+
+ 
+
+
+ 
+ 
+ 
+
+
+ 
+ 
+
+
+
+
+
+ +
+
+
+ +
+ YOLOv5🚀是一个在COCO数据集上预训练的物体检测架构和模型系列，它代表了Ultralytics对未来视觉AI方法的公开研究，其中包含了在数千小时的研究和开发中所获得的经验和最佳实践。 +
+ + +
+
+
+
+
+
+
+
+
+ + YOLOv5🚀是一个在COCO数据集上预训练的物体检测架构和模型系列，它代表了Ultralytics对未来视觉AI方法的公开研究，其中包含了在数千小时的研究和开发中所获得的经验和最佳实践。 +
+ + +
+
+
+
+
+
+## 
+
+##
+ - **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart)
+ - **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/AWS-Quickstart)
+ - **Docker Image**. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/Docker-Quickstart)
+
+ ## Status
+
+
+
+ If this badge is green, all [YOLOv5 GitHub Actions](https://github.com/ultralytics/yolov5/actions) Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 [training](https://github.com/ultralytics/yolov5/blob/master/train.py), [validation](https://github.com/ultralytics/yolov5/blob/master/val.py), [inference](https://github.com/ultralytics/yolov5/blob/master/detect.py), [export](https://github.com/ultralytics/yolov5/blob/master/export.py) and [benchmarks](https://github.com/ultralytics/yolov5/blob/master/benchmarks.py) on MacOS, Windows, and Ubuntu every 24 hours and on every commit.
+
diff --git a/src/FireDetect/.github/workflows/stale.yml b/src/FireDetect/.github/workflows/stale.yml
new file mode 100644
index 0000000..9067c34
--- /dev/null
+++ b/src/FireDetect/.github/workflows/stale.yml
@@ -0,0 +1,40 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+
+name: Close stale issues
+on:
+ schedule:
+ - cron: '0 0 * * *' # Runs at 00:00 UTC every day
+
+jobs:
+ stale:
+ runs-on: ubuntu-latest
+ steps:
+ - uses: actions/stale@v6
+ with:
+ repo-token: ${{ secrets.GITHUB_TOKEN }}
+ stale-issue-message: |
+ 👋 Hello, this issue has been automatically marked as stale because it has not had recent activity. Please note it will be closed if no further activity occurs.
+
+ Access additional [YOLOv5](https://ultralytics.com/yolov5) 🚀 resources:
+ - **Wiki** – https://github.com/ultralytics/yolov5/wiki
+ - **Tutorials** – https://github.com/ultralytics/yolov5#tutorials
+ - **Docs** – https://docs.ultralytics.com
+
+ Access additional [Ultralytics](https://ultralytics.com) ⚡ resources:
+ - **Ultralytics HUB** – https://ultralytics.com/hub
+ - **Vision API** – https://ultralytics.com/yolov5
+ - **About Us** – https://ultralytics.com/about
+ - **Join Our Team** – https://ultralytics.com/work
+ - **Contact Us** – https://ultralytics.com/contact
+
+ Feel free to inform us of any other **issues** you discover or **feature requests** that come to mind in the future. Pull Requests (PRs) are also always welcomed!
+
+ Thank you for your contributions to YOLOv5 🚀 and Vision AI ⭐!
+
+ stale-pr-message: 'This pull request has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions YOLOv5 🚀 and Vision AI ⭐.'
+ days-before-issue-stale: 30
+ days-before-issue-close: 10
+ days-before-pr-stale: 90
+ days-before-pr-close: 30
+ exempt-issue-labels: 'documentation,tutorial,TODO'
+ operations-per-run: 300 # The maximum number of operations per run, used to control rate limiting.
diff --git a/src/FireDetect/SengJingDetect.py b/src/FireDetect/SengJingDetect.py
new file mode 100644
index 0000000..ff721a6
--- /dev/null
+++ b/src/FireDetect/SengJingDetect.py
@@ -0,0 +1,597 @@
+import math
+import threading
+import time
+import contextlib
+import numpy as np
+import torch
+import torchvision
+from models.common import DetectMultiBackend
+import cv2
+from utils.plots import Annotator
+import pkg_resources as pkg
+import platform
+import logging
+import logging.config
+import os
+from PIL import Image, ImageDraw, ImageFont
+from pathlib import Path
+def is_ascii(s=''):
+ # Is string composed of all ASCII (no UTF) characters? (note str().isascii() introduced in python 3.7)
+ s = str(s) # convert list, tuple, None, etc. to str
+ return len(s.encode().decode('ascii', 'ignore')) == len(s)
+
+
+LOGGING_NAME = "yolov5"
+def set_logging(name=LOGGING_NAME, verbose=True):
+ # sets up logging for the given name
+ rank = int(os.getenv('RANK', -1)) # rank in world for Multi-GPU trainings
+ level = logging.INFO if verbose and rank in {-1, 0} else logging.ERROR
+ logging.config.dictConfig({
+ "version": 1,
+ "disable_existing_loggers": False,
+ "formatters": {
+ name: {
+ "format": "%(message)s"}},
+ "handlers": {
+ name: {
+ "class": "logging.StreamHandler",
+ "formatter": name,
+ "level": level,}},
+ "loggers": {
+ name: {
+ "level": level,
+ "handlers": [name],
+ "propagate": False,}}})
+set_logging(LOGGING_NAME) # run before defining LOGGER
+LOGGER = logging.getLogger(LOGGING_NAME) # define globally (used in train.py, val.py, detect.py, etc.)
+def emojis(str=''):
+ # Return platform-dependent emoji-safe version of string
+ return str.encode().decode('ascii', 'ignore') if platform.system() == 'Windows' else str
+
+
+def check_version(current='0.0.0', minimum='0.0.0', name='version ', pinned=False, hard=False, verbose=False):
+ # Check version vs. required version
+ current, minimum = (pkg.parse_version(x) for x in (current, minimum))
+ result = (current == minimum) if pinned else (current >= minimum) # bool
+ s = f'WARNING ⚠️ {name}{minimum} is required by YOLOv5, but {name}{current} is currently installed' # string
+ if hard:
+ assert result, emojis(s) # assert min requirements met
+ if verbose and not result:
+ LOGGER.warning(s)
+ return result
+
+
+def smart_inference_mode(torch_1_9=check_version(torch.__version__, '1.9.0')):
+ # Applies torch.inference_mode() decorator if torch>=1.9.0 else torch.no_grad() decorator
+ def decorate(fn):
+ return (torch.inference_mode if torch_1_9 else torch.no_grad)()(fn)
+
+ return decorate
+
+
+def box_iou(box1, box2, eps=1e-7):
+ # https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py
+ """
+ Return intersection-over-union (Jaccard index) of boxes.
+ Both sets of boxes are expected to be in (x1, y1, x2, y2) format.
+ Arguments:
+ box1 (Tensor[N, 4])
+ box2 (Tensor[M, 4])
+ Returns:
+ iou (Tensor[N, M]): the NxM matrix containing the pairwise
+ IoU values for every element in boxes1 and boxes2
+ """
+
+ # inter(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2)
+ (a1, a2), (b1, b2) = box1.unsqueeze(1).chunk(2, 2), box2.unsqueeze(0).chunk(2, 2)
+ inter = (torch.min(a2, b2) - torch.max(a1, b1)).clamp(0).prod(2)
+
+ # IoU = inter / (area1 + area2 - inter)
+ return inter / ((a2 - a1).prod(2) + (b2 - b1).prod(2) - inter + eps)
+
+def xywh2xyxy(x):
+ # Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right
+ y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
+ y[:, 0] = x[:, 0] - x[:, 2] / 2 # top left x
+ y[:, 1] = x[:, 1] - x[:, 3] / 2 # top left y
+ y[:, 2] = x[:, 0] + x[:, 2] / 2 # bottom right x
+ y[:, 3] = x[:, 1] + x[:, 3] / 2 # bottom right y
+ return y
+
+
+def xyxy2xywh(x):
+ # Convert nx4 boxes from [x1, y1, x2, y2] to [x, y, w, h] where xy1=top-left, xy2=bottom-right
+ y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
+ y[:, 0] = (x[:, 0] + x[:, 2]) / 2 # x center
+ y[:, 1] = (x[:, 1] + x[:, 3]) / 2 # y center
+ y[:, 2] = x[:, 2] - x[:, 0] # width
+ y[:, 3] = x[:, 3] - x[:, 1] # height
+ return y
+
+def clip_boxes(boxes, shape):
+ # Clip boxes (xyxy) to image shape (height, width)
+ if isinstance(boxes, torch.Tensor): # faster individually
+ boxes[:, 0].clamp_(0, shape[1]) # x1
+ boxes[:, 1].clamp_(0, shape[0]) # y1
+ boxes[:, 2].clamp_(0, shape[1]) # x2
+ boxes[:, 3].clamp_(0, shape[0]) # y2
+ else: # np.array (faster grouped)
+ boxes[:, [0, 2]] = boxes[:, [0, 2]].clip(0, shape[1]) # x1, x2
+ boxes[:, [1, 3]] = boxes[:, [1, 3]].clip(0, shape[0]) # y1, y2
+
+def scale_boxes(img1_shape, boxes, img0_shape, ratio_pad=None):
+ # Rescale boxes (xyxy) from img1_shape to img0_shape
+ if ratio_pad is None: # calculate from img0_shape
+ gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1]) # gain = old / new
+ pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2 # wh padding
+ else:
+ gain = ratio_pad[0][0]
+ pad = ratio_pad[1]
+
+ boxes[:, [0, 2]] -= pad[0] # x padding
+ boxes[:, [1, 3]] -= pad[1] # y padding
+ boxes[:, :4] /= gain
+ clip_boxes(boxes, img0_shape)
+ return boxes
+
+def letterbox(im, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleFill=False, scaleup=True, stride=32):
+ # Resize and pad image while meeting stride-multiple constraints
+ shape = im.shape[:2] # current shape [height, width]
+ if isinstance(new_shape, int):
+ new_shape = (new_shape, new_shape)
+
+ # Scale ratio (new / old)
+ r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
+ if not scaleup: # only scale down, do not scale up (for better val mAP)
+ r = min(r, 1.0)
+
+ # Compute padding
+ ratio = r, r # width, height ratios
+ new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
+ dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] # wh padding
+ if auto: # minimum rectangle
+ dw, dh = np.mod(dw, stride), np.mod(dh, stride) # wh padding
+ elif scaleFill: # stretch
+ dw, dh = 0.0, 0.0
+ new_unpad = (new_shape[1], new_shape[0])
+ ratio = new_shape[1] / shape[1], new_shape[0] / shape[0] # width, height ratios
+
+ dw /= 2 # divide padding into 2 sides
+ dh /= 2
+
+ if shape[::-1] != new_unpad: # resize
+ im = cv2.resize(im, new_unpad, interpolation=cv2.INTER_LINEAR)
+ top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
+ left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
+ im = cv2.copyMakeBorder(im, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # add border
+ return im, ratio, (dw, dh)
+
+
+
+
+def non_max_suppression(
+ prediction,
+ conf_thres=0.25,
+ iou_thres=0.45,
+ classes=None,
+ agnostic=False,
+ multi_label=False,
+ labels=(),
+ max_det=300,
+ nm=0, # number of masks
+):
+ """Non-Maximum Suppression (NMS) on inference results to reject overlapping detections
+
+ Returns:
+ list of detections, on (n,6) tensor per image [xyxy, conf, cls]
+ """
+# YOLOv5 model in validation model, output = (inference_out, loss_out)
+ prediction = prediction[0] # select only inference output
+ nc = prediction.shape[2] - nm - 5 # number of classes
+ xc = prediction[..., 4] > conf_thres # candidates
+ # Checks
+ mi = 5 + nc # mask start index
+ output = [torch.zeros((0, 6 + nm), device=prediction.device)]
+ for xi, x in enumerate(prediction): # image index, image inference
+ # Apply constraints
+ # x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0 # width-height
+ x = x[xc[xi]] # confidence
+
+ # Cat apriori labels if autolabelling
+ if labels and len(labels[xi]):
+ lb = labels[xi]
+ v = torch.zeros((len(lb), nc + nm + 5), device=x.device)
+ v[:, :4] = lb[:, 1:5] # box
+ v[:, 4] = 1.0 # conf
+ v[range(len(lb)), lb[:, 0].long() + 5] = 1.0 # cls
+ x = torch.cat((x, v), 0)
+
+ # If none remain process next image
+ if not x.shape[0]:
+ continue
+
+ # Compute conf
+ x[:, 5:] *= x[:, 4:5] # conf = obj_conf * cls_conf
+
+ # Box/Mask
+ box = xywh2xyxy(x[:, :4]) # center_x, center_y, width, height) to (x1, y1, x2, y2)
+ mask = x[:, mi:] # zero columns if no masks
+
+ # Detections matrix nx6 (xyxy, conf, cls)
+
+ conf, j = x[:, 5:mi].max(1, keepdim=True)
+ x = torch.cat((box, conf, j.float(), mask), 1)[conf.view(-1) > conf_thres]
+
+ # Filter by class
+ if classes is not None:
+ x = x[(x[:, 5:6] == torch.tensor(classes, device=x.device)).any(1)]
+
+ # Apply finite constraint
+ # if not torch.isfinite(x).all():
+ # x = x[torch.isfinite(x).all(1)]
+
+ # Check shape
+ n = x.shape[0] # number of boxes
+ if not n: # no boxes
+ continue
+
+ x = x[x[:, 4].argsort(descending=True)] # sort by confidence
+
+ # Batched NMS
+ c = x[:, 5:6] # classes
+ boxes, scores = x[:, :4] + c, x[:, 4] # boxes (offset by class), scores
+ i = torchvision.ops.nms(boxes, scores, iou_thres) # NMS
+ # limit detections
+ i = i[:max_det]
+ # update boxes as boxes(i,4) = weights(i,n) * boxes(n,4)
+ iou = box_iou(boxes[i], boxes) > iou_thres # iou matrix
+ weights = iou * scores[None] # box weights
+ x[i, :4] = torch.mm(weights, x[:, :4]).float() / weights.sum(1, keepdim=True) # merged boxes
+ i = i[iou.sum(1) > 1] # require redundancy
+
+ output[xi] = x[i]
+ return output
+
+def is_writeable(dir, test=False):
+ # Return True if directory has write permissions, test opening a file with write permissions if test=True
+ if not test:
+ return os.access(dir, os.W_OK) # possible issues on Windows
+ file = Path(dir) / 'tmp.txt'
+ try:
+ with open(file, 'w'): # open file with write permissions
+ pass
+ file.unlink() # remove file
+ return True
+ except OSError:
+ return False
+def user_config_dir(dir='Ultralytics', env_var='YOLOV5_CONFIG_DIR'):
+ # Return path of user configuration directory. Prefer environment variable if exists. Make dir if required.
+ env = os.getenv(env_var)
+ if env:
+ path = Path(env) # use environment variable
+ else:
+ cfg = {'Windows': 'AppData/Roaming', 'Linux': '.config', 'Darwin': 'Library/Application Support'} # 3 OS dirs
+ path = Path.home() / cfg.get(platform.system(), '') # OS-specific config dir
+ path = (path if is_writeable(path) else Path('/tmp')) / dir # GCP and AWS lambda fix, only /tmp is writeable
+ path.mkdir(exist_ok=True) # make if required
+ return path
+
+CONFIG_DIR = user_config_dir() # Ultralytics settings dir
+FONT = 'Arial.ttf' # https://ultralytics.com/assets/Arial.ttf
+
+def check_font(font=FONT, progress=False):
+ # Download font to CONFIG_DIR if necessary
+ font = Path(font)
+ file = CONFIG_DIR / font.name
+ if not font.exists() and not file.exists():
+ url = f'https://ultralytics.com/assets/{font.name}'
+ LOGGER.info(f'Downloading {url} to {file}...')
+ torch.hub.download_url_to_file(url, str(file), progress=progress)
+def check_python(minimum='3.7.0'):
+ # Check current python version vs. required python version
+ check_version(platform.python_version(), minimum, name='Python ', hard=True)
+
+ class TryExcept(contextlib.ContextDecorator):
+ # YOLOv5 TryExcept class. Usage: @TryExcept() decorator or 'with TryExcept():' context manager
+ def __init__(self, msg=''):
+ self.msg = msg
+
+ def __enter__(self):
+ pass
+
+ def __exit__(self, exc_type, value, traceback):
+ if value:
+ print(emojis(f"{self.msg}{': ' if self.msg else ''}{value}"))
+ return True
+class TryExcept(contextlib.ContextDecorator):
+ # YOLOv5 TryExcept class. Usage: @TryExcept() decorator or 'with TryExcept():' context manager
+ def __init__(self, msg=''):
+ self.msg = msg
+
+ def __enter__(self):
+ pass
+
+ def __exit__(self, exc_type, value, traceback):
+ if value:
+ print(emojis(f"{self.msg}{': ' if self.msg else ''}{value}"))
+ return True
+
+
+
+def colorstr(*input):
+ # Colors a string https://en.wikipedia.org/wiki/ANSI_escape_code, i.e. colorstr('blue', 'hello world')
+ *args, string = input if len(input) > 1 else ('blue', 'bold', input[0]) # color arguments, string
+ colors = {
+ 'black': '\033[30m', # basic colors
+ 'red': '\033[31m',
+ 'green': '\033[32m',
+ 'yellow': '\033[33m',
+ 'blue': '\033[34m',
+ 'magenta': '\033[35m',
+ 'cyan': '\033[36m',
+ 'white': '\033[37m',
+ 'bright_black': '\033[90m', # bright colors
+ 'bright_red': '\033[91m',
+ 'bright_green': '\033[92m',
+ 'bright_yellow': '\033[93m',
+ 'bright_blue': '\033[94m',
+ 'bright_magenta': '\033[95m',
+ 'bright_cyan': '\033[96m',
+ 'bright_white': '\033[97m',
+ 'end': '\033[0m', # misc
+ 'bold': '\033[1m',
+ 'underline': '\033[4m'}
+ return ''.join(colors[x] for x in args) + f'{string}' + colors['end']
+
+
+@TryExcept()
+# def check_requirements(requirements='requirements.txt', exclude=(), install=True, cmds=''):
+# # Check installed dependencies meet YOLOv5 requirements (pass *.txt file or list of packages or single package str)
+# prefix = colorstr('red', 'bold', 'requirements:')
+# check_python() # check python version
+# if isinstance(requirements, Path): # requirements.txt file
+# file = requirements.resolve()
+# assert file.exists(), f"{prefix} {file} not found, check failed."
+# with file.open() as f:
+# requirements = [f'{x.name}{x.specifier}' for x in pkg.parse_requirements(f) if x.name not in exclude]
+# elif isinstance(requirements, str):
+# requirements = [requirements]
+#
+# s = ''
+# n = 0
+# for r in requirements:
+# try:
+# pkg.require(r)
+# except (pkg.VersionConflict, pkg.DistributionNotFound): # exception if requirements not met
+# s += f'"{r}" '
+# n += 1
+#
+# if s and install and AUTOINSTALL: # check environment variable
+# LOGGER.info(f"{prefix} YOLOv5 requirement{'s' * (n > 1)} {s}not found, attempting AutoUpdate...")
+# try:
+# # assert check_online(), "AutoUpdate skipped (offline)"
+# LOGGER.info(check_output(f'pip install {s} {cmds}', shell=True).decode())
+# source = file if 'file' in locals() else requirements
+# s = f"{prefix} {n} package{'s' * (n > 1)} updated per {source}\n" \
+# f"{prefix} ⚠️ {colorstr('bold', 'Restart runtime or rerun command for updates to take effect')}\n"
+# LOGGER.info(s)
+# except Exception as e:
+# LOGGER.warning(f'{prefix} ❌ {e}')
+
+# def check_pil_font(font=FONT, size=10):
+# # Return a PIL TrueType Font, downloading to CONFIG_DIR if necessary
+# font = Path(font)
+# font = font if font.exists() else (CONFIG_DIR / font.name)
+# try:
+# return ImageFont.truetype(str(font) if font.exists() else font.name, size)
+# except Exception: # download if missing
+# try:
+# check_font(font)
+# return ImageFont.truetype(str(font), size)
+# except TypeError:
+# check_requirements('Pillow>=8.4.0') # known issue https://github.com/ultralytics/yolov5/issues/5374
+# except URLError: # not online
+# return ImageFont.load_default()
+
+
+
+def scale_image(im1_shape, masks, im0_shape, ratio_pad=None):
+ """
+ img1_shape: model input shape, [h, w]
+ img0_shape: origin pic shape, [h, w, 3]
+ masks: [h, w, num]
+ """
+ # Rescale coordinates (xyxy) from im1_shape to im0_shape
+ if ratio_pad is None: # calculate from im0_shape
+ gain = min(im1_shape[0] / im0_shape[0], im1_shape[1] / im0_shape[1]) # gain = old / new
+ pad = (im1_shape[1] - im0_shape[1] * gain) / 2, (im1_shape[0] - im0_shape[0] * gain) / 2 # wh padding
+ else:
+ pad = ratio_pad[1]
+ top, left = int(pad[1]), int(pad[0]) # y, x
+ bottom, right = int(im1_shape[0] - pad[1]), int(im1_shape[1] - pad[0])
+
+ if len(masks.shape) < 2:
+ raise ValueError(f'"len of masks shape" should be 2 or 3, but got {len(masks.shape)}')
+ masks = masks[top:bottom, left:right]
+ # masks = masks.permute(2, 0, 1).contiguous()
+ # masks = F.interpolate(masks[None], im0_shape[:2], mode='bilinear', align_corners=False)[0]
+ # masks = masks.permute(1, 2, 0).contiguous()
+ masks = cv2.resize(masks, (im0_shape[1], im0_shape[0]))
+
+ if len(masks.shape) == 2:
+ masks = masks[:, :, None]
+ return masks
+# class Annotator:
+# # YOLOv5 Annotator for train/val mosaics and jpgs and detect/hub inference annotations
+# def __init__(self, im, line_width=None, font_size=None, font='Arial.ttf', pil=False, example='abc'):
+# assert im.data.contiguous, 'Image not contiguous. Apply np.ascontiguousarray(im) to Annotator() input images.'
+# non_ascii = not is_ascii(example) # non-latin labels, i.e. asian, arabic, cyrillic
+# self.pil = pil or non_ascii
+# if self.pil: # use PIL
+# self.im = im if isinstance(im, Image.Image) else Image.fromarray(im)
+# self.draw = ImageDraw.Draw(self.im)
+# self.font = check_pil_font(font='Arial.Unicode.ttf' if non_ascii else font,
+# size=font_size or max(round(sum(self.im.size) / 2 * 0.035), 12))
+# else: # use cv2
+# self.im = im
+# self.lw = line_width or max(round(sum(im.shape) / 2 * 0.003), 2) # line width
+#
+# def box_label(self, box, label='', color=(128, 128, 128), txt_color=(255, 255, 255)):
+# # Add one xyxy box to image with label
+# if self.pil or not is_ascii(label):
+# self.draw.rectangle(box, width=self.lw, outline=color) # box
+# if label:
+# w, h = self.font.getsize(label) # text width, height
+# outside = box[1] - h >= 0 # label fits outside box
+# self.draw.rectangle(
+# (box[0], box[1] - h if outside else box[1], box[0] + w + 1,
+# box[1] + 1 if outside else box[1] + h + 1),
+# fill=color,
+# )
+# # self.draw.text((box[0], box[1]), label, fill=txt_color, font=self.font, anchor='ls') # for PIL>8.0
+# self.draw.text((box[0], box[1] - h if outside else box[1]), label, fill=txt_color, font=self.font)
+# else: # cv2
+# p1, p2 = (int(box[0]), int(box[1])), (int(box[2]), int(box[3]))
+# cv2.rectangle(self.im, p1, p2, color, thickness=self.lw, lineType=cv2.LINE_AA)
+# if label:
+# tf = max(self.lw - 1, 1) # font thickness
+# w, h = cv2.getTextSize(label, 0, fontScale=self.lw / 3, thickness=tf)[0] # text width, height
+# outside = p1[1] - h >= 3
+# p2 = p1[0] + w, p1[1] - h - 3 if outside else p1[1] + h + 3
+# cv2.rectangle(self.im, p1, p2, color, -1, cv2.LINE_AA) # filled
+# cv2.putText(self.im,
+# label, (p1[0], p1[1] - 2 if outside else p1[1] + h + 2),
+# 0,
+# self.lw / 3,
+# txt_color,
+# thickness=tf,
+# lineType=cv2.LINE_AA)
+#
+# def masks(self, masks, colors, im_gpu=None, alpha=0.5):
+# """Plot masks at once.
+# Args:
+# masks (tensor): predicted masks on cuda, shape: [n, h, w]
+# colors (List[List[Int]]): colors for predicted masks, [[r, g, b] * n]
+# im_gpu (tensor): img is in cuda, shape: [3, h, w], range: [0, 1]
+# alpha (float): mask transparency: 0.0 fully transparent, 1.0 opaque
+# """
+# if self.pil:
+# # convert to numpy first
+# self.im = np.asarray(self.im).copy()
+# if im_gpu is None:
+# # Add multiple masks of shape(h,w,n) with colors list([r,g,b], [r,g,b], ...)
+# if len(masks) == 0:
+# return
+# if isinstance(masks, torch.Tensor):
+# masks = torch.as_tensor(masks, dtype=torch.uint8)
+# masks = masks.permute(1, 2, 0).contiguous()
+# masks = masks.cpu().numpy()
+# # masks = np.ascontiguousarray(masks.transpose(1, 2, 0))
+# masks = scale_image(masks.shape[:2], masks, self.im.shape)
+# masks = np.asarray(masks, dtype=np.float32)
+# colors = np.asarray(colors, dtype=np.float32) # shape(n,3)
+# s = masks.sum(2, keepdims=True).clip(0, 1) # add all masks together
+# masks = (masks @ colors).clip(0, 255) # (h,w,n) @ (n,3) = (h,w,3)
+# self.im[:] = masks * alpha + self.im * (1 - s * alpha)
+# else:
+# if len(masks) == 0:
+# self.im[:] = im_gpu.permute(1, 2, 0).contiguous().cpu().numpy() * 255
+# colors = torch.tensor(colors, device=im_gpu.device, dtype=torch.float32) / 255.0
+# colors = colors[:, None, None] # shape(n,1,1,3)
+# masks = masks.unsqueeze(3) # shape(n,h,w,1)
+# masks_color = masks * (colors * alpha) # shape(n,h,w,3)
+#
+# inv_alph_masks = (1 - masks * alpha).cumprod(0) # shape(n,h,w,1)
+# mcs = (masks_color * inv_alph_masks).sum(0) * 2 # mask color summand shape(n,h,w,3)
+#
+# im_gpu = im_gpu.flip(dims=[0]) # flip channel
+# im_gpu = im_gpu.permute(1, 2, 0).contiguous() # shape(h,w,3)
+# im_gpu = im_gpu * inv_alph_masks[-1] + mcs
+# im_mask = (im_gpu * 255).byte().cpu().numpy()
+# self.im[:] = scale_image(im_gpu.shape, im_mask, self.im.shape)
+# if self.pil:
+# # convert im back to PIL and update draw
+# self.fromarray(self.im)
+#
+# def rectangle(self, xy, fill=None, outline=None, width=1):
+# # Add rectangle to image (PIL-only)
+# self.draw.rectangle(xy, fill, outline, width)
+#
+# def text(self, xy, text, txt_color=(255, 255, 255), anchor='top'):
+# # Add text to image (PIL-only)
+# if anchor == 'bottom': # start y from font bottom
+# w, h = self.font.getsize(text) # text width, height
+# xy[1] += 1 - h
+# self.draw.text(xy, text, fill=txt_color, font=self.font)
+#
+# def fromarray(self, im):
+# # Update self.im from a numpy array
+# self.im = im if isinstance(im, Image.Image) else Image.fromarray(im)
+# self.draw = ImageDraw.Draw(self.im)
+#
+# def result(self):
+# # Return annotated image as array
+# return np.asarray(self.im)
+
+@smart_inference_mode()
+def run():
+ # Load model
+ device = torch.device('cuda:0')
+ model = DetectMultiBackend(weights='fire.pt', device=device, dnn=False, data=False, fp16=True)
+ stride, names, pt = model.stride, model.names, model.pt
+ #IP摄像头
+ cap = cv2.VideoCapture("http://admin:admin@10.129.50.72:8081") # URL of the IP camera
+ # 读取图片
+ while True:
+ ret, img0 = cap.read()
+ #图片旋转
+ img0 = np.rot90(img0,3)
+
+ img0 = np.array(img0)
+ img0 = cv2.cvtColor(img0, cv2.COLOR_BGRA2BGR)
+
+
+ # 处理图片
+ im = letterbox(img0, (640, 640), stride=32, auto=True)[0] # padded resize
+ im = im.transpose((2, 0, 1))[::-1] # HWC to CHW, BGR to RGB
+ im = np.ascontiguousarray(im) # contiguous
+ im = torch.from_numpy(im).to(model.device)
+ im = im.half() if model.fp16 else im.float() # uint8 to fp16/32
+ im /= 255 # 0 - 255 to 0.0 - 1.0
+ if len(im.shape) == 3:
+ im = im[None] # expand for batch dim
+
+ # 推理
+ pred = model(im, augment=False, visualize=False)
+ # 非极大值抑制
+ pred = non_max_suppression(pred, conf_thres=0.4, iou_thres=0.05, classes=None, max_det=1000)
+
+ # 处理推理内容
+
+ for i, det in enumerate(pred):
+ # 画框
+ annotator = Annotator(img0, line_width=2)
+ if len(det):
+ target_list = []
+ # 将转换后的图片画框结果转换成原图上的结果
+ det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], img0.shape).round()
+ for *xyxy, conf, cls in reversed(det): # 处理推理出来每个目标的信息
+ # 将xyxy(左上角+右下角)格式转为xywh(中心点+宽长)格式，并除上w，h做归一化，转化为列表再保存
+ xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4))).view(-1).tolist() # normalized xywh
+
+ annotator.box_label(xyxy, label=f'[{names[int(cls)]} {conf:.2f}]',
+ color=(34, 139, 34),
+ txt_color=(0, 191, 255))
+
+
+ target_list.append(xywh)
+
+
+
+ im0 = annotator.result()
+ cv2.imshow('window', im0)
+ cv2.waitKey(1)
+
+
+if __name__ == "__main__":
+ run()
diff --git a/src/FireDetect/data/Argoverse.yaml b/src/FireDetect/data/Argoverse.yaml
new file mode 100644
index 0000000..558151d
--- /dev/null
+++ b/src/FireDetect/data/Argoverse.yaml
@@ -0,0 +1,74 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# Argoverse-HD dataset (ring-front-center camera) http://www.cs.cmu.edu/~mengtial/proj/streaming/ by Argo AI
+# Example usage: python train.py --data Argoverse.yaml
+# parent
+# ├── yolov5
+# └── datasets
+# └── Argoverse ← downloads here (31.3 GB)
+
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/Argoverse # dataset root dir
+train: Argoverse-1.1/images/train/ # train images (relative to 'path') 39384 images
+val: Argoverse-1.1/images/val/ # val images (relative to 'path') 15062 images
+test: Argoverse-1.1/images/test/ # test images (optional) https://eval.ai/web/challenges/challenge-page/800/overview
+
+# Classes
+names:
+ 0: person
+ 1: bicycle
+ 2: car
+ 3: motorcycle
+ 4: bus
+ 5: truck
+ 6: traffic_light
+ 7: stop_sign
+
+
+# Download script/URL (optional) ---------------------------------------------------------------------------------------
+download: |
+ import json
+
+ from tqdm import tqdm
+ from utils.general import download, Path
+
+
+ def argoverse2yolo(set):
+ labels = {}
+ a = json.load(open(set, "rb"))
+ for annot in tqdm(a['annotations'], desc=f"Converting {set} to YOLOv5 format..."):
+ img_id = annot['image_id']
+ img_name = a['images'][img_id]['name']
+ img_label_name = f'{img_name[:-3]}txt'
+
+ cls = annot['category_id'] # instance class id
+ x_center, y_center, width, height = annot['bbox']
+ x_center = (x_center + width / 2) / 1920.0 # offset and scale
+ y_center = (y_center + height / 2) / 1200.0 # offset and scale
+ width /= 1920.0 # scale
+ height /= 1200.0 # scale
+
+ img_dir = set.parents[2] / 'Argoverse-1.1' / 'labels' / a['seq_dirs'][a['images'][annot['image_id']]['sid']]
+ if not img_dir.exists():
+ img_dir.mkdir(parents=True, exist_ok=True)
+
+ k = str(img_dir / img_label_name)
+ if k not in labels:
+ labels[k] = []
+ labels[k].append(f"{cls} {x_center} {y_center} {width} {height}\n")
+
+ for k in labels:
+ with open(k, "w") as f:
+ f.writelines(labels[k])
+
+
+ # Download
+ dir = Path(yaml['path']) # dataset root dir
+ urls = ['https://argoverse-hd.s3.us-east-2.amazonaws.com/Argoverse-HD-Full.zip']
+ download(urls, dir=dir, delete=False)
+
+ # Convert
+ annotations_dir = 'Argoverse-HD/annotations/'
+ (dir / 'Argoverse-1.1' / 'tracking').rename(dir / 'Argoverse-1.1' / 'images') # rename 'tracking' to 'images'
+ for d in "train.json", "val.json":
+ argoverse2yolo(dir / annotations_dir / d) # convert VisDrone annotations to YOLO labels
diff --git a/src/FireDetect/data/GlobalWheat2020.yaml b/src/FireDetect/data/GlobalWheat2020.yaml
new file mode 100644
index 0000000..01812d0
--- /dev/null
+++ b/src/FireDetect/data/GlobalWheat2020.yaml
@@ -0,0 +1,54 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# Global Wheat 2020 dataset http://www.global-wheat.com/ by University of Saskatchewan
+# Example usage: python train.py --data GlobalWheat2020.yaml
+# parent
+# ├── yolov5
+# └── datasets
+# └── GlobalWheat2020 ← downloads here (7.0 GB)
+
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/GlobalWheat2020 # dataset root dir
+train: # train images (relative to 'path') 3422 images
+ - images/arvalis_1
+ - images/arvalis_2
+ - images/arvalis_3
+ - images/ethz_1
+ - images/rres_1
+ - images/inrae_1
+ - images/usask_1
+val: # val images (relative to 'path') 748 images (WARNING: train set contains ethz_1)
+ - images/ethz_1
+test: # test images (optional) 1276 images
+ - images/utokyo_1
+ - images/utokyo_2
+ - images/nau_1
+ - images/uq_1
+
+# Classes
+names:
+ 0: wheat_head
+
+
+# Download script/URL (optional) ---------------------------------------------------------------------------------------
+download: |
+ from utils.general import download, Path
+
+
+ # Download
+ dir = Path(yaml['path']) # dataset root dir
+ urls = ['https://zenodo.org/record/4298502/files/global-wheat-codalab-official.zip',
+ 'https://github.com/ultralytics/yolov5/releases/download/v1.0/GlobalWheat2020_labels.zip']
+ download(urls, dir=dir)
+
+ # Make Directories
+ for p in 'annotations', 'images', 'labels':
+ (dir / p).mkdir(parents=True, exist_ok=True)
+
+ # Move
+ for p in 'arvalis_1', 'arvalis_2', 'arvalis_3', 'ethz_1', 'rres_1', 'inrae_1', 'usask_1', \
+ 'utokyo_1', 'utokyo_2', 'nau_1', 'uq_1':
+ (dir / p).rename(dir / 'images' / p) # move to /images
+ f = (dir / p).with_suffix('.json') # json file
+ if f.exists():
+ f.rename((dir / 'annotations' / p).with_suffix('.json')) # move to /annotations
diff --git a/src/FireDetect/data/ImageNet.yaml b/src/FireDetect/data/ImageNet.yaml
new file mode 100644
index 0000000..14f1295
--- /dev/null
+++ b/src/FireDetect/data/ImageNet.yaml
@@ -0,0 +1,1022 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# ImageNet-1k dataset https://www.image-net.org/index.php by Stanford University
+# Simplified class names from https://github.com/anishathalye/imagenet-simple-labels
+# Example usage: python classify/train.py --data imagenet
+# parent
+# ├── yolov5
+# └── datasets
+# └── imagenet ← downloads here (144 GB)
+
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/imagenet # dataset root dir
+train: train # train images (relative to 'path') 1281167 images
+val: val # val images (relative to 'path') 50000 images
+test: # test images (optional)
+
+# Classes
+names:
+ 0: tench
+ 1: goldfish
+ 2: great white shark
+ 3: tiger shark
+ 4: hammerhead shark
+ 5: electric ray
+ 6: stingray
+ 7: cock
+ 8: hen
+ 9: ostrich
+ 10: brambling
+ 11: goldfinch
+ 12: house finch
+ 13: junco
+ 14: indigo bunting
+ 15: American robin
+ 16: bulbul
+ 17: jay
+ 18: magpie
+ 19: chickadee
+ 20: American dipper
+ 21: kite
+ 22: bald eagle
+ 23: vulture
+ 24: great grey owl
+ 25: fire salamander
+ 26: smooth newt
+ 27: newt
+ 28: spotted salamander
+ 29: axolotl
+ 30: American bullfrog
+ 31: tree frog
+ 32: tailed frog
+ 33: loggerhead sea turtle
+ 34: leatherback sea turtle
+ 35: mud turtle
+ 36: terrapin
+ 37: box turtle
+ 38: banded gecko
+ 39: green iguana
+ 40: Carolina anole
+ 41: desert grassland whiptail lizard
+ 42: agama
+ 43: frilled-necked lizard
+ 44: alligator lizard
+ 45: Gila monster
+ 46: European green lizard
+ 47: chameleon
+ 48: Komodo dragon
+ 49: Nile crocodile
+ 50: American alligator
+ 51: triceratops
+ 52: worm snake
+ 53: ring-necked snake
+ 54: eastern hog-nosed snake
+ 55: smooth green snake
+ 56: kingsnake
+ 57: garter snake
+ 58: water snake
+ 59: vine snake
+ 60: night snake
+ 61: boa constrictor
+ 62: African rock python
+ 63: Indian cobra
+ 64: green mamba
+ 65: sea snake
+ 66: Saharan horned viper
+ 67: eastern diamondback rattlesnake
+ 68: sidewinder
+ 69: trilobite
+ 70: harvestman
+ 71: scorpion
+ 72: yellow garden spider
+ 73: barn spider
+ 74: European garden spider
+ 75: southern black widow
+ 76: tarantula
+ 77: wolf spider
+ 78: tick
+ 79: centipede
+ 80: black grouse
+ 81: ptarmigan
+ 82: ruffed grouse
+ 83: prairie grouse
+ 84: peacock
+ 85: quail
+ 86: partridge
+ 87: grey parrot
+ 88: macaw
+ 89: sulphur-crested cockatoo
+ 90: lorikeet
+ 91: coucal
+ 92: bee eater
+ 93: hornbill
+ 94: hummingbird
+ 95: jacamar
+ 96: toucan
+ 97: duck
+ 98: red-breasted merganser
+ 99: goose
+ 100: black swan
+ 101: tusker
+ 102: echidna
+ 103: platypus
+ 104: wallaby
+ 105: koala
+ 106: wombat
+ 107: jellyfish
+ 108: sea anemone
+ 109: brain coral
+ 110: flatworm
+ 111: nematode
+ 112: conch
+ 113: snail
+ 114: slug
+ 115: sea slug
+ 116: chiton
+ 117: chambered nautilus
+ 118: Dungeness crab
+ 119: rock crab
+ 120: fiddler crab
+ 121: red king crab
+ 122: American lobster
+ 123: spiny lobster
+ 124: crayfish
+ 125: hermit crab
+ 126: isopod
+ 127: white stork
+ 128: black stork
+ 129: spoonbill
+ 130: flamingo
+ 131: little blue heron
+ 132: great egret
+ 133: bittern
+ 134: crane (bird)
+ 135: limpkin
+ 136: common gallinule
+ 137: American coot
+ 138: bustard
+ 139: ruddy turnstone
+ 140: dunlin
+ 141: common redshank
+ 142: dowitcher
+ 143: oystercatcher
+ 144: pelican
+ 145: king penguin
+ 146: albatross
+ 147: grey whale
+ 148: killer whale
+ 149: dugong
+ 150: sea lion
+ 151: Chihuahua
+ 152: Japanese Chin
+ 153: Maltese
+ 154: Pekingese
+ 155: Shih Tzu
+ 156: King Charles Spaniel
+ 157: Papillon
+ 158: toy terrier
+ 159: Rhodesian Ridgeback
+ 160: Afghan Hound
+ 161: Basset Hound
+ 162: Beagle
+ 163: Bloodhound
+ 164: Bluetick Coonhound
+ 165: Black and Tan Coonhound
+ 166: Treeing Walker Coonhound
+ 167: English foxhound
+ 168: Redbone Coonhound
+ 169: borzoi
+ 170: Irish Wolfhound
+ 171: Italian Greyhound
+ 172: Whippet
+ 173: Ibizan Hound
+ 174: Norwegian Elkhound
+ 175: Otterhound
+ 176: Saluki
+ 177: Scottish Deerhound
+ 178: Weimaraner
+ 179: Staffordshire Bull Terrier
+ 180: American Staffordshire Terrier
+ 181: Bedlington Terrier
+ 182: Border Terrier
+ 183: Kerry Blue Terrier
+ 184: Irish Terrier
+ 185: Norfolk Terrier
+ 186: Norwich Terrier
+ 187: Yorkshire Terrier
+ 188: Wire Fox Terrier
+ 189: Lakeland Terrier
+ 190: Sealyham Terrier
+ 191: Airedale Terrier
+ 192: Cairn Terrier
+ 193: Australian Terrier
+ 194: Dandie Dinmont Terrier
+ 195: Boston Terrier
+ 196: Miniature Schnauzer
+ 197: Giant Schnauzer
+ 198: Standard Schnauzer
+ 199: Scottish Terrier
+ 200: Tibetan Terrier
+ 201: Australian Silky Terrier
+ 202: Soft-coated Wheaten Terrier
+ 203: West Highland White Terrier
+ 204: Lhasa Apso
+ 205: Flat-Coated Retriever
+ 206: Curly-coated Retriever
+ 207: Golden Retriever
+ 208: Labrador Retriever
+ 209: Chesapeake Bay Retriever
+ 210: German Shorthaired Pointer
+ 211: Vizsla
+ 212: English Setter
+ 213: Irish Setter
+ 214: Gordon Setter
+ 215: Brittany
+ 216: Clumber Spaniel
+ 217: English Springer Spaniel
+ 218: Welsh Springer Spaniel
+ 219: Cocker Spaniels
+ 220: Sussex Spaniel
+ 221: Irish Water Spaniel
+ 222: Kuvasz
+ 223: Schipperke
+ 224: Groenendael
+ 225: Malinois
+ 226: Briard
+ 227: Australian Kelpie
+ 228: Komondor
+ 229: Old English Sheepdog
+ 230: Shetland Sheepdog
+ 231: collie
+ 232: Border Collie
+ 233: Bouvier des Flandres
+ 234: Rottweiler
+ 235: German Shepherd Dog
+ 236: Dobermann
+ 237: Miniature Pinscher
+ 238: Greater Swiss Mountain Dog
+ 239: Bernese Mountain Dog
+ 240: Appenzeller Sennenhund
+ 241: Entlebucher Sennenhund
+ 242: Boxer
+ 243: Bullmastiff
+ 244: Tibetan Mastiff
+ 245: French Bulldog
+ 246: Great Dane
+ 247: St. Bernard
+ 248: husky
+ 249: Alaskan Malamute
+ 250: Siberian Husky
+ 251: Dalmatian
+ 252: Affenpinscher
+ 253: Basenji
+ 254: pug
+ 255: Leonberger
+ 256: Newfoundland
+ 257: Pyrenean Mountain Dog
+ 258: Samoyed
+ 259: Pomeranian
+ 260: Chow Chow
+ 261: Keeshond
+ 262: Griffon Bruxellois
+ 263: Pembroke Welsh Corgi
+ 264: Cardigan Welsh Corgi
+ 265: Toy Poodle
+ 266: Miniature Poodle
+ 267: Standard Poodle
+ 268: Mexican hairless dog
+ 269: grey wolf
+ 270: Alaskan tundra wolf
+ 271: red wolf
+ 272: coyote
+ 273: dingo
+ 274: dhole
+ 275: African wild dog
+ 276: hyena
+ 277: red fox
+ 278: kit fox
+ 279: Arctic fox
+ 280: grey fox
+ 281: tabby cat
+ 282: tiger cat
+ 283: Persian cat
+ 284: Siamese cat
+ 285: Egyptian Mau
+ 286: cougar
+ 287: lynx
+ 288: leopard
+ 289: snow leopard
+ 290: jaguar
+ 291: lion
+ 292: tiger
+ 293: cheetah
+ 294: brown bear
+ 295: American black bear
+ 296: polar bear
+ 297: sloth bear
+ 298: mongoose
+ 299: meerkat
+ 300: tiger beetle
+ 301: ladybug
+ 302: ground beetle
+ 303: longhorn beetle
+ 304: leaf beetle
+ 305: dung beetle
+ 306: rhinoceros beetle
+ 307: weevil
+ 308: fly
+ 309: bee
+ 310: ant
+ 311: grasshopper
+ 312: cricket
+ 313: stick insect
+ 314: cockroach
+ 315: mantis
+ 316: cicada
+ 317: leafhopper
+ 318: lacewing
+ 319: dragonfly
+ 320: damselfly
+ 321: red admiral
+ 322: ringlet
+ 323: monarch butterfly
+ 324: small white
+ 325: sulphur butterfly
+ 326: gossamer-winged butterfly
+ 327: starfish
+ 328: sea urchin
+ 329: sea cucumber
+ 330: cottontail rabbit
+ 331: hare
+ 332: Angora rabbit
+ 333: hamster
+ 334: porcupine
+ 335: fox squirrel
+ 336: marmot
+ 337: beaver
+ 338: guinea pig
+ 339: common sorrel
+ 340: zebra
+ 341: pig
+ 342: wild boar
+ 343: warthog
+ 344: hippopotamus
+ 345: ox
+ 346: water buffalo
+ 347: bison
+ 348: ram
+ 349: bighorn sheep
+ 350: Alpine ibex
+ 351: hartebeest
+ 352: impala
+ 353: gazelle
+ 354: dromedary
+ 355: llama
+ 356: weasel
+ 357: mink
+ 358: European polecat
+ 359: black-footed ferret
+ 360: otter
+ 361: skunk
+ 362: badger
+ 363: armadillo
+ 364: three-toed sloth
+ 365: orangutan
+ 366: gorilla
+ 367: chimpanzee
+ 368: gibbon
+ 369: siamang
+ 370: guenon
+ 371: patas monkey
+ 372: baboon
+ 373: macaque
+ 374: langur
+ 375: black-and-white colobus
+ 376: proboscis monkey
+ 377: marmoset
+ 378: white-headed capuchin
+ 379: howler monkey
+ 380: titi
+ 381: Geoffroy's spider monkey
+ 382: common squirrel monkey
+ 383: ring-tailed lemur
+ 384: indri
+ 385: Asian elephant
+ 386: African bush elephant
+ 387: red panda
+ 388: giant panda
+ 389: snoek
+ 390: eel
+ 391: coho salmon
+ 392: rock beauty
+ 393: clownfish
+ 394: sturgeon
+ 395: garfish
+ 396: lionfish
+ 397: pufferfish
+ 398: abacus
+ 399: abaya
+ 400: academic gown
+ 401: accordion
+ 402: acoustic guitar
+ 403: aircraft carrier
+ 404: airliner
+ 405: airship
+ 406: altar
+ 407: ambulance
+ 408: amphibious vehicle
+ 409: analog clock
+ 410: apiary
+ 411: apron
+ 412: waste container
+ 413: assault rifle
+ 414: backpack
+ 415: bakery
+ 416: balance beam
+ 417: balloon
+ 418: ballpoint pen
+ 419: Band-Aid
+ 420: banjo
+ 421: baluster
+ 422: barbell
+ 423: barber chair
+ 424: barbershop
+ 425: barn
+ 426: barometer
+ 427: barrel
+ 428: wheelbarrow
+ 429: baseball
+ 430: basketball
+ 431: bassinet
+ 432: bassoon
+ 433: swimming cap
+ 434: bath towel
+ 435: bathtub
+ 436: station wagon
+ 437: lighthouse
+ 438: beaker
+ 439: military cap
+ 440: beer bottle
+ 441: beer glass
+ 442: bell-cot
+ 443: bib
+ 444: tandem bicycle
+ 445: bikini
+ 446: ring binder
+ 447: binoculars
+ 448: birdhouse
+ 449: boathouse
+ 450: bobsleigh
+ 451: bolo tie
+ 452: poke bonnet
+ 453: bookcase
+ 454: bookstore
+ 455: bottle cap
+ 456: bow
+ 457: bow tie
+ 458: brass
+ 459: bra
+ 460: breakwater
+ 461: breastplate
+ 462: broom
+ 463: bucket
+ 464: buckle
+ 465: bulletproof vest
+ 466: high-speed train
+ 467: butcher shop
+ 468: taxicab
+ 469: cauldron
+ 470: candle
+ 471: cannon
+ 472: canoe
+ 473: can opener
+ 474: cardigan
+ 475: car mirror
+ 476: carousel
+ 477: tool kit
+ 478: carton
+ 479: car wheel
+ 480: automated teller machine
+ 481: cassette
+ 482: cassette player
+ 483: castle
+ 484: catamaran
+ 485: CD player
+ 486: cello
+ 487: mobile phone
+ 488: chain
+ 489: chain-link fence
+ 490: chain mail
+ 491: chainsaw
+ 492: chest
+ 493: chiffonier
+ 494: chime
+ 495: china cabinet
+ 496: Christmas stocking
+ 497: church
+ 498: movie theater
+ 499: cleaver
+ 500: cliff dwelling
+ 501: cloak
+ 502: clogs
+ 503: cocktail shaker
+ 504: coffee mug
+ 505: coffeemaker
+ 506: coil
+ 507: combination lock
+ 508: computer keyboard
+ 509: confectionery store
+ 510: container ship
+ 511: convertible
+ 512: corkscrew
+ 513: cornet
+ 514: cowboy boot
+ 515: cowboy hat
+ 516: cradle
+ 517: crane (machine)
+ 518: crash helmet
+ 519: crate
+ 520: infant bed
+ 521: Crock Pot
+ 522: croquet ball
+ 523: crutch
+ 524: cuirass
+ 525: dam
+ 526: desk
+ 527: desktop computer
+ 528: rotary dial telephone
+ 529: diaper
+ 530: digital clock
+ 531: digital watch
+ 532: dining table
+ 533: dishcloth
+ 534: dishwasher
+ 535: disc brake
+ 536: dock
+ 537: dog sled
+ 538: dome
+ 539: doormat
+ 540: drilling rig
+ 541: drum
+ 542: drumstick
+ 543: dumbbell
+ 544: Dutch oven
+ 545: electric fan
+ 546: electric guitar
+ 547: electric locomotive
+ 548: entertainment center
+ 549: envelope
+ 550: espresso machine
+ 551: face powder
+ 552: feather boa
+ 553: filing cabinet
+ 554: fireboat
+ 555: fire engine
+ 556: fire screen sheet
+ 557: flagpole
+ 558: flute
+ 559: folding chair
+ 560: football helmet
+ 561: forklift
+ 562: fountain
+ 563: fountain pen
+ 564: four-poster bed
+ 565: freight car
+ 566: French horn
+ 567: frying pan
+ 568: fur coat
+ 569: garbage truck
+ 570: gas mask
+ 571: gas pump
+ 572: goblet
+ 573: go-kart
+ 574: golf ball
+ 575: golf cart
+ 576: gondola
+ 577: gong
+ 578: gown
+ 579: grand piano
+ 580: greenhouse
+ 581: grille
+ 582: grocery store
+ 583: guillotine
+ 584: barrette
+ 585: hair spray
+ 586: half-track
+ 587: hammer
+ 588: hamper
+ 589: hair dryer
+ 590: hand-held computer
+ 591: handkerchief
+ 592: hard disk drive
+ 593: harmonica
+ 594: harp
+ 595: harvester
+ 596: hatchet
+ 597: holster
+ 598: home theater
+ 599: honeycomb
+ 600: hook
+ 601: hoop skirt
+ 602: horizontal bar
+ 603: horse-drawn vehicle
+ 604: hourglass
+ 605: iPod
+ 606: clothes iron
+ 607: jack-o'-lantern
+ 608: jeans
+ 609: jeep
+ 610: T-shirt
+ 611: jigsaw puzzle
+ 612: pulled rickshaw
+ 613: joystick
+ 614: kimono
+ 615: knee pad
+ 616: knot
+ 617: lab coat
+ 618: ladle
+ 619: lampshade
+ 620: laptop computer
+ 621: lawn mower
+ 622: lens cap
+ 623: paper knife
+ 624: library
+ 625: lifeboat
+ 626: lighter
+ 627: limousine
+ 628: ocean liner
+ 629: lipstick
+ 630: slip-on shoe
+ 631: lotion
+ 632: speaker
+ 633: loupe
+ 634: sawmill
+ 635: magnetic compass
+ 636: mail bag
+ 637: mailbox
+ 638: tights
+ 639: tank suit
+ 640: manhole cover
+ 641: maraca
+ 642: marimba
+ 643: mask
+ 644: match
+ 645: maypole
+ 646: maze
+ 647: measuring cup
+ 648: medicine chest
+ 649: megalith
+ 650: microphone
+ 651: microwave oven
+ 652: military uniform
+ 653: milk can
+ 654: minibus
+ 655: miniskirt
+ 656: minivan
+ 657: missile
+ 658: mitten
+ 659: mixing bowl
+ 660: mobile home
+ 661: Model T
+ 662: modem
+ 663: monastery
+ 664: monitor
+ 665: moped
+ 666: mortar
+ 667: square academic cap
+ 668: mosque
+ 669: mosquito net
+ 670: scooter
+ 671: mountain bike
+ 672: tent
+ 673: computer mouse
+ 674: mousetrap
+ 675: moving van
+ 676: muzzle
+ 677: nail
+ 678: neck brace
+ 679: necklace
+ 680: nipple
+ 681: notebook computer
+ 682: obelisk
+ 683: oboe
+ 684: ocarina
+ 685: odometer
+ 686: oil filter
+ 687: organ
+ 688: oscilloscope
+ 689: overskirt
+ 690: bullock cart
+ 691: oxygen mask
+ 692: packet
+ 693: paddle
+ 694: paddle wheel
+ 695: padlock
+ 696: paintbrush
+ 697: pajamas
+ 698: palace
+ 699: pan flute
+ 700: paper towel
+ 701: parachute
+ 702: parallel bars
+ 703: park bench
+ 704: parking meter
+ 705: passenger car
+ 706: patio
+ 707: payphone
+ 708: pedestal
+ 709: pencil case
+ 710: pencil sharpener
+ 711: perfume
+ 712: Petri dish
+ 713: photocopier
+ 714: plectrum
+ 715: Pickelhaube
+ 716: picket fence
+ 717: pickup truck
+ 718: pier
+ 719: piggy bank
+ 720: pill bottle
+ 721: pillow
+ 722: ping-pong ball
+ 723: pinwheel
+ 724: pirate ship
+ 725: pitcher
+ 726: hand plane
+ 727: planetarium
+ 728: plastic bag
+ 729: plate rack
+ 730: plow
+ 731: plunger
+ 732: Polaroid camera
+ 733: pole
+ 734: police van
+ 735: poncho
+ 736: billiard table
+ 737: soda bottle
+ 738: pot
+ 739: potter's wheel
+ 740: power drill
+ 741: prayer rug
+ 742: printer
+ 743: prison
+ 744: projectile
+ 745: projector
+ 746: hockey puck
+ 747: punching bag
+ 748: purse
+ 749: quill
+ 750: quilt
+ 751: race car
+ 752: racket
+ 753: radiator
+ 754: radio
+ 755: radio telescope
+ 756: rain barrel
+ 757: recreational vehicle
+ 758: reel
+ 759: reflex camera
+ 760: refrigerator
+ 761: remote control
+ 762: restaurant
+ 763: revolver
+ 764: rifle
+ 765: rocking chair
+ 766: rotisserie
+ 767: eraser
+ 768: rugby ball
+ 769: ruler
+ 770: running shoe
+ 771: safe
+ 772: safety pin
+ 773: salt shaker
+ 774: sandal
+ 775: sarong
+ 776: saxophone
+ 777: scabbard
+ 778: weighing scale
+ 779: school bus
+ 780: schooner
+ 781: scoreboard
+ 782: CRT screen
+ 783: screw
+ 784: screwdriver
+ 785: seat belt
+ 786: sewing machine
+ 787: shield
+ 788: shoe store
+ 789: shoji
+ 790: shopping basket
+ 791: shopping cart
+ 792: shovel
+ 793: shower cap
+ 794: shower curtain
+ 795: ski
+ 796: ski mask
+ 797: sleeping bag
+ 798: slide rule
+ 799: sliding door
+ 800: slot machine
+ 801: snorkel
+ 802: snowmobile
+ 803: snowplow
+ 804: soap dispenser
+ 805: soccer ball
+ 806: sock
+ 807: solar thermal collector
+ 808: sombrero
+ 809: soup bowl
+ 810: space bar
+ 811: space heater
+ 812: space shuttle
+ 813: spatula
+ 814: motorboat
+ 815: spider web
+ 816: spindle
+ 817: sports car
+ 818: spotlight
+ 819: stage
+ 820: steam locomotive
+ 821: through arch bridge
+ 822: steel drum
+ 823: stethoscope
+ 824: scarf
+ 825: stone wall
+ 826: stopwatch
+ 827: stove
+ 828: strainer
+ 829: tram
+ 830: stretcher
+ 831: couch
+ 832: stupa
+ 833: submarine
+ 834: suit
+ 835: sundial
+ 836: sunglass
+ 837: sunglasses
+ 838: sunscreen
+ 839: suspension bridge
+ 840: mop
+ 841: sweatshirt
+ 842: swimsuit
+ 843: swing
+ 844: switch
+ 845: syringe
+ 846: table lamp
+ 847: tank
+ 848: tape player
+ 849: teapot
+ 850: teddy bear
+ 851: television
+ 852: tennis ball
+ 853: thatched roof
+ 854: front curtain
+ 855: thimble
+ 856: threshing machine
+ 857: throne
+ 858: tile roof
+ 859: toaster
+ 860: tobacco shop
+ 861: toilet seat
+ 862: torch
+ 863: totem pole
+ 864: tow truck
+ 865: toy store
+ 866: tractor
+ 867: semi-trailer truck
+ 868: tray
+ 869: trench coat
+ 870: tricycle
+ 871: trimaran
+ 872: tripod
+ 873: triumphal arch
+ 874: trolleybus
+ 875: trombone
+ 876: tub
+ 877: turnstile
+ 878: typewriter keyboard
+ 879: umbrella
+ 880: unicycle
+ 881: upright piano
+ 882: vacuum cleaner
+ 883: vase
+ 884: vault
+ 885: velvet
+ 886: vending machine
+ 887: vestment
+ 888: viaduct
+ 889: violin
+ 890: volleyball
+ 891: waffle iron
+ 892: wall clock
+ 893: wallet
+ 894: wardrobe
+ 895: military aircraft
+ 896: sink
+ 897: washing machine
+ 898: water bottle
+ 899: water jug
+ 900: water tower
+ 901: whiskey jug
+ 902: whistle
+ 903: wig
+ 904: window screen
+ 905: window shade
+ 906: Windsor tie
+ 907: wine bottle
+ 908: wing
+ 909: wok
+ 910: wooden spoon
+ 911: wool
+ 912: split-rail fence
+ 913: shipwreck
+ 914: yawl
+ 915: yurt
+ 916: website
+ 917: comic book
+ 918: crossword
+ 919: traffic sign
+ 920: traffic light
+ 921: dust jacket
+ 922: menu
+ 923: plate
+ 924: guacamole
+ 925: consomme
+ 926: hot pot
+ 927: trifle
+ 928: ice cream
+ 929: ice pop
+ 930: baguette
+ 931: bagel
+ 932: pretzel
+ 933: cheeseburger
+ 934: hot dog
+ 935: mashed potato
+ 936: cabbage
+ 937: broccoli
+ 938: cauliflower
+ 939: zucchini
+ 940: spaghetti squash
+ 941: acorn squash
+ 942: butternut squash
+ 943: cucumber
+ 944: artichoke
+ 945: bell pepper
+ 946: cardoon
+ 947: mushroom
+ 948: Granny Smith
+ 949: strawberry
+ 950: orange
+ 951: lemon
+ 952: fig
+ 953: pineapple
+ 954: banana
+ 955: jackfruit
+ 956: custard apple
+ 957: pomegranate
+ 958: hay
+ 959: carbonara
+ 960: chocolate syrup
+ 961: dough
+ 962: meatloaf
+ 963: pizza
+ 964: pot pie
+ 965: burrito
+ 966: red wine
+ 967: espresso
+ 968: cup
+ 969: eggnog
+ 970: alp
+ 971: bubble
+ 972: cliff
+ 973: coral reef
+ 974: geyser
+ 975: lakeshore
+ 976: promontory
+ 977: shoal
+ 978: seashore
+ 979: valley
+ 980: volcano
+ 981: baseball player
+ 982: bridegroom
+ 983: scuba diver
+ 984: rapeseed
+ 985: daisy
+ 986: yellow lady's slipper
+ 987: corn
+ 988: acorn
+ 989: rose hip
+ 990: horse chestnut seed
+ 991: coral fungus
+ 992: agaric
+ 993: gyromitra
+ 994: stinkhorn mushroom
+ 995: earth star
+ 996: hen-of-the-woods
+ 997: bolete
+ 998: ear
+ 999: toilet paper
+
+
+# Download script/URL (optional)
+download: data/scripts/get_imagenet.sh
diff --git a/src/FireDetect/data/Objects365.yaml b/src/FireDetect/data/Objects365.yaml
new file mode 100644
index 0000000..05b26a1
--- /dev/null
+++ b/src/FireDetect/data/Objects365.yaml
@@ -0,0 +1,438 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# Objects365 dataset https://www.objects365.org/ by Megvii
+# Example usage: python train.py --data Objects365.yaml
+# parent
+# ├── yolov5
+# └── datasets
+# └── Objects365 ← downloads here (712 GB = 367G data + 345G zips)
+
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/Objects365 # dataset root dir
+train: images/train # train images (relative to 'path') 1742289 images
+val: images/val # val images (relative to 'path') 80000 images
+test: # test images (optional)
+
+# Classes
+names:
+ 0: Person
+ 1: Sneakers
+ 2: Chair
+ 3: Other Shoes
+ 4: Hat
+ 5: Car
+ 6: Lamp
+ 7: Glasses
+ 8: Bottle
+ 9: Desk
+ 10: Cup
+ 11: Street Lights
+ 12: Cabinet/shelf
+ 13: Handbag/Satchel
+ 14: Bracelet
+ 15: Plate
+ 16: Picture/Frame
+ 17: Helmet
+ 18: Book
+ 19: Gloves
+ 20: Storage box
+ 21: Boat
+ 22: Leather Shoes
+ 23: Flower
+ 24: Bench
+ 25: Potted Plant
+ 26: Bowl/Basin
+ 27: Flag
+ 28: Pillow
+ 29: Boots
+ 30: Vase
+ 31: Microphone
+ 32: Necklace
+ 33: Ring
+ 34: SUV
+ 35: Wine Glass
+ 36: Belt
+ 37: Monitor/TV
+ 38: Backpack
+ 39: Umbrella
+ 40: Traffic Light
+ 41: Speaker
+ 42: Watch
+ 43: Tie
+ 44: Trash bin Can
+ 45: Slippers
+ 46: Bicycle
+ 47: Stool
+ 48: Barrel/bucket
+ 49: Van
+ 50: Couch
+ 51: Sandals
+ 52: Basket
+ 53: Drum
+ 54: Pen/Pencil
+ 55: Bus
+ 56: Wild Bird
+ 57: High Heels
+ 58: Motorcycle
+ 59: Guitar
+ 60: Carpet
+ 61: Cell Phone
+ 62: Bread
+ 63: Camera
+ 64: Canned
+ 65: Truck
+ 66: Traffic cone
+ 67: Cymbal
+ 68: Lifesaver
+ 69: Towel
+ 70: Stuffed Toy
+ 71: Candle
+ 72: Sailboat
+ 73: Laptop
+ 74: Awning
+ 75: Bed
+ 76: Faucet
+ 77: Tent
+ 78: Horse
+ 79: Mirror
+ 80: Power outlet
+ 81: Sink
+ 82: Apple
+ 83: Air Conditioner
+ 84: Knife
+ 85: Hockey Stick
+ 86: Paddle
+ 87: Pickup Truck
+ 88: Fork
+ 89: Traffic Sign
+ 90: Balloon
+ 91: Tripod
+ 92: Dog
+ 93: Spoon
+ 94: Clock
+ 95: Pot
+ 96: Cow
+ 97: Cake
+ 98: Dinning Table
+ 99: Sheep
+ 100: Hanger
+ 101: Blackboard/Whiteboard
+ 102: Napkin
+ 103: Other Fish
+ 104: Orange/Tangerine
+ 105: Toiletry
+ 106: Keyboard
+ 107: Tomato
+ 108: Lantern
+ 109: Machinery Vehicle
+ 110: Fan
+ 111: Green Vegetables
+ 112: Banana
+ 113: Baseball Glove
+ 114: Airplane
+ 115: Mouse
+ 116: Train
+ 117: Pumpkin
+ 118: Soccer
+ 119: Skiboard
+ 120: Luggage
+ 121: Nightstand
+ 122: Tea pot
+ 123: Telephone
+ 124: Trolley
+ 125: Head Phone
+ 126: Sports Car
+ 127: Stop Sign
+ 128: Dessert
+ 129: Scooter
+ 130: Stroller
+ 131: Crane
+ 132: Remote
+ 133: Refrigerator
+ 134: Oven
+ 135: Lemon
+ 136: Duck
+ 137: Baseball Bat
+ 138: Surveillance Camera
+ 139: Cat
+ 140: Jug
+ 141: Broccoli
+ 142: Piano
+ 143: Pizza
+ 144: Elephant
+ 145: Skateboard
+ 146: Surfboard
+ 147: Gun
+ 148: Skating and Skiing shoes
+ 149: Gas stove
+ 150: Donut
+ 151: Bow Tie
+ 152: Carrot
+ 153: Toilet
+ 154: Kite
+ 155: Strawberry
+ 156: Other Balls
+ 157: Shovel
+ 158: Pepper
+ 159: Computer Box
+ 160: Toilet Paper
+ 161: Cleaning Products
+ 162: Chopsticks
+ 163: Microwave
+ 164: Pigeon
+ 165: Baseball
+ 166: Cutting/chopping Board
+ 167: Coffee Table
+ 168: Side Table
+ 169: Scissors
+ 170: Marker
+ 171: Pie
+ 172: Ladder
+ 173: Snowboard
+ 174: Cookies
+ 175: Radiator
+ 176: Fire Hydrant
+ 177: Basketball
+ 178: Zebra
+ 179: Grape
+ 180: Giraffe
+ 181: Potato
+ 182: Sausage
+ 183: Tricycle
+ 184: Violin
+ 185: Egg
+ 186: Fire Extinguisher
+ 187: Candy
+ 188: Fire Truck
+ 189: Billiards
+ 190: Converter
+ 191: Bathtub
+ 192: Wheelchair
+ 193: Golf Club
+ 194: Briefcase
+ 195: Cucumber
+ 196: Cigar/Cigarette
+ 197: Paint Brush
+ 198: Pear
+ 199: Heavy Truck
+ 200: Hamburger
+ 201: Extractor
+ 202: Extension Cord
+ 203: Tong
+ 204: Tennis Racket
+ 205: Folder
+ 206: American Football
+ 207: earphone
+ 208: Mask
+ 209: Kettle
+ 210: Tennis
+ 211: Ship
+ 212: Swing
+ 213: Coffee Machine
+ 214: Slide
+ 215: Carriage
+ 216: Onion
+ 217: Green beans
+ 218: Projector
+ 219: Frisbee
+ 220: Washing Machine/Drying Machine
+ 221: Chicken
+ 222: Printer
+ 223: Watermelon
+ 224: Saxophone
+ 225: Tissue
+ 226: Toothbrush
+ 227: Ice cream
+ 228: Hot-air balloon
+ 229: Cello
+ 230: French Fries
+ 231: Scale
+ 232: Trophy
+ 233: Cabbage
+ 234: Hot dog
+ 235: Blender
+ 236: Peach
+ 237: Rice
+ 238: Wallet/Purse
+ 239: Volleyball
+ 240: Deer
+ 241: Goose
+ 242: Tape
+ 243: Tablet
+ 244: Cosmetics
+ 245: Trumpet
+ 246: Pineapple
+ 247: Golf Ball
+ 248: Ambulance
+ 249: Parking meter
+ 250: Mango
+ 251: Key
+ 252: Hurdle
+ 253: Fishing Rod
+ 254: Medal
+ 255: Flute
+ 256: Brush
+ 257: Penguin
+ 258: Megaphone
+ 259: Corn
+ 260: Lettuce
+ 261: Garlic
+ 262: Swan
+ 263: Helicopter
+ 264: Green Onion
+ 265: Sandwich
+ 266: Nuts
+ 267: Speed Limit Sign
+ 268: Induction Cooker
+ 269: Broom
+ 270: Trombone
+ 271: Plum
+ 272: Rickshaw
+ 273: Goldfish
+ 274: Kiwi fruit
+ 275: Router/modem
+ 276: Poker Card
+ 277: Toaster
+ 278: Shrimp
+ 279: Sushi
+ 280: Cheese
+ 281: Notepaper
+ 282: Cherry
+ 283: Pliers
+ 284: CD
+ 285: Pasta
+ 286: Hammer
+ 287: Cue
+ 288: Avocado
+ 289: Hamimelon
+ 290: Flask
+ 291: Mushroom
+ 292: Screwdriver
+ 293: Soap
+ 294: Recorder
+ 295: Bear
+ 296: Eggplant
+ 297: Board Eraser
+ 298: Coconut
+ 299: Tape Measure/Ruler
+ 300: Pig
+ 301: Showerhead
+ 302: Globe
+ 303: Chips
+ 304: Steak
+ 305: Crosswalk Sign
+ 306: Stapler
+ 307: Camel
+ 308: Formula 1
+ 309: Pomegranate
+ 310: Dishwasher
+ 311: Crab
+ 312: Hoverboard
+ 313: Meat ball
+ 314: Rice Cooker
+ 315: Tuba
+ 316: Calculator
+ 317: Papaya
+ 318: Antelope
+ 319: Parrot
+ 320: Seal
+ 321: Butterfly
+ 322: Dumbbell
+ 323: Donkey
+ 324: Lion
+ 325: Urinal
+ 326: Dolphin
+ 327: Electric Drill
+ 328: Hair Dryer
+ 329: Egg tart
+ 330: Jellyfish
+ 331: Treadmill
+ 332: Lighter
+ 333: Grapefruit
+ 334: Game board
+ 335: Mop
+ 336: Radish
+ 337: Baozi
+ 338: Target
+ 339: French
+ 340: Spring Rolls
+ 341: Monkey
+ 342: Rabbit
+ 343: Pencil Case
+ 344: Yak
+ 345: Red Cabbage
+ 346: Binoculars
+ 347: Asparagus
+ 348: Barbell
+ 349: Scallop
+ 350: Noddles
+ 351: Comb
+ 352: Dumpling
+ 353: Oyster
+ 354: Table Tennis paddle
+ 355: Cosmetics Brush/Eyeliner Pencil
+ 356: Chainsaw
+ 357: Eraser
+ 358: Lobster
+ 359: Durian
+ 360: Okra
+ 361: Lipstick
+ 362: Cosmetics Mirror
+ 363: Curling
+ 364: Table Tennis
+
+
+# Download script/URL (optional) ---------------------------------------------------------------------------------------
+download: |
+ from tqdm import tqdm
+
+ from utils.general import Path, check_requirements, download, np, xyxy2xywhn
+
+ check_requirements(('pycocotools>=2.0',))
+ from pycocotools.coco import COCO
+
+ # Make Directories
+ dir = Path(yaml['path']) # dataset root dir
+ for p in 'images', 'labels':
+ (dir / p).mkdir(parents=True, exist_ok=True)
+ for q in 'train', 'val':
+ (dir / p / q).mkdir(parents=True, exist_ok=True)
+
+ # Train, Val Splits
+ for split, patches in [('train', 50 + 1), ('val', 43 + 1)]:
+ print(f"Processing {split} in {patches} patches ...")
+ images, labels = dir / 'images' / split, dir / 'labels' / split
+
+ # Download
+ url = f"https://dorc.ks3-cn-beijing.ksyun.com/data-set/2020Objects365%E6%95%B0%E6%8D%AE%E9%9B%86/{split}/"
+ if split == 'train':
+ download([f'{url}zhiyuan_objv2_{split}.tar.gz'], dir=dir, delete=False) # annotations json
+ download([f'{url}patch{i}.tar.gz' for i in range(patches)], dir=images, curl=True, delete=False, threads=8)
+ elif split == 'val':
+ download([f'{url}zhiyuan_objv2_{split}.json'], dir=dir, delete=False) # annotations json
+ download([f'{url}images/v1/patch{i}.tar.gz' for i in range(15 + 1)], dir=images, curl=True, delete=False, threads=8)
+ download([f'{url}images/v2/patch{i}.tar.gz' for i in range(16, patches)], dir=images, curl=True, delete=False, threads=8)
+
+ # Move
+ for f in tqdm(images.rglob('*.jpg'), desc=f'Moving {split} images'):
+ f.rename(images / f.name) # move to /images/{split}
+
+ # Labels
+ coco = COCO(dir / f'zhiyuan_objv2_{split}.json')
+ names = [x["name"] for x in coco.loadCats(coco.getCatIds())]
+ for cid, cat in enumerate(names):
+ catIds = coco.getCatIds(catNms=[cat])
+ imgIds = coco.getImgIds(catIds=catIds)
+ for im in tqdm(coco.loadImgs(imgIds), desc=f'Class {cid + 1}/{len(names)} {cat}'):
+ width, height = im["width"], im["height"]
+ path = Path(im["file_name"]) # image filename
+ try:
+ with open(labels / path.with_suffix('.txt').name, 'a') as file:
+ annIds = coco.getAnnIds(imgIds=im["id"], catIds=catIds, iscrowd=None)
+ for a in coco.loadAnns(annIds):
+ x, y, w, h = a['bbox'] # bounding box in xywh (xy top-left corner)
+ xyxy = np.array([x, y, x + w, y + h])[None] # pixels(1,4)
+ x, y, w, h = xyxy2xywhn(xyxy, w=width, h=height, clip=True)[0] # normalized and clipped
+ file.write(f"{cid} {x:.5f} {y:.5f} {w:.5f} {h:.5f}\n")
+ except Exception as e:
+ print(e)
diff --git a/src/FireDetect/data/SKU-110K.yaml b/src/FireDetect/data/SKU-110K.yaml
new file mode 100644
index 0000000..edae717
--- /dev/null
+++ b/src/FireDetect/data/SKU-110K.yaml
@@ -0,0 +1,53 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# SKU-110K retail items dataset https://github.com/eg4000/SKU110K_CVPR19 by Trax Retail
+# Example usage: python train.py --data SKU-110K.yaml
+# parent
+# ├── yolov5
+# └── datasets
+# └── SKU-110K ← downloads here (13.6 GB)
+
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/SKU-110K # dataset root dir
+train: train.txt # train images (relative to 'path') 8219 images
+val: val.txt # val images (relative to 'path') 588 images
+test: test.txt # test images (optional) 2936 images
+
+# Classes
+names:
+ 0: object
+
+
+# Download script/URL (optional) ---------------------------------------------------------------------------------------
+download: |
+ import shutil
+ from tqdm import tqdm
+ from utils.general import np, pd, Path, download, xyxy2xywh
+
+
+ # Download
+ dir = Path(yaml['path']) # dataset root dir
+ parent = Path(dir.parent) # download dir
+ urls = ['http://trax-geometry.s3.amazonaws.com/cvpr_challenge/SKU110K_fixed.tar.gz']
+ download(urls, dir=parent, delete=False)
+
+ # Rename directories
+ if dir.exists():
+ shutil.rmtree(dir)
+ (parent / 'SKU110K_fixed').rename(dir) # rename dir
+ (dir / 'labels').mkdir(parents=True, exist_ok=True) # create labels dir
+
+ # Convert labels
+ names = 'image', 'x1', 'y1', 'x2', 'y2', 'class', 'image_width', 'image_height' # column names
+ for d in 'annotations_train.csv', 'annotations_val.csv', 'annotations_test.csv':
+ x = pd.read_csv(dir / 'annotations' / d, names=names).values # annotations
+ images, unique_images = x[:, 0], np.unique(x[:, 0])
+ with open((dir / d).with_suffix('.txt').__str__().replace('annotations_', ''), 'w') as f:
+ f.writelines(f'./images/{s}\n' for s in unique_images)
+ for im in tqdm(unique_images, desc=f'Converting {dir / d}'):
+ cls = 0 # single-class dataset
+ with open((dir / 'labels' / im).with_suffix('.txt'), 'a') as f:
+ for r in x[images == im]:
+ w, h = r[6], r[7] # image width, height
+ xywh = xyxy2xywh(np.array([[r[1] / w, r[2] / h, r[3] / w, r[4] / h]]))[0] # instance
+ f.write(f"{cls} {xywh[0]:.5f} {xywh[1]:.5f} {xywh[2]:.5f} {xywh[3]:.5f}\n") # write label
diff --git a/src/FireDetect/data/VOC.yaml b/src/FireDetect/data/VOC.yaml
new file mode 100644
index 0000000..27d3810
--- /dev/null
+++ b/src/FireDetect/data/VOC.yaml
@@ -0,0 +1,100 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC by University of Oxford
+# Example usage: python train.py --data VOC.yaml
+# parent
+# ├── yolov5
+# └── datasets
+# └── VOC ← downloads here (2.8 GB)
+
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/VOC
+train: # train images (relative to 'path') 16551 images
+ - images/train2012
+ - images/train2007
+ - images/val2012
+ - images/val2007
+val: # val images (relative to 'path') 4952 images
+ - images/test2007
+test: # test images (optional)
+ - images/test2007
+
+# Classes
+names:
+ 0: aeroplane
+ 1: bicycle
+ 2: bird
+ 3: boat
+ 4: bottle
+ 5: bus
+ 6: car
+ 7: cat
+ 8: chair
+ 9: cow
+ 10: diningtable
+ 11: dog
+ 12: horse
+ 13: motorbike
+ 14: person
+ 15: pottedplant
+ 16: sheep
+ 17: sofa
+ 18: train
+ 19: tvmonitor
+
+
+# Download script/URL (optional) ---------------------------------------------------------------------------------------
+download: |
+ import xml.etree.ElementTree as ET
+
+ from tqdm import tqdm
+ from utils.general import download, Path
+
+
+ def convert_label(path, lb_path, year, image_id):
+ def convert_box(size, box):
+ dw, dh = 1. / size[0], 1. / size[1]
+ x, y, w, h = (box[0] + box[1]) / 2.0 - 1, (box[2] + box[3]) / 2.0 - 1, box[1] - box[0], box[3] - box[2]
+ return x * dw, y * dh, w * dw, h * dh
+
+ in_file = open(path / f'VOC{year}/Annotations/{image_id}.xml')
+ out_file = open(lb_path, 'w')
+ tree = ET.parse(in_file)
+ root = tree.getroot()
+ size = root.find('size')
+ w = int(size.find('width').text)
+ h = int(size.find('height').text)
+
+ names = list(yaml['names'].values()) # names list
+ for obj in root.iter('object'):
+ cls = obj.find('name').text
+ if cls in names and int(obj.find('difficult').text) != 1:
+ xmlbox = obj.find('bndbox')
+ bb = convert_box((w, h), [float(xmlbox.find(x).text) for x in ('xmin', 'xmax', 'ymin', 'ymax')])
+ cls_id = names.index(cls) # class id
+ out_file.write(" ".join([str(a) for a in (cls_id, *bb)]) + '\n')
+
+
+ # Download
+ dir = Path(yaml['path']) # dataset root dir
+ url = 'https://github.com/ultralytics/yolov5/releases/download/v1.0/'
+ urls = [f'{url}VOCtrainval_06-Nov-2007.zip', # 446MB, 5012 images
+ f'{url}VOCtest_06-Nov-2007.zip', # 438MB, 4953 images
+ f'{url}VOCtrainval_11-May-2012.zip'] # 1.95GB, 17126 images
+ download(urls, dir=dir / 'images', delete=False, curl=True, threads=3)
+
+ # Convert
+ path = dir / 'images/VOCdevkit'
+ for year, image_set in ('2012', 'train'), ('2012', 'val'), ('2007', 'train'), ('2007', 'val'), ('2007', 'test'):
+ imgs_path = dir / 'images' / f'{image_set}{year}'
+ lbs_path = dir / 'labels' / f'{image_set}{year}'
+ imgs_path.mkdir(exist_ok=True, parents=True)
+ lbs_path.mkdir(exist_ok=True, parents=True)
+
+ with open(path / f'VOC{year}/ImageSets/Main/{image_set}.txt') as f:
+ image_ids = f.read().strip().split()
+ for id in tqdm(image_ids, desc=f'{image_set}{year}'):
+ f = path / f'VOC{year}/JPEGImages/{id}.jpg' # old img path
+ lb_path = (lbs_path / f.name).with_suffix('.txt') # new label path
+ f.rename(imgs_path / f.name) # move image
+ convert_label(path, lb_path, year, id) # convert labels to YOLO format
diff --git a/src/FireDetect/data/VisDrone.yaml b/src/FireDetect/data/VisDrone.yaml
new file mode 100644
index 0000000..a8bcf8e
--- /dev/null
+++ b/src/FireDetect/data/VisDrone.yaml
@@ -0,0 +1,70 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# VisDrone2019-DET dataset https://github.com/VisDrone/VisDrone-Dataset by Tianjin University
+# Example usage: python train.py --data VisDrone.yaml
+# parent
+# ├── yolov5
+# └── datasets
+# └── VisDrone ← downloads here (2.3 GB)
+
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/VisDrone # dataset root dir
+train: VisDrone2019-DET-train/images # train images (relative to 'path') 6471 images
+val: VisDrone2019-DET-val/images # val images (relative to 'path') 548 images
+test: VisDrone2019-DET-test-dev/images # test images (optional) 1610 images
+
+# Classes
+names:
+ 0: pedestrian
+ 1: people
+ 2: bicycle
+ 3: car
+ 4: van
+ 5: truck
+ 6: tricycle
+ 7: awning-tricycle
+ 8: bus
+ 9: motor
+
+
+# Download script/URL (optional) ---------------------------------------------------------------------------------------
+download: |
+ from utils.general import download, os, Path
+
+ def visdrone2yolo(dir):
+ from PIL import Image
+ from tqdm import tqdm
+
+ def convert_box(size, box):
+ # Convert VisDrone box to YOLO xywh box
+ dw = 1. / size[0]
+ dh = 1. / size[1]
+ return (box[0] + box[2] / 2) * dw, (box[1] + box[3] / 2) * dh, box[2] * dw, box[3] * dh
+
+ (dir / 'labels').mkdir(parents=True, exist_ok=True) # make labels directory
+ pbar = tqdm((dir / 'annotations').glob('*.txt'), desc=f'Converting {dir}')
+ for f in pbar:
+ img_size = Image.open((dir / 'images' / f.name).with_suffix('.jpg')).size
+ lines = []
+ with open(f, 'r') as file: # read annotation.txt
+ for row in [x.split(',') for x in file.read().strip().splitlines()]:
+ if row[4] == '0': # VisDrone 'ignored regions' class 0
+ continue
+ cls = int(row[5]) - 1
+ box = convert_box(img_size, tuple(map(int, row[:4])))
+ lines.append(f"{cls} {' '.join(f'{x:.6f}' for x in box)}\n")
+ with open(str(f).replace(os.sep + 'annotations' + os.sep, os.sep + 'labels' + os.sep), 'w') as fl:
+ fl.writelines(lines) # write label.txt
+
+
+ # Download
+ dir = Path(yaml['path']) # dataset root dir
+ urls = ['https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-train.zip',
+ 'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-val.zip',
+ 'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-test-dev.zip',
+ 'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-test-challenge.zip']
+ download(urls, dir=dir, curl=True, threads=4)
+
+ # Convert
+ for d in 'VisDrone2019-DET-train', 'VisDrone2019-DET-val', 'VisDrone2019-DET-test-dev':
+ visdrone2yolo(dir / d) # convert VisDrone annotations to YOLO labels
diff --git a/src/FireDetect/data/coco.yaml b/src/FireDetect/data/coco.yaml
new file mode 100644
index 0000000..d64dfc7
--- /dev/null
+++ b/src/FireDetect/data/coco.yaml
@@ -0,0 +1,116 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# COCO 2017 dataset http://cocodataset.org by Microsoft
+# Example usage: python train.py --data coco.yaml
+# parent
+# ├── yolov5
+# └── datasets
+# └── coco ← downloads here (20.1 GB)
+
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/coco # dataset root dir
+train: train2017.txt # train images (relative to 'path') 118287 images
+val: val2017.txt # val images (relative to 'path') 5000 images
+test: test-dev2017.txt # 20288 of 40670 images, submit to https://competitions.codalab.org/competitions/20794
+
+# Classes
+names:
+ 0: person
+ 1: bicycle
+ 2: car
+ 3: motorcycle
+ 4: airplane
+ 5: bus
+ 6: train
+ 7: truck
+ 8: boat
+ 9: traffic light
+ 10: fire hydrant
+ 11: stop sign
+ 12: parking meter
+ 13: bench
+ 14: bird
+ 15: cat
+ 16: dog
+ 17: horse
+ 18: sheep
+ 19: cow
+ 20: elephant
+ 21: bear
+ 22: zebra
+ 23: giraffe
+ 24: backpack
+ 25: umbrella
+ 26: handbag
+ 27: tie
+ 28: suitcase
+ 29: frisbee
+ 30: skis
+ 31: snowboard
+ 32: sports ball
+ 33: kite
+ 34: baseball bat
+ 35: baseball glove
+ 36: skateboard
+ 37: surfboard
+ 38: tennis racket
+ 39: bottle
+ 40: wine glass
+ 41: cup
+ 42: fork
+ 43: knife
+ 44: spoon
+ 45: bowl
+ 46: banana
+ 47: apple
+ 48: sandwich
+ 49: orange
+ 50: broccoli
+ 51: carrot
+ 52: hot dog
+ 53: pizza
+ 54: donut
+ 55: cake
+ 56: chair
+ 57: couch
+ 58: potted plant
+ 59: bed
+ 60: dining table
+ 61: toilet
+ 62: tv
+ 63: laptop
+ 64: mouse
+ 65: remote
+ 66: keyboard
+ 67: cell phone
+ 68: microwave
+ 69: oven
+ 70: toaster
+ 71: sink
+ 72: refrigerator
+ 73: book
+ 74: clock
+ 75: vase
+ 76: scissors
+ 77: teddy bear
+ 78: hair drier
+ 79: toothbrush
+
+
+# Download script/URL (optional)
+download: |
+ from utils.general import download, Path
+
+
+ # Download labels
+ segments = False # segment or box labels
+ dir = Path(yaml['path']) # dataset root dir
+ url = 'https://github.com/ultralytics/yolov5/releases/download/v1.0/'
+ urls = [url + ('coco2017labels-segments.zip' if segments else 'coco2017labels.zip')] # labels
+ download(urls, dir=dir.parent)
+
+ # Download data
+ urls = ['http://images.cocodataset.org/zips/train2017.zip', # 19G, 118k images
+ 'http://images.cocodataset.org/zips/val2017.zip', # 1G, 5k images
+ 'http://images.cocodataset.org/zips/test2017.zip'] # 7G, 41k images (optional)
+ download(urls, dir=dir / 'images', threads=3)
diff --git a/src/FireDetect/data/coco128-seg.yaml b/src/FireDetect/data/coco128-seg.yaml
new file mode 100644
index 0000000..5e81910
--- /dev/null
+++ b/src/FireDetect/data/coco128-seg.yaml
@@ -0,0 +1,101 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# COCO128-seg dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics
+# Example usage: python train.py --data coco128.yaml
+# parent
+# ├── yolov5
+# └── datasets
+# └── coco128-seg ← downloads here (7 MB)
+
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/coco128-seg # dataset root dir
+train: images/train2017 # train images (relative to 'path') 128 images
+val: images/train2017 # val images (relative to 'path') 128 images
+test: # test images (optional)
+
+# Classes
+names:
+ 0: person
+ 1: bicycle
+ 2: car
+ 3: motorcycle
+ 4: airplane
+ 5: bus
+ 6: train
+ 7: truck
+ 8: boat
+ 9: traffic light
+ 10: fire hydrant
+ 11: stop sign
+ 12: parking meter
+ 13: bench
+ 14: bird
+ 15: cat
+ 16: dog
+ 17: horse
+ 18: sheep
+ 19: cow
+ 20: elephant
+ 21: bear
+ 22: zebra
+ 23: giraffe
+ 24: backpack
+ 25: umbrella
+ 26: handbag
+ 27: tie
+ 28: suitcase
+ 29: frisbee
+ 30: skis
+ 31: snowboard
+ 32: sports ball
+ 33: kite
+ 34: baseball bat
+ 35: baseball glove
+ 36: skateboard
+ 37: surfboard
+ 38: tennis racket
+ 39: bottle
+ 40: wine glass
+ 41: cup
+ 42: fork
+ 43: knife
+ 44: spoon
+ 45: bowl
+ 46: banana
+ 47: apple
+ 48: sandwich
+ 49: orange
+ 50: broccoli
+ 51: carrot
+ 52: hot dog
+ 53: pizza
+ 54: donut
+ 55: cake
+ 56: chair
+ 57: couch
+ 58: potted plant
+ 59: bed
+ 60: dining table
+ 61: toilet
+ 62: tv
+ 63: laptop
+ 64: mouse
+ 65: remote
+ 66: keyboard
+ 67: cell phone
+ 68: microwave
+ 69: oven
+ 70: toaster
+ 71: sink
+ 72: refrigerator
+ 73: book
+ 74: clock
+ 75: vase
+ 76: scissors
+ 77: teddy bear
+ 78: hair drier
+ 79: toothbrush
+
+
+# Download script/URL (optional)
+download: https://ultralytics.com/assets/coco128-seg.zip
diff --git a/src/FireDetect/data/coco128.yaml b/src/FireDetect/data/coco128.yaml
new file mode 100644
index 0000000..1255673
--- /dev/null
+++ b/src/FireDetect/data/coco128.yaml
@@ -0,0 +1,101 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# COCO128 dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics
+# Example usage: python train.py --data coco128.yaml
+# parent
+# ├── yolov5
+# └── datasets
+# └── coco128 ← downloads here (7 MB)
+
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/coco128 # dataset root dir
+train: images/train2017 # train images (relative to 'path') 128 images
+val: images/train2017 # val images (relative to 'path') 128 images
+test: # test images (optional)
+
+# Classes
+names:
+ 0: person
+ 1: bicycle
+ 2: car
+ 3: motorcycle
+ 4: airplane
+ 5: bus
+ 6: train
+ 7: truck
+ 8: boat
+ 9: traffic light
+ 10: fire hydrant
+ 11: stop sign
+ 12: parking meter
+ 13: bench
+ 14: bird
+ 15: cat
+ 16: dog
+ 17: horse
+ 18: sheep
+ 19: cow
+ 20: elephant
+ 21: bear
+ 22: zebra
+ 23: giraffe
+ 24: backpack
+ 25: umbrella
+ 26: handbag
+ 27: tie
+ 28: suitcase
+ 29: frisbee
+ 30: skis
+ 31: snowboard
+ 32: sports ball
+ 33: kite
+ 34: baseball bat
+ 35: baseball glove
+ 36: skateboard
+ 37: surfboard
+ 38: tennis racket
+ 39: bottle
+ 40: wine glass
+ 41: cup
+ 42: fork
+ 43: knife
+ 44: spoon
+ 45: bowl
+ 46: banana
+ 47: apple
+ 48: sandwich
+ 49: orange
+ 50: broccoli
+ 51: carrot
+ 52: hot dog
+ 53: pizza
+ 54: donut
+ 55: cake
+ 56: chair
+ 57: couch
+ 58: potted plant
+ 59: bed
+ 60: dining table
+ 61: toilet
+ 62: tv
+ 63: laptop
+ 64: mouse
+ 65: remote
+ 66: keyboard
+ 67: cell phone
+ 68: microwave
+ 69: oven
+ 70: toaster
+ 71: sink
+ 72: refrigerator
+ 73: book
+ 74: clock
+ 75: vase
+ 76: scissors
+ 77: teddy bear
+ 78: hair drier
+ 79: toothbrush
+
+
+# Download script/URL (optional)
+download: https://ultralytics.com/assets/coco128.zip
diff --git a/src/FireDetect/data/hyps/hyp.Objects365.yaml b/src/FireDetect/data/hyps/hyp.Objects365.yaml
new file mode 100644
index 0000000..7497174
--- /dev/null
+++ b/src/FireDetect/data/hyps/hyp.Objects365.yaml
@@ -0,0 +1,34 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# Hyperparameters for Objects365 training
+# python train.py --weights yolov5m.pt --data Objects365.yaml --evolve
+# See Hyperparameter Evolution tutorial for details https://github.com/ultralytics/yolov5#tutorials
+
+lr0: 0.00258
+lrf: 0.17
+momentum: 0.779
+weight_decay: 0.00058
+warmup_epochs: 1.33
+warmup_momentum: 0.86
+warmup_bias_lr: 0.0711
+box: 0.0539
+cls: 0.299
+cls_pw: 0.825
+obj: 0.632
+obj_pw: 1.0
+iou_t: 0.2
+anchor_t: 3.44
+anchors: 3.2
+fl_gamma: 0.0
+hsv_h: 0.0188
+hsv_s: 0.704
+hsv_v: 0.36
+degrees: 0.0
+translate: 0.0902
+scale: 0.491
+shear: 0.0
+perspective: 0.0
+flipud: 0.0
+fliplr: 0.5
+mosaic: 1.0
+mixup: 0.0
+copy_paste: 0.0
diff --git a/src/FireDetect/data/hyps/hyp.VOC.yaml b/src/FireDetect/data/hyps/hyp.VOC.yaml
new file mode 100644
index 0000000..0aa4e7d
--- /dev/null
+++ b/src/FireDetect/data/hyps/hyp.VOC.yaml
@@ -0,0 +1,40 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# Hyperparameters for VOC training
+# python train.py --batch 128 --weights yolov5m6.pt --data VOC.yaml --epochs 50 --img 512 --hyp hyp.scratch-med.yaml --evolve
+# See Hyperparameter Evolution tutorial for details https://github.com/ultralytics/yolov5#tutorials
+
+# YOLOv5 Hyperparameter Evolution Results
+# Best generation: 467
+# Last generation: 996
+# metrics/precision, metrics/recall, metrics/mAP_0.5, metrics/mAP_0.5:0.95, val/box_loss, val/obj_loss, val/cls_loss
+# 0.87729, 0.85125, 0.91286, 0.72664, 0.0076739, 0.0042529, 0.0013865
+
+lr0: 0.00334
+lrf: 0.15135
+momentum: 0.74832
+weight_decay: 0.00025
+warmup_epochs: 3.3835
+warmup_momentum: 0.59462
+warmup_bias_lr: 0.18657
+box: 0.02
+cls: 0.21638
+cls_pw: 0.5
+obj: 0.51728
+obj_pw: 0.67198
+iou_t: 0.2
+anchor_t: 3.3744
+fl_gamma: 0.0
+hsv_h: 0.01041
+hsv_s: 0.54703
+hsv_v: 0.27739
+degrees: 0.0
+translate: 0.04591
+scale: 0.75544
+shear: 0.0
+perspective: 0.0
+flipud: 0.0
+fliplr: 0.5
+mosaic: 0.85834
+mixup: 0.04266
+copy_paste: 0.0
+anchors: 3.412
diff --git a/src/FireDetect/data/hyps/hyp.scratch-high.yaml b/src/FireDetect/data/hyps/hyp.scratch-high.yaml
new file mode 100644
index 0000000..123cc84
--- /dev/null
+++ b/src/FireDetect/data/hyps/hyp.scratch-high.yaml
@@ -0,0 +1,34 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# Hyperparameters for high-augmentation COCO training from scratch
+# python train.py --batch 32 --cfg yolov5m6.yaml --weights '' --data coco.yaml --img 1280 --epochs 300
+# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials
+
+lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3)
+lrf: 0.1 # final OneCycleLR learning rate (lr0 * lrf)
+momentum: 0.937 # SGD momentum/Adam beta1
+weight_decay: 0.0005 # optimizer weight decay 5e-4
+warmup_epochs: 3.0 # warmup epochs (fractions ok)
+warmup_momentum: 0.8 # warmup initial momentum
+warmup_bias_lr: 0.1 # warmup initial bias lr
+box: 0.05 # box loss gain
+cls: 0.3 # cls loss gain
+cls_pw: 1.0 # cls BCELoss positive_weight
+obj: 0.7 # obj loss gain (scale with pixels)
+obj_pw: 1.0 # obj BCELoss positive_weight
+iou_t: 0.20 # IoU training threshold
+anchor_t: 4.0 # anchor-multiple threshold
+# anchors: 3 # anchors per output layer (0 to ignore)
+fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5)
+hsv_h: 0.015 # image HSV-Hue augmentation (fraction)
+hsv_s: 0.7 # image HSV-Saturation augmentation (fraction)
+hsv_v: 0.4 # image HSV-Value augmentation (fraction)
+degrees: 0.0 # image rotation (+/- deg)
+translate: 0.1 # image translation (+/- fraction)
+scale: 0.9 # image scale (+/- gain)
+shear: 0.0 # image shear (+/- deg)
+perspective: 0.0 # image perspective (+/- fraction), range 0-0.001
+flipud: 0.0 # image flip up-down (probability)
+fliplr: 0.5 # image flip left-right (probability)
+mosaic: 1.0 # image mosaic (probability)
+mixup: 0.1 # image mixup (probability)
+copy_paste: 0.1 # segment copy-paste (probability)
diff --git a/src/FireDetect/data/hyps/hyp.scratch-low.yaml b/src/FireDetect/data/hyps/hyp.scratch-low.yaml
new file mode 100644
index 0000000..b9ef1d5
--- /dev/null
+++ b/src/FireDetect/data/hyps/hyp.scratch-low.yaml
@@ -0,0 +1,34 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# Hyperparameters for low-augmentation COCO training from scratch
+# python train.py --batch 64 --cfg yolov5n6.yaml --weights '' --data coco.yaml --img 640 --epochs 300 --linear
+# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials
+
+lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3)
+lrf: 0.01 # final OneCycleLR learning rate (lr0 * lrf)
+momentum: 0.937 # SGD momentum/Adam beta1
+weight_decay: 0.0005 # optimizer weight decay 5e-4
+warmup_epochs: 3.0 # warmup epochs (fractions ok)
+warmup_momentum: 0.8 # warmup initial momentum
+warmup_bias_lr: 0.1 # warmup initial bias lr
+box: 0.05 # box loss gain
+cls: 0.5 # cls loss gain
+cls_pw: 1.0 # cls BCELoss positive_weight
+obj: 1.0 # obj loss gain (scale with pixels)
+obj_pw: 1.0 # obj BCELoss positive_weight
+iou_t: 0.20 # IoU training threshold
+anchor_t: 4.0 # anchor-multiple threshold
+# anchors: 3 # anchors per output layer (0 to ignore)
+fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5)
+hsv_h: 0.015 # image HSV-Hue augmentation (fraction)
+hsv_s: 0.7 # image HSV-Saturation augmentation (fraction)
+hsv_v: 0.4 # image HSV-Value augmentation (fraction)
+degrees: 0.0 # image rotation (+/- deg)
+translate: 0.1 # image translation (+/- fraction)
+scale: 0.5 # image scale (+/- gain)
+shear: 0.0 # image shear (+/- deg)
+perspective: 0.0 # image perspective (+/- fraction), range 0-0.001
+flipud: 0.0 # image flip up-down (probability)
+fliplr: 0.5 # image flip left-right (probability)
+mosaic: 1.0 # image mosaic (probability)
+mixup: 0.0 # image mixup (probability)
+copy_paste: 0.0 # segment copy-paste (probability)
diff --git a/src/FireDetect/data/hyps/hyp.scratch-med.yaml b/src/FireDetect/data/hyps/hyp.scratch-med.yaml
new file mode 100644
index 0000000..d6867d7
--- /dev/null
+++ b/src/FireDetect/data/hyps/hyp.scratch-med.yaml
@@ -0,0 +1,34 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# Hyperparameters for medium-augmentation COCO training from scratch
+# python train.py --batch 32 --cfg yolov5m6.yaml --weights '' --data coco.yaml --img 1280 --epochs 300
+# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials
+
+lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3)
+lrf: 0.1 # final OneCycleLR learning rate (lr0 * lrf)
+momentum: 0.937 # SGD momentum/Adam beta1
+weight_decay: 0.0005 # optimizer weight decay 5e-4
+warmup_epochs: 3.0 # warmup epochs (fractions ok)
+warmup_momentum: 0.8 # warmup initial momentum
+warmup_bias_lr: 0.1 # warmup initial bias lr
+box: 0.05 # box loss gain
+cls: 0.3 # cls loss gain
+cls_pw: 1.0 # cls BCELoss positive_weight
+obj: 0.7 # obj loss gain (scale with pixels)
+obj_pw: 1.0 # obj BCELoss positive_weight
+iou_t: 0.20 # IoU training threshold
+anchor_t: 4.0 # anchor-multiple threshold
+# anchors: 3 # anchors per output layer (0 to ignore)
+fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5)
+hsv_h: 0.015 # image HSV-Hue augmentation (fraction)
+hsv_s: 0.7 # image HSV-Saturation augmentation (fraction)
+hsv_v: 0.4 # image HSV-Value augmentation (fraction)
+degrees: 0.0 # image rotation (+/- deg)
+translate: 0.1 # image translation (+/- fraction)
+scale: 0.9 # image scale (+/- gain)
+shear: 0.0 # image shear (+/- deg)
+perspective: 0.0 # image perspective (+/- fraction), range 0-0.001
+flipud: 0.0 # image flip up-down (probability)
+fliplr: 0.5 # image flip left-right (probability)
+mosaic: 1.0 # image mosaic (probability)
+mixup: 0.1 # image mixup (probability)
+copy_paste: 0.0 # segment copy-paste (probability)
diff --git a/src/FireDetect/data/images/bus.jpg b/src/FireDetect/data/images/bus.jpg
new file mode 100644
index 0000000..b43e311
Binary files /dev/null and b/src/FireDetect/data/images/bus.jpg differ
diff --git a/src/FireDetect/data/images/zidane.jpg b/src/FireDetect/data/images/zidane.jpg
new file mode 100644
index 0000000..92d72ea
Binary files /dev/null and b/src/FireDetect/data/images/zidane.jpg differ
diff --git a/src/FireDetect/data/scripts/download_weights.sh b/src/FireDetect/data/scripts/download_weights.sh
new file mode 100644
index 0000000..31e0a15
--- /dev/null
+++ b/src/FireDetect/data/scripts/download_weights.sh
@@ -0,0 +1,22 @@
+#!/bin/bash
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# Download latest models from https://github.com/ultralytics/yolov5/releases
+# Example usage: bash data/scripts/download_weights.sh
+# parent
+# └── yolov5
+# ├── yolov5s.pt ← downloads here
+# ├── yolov5m.pt
+# └── ...
+
+python - <
+
+## About ClearML
+
+[ClearML](https://cutt.ly/yolov5-tutorial-clearml) is an [open-source](https://github.com/allegroai/clearml) toolbox designed to save you time ⏱️.
+
+🔨 Track every YOLOv5 training run in the experiment manager
+
+🔧 Version and easily access your custom training data with the integrated ClearML Data Versioning Tool
+
+🔦 Remotely train and monitor your YOLOv5 training runs using ClearML Agent
+
+🔬 Get the very best mAP using ClearML Hyperparameter Optimization
+
+🔭 Turn your newly trained YOLOv5 model into an API with just a few commands using ClearML Serving
+
+
+
+# YOLOv5 with Comet
+
+This guide will cover how to use YOLOv5 with [Comet](https://bit.ly/yolov5-readme-comet)
+
+# About Comet
+
+Comet builds tools that help data scientists, engineers, and team leaders accelerate and optimize machine learning and deep learning models.
+
+Track and visualize model metrics in real time, save your hyperparameters, datasets, and model checkpoints, and visualize your model predictions with [Comet Custom Panels](https://bit.ly/yolov5-colab-comet-panels)!
+Comet makes sure you never lose track of your work and makes it easy to share results and collaborate across teams of all sizes!
+
+# Getting Started
+
+## Install Comet
+
+```shell
+pip install comet_ml
+```
+
+## Configure Comet Credentials
+
+There are two ways to configure Comet with YOLOv5.
+
+You can either set your credentials through enviroment variables
+
+**Environment Variables**
+
+```shell
+export COMET_API_KEY=
+
+# Try out an Example!
+Check out an example of a [completed run here](https://www.comet.com/examples/comet-example-yolov5/a0e29e0e9b984e4a822db2a62d0cb357?experiment-tab=chart&showOutliers=true&smoothing=0&transformY=smoothing&xAxis=step&ref=yolov5&utm_source=yolov5&utm_medium=affilliate&utm_campaign=yolov5_comet_integration)
+
+Or better yet, try it out yourself in this Colab Notebook
+
+[](https://colab.research.google.com/drive/1RG0WOQyxlDlo5Km8GogJpIEJlg_5lyYO?usp=sharing)
+
+# Log automatically
+
+By default, Comet will log the following items
+
+## Metrics
+- Box Loss, Object Loss, Classification Loss for the training and validation data
+- mAP_0.5, mAP_0.5:0.95 metrics for the validation data.
+- Precision and Recall for the validation data
+
+## Parameters
+
+- Model Hyperparameters
+- All parameters passed through the command line options
+
+## Visualizations
+
+- Confusion Matrix of the model predictions on the validation data
+- Plots for the PR and F1 curves across all classes
+- Correlogram of the Class Labels
+
+# Configure Comet Logging
+
+Comet can be configured to log additional data either through command line flags passed to the training script
+or through environment variables.
+
+```shell
+export COMET_MODE=online # Set whether to run Comet in 'online' or 'offline' mode. Defaults to online
+export COMET_MODEL_NAME=
+
+You can preview the data directly in the Comet UI.
+
+
+Artifacts are versioned and also support adding metadata about the dataset. Comet will automatically log the metadata from your dataset `yaml` file
+
+
+### Using a saved Artifact
+
+If you would like to use a dataset from Comet Artifacts, set the `path` variable in your dataset `yaml` file to point to the following Artifact resource URL.
+
+```
+# contents of artifact.yaml file
+path: "comet://
+
+## Resuming a Training Run
+
+If your training run is interrupted for any reason, e.g. disrupted internet connection, you can resume the run using the `resume` flag and the Comet Run Path.
+
+The Run Path has the following format `comet://
diff --git a/src/FireDetect/utils/loggers/comet/__init__.py b/src/FireDetect/utils/loggers/comet/__init__.py
new file mode 100644
index 0000000..b0318f8
--- /dev/null
+++ b/src/FireDetect/utils/loggers/comet/__init__.py
@@ -0,0 +1,508 @@
+import glob
+import json
+import logging
+import os
+import sys
+from pathlib import Path
+
+logger = logging.getLogger(__name__)
+
+FILE = Path(__file__).resolve()
+ROOT = FILE.parents[3] # YOLOv5 root directory
+if str(ROOT) not in sys.path:
+ sys.path.append(str(ROOT)) # add ROOT to PATH
+
+try:
+ import comet_ml
+
+ # Project Configuration
+ config = comet_ml.config.get_config()
+ COMET_PROJECT_NAME = config.get_string(os.getenv("COMET_PROJECT_NAME"), "comet.project_name", default="yolov5")
+except (ModuleNotFoundError, ImportError):
+ comet_ml = None
+ COMET_PROJECT_NAME = None
+
+import PIL
+import torch
+import torchvision.transforms as T
+import yaml
+
+from utils.dataloaders import img2label_paths
+from utils.general import check_dataset, scale_boxes, xywh2xyxy
+from utils.metrics import box_iou
+
+COMET_PREFIX = "comet://"
+
+COMET_MODE = os.getenv("COMET_MODE", "online")
+
+# Model Saving Settings
+COMET_MODEL_NAME = os.getenv("COMET_MODEL_NAME", "yolov5")
+
+# Dataset Artifact Settings
+COMET_UPLOAD_DATASET = os.getenv("COMET_UPLOAD_DATASET", "false").lower() == "true"
+
+# Evaluation Settings
+COMET_LOG_CONFUSION_MATRIX = os.getenv("COMET_LOG_CONFUSION_MATRIX", "true").lower() == "true"
+COMET_LOG_PREDICTIONS = os.getenv("COMET_LOG_PREDICTIONS", "true").lower() == "true"
+COMET_MAX_IMAGE_UPLOADS = int(os.getenv("COMET_MAX_IMAGE_UPLOADS", 100))
+
+# Confusion Matrix Settings
+CONF_THRES = float(os.getenv("CONF_THRES", 0.001))
+IOU_THRES = float(os.getenv("IOU_THRES", 0.6))
+
+# Batch Logging Settings
+COMET_LOG_BATCH_METRICS = os.getenv("COMET_LOG_BATCH_METRICS", "false").lower() == "true"
+COMET_BATCH_LOGGING_INTERVAL = os.getenv("COMET_BATCH_LOGGING_INTERVAL", 1)
+COMET_PREDICTION_LOGGING_INTERVAL = os.getenv("COMET_PREDICTION_LOGGING_INTERVAL", 1)
+COMET_LOG_PER_CLASS_METRICS = os.getenv("COMET_LOG_PER_CLASS_METRICS", "false").lower() == "true"
+
+RANK = int(os.getenv("RANK", -1))
+
+to_pil = T.ToPILImage()
+
+
+class CometLogger:
+ """Log metrics, parameters, source code, models and much more
+ with Comet
+ """
+
+ def __init__(self, opt, hyp, run_id=None, job_type="Training", **experiment_kwargs) -> None:
+ self.job_type = job_type
+ self.opt = opt
+ self.hyp = hyp
+
+ # Comet Flags
+ self.comet_mode = COMET_MODE
+
+ self.save_model = opt.save_period > -1
+ self.model_name = COMET_MODEL_NAME
+
+ # Batch Logging Settings
+ self.log_batch_metrics = COMET_LOG_BATCH_METRICS
+ self.comet_log_batch_interval = COMET_BATCH_LOGGING_INTERVAL
+
+ # Dataset Artifact Settings
+ self.upload_dataset = self.opt.upload_dataset if self.opt.upload_dataset else COMET_UPLOAD_DATASET
+ self.resume = self.opt.resume
+
+ # Default parameters to pass to Experiment objects
+ self.default_experiment_kwargs = {
+ "log_code": False,
+ "log_env_gpu": True,
+ "log_env_cpu": True,
+ "project_name": COMET_PROJECT_NAME,}
+ self.default_experiment_kwargs.update(experiment_kwargs)
+ self.experiment = self._get_experiment(self.comet_mode, run_id)
+
+ self.data_dict = self.check_dataset(self.opt.data)
+ self.class_names = self.data_dict["names"]
+ self.num_classes = self.data_dict["nc"]
+
+ self.logged_images_count = 0
+ self.max_images = COMET_MAX_IMAGE_UPLOADS
+
+ if run_id is None:
+ self.experiment.log_other("Created from", "YOLOv5")
+ if not isinstance(self.experiment, comet_ml.OfflineExperiment):
+ workspace, project_name, experiment_id = self.experiment.url.split("/")[-3:]
+ self.experiment.log_other(
+ "Run Path",
+ f"{workspace}/{project_name}/{experiment_id}",
+ )
+ self.log_parameters(vars(opt))
+ self.log_parameters(self.opt.hyp)
+ self.log_asset_data(
+ self.opt.hyp,
+ name="hyperparameters.json",
+ metadata={"type": "hyp-config-file"},
+ )
+ self.log_asset(
+ f"{self.opt.save_dir}/opt.yaml",
+ metadata={"type": "opt-config-file"},
+ )
+
+ self.comet_log_confusion_matrix = COMET_LOG_CONFUSION_MATRIX
+
+ if hasattr(self.opt, "conf_thres"):
+ self.conf_thres = self.opt.conf_thres
+ else:
+ self.conf_thres = CONF_THRES
+ if hasattr(self.opt, "iou_thres"):
+ self.iou_thres = self.opt.iou_thres
+ else:
+ self.iou_thres = IOU_THRES
+
+ self.log_parameters({"val_iou_threshold": self.iou_thres, "val_conf_threshold": self.conf_thres})
+
+ self.comet_log_predictions = COMET_LOG_PREDICTIONS
+ if self.opt.bbox_interval == -1:
+ self.comet_log_prediction_interval = 1 if self.opt.epochs < 10 else self.opt.epochs // 10
+ else:
+ self.comet_log_prediction_interval = self.opt.bbox_interval
+
+ if self.comet_log_predictions:
+ self.metadata_dict = {}
+ self.logged_image_names = []
+
+ self.comet_log_per_class_metrics = COMET_LOG_PER_CLASS_METRICS
+
+ self.experiment.log_others({
+ "comet_mode": COMET_MODE,
+ "comet_max_image_uploads": COMET_MAX_IMAGE_UPLOADS,
+ "comet_log_per_class_metrics": COMET_LOG_PER_CLASS_METRICS,
+ "comet_log_batch_metrics": COMET_LOG_BATCH_METRICS,
+ "comet_log_confusion_matrix": COMET_LOG_CONFUSION_MATRIX,
+ "comet_model_name": COMET_MODEL_NAME,})
+
+ # Check if running the Experiment with the Comet Optimizer
+ if hasattr(self.opt, "comet_optimizer_id"):
+ self.experiment.log_other("optimizer_id", self.opt.comet_optimizer_id)
+ self.experiment.log_other("optimizer_objective", self.opt.comet_optimizer_objective)
+ self.experiment.log_other("optimizer_metric", self.opt.comet_optimizer_metric)
+ self.experiment.log_other("optimizer_parameters", json.dumps(self.hyp))
+
+ def _get_experiment(self, mode, experiment_id=None):
+ if mode == "offline":
+ if experiment_id is not None:
+ return comet_ml.ExistingOfflineExperiment(
+ previous_experiment=experiment_id,
+ **self.default_experiment_kwargs,
+ )
+
+ return comet_ml.OfflineExperiment(**self.default_experiment_kwargs,)
+
+ else:
+ try:
+ if experiment_id is not None:
+ return comet_ml.ExistingExperiment(
+ previous_experiment=experiment_id,
+ **self.default_experiment_kwargs,
+ )
+
+ return comet_ml.Experiment(**self.default_experiment_kwargs)
+
+ except ValueError:
+ logger.warning("COMET WARNING: "
+ "Comet credentials have not been set. "
+ "Comet will default to offline logging. "
+ "Please set your credentials to enable online logging.")
+ return self._get_experiment("offline", experiment_id)
+
+ return
+
+ def log_metrics(self, log_dict, **kwargs):
+ self.experiment.log_metrics(log_dict, **kwargs)
+
+ def log_parameters(self, log_dict, **kwargs):
+ self.experiment.log_parameters(log_dict, **kwargs)
+
+ def log_asset(self, asset_path, **kwargs):
+ self.experiment.log_asset(asset_path, **kwargs)
+
+ def log_asset_data(self, asset, **kwargs):
+ self.experiment.log_asset_data(asset, **kwargs)
+
+ def log_image(self, img, **kwargs):
+ self.experiment.log_image(img, **kwargs)
+
+ def log_model(self, path, opt, epoch, fitness_score, best_model=False):
+ if not self.save_model:
+ return
+
+ model_metadata = {
+ "fitness_score": fitness_score[-1],
+ "epochs_trained": epoch + 1,
+ "save_period": opt.save_period,
+ "total_epochs": opt.epochs,}
+
+ model_files = glob.glob(f"{path}/*.pt")
+ for model_path in model_files:
+ name = Path(model_path).name
+
+ self.experiment.log_model(
+ self.model_name,
+ file_or_folder=model_path,
+ file_name=name,
+ metadata=model_metadata,
+ overwrite=True,
+ )
+
+ def check_dataset(self, data_file):
+ with open(data_file) as f:
+ data_config = yaml.safe_load(f)
+
+ if data_config['path'].startswith(COMET_PREFIX):
+ path = data_config['path'].replace(COMET_PREFIX, "")
+ data_dict = self.download_dataset_artifact(path)
+
+ return data_dict
+
+ self.log_asset(self.opt.data, metadata={"type": "data-config-file"})
+
+ return check_dataset(data_file)
+
+ def log_predictions(self, image, labelsn, path, shape, predn):
+ if self.logged_images_count >= self.max_images:
+ return
+ detections = predn[predn[:, 4] > self.conf_thres]
+ iou = box_iou(labelsn[:, 1:], detections[:, :4])
+ mask, _ = torch.where(iou > self.iou_thres)
+ if len(mask) == 0:
+ return
+
+ filtered_detections = detections[mask]
+ filtered_labels = labelsn[mask]
+
+ image_id = path.split("/")[-1].split(".")[0]
+ image_name = f"{image_id}_curr_epoch_{self.experiment.curr_epoch}"
+ if image_name not in self.logged_image_names:
+ native_scale_image = PIL.Image.open(path)
+ self.log_image(native_scale_image, name=image_name)
+ self.logged_image_names.append(image_name)
+
+ metadata = []
+ for cls, *xyxy in filtered_labels.tolist():
+ metadata.append({
+ "label": f"{self.class_names[int(cls)]}-gt",
+ "score": 100,
+ "box": {
+ "x": xyxy[0],
+ "y": xyxy[1],
+ "x2": xyxy[2],
+ "y2": xyxy[3]},})
+ for *xyxy, conf, cls in filtered_detections.tolist():
+ metadata.append({
+ "label": f"{self.class_names[int(cls)]}",
+ "score": conf * 100,
+ "box": {
+ "x": xyxy[0],
+ "y": xyxy[1],
+ "x2": xyxy[2],
+ "y2": xyxy[3]},})
+
+ self.metadata_dict[image_name] = metadata
+ self.logged_images_count += 1
+
+ return
+
+ def preprocess_prediction(self, image, labels, shape, pred):
+ nl, _ = labels.shape[0], pred.shape[0]
+
+ # Predictions
+ if self.opt.single_cls:
+ pred[:, 5] = 0
+
+ predn = pred.clone()
+ scale_boxes(image.shape[1:], predn[:, :4], shape[0], shape[1])
+
+ labelsn = None
+ if nl:
+ tbox = xywh2xyxy(labels[:, 1:5]) # target boxes
+ scale_boxes(image.shape[1:], tbox, shape[0], shape[1]) # native-space labels
+ labelsn = torch.cat((labels[:, 0:1], tbox), 1) # native-space labels
+ scale_boxes(image.shape[1:], predn[:, :4], shape[0], shape[1]) # native-space pred
+
+ return predn, labelsn
+
+ def add_assets_to_artifact(self, artifact, path, asset_path, split):
+ img_paths = sorted(glob.glob(f"{asset_path}/*"))
+ label_paths = img2label_paths(img_paths)
+
+ for image_file, label_file in zip(img_paths, label_paths):
+ image_logical_path, label_logical_path = map(lambda x: os.path.relpath(x, path), [image_file, label_file])
+
+ try:
+ artifact.add(image_file, logical_path=image_logical_path, metadata={"split": split})
+ artifact.add(label_file, logical_path=label_logical_path, metadata={"split": split})
+ except ValueError as e:
+ logger.error('COMET ERROR: Error adding file to Artifact. Skipping file.')
+ logger.error(f"COMET ERROR: {e}")
+ continue
+
+ return artifact
+
+ def upload_dataset_artifact(self):
+ dataset_name = self.data_dict.get("dataset_name", "yolov5-dataset")
+ path = str((ROOT / Path(self.data_dict["path"])).resolve())
+
+ metadata = self.data_dict.copy()
+ for key in ["train", "val", "test"]:
+ split_path = metadata.get(key)
+ if split_path is not None:
+ metadata[key] = split_path.replace(path, "")
+
+ artifact = comet_ml.Artifact(name=dataset_name, artifact_type="dataset", metadata=metadata)
+ for key in metadata.keys():
+ if key in ["train", "val", "test"]:
+ if isinstance(self.upload_dataset, str) and (key != self.upload_dataset):
+ continue
+
+ asset_path = self.data_dict.get(key)
+ if asset_path is not None:
+ artifact = self.add_assets_to_artifact(artifact, path, asset_path, key)
+
+ self.experiment.log_artifact(artifact)
+
+ return
+
+ def download_dataset_artifact(self, artifact_path):
+ logged_artifact = self.experiment.get_artifact(artifact_path)
+ artifact_save_dir = str(Path(self.opt.save_dir) / logged_artifact.name)
+ logged_artifact.download(artifact_save_dir)
+
+ metadata = logged_artifact.metadata
+ data_dict = metadata.copy()
+ data_dict["path"] = artifact_save_dir
+
+ metadata_names = metadata.get("names")
+ if type(metadata_names) == dict:
+ data_dict["names"] = {int(k): v for k, v in metadata.get("names").items()}
+ elif type(metadata_names) == list:
+ data_dict["names"] = {int(k): v for k, v in zip(range(len(metadata_names)), metadata_names)}
+ else:
+ raise "Invalid 'names' field in dataset yaml file. Please use a list or dictionary"
+
+ data_dict = self.update_data_paths(data_dict)
+ return data_dict
+
+ def update_data_paths(self, data_dict):
+ path = data_dict.get("path", "")
+
+ for split in ["train", "val", "test"]:
+ if data_dict.get(split):
+ split_path = data_dict.get(split)
+ data_dict[split] = (f"{path}/{split_path}" if isinstance(split, str) else [
+ f"{path}/{x}" for x in split_path])
+
+ return data_dict
+
+ def on_pretrain_routine_end(self, paths):
+ if self.opt.resume:
+ return
+
+ for path in paths:
+ self.log_asset(str(path))
+
+ if self.upload_dataset:
+ if not self.resume:
+ self.upload_dataset_artifact()
+
+ return
+
+ def on_train_start(self):
+ self.log_parameters(self.hyp)
+
+ def on_train_epoch_start(self):
+ return
+
+ def on_train_epoch_end(self, epoch):
+ self.experiment.curr_epoch = epoch
+
+ return
+
+ def on_train_batch_start(self):
+ return
+
+ def on_train_batch_end(self, log_dict, step):
+ self.experiment.curr_step = step
+ if self.log_batch_metrics and (step % self.comet_log_batch_interval == 0):
+ self.log_metrics(log_dict, step=step)
+
+ return
+
+ def on_train_end(self, files, save_dir, last, best, epoch, results):
+ if self.comet_log_predictions:
+ curr_epoch = self.experiment.curr_epoch
+ self.experiment.log_asset_data(self.metadata_dict, "image-metadata.json", epoch=curr_epoch)
+
+ for f in files:
+ self.log_asset(f, metadata={"epoch": epoch})
+ self.log_asset(f"{save_dir}/results.csv", metadata={"epoch": epoch})
+
+ if not self.opt.evolve:
+ model_path = str(best if best.exists() else last)
+ name = Path(model_path).name
+ if self.save_model:
+ self.experiment.log_model(
+ self.model_name,
+ file_or_folder=model_path,
+ file_name=name,
+ overwrite=True,
+ )
+
+ # Check if running Experiment with Comet Optimizer
+ if hasattr(self.opt, 'comet_optimizer_id'):
+ metric = results.get(self.opt.comet_optimizer_metric)
+ self.experiment.log_other('optimizer_metric_value', metric)
+
+ self.finish_run()
+
+ def on_val_start(self):
+ return
+
+ def on_val_batch_start(self):
+ return
+
+ def on_val_batch_end(self, batch_i, images, targets, paths, shapes, outputs):
+ if not (self.comet_log_predictions and ((batch_i + 1) % self.comet_log_prediction_interval == 0)):
+ return
+
+ for si, pred in enumerate(outputs):
+ if len(pred) == 0:
+ continue
+
+ image = images[si]
+ labels = targets[targets[:, 0] == si, 1:]
+ shape = shapes[si]
+ path = paths[si]
+ predn, labelsn = self.preprocess_prediction(image, labels, shape, pred)
+ if labelsn is not None:
+ self.log_predictions(image, labelsn, path, shape, predn)
+
+ return
+
+ def on_val_end(self, nt, tp, fp, p, r, f1, ap, ap50, ap_class, confusion_matrix):
+ if self.comet_log_per_class_metrics:
+ if self.num_classes > 1:
+ for i, c in enumerate(ap_class):
+ class_name = self.class_names[c]
+ self.experiment.log_metrics(
+ {
+ 'mAP@.5': ap50[i],
+ 'mAP@.5:.95': ap[i],
+ 'precision': p[i],
+ 'recall': r[i],
+ 'f1': f1[i],
+ 'true_positives': tp[i],
+ 'false_positives': fp[i],
+ 'support': nt[c]},
+ prefix=class_name)
+
+ if self.comet_log_confusion_matrix:
+ epoch = self.experiment.curr_epoch
+ class_names = list(self.class_names.values())
+ class_names.append("background")
+ num_classes = len(class_names)
+
+ self.experiment.log_confusion_matrix(
+ matrix=confusion_matrix.matrix,
+ max_categories=num_classes,
+ labels=class_names,
+ epoch=epoch,
+ column_label='Actual Category',
+ row_label='Predicted Category',
+ file_name=f"confusion-matrix-epoch-{epoch}.json",
+ )
+
+ def on_fit_epoch_end(self, result, epoch):
+ self.log_metrics(result, epoch=epoch)
+
+ def on_model_save(self, last, epoch, final_epoch, best_fitness, fi):
+ if ((epoch + 1) % self.opt.save_period == 0 and not final_epoch) and self.opt.save_period != -1:
+ self.log_model(last.parent, self.opt, epoch, fi, best_model=best_fitness == fi)
+
+ def on_params_update(self, params):
+ self.log_parameters(params)
+
+ def finish_run(self):
+ self.experiment.end()
diff --git a/src/FireDetect/utils/loggers/comet/comet_utils.py b/src/FireDetect/utils/loggers/comet/comet_utils.py
new file mode 100644
index 0000000..3cbd451
--- /dev/null
+++ b/src/FireDetect/utils/loggers/comet/comet_utils.py
@@ -0,0 +1,150 @@
+import logging
+import os
+from urllib.parse import urlparse
+
+try:
+ import comet_ml
+except (ModuleNotFoundError, ImportError):
+ comet_ml = None
+
+import yaml
+
+logger = logging.getLogger(__name__)
+
+COMET_PREFIX = "comet://"
+COMET_MODEL_NAME = os.getenv("COMET_MODEL_NAME", "yolov5")
+COMET_DEFAULT_CHECKPOINT_FILENAME = os.getenv("COMET_DEFAULT_CHECKPOINT_FILENAME", "last.pt")
+
+
+def download_model_checkpoint(opt, experiment):
+ model_dir = f"{opt.project}/{experiment.name}"
+ os.makedirs(model_dir, exist_ok=True)
+
+ model_name = COMET_MODEL_NAME
+ model_asset_list = experiment.get_model_asset_list(model_name)
+
+ if len(model_asset_list) == 0:
+ logger.error(f"COMET ERROR: No checkpoints found for model name : {model_name}")
+ return
+
+ model_asset_list = sorted(
+ model_asset_list,
+ key=lambda x: x["step"],
+ reverse=True,
+ )
+ logged_checkpoint_map = {asset["fileName"]: asset["assetId"] for asset in model_asset_list}
+
+ resource_url = urlparse(opt.weights)
+ checkpoint_filename = resource_url.query
+
+ if checkpoint_filename:
+ asset_id = logged_checkpoint_map.get(checkpoint_filename)
+ else:
+ asset_id = logged_checkpoint_map.get(COMET_DEFAULT_CHECKPOINT_FILENAME)
+ checkpoint_filename = COMET_DEFAULT_CHECKPOINT_FILENAME
+
+ if asset_id is None:
+ logger.error(f"COMET ERROR: Checkpoint {checkpoint_filename} not found in the given Experiment")
+ return
+
+ try:
+ logger.info(f"COMET INFO: Downloading checkpoint {checkpoint_filename}")
+ asset_filename = checkpoint_filename
+
+ model_binary = experiment.get_asset(asset_id, return_type="binary", stream=False)
+ model_download_path = f"{model_dir}/{asset_filename}"
+ with open(model_download_path, "wb") as f:
+ f.write(model_binary)
+
+ opt.weights = model_download_path
+
+ except Exception as e:
+ logger.warning("COMET WARNING: Unable to download checkpoint from Comet")
+ logger.exception(e)
+
+
+def set_opt_parameters(opt, experiment):
+ """Update the opts Namespace with parameters
+ from Comet's ExistingExperiment when resuming a run
+
+ Args:
+ opt (argparse.Namespace): Namespace of command line options
+ experiment (comet_ml.APIExperiment): Comet API Experiment object
+ """
+ asset_list = experiment.get_asset_list()
+ resume_string = opt.resume
+
+ for asset in asset_list:
+ if asset["fileName"] == "opt.yaml":
+ asset_id = asset["assetId"]
+ asset_binary = experiment.get_asset(asset_id, return_type="binary", stream=False)
+ opt_dict = yaml.safe_load(asset_binary)
+ for key, value in opt_dict.items():
+ setattr(opt, key, value)
+ opt.resume = resume_string
+
+ # Save hyperparameters to YAML file
+ # Necessary to pass checks in training script
+ save_dir = f"{opt.project}/{experiment.name}"
+ os.makedirs(save_dir, exist_ok=True)
+
+ hyp_yaml_path = f"{save_dir}/hyp.yaml"
+ with open(hyp_yaml_path, "w") as f:
+ yaml.dump(opt.hyp, f)
+ opt.hyp = hyp_yaml_path
+
+
+def check_comet_weights(opt):
+ """Downloads model weights from Comet and updates the
+ weights path to point to saved weights location
+
+ Args:
+ opt (argparse.Namespace): Command Line arguments passed
+ to YOLOv5 training script
+
+ Returns:
+ None/bool: Return True if weights are successfully downloaded
+ else return None
+ """
+ if comet_ml is None:
+ return
+
+ if isinstance(opt.weights, str):
+ if opt.weights.startswith(COMET_PREFIX):
+ api = comet_ml.API()
+ resource = urlparse(opt.weights)
+ experiment_path = f"{resource.netloc}{resource.path}"
+ experiment = api.get(experiment_path)
+ download_model_checkpoint(opt, experiment)
+ return True
+
+ return None
+
+
+def check_comet_resume(opt):
+ """Restores run parameters to its original state based on the model checkpoint
+ and logged Experiment parameters.
+
+ Args:
+ opt (argparse.Namespace): Command Line arguments passed
+ to YOLOv5 training script
+
+ Returns:
+ None/bool: Return True if the run is restored successfully
+ else return None
+ """
+ if comet_ml is None:
+ return
+
+ if isinstance(opt.resume, str):
+ if opt.resume.startswith(COMET_PREFIX):
+ api = comet_ml.API()
+ resource = urlparse(opt.resume)
+ experiment_path = f"{resource.netloc}{resource.path}"
+ experiment = api.get(experiment_path)
+ set_opt_parameters(opt, experiment)
+ download_model_checkpoint(opt, experiment)
+
+ return True
+
+ return None
diff --git a/src/FireDetect/utils/loggers/comet/hpo.py b/src/FireDetect/utils/loggers/comet/hpo.py
new file mode 100644
index 0000000..7dd5c92
--- /dev/null
+++ b/src/FireDetect/utils/loggers/comet/hpo.py
@@ -0,0 +1,118 @@
+import argparse
+import json
+import logging
+import os
+import sys
+from pathlib import Path
+
+import comet_ml
+
+logger = logging.getLogger(__name__)
+
+FILE = Path(__file__).resolve()
+ROOT = FILE.parents[3] # YOLOv5 root directory
+if str(ROOT) not in sys.path:
+ sys.path.append(str(ROOT)) # add ROOT to PATH
+
+from train import train
+from utils.callbacks import Callbacks
+from utils.general import increment_path
+from utils.torch_utils import select_device
+
+# Project Configuration
+config = comet_ml.config.get_config()
+COMET_PROJECT_NAME = config.get_string(os.getenv("COMET_PROJECT_NAME"), "comet.project_name", default="yolov5")
+
+
+def get_args(known=False):
+ parser = argparse.ArgumentParser()
+ parser.add_argument('--weights', type=str, default=ROOT / 'yolov5s.pt', help='initial weights path')
+ parser.add_argument('--cfg', type=str, default='', help='model.yaml path')
+ parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='dataset.yaml path')
+ parser.add_argument('--hyp', type=str, default=ROOT / 'data/hyps/hyp.scratch-low.yaml', help='hyperparameters path')
+ parser.add_argument('--epochs', type=int, default=300, help='total training epochs')
+ parser.add_argument('--batch-size', type=int, default=16, help='total batch size for all GPUs, -1 for autobatch')
+ parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=640, help='train, val image size (pixels)')
+ parser.add_argument('--rect', action='store_true', help='rectangular training')
+ parser.add_argument('--resume', nargs='?', const=True, default=False, help='resume most recent training')
+ parser.add_argument('--nosave', action='store_true', help='only save final checkpoint')
+ parser.add_argument('--noval', action='store_true', help='only validate final epoch')
+ parser.add_argument('--noautoanchor', action='store_true', help='disable AutoAnchor')
+ parser.add_argument('--noplots', action='store_true', help='save no plot files')
+ parser.add_argument('--evolve', type=int, nargs='?', const=300, help='evolve hyperparameters for x generations')
+ parser.add_argument('--bucket', type=str, default='', help='gsutil bucket')
+ parser.add_argument('--cache', type=str, nargs='?', const='ram', help='--cache images in "ram" (default) or "disk"')
+ parser.add_argument('--image-weights', action='store_true', help='use weighted image selection for training')
+ parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
+ parser.add_argument('--multi-scale', action='store_true', help='vary img-size +/- 50%%')
+ parser.add_argument('--single-cls', action='store_true', help='train multi-class data as single-class')
+ parser.add_argument('--optimizer', type=str, choices=['SGD', 'Adam', 'AdamW'], default='SGD', help='optimizer')
+ parser.add_argument('--sync-bn', action='store_true', help='use SyncBatchNorm, only available in DDP mode')
+ parser.add_argument('--workers', type=int, default=8, help='max dataloader workers (per RANK in DDP mode)')
+ parser.add_argument('--project', default=ROOT / 'runs/train', help='save to project/name')
+ parser.add_argument('--name', default='exp', help='save to project/name')
+ parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
+ parser.add_argument('--quad', action='store_true', help='quad dataloader')
+ parser.add_argument('--cos-lr', action='store_true', help='cosine LR scheduler')
+ parser.add_argument('--label-smoothing', type=float, default=0.0, help='Label smoothing epsilon')
+ parser.add_argument('--patience', type=int, default=100, help='EarlyStopping patience (epochs without improvement)')
+ parser.add_argument('--freeze', nargs='+', type=int, default=[0], help='Freeze layers: backbone=10, first3=0 1 2')
+ parser.add_argument('--save-period', type=int, default=-1, help='Save checkpoint every x epochs (disabled if < 1)')
+ parser.add_argument('--seed', type=int, default=0, help='Global training seed')
+ parser.add_argument('--local_rank', type=int, default=-1, help='Automatic DDP Multi-GPU argument, do not modify')
+
+ # Weights & Biases arguments
+ parser.add_argument('--entity', default=None, help='W&B: Entity')
+ parser.add_argument('--upload_dataset', nargs='?', const=True, default=False, help='W&B: Upload data, "val" option')
+ parser.add_argument('--bbox_interval', type=int, default=-1, help='W&B: Set bounding-box image logging interval')
+ parser.add_argument('--artifact_alias', type=str, default='latest', help='W&B: Version of dataset artifact to use')
+
+ # Comet Arguments
+ parser.add_argument("--comet_optimizer_config", type=str, help="Comet: Path to a Comet Optimizer Config File.")
+ parser.add_argument("--comet_optimizer_id", type=str, help="Comet: ID of the Comet Optimizer sweep.")
+ parser.add_argument("--comet_optimizer_objective", type=str, help="Comet: Set to 'minimize' or 'maximize'.")
+ parser.add_argument("--comet_optimizer_metric", type=str, help="Comet: Metric to Optimize.")
+ parser.add_argument("--comet_optimizer_workers",
+ type=int,
+ default=1,
+ help="Comet: Number of Parallel Workers to use with the Comet Optimizer.")
+
+ return parser.parse_known_args()[0] if known else parser.parse_args()
+
+
+def run(parameters, opt):
+ hyp_dict = {k: v for k, v in parameters.items() if k not in ["epochs", "batch_size"]}
+
+ opt.save_dir = str(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok or opt.evolve))
+ opt.batch_size = parameters.get("batch_size")
+ opt.epochs = parameters.get("epochs")
+
+ device = select_device(opt.device, batch_size=opt.batch_size)
+ train(hyp_dict, opt, device, callbacks=Callbacks())
+
+
+if __name__ == "__main__":
+ opt = get_args(known=True)
+
+ opt.weights = str(opt.weights)
+ opt.cfg = str(opt.cfg)
+ opt.data = str(opt.data)
+ opt.project = str(opt.project)
+
+ optimizer_id = os.getenv("COMET_OPTIMIZER_ID")
+ if optimizer_id is None:
+ with open(opt.comet_optimizer_config) as f:
+ optimizer_config = json.load(f)
+ optimizer = comet_ml.Optimizer(optimizer_config)
+ else:
+ optimizer = comet_ml.Optimizer(optimizer_id)
+
+ opt.comet_optimizer_id = optimizer.id
+ status = optimizer.status()
+
+ opt.comet_optimizer_objective = status["spec"]["objective"]
+ opt.comet_optimizer_metric = status["spec"]["metric"]
+
+ logger.info("COMET INFO: Starting Hyperparameter Sweep")
+ for parameter in optimizer.get_parameters():
+ run(parameter["parameters"], opt)
diff --git a/src/FireDetect/utils/loggers/comet/optimizer_config.json b/src/FireDetect/utils/loggers/comet/optimizer_config.json
new file mode 100644
index 0000000..83dddda
--- /dev/null
+++ b/src/FireDetect/utils/loggers/comet/optimizer_config.json
@@ -0,0 +1,209 @@
+{
+ "algorithm": "random",
+ "parameters": {
+ "anchor_t": {
+ "type": "discrete",
+ "values": [
+ 2,
+ 8
+ ]
+ },
+ "batch_size": {
+ "type": "discrete",
+ "values": [
+ 16,
+ 32,
+ 64
+ ]
+ },
+ "box": {
+ "type": "discrete",
+ "values": [
+ 0.02,
+ 0.2
+ ]
+ },
+ "cls": {
+ "type": "discrete",
+ "values": [
+ 0.2
+ ]
+ },
+ "cls_pw": {
+ "type": "discrete",
+ "values": [
+ 0.5
+ ]
+ },
+ "copy_paste": {
+ "type": "discrete",
+ "values": [
+ 1
+ ]
+ },
+ "degrees": {
+ "type": "discrete",
+ "values": [
+ 0,
+ 45
+ ]
+ },
+ "epochs": {
+ "type": "discrete",
+ "values": [
+ 5
+ ]
+ },
+ "fl_gamma": {
+ "type": "discrete",
+ "values": [
+ 0
+ ]
+ },
+ "fliplr": {
+ "type": "discrete",
+ "values": [
+ 0
+ ]
+ },
+ "flipud": {
+ "type": "discrete",
+ "values": [
+ 0
+ ]
+ },
+ "hsv_h": {
+ "type": "discrete",
+ "values": [
+ 0
+ ]
+ },
+ "hsv_s": {
+ "type": "discrete",
+ "values": [
+ 0
+ ]
+ },
+ "hsv_v": {
+ "type": "discrete",
+ "values": [
+ 0
+ ]
+ },
+ "iou_t": {
+ "type": "discrete",
+ "values": [
+ 0.7
+ ]
+ },
+ "lr0": {
+ "type": "discrete",
+ "values": [
+ 1e-05,
+ 0.1
+ ]
+ },
+ "lrf": {
+ "type": "discrete",
+ "values": [
+ 0.01,
+ 1
+ ]
+ },
+ "mixup": {
+ "type": "discrete",
+ "values": [
+ 1
+ ]
+ },
+ "momentum": {
+ "type": "discrete",
+ "values": [
+ 0.6
+ ]
+ },
+ "mosaic": {
+ "type": "discrete",
+ "values": [
+ 0
+ ]
+ },
+ "obj": {
+ "type": "discrete",
+ "values": [
+ 0.2
+ ]
+ },
+ "obj_pw": {
+ "type": "discrete",
+ "values": [
+ 0.5
+ ]
+ },
+ "optimizer": {
+ "type": "categorical",
+ "values": [
+ "SGD",
+ "Adam",
+ "AdamW"
+ ]
+ },
+ "perspective": {
+ "type": "discrete",
+ "values": [
+ 0
+ ]
+ },
+ "scale": {
+ "type": "discrete",
+ "values": [
+ 0
+ ]
+ },
+ "shear": {
+ "type": "discrete",
+ "values": [
+ 0
+ ]
+ },
+ "translate": {
+ "type": "discrete",
+ "values": [
+ 0
+ ]
+ },
+ "warmup_bias_lr": {
+ "type": "discrete",
+ "values": [
+ 0,
+ 0.2
+ ]
+ },
+ "warmup_epochs": {
+ "type": "discrete",
+ "values": [
+ 5
+ ]
+ },
+ "warmup_momentum": {
+ "type": "discrete",
+ "values": [
+ 0,
+ 0.95
+ ]
+ },
+ "weight_decay": {
+ "type": "discrete",
+ "values": [
+ 0,
+ 0.001
+ ]
+ }
+ },
+ "spec": {
+ "maxCombo": 0,
+ "metric": "metrics/mAP_0.5",
+ "objective": "maximize"
+ },
+ "trials": 1
+}
diff --git a/src/FireDetect/utils/loggers/wandb/README.md b/src/FireDetect/utils/loggers/wandb/README.md
new file mode 100644
index 0000000..d78324b
--- /dev/null
+++ b/src/FireDetect/utils/loggers/wandb/README.md
@@ -0,0 +1,162 @@
+📚 This guide explains how to use **Weights & Biases** (W&B) with YOLOv5 🚀. UPDATED 29 September 2021.
+
+- [About Weights & Biases](#about-weights-&-biases)
+- [First-Time Setup](#first-time-setup)
+- [Viewing runs](#viewing-runs)
+- [Disabling wandb](#disabling-wandb)
+- [Advanced Usage: Dataset Versioning and Evaluation](#advanced-usage)
+- [Reports: Share your work with the world!](#reports)
+
+## About Weights & Biases
+
+Think of [W&B](https://wandb.ai/site?utm_campaign=repo_yolo_wandbtutorial) like GitHub for machine learning models. With a few lines of code, save everything you need to debug, compare and reproduce your models — architecture, hyperparameters, git commits, model weights, GPU usage, and even datasets and predictions.
+
+Used by top researchers including teams at OpenAI, Lyft, Github, and MILA, W&B is part of the new standard of best practices for machine learning. How W&B can help you optimize your machine learning workflows:
+
+- [Debug](https://wandb.ai/wandb/getting-started/reports/Visualize-Debug-Machine-Learning-Models--VmlldzoyNzY5MDk#Free-2) model performance in real time
+- [GPU usage](https://wandb.ai/wandb/getting-started/reports/Visualize-Debug-Machine-Learning-Models--VmlldzoyNzY5MDk#System-4) visualized automatically
+- [Custom charts](https://wandb.ai/wandb/customizable-charts/reports/Powerful-Custom-Charts-To-Debug-Model-Peformance--VmlldzoyNzY4ODI) for powerful, extensible visualization
+- [Share insights](https://wandb.ai/wandb/getting-started/reports/Visualize-Debug-Machine-Learning-Models--VmlldzoyNzY5MDk#Share-8) interactively with collaborators
+- [Optimize hyperparameters](https://docs.wandb.com/sweeps) efficiently
+- [Track](https://docs.wandb.com/artifacts) datasets, pipelines, and production models
+
+## First-Time Setup
+
+
+
+
+
+ 
$ python train.py --upload_data val
+
+
+
+{dataset}_wandb.yaml file which can be used to train from dataset artifact.
+ $ python utils/logger/wandb/log_dataset.py --project ... --name ... --data ..
+
+
+
+ $ python train.py --data {data}_wandb.yaml
+
+
+
+ $ python train.py --save_period 1
+
+
+
+--resume argument starts with wandb-artifact:// prefix followed by the run path, i.e, wandb-artifact://username/project/runid . This doesn't require the model checkpoint to be present on the local system.
+
+ $ python train.py --resume wandb-artifact://{run_path}
+
+
+
+--upload_dataset or
+ train from _wandb.yaml file and set --save_period
+
+ $ python train.py --resume wandb-artifact://{run_path}
+
+
+
+
+
+## Environments
+
+YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled):
+
+- **Google Colab and Kaggle** notebooks with free GPU:
+- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart)
+- **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/AWS-Quickstart)
+- **Docker Image**. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/Docker-Quickstart)
+
+## Status
+
+
+
+If this badge is green, all [YOLOv5 GitHub Actions](https://github.com/ultralytics/yolov5/actions) Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training ([train.py](https://github.com/ultralytics/yolov5/blob/master/train.py)), validation ([val.py](https://github.com/ultralytics/yolov5/blob/master/val.py)), inference ([detect.py](https://github.com/ultralytics/yolov5/blob/master/detect.py)) and export ([export.py](https://github.com/ultralytics/yolov5/blob/master/export.py)) on macOS, Windows, and Ubuntu every 24 hours and on every commit.
diff --git a/src/FireDetect/utils/loggers/wandb/__init__.py b/src/FireDetect/utils/loggers/wandb/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/FireDetect/utils/loggers/wandb/log_dataset.py b/src/FireDetect/utils/loggers/wandb/log_dataset.py
new file mode 100644
index 0000000..06e81fb
--- /dev/null
+++ b/src/FireDetect/utils/loggers/wandb/log_dataset.py
@@ -0,0 +1,27 @@
+import argparse
+
+from wandb_utils import WandbLogger
+
+from utils.general import LOGGER
+
+WANDB_ARTIFACT_PREFIX = 'wandb-artifact://'
+
+
+def create_dataset_artifact(opt):
+ logger = WandbLogger(opt, None, job_type='Dataset Creation') # TODO: return value unused
+ if not logger.wandb:
+ LOGGER.info("install wandb using `pip install wandb` to log the dataset")
+
+
+if __name__ == '__main__':
+ parser = argparse.ArgumentParser()
+ parser.add_argument('--data', type=str, default='data/coco128.yaml', help='data.yaml path')
+ parser.add_argument('--single-cls', action='store_true', help='train as single-class dataset')
+ parser.add_argument('--project', type=str, default='YOLOv5', help='name of W&B Project')
+ parser.add_argument('--entity', default=None, help='W&B entity')
+ parser.add_argument('--name', type=str, default='log dataset', help='name of W&B run')
+
+ opt = parser.parse_args()
+ opt.resume = False # Explicitly disallow resume check for dataset upload job
+
+ create_dataset_artifact(opt)
diff --git a/src/FireDetect/utils/loggers/wandb/sweep.py b/src/FireDetect/utils/loggers/wandb/sweep.py
new file mode 100644
index 0000000..d49ea6f
--- /dev/null
+++ b/src/FireDetect/utils/loggers/wandb/sweep.py
@@ -0,0 +1,41 @@
+import sys
+from pathlib import Path
+
+import wandb
+
+FILE = Path(__file__).resolve()
+ROOT = FILE.parents[3] # YOLOv5 root directory
+if str(ROOT) not in sys.path:
+ sys.path.append(str(ROOT)) # add ROOT to PATH
+
+from train import parse_opt, train
+from utils.callbacks import Callbacks
+from utils.general import increment_path
+from utils.torch_utils import select_device
+
+
+def sweep():
+ wandb.init()
+ # Get hyp dict from sweep agent. Copy because train() modifies parameters which confused wandb.
+ hyp_dict = vars(wandb.config).get("_items").copy()
+
+ # Workaround: get necessary opt args
+ opt = parse_opt(known=True)
+ opt.batch_size = hyp_dict.get("batch_size")
+ opt.save_dir = str(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok or opt.evolve))
+ opt.epochs = hyp_dict.get("epochs")
+ opt.nosave = True
+ opt.data = hyp_dict.get("data")
+ opt.weights = str(opt.weights)
+ opt.cfg = str(opt.cfg)
+ opt.data = str(opt.data)
+ opt.hyp = str(opt.hyp)
+ opt.project = str(opt.project)
+ device = select_device(opt.device, batch_size=opt.batch_size)
+
+ # train
+ train(hyp_dict, opt, device, callbacks=Callbacks())
+
+
+if __name__ == "__main__":
+ sweep()
diff --git a/src/FireDetect/utils/loggers/wandb/sweep.yaml b/src/FireDetect/utils/loggers/wandb/sweep.yaml
new file mode 100644
index 0000000..688b1ea
--- /dev/null
+++ b/src/FireDetect/utils/loggers/wandb/sweep.yaml
@@ -0,0 +1,143 @@
+# Hyperparameters for training
+# To set range-
+# Provide min and max values as:
+# parameter:
+#
+# min: scalar
+# max: scalar
+# OR
+#
+# Set a specific list of search space-
+# parameter:
+# values: [scalar1, scalar2, scalar3...]
+#
+# You can use grid, bayesian and hyperopt search strategy
+# For more info on configuring sweeps visit - https://docs.wandb.ai/guides/sweeps/configuration
+
+program: utils/loggers/wandb/sweep.py
+method: random
+metric:
+ name: metrics/mAP_0.5
+ goal: maximize
+
+parameters:
+ # hyperparameters: set either min, max range or values list
+ data:
+ value: "data/coco128.yaml"
+ batch_size:
+ values: [64]
+ epochs:
+ values: [10]
+
+ lr0:
+ distribution: uniform
+ min: 1e-5
+ max: 1e-1
+ lrf:
+ distribution: uniform
+ min: 0.01
+ max: 1.0
+ momentum:
+ distribution: uniform
+ min: 0.6
+ max: 0.98
+ weight_decay:
+ distribution: uniform
+ min: 0.0
+ max: 0.001
+ warmup_epochs:
+ distribution: uniform
+ min: 0.0
+ max: 5.0
+ warmup_momentum:
+ distribution: uniform
+ min: 0.0
+ max: 0.95
+ warmup_bias_lr:
+ distribution: uniform
+ min: 0.0
+ max: 0.2
+ box:
+ distribution: uniform
+ min: 0.02
+ max: 0.2
+ cls:
+ distribution: uniform
+ min: 0.2
+ max: 4.0
+ cls_pw:
+ distribution: uniform
+ min: 0.5
+ max: 2.0
+ obj:
+ distribution: uniform
+ min: 0.2
+ max: 4.0
+ obj_pw:
+ distribution: uniform
+ min: 0.5
+ max: 2.0
+ iou_t:
+ distribution: uniform
+ min: 0.1
+ max: 0.7
+ anchor_t:
+ distribution: uniform
+ min: 2.0
+ max: 8.0
+ fl_gamma:
+ distribution: uniform
+ min: 0.0
+ max: 4.0
+ hsv_h:
+ distribution: uniform
+ min: 0.0
+ max: 0.1
+ hsv_s:
+ distribution: uniform
+ min: 0.0
+ max: 0.9
+ hsv_v:
+ distribution: uniform
+ min: 0.0
+ max: 0.9
+ degrees:
+ distribution: uniform
+ min: 0.0
+ max: 45.0
+ translate:
+ distribution: uniform
+ min: 0.0
+ max: 0.9
+ scale:
+ distribution: uniform
+ min: 0.0
+ max: 0.9
+ shear:
+ distribution: uniform
+ min: 0.0
+ max: 10.0
+ perspective:
+ distribution: uniform
+ min: 0.0
+ max: 0.001
+ flipud:
+ distribution: uniform
+ min: 0.0
+ max: 1.0
+ fliplr:
+ distribution: uniform
+ min: 0.0
+ max: 1.0
+ mosaic:
+ distribution: uniform
+ min: 0.0
+ max: 1.0
+ mixup:
+ distribution: uniform
+ min: 0.0
+ max: 1.0
+ copy_paste:
+ distribution: uniform
+ min: 0.0
+ max: 1.0
diff --git a/src/FireDetect/utils/loggers/wandb/wandb_utils.py b/src/FireDetect/utils/loggers/wandb/wandb_utils.py
new file mode 100644
index 0000000..238f4ed
--- /dev/null
+++ b/src/FireDetect/utils/loggers/wandb/wandb_utils.py
@@ -0,0 +1,589 @@
+"""Utilities and tools for tracking runs with Weights & Biases."""
+
+import logging
+import os
+import sys
+from contextlib import contextmanager
+from pathlib import Path
+from typing import Dict
+
+import yaml
+from tqdm import tqdm
+
+FILE = Path(__file__).resolve()
+ROOT = FILE.parents[3] # YOLOv5 root directory
+if str(ROOT) not in sys.path:
+ sys.path.append(str(ROOT)) # add ROOT to PATH
+
+from utils.dataloaders import LoadImagesAndLabels, img2label_paths
+from utils.general import LOGGER, check_dataset, check_file
+
+try:
+ import wandb
+
+ assert hasattr(wandb, '__version__') # verify package import not local dir
+except (ImportError, AssertionError):
+ wandb = None
+
+RANK = int(os.getenv('RANK', -1))
+WANDB_ARTIFACT_PREFIX = 'wandb-artifact://'
+
+
+def remove_prefix(from_string, prefix=WANDB_ARTIFACT_PREFIX):
+ return from_string[len(prefix):]
+
+
+def check_wandb_config_file(data_config_file):
+ wandb_config = '_wandb.'.join(data_config_file.rsplit('.', 1)) # updated data.yaml path
+ if Path(wandb_config).is_file():
+ return wandb_config
+ return data_config_file
+
+
+def check_wandb_dataset(data_file):
+ is_trainset_wandb_artifact = False
+ is_valset_wandb_artifact = False
+ if isinstance(data_file, dict):
+ # In that case another dataset manager has already processed it and we don't have to
+ return data_file
+ if check_file(data_file) and data_file.endswith('.yaml'):
+ with open(data_file, errors='ignore') as f:
+ data_dict = yaml.safe_load(f)
+ is_trainset_wandb_artifact = isinstance(data_dict['train'],
+ str) and data_dict['train'].startswith(WANDB_ARTIFACT_PREFIX)
+ is_valset_wandb_artifact = isinstance(data_dict['val'],
+ str) and data_dict['val'].startswith(WANDB_ARTIFACT_PREFIX)
+ if is_trainset_wandb_artifact or is_valset_wandb_artifact:
+ return data_dict
+ else:
+ return check_dataset(data_file)
+
+
+def get_run_info(run_path):
+ run_path = Path(remove_prefix(run_path, WANDB_ARTIFACT_PREFIX))
+ run_id = run_path.stem
+ project = run_path.parent.stem
+ entity = run_path.parent.parent.stem
+ model_artifact_name = 'run_' + run_id + '_model'
+ return entity, project, run_id, model_artifact_name
+
+
+def check_wandb_resume(opt):
+ process_wandb_config_ddp_mode(opt) if RANK not in [-1, 0] else None
+ if isinstance(opt.resume, str):
+ if opt.resume.startswith(WANDB_ARTIFACT_PREFIX):
+ if RANK not in [-1, 0]: # For resuming DDP runs
+ entity, project, run_id, model_artifact_name = get_run_info(opt.resume)
+ api = wandb.Api()
+ artifact = api.artifact(entity + '/' + project + '/' + model_artifact_name + ':latest')
+ modeldir = artifact.download()
+ opt.weights = str(Path(modeldir) / "last.pt")
+ return True
+ return None
+
+
+def process_wandb_config_ddp_mode(opt):
+ with open(check_file(opt.data), errors='ignore') as f:
+ data_dict = yaml.safe_load(f) # data dict
+ train_dir, val_dir = None, None
+ if isinstance(data_dict['train'], str) and data_dict['train'].startswith(WANDB_ARTIFACT_PREFIX):
+ api = wandb.Api()
+ train_artifact = api.artifact(remove_prefix(data_dict['train']) + ':' + opt.artifact_alias)
+ train_dir = train_artifact.download()
+ train_path = Path(train_dir) / 'data/images/'
+ data_dict['train'] = str(train_path)
+
+ if isinstance(data_dict['val'], str) and data_dict['val'].startswith(WANDB_ARTIFACT_PREFIX):
+ api = wandb.Api()
+ val_artifact = api.artifact(remove_prefix(data_dict['val']) + ':' + opt.artifact_alias)
+ val_dir = val_artifact.download()
+ val_path = Path(val_dir) / 'data/images/'
+ data_dict['val'] = str(val_path)
+ if train_dir or val_dir:
+ ddp_data_path = str(Path(val_dir) / 'wandb_local_data.yaml')
+ with open(ddp_data_path, 'w') as f:
+ yaml.safe_dump(data_dict, f)
+ opt.data = ddp_data_path
+
+
+class WandbLogger():
+ """Log training runs, datasets, models, and predictions to Weights & Biases.
+
+ This logger sends information to W&B at wandb.ai. By default, this information
+ includes hyperparameters, system configuration and metrics, model metrics,
+ and basic data metrics and analyses.
+
+ By providing additional command line arguments to train.py, datasets,
+ models and predictions can also be logged.
+
+ For more on how this logger is used, see the Weights & Biases documentation:
+ https://docs.wandb.com/guides/integrations/yolov5
+ """
+
+ def __init__(self, opt, run_id=None, job_type='Training'):
+ """
+ - Initialize WandbLogger instance
+ - Upload dataset if opt.upload_dataset is True
+ - Setup training processes if job_type is 'Training'
+
+ arguments:
+ opt (namespace) -- Commandline arguments for this run
+ run_id (str) -- Run ID of W&B run to be resumed
+ job_type (str) -- To set the job_type for this run
+
+ """
+ # Temporary-fix
+ if opt.upload_dataset:
+ opt.upload_dataset = False
+ # LOGGER.info("Uploading Dataset functionality is not being supported temporarily due to a bug.")
+
+ # Pre-training routine --
+ self.job_type = job_type
+ self.wandb, self.wandb_run = wandb, None if not wandb else wandb.run
+ self.val_artifact, self.train_artifact = None, None
+ self.train_artifact_path, self.val_artifact_path = None, None
+ self.result_artifact = None
+ self.val_table, self.result_table = None, None
+ self.bbox_media_panel_images = []
+ self.val_table_path_map = None
+ self.max_imgs_to_log = 16
+ self.wandb_artifact_data_dict = None
+ self.data_dict = None
+ # It's more elegant to stick to 1 wandb.init call,
+ # but useful config data is overwritten in the WandbLogger's wandb.init call
+ if isinstance(opt.resume, str): # checks resume from artifact
+ if opt.resume.startswith(WANDB_ARTIFACT_PREFIX):
+ entity, project, run_id, model_artifact_name = get_run_info(opt.resume)
+ model_artifact_name = WANDB_ARTIFACT_PREFIX + model_artifact_name
+ assert wandb, 'install wandb to resume wandb runs'
+ # Resume wandb-artifact:// runs here| workaround for not overwriting wandb.config
+ self.wandb_run = wandb.init(id=run_id,
+ project=project,
+ entity=entity,
+ resume='allow',
+ allow_val_change=True)
+ opt.resume = model_artifact_name
+ elif self.wandb:
+ self.wandb_run = wandb.init(config=opt,
+ resume="allow",
+ project='YOLOv5' if opt.project == 'runs/train' else Path(opt.project).stem,
+ entity=opt.entity,
+ name=opt.name if opt.name != 'exp' else None,
+ job_type=job_type,
+ id=run_id,
+ allow_val_change=True) if not wandb.run else wandb.run
+ if self.wandb_run:
+ if self.job_type == 'Training':
+ if opt.upload_dataset:
+ if not opt.resume:
+ self.wandb_artifact_data_dict = self.check_and_upload_dataset(opt)
+
+ if isinstance(opt.data, dict):
+ # This means another dataset manager has already processed the dataset info (e.g. ClearML)
+ # and they will have stored the already processed dict in opt.data
+ self.data_dict = opt.data
+ elif opt.resume:
+ # resume from artifact
+ if isinstance(opt.resume, str) and opt.resume.startswith(WANDB_ARTIFACT_PREFIX):
+ self.data_dict = dict(self.wandb_run.config.data_dict)
+ else: # local resume
+ self.data_dict = check_wandb_dataset(opt.data)
+ else:
+ self.data_dict = check_wandb_dataset(opt.data)
+ self.wandb_artifact_data_dict = self.wandb_artifact_data_dict or self.data_dict
+
+ # write data_dict to config. useful for resuming from artifacts. Do this only when not resuming.
+ self.wandb_run.config.update({'data_dict': self.wandb_artifact_data_dict}, allow_val_change=True)
+ self.setup_training(opt)
+
+ if self.job_type == 'Dataset Creation':
+ self.wandb_run.config.update({"upload_dataset": True})
+ self.data_dict = self.check_and_upload_dataset(opt)
+
+ def check_and_upload_dataset(self, opt):
+ """
+ Check if the dataset format is compatible and upload it as W&B artifact
+
+ arguments:
+ opt (namespace)-- Commandline arguments for current run
+
+ returns:
+ Updated dataset info dictionary where local dataset paths are replaced by WAND_ARFACT_PREFIX links.
+ """
+ assert wandb, 'Install wandb to upload dataset'
+ config_path = self.log_dataset_artifact(opt.data, opt.single_cls,
+ 'YOLOv5' if opt.project == 'runs/train' else Path(opt.project).stem)
+ with open(config_path, errors='ignore') as f:
+ wandb_data_dict = yaml.safe_load(f)
+ return wandb_data_dict
+
+ def setup_training(self, opt):
+ """
+ Setup the necessary processes for training YOLO models:
+ - Attempt to download model checkpoint and dataset artifacts if opt.resume stats with WANDB_ARTIFACT_PREFIX
+ - Update data_dict, to contain info of previous run if resumed and the paths of dataset artifact if downloaded
+ - Setup log_dict, initialize bbox_interval
+
+ arguments:
+ opt (namespace) -- commandline arguments for this run
+
+ """
+ self.log_dict, self.current_epoch = {}, 0
+ self.bbox_interval = opt.bbox_interval
+ if isinstance(opt.resume, str):
+ modeldir, _ = self.download_model_artifact(opt)
+ if modeldir:
+ self.weights = Path(modeldir) / "last.pt"
+ config = self.wandb_run.config
+ opt.weights, opt.save_period, opt.batch_size, opt.bbox_interval, opt.epochs, opt.hyp, opt.imgsz = str(
+ self.weights), config.save_period, config.batch_size, config.bbox_interval, config.epochs,\
+ config.hyp, config.imgsz
+ data_dict = self.data_dict
+ if self.val_artifact is None: # If --upload_dataset is set, use the existing artifact, don't download
+ self.train_artifact_path, self.train_artifact = self.download_dataset_artifact(
+ data_dict.get('train'), opt.artifact_alias)
+ self.val_artifact_path, self.val_artifact = self.download_dataset_artifact(
+ data_dict.get('val'), opt.artifact_alias)
+
+ if self.train_artifact_path is not None:
+ train_path = Path(self.train_artifact_path) / 'data/images/'
+ data_dict['train'] = str(train_path)
+ if self.val_artifact_path is not None:
+ val_path = Path(self.val_artifact_path) / 'data/images/'
+ data_dict['val'] = str(val_path)
+
+ if self.val_artifact is not None:
+ self.result_artifact = wandb.Artifact("run_" + wandb.run.id + "_progress", "evaluation")
+ columns = ["epoch", "id", "ground truth", "prediction"]
+ columns.extend(self.data_dict['names'])
+ self.result_table = wandb.Table(columns)
+ self.val_table = self.val_artifact.get("val")
+ if self.val_table_path_map is None:
+ self.map_val_table_path()
+ if opt.bbox_interval == -1:
+ self.bbox_interval = opt.bbox_interval = (opt.epochs // 10) if opt.epochs > 10 else 1
+ if opt.evolve or opt.noplots:
+ self.bbox_interval = opt.bbox_interval = opt.epochs + 1 # disable bbox_interval
+ train_from_artifact = self.train_artifact_path is not None and self.val_artifact_path is not None
+ # Update the the data_dict to point to local artifacts dir
+ if train_from_artifact:
+ self.data_dict = data_dict
+
+ def download_dataset_artifact(self, path, alias):
+ """
+ download the model checkpoint artifact if the path starts with WANDB_ARTIFACT_PREFIX
+
+ arguments:
+ path -- path of the dataset to be used for training
+ alias (str)-- alias of the artifact to be download/used for training
+
+ returns:
+ (str, wandb.Artifact) -- path of the downladed dataset and it's corresponding artifact object if dataset
+ is found otherwise returns (None, None)
+ """
+ if isinstance(path, str) and path.startswith(WANDB_ARTIFACT_PREFIX):
+ artifact_path = Path(remove_prefix(path, WANDB_ARTIFACT_PREFIX) + ":" + alias)
+ dataset_artifact = wandb.use_artifact(artifact_path.as_posix().replace("\\", "/"))
+ assert dataset_artifact is not None, "'Error: W&B dataset artifact doesn\'t exist'"
+ datadir = dataset_artifact.download()
+ return datadir, dataset_artifact
+ return None, None
+
+ def download_model_artifact(self, opt):
+ """
+ download the model checkpoint artifact if the resume path starts with WANDB_ARTIFACT_PREFIX
+
+ arguments:
+ opt (namespace) -- Commandline arguments for this run
+ """
+ if opt.resume.startswith(WANDB_ARTIFACT_PREFIX):
+ model_artifact = wandb.use_artifact(remove_prefix(opt.resume, WANDB_ARTIFACT_PREFIX) + ":latest")
+ assert model_artifact is not None, 'Error: W&B model artifact doesn\'t exist'
+ modeldir = model_artifact.download()
+ # epochs_trained = model_artifact.metadata.get('epochs_trained')
+ total_epochs = model_artifact.metadata.get('total_epochs')
+ is_finished = total_epochs is None
+ assert not is_finished, 'training is finished, can only resume incomplete runs.'
+ return modeldir, model_artifact
+ return None, None
+
+ def log_model(self, path, opt, epoch, fitness_score, best_model=False):
+ """
+ Log the model checkpoint as W&B artifact
+
+ arguments:
+ path (Path) -- Path of directory containing the checkpoints
+ opt (namespace) -- Command line arguments for this run
+ epoch (int) -- Current epoch number
+ fitness_score (float) -- fitness score for current epoch
+ best_model (boolean) -- Boolean representing if the current checkpoint is the best yet.
+ """
+ model_artifact = wandb.Artifact('run_' + wandb.run.id + '_model',
+ type='model',
+ metadata={
+ 'original_url': str(path),
+ 'epochs_trained': epoch + 1,
+ 'save period': opt.save_period,
+ 'project': opt.project,
+ 'total_epochs': opt.epochs,
+ 'fitness_score': fitness_score})
+ model_artifact.add_file(str(path / 'last.pt'), name='last.pt')
+ wandb.log_artifact(model_artifact,
+ aliases=['latest', 'last', 'epoch ' + str(self.current_epoch), 'best' if best_model else ''])
+ LOGGER.info(f"Saving model artifact on epoch {epoch + 1}")
+
+ def log_dataset_artifact(self, data_file, single_cls, project, overwrite_config=False):
+ """
+ Log the dataset as W&B artifact and return the new data file with W&B links
+
+ arguments:
+ data_file (str) -- the .yaml file with information about the dataset like - path, classes etc.
+ single_class (boolean) -- train multi-class data as single-class
+ project (str) -- project name. Used to construct the artifact path
+ overwrite_config (boolean) -- overwrites the data.yaml file if set to true otherwise creates a new
+ file with _wandb postfix. Eg -> data_wandb.yaml
+
+ returns:
+ the new .yaml file with artifact links. it can be used to start training directly from artifacts
+ """
+ upload_dataset = self.wandb_run.config.upload_dataset
+ log_val_only = isinstance(upload_dataset, str) and upload_dataset == 'val'
+ self.data_dict = check_dataset(data_file) # parse and check
+ data = dict(self.data_dict)
+ nc, names = (1, ['item']) if single_cls else (int(data['nc']), data['names'])
+ names = {k: v for k, v in enumerate(names)} # to index dictionary
+
+ # log train set
+ if not log_val_only:
+ self.train_artifact = self.create_dataset_table(LoadImagesAndLabels(data['train'], rect=True, batch_size=1),
+ names,
+ name='train') if data.get('train') else None
+ if data.get('train'):
+ data['train'] = WANDB_ARTIFACT_PREFIX + str(Path(project) / 'train')
+
+ self.val_artifact = self.create_dataset_table(
+ LoadImagesAndLabels(data['val'], rect=True, batch_size=1), names, name='val') if data.get('val') else None
+ if data.get('val'):
+ data['val'] = WANDB_ARTIFACT_PREFIX + str(Path(project) / 'val')
+
+ path = Path(data_file)
+ # create a _wandb.yaml file with artifacts links if both train and test set are logged
+ if not log_val_only:
+ path = (path.stem if overwrite_config else path.stem + '_wandb') + '.yaml' # updated data.yaml path
+ path = ROOT / 'data' / path
+ data.pop('download', None)
+ data.pop('path', None)
+ with open(path, 'w') as f:
+ yaml.safe_dump(data, f)
+ LOGGER.info(f"Created dataset config file {path}")
+
+ if self.job_type == 'Training': # builds correct artifact pipeline graph
+ if not log_val_only:
+ self.wandb_run.log_artifact(
+ self.train_artifact) # calling use_artifact downloads the dataset. NOT NEEDED!
+ self.wandb_run.use_artifact(self.val_artifact)
+ self.val_artifact.wait()
+ self.val_table = self.val_artifact.get('val')
+ self.map_val_table_path()
+ else:
+ self.wandb_run.log_artifact(self.train_artifact)
+ self.wandb_run.log_artifact(self.val_artifact)
+ return path
+
+ def map_val_table_path(self):
+ """
+ Map the validation dataset Table like name of file -> it's id in the W&B Table.
+ Useful for - referencing artifacts for evaluation.
+ """
+ self.val_table_path_map = {}
+ LOGGER.info("Mapping dataset")
+ for i, data in enumerate(tqdm(self.val_table.data)):
+ self.val_table_path_map[data[3]] = data[0]
+
+ def create_dataset_table(self, dataset: LoadImagesAndLabels, class_to_id: Dict[int, str], name: str = 'dataset'):
+ """
+ Create and return W&B artifact containing W&B Table of the dataset.
+
+ arguments:
+ dataset -- instance of LoadImagesAndLabels class used to iterate over the data to build Table
+ class_to_id -- hash map that maps class ids to labels
+ name -- name of the artifact
+
+ returns:
+ dataset artifact to be logged or used
+ """
+ # TODO: Explore multiprocessing to slpit this loop parallely| This is essential for speeding up the the logging
+ artifact = wandb.Artifact(name=name, type="dataset")
+ img_files = tqdm([dataset.path]) if isinstance(dataset.path, str) and Path(dataset.path).is_dir() else None
+ img_files = tqdm(dataset.im_files) if not img_files else img_files
+ for img_file in img_files:
+ if Path(img_file).is_dir():
+ artifact.add_dir(img_file, name='data/images')
+ labels_path = 'labels'.join(dataset.path.rsplit('images', 1))
+ artifact.add_dir(labels_path, name='data/labels')
+ else:
+ artifact.add_file(img_file, name='data/images/' + Path(img_file).name)
+ label_file = Path(img2label_paths([img_file])[0])
+ artifact.add_file(str(label_file), name='data/labels/' +
+ label_file.name) if label_file.exists() else None
+ table = wandb.Table(columns=["id", "train_image", "Classes", "name"])
+ class_set = wandb.Classes([{'id': id, 'name': name} for id, name in class_to_id.items()])
+ for si, (img, labels, paths, shapes) in enumerate(tqdm(dataset)):
+ box_data, img_classes = [], {}
+ for cls, *xywh in labels[:, 1:].tolist():
+ cls = int(cls)
+ box_data.append({
+ "position": {
+ "middle": [xywh[0], xywh[1]],
+ "width": xywh[2],
+ "height": xywh[3]},
+ "class_id": cls,
+ "box_caption": "%s" % (class_to_id[cls])})
+ img_classes[cls] = class_to_id[cls]
+ boxes = {"ground_truth": {"box_data": box_data, "class_labels": class_to_id}} # inference-space
+ table.add_data(si, wandb.Image(paths, classes=class_set, boxes=boxes), list(img_classes.values()),
+ Path(paths).name)
+ artifact.add(table, name)
+ return artifact
+
+ def log_training_progress(self, predn, path, names):
+ """
+ Build evaluation Table. Uses reference from validation dataset table.
+
+ arguments:
+ predn (list): list of predictions in the native space in the format - [xmin, ymin, xmax, ymax, confidence, class]
+ path (str): local path of the current evaluation image
+ names (dict(int, str)): hash map that maps class ids to labels
+ """
+ class_set = wandb.Classes([{'id': id, 'name': name} for id, name in names.items()])
+ box_data = []
+ avg_conf_per_class = [0] * len(self.data_dict['names'])
+ pred_class_count = {}
+ for *xyxy, conf, cls in predn.tolist():
+ if conf >= 0.25:
+ cls = int(cls)
+ box_data.append({
+ "position": {
+ "minX": xyxy[0],
+ "minY": xyxy[1],
+ "maxX": xyxy[2],
+ "maxY": xyxy[3]},
+ "class_id": cls,
+ "box_caption": f"{names[cls]} {conf:.3f}",
+ "scores": {
+ "class_score": conf},
+ "domain": "pixel"})
+ avg_conf_per_class[cls] += conf
+
+ if cls in pred_class_count:
+ pred_class_count[cls] += 1
+ else:
+ pred_class_count[cls] = 1
+
+ for pred_class in pred_class_count.keys():
+ avg_conf_per_class[pred_class] = avg_conf_per_class[pred_class] / pred_class_count[pred_class]
+
+ boxes = {"predictions": {"box_data": box_data, "class_labels": names}} # inference-space
+ id = self.val_table_path_map[Path(path).name]
+ self.result_table.add_data(self.current_epoch, id, self.val_table.data[id][1],
+ wandb.Image(self.val_table.data[id][1], boxes=boxes, classes=class_set),
+ *avg_conf_per_class)
+
+ def val_one_image(self, pred, predn, path, names, im):
+ """
+ Log validation data for one image. updates the result Table if validation dataset is uploaded and log bbox media panel
+
+ arguments:
+ pred (list): list of scaled predictions in the format - [xmin, ymin, xmax, ymax, confidence, class]
+ predn (list): list of predictions in the native space - [xmin, ymin, xmax, ymax, confidence, class]
+ path (str): local path of the current evaluation image
+ """
+ if self.val_table and self.result_table: # Log Table if Val dataset is uploaded as artifact
+ self.log_training_progress(predn, path, names)
+
+ if len(self.bbox_media_panel_images) < self.max_imgs_to_log and self.current_epoch > 0:
+ if self.current_epoch % self.bbox_interval == 0:
+ box_data = [{
+ "position": {
+ "minX": xyxy[0],
+ "minY": xyxy[1],
+ "maxX": xyxy[2],
+ "maxY": xyxy[3]},
+ "class_id": int(cls),
+ "box_caption": f"{names[int(cls)]} {conf:.3f}",
+ "scores": {
+ "class_score": conf},
+ "domain": "pixel"} for *xyxy, conf, cls in pred.tolist()]
+ boxes = {"predictions": {"box_data": box_data, "class_labels": names}} # inference-space
+ self.bbox_media_panel_images.append(wandb.Image(im, boxes=boxes, caption=path.name))
+
+ def log(self, log_dict):
+ """
+ save the metrics to the logging dictionary
+
+ arguments:
+ log_dict (Dict) -- metrics/media to be logged in current step
+ """
+ if self.wandb_run:
+ for key, value in log_dict.items():
+ self.log_dict[key] = value
+
+ def end_epoch(self, best_result=False):
+ """
+ commit the log_dict, model artifacts and Tables to W&B and flush the log_dict.
+
+ arguments:
+ best_result (boolean): Boolean representing if the result of this evaluation is best or not
+ """
+ if self.wandb_run:
+ with all_logging_disabled():
+ if self.bbox_media_panel_images:
+ self.log_dict["BoundingBoxDebugger"] = self.bbox_media_panel_images
+ try:
+ wandb.log(self.log_dict)
+ except BaseException as e:
+ LOGGER.info(
+ f"An error occurred in wandb logger. The training will proceed without interruption. More info\n{e}"
+ )
+ self.wandb_run.finish()
+ self.wandb_run = None
+
+ self.log_dict = {}
+ self.bbox_media_panel_images = []
+ if self.result_artifact:
+ self.result_artifact.add(self.result_table, 'result')
+ wandb.log_artifact(self.result_artifact,
+ aliases=[
+ 'latest', 'last', 'epoch ' + str(self.current_epoch),
+ ('best' if best_result else '')])
+
+ wandb.log({"evaluation": self.result_table})
+ columns = ["epoch", "id", "ground truth", "prediction"]
+ columns.extend(self.data_dict['names'])
+ self.result_table = wandb.Table(columns)
+ self.result_artifact = wandb.Artifact("run_" + wandb.run.id + "_progress", "evaluation")
+
+ def finish_run(self):
+ """
+ Log metrics if any and finish the current W&B run
+ """
+ if self.wandb_run:
+ if self.log_dict:
+ with all_logging_disabled():
+ wandb.log(self.log_dict)
+ wandb.run.finish()
+
+
+@contextmanager
+def all_logging_disabled(highest_level=logging.CRITICAL):
+ """ source - https://gist.github.com/simon-weber/7853144
+ A context manager that will prevent any logging messages triggered during the body from being processed.
+ :param highest_level: the maximum logging level in use.
+ This would only need to be changed if a custom level greater than CRITICAL is defined.
+ """
+ previous_level = logging.root.manager.disable
+ logging.disable(highest_level)
+ try:
+ yield
+ finally:
+ logging.disable(previous_level)
diff --git a/src/FireDetect/utils/loss.py b/src/FireDetect/utils/loss.py
new file mode 100644
index 0000000..9b9c3d9
--- /dev/null
+++ b/src/FireDetect/utils/loss.py
@@ -0,0 +1,234 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+"""
+Loss functions
+"""
+
+import torch
+import torch.nn as nn
+
+from utils.metrics import bbox_iou
+from utils.torch_utils import de_parallel
+
+
+def smooth_BCE(eps=0.1): # https://github.com/ultralytics/yolov3/issues/238#issuecomment-598028441
+ # return positive, negative label smoothing BCE targets
+ return 1.0 - 0.5 * eps, 0.5 * eps
+
+
+class BCEBlurWithLogitsLoss(nn.Module):
+ # BCEwithLogitLoss() with reduced missing label effects.
+ def __init__(self, alpha=0.05):
+ super().__init__()
+ self.loss_fcn = nn.BCEWithLogitsLoss(reduction='none') # must be nn.BCEWithLogitsLoss()
+ self.alpha = alpha
+
+ def forward(self, pred, true):
+ loss = self.loss_fcn(pred, true)
+ pred = torch.sigmoid(pred) # prob from logits
+ dx = pred - true # reduce only missing label effects
+ # dx = (pred - true).abs() # reduce missing label and false label effects
+ alpha_factor = 1 - torch.exp((dx - 1) / (self.alpha + 1e-4))
+ loss *= alpha_factor
+ return loss.mean()
+
+
+class FocalLoss(nn.Module):
+ # Wraps focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5)
+ def __init__(self, loss_fcn, gamma=1.5, alpha=0.25):
+ super().__init__()
+ self.loss_fcn = loss_fcn # must be nn.BCEWithLogitsLoss()
+ self.gamma = gamma
+ self.alpha = alpha
+ self.reduction = loss_fcn.reduction
+ self.loss_fcn.reduction = 'none' # required to apply FL to each element
+
+ def forward(self, pred, true):
+ loss = self.loss_fcn(pred, true)
+ # p_t = torch.exp(-loss)
+ # loss *= self.alpha * (1.000001 - p_t) ** self.gamma # non-zero power for gradient stability
+
+ # TF implementation https://github.com/tensorflow/addons/blob/v0.7.1/tensorflow_addons/losses/focal_loss.py
+ pred_prob = torch.sigmoid(pred) # prob from logits
+ p_t = true * pred_prob + (1 - true) * (1 - pred_prob)
+ alpha_factor = true * self.alpha + (1 - true) * (1 - self.alpha)
+ modulating_factor = (1.0 - p_t) ** self.gamma
+ loss *= alpha_factor * modulating_factor
+
+ if self.reduction == 'mean':
+ return loss.mean()
+ elif self.reduction == 'sum':
+ return loss.sum()
+ else: # 'none'
+ return loss
+
+
+class QFocalLoss(nn.Module):
+ # Wraps Quality focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5)
+ def __init__(self, loss_fcn, gamma=1.5, alpha=0.25):
+ super().__init__()
+ self.loss_fcn = loss_fcn # must be nn.BCEWithLogitsLoss()
+ self.gamma = gamma
+ self.alpha = alpha
+ self.reduction = loss_fcn.reduction
+ self.loss_fcn.reduction = 'none' # required to apply FL to each element
+
+ def forward(self, pred, true):
+ loss = self.loss_fcn(pred, true)
+
+ pred_prob = torch.sigmoid(pred) # prob from logits
+ alpha_factor = true * self.alpha + (1 - true) * (1 - self.alpha)
+ modulating_factor = torch.abs(true - pred_prob) ** self.gamma
+ loss *= alpha_factor * modulating_factor
+
+ if self.reduction == 'mean':
+ return loss.mean()
+ elif self.reduction == 'sum':
+ return loss.sum()
+ else: # 'none'
+ return loss
+
+
+class ComputeLoss:
+ sort_obj_iou = False
+
+ # Compute losses
+ def __init__(self, model, autobalance=False):
+ device = next(model.parameters()).device # get model device
+ h = model.hyp # hyperparameters
+
+ # Define criteria
+ BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['cls_pw']], device=device))
+ BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['obj_pw']], device=device))
+
+ # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3
+ self.cp, self.cn = smooth_BCE(eps=h.get('label_smoothing', 0.0)) # positive, negative BCE targets
+
+ # Focal loss
+ g = h['fl_gamma'] # focal loss gamma
+ if g > 0:
+ BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g)
+
+ m = de_parallel(model).model[-1] # Detect() module
+ self.balance = {3: [4.0, 1.0, 0.4]}.get(m.nl, [4.0, 1.0, 0.25, 0.06, 0.02]) # P3-P7
+ self.ssi = list(m.stride).index(16) if autobalance else 0 # stride 16 index
+ self.BCEcls, self.BCEobj, self.gr, self.hyp, self.autobalance = BCEcls, BCEobj, 1.0, h, autobalance
+ self.na = m.na # number of anchors
+ self.nc = m.nc # number of classes
+ self.nl = m.nl # number of layers
+ self.anchors = m.anchors
+ self.device = device
+
+ def __call__(self, p, targets): # predictions, targets
+ lcls = torch.zeros(1, device=self.device) # class loss
+ lbox = torch.zeros(1, device=self.device) # box loss
+ lobj = torch.zeros(1, device=self.device) # object loss
+ tcls, tbox, indices, anchors = self.build_targets(p, targets) # targets
+
+ # Losses
+ for i, pi in enumerate(p): # layer index, layer predictions
+ b, a, gj, gi = indices[i] # image, anchor, gridy, gridx
+ tobj = torch.zeros(pi.shape[:4], dtype=pi.dtype, device=self.device) # target obj
+
+ n = b.shape[0] # number of targets
+ if n:
+ # pxy, pwh, _, pcls = pi[b, a, gj, gi].tensor_split((2, 4, 5), dim=1) # faster, requires torch 1.8.0
+ pxy, pwh, _, pcls = pi[b, a, gj, gi].split((2, 2, 1, self.nc), 1) # target-subset of predictions
+
+ # Regression
+ pxy = pxy.sigmoid() * 2 - 0.5
+ pwh = (pwh.sigmoid() * 2) ** 2 * anchors[i]
+ pbox = torch.cat((pxy, pwh), 1) # predicted box
+ iou = bbox_iou(pbox, tbox[i], CIoU=True).squeeze() # iou(prediction, target)
+ lbox += (1.0 - iou).mean() # iou loss
+
+ # Objectness
+ iou = iou.detach().clamp(0).type(tobj.dtype)
+ if self.sort_obj_iou:
+ j = iou.argsort()
+ b, a, gj, gi, iou = b[j], a[j], gj[j], gi[j], iou[j]
+ if self.gr < 1:
+ iou = (1.0 - self.gr) + self.gr * iou
+ tobj[b, a, gj, gi] = iou # iou ratio
+
+ # Classification
+ if self.nc > 1: # cls loss (only if multiple classes)
+ t = torch.full_like(pcls, self.cn, device=self.device) # targets
+ t[range(n), tcls[i]] = self.cp
+ lcls += self.BCEcls(pcls, t) # BCE
+
+ # Append targets to text file
+ # with open('targets.txt', 'a') as file:
+ # [file.write('%11.5g ' * 4 % tuple(x) + '\n') for x in torch.cat((txy[i], twh[i]), 1)]
+
+ obji = self.BCEobj(pi[..., 4], tobj)
+ lobj += obji * self.balance[i] # obj loss
+ if self.autobalance:
+ self.balance[i] = self.balance[i] * 0.9999 + 0.0001 / obji.detach().item()
+
+ if self.autobalance:
+ self.balance = [x / self.balance[self.ssi] for x in self.balance]
+ lbox *= self.hyp['box']
+ lobj *= self.hyp['obj']
+ lcls *= self.hyp['cls']
+ bs = tobj.shape[0] # batch size
+
+ return (lbox + lobj + lcls) * bs, torch.cat((lbox, lobj, lcls)).detach()
+
+ def build_targets(self, p, targets):
+ # Build targets for compute_loss(), input targets(image,class,x,y,w,h)
+ na, nt = self.na, targets.shape[0] # number of anchors, targets
+ tcls, tbox, indices, anch = [], [], [], []
+ gain = torch.ones(7, device=self.device) # normalized to gridspace gain
+ ai = torch.arange(na, device=self.device).float().view(na, 1).repeat(1, nt) # same as .repeat_interleave(nt)
+ targets = torch.cat((targets.repeat(na, 1, 1), ai[..., None]), 2) # append anchor indices
+
+ g = 0.5 # bias
+ off = torch.tensor(
+ [
+ [0, 0],
+ [1, 0],
+ [0, 1],
+ [-1, 0],
+ [0, -1], # j,k,l,m
+ # [1, 1], [1, -1], [-1, 1], [-1, -1], # jk,jm,lk,lm
+ ],
+ device=self.device).float() * g # offsets
+
+ for i in range(self.nl):
+ anchors, shape = self.anchors[i], p[i].shape
+ gain[2:6] = torch.tensor(shape)[[3, 2, 3, 2]] # xyxy gain
+
+ # Match targets to anchors
+ t = targets * gain # shape(3,n,7)
+ if nt:
+ # Matches
+ r = t[..., 4:6] / anchors[:, None] # wh ratio
+ j = torch.max(r, 1 / r).max(2)[0] < self.hyp['anchor_t'] # compare
+ # j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t'] # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2))
+ t = t[j] # filter
+
+ # Offsets
+ gxy = t[:, 2:4] # grid xy
+ gxi = gain[[2, 3]] - gxy # inverse
+ j, k = ((gxy % 1 < g) & (gxy > 1)).T
+ l, m = ((gxi % 1 < g) & (gxi > 1)).T
+ j = torch.stack((torch.ones_like(j), j, k, l, m))
+ t = t.repeat((5, 1, 1))[j]
+ offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j]
+ else:
+ t = targets[0]
+ offsets = 0
+
+ # Define
+ bc, gxy, gwh, a = t.chunk(4, 1) # (image, class), grid xy, grid wh, anchors
+ a, (b, c) = a.long().view(-1), bc.long().T # anchors, image, class
+ gij = (gxy - offsets).long()
+ gi, gj = gij.T # grid indices
+
+ # Append
+ indices.append((b, a, gj.clamp_(0, shape[2] - 1), gi.clamp_(0, shape[3] - 1))) # image, anchor, grid
+ tbox.append(torch.cat((gxy - gij, gwh), 1)) # box
+ anch.append(anchors[a]) # anchors
+ tcls.append(c) # class
+
+ return tcls, tbox, indices, anch
diff --git a/src/FireDetect/utils/metrics.py b/src/FireDetect/utils/metrics.py
new file mode 100644
index 0000000..65ea463
--- /dev/null
+++ b/src/FireDetect/utils/metrics.py
@@ -0,0 +1,363 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+"""
+Model validation metrics
+"""
+
+import math
+import warnings
+from pathlib import Path
+
+import matplotlib.pyplot as plt
+import numpy as np
+import torch
+
+from utils import TryExcept, threaded
+
+
+def fitness(x):
+ # Model fitness as a weighted combination of metrics
+ w = [0.0, 0.0, 0.1, 0.9] # weights for [P, R, mAP@0.5, mAP@0.5:0.95]
+ return (x[:, :4] * w).sum(1)
+
+
+def smooth(y, f=0.05):
+ # Box filter of fraction f
+ nf = round(len(y) * f * 2) // 2 + 1 # number of filter elements (must be odd)
+ p = np.ones(nf // 2) # ones padding
+ yp = np.concatenate((p * y[0], y, p * y[-1]), 0) # y padded
+ return np.convolve(yp, np.ones(nf) / nf, mode='valid') # y-smoothed
+
+
+def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir='.', names=(), eps=1e-16, prefix=""):
+ """ Compute the average precision, given the recall and precision curves.
+ Source: https://github.com/rafaelpadilla/Object-Detection-Metrics.
+ # Arguments
+ tp: True positives (nparray, nx1 or nx10).
+ conf: Objectness value from 0-1 (nparray).
+ pred_cls: Predicted object classes (nparray).
+ target_cls: True object classes (nparray).
+ plot: Plot precision-recall curve at mAP@0.5
+ save_dir: Plot save directory
+ # Returns
+ The average precision as computed in py-faster-rcnn.
+ """
+
+ # Sort by objectness
+ i = np.argsort(-conf)
+ tp, conf, pred_cls = tp[i], conf[i], pred_cls[i]
+
+ # Find unique classes
+ unique_classes, nt = np.unique(target_cls, return_counts=True)
+ nc = unique_classes.shape[0] # number of classes, number of detections
+
+ # Create Precision-Recall curve and compute AP for each class
+ px, py = np.linspace(0, 1, 1000), [] # for plotting
+ ap, p, r = np.zeros((nc, tp.shape[1])), np.zeros((nc, 1000)), np.zeros((nc, 1000))
+ for ci, c in enumerate(unique_classes):
+ i = pred_cls == c
+ n_l = nt[ci] # number of labels
+ n_p = i.sum() # number of predictions
+ if n_p == 0 or n_l == 0:
+ continue
+
+ # Accumulate FPs and TPs
+ fpc = (1 - tp[i]).cumsum(0)
+ tpc = tp[i].cumsum(0)
+
+ # Recall
+ recall = tpc / (n_l + eps) # recall curve
+ r[ci] = np.interp(-px, -conf[i], recall[:, 0], left=0) # negative x, xp because xp decreases
+
+ # Precision
+ precision = tpc / (tpc + fpc) # precision curve
+ p[ci] = np.interp(-px, -conf[i], precision[:, 0], left=1) # p at pr_score
+
+ # AP from recall-precision curve
+ for j in range(tp.shape[1]):
+ ap[ci, j], mpre, mrec = compute_ap(recall[:, j], precision[:, j])
+ if plot and j == 0:
+ py.append(np.interp(px, mrec, mpre)) # precision at mAP@0.5
+
+ # Compute F1 (harmonic mean of precision and recall)
+ f1 = 2 * p * r / (p + r + eps)
+ names = [v for k, v in names.items() if k in unique_classes] # list: only classes that have data
+ names = dict(enumerate(names)) # to dict
+ if plot:
+ plot_pr_curve(px, py, ap, Path(save_dir) / f'{prefix}PR_curve.png', names)
+ plot_mc_curve(px, f1, Path(save_dir) / f'{prefix}F1_curve.png', names, ylabel='F1')
+ plot_mc_curve(px, p, Path(save_dir) / f'{prefix}P_curve.png', names, ylabel='Precision')
+ plot_mc_curve(px, r, Path(save_dir) / f'{prefix}R_curve.png', names, ylabel='Recall')
+
+ i = smooth(f1.mean(0), 0.1).argmax() # max F1 index
+ p, r, f1 = p[:, i], r[:, i], f1[:, i]
+ tp = (r * nt).round() # true positives
+ fp = (tp / (p + eps) - tp).round() # false positives
+ return tp, fp, p, r, f1, ap, unique_classes.astype(int)
+
+
+def compute_ap(recall, precision):
+ """ Compute the average precision, given the recall and precision curves
+ # Arguments
+ recall: The recall curve (list)
+ precision: The precision curve (list)
+ # Returns
+ Average precision, precision curve, recall curve
+ """
+
+ # Append sentinel values to beginning and end
+ mrec = np.concatenate(([0.0], recall, [1.0]))
+ mpre = np.concatenate(([1.0], precision, [0.0]))
+
+ # Compute the precision envelope
+ mpre = np.flip(np.maximum.accumulate(np.flip(mpre)))
+
+ # Integrate area under curve
+ method = 'interp' # methods: 'continuous', 'interp'
+ if method == 'interp':
+ x = np.linspace(0, 1, 101) # 101-point interp (COCO)
+ ap = np.trapz(np.interp(x, mrec, mpre), x) # integrate
+ else: # 'continuous'
+ i = np.where(mrec[1:] != mrec[:-1])[0] # points where x axis (recall) changes
+ ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1]) # area under curve
+
+ return ap, mpre, mrec
+
+
+class ConfusionMatrix:
+ # Updated version of https://github.com/kaanakan/object_detection_confusion_matrix
+ def __init__(self, nc, conf=0.25, iou_thres=0.45):
+ self.matrix = np.zeros((nc + 1, nc + 1))
+ self.nc = nc # number of classes
+ self.conf = conf
+ self.iou_thres = iou_thres
+
+ def process_batch(self, detections, labels):
+ """
+ Return intersection-over-union (Jaccard index) of boxes.
+ Both sets of boxes are expected to be in (x1, y1, x2, y2) format.
+ Arguments:
+ detections (Array[N, 6]), x1, y1, x2, y2, conf, class
+ labels (Array[M, 5]), class, x1, y1, x2, y2
+ Returns:
+ None, updates confusion matrix accordingly
+ """
+ if detections is None:
+ gt_classes = labels.int()
+ for gc in gt_classes:
+ self.matrix[self.nc, gc] += 1 # background FN
+ return
+
+ detections = detections[detections[:, 4] > self.conf]
+ gt_classes = labels[:, 0].int()
+ detection_classes = detections[:, 5].int()
+ iou = box_iou(labels[:, 1:], detections[:, :4])
+
+ x = torch.where(iou > self.iou_thres)
+ if x[0].shape[0]:
+ matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy()
+ if x[0].shape[0] > 1:
+ matches = matches[matches[:, 2].argsort()[::-1]]
+ matches = matches[np.unique(matches[:, 1], return_index=True)[1]]
+ matches = matches[matches[:, 2].argsort()[::-1]]
+ matches = matches[np.unique(matches[:, 0], return_index=True)[1]]
+ else:
+ matches = np.zeros((0, 3))
+
+ n = matches.shape[0] > 0
+ m0, m1, _ = matches.transpose().astype(int)
+ for i, gc in enumerate(gt_classes):
+ j = m0 == i
+ if n and sum(j) == 1:
+ self.matrix[detection_classes[m1[j]], gc] += 1 # correct
+ else:
+ self.matrix[self.nc, gc] += 1 # true background
+
+ if n:
+ for i, dc in enumerate(detection_classes):
+ if not any(m1 == i):
+ self.matrix[dc, self.nc] += 1 # predicted background
+
+ def matrix(self):
+ return self.matrix
+
+ def tp_fp(self):
+ tp = self.matrix.diagonal() # true positives
+ fp = self.matrix.sum(1) - tp # false positives
+ # fn = self.matrix.sum(0) - tp # false negatives (missed detections)
+ return tp[:-1], fp[:-1] # remove background class
+
+ @TryExcept('WARNING ⚠️ ConfusionMatrix plot failure')
+ def plot(self, normalize=True, save_dir='', names=()):
+ import seaborn as sn
+
+ array = self.matrix / ((self.matrix.sum(0).reshape(1, -1) + 1E-9) if normalize else 1) # normalize columns
+ array[array < 0.005] = np.nan # don't annotate (would appear as 0.00)
+
+ fig, ax = plt.subplots(1, 1, figsize=(12, 9), tight_layout=True)
+ nc, nn = self.nc, len(names) # number of classes, names
+ sn.set(font_scale=1.0 if nc < 50 else 0.8) # for label size
+ labels = (0 < nn < 99) and (nn == nc) # apply names to ticklabels
+ ticklabels = (names + ['background']) if labels else "auto"
+ with warnings.catch_warnings():
+ warnings.simplefilter('ignore') # suppress empty matrix RuntimeWarning: All-NaN slice encountered
+ sn.heatmap(array,
+ ax=ax,
+ annot=nc < 30,
+ annot_kws={
+ "size": 8},
+ cmap='Blues',
+ fmt='.2f',
+ square=True,
+ vmin=0.0,
+ xticklabels=ticklabels,
+ yticklabels=ticklabels).set_facecolor((1, 1, 1))
+ ax.set_ylabel('True')
+ ax.set_ylabel('Predicted')
+ ax.set_title('Confusion Matrix')
+ fig.savefig(Path(save_dir) / 'confusion_matrix.png', dpi=250)
+ plt.close(fig)
+
+ def print(self):
+ for i in range(self.nc + 1):
+ print(' '.join(map(str, self.matrix[i])))
+
+
+def bbox_iou(box1, box2, xywh=True, GIoU=False, DIoU=False, CIoU=False, eps=1e-7):
+ # Returns Intersection over Union (IoU) of box1(1,4) to box2(n,4)
+
+ # Get the coordinates of bounding boxes
+ if xywh: # transform from xywh to xyxy
+ (x1, y1, w1, h1), (x2, y2, w2, h2) = box1.chunk(4, -1), box2.chunk(4, -1)
+ w1_, h1_, w2_, h2_ = w1 / 2, h1 / 2, w2 / 2, h2 / 2
+ b1_x1, b1_x2, b1_y1, b1_y2 = x1 - w1_, x1 + w1_, y1 - h1_, y1 + h1_
+ b2_x1, b2_x2, b2_y1, b2_y2 = x2 - w2_, x2 + w2_, y2 - h2_, y2 + h2_
+ else: # x1, y1, x2, y2 = box1
+ b1_x1, b1_y1, b1_x2, b1_y2 = box1.chunk(4, -1)
+ b2_x1, b2_y1, b2_x2, b2_y2 = box2.chunk(4, -1)
+ w1, h1 = b1_x2 - b1_x1, b1_y2 - b1_y1 + eps
+ w2, h2 = b2_x2 - b2_x1, b2_y2 - b2_y1 + eps
+
+ # Intersection area
+ inter = (torch.min(b1_x2, b2_x2) - torch.max(b1_x1, b2_x1)).clamp(0) * \
+ (torch.min(b1_y2, b2_y2) - torch.max(b1_y1, b2_y1)).clamp(0)
+
+ # Union Area
+ union = w1 * h1 + w2 * h2 - inter + eps
+
+ # IoU
+ iou = inter / union
+ if CIoU or DIoU or GIoU:
+ cw = torch.max(b1_x2, b2_x2) - torch.min(b1_x1, b2_x1) # convex (smallest enclosing box) width
+ ch = torch.max(b1_y2, b2_y2) - torch.min(b1_y1, b2_y1) # convex height
+ if CIoU or DIoU: # Distance or Complete IoU https://arxiv.org/abs/1911.08287v1
+ c2 = cw ** 2 + ch ** 2 + eps # convex diagonal squared
+ rho2 = ((b2_x1 + b2_x2 - b1_x1 - b1_x2) ** 2 + (b2_y1 + b2_y2 - b1_y1 - b1_y2) ** 2) / 4 # center dist ** 2
+ if CIoU: # https://github.com/Zzh-tju/DIoU-SSD-pytorch/blob/master/utils/box/box_utils.py#L47
+ v = (4 / math.pi ** 2) * torch.pow(torch.atan(w2 / h2) - torch.atan(w1 / h1), 2)
+ with torch.no_grad():
+ alpha = v / (v - iou + (1 + eps))
+ return iou - (rho2 / c2 + v * alpha) # CIoU
+ return iou - rho2 / c2 # DIoU
+ c_area = cw * ch + eps # convex area
+ return iou - (c_area - union) / c_area # GIoU https://arxiv.org/pdf/1902.09630.pdf
+ return iou # IoU
+
+
+def box_iou(box1, box2, eps=1e-7):
+ # https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py
+ """
+ Return intersection-over-union (Jaccard index) of boxes.
+ Both sets of boxes are expected to be in (x1, y1, x2, y2) format.
+ Arguments:
+ box1 (Tensor[N, 4])
+ box2 (Tensor[M, 4])
+ Returns:
+ iou (Tensor[N, M]): the NxM matrix containing the pairwise
+ IoU values for every element in boxes1 and boxes2
+ """
+
+ # inter(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2)
+ (a1, a2), (b1, b2) = box1.unsqueeze(1).chunk(2, 2), box2.unsqueeze(0).chunk(2, 2)
+ inter = (torch.min(a2, b2) - torch.max(a1, b1)).clamp(0).prod(2)
+
+ # IoU = inter / (area1 + area2 - inter)
+ return inter / ((a2 - a1).prod(2) + (b2 - b1).prod(2) - inter + eps)
+
+
+def bbox_ioa(box1, box2, eps=1e-7):
+ """ Returns the intersection over box2 area given box1, box2. Boxes are x1y1x2y2
+ box1: np.array of shape(4)
+ box2: np.array of shape(nx4)
+ returns: np.array of shape(n)
+ """
+
+ # Get the coordinates of bounding boxes
+ b1_x1, b1_y1, b1_x2, b1_y2 = box1
+ b2_x1, b2_y1, b2_x2, b2_y2 = box2.T
+
+ # Intersection area
+ inter_area = (np.minimum(b1_x2, b2_x2) - np.maximum(b1_x1, b2_x1)).clip(0) * \
+ (np.minimum(b1_y2, b2_y2) - np.maximum(b1_y1, b2_y1)).clip(0)
+
+ # box2 area
+ box2_area = (b2_x2 - b2_x1) * (b2_y2 - b2_y1) + eps
+
+ # Intersection over box2 area
+ return inter_area / box2_area
+
+
+def wh_iou(wh1, wh2, eps=1e-7):
+ # Returns the nxm IoU matrix. wh1 is nx2, wh2 is mx2
+ wh1 = wh1[:, None] # [N,1,2]
+ wh2 = wh2[None] # [1,M,2]
+ inter = torch.min(wh1, wh2).prod(2) # [N,M]
+ return inter / (wh1.prod(2) + wh2.prod(2) - inter + eps) # iou = inter / (area1 + area2 - inter)
+
+
+# Plots ----------------------------------------------------------------------------------------------------------------
+
+
+@threaded
+def plot_pr_curve(px, py, ap, save_dir=Path('pr_curve.png'), names=()):
+ # Precision-recall curve
+ fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True)
+ py = np.stack(py, axis=1)
+
+ if 0 < len(names) < 21: # display per-class legend if < 21 classes
+ for i, y in enumerate(py.T):
+ ax.plot(px, y, linewidth=1, label=f'{names[i]} {ap[i, 0]:.3f}') # plot(recall, precision)
+ else:
+ ax.plot(px, py, linewidth=1, color='grey') # plot(recall, precision)
+
+ ax.plot(px, py.mean(1), linewidth=3, color='blue', label='all classes %.3f mAP@0.5' % ap[:, 0].mean())
+ ax.set_xlabel('Recall')
+ ax.set_ylabel('Precision')
+ ax.set_xlim(0, 1)
+ ax.set_ylim(0, 1)
+ ax.legend(bbox_to_anchor=(1.04, 1), loc="upper left")
+ ax.set_title('Precision-Recall Curve')
+ fig.savefig(save_dir, dpi=250)
+ plt.close(fig)
+
+
+@threaded
+def plot_mc_curve(px, py, save_dir=Path('mc_curve.png'), names=(), xlabel='Confidence', ylabel='Metric'):
+ # Metric-confidence curve
+ fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True)
+
+ if 0 < len(names) < 21: # display per-class legend if < 21 classes
+ for i, y in enumerate(py):
+ ax.plot(px, y, linewidth=1, label=f'{names[i]}') # plot(confidence, metric)
+ else:
+ ax.plot(px, py.T, linewidth=1, color='grey') # plot(confidence, metric)
+
+ y = smooth(py.mean(0), 0.05)
+ ax.plot(px, y, linewidth=3, color='blue', label=f'all classes {y.max():.2f} at {px[y.argmax()]:.3f}')
+ ax.set_xlabel(xlabel)
+ ax.set_ylabel(ylabel)
+ ax.set_xlim(0, 1)
+ ax.set_ylim(0, 1)
+ ax.legend(bbox_to_anchor=(1.04, 1), loc="upper left")
+ ax.set_title(f'{ylabel}-Confidence Curve')
+ fig.savefig(save_dir, dpi=250)
+ plt.close(fig)
diff --git a/src/FireDetect/utils/plots.py b/src/FireDetect/utils/plots.py
new file mode 100644
index 0000000..36df271
--- /dev/null
+++ b/src/FireDetect/utils/plots.py
@@ -0,0 +1,575 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+"""
+Plotting utils
+"""
+
+import contextlib
+import math
+import os
+from copy import copy
+from pathlib import Path
+from urllib.error import URLError
+
+import cv2
+import matplotlib
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import seaborn as sn
+import torch
+from PIL import Image, ImageDraw, ImageFont
+
+from utils import TryExcept, threaded
+from utils.general import (CONFIG_DIR, FONT, LOGGER, check_font, check_requirements, clip_boxes, increment_path,
+ is_ascii, xywh2xyxy, xyxy2xywh)
+from utils.metrics import fitness
+from utils.segment.general import scale_image
+
+# Settings
+RANK = int(os.getenv('RANK', -1))
+matplotlib.rc('font', **{'size': 11})
+matplotlib.use('Agg') # for writing to files only
+
+
+class Colors:
+ # Ultralytics color palette https://ultralytics.com/
+ def __init__(self):
+ # hex = matplotlib.colors.TABLEAU_COLORS.values()
+ hexs = ('FF3838', 'FF9D97', 'FF701F', 'FFB21D', 'CFD231', '48F90A', '92CC17', '3DDB86', '1A9334', '00D4BB',
+ '2C99A8', '00C2FF', '344593', '6473FF', '0018EC', '8438FF', '520085', 'CB38FF', 'FF95C8', 'FF37C7')
+ self.palette = [self.hex2rgb(f'#{c}') for c in hexs]
+ self.n = len(self.palette)
+
+ def __call__(self, i, bgr=False):
+ c = self.palette[int(i) % self.n]
+ return (c[2], c[1], c[0]) if bgr else c
+
+ @staticmethod
+ def hex2rgb(h): # rgb order (PIL)
+ return tuple(int(h[1 + i:1 + i + 2], 16) for i in (0, 2, 4))
+
+
+colors = Colors() # create instance for 'from utils.plots import colors'
+
+
+def check_pil_font(font=FONT, size=10):
+ # Return a PIL TrueType Font, downloading to CONFIG_DIR if necessary
+ font = Path(font)
+ font = font if font.exists() else (CONFIG_DIR / font.name)
+ try:
+ return ImageFont.truetype(str(font) if font.exists() else font.name, size)
+ except Exception: # download if missing
+ try:
+ check_font(font)
+ return ImageFont.truetype(str(font), size)
+ except TypeError:
+ check_requirements('Pillow>=8.4.0') # known issue https://github.com/ultralytics/yolov5/issues/5374
+ except URLError: # not online
+ return ImageFont.load_default()
+
+
+class Annotator:
+ # YOLOv5 Annotator for train/val mosaics and jpgs and detect/hub inference annotations
+ def __init__(self, im, line_width=None, font_size=None, font='Arial.ttf', pil=False, example='abc'):
+ assert im.data.contiguous, 'Image not contiguous. Apply np.ascontiguousarray(im) to Annotator() input images.'
+ non_ascii = not is_ascii(example) # non-latin labels, i.e. asian, arabic, cyrillic
+ self.pil = pil or non_ascii
+ if self.pil: # use PIL
+ self.im = im if isinstance(im, Image.Image) else Image.fromarray(im)
+ self.draw = ImageDraw.Draw(self.im)
+ self.font = check_pil_font(font='Arial.Unicode.ttf' if non_ascii else font,
+ size=font_size or max(round(sum(self.im.size) / 2 * 0.035), 12))
+ else: # use cv2
+ self.im = im
+ self.lw = line_width or max(round(sum(im.shape) / 2 * 0.003), 2) # line width
+
+ def box_label(self, box, label='', color=(128, 128, 128), txt_color=(255, 255, 255)):
+ # Add one xyxy box to image with label
+ if self.pil or not is_ascii(label):
+ self.draw.rectangle(box, width=self.lw, outline=color) # box
+ if label:
+ w, h = self.font.getsize(label) # text width, height
+ outside = box[1] - h >= 0 # label fits outside box
+ self.draw.rectangle(
+ (box[0], box[1] - h if outside else box[1], box[0] + w + 1,
+ box[1] + 1 if outside else box[1] + h + 1),
+ fill=color,
+ )
+ # self.draw.text((box[0], box[1]), label, fill=txt_color, font=self.font, anchor='ls') # for PIL>8.0
+ self.draw.text((box[0], box[1] - h if outside else box[1]), label, fill=txt_color, font=self.font)
+ else: # cv2
+ p1, p2 = (int(box[0]), int(box[1])), (int(box[2]), int(box[3]))
+ cv2.rectangle(self.im, p1, p2, color, thickness=self.lw, lineType=cv2.LINE_AA)
+ if label:
+ tf = max(self.lw - 1, 1) # font thickness
+ w, h = cv2.getTextSize(label, 0, fontScale=self.lw / 3, thickness=tf)[0] # text width, height
+ outside = p1[1] - h >= 3
+ p2 = p1[0] + w, p1[1] - h - 3 if outside else p1[1] + h + 3
+ cv2.rectangle(self.im, p1, p2, color, -1, cv2.LINE_AA) # filled
+ cv2.putText(self.im,
+ label, (p1[0], p1[1] - 2 if outside else p1[1] + h + 2),
+ 0,
+ self.lw / 3,
+ txt_color,
+ thickness=tf,
+ lineType=cv2.LINE_AA)
+
+ def masks(self, masks, colors, im_gpu=None, alpha=0.5):
+ """Plot masks at once.
+ Args:
+ masks (tensor): predicted masks on cuda, shape: [n, h, w]
+ colors (List[List[Int]]): colors for predicted masks, [[r, g, b] * n]
+ im_gpu (tensor): img is in cuda, shape: [3, h, w], range: [0, 1]
+ alpha (float): mask transparency: 0.0 fully transparent, 1.0 opaque
+ """
+ if self.pil:
+ # convert to numpy first
+ self.im = np.asarray(self.im).copy()
+ if im_gpu is None:
+ # Add multiple masks of shape(h,w,n) with colors list([r,g,b], [r,g,b], ...)
+ if len(masks) == 0:
+ return
+ if isinstance(masks, torch.Tensor):
+ masks = torch.as_tensor(masks, dtype=torch.uint8)
+ masks = masks.permute(1, 2, 0).contiguous()
+ masks = masks.cpu().numpy()
+ # masks = np.ascontiguousarray(masks.transpose(1, 2, 0))
+ masks = scale_image(masks.shape[:2], masks, self.im.shape)
+ masks = np.asarray(masks, dtype=np.float32)
+ colors = np.asarray(colors, dtype=np.float32) # shape(n,3)
+ s = masks.sum(2, keepdims=True).clip(0, 1) # add all masks together
+ masks = (masks @ colors).clip(0, 255) # (h,w,n) @ (n,3) = (h,w,3)
+ self.im[:] = masks * alpha + self.im * (1 - s * alpha)
+ else:
+ if len(masks) == 0:
+ self.im[:] = im_gpu.permute(1, 2, 0).contiguous().cpu().numpy() * 255
+ colors = torch.tensor(colors, device=im_gpu.device, dtype=torch.float32) / 255.0
+ colors = colors[:, None, None] # shape(n,1,1,3)
+ masks = masks.unsqueeze(3) # shape(n,h,w,1)
+ masks_color = masks * (colors * alpha) # shape(n,h,w,3)
+
+ inv_alph_masks = (1 - masks * alpha).cumprod(0) # shape(n,h,w,1)
+ mcs = (masks_color * inv_alph_masks).sum(0) * 2 # mask color summand shape(n,h,w,3)
+
+ im_gpu = im_gpu.flip(dims=[0]) # flip channel
+ im_gpu = im_gpu.permute(1, 2, 0).contiguous() # shape(h,w,3)
+ im_gpu = im_gpu * inv_alph_masks[-1] + mcs
+ im_mask = (im_gpu * 255).byte().cpu().numpy()
+ self.im[:] = scale_image(im_gpu.shape, im_mask, self.im.shape)
+ if self.pil:
+ # convert im back to PIL and update draw
+ self.fromarray(self.im)
+
+ def rectangle(self, xy, fill=None, outline=None, width=1):
+ # Add rectangle to image (PIL-only)
+ self.draw.rectangle(xy, fill, outline, width)
+
+ def text(self, xy, text, txt_color=(255, 255, 255), anchor='top'):
+ # Add text to image (PIL-only)
+ if anchor == 'bottom': # start y from font bottom
+ w, h = self.font.getsize(text) # text width, height
+ xy[1] += 1 - h
+ self.draw.text(xy, text, fill=txt_color, font=self.font)
+
+ def fromarray(self, im):
+ # Update self.im from a numpy array
+ self.im = im if isinstance(im, Image.Image) else Image.fromarray(im)
+ self.draw = ImageDraw.Draw(self.im)
+
+ def result(self):
+ # Return annotated image as array
+ return np.asarray(self.im)
+
+
+def feature_visualization(x, module_type, stage, n=32, save_dir=Path('runs/detect/exp')):
+ """
+ x: Features to be visualized
+ module_type: Module type
+ stage: Module stage within model
+ n: Maximum number of feature maps to plot
+ save_dir: Directory to save results
+ """
+ if 'Detect' not in module_type:
+ batch, channels, height, width = x.shape # batch, channels, height, width
+ if height > 1 and width > 1:
+ f = save_dir / f"stage{stage}_{module_type.split('.')[-1]}_features.png" # filename
+
+ blocks = torch.chunk(x[0].cpu(), channels, dim=0) # select batch index 0, block by channels
+ n = min(n, channels) # number of plots
+ fig, ax = plt.subplots(math.ceil(n / 8), 8, tight_layout=True) # 8 rows x n/8 cols
+ ax = ax.ravel()
+ plt.subplots_adjust(wspace=0.05, hspace=0.05)
+ for i in range(n):
+ ax[i].imshow(blocks[i].squeeze()) # cmap='gray'
+ ax[i].axis('off')
+
+ LOGGER.info(f'Saving {f}... ({n}/{channels})')
+ plt.savefig(f, dpi=300, bbox_inches='tight')
+ plt.close()
+ np.save(str(f.with_suffix('.npy')), x[0].cpu().numpy()) # npy save
+
+
+def hist2d(x, y, n=100):
+ # 2d histogram used in labels.png and evolve.png
+ xedges, yedges = np.linspace(x.min(), x.max(), n), np.linspace(y.min(), y.max(), n)
+ hist, xedges, yedges = np.histogram2d(x, y, (xedges, yedges))
+ xidx = np.clip(np.digitize(x, xedges) - 1, 0, hist.shape[0] - 1)
+ yidx = np.clip(np.digitize(y, yedges) - 1, 0, hist.shape[1] - 1)
+ return np.log(hist[xidx, yidx])
+
+
+def butter_lowpass_filtfilt(data, cutoff=1500, fs=50000, order=5):
+ from scipy.signal import butter, filtfilt
+
+ # https://stackoverflow.com/questions/28536191/how-to-filter-smooth-with-scipy-numpy
+ def butter_lowpass(cutoff, fs, order):
+ nyq = 0.5 * fs
+ normal_cutoff = cutoff / nyq
+ return butter(order, normal_cutoff, btype='low', analog=False)
+
+ b, a = butter_lowpass(cutoff, fs, order=order)
+ return filtfilt(b, a, data) # forward-backward filter
+
+
+def output_to_target(output, max_det=300):
+ # Convert model output to target format [batch_id, class_id, x, y, w, h, conf] for plotting
+ targets = []
+ for i, o in enumerate(output):
+ box, conf, cls = o[:max_det, :6].cpu().split((4, 1, 1), 1)
+ j = torch.full((conf.shape[0], 1), i)
+ targets.append(torch.cat((j, cls, xyxy2xywh(box), conf), 1))
+ return torch.cat(targets, 0).numpy()
+
+
+@threaded
+def plot_images(images, targets, paths=None, fname='images.jpg', names=None):
+ # Plot image grid with labels
+ if isinstance(images, torch.Tensor):
+ images = images.cpu().float().numpy()
+ if isinstance(targets, torch.Tensor):
+ targets = targets.cpu().numpy()
+
+ max_size = 1920 # max image size
+ max_subplots = 16 # max image subplots, i.e. 4x4
+ bs, _, h, w = images.shape # batch size, _, height, width
+ bs = min(bs, max_subplots) # limit plot images
+ ns = np.ceil(bs ** 0.5) # number of subplots (square)
+ if np.max(images[0]) <= 1:
+ images *= 255 # de-normalise (optional)
+
+ # Build Image
+ mosaic = np.full((int(ns * h), int(ns * w), 3), 255, dtype=np.uint8) # init
+ for i, im in enumerate(images):
+ if i == max_subplots: # if last batch has fewer images than we expect
+ break
+ x, y = int(w * (i // ns)), int(h * (i % ns)) # block origin
+ im = im.transpose(1, 2, 0)
+ mosaic[y:y + h, x:x + w, :] = im
+
+ # Resize (optional)
+ scale = max_size / ns / max(h, w)
+ if scale < 1:
+ h = math.ceil(scale * h)
+ w = math.ceil(scale * w)
+ mosaic = cv2.resize(mosaic, tuple(int(x * ns) for x in (w, h)))
+
+ # Annotate
+ fs = int((h + w) * ns * 0.01) # font size
+ annotator = Annotator(mosaic, line_width=round(fs / 10), font_size=fs, pil=True, example=names)
+ for i in range(i + 1):
+ x, y = int(w * (i // ns)), int(h * (i % ns)) # block origin
+ annotator.rectangle([x, y, x + w, y + h], None, (255, 255, 255), width=2) # borders
+ if paths:
+ annotator.text((x + 5, y + 5), text=Path(paths[i]).name[:40], txt_color=(220, 220, 220)) # filenames
+ if len(targets) > 0:
+ ti = targets[targets[:, 0] == i] # image targets
+ boxes = xywh2xyxy(ti[:, 2:6]).T
+ classes = ti[:, 1].astype('int')
+ labels = ti.shape[1] == 6 # labels if no conf column
+ conf = None if labels else ti[:, 6] # check for confidence presence (label vs pred)
+
+ if boxes.shape[1]:
+ if boxes.max() <= 1.01: # if normalized with tolerance 0.01
+ boxes[[0, 2]] *= w # scale to pixels
+ boxes[[1, 3]] *= h
+ elif scale < 1: # absolute coords need scale if image scales
+ boxes *= scale
+ boxes[[0, 2]] += x
+ boxes[[1, 3]] += y
+ for j, box in enumerate(boxes.T.tolist()):
+ cls = classes[j]
+ color = colors(cls)
+ cls = names[cls] if names else cls
+ if labels or conf[j] > 0.25: # 0.25 conf thresh
+ label = f'{cls}' if labels else f'{cls} {conf[j]:.1f}'
+ annotator.box_label(box, label, color=color)
+ annotator.im.save(fname) # save
+
+
+def plot_lr_scheduler(optimizer, scheduler, epochs=300, save_dir=''):
+ # Plot LR simulating training for full epochs
+ optimizer, scheduler = copy(optimizer), copy(scheduler) # do not modify originals
+ y = []
+ for _ in range(epochs):
+ scheduler.step()
+ y.append(optimizer.param_groups[0]['lr'])
+ plt.plot(y, '.-', label='LR')
+ plt.xlabel('epoch')
+ plt.ylabel('LR')
+ plt.grid()
+ plt.xlim(0, epochs)
+ plt.ylim(0)
+ plt.savefig(Path(save_dir) / 'LR.png', dpi=200)
+ plt.close()
+
+
+def plot_val_txt(): # from utils.plots import *; plot_val()
+ # Plot val.txt histograms
+ x = np.loadtxt('val.txt', dtype=np.float32)
+ box = xyxy2xywh(x[:, :4])
+ cx, cy = box[:, 0], box[:, 1]
+
+ fig, ax = plt.subplots(1, 1, figsize=(6, 6), tight_layout=True)
+ ax.hist2d(cx, cy, bins=600, cmax=10, cmin=0)
+ ax.set_aspect('equal')
+ plt.savefig('hist2d.png', dpi=300)
+
+ fig, ax = plt.subplots(1, 2, figsize=(12, 6), tight_layout=True)
+ ax[0].hist(cx, bins=600)
+ ax[1].hist(cy, bins=600)
+ plt.savefig('hist1d.png', dpi=200)
+
+
+def plot_targets_txt(): # from utils.plots import *; plot_targets_txt()
+ # Plot targets.txt histograms
+ x = np.loadtxt('targets.txt', dtype=np.float32).T
+ s = ['x targets', 'y targets', 'width targets', 'height targets']
+ fig, ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True)
+ ax = ax.ravel()
+ for i in range(4):
+ ax[i].hist(x[i], bins=100, label=f'{x[i].mean():.3g} +/- {x[i].std():.3g}')
+ ax[i].legend()
+ ax[i].set_title(s[i])
+ plt.savefig('targets.jpg', dpi=200)
+
+
+def plot_val_study(file='', dir='', x=None): # from utils.plots import *; plot_val_study()
+ # Plot file=study.txt generated by val.py (or plot all study*.txt in dir)
+ save_dir = Path(file).parent if file else Path(dir)
+ plot2 = False # plot additional results
+ if plot2:
+ ax = plt.subplots(2, 4, figsize=(10, 6), tight_layout=True)[1].ravel()
+
+ fig2, ax2 = plt.subplots(1, 1, figsize=(8, 4), tight_layout=True)
+ # for f in [save_dir / f'study_coco_{x}.txt' for x in ['yolov5n6', 'yolov5s6', 'yolov5m6', 'yolov5l6', 'yolov5x6']]:
+ for f in sorted(save_dir.glob('study*.txt')):
+ y = np.loadtxt(f, dtype=np.float32, usecols=[0, 1, 2, 3, 7, 8, 9], ndmin=2).T
+ x = np.arange(y.shape[1]) if x is None else np.array(x)
+ if plot2:
+ s = ['P', 'R', 'mAP@.5', 'mAP@.5:.95', 't_preprocess (ms/img)', 't_inference (ms/img)', 't_NMS (ms/img)']
+ for i in range(7):
+ ax[i].plot(x, y[i], '.-', linewidth=2, markersize=8)
+ ax[i].set_title(s[i])
+
+ j = y[3].argmax() + 1
+ ax2.plot(y[5, 1:j],
+ y[3, 1:j] * 1E2,
+ '.-',
+ linewidth=2,
+ markersize=8,
+ label=f.stem.replace('study_coco_', '').replace('yolo', 'YOLO'))
+
+ ax2.plot(1E3 / np.array([209, 140, 97, 58, 35, 18]), [34.6, 40.5, 43.0, 47.5, 49.7, 51.5],
+ 'k.-',
+ linewidth=2,
+ markersize=8,
+ alpha=.25,
+ label='EfficientDet')
+
+ ax2.grid(alpha=0.2)
+ ax2.set_yticks(np.arange(20, 60, 5))
+ ax2.set_xlim(0, 57)
+ ax2.set_ylim(25, 55)
+ ax2.set_xlabel('GPU Speed (ms/img)')
+ ax2.set_ylabel('COCO AP val')
+ ax2.legend(loc='lower right')
+ f = save_dir / 'study.png'
+ print(f'Saving {f}...')
+ plt.savefig(f, dpi=300)
+
+
+@TryExcept() # known issue https://github.com/ultralytics/yolov5/issues/5395
+def plot_labels(labels, names=(), save_dir=Path('')):
+ # plot dataset labels
+ LOGGER.info(f"Plotting labels to {save_dir / 'labels.jpg'}... ")
+ c, b = labels[:, 0], labels[:, 1:].transpose() # classes, boxes
+ nc = int(c.max() + 1) # number of classes
+ x = pd.DataFrame(b.transpose(), columns=['x', 'y', 'width', 'height'])
+
+ # seaborn correlogram
+ sn.pairplot(x, corner=True, diag_kind='auto', kind='hist', diag_kws=dict(bins=50), plot_kws=dict(pmax=0.9))
+ plt.savefig(save_dir / 'labels_correlogram.jpg', dpi=200)
+ plt.close()
+
+ # matplotlib labels
+ matplotlib.use('svg') # faster
+ ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True)[1].ravel()
+ y = ax[0].hist(c, bins=np.linspace(0, nc, nc + 1) - 0.5, rwidth=0.8)
+ with contextlib.suppress(Exception): # color histogram bars by class
+ [y[2].patches[i].set_color([x / 255 for x in colors(i)]) for i in range(nc)] # known issue #3195
+ ax[0].set_ylabel('instances')
+ if 0 < len(names) < 30:
+ ax[0].set_xticks(range(len(names)))
+ ax[0].set_xticklabels(list(names.values()), rotation=90, fontsize=10)
+ else:
+ ax[0].set_xlabel('classes')
+ sn.histplot(x, x='x', y='y', ax=ax[2], bins=50, pmax=0.9)
+ sn.histplot(x, x='width', y='height', ax=ax[3], bins=50, pmax=0.9)
+
+ # rectangles
+ labels[:, 1:3] = 0.5 # center
+ labels[:, 1:] = xywh2xyxy(labels[:, 1:]) * 2000
+ img = Image.fromarray(np.ones((2000, 2000, 3), dtype=np.uint8) * 255)
+ for cls, *box in labels[:1000]:
+ ImageDraw.Draw(img).rectangle(box, width=1, outline=colors(cls)) # plot
+ ax[1].imshow(img)
+ ax[1].axis('off')
+
+ for a in [0, 1, 2, 3]:
+ for s in ['top', 'right', 'left', 'bottom']:
+ ax[a].spines[s].set_visible(False)
+
+ plt.savefig(save_dir / 'labels.jpg', dpi=200)
+ matplotlib.use('Agg')
+ plt.close()
+
+
+def imshow_cls(im, labels=None, pred=None, names=None, nmax=25, verbose=False, f=Path('images.jpg')):
+ # Show classification image grid with labels (optional) and predictions (optional)
+ from utils.augmentations import denormalize
+
+ names = names or [f'class{i}' for i in range(1000)]
+ blocks = torch.chunk(denormalize(im.clone()).cpu().float(), len(im),
+ dim=0) # select batch index 0, block by channels
+ n = min(len(blocks), nmax) # number of plots
+ m = min(8, round(n ** 0.5)) # 8 x 8 default
+ fig, ax = plt.subplots(math.ceil(n / m), m) # 8 rows x n/8 cols
+ ax = ax.ravel() if m > 1 else [ax]
+ # plt.subplots_adjust(wspace=0.05, hspace=0.05)
+ for i in range(n):
+ ax[i].imshow(blocks[i].squeeze().permute((1, 2, 0)).numpy().clip(0.0, 1.0))
+ ax[i].axis('off')
+ if labels is not None:
+ s = names[labels[i]] + (f'—{names[pred[i]]}' if pred is not None else '')
+ ax[i].set_title(s, fontsize=8, verticalalignment='top')
+ plt.savefig(f, dpi=300, bbox_inches='tight')
+ plt.close()
+ if verbose:
+ LOGGER.info(f"Saving {f}")
+ if labels is not None:
+ LOGGER.info('True: ' + ' '.join(f'{names[i]:3s}' for i in labels[:nmax]))
+ if pred is not None:
+ LOGGER.info('Predicted:' + ' '.join(f'{names[i]:3s}' for i in pred[:nmax]))
+ return f
+
+
+def plot_evolve(evolve_csv='path/to/evolve.csv'): # from utils.plots import *; plot_evolve()
+ # Plot evolve.csv hyp evolution results
+ evolve_csv = Path(evolve_csv)
+ data = pd.read_csv(evolve_csv)
+ keys = [x.strip() for x in data.columns]
+ x = data.values
+ f = fitness(x)
+ j = np.argmax(f) # max fitness index
+ plt.figure(figsize=(10, 12), tight_layout=True)
+ matplotlib.rc('font', **{'size': 8})
+ print(f'Best results from row {j} of {evolve_csv}:')
+ for i, k in enumerate(keys[7:]):
+ v = x[:, 7 + i]
+ mu = v[j] # best single result
+ plt.subplot(6, 5, i + 1)
+ plt.scatter(v, f, c=hist2d(v, f, 20), cmap='viridis', alpha=.8, edgecolors='none')
+ plt.plot(mu, f.max(), 'k+', markersize=15)
+ plt.title(f'{k} = {mu:.3g}', fontdict={'size': 9}) # limit to 40 characters
+ if i % 5 != 0:
+ plt.yticks([])
+ print(f'{k:>15}: {mu:.3g}')
+ f = evolve_csv.with_suffix('.png') # filename
+ plt.savefig(f, dpi=200)
+ plt.close()
+ print(f'Saved {f}')
+
+
+def plot_results(file='path/to/results.csv', dir=''):
+ # Plot training results.csv. Usage: from utils.plots import *; plot_results('path/to/results.csv')
+ save_dir = Path(file).parent if file else Path(dir)
+ fig, ax = plt.subplots(2, 5, figsize=(12, 6), tight_layout=True)
+ ax = ax.ravel()
+ files = list(save_dir.glob('results*.csv'))
+ assert len(files), f'No results.csv files found in {save_dir.resolve()}, nothing to plot.'
+ for f in files:
+ try:
+ data = pd.read_csv(f)
+ s = [x.strip() for x in data.columns]
+ x = data.values[:, 0]
+ for i, j in enumerate([1, 2, 3, 4, 5, 8, 9, 10, 6, 7]):
+ y = data.values[:, j].astype('float')
+ # y[y == 0] = np.nan # don't show zero values
+ ax[i].plot(x, y, marker='.', label=f.stem, linewidth=2, markersize=8)
+ ax[i].set_title(s[j], fontsize=12)
+ # if j in [8, 9, 10]: # share train and val loss y axes
+ # ax[i].get_shared_y_axes().join(ax[i], ax[i - 5])
+ except Exception as e:
+ LOGGER.info(f'Warning: Plotting error for {f}: {e}')
+ ax[1].legend()
+ fig.savefig(save_dir / 'results.png', dpi=200)
+ plt.close()
+
+
+def profile_idetection(start=0, stop=0, labels=(), save_dir=''):
+ # Plot iDetection '*.txt' per-image logs. from utils.plots import *; profile_idetection()
+ ax = plt.subplots(2, 4, figsize=(12, 6), tight_layout=True)[1].ravel()
+ s = ['Images', 'Free Storage (GB)', 'RAM Usage (GB)', 'Battery', 'dt_raw (ms)', 'dt_smooth (ms)', 'real-world FPS']
+ files = list(Path(save_dir).glob('frames*.txt'))
+ for fi, f in enumerate(files):
+ try:
+ results = np.loadtxt(f, ndmin=2).T[:, 90:-30] # clip first and last rows
+ n = results.shape[1] # number of rows
+ x = np.arange(start, min(stop, n) if stop else n)
+ results = results[:, x]
+ t = (results[0] - results[0].min()) # set t0=0s
+ results[0] = x
+ for i, a in enumerate(ax):
+ if i < len(results):
+ label = labels[fi] if len(labels) else f.stem.replace('frames_', '')
+ a.plot(t, results[i], marker='.', label=label, linewidth=1, markersize=5)
+ a.set_title(s[i])
+ a.set_xlabel('time (s)')
+ # if fi == len(files) - 1:
+ # a.set_ylim(bottom=0)
+ for side in ['top', 'right']:
+ a.spines[side].set_visible(False)
+ else:
+ a.remove()
+ except Exception as e:
+ print(f'Warning: Plotting error for {f}; {e}')
+ ax[1].legend()
+ plt.savefig(Path(save_dir) / 'idetection_profile.png', dpi=200)
+
+
+def save_one_box(xyxy, im, file=Path('im.jpg'), gain=1.02, pad=10, square=False, BGR=False, save=True):
+ # Save image crop as {file} with crop size multiple {gain} and {pad} pixels. Save and/or return crop
+ xyxy = torch.tensor(xyxy).view(-1, 4)
+ b = xyxy2xywh(xyxy) # boxes
+ if square:
+ b[:, 2:] = b[:, 2:].max(1)[0].unsqueeze(1) # attempt rectangle to square
+ b[:, 2:] = b[:, 2:] * gain + pad # box wh * gain + pad
+ xyxy = xywh2xyxy(b).long()
+ clip_boxes(xyxy, im.shape)
+ crop = im[int(xyxy[0, 1]):int(xyxy[0, 3]), int(xyxy[0, 0]):int(xyxy[0, 2]), ::(1 if BGR else -1)]
+ if save:
+ file.parent.mkdir(parents=True, exist_ok=True) # make directory
+ f = str(increment_path(file).with_suffix('.jpg'))
+ # cv2.imwrite(f, crop) # save BGR, https://github.com/ultralytics/yolov5/issues/7007 chroma subsampling issue
+ Image.fromarray(crop[..., ::-1]).save(f, quality=95, subsampling=0) # save RGB
+ return crop
diff --git a/src/FireDetect/utils/segment/__init__.py b/src/FireDetect/utils/segment/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/FireDetect/utils/segment/__pycache__/__init__.cpython-38.pyc b/src/FireDetect/utils/segment/__pycache__/__init__.cpython-38.pyc
new file mode 100644
index 0000000..172f6fa
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diff --git a/src/FireDetect/utils/segment/__pycache__/general.cpython-38.pyc b/src/FireDetect/utils/segment/__pycache__/general.cpython-38.pyc
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diff --git a/src/FireDetect/utils/segment/augmentations.py b/src/FireDetect/utils/segment/augmentations.py
new file mode 100644
index 0000000..169adde
--- /dev/null
+++ b/src/FireDetect/utils/segment/augmentations.py
@@ -0,0 +1,104 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+"""
+Image augmentation functions
+"""
+
+import math
+import random
+
+import cv2
+import numpy as np
+
+from ..augmentations import box_candidates
+from ..general import resample_segments, segment2box
+
+
+def mixup(im, labels, segments, im2, labels2, segments2):
+ # Applies MixUp augmentation https://arxiv.org/pdf/1710.09412.pdf
+ r = np.random.beta(32.0, 32.0) # mixup ratio, alpha=beta=32.0
+ im = (im * r + im2 * (1 - r)).astype(np.uint8)
+ labels = np.concatenate((labels, labels2), 0)
+ segments = np.concatenate((segments, segments2), 0)
+ return im, labels, segments
+
+
+def random_perspective(im,
+ targets=(),
+ segments=(),
+ degrees=10,
+ translate=.1,
+ scale=.1,
+ shear=10,
+ perspective=0.0,
+ border=(0, 0)):
+ # torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(.1, .1), scale=(.9, 1.1), shear=(-10, 10))
+ # targets = [cls, xyxy]
+
+ height = im.shape[0] + border[0] * 2 # shape(h,w,c)
+ width = im.shape[1] + border[1] * 2
+
+ # Center
+ C = np.eye(3)
+ C[0, 2] = -im.shape[1] / 2 # x translation (pixels)
+ C[1, 2] = -im.shape[0] / 2 # y translation (pixels)
+
+ # Perspective
+ P = np.eye(3)
+ P[2, 0] = random.uniform(-perspective, perspective) # x perspective (about y)
+ P[2, 1] = random.uniform(-perspective, perspective) # y perspective (about x)
+
+ # Rotation and Scale
+ R = np.eye(3)
+ a = random.uniform(-degrees, degrees)
+ # a += random.choice([-180, -90, 0, 90]) # add 90deg rotations to small rotations
+ s = random.uniform(1 - scale, 1 + scale)
+ # s = 2 ** random.uniform(-scale, scale)
+ R[:2] = cv2.getRotationMatrix2D(angle=a, center=(0, 0), scale=s)
+
+ # Shear
+ S = np.eye(3)
+ S[0, 1] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # x shear (deg)
+ S[1, 0] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # y shear (deg)
+
+ # Translation
+ T = np.eye(3)
+ T[0, 2] = (random.uniform(0.5 - translate, 0.5 + translate) * width) # x translation (pixels)
+ T[1, 2] = (random.uniform(0.5 - translate, 0.5 + translate) * height) # y translation (pixels)
+
+ # Combined rotation matrix
+ M = T @ S @ R @ P @ C # order of operations (right to left) is IMPORTANT
+ if (border[0] != 0) or (border[1] != 0) or (M != np.eye(3)).any(): # image changed
+ if perspective:
+ im = cv2.warpPerspective(im, M, dsize=(width, height), borderValue=(114, 114, 114))
+ else: # affine
+ im = cv2.warpAffine(im, M[:2], dsize=(width, height), borderValue=(114, 114, 114))
+
+ # Visualize
+ # import matplotlib.pyplot as plt
+ # ax = plt.subplots(1, 2, figsize=(12, 6))[1].ravel()
+ # ax[0].imshow(im[:, :, ::-1]) # base
+ # ax[1].imshow(im2[:, :, ::-1]) # warped
+
+ # Transform label coordinates
+ n = len(targets)
+ new_segments = []
+ if n:
+ new = np.zeros((n, 4))
+ segments = resample_segments(segments) # upsample
+ for i, segment in enumerate(segments):
+ xy = np.ones((len(segment), 3))
+ xy[:, :2] = segment
+ xy = xy @ M.T # transform
+ xy = (xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2]) # perspective rescale or affine
+
+ # clip
+ new[i] = segment2box(xy, width, height)
+ new_segments.append(xy)
+
+ # filter candidates
+ i = box_candidates(box1=targets[:, 1:5].T * s, box2=new.T, area_thr=0.01)
+ targets = targets[i]
+ targets[:, 1:5] = new[i]
+ new_segments = np.array(new_segments)[i]
+
+ return im, targets, new_segments
diff --git a/src/FireDetect/utils/segment/dataloaders.py b/src/FireDetect/utils/segment/dataloaders.py
new file mode 100644
index 0000000..9de6f0f
--- /dev/null
+++ b/src/FireDetect/utils/segment/dataloaders.py
@@ -0,0 +1,331 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+"""
+Dataloaders
+"""
+
+import os
+import random
+
+import cv2
+import numpy as np
+import torch
+from torch.utils.data import DataLoader, distributed
+
+from ..augmentations import augment_hsv, copy_paste, letterbox
+from ..dataloaders import InfiniteDataLoader, LoadImagesAndLabels, seed_worker
+from ..general import LOGGER, xyn2xy, xywhn2xyxy, xyxy2xywhn
+from ..torch_utils import torch_distributed_zero_first
+from .augmentations import mixup, random_perspective
+
+RANK = int(os.getenv('RANK', -1))
+
+
+def create_dataloader(path,
+ imgsz,
+ batch_size,
+ stride,
+ single_cls=False,
+ hyp=None,
+ augment=False,
+ cache=False,
+ pad=0.0,
+ rect=False,
+ rank=-1,
+ workers=8,
+ image_weights=False,
+ quad=False,
+ prefix='',
+ shuffle=False,
+ mask_downsample_ratio=1,
+ overlap_mask=False):
+ if rect and shuffle:
+ LOGGER.warning('WARNING ⚠️ --rect is incompatible with DataLoader shuffle, setting shuffle=False')
+ shuffle = False
+ with torch_distributed_zero_first(rank): # init dataset *.cache only once if DDP
+ dataset = LoadImagesAndLabelsAndMasks(
+ path,
+ imgsz,
+ batch_size,
+ augment=augment, # augmentation
+ hyp=hyp, # hyperparameters
+ rect=rect, # rectangular batches
+ cache_images=cache,
+ single_cls=single_cls,
+ stride=int(stride),
+ pad=pad,
+ image_weights=image_weights,
+ prefix=prefix,
+ downsample_ratio=mask_downsample_ratio,
+ overlap=overlap_mask)
+
+ batch_size = min(batch_size, len(dataset))
+ nd = torch.cuda.device_count() # number of CUDA devices
+ nw = min([os.cpu_count() // max(nd, 1), batch_size if batch_size > 1 else 0, workers]) # number of workers
+ sampler = None if rank == -1 else distributed.DistributedSampler(dataset, shuffle=shuffle)
+ loader = DataLoader if image_weights else InfiniteDataLoader # only DataLoader allows for attribute updates
+ generator = torch.Generator()
+ generator.manual_seed(6148914691236517205 + RANK)
+ return loader(
+ dataset,
+ batch_size=batch_size,
+ shuffle=shuffle and sampler is None,
+ num_workers=nw,
+ sampler=sampler,
+ pin_memory=True,
+ collate_fn=LoadImagesAndLabelsAndMasks.collate_fn4 if quad else LoadImagesAndLabelsAndMasks.collate_fn,
+ worker_init_fn=seed_worker,
+ generator=generator,
+ ), dataset
+
+
+class LoadImagesAndLabelsAndMasks(LoadImagesAndLabels): # for training/testing
+
+ def __init__(
+ self,
+ path,
+ img_size=640,
+ batch_size=16,
+ augment=False,
+ hyp=None,
+ rect=False,
+ image_weights=False,
+ cache_images=False,
+ single_cls=False,
+ stride=32,
+ pad=0,
+ min_items=0,
+ prefix="",
+ downsample_ratio=1,
+ overlap=False,
+ ):
+ super().__init__(path, img_size, batch_size, augment, hyp, rect, image_weights, cache_images, single_cls,
+ stride, pad, min_items, prefix)
+ self.downsample_ratio = downsample_ratio
+ self.overlap = overlap
+
+ def __getitem__(self, index):
+ index = self.indices[index] # linear, shuffled, or image_weights
+
+ hyp = self.hyp
+ mosaic = self.mosaic and random.random() < hyp['mosaic']
+ masks = []
+ if mosaic:
+ # Load mosaic
+ img, labels, segments = self.load_mosaic(index)
+ shapes = None
+
+ # MixUp augmentation
+ if random.random() < hyp["mixup"]:
+ img, labels, segments = mixup(img, labels, segments, *self.load_mosaic(random.randint(0, self.n - 1)))
+
+ else:
+ # Load image
+ img, (h0, w0), (h, w) = self.load_image(index)
+
+ # Letterbox
+ shape = self.batch_shapes[self.batch[index]] if self.rect else self.img_size # final letterboxed shape
+ img, ratio, pad = letterbox(img, shape, auto=False, scaleup=self.augment)
+ shapes = (h0, w0), ((h / h0, w / w0), pad) # for COCO mAP rescaling
+
+ labels = self.labels[index].copy()
+ # [array, array, ....], array.shape=(num_points, 2), xyxyxyxy
+ segments = self.segments[index].copy()
+ if len(segments):
+ for i_s in range(len(segments)):
+ segments[i_s] = xyn2xy(
+ segments[i_s],
+ ratio[0] * w,
+ ratio[1] * h,
+ padw=pad[0],
+ padh=pad[1],
+ )
+ if labels.size: # normalized xywh to pixel xyxy format
+ labels[:, 1:] = xywhn2xyxy(labels[:, 1:], ratio[0] * w, ratio[1] * h, padw=pad[0], padh=pad[1])
+
+ if self.augment:
+ img, labels, segments = random_perspective(img,
+ labels,
+ segments=segments,
+ degrees=hyp["degrees"],
+ translate=hyp["translate"],
+ scale=hyp["scale"],
+ shear=hyp["shear"],
+ perspective=hyp["perspective"])
+
+ nl = len(labels) # number of labels
+ if nl:
+ labels[:, 1:5] = xyxy2xywhn(labels[:, 1:5], w=img.shape[1], h=img.shape[0], clip=True, eps=1e-3)
+ if self.overlap:
+ masks, sorted_idx = polygons2masks_overlap(img.shape[:2],
+ segments,
+ downsample_ratio=self.downsample_ratio)
+ masks = masks[None] # (640, 640) -> (1, 640, 640)
+ labels = labels[sorted_idx]
+ else:
+ masks = polygons2masks(img.shape[:2], segments, color=1, downsample_ratio=self.downsample_ratio)
+
+ masks = (torch.from_numpy(masks) if len(masks) else torch.zeros(1 if self.overlap else nl, img.shape[0] //
+ self.downsample_ratio, img.shape[1] //
+ self.downsample_ratio))
+ # TODO: albumentations support
+ if self.augment:
+ # Albumentations
+ # there are some augmentation that won't change boxes and masks,
+ # so just be it for now.
+ img, labels = self.albumentations(img, labels)
+ nl = len(labels) # update after albumentations
+
+ # HSV color-space
+ augment_hsv(img, hgain=hyp["hsv_h"], sgain=hyp["hsv_s"], vgain=hyp["hsv_v"])
+
+ # Flip up-down
+ if random.random() < hyp["flipud"]:
+ img = np.flipud(img)
+ if nl:
+ labels[:, 2] = 1 - labels[:, 2]
+ masks = torch.flip(masks, dims=[1])
+
+ # Flip left-right
+ if random.random() < hyp["fliplr"]:
+ img = np.fliplr(img)
+ if nl:
+ labels[:, 1] = 1 - labels[:, 1]
+ masks = torch.flip(masks, dims=[2])
+
+ # Cutouts # labels = cutout(img, labels, p=0.5)
+
+ labels_out = torch.zeros((nl, 6))
+ if nl:
+ labels_out[:, 1:] = torch.from_numpy(labels)
+
+ # Convert
+ img = img.transpose((2, 0, 1))[::-1] # HWC to CHW, BGR to RGB
+ img = np.ascontiguousarray(img)
+
+ return (torch.from_numpy(img), labels_out, self.im_files[index], shapes, masks)
+
+ def load_mosaic(self, index):
+ # YOLOv5 4-mosaic loader. Loads 1 image + 3 random images into a 4-image mosaic
+ labels4, segments4 = [], []
+ s = self.img_size
+ yc, xc = (int(random.uniform(-x, 2 * s + x)) for x in self.mosaic_border) # mosaic center x, y
+
+ # 3 additional image indices
+ indices = [index] + random.choices(self.indices, k=3) # 3 additional image indices
+ for i, index in enumerate(indices):
+ # Load image
+ img, _, (h, w) = self.load_image(index)
+
+ # place img in img4
+ if i == 0: # top left
+ img4 = np.full((s * 2, s * 2, img.shape[2]), 114, dtype=np.uint8) # base image with 4 tiles
+ x1a, y1a, x2a, y2a = max(xc - w, 0), max(yc - h, 0), xc, yc # xmin, ymin, xmax, ymax (large image)
+ x1b, y1b, x2b, y2b = w - (x2a - x1a), h - (y2a - y1a), w, h # xmin, ymin, xmax, ymax (small image)
+ elif i == 1: # top right
+ x1a, y1a, x2a, y2a = xc, max(yc - h, 0), min(xc + w, s * 2), yc
+ x1b, y1b, x2b, y2b = 0, h - (y2a - y1a), min(w, x2a - x1a), h
+ elif i == 2: # bottom left
+ x1a, y1a, x2a, y2a = max(xc - w, 0), yc, xc, min(s * 2, yc + h)
+ x1b, y1b, x2b, y2b = w - (x2a - x1a), 0, w, min(y2a - y1a, h)
+ elif i == 3: # bottom right
+ x1a, y1a, x2a, y2a = xc, yc, min(xc + w, s * 2), min(s * 2, yc + h)
+ x1b, y1b, x2b, y2b = 0, 0, min(w, x2a - x1a), min(y2a - y1a, h)
+
+ img4[y1a:y2a, x1a:x2a] = img[y1b:y2b, x1b:x2b] # img4[ymin:ymax, xmin:xmax]
+ padw = x1a - x1b
+ padh = y1a - y1b
+
+ labels, segments = self.labels[index].copy(), self.segments[index].copy()
+
+ if labels.size:
+ labels[:, 1:] = xywhn2xyxy(labels[:, 1:], w, h, padw, padh) # normalized xywh to pixel xyxy format
+ segments = [xyn2xy(x, w, h, padw, padh) for x in segments]
+ labels4.append(labels)
+ segments4.extend(segments)
+
+ # Concat/clip labels
+ labels4 = np.concatenate(labels4, 0)
+ for x in (labels4[:, 1:], *segments4):
+ np.clip(x, 0, 2 * s, out=x) # clip when using random_perspective()
+ # img4, labels4 = replicate(img4, labels4) # replicate
+
+ # Augment
+ img4, labels4, segments4 = copy_paste(img4, labels4, segments4, p=self.hyp["copy_paste"])
+ img4, labels4, segments4 = random_perspective(img4,
+ labels4,
+ segments4,
+ degrees=self.hyp["degrees"],
+ translate=self.hyp["translate"],
+ scale=self.hyp["scale"],
+ shear=self.hyp["shear"],
+ perspective=self.hyp["perspective"],
+ border=self.mosaic_border) # border to remove
+ return img4, labels4, segments4
+
+ @staticmethod
+ def collate_fn(batch):
+ img, label, path, shapes, masks = zip(*batch) # transposed
+ batched_masks = torch.cat(masks, 0)
+ for i, l in enumerate(label):
+ l[:, 0] = i # add target image index for build_targets()
+ return torch.stack(img, 0), torch.cat(label, 0), path, shapes, batched_masks
+
+
+def polygon2mask(img_size, polygons, color=1, downsample_ratio=1):
+ """
+ Args:
+ img_size (tuple): The image size.
+ polygons (np.ndarray): [N, M], N is the number of polygons,
+ M is the number of points(Be divided by 2).
+ """
+ mask = np.zeros(img_size, dtype=np.uint8)
+ polygons = np.asarray(polygons)
+ polygons = polygons.astype(np.int32)
+ shape = polygons.shape
+ polygons = polygons.reshape(shape[0], -1, 2)
+ cv2.fillPoly(mask, polygons, color=color)
+ nh, nw = (img_size[0] // downsample_ratio, img_size[1] // downsample_ratio)
+ # NOTE: fillPoly firstly then resize is trying the keep the same way
+ # of loss calculation when mask-ratio=1.
+ mask = cv2.resize(mask, (nw, nh))
+ return mask
+
+
+def polygons2masks(img_size, polygons, color, downsample_ratio=1):
+ """
+ Args:
+ img_size (tuple): The image size.
+ polygons (list[np.ndarray]): each polygon is [N, M],
+ N is the number of polygons,
+ M is the number of points(Be divided by 2).
+ """
+ masks = []
+ for si in range(len(polygons)):
+ mask = polygon2mask(img_size, [polygons[si].reshape(-1)], color, downsample_ratio)
+ masks.append(mask)
+ return np.array(masks)
+
+
+def polygons2masks_overlap(img_size, segments, downsample_ratio=1):
+ """Return a (640, 640) overlap mask."""
+ masks = np.zeros((img_size[0] // downsample_ratio, img_size[1] // downsample_ratio),
+ dtype=np.int32 if len(segments) > 255 else np.uint8)
+ areas = []
+ ms = []
+ for si in range(len(segments)):
+ mask = polygon2mask(
+ img_size,
+ [segments[si].reshape(-1)],
+ downsample_ratio=downsample_ratio,
+ color=1,
+ )
+ ms.append(mask)
+ areas.append(mask.sum())
+ areas = np.asarray(areas)
+ index = np.argsort(-areas)
+ ms = np.array(ms)[index]
+ for i in range(len(segments)):
+ mask = ms[i] * (i + 1)
+ masks = masks + mask
+ masks = np.clip(masks, a_min=0, a_max=i + 1)
+ return masks, index
diff --git a/src/FireDetect/utils/segment/general.py b/src/FireDetect/utils/segment/general.py
new file mode 100644
index 0000000..b526333
--- /dev/null
+++ b/src/FireDetect/utils/segment/general.py
@@ -0,0 +1,137 @@
+import cv2
+import numpy as np
+import torch
+import torch.nn.functional as F
+
+
+def crop_mask(masks, boxes):
+ """
+ "Crop" predicted masks by zeroing out everything not in the predicted bbox.
+ Vectorized by Chong (thanks Chong).
+
+ Args:
+ - masks should be a size [h, w, n] tensor of masks
+ - boxes should be a size [n, 4] tensor of bbox coords in relative point form
+ """
+
+ n, h, w = masks.shape
+ x1, y1, x2, y2 = torch.chunk(boxes[:, :, None], 4, 1) # x1 shape(1,1,n)
+ r = torch.arange(w, device=masks.device, dtype=x1.dtype)[None, None, :] # rows shape(1,w,1)
+ c = torch.arange(h, device=masks.device, dtype=x1.dtype)[None, :, None] # cols shape(h,1,1)
+
+ return masks * ((r >= x1) * (r < x2) * (c >= y1) * (c < y2))
+
+
+def process_mask_upsample(protos, masks_in, bboxes, shape):
+ """
+ Crop after upsample.
+ proto_out: [mask_dim, mask_h, mask_w]
+ out_masks: [n, mask_dim], n is number of masks after nms
+ bboxes: [n, 4], n is number of masks after nms
+ shape:input_image_size, (h, w)
+
+ return: h, w, n
+ """
+
+ c, mh, mw = protos.shape # CHW
+ masks = (masks_in @ protos.float().view(c, -1)).sigmoid().view(-1, mh, mw)
+ masks = F.interpolate(masks[None], shape, mode='bilinear', align_corners=False)[0] # CHW
+ masks = crop_mask(masks, bboxes) # CHW
+ return masks.gt_(0.5)
+
+
+def process_mask(protos, masks_in, bboxes, shape, upsample=False):
+ """
+ Crop before upsample.
+ proto_out: [mask_dim, mask_h, mask_w]
+ out_masks: [n, mask_dim], n is number of masks after nms
+ bboxes: [n, 4], n is number of masks after nms
+ shape:input_image_size, (h, w)
+
+ return: h, w, n
+ """
+
+ c, mh, mw = protos.shape # CHW
+ ih, iw = shape
+ masks = (masks_in @ protos.float().view(c, -1)).sigmoid().view(-1, mh, mw) # CHW
+
+ downsampled_bboxes = bboxes.clone()
+ downsampled_bboxes[:, 0] *= mw / iw
+ downsampled_bboxes[:, 2] *= mw / iw
+ downsampled_bboxes[:, 3] *= mh / ih
+ downsampled_bboxes[:, 1] *= mh / ih
+
+ masks = crop_mask(masks, downsampled_bboxes) # CHW
+ if upsample:
+ masks = F.interpolate(masks[None], shape, mode='bilinear', align_corners=False)[0] # CHW
+ return masks.gt_(0.5)
+
+
+def scale_image(im1_shape, masks, im0_shape, ratio_pad=None):
+ """
+ img1_shape: model input shape, [h, w]
+ img0_shape: origin pic shape, [h, w, 3]
+ masks: [h, w, num]
+ """
+ # Rescale coordinates (xyxy) from im1_shape to im0_shape
+ if ratio_pad is None: # calculate from im0_shape
+ gain = min(im1_shape[0] / im0_shape[0], im1_shape[1] / im0_shape[1]) # gain = old / new
+ pad = (im1_shape[1] - im0_shape[1] * gain) / 2, (im1_shape[0] - im0_shape[0] * gain) / 2 # wh padding
+ else:
+ pad = ratio_pad[1]
+ top, left = int(pad[1]), int(pad[0]) # y, x
+ bottom, right = int(im1_shape[0] - pad[1]), int(im1_shape[1] - pad[0])
+
+ if len(masks.shape) < 2:
+ raise ValueError(f'"len of masks shape" should be 2 or 3, but got {len(masks.shape)}')
+ masks = masks[top:bottom, left:right]
+ # masks = masks.permute(2, 0, 1).contiguous()
+ # masks = F.interpolate(masks[None], im0_shape[:2], mode='bilinear', align_corners=False)[0]
+ # masks = masks.permute(1, 2, 0).contiguous()
+ masks = cv2.resize(masks, (im0_shape[1], im0_shape[0]))
+
+ if len(masks.shape) == 2:
+ masks = masks[:, :, None]
+ return masks
+
+
+def mask_iou(mask1, mask2, eps=1e-7):
+ """
+ mask1: [N, n] m1 means number of predicted objects
+ mask2: [M, n] m2 means number of gt objects
+ Note: n means image_w x image_h
+
+ return: masks iou, [N, M]
+ """
+ intersection = torch.matmul(mask1, mask2.t()).clamp(0)
+ union = (mask1.sum(1)[:, None] + mask2.sum(1)[None]) - intersection # (area1 + area2) - intersection
+ return intersection / (union + eps)
+
+
+def masks_iou(mask1, mask2, eps=1e-7):
+ """
+ mask1: [N, n] m1 means number of predicted objects
+ mask2: [N, n] m2 means number of gt objects
+ Note: n means image_w x image_h
+
+ return: masks iou, (N, )
+ """
+ intersection = (mask1 * mask2).sum(1).clamp(0) # (N, )
+ union = (mask1.sum(1) + mask2.sum(1))[None] - intersection # (area1 + area2) - intersection
+ return intersection / (union + eps)
+
+
+def masks2segments(masks, strategy='largest'):
+ # Convert masks(n,160,160) into segments(n,xy)
+ segments = []
+ for x in masks.int().cpu().numpy().astype('uint8'):
+ c = cv2.findContours(x, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[0]
+ if c:
+ if strategy == 'concat': # concatenate all segments
+ c = np.concatenate([x.reshape(-1, 2) for x in c])
+ elif strategy == 'largest': # select largest segment
+ c = np.array(c[np.array([len(x) for x in c]).argmax()]).reshape(-1, 2)
+ else:
+ c = np.zeros((0, 2)) # no segments found
+ segments.append(c.astype('float32'))
+ return segments
diff --git a/src/FireDetect/utils/segment/loss.py b/src/FireDetect/utils/segment/loss.py
new file mode 100644
index 0000000..b45b2c2
--- /dev/null
+++ b/src/FireDetect/utils/segment/loss.py
@@ -0,0 +1,186 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from ..general import xywh2xyxy
+from ..loss import FocalLoss, smooth_BCE
+from ..metrics import bbox_iou
+from ..torch_utils import de_parallel
+from .general import crop_mask
+
+
+class ComputeLoss:
+ # Compute losses
+ def __init__(self, model, autobalance=False, overlap=False):
+ self.sort_obj_iou = False
+ self.overlap = overlap
+ device = next(model.parameters()).device # get model device
+ h = model.hyp # hyperparameters
+ self.device = device
+
+ # Define criteria
+ BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['cls_pw']], device=device))
+ BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['obj_pw']], device=device))
+
+ # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3
+ self.cp, self.cn = smooth_BCE(eps=h.get('label_smoothing', 0.0)) # positive, negative BCE targets
+
+ # Focal loss
+ g = h['fl_gamma'] # focal loss gamma
+ if g > 0:
+ BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g)
+
+ m = de_parallel(model).model[-1] # Detect() module
+ self.balance = {3: [4.0, 1.0, 0.4]}.get(m.nl, [4.0, 1.0, 0.25, 0.06, 0.02]) # P3-P7
+ self.ssi = list(m.stride).index(16) if autobalance else 0 # stride 16 index
+ self.BCEcls, self.BCEobj, self.gr, self.hyp, self.autobalance = BCEcls, BCEobj, 1.0, h, autobalance
+ self.na = m.na # number of anchors
+ self.nc = m.nc # number of classes
+ self.nl = m.nl # number of layers
+ self.nm = m.nm # number of masks
+ self.anchors = m.anchors
+ self.device = device
+
+ def __call__(self, preds, targets, masks): # predictions, targets, model
+ p, proto = preds
+ bs, nm, mask_h, mask_w = proto.shape # batch size, number of masks, mask height, mask width
+ lcls = torch.zeros(1, device=self.device)
+ lbox = torch.zeros(1, device=self.device)
+ lobj = torch.zeros(1, device=self.device)
+ lseg = torch.zeros(1, device=self.device)
+ tcls, tbox, indices, anchors, tidxs, xywhn = self.build_targets(p, targets) # targets
+
+ # Losses
+ for i, pi in enumerate(p): # layer index, layer predictions
+ b, a, gj, gi = indices[i] # image, anchor, gridy, gridx
+ tobj = torch.zeros(pi.shape[:4], dtype=pi.dtype, device=self.device) # target obj
+
+ n = b.shape[0] # number of targets
+ if n:
+ pxy, pwh, _, pcls, pmask = pi[b, a, gj, gi].split((2, 2, 1, self.nc, nm), 1) # subset of predictions
+
+ # Box regression
+ pxy = pxy.sigmoid() * 2 - 0.5
+ pwh = (pwh.sigmoid() * 2) ** 2 * anchors[i]
+ pbox = torch.cat((pxy, pwh), 1) # predicted box
+ iou = bbox_iou(pbox, tbox[i], CIoU=True).squeeze() # iou(prediction, target)
+ lbox += (1.0 - iou).mean() # iou loss
+
+ # Objectness
+ iou = iou.detach().clamp(0).type(tobj.dtype)
+ if self.sort_obj_iou:
+ j = iou.argsort()
+ b, a, gj, gi, iou = b[j], a[j], gj[j], gi[j], iou[j]
+ if self.gr < 1:
+ iou = (1.0 - self.gr) + self.gr * iou
+ tobj[b, a, gj, gi] = iou # iou ratio
+
+ # Classification
+ if self.nc > 1: # cls loss (only if multiple classes)
+ t = torch.full_like(pcls, self.cn, device=self.device) # targets
+ t[range(n), tcls[i]] = self.cp
+ lcls += self.BCEcls(pcls, t) # BCE
+
+ # Mask regression
+ if tuple(masks.shape[-2:]) != (mask_h, mask_w): # downsample
+ masks = F.interpolate(masks[None], (mask_h, mask_w), mode="nearest")[0]
+ marea = xywhn[i][:, 2:].prod(1) # mask width, height normalized
+ mxyxy = xywh2xyxy(xywhn[i] * torch.tensor([mask_w, mask_h, mask_w, mask_h], device=self.device))
+ for bi in b.unique():
+ j = b == bi # matching index
+ if self.overlap:
+ mask_gti = torch.where(masks[bi][None] == tidxs[i][j].view(-1, 1, 1), 1.0, 0.0)
+ else:
+ mask_gti = masks[tidxs[i]][j]
+ lseg += self.single_mask_loss(mask_gti, pmask[j], proto[bi], mxyxy[j], marea[j])
+
+ obji = self.BCEobj(pi[..., 4], tobj)
+ lobj += obji * self.balance[i] # obj loss
+ if self.autobalance:
+ self.balance[i] = self.balance[i] * 0.9999 + 0.0001 / obji.detach().item()
+
+ if self.autobalance:
+ self.balance = [x / self.balance[self.ssi] for x in self.balance]
+ lbox *= self.hyp["box"]
+ lobj *= self.hyp["obj"]
+ lcls *= self.hyp["cls"]
+ lseg *= self.hyp["box"] / bs
+
+ loss = lbox + lobj + lcls + lseg
+ return loss * bs, torch.cat((lbox, lseg, lobj, lcls)).detach()
+
+ def single_mask_loss(self, gt_mask, pred, proto, xyxy, area):
+ # Mask loss for one image
+ pred_mask = (pred @ proto.view(self.nm, -1)).view(-1, *proto.shape[1:]) # (n,32) @ (32,80,80) -> (n,80,80)
+ loss = F.binary_cross_entropy_with_logits(pred_mask, gt_mask, reduction="none")
+ return (crop_mask(loss, xyxy).mean(dim=(1, 2)) / area).mean()
+
+ def build_targets(self, p, targets):
+ # Build targets for compute_loss(), input targets(image,class,x,y,w,h)
+ na, nt = self.na, targets.shape[0] # number of anchors, targets
+ tcls, tbox, indices, anch, tidxs, xywhn = [], [], [], [], [], []
+ gain = torch.ones(8, device=self.device) # normalized to gridspace gain
+ ai = torch.arange(na, device=self.device).float().view(na, 1).repeat(1, nt) # same as .repeat_interleave(nt)
+ if self.overlap:
+ batch = p[0].shape[0]
+ ti = []
+ for i in range(batch):
+ num = (targets[:, 0] == i).sum() # find number of targets of each image
+ ti.append(torch.arange(num, device=self.device).float().view(1, num).repeat(na, 1) + 1) # (na, num)
+ ti = torch.cat(ti, 1) # (na, nt)
+ else:
+ ti = torch.arange(nt, device=self.device).float().view(1, nt).repeat(na, 1)
+ targets = torch.cat((targets.repeat(na, 1, 1), ai[..., None], ti[..., None]), 2) # append anchor indices
+
+ g = 0.5 # bias
+ off = torch.tensor(
+ [
+ [0, 0],
+ [1, 0],
+ [0, 1],
+ [-1, 0],
+ [0, -1], # j,k,l,m
+ # [1, 1], [1, -1], [-1, 1], [-1, -1], # jk,jm,lk,lm
+ ],
+ device=self.device).float() * g # offsets
+
+ for i in range(self.nl):
+ anchors, shape = self.anchors[i], p[i].shape
+ gain[2:6] = torch.tensor(shape)[[3, 2, 3, 2]] # xyxy gain
+
+ # Match targets to anchors
+ t = targets * gain # shape(3,n,7)
+ if nt:
+ # Matches
+ r = t[..., 4:6] / anchors[:, None] # wh ratio
+ j = torch.max(r, 1 / r).max(2)[0] < self.hyp['anchor_t'] # compare
+ # j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t'] # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2))
+ t = t[j] # filter
+
+ # Offsets
+ gxy = t[:, 2:4] # grid xy
+ gxi = gain[[2, 3]] - gxy # inverse
+ j, k = ((gxy % 1 < g) & (gxy > 1)).T
+ l, m = ((gxi % 1 < g) & (gxi > 1)).T
+ j = torch.stack((torch.ones_like(j), j, k, l, m))
+ t = t.repeat((5, 1, 1))[j]
+ offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j]
+ else:
+ t = targets[0]
+ offsets = 0
+
+ # Define
+ bc, gxy, gwh, at = t.chunk(4, 1) # (image, class), grid xy, grid wh, anchors
+ (a, tidx), (b, c) = at.long().T, bc.long().T # anchors, image, class
+ gij = (gxy - offsets).long()
+ gi, gj = gij.T # grid indices
+
+ # Append
+ indices.append((b, a, gj.clamp_(0, shape[2] - 1), gi.clamp_(0, shape[3] - 1))) # image, anchor, grid
+ tbox.append(torch.cat((gxy - gij, gwh), 1)) # box
+ anch.append(anchors[a]) # anchors
+ tcls.append(c) # class
+ tidxs.append(tidx)
+ xywhn.append(torch.cat((gxy, gwh), 1) / gain[2:6]) # xywh normalized
+
+ return tcls, tbox, indices, anch, tidxs, xywhn
diff --git a/src/FireDetect/utils/segment/metrics.py b/src/FireDetect/utils/segment/metrics.py
new file mode 100644
index 0000000..b09ce23
--- /dev/null
+++ b/src/FireDetect/utils/segment/metrics.py
@@ -0,0 +1,210 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+"""
+Model validation metrics
+"""
+
+import numpy as np
+
+from ..metrics import ap_per_class
+
+
+def fitness(x):
+ # Model fitness as a weighted combination of metrics
+ w = [0.0, 0.0, 0.1, 0.9, 0.0, 0.0, 0.1, 0.9]
+ return (x[:, :8] * w).sum(1)
+
+
+def ap_per_class_box_and_mask(
+ tp_m,
+ tp_b,
+ conf,
+ pred_cls,
+ target_cls,
+ plot=False,
+ save_dir=".",
+ names=(),
+):
+ """
+ Args:
+ tp_b: tp of boxes.
+ tp_m: tp of masks.
+ other arguments see `func: ap_per_class`.
+ """
+ results_boxes = ap_per_class(tp_b,
+ conf,
+ pred_cls,
+ target_cls,
+ plot=plot,
+ save_dir=save_dir,
+ names=names,
+ prefix="Box")[2:]
+ results_masks = ap_per_class(tp_m,
+ conf,
+ pred_cls,
+ target_cls,
+ plot=plot,
+ save_dir=save_dir,
+ names=names,
+ prefix="Mask")[2:]
+
+ results = {
+ "boxes": {
+ "p": results_boxes[0],
+ "r": results_boxes[1],
+ "ap": results_boxes[3],
+ "f1": results_boxes[2],
+ "ap_class": results_boxes[4]},
+ "masks": {
+ "p": results_masks[0],
+ "r": results_masks[1],
+ "ap": results_masks[3],
+ "f1": results_masks[2],
+ "ap_class": results_masks[4]}}
+ return results
+
+
+class Metric:
+
+ def __init__(self) -> None:
+ self.p = [] # (nc, )
+ self.r = [] # (nc, )
+ self.f1 = [] # (nc, )
+ self.all_ap = [] # (nc, 10)
+ self.ap_class_index = [] # (nc, )
+
+ @property
+ def ap50(self):
+ """AP@0.5 of all classes.
+ Return:
+ (nc, ) or [].
+ """
+ return self.all_ap[:, 0] if len(self.all_ap) else []
+
+ @property
+ def ap(self):
+ """AP@0.5:0.95
+ Return:
+ (nc, ) or [].
+ """
+ return self.all_ap.mean(1) if len(self.all_ap) else []
+
+ @property
+ def mp(self):
+ """mean precision of all classes.
+ Return:
+ float.
+ """
+ return self.p.mean() if len(self.p) else 0.0
+
+ @property
+ def mr(self):
+ """mean recall of all classes.
+ Return:
+ float.
+ """
+ return self.r.mean() if len(self.r) else 0.0
+
+ @property
+ def map50(self):
+ """Mean AP@0.5 of all classes.
+ Return:
+ float.
+ """
+ return self.all_ap[:, 0].mean() if len(self.all_ap) else 0.0
+
+ @property
+ def map(self):
+ """Mean AP@0.5:0.95 of all classes.
+ Return:
+ float.
+ """
+ return self.all_ap.mean() if len(self.all_ap) else 0.0
+
+ def mean_results(self):
+ """Mean of results, return mp, mr, map50, map"""
+ return (self.mp, self.mr, self.map50, self.map)
+
+ def class_result(self, i):
+ """class-aware result, return p[i], r[i], ap50[i], ap[i]"""
+ return (self.p[i], self.r[i], self.ap50[i], self.ap[i])
+
+ def get_maps(self, nc):
+ maps = np.zeros(nc) + self.map
+ for i, c in enumerate(self.ap_class_index):
+ maps[c] = self.ap[i]
+ return maps
+
+ def update(self, results):
+ """
+ Args:
+ results: tuple(p, r, ap, f1, ap_class)
+ """
+ p, r, all_ap, f1, ap_class_index = results
+ self.p = p
+ self.r = r
+ self.all_ap = all_ap
+ self.f1 = f1
+ self.ap_class_index = ap_class_index
+
+
+class Metrics:
+ """Metric for boxes and masks."""
+
+ def __init__(self) -> None:
+ self.metric_box = Metric()
+ self.metric_mask = Metric()
+
+ def update(self, results):
+ """
+ Args:
+ results: Dict{'boxes': Dict{}, 'masks': Dict{}}
+ """
+ self.metric_box.update(list(results["boxes"].values()))
+ self.metric_mask.update(list(results["masks"].values()))
+
+ def mean_results(self):
+ return self.metric_box.mean_results() + self.metric_mask.mean_results()
+
+ def class_result(self, i):
+ return self.metric_box.class_result(i) + self.metric_mask.class_result(i)
+
+ def get_maps(self, nc):
+ return self.metric_box.get_maps(nc) + self.metric_mask.get_maps(nc)
+
+ @property
+ def ap_class_index(self):
+ # boxes and masks have the same ap_class_index
+ return self.metric_box.ap_class_index
+
+
+KEYS = [
+ "train/box_loss",
+ "train/seg_loss", # train loss
+ "train/obj_loss",
+ "train/cls_loss",
+ "metrics/precision(B)",
+ "metrics/recall(B)",
+ "metrics/mAP_0.5(B)",
+ "metrics/mAP_0.5:0.95(B)", # metrics
+ "metrics/precision(M)",
+ "metrics/recall(M)",
+ "metrics/mAP_0.5(M)",
+ "metrics/mAP_0.5:0.95(M)", # metrics
+ "val/box_loss",
+ "val/seg_loss", # val loss
+ "val/obj_loss",
+ "val/cls_loss",
+ "x/lr0",
+ "x/lr1",
+ "x/lr2",]
+
+BEST_KEYS = [
+ "best/epoch",
+ "best/precision(B)",
+ "best/recall(B)",
+ "best/mAP_0.5(B)",
+ "best/mAP_0.5:0.95(B)",
+ "best/precision(M)",
+ "best/recall(M)",
+ "best/mAP_0.5(M)",
+ "best/mAP_0.5:0.95(M)",]
diff --git a/src/FireDetect/utils/segment/plots.py b/src/FireDetect/utils/segment/plots.py
new file mode 100644
index 0000000..9b90900
--- /dev/null
+++ b/src/FireDetect/utils/segment/plots.py
@@ -0,0 +1,143 @@
+import contextlib
+import math
+from pathlib import Path
+
+import cv2
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import torch
+
+from .. import threaded
+from ..general import xywh2xyxy
+from ..plots import Annotator, colors
+
+
+@threaded
+def plot_images_and_masks(images, targets, masks, paths=None, fname='images.jpg', names=None):
+ # Plot image grid with labels
+ if isinstance(images, torch.Tensor):
+ images = images.cpu().float().numpy()
+ if isinstance(targets, torch.Tensor):
+ targets = targets.cpu().numpy()
+ if isinstance(masks, torch.Tensor):
+ masks = masks.cpu().numpy().astype(int)
+
+ max_size = 1920 # max image size
+ max_subplots = 16 # max image subplots, i.e. 4x4
+ bs, _, h, w = images.shape # batch size, _, height, width
+ bs = min(bs, max_subplots) # limit plot images
+ ns = np.ceil(bs ** 0.5) # number of subplots (square)
+ if np.max(images[0]) <= 1:
+ images *= 255 # de-normalise (optional)
+
+ # Build Image
+ mosaic = np.full((int(ns * h), int(ns * w), 3), 255, dtype=np.uint8) # init
+ for i, im in enumerate(images):
+ if i == max_subplots: # if last batch has fewer images than we expect
+ break
+ x, y = int(w * (i // ns)), int(h * (i % ns)) # block origin
+ im = im.transpose(1, 2, 0)
+ mosaic[y:y + h, x:x + w, :] = im
+
+ # Resize (optional)
+ scale = max_size / ns / max(h, w)
+ if scale < 1:
+ h = math.ceil(scale * h)
+ w = math.ceil(scale * w)
+ mosaic = cv2.resize(mosaic, tuple(int(x * ns) for x in (w, h)))
+
+ # Annotate
+ fs = int((h + w) * ns * 0.01) # font size
+ annotator = Annotator(mosaic, line_width=round(fs / 10), font_size=fs, pil=True, example=names)
+ for i in range(i + 1):
+ x, y = int(w * (i // ns)), int(h * (i % ns)) # block origin
+ annotator.rectangle([x, y, x + w, y + h], None, (255, 255, 255), width=2) # borders
+ if paths:
+ annotator.text((x + 5, y + 5 + h), text=Path(paths[i]).name[:40], txt_color=(220, 220, 220)) # filenames
+ if len(targets) > 0:
+ idx = targets[:, 0] == i
+ ti = targets[idx] # image targets
+
+ boxes = xywh2xyxy(ti[:, 2:6]).T
+ classes = ti[:, 1].astype('int')
+ labels = ti.shape[1] == 6 # labels if no conf column
+ conf = None if labels else ti[:, 6] # check for confidence presence (label vs pred)
+
+ if boxes.shape[1]:
+ if boxes.max() <= 1.01: # if normalized with tolerance 0.01
+ boxes[[0, 2]] *= w # scale to pixels
+ boxes[[1, 3]] *= h
+ elif scale < 1: # absolute coords need scale if image scales
+ boxes *= scale
+ boxes[[0, 2]] += x
+ boxes[[1, 3]] += y
+ for j, box in enumerate(boxes.T.tolist()):
+ cls = classes[j]
+ color = colors(cls)
+ cls = names[cls] if names else cls
+ if labels or conf[j] > 0.25: # 0.25 conf thresh
+ label = f'{cls}' if labels else f'{cls} {conf[j]:.1f}'
+ annotator.box_label(box, label, color=color)
+
+ # Plot masks
+ if len(masks):
+ if masks.max() > 1.0: # mean that masks are overlap
+ image_masks = masks[[i]] # (1, 640, 640)
+ nl = len(ti)
+ index = np.arange(nl).reshape(nl, 1, 1) + 1
+ image_masks = np.repeat(image_masks, nl, axis=0)
+ image_masks = np.where(image_masks == index, 1.0, 0.0)
+ else:
+ image_masks = masks[idx]
+
+ im = np.asarray(annotator.im).copy()
+ for j, box in enumerate(boxes.T.tolist()):
+ if labels or conf[j] > 0.25: # 0.25 conf thresh
+ color = colors(classes[j])
+ mh, mw = image_masks[j].shape
+ if mh != h or mw != w:
+ mask = image_masks[j].astype(np.uint8)
+ mask = cv2.resize(mask, (w, h))
+ mask = mask.astype(bool)
+ else:
+ mask = image_masks[j].astype(bool)
+ with contextlib.suppress(Exception):
+ im[y:y + h, x:x + w, :][mask] = im[y:y + h, x:x + w, :][mask] * 0.4 + np.array(color) * 0.6
+ annotator.fromarray(im)
+ annotator.im.save(fname) # save
+
+
+def plot_results_with_masks(file="path/to/results.csv", dir="", best=True):
+ # Plot training results.csv. Usage: from utils.plots import *; plot_results('path/to/results.csv')
+ save_dir = Path(file).parent if file else Path(dir)
+ fig, ax = plt.subplots(2, 8, figsize=(18, 6), tight_layout=True)
+ ax = ax.ravel()
+ files = list(save_dir.glob("results*.csv"))
+ assert len(files), f"No results.csv files found in {save_dir.resolve()}, nothing to plot."
+ for f in files:
+ try:
+ data = pd.read_csv(f)
+ index = np.argmax(0.9 * data.values[:, 8] + 0.1 * data.values[:, 7] + 0.9 * data.values[:, 12] +
+ 0.1 * data.values[:, 11])
+ s = [x.strip() for x in data.columns]
+ x = data.values[:, 0]
+ for i, j in enumerate([1, 2, 3, 4, 5, 6, 9, 10, 13, 14, 15, 16, 7, 8, 11, 12]):
+ y = data.values[:, j]
+ # y[y == 0] = np.nan # don't show zero values
+ ax[i].plot(x, y, marker=".", label=f.stem, linewidth=2, markersize=2)
+ if best:
+ # best
+ ax[i].scatter(index, y[index], color="r", label=f"best:{index}", marker="*", linewidth=3)
+ ax[i].set_title(s[j] + f"\n{round(y[index], 5)}")
+ else:
+ # last
+ ax[i].scatter(x[-1], y[-1], color="r", label="last", marker="*", linewidth=3)
+ ax[i].set_title(s[j] + f"\n{round(y[-1], 5)}")
+ # if j in [8, 9, 10]: # share train and val loss y axes
+ # ax[i].get_shared_y_axes().join(ax[i], ax[i - 5])
+ except Exception as e:
+ print(f"Warning: Plotting error for {f}: {e}")
+ ax[1].legend()
+ fig.savefig(save_dir / "results.png", dpi=200)
+ plt.close()
diff --git a/src/FireDetect/utils/torch_utils.py b/src/FireDetect/utils/torch_utils.py
new file mode 100644
index 0000000..77549b0
--- /dev/null
+++ b/src/FireDetect/utils/torch_utils.py
@@ -0,0 +1,432 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+"""
+PyTorch utils
+"""
+
+import math
+import os
+import platform
+import subprocess
+import time
+import warnings
+from contextlib import contextmanager
+from copy import deepcopy
+from pathlib import Path
+
+import torch
+import torch.distributed as dist
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.nn.parallel import DistributedDataParallel as DDP
+
+from utils.general import LOGGER, check_version, colorstr, file_date, git_describe
+
+LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1)) # https://pytorch.org/docs/stable/elastic/run.html
+RANK = int(os.getenv('RANK', -1))
+WORLD_SIZE = int(os.getenv('WORLD_SIZE', 1))
+
+try:
+ import thop # for FLOPs computation
+except ImportError:
+ thop = None
+
+# Suppress PyTorch warnings
+warnings.filterwarnings('ignore', message='User provided device_type of \'cuda\', but CUDA is not available. Disabling')
+warnings.filterwarnings('ignore', category=UserWarning)
+
+
+def smart_inference_mode(torch_1_9=check_version(torch.__version__, '1.9.0')):
+ # Applies torch.inference_mode() decorator if torch>=1.9.0 else torch.no_grad() decorator
+ def decorate(fn):
+ return (torch.inference_mode if torch_1_9 else torch.no_grad)()(fn)
+
+ return decorate
+
+
+def smartCrossEntropyLoss(label_smoothing=0.0):
+ # Returns nn.CrossEntropyLoss with label smoothing enabled for torch>=1.10.0
+ if check_version(torch.__version__, '1.10.0'):
+ return nn.CrossEntropyLoss(label_smoothing=label_smoothing)
+ if label_smoothing > 0:
+ LOGGER.warning(f'WARNING ⚠️ label smoothing {label_smoothing} requires torch>=1.10.0')
+ return nn.CrossEntropyLoss()
+
+
+def smart_DDP(model):
+ # Model DDP creation with checks
+ assert not check_version(torch.__version__, '1.12.0', pinned=True), \
+ 'torch==1.12.0 torchvision==0.13.0 DDP training is not supported due to a known issue. ' \
+ 'Please upgrade or downgrade torch to use DDP. See https://github.com/ultralytics/yolov5/issues/8395'
+ if check_version(torch.__version__, '1.11.0'):
+ return DDP(model, device_ids=[LOCAL_RANK], output_device=LOCAL_RANK, static_graph=True)
+ else:
+ return DDP(model, device_ids=[LOCAL_RANK], output_device=LOCAL_RANK)
+
+
+def reshape_classifier_output(model, n=1000):
+ # Update a TorchVision classification model to class count 'n' if required
+ from models.common import Classify
+ name, m = list((model.model if hasattr(model, 'model') else model).named_children())[-1] # last module
+ if isinstance(m, Classify): # YOLOv5 Classify() head
+ if m.linear.out_features != n:
+ m.linear = nn.Linear(m.linear.in_features, n)
+ elif isinstance(m, nn.Linear): # ResNet, EfficientNet
+ if m.out_features != n:
+ setattr(model, name, nn.Linear(m.in_features, n))
+ elif isinstance(m, nn.Sequential):
+ types = [type(x) for x in m]
+ if nn.Linear in types:
+ i = types.index(nn.Linear) # nn.Linear index
+ if m[i].out_features != n:
+ m[i] = nn.Linear(m[i].in_features, n)
+ elif nn.Conv2d in types:
+ i = types.index(nn.Conv2d) # nn.Conv2d index
+ if m[i].out_channels != n:
+ m[i] = nn.Conv2d(m[i].in_channels, n, m[i].kernel_size, m[i].stride, bias=m[i].bias is not None)
+
+
+@contextmanager
+def torch_distributed_zero_first(local_rank: int):
+ # Decorator to make all processes in distributed training wait for each local_master to do something
+ if local_rank not in [-1, 0]:
+ dist.barrier(device_ids=[local_rank])
+ yield
+ if local_rank == 0:
+ dist.barrier(device_ids=[0])
+
+
+def device_count():
+ # Returns number of CUDA devices available. Safe version of torch.cuda.device_count(). Supports Linux and Windows
+ assert platform.system() in ('Linux', 'Windows'), 'device_count() only supported on Linux or Windows'
+ try:
+ cmd = 'nvidia-smi -L | wc -l' if platform.system() == 'Linux' else 'nvidia-smi -L | find /c /v ""' # Windows
+ return int(subprocess.run(cmd, shell=True, capture_output=True, check=True).stdout.decode().split()[-1])
+ except Exception:
+ return 0
+
+
+def select_device(device='', batch_size=0, newline=True):
+ # device = None or 'cpu' or 0 or '0' or '0,1,2,3'
+ s = f'YOLOv5 🚀 {git_describe() or file_date()} Python-{platform.python_version()} torch-{torch.__version__} '
+ device = str(device).strip().lower().replace('cuda:', '').replace('none', '') # to string, 'cuda:0' to '0'
+ cpu = device == 'cpu'
+ mps = device == 'mps' # Apple Metal Performance Shaders (MPS)
+ if cpu or mps:
+ os.environ['CUDA_VISIBLE_DEVICES'] = '-1' # force torch.cuda.is_available() = False
+ elif device: # non-cpu device requested
+ os.environ['CUDA_VISIBLE_DEVICES'] = device # set environment variable - must be before assert is_available()
+ assert torch.cuda.is_available() and torch.cuda.device_count() >= len(device.replace(',', '')), \
+ f"Invalid CUDA '--device {device}' requested, use '--device cpu' or pass valid CUDA device(s)"
+
+ if not cpu and not mps and torch.cuda.is_available(): # prefer GPU if available
+ devices = device.split(',') if device else '0' # range(torch.cuda.device_count()) # i.e. 0,1,6,7
+ n = len(devices) # device count
+ if n > 1 and batch_size > 0: # check batch_size is divisible by device_count
+ assert batch_size % n == 0, f'batch-size {batch_size} not multiple of GPU count {n}'
+ space = ' ' * (len(s) + 1)
+ for i, d in enumerate(devices):
+ p = torch.cuda.get_device_properties(i)
+ s += f"{'' if i == 0 else space}CUDA:{d} ({p.name}, {p.total_memory / (1 << 20):.0f}MiB)\n" # bytes to MB
+ arg = 'cuda:0'
+ elif mps and getattr(torch, 'has_mps', False) and torch.backends.mps.is_available(): # prefer MPS if available
+ s += 'MPS\n'
+ arg = 'mps'
+ else: # revert to CPU
+ s += 'CPU\n'
+ arg = 'cpu'
+
+ if not newline:
+ s = s.rstrip()
+ LOGGER.info(s)
+ return torch.device(arg)
+
+
+def time_sync():
+ # PyTorch-accurate time
+ if torch.cuda.is_available():
+ torch.cuda.synchronize()
+ return time.time()
+
+
+def profile(input, ops, n=10, device=None):
+ """ YOLOv5 speed/memory/FLOPs profiler
+ Usage:
+ input = torch.randn(16, 3, 640, 640)
+ m1 = lambda x: x * torch.sigmoid(x)
+ m2 = nn.SiLU()
+ profile(input, [m1, m2], n=100) # profile over 100 iterations
+ """
+ results = []
+ if not isinstance(device, torch.device):
+ device = select_device(device)
+ print(f"{'Params':>12s}{'GFLOPs':>12s}{'GPU_mem (GB)':>14s}{'forward (ms)':>14s}{'backward (ms)':>14s}"
+ f"{'input':>24s}{'output':>24s}")
+
+ for x in input if isinstance(input, list) else [input]:
+ x = x.to(device)
+ x.requires_grad = True
+ for m in ops if isinstance(ops, list) else [ops]:
+ m = m.to(device) if hasattr(m, 'to') else m # device
+ m = m.half() if hasattr(m, 'half') and isinstance(x, torch.Tensor) and x.dtype is torch.float16 else m
+ tf, tb, t = 0, 0, [0, 0, 0] # dt forward, backward
+ try:
+ flops = thop.profile(m, inputs=(x,), verbose=False)[0] / 1E9 * 2 # GFLOPs
+ except Exception:
+ flops = 0
+
+ try:
+ for _ in range(n):
+ t[0] = time_sync()
+ y = m(x)
+ t[1] = time_sync()
+ try:
+ _ = (sum(yi.sum() for yi in y) if isinstance(y, list) else y).sum().backward()
+ t[2] = time_sync()
+ except Exception: # no backward method
+ # print(e) # for debug
+ t[2] = float('nan')
+ tf += (t[1] - t[0]) * 1000 / n # ms per op forward
+ tb += (t[2] - t[1]) * 1000 / n # ms per op backward
+ mem = torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0 # (GB)
+ s_in, s_out = (tuple(x.shape) if isinstance(x, torch.Tensor) else 'list' for x in (x, y)) # shapes
+ p = sum(x.numel() for x in m.parameters()) if isinstance(m, nn.Module) else 0 # parameters
+ print(f'{p:12}{flops:12.4g}{mem:>14.3f}{tf:14.4g}{tb:14.4g}{str(s_in):>24s}{str(s_out):>24s}')
+ results.append([p, flops, mem, tf, tb, s_in, s_out])
+ except Exception as e:
+ print(e)
+ results.append(None)
+ torch.cuda.empty_cache()
+ return results
+
+
+def is_parallel(model):
+ # Returns True if model is of type DP or DDP
+ return type(model) in (nn.parallel.DataParallel, nn.parallel.DistributedDataParallel)
+
+
+def de_parallel(model):
+ # De-parallelize a model: returns single-GPU model if model is of type DP or DDP
+ return model.module if is_parallel(model) else model
+
+
+def initialize_weights(model):
+ for m in model.modules():
+ t = type(m)
+ if t is nn.Conv2d:
+ pass # nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
+ elif t is nn.BatchNorm2d:
+ m.eps = 1e-3
+ m.momentum = 0.03
+ elif t in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU]:
+ m.inplace = True
+
+
+def find_modules(model, mclass=nn.Conv2d):
+ # Finds layer indices matching module class 'mclass'
+ return [i for i, m in enumerate(model.module_list) if isinstance(m, mclass)]
+
+
+def sparsity(model):
+ # Return global model sparsity
+ a, b = 0, 0
+ for p in model.parameters():
+ a += p.numel()
+ b += (p == 0).sum()
+ return b / a
+
+
+def prune(model, amount=0.3):
+ # Prune model to requested global sparsity
+ import torch.nn.utils.prune as prune
+ for name, m in model.named_modules():
+ if isinstance(m, nn.Conv2d):
+ prune.l1_unstructured(m, name='weight', amount=amount) # prune
+ prune.remove(m, 'weight') # make permanent
+ LOGGER.info(f'Model pruned to {sparsity(model):.3g} global sparsity')
+
+
+def fuse_conv_and_bn(conv, bn):
+ # Fuse Conv2d() and BatchNorm2d() layers https://tehnokv.com/posts/fusing-batchnorm-and-conv/
+ fusedconv = nn.Conv2d(conv.in_channels,
+ conv.out_channels,
+ kernel_size=conv.kernel_size,
+ stride=conv.stride,
+ padding=conv.padding,
+ dilation=conv.dilation,
+ groups=conv.groups,
+ bias=True).requires_grad_(False).to(conv.weight.device)
+
+ # Prepare filters
+ w_conv = conv.weight.clone().view(conv.out_channels, -1)
+ w_bn = torch.diag(bn.weight.div(torch.sqrt(bn.eps + bn.running_var)))
+ fusedconv.weight.copy_(torch.mm(w_bn, w_conv).view(fusedconv.weight.shape))
+
+ # Prepare spatial bias
+ b_conv = torch.zeros(conv.weight.size(0), device=conv.weight.device) if conv.bias is None else conv.bias
+ b_bn = bn.bias - bn.weight.mul(bn.running_mean).div(torch.sqrt(bn.running_var + bn.eps))
+ fusedconv.bias.copy_(torch.mm(w_bn, b_conv.reshape(-1, 1)).reshape(-1) + b_bn)
+
+ return fusedconv
+
+
+def model_info(model, verbose=False, imgsz=640):
+ # Model information. img_size may be int or list, i.e. img_size=640 or img_size=[640, 320]
+ n_p = sum(x.numel() for x in model.parameters()) # number parameters
+ n_g = sum(x.numel() for x in model.parameters() if x.requires_grad) # number gradients
+ if verbose:
+ print(f"{'layer':>5} {'name':>40} {'gradient':>9} {'parameters':>12} {'shape':>20} {'mu':>10} {'sigma':>10}")
+ for i, (name, p) in enumerate(model.named_parameters()):
+ name = name.replace('module_list.', '')
+ print('%5g %40s %9s %12g %20s %10.3g %10.3g' %
+ (i, name, p.requires_grad, p.numel(), list(p.shape), p.mean(), p.std()))
+
+ try: # FLOPs
+ p = next(model.parameters())
+ stride = max(int(model.stride.max()), 32) if hasattr(model, 'stride') else 32 # max stride
+ im = torch.empty((1, p.shape[1], stride, stride), device=p.device) # input image in BCHW format
+ flops = thop.profile(deepcopy(model), inputs=(im,), verbose=False)[0] / 1E9 * 2 # stride GFLOPs
+ imgsz = imgsz if isinstance(imgsz, list) else [imgsz, imgsz] # expand if int/float
+ fs = f', {flops * imgsz[0] / stride * imgsz[1] / stride:.1f} GFLOPs' # 640x640 GFLOPs
+ except Exception:
+ fs = ''
+
+ name = Path(model.yaml_file).stem.replace('yolov5', 'YOLOv5') if hasattr(model, 'yaml_file') else 'Model'
+ LOGGER.info(f"{name} summary: {len(list(model.modules()))} layers, {n_p} parameters, {n_g} gradients{fs}")
+
+
+def scale_img(img, ratio=1.0, same_shape=False, gs=32): # img(16,3,256,416)
+ # Scales img(bs,3,y,x) by ratio constrained to gs-multiple
+ if ratio == 1.0:
+ return img
+ h, w = img.shape[2:]
+ s = (int(h * ratio), int(w * ratio)) # new size
+ img = F.interpolate(img, size=s, mode='bilinear', align_corners=False) # resize
+ if not same_shape: # pad/crop img
+ h, w = (math.ceil(x * ratio / gs) * gs for x in (h, w))
+ return F.pad(img, [0, w - s[1], 0, h - s[0]], value=0.447) # value = imagenet mean
+
+
+def copy_attr(a, b, include=(), exclude=()):
+ # Copy attributes from b to a, options to only include [...] and to exclude [...]
+ for k, v in b.__dict__.items():
+ if (len(include) and k not in include) or k.startswith('_') or k in exclude:
+ continue
+ else:
+ setattr(a, k, v)
+
+
+def smart_optimizer(model, name='Adam', lr=0.001, momentum=0.9, decay=1e-5):
+ # YOLOv5 3-param group optimizer: 0) weights with decay, 1) weights no decay, 2) biases no decay
+ g = [], [], [] # optimizer parameter groups
+ bn = tuple(v for k, v in nn.__dict__.items() if 'Norm' in k) # normalization layers, i.e. BatchNorm2d()
+ for v in model.modules():
+ for p_name, p in v.named_parameters(recurse=0):
+ if p_name == 'bias': # bias (no decay)
+ g[2].append(p)
+ elif p_name == 'weight' and isinstance(v, bn): # weight (no decay)
+ g[1].append(p)
+ else:
+ g[0].append(p) # weight (with decay)
+
+ if name == 'Adam':
+ optimizer = torch.optim.Adam(g[2], lr=lr, betas=(momentum, 0.999)) # adjust beta1 to momentum
+ elif name == 'AdamW':
+ optimizer = torch.optim.AdamW(g[2], lr=lr, betas=(momentum, 0.999), weight_decay=0.0)
+ elif name == 'RMSProp':
+ optimizer = torch.optim.RMSprop(g[2], lr=lr, momentum=momentum)
+ elif name == 'SGD':
+ optimizer = torch.optim.SGD(g[2], lr=lr, momentum=momentum, nesterov=True)
+ else:
+ raise NotImplementedError(f'Optimizer {name} not implemented.')
+
+ optimizer.add_param_group({'params': g[0], 'weight_decay': decay}) # add g0 with weight_decay
+ optimizer.add_param_group({'params': g[1], 'weight_decay': 0.0}) # add g1 (BatchNorm2d weights)
+ LOGGER.info(f"{colorstr('optimizer:')} {type(optimizer).__name__}(lr={lr}) with parameter groups "
+ f"{len(g[1])} weight(decay=0.0), {len(g[0])} weight(decay={decay}), {len(g[2])} bias")
+ return optimizer
+
+
+def smart_hub_load(repo='ultralytics/yolov5', model='yolov5s', **kwargs):
+ # YOLOv5 torch.hub.load() wrapper with smart error/issue handling
+ if check_version(torch.__version__, '1.9.1'):
+ kwargs['skip_validation'] = True # validation causes GitHub API rate limit errors
+ if check_version(torch.__version__, '1.12.0'):
+ kwargs['trust_repo'] = True # argument required starting in torch 0.12
+ try:
+ return torch.hub.load(repo, model, **kwargs)
+ except Exception:
+ return torch.hub.load(repo, model, force_reload=True, **kwargs)
+
+
+def smart_resume(ckpt, optimizer, ema=None, weights='yolov5s.pt', epochs=300, resume=True):
+ # Resume training from a partially trained checkpoint
+ best_fitness = 0.0
+ start_epoch = ckpt['epoch'] + 1
+ if ckpt['optimizer'] is not None:
+ optimizer.load_state_dict(ckpt['optimizer']) # optimizer
+ best_fitness = ckpt['best_fitness']
+ if ema and ckpt.get('ema'):
+ ema.ema.load_state_dict(ckpt['ema'].float().state_dict()) # EMA
+ ema.updates = ckpt['updates']
+ if resume:
+ assert start_epoch > 0, f'{weights} training to {epochs} epochs is finished, nothing to resume.\n' \
+ f"Start a new training without --resume, i.e. 'python train.py --weights {weights}'"
+ LOGGER.info(f'Resuming training from {weights} from epoch {start_epoch} to {epochs} total epochs')
+ if epochs < start_epoch:
+ LOGGER.info(f"{weights} has been trained for {ckpt['epoch']} epochs. Fine-tuning for {epochs} more epochs.")
+ epochs += ckpt['epoch'] # finetune additional epochs
+ return best_fitness, start_epoch, epochs
+
+
+class EarlyStopping:
+ # YOLOv5 simple early stopper
+ def __init__(self, patience=30):
+ self.best_fitness = 0.0 # i.e. mAP
+ self.best_epoch = 0
+ self.patience = patience or float('inf') # epochs to wait after fitness stops improving to stop
+ self.possible_stop = False # possible stop may occur next epoch
+
+ def __call__(self, epoch, fitness):
+ if fitness >= self.best_fitness: # >= 0 to allow for early zero-fitness stage of training
+ self.best_epoch = epoch
+ self.best_fitness = fitness
+ delta = epoch - self.best_epoch # epochs without improvement
+ self.possible_stop = delta >= (self.patience - 1) # possible stop may occur next epoch
+ stop = delta >= self.patience # stop training if patience exceeded
+ if stop:
+ LOGGER.info(f'Stopping training early as no improvement observed in last {self.patience} epochs. '
+ f'Best results observed at epoch {self.best_epoch}, best model saved as best.pt.\n'
+ f'To update EarlyStopping(patience={self.patience}) pass a new patience value, '
+ f'i.e. `python train.py --patience 300` or use `--patience 0` to disable EarlyStopping.')
+ return stop
+
+
+class ModelEMA:
+ """ Updated Exponential Moving Average (EMA) from https://github.com/rwightman/pytorch-image-models
+ Keeps a moving average of everything in the model state_dict (parameters and buffers)
+ For EMA details see https://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverage
+ """
+
+ def __init__(self, model, decay=0.9999, tau=2000, updates=0):
+ # Create EMA
+ self.ema = deepcopy(de_parallel(model)).eval() # FP32 EMA
+ self.updates = updates # number of EMA updates
+ self.decay = lambda x: decay * (1 - math.exp(-x / tau)) # decay exponential ramp (to help early epochs)
+ for p in self.ema.parameters():
+ p.requires_grad_(False)
+
+ def update(self, model):
+ # Update EMA parameters
+ self.updates += 1
+ d = self.decay(self.updates)
+
+ msd = de_parallel(model).state_dict() # model state_dict
+ for k, v in self.ema.state_dict().items():
+ if v.dtype.is_floating_point: # true for FP16 and FP32
+ v *= d
+ v += (1 - d) * msd[k].detach()
+ # assert v.dtype == msd[k].dtype == torch.float32, f'{k}: EMA {v.dtype} and model {msd[k].dtype} must be FP32'
+
+ def update_attr(self, model, include=(), exclude=('process_group', 'reducer')):
+ # Update EMA attributes
+ copy_attr(self.ema, model, include, exclude)
diff --git a/src/FireDetect/utils/triton.py b/src/FireDetect/utils/triton.py
new file mode 100644
index 0000000..a94ef0a
--- /dev/null
+++ b/src/FireDetect/utils/triton.py
@@ -0,0 +1,85 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+""" Utils to interact with the Triton Inference Server
+"""
+
+import typing
+from urllib.parse import urlparse
+
+import torch
+
+
+class TritonRemoteModel:
+ """ A wrapper over a model served by the Triton Inference Server. It can
+ be configured to communicate over GRPC or HTTP. It accepts Torch Tensors
+ as input and returns them as outputs.
+ """
+
+ def __init__(self, url: str):
+ """
+ Keyword arguments:
+ url: Fully qualified address of the Triton server - for e.g. grpc://localhost:8000
+ """
+
+ parsed_url = urlparse(url)
+ if parsed_url.scheme == "grpc":
+ from tritonclient.grpc import InferenceServerClient, InferInput
+
+ self.client = InferenceServerClient(parsed_url.netloc) # Triton GRPC client
+ model_repository = self.client.get_model_repository_index()
+ self.model_name = model_repository.models[0].name
+ self.metadata = self.client.get_model_metadata(self.model_name, as_json=True)
+
+ def create_input_placeholders() -> typing.List[InferInput]:
+ return [
+ InferInput(i['name'], [int(s) for s in i["shape"]], i['datatype']) for i in self.metadata['inputs']]
+
+ else:
+ from tritonclient.http import InferenceServerClient, InferInput
+
+ self.client = InferenceServerClient(parsed_url.netloc) # Triton HTTP client
+ model_repository = self.client.get_model_repository_index()
+ self.model_name = model_repository[0]['name']
+ self.metadata = self.client.get_model_metadata(self.model_name)
+
+ def create_input_placeholders() -> typing.List[InferInput]:
+ return [
+ InferInput(i['name'], [int(s) for s in i["shape"]], i['datatype']) for i in self.metadata['inputs']]
+
+ self._create_input_placeholders_fn = create_input_placeholders
+
+ @property
+ def runtime(self):
+ """Returns the model runtime"""
+ return self.metadata.get("backend", self.metadata.get("platform"))
+
+ def __call__(self, *args, **kwargs) -> typing.Union[torch.Tensor, typing.Tuple[torch.Tensor, ...]]:
+ """ Invokes the model. Parameters can be provided via args or kwargs.
+ args, if provided, are assumed to match the order of inputs of the model.
+ kwargs are matched with the model input names.
+ """
+ inputs = self._create_inputs(*args, **kwargs)
+ response = self.client.infer(model_name=self.model_name, inputs=inputs)
+ result = []
+ for output in self.metadata['outputs']:
+ tensor = torch.as_tensor(response.as_numpy(output['name']))
+ result.append(tensor)
+ return result[0] if len(result) == 1 else result
+
+ def _create_inputs(self, *args, **kwargs):
+ args_len, kwargs_len = len(args), len(kwargs)
+ if not args_len and not kwargs_len:
+ raise RuntimeError("No inputs provided.")
+ if args_len and kwargs_len:
+ raise RuntimeError("Cannot specify args and kwargs at the same time")
+
+ placeholders = self._create_input_placeholders_fn()
+ if args_len:
+ if args_len != len(placeholders):
+ raise RuntimeError(f"Expected {len(placeholders)} inputs, got {args_len}.")
+ for input, value in zip(placeholders, args):
+ input.set_data_from_numpy(value.cpu().numpy())
+ else:
+ for input in placeholders:
+ value = kwargs[input.name]
+ input.set_data_from_numpy(value.cpu().numpy())
+ return placeholders