图像识别和高空避障的详细设计顺序图、活动图

src/Guide_stick_system/MonocularDistanceDetect-YOLOV5-RKNN-CPP-MultiThread

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-Subproject commit 64a5c7595d7404f78caf82348af9ae30b105f5b4

src/Guide_stick_system/image_recognition/LICENSE

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-IS WITH YOU.  SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
-ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
-
-  16. Limitation of Liability.
-
-  IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
-WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
-THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
-GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
-USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
-DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
-PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
-EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
-SUCH DAMAGES.
-
-  17. Interpretation of Sections 15 and 16.
-
-  If the disclaimer of warranty and limitation of liability provided
-above cannot be given local legal effect according to their terms,
-reviewing courts shall apply local law that most closely approximates
-an absolute waiver of all civil liability in connection with the
-Program, unless a warranty or assumption of liability accompanies a
-copy of the Program in return for a fee.
-
-                     END OF TERMS AND CONDITIONS
-
-            How to Apply These Terms to Your New Programs
-
-  If you develop a new program, and you want it to be of the greatest
-possible use to the public, the best way to achieve this is to make it
-free software which everyone can redistribute and change under these terms.
-
-  To do so, attach the following notices to the program.  It is safest
-to attach them to the start of each source file to most effectively
-state the exclusion of warranty; and each file should have at least
-the "copyright" line and a pointer to where the full notice is found.
-
-    <one line to give the program's name and a brief idea of what it does.>
-    Copyright (C) <year>  <name of author>
-
-    This program is free software: you can redistribute it and/or modify
-    it under the terms of the GNU General Public License as published by
-    the Free Software Foundation, either version 3 of the License, or
-    (at your option) any later version.
-
-    This program is distributed in the hope that it will be useful,
-    but WITHOUT ANY WARRANTY; without even the implied warranty of
-    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
-    GNU General Public License for more details.
-
-    You should have received a copy of the GNU General Public License
-    along with this program.  If not, see <http://www.gnu.org/licenses/>.
-
-Also add information on how to contact you by electronic and paper mail.
-
-  If the program does terminal interaction, make it output a short
-notice like this when it starts in an interactive mode:
-
-    <program>  Copyright (C) <year>  <name of author>
-    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
-    This is free software, and you are welcome to redistribute it
-    under certain conditions; type `show c' for details.
-
-The hypothetical commands `show w' and `show c' should show the appropriate
-parts of the General Public License.  Of course, your program's commands
-might be different; for a GUI interface, you would use an "about box".
-
-  You should also get your employer (if you work as a programmer) or school,
-if any, to sign a "copyright disclaimer" for the program, if necessary.
-For more information on this, and how to apply and follow the GNU GPL, see
-<http://www.gnu.org/licenses/>.
-
-  The GNU General Public License does not permit incorporating your program
-into proprietary programs.  If your program is a subroutine library, you
-may consider it more useful to permit linking proprietary applications with
-the library.  If this is what you want to do, use the GNU Lesser General
-Public License instead of this License.  But first, please read
-<http://www.gnu.org/philosophy/why-not-lgpl.html>.

src/Guide_stick_system/image_recognition/data/Argoverse.yaml

@@ -1,67 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-# Argoverse-HD dataset (ring-front-center camera) http://www.cs.cmu.edu/~mengtial/proj/streaming/
-# Example usage: python train.py --data Argoverse.yaml
-# parent
-# ├── yolov5
-# └── datasets
-#     └── Argoverse  ← downloads here
-
-
-# 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
-nc: 8  # number of classes
-names: ['person',  'bicycle',  'car',  'motorcycle',  'bus',  'truck',  'traffic_light',  'stop_sign']  # class names
-
-
-# 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 = 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('../datasets/Argoverse')  # 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

src/Guide_stick_system/image_recognition/data/GlobalWheat2020.yaml

@@ -1,53 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-# Global Wheat 2020 dataset http://www.global-wheat.com/
-# Example usage: python train.py --data GlobalWheat2020.yaml
-# parent
-# ├── yolov5
-# └── datasets
-#     └── GlobalWheat2020  ← downloads here
-
-
-# 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
-nc: 1  # number of classes
-names: ['wheat_head']  # class names
-
-
-# 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

src/Guide_stick_system/image_recognition/data/Objects365.yaml

@@ -1,112 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-# Objects365 dataset https://www.objects365.org/
-# Example usage: python train.py --data Objects365.yaml
-# parent
-# ├── yolov5
-# └── datasets
-#     └── Objects365  ← downloads here
-
-
-# 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
-nc: 365  # number of classes
-names: ['Person', 'Sneakers', 'Chair', 'Other Shoes', 'Hat', 'Car', 'Lamp', 'Glasses', 'Bottle', 'Desk', 'Cup',
-        'Street Lights', 'Cabinet/shelf', 'Handbag/Satchel', 'Bracelet', 'Plate', 'Picture/Frame', 'Helmet', 'Book',
-        'Gloves', 'Storage box', 'Boat', 'Leather Shoes', 'Flower', 'Bench', 'Potted Plant', 'Bowl/Basin', 'Flag',
-        'Pillow', 'Boots', 'Vase', 'Microphone', 'Necklace', 'Ring', 'SUV', 'Wine Glass', 'Belt', 'Monitor/TV',
-        'Backpack', 'Umbrella', 'Traffic Light', 'Speaker', 'Watch', 'Tie', 'Trash bin Can', 'Slippers', 'Bicycle',
-        'Stool', 'Barrel/bucket', 'Van', 'Couch', 'Sandals', 'Basket', 'Drum', 'Pen/Pencil', 'Bus', 'Wild Bird',
-        'High Heels', 'Motorcycle', 'Guitar', 'Carpet', 'Cell Phone', 'Bread', 'Camera', 'Canned', 'Truck',
-        'Traffic cone', 'Cymbal', 'Lifesaver', 'Towel', 'Stuffed Toy', 'Candle', 'Sailboat', 'Laptop', 'Awning',
-        'Bed', 'Faucet', 'Tent', 'Horse', 'Mirror', 'Power outlet', 'Sink', 'Apple', 'Air Conditioner', 'Knife',
-        'Hockey Stick', 'Paddle', 'Pickup Truck', 'Fork', 'Traffic Sign', 'Balloon', 'Tripod', 'Dog', 'Spoon', 'Clock',
-        'Pot', 'Cow', 'Cake', 'Dinning Table', 'Sheep', 'Hanger', 'Blackboard/Whiteboard', 'Napkin', 'Other Fish',
-        'Orange/Tangerine', 'Toiletry', 'Keyboard', 'Tomato', 'Lantern', 'Machinery Vehicle', 'Fan',
-        'Green Vegetables', 'Banana', 'Baseball Glove', 'Airplane', 'Mouse', 'Train', 'Pumpkin', 'Soccer', 'Skiboard',
-        'Luggage', 'Nightstand', 'Tea pot', 'Telephone', 'Trolley', 'Head Phone', 'Sports Car', 'Stop Sign',
-        'Dessert', 'Scooter', 'Stroller', 'Crane', 'Remote', 'Refrigerator', 'Oven', 'Lemon', 'Duck', 'Baseball Bat',
-        'Surveillance Camera', 'Cat', 'Jug', 'Broccoli', 'Piano', 'Pizza', 'Elephant', 'Skateboard', 'Surfboard',
-        'Gun', 'Skating and Skiing shoes', 'Gas stove', 'Donut', 'Bow Tie', 'Carrot', 'Toilet', 'Kite', 'Strawberry',
-        'Other Balls', 'Shovel', 'Pepper', 'Computer Box', 'Toilet Paper', 'Cleaning Products', 'Chopsticks',
-        'Microwave', 'Pigeon', 'Baseball', 'Cutting/chopping Board', 'Coffee Table', 'Side Table', 'Scissors',
-        'Marker', 'Pie', 'Ladder', 'Snowboard', 'Cookies', 'Radiator', 'Fire Hydrant', 'Basketball', 'Zebra', 'Grape',
-        'Giraffe', 'Potato', 'Sausage', 'Tricycle', 'Violin', 'Egg', 'Fire Extinguisher', 'Candy', 'Fire Truck',
-        'Billiards', 'Converter', 'Bathtub', 'Wheelchair', 'Golf Club', 'Briefcase', 'Cucumber', 'Cigar/Cigarette',
-        'Paint Brush', 'Pear', 'Heavy Truck', 'Hamburger', 'Extractor', 'Extension Cord', 'Tong', 'Tennis Racket',
-        'Folder', 'American Football', 'earphone', 'Mask', 'Kettle', 'Tennis', 'Ship', 'Swing', 'Coffee Machine',
-        'Slide', 'Carriage', 'Onion', 'Green beans', 'Projector', 'Frisbee', 'Washing Machine/Drying Machine',
-        'Chicken', 'Printer', 'Watermelon', 'Saxophone', 'Tissue', 'Toothbrush', 'Ice cream', 'Hot-air balloon',
-        'Cello', 'French Fries', 'Scale', 'Trophy', 'Cabbage', 'Hot dog', 'Blender', 'Peach', 'Rice', 'Wallet/Purse',
-        'Volleyball', 'Deer', 'Goose', 'Tape', 'Tablet', 'Cosmetics', 'Trumpet', 'Pineapple', 'Golf Ball',
-        'Ambulance', 'Parking meter', 'Mango', 'Key', 'Hurdle', 'Fishing Rod', 'Medal', 'Flute', 'Brush', 'Penguin',
-        'Megaphone', 'Corn', 'Lettuce', 'Garlic', 'Swan', 'Helicopter', 'Green Onion', 'Sandwich', 'Nuts',
-        'Speed Limit Sign', 'Induction Cooker', 'Broom', 'Trombone', 'Plum', 'Rickshaw', 'Goldfish', 'Kiwi fruit',
-        'Router/modem', 'Poker Card', 'Toaster', 'Shrimp', 'Sushi', 'Cheese', 'Notepaper', 'Cherry', 'Pliers', 'CD',
-        'Pasta', 'Hammer', 'Cue', 'Avocado', 'Hamimelon', 'Flask', 'Mushroom', 'Screwdriver', 'Soap', 'Recorder',
-        'Bear', 'Eggplant', 'Board Eraser', 'Coconut', 'Tape Measure/Ruler', 'Pig', 'Showerhead', 'Globe', 'Chips',
-        'Steak', 'Crosswalk Sign', 'Stapler', 'Camel', 'Formula 1', 'Pomegranate', 'Dishwasher', 'Crab',
-        'Hoverboard', 'Meat ball', 'Rice Cooker', 'Tuba', 'Calculator', 'Papaya', 'Antelope', 'Parrot', 'Seal',
-        'Butterfly', 'Dumbbell', 'Donkey', 'Lion', 'Urinal', 'Dolphin', 'Electric Drill', 'Hair Dryer', 'Egg tart',
-        'Jellyfish', 'Treadmill', 'Lighter', 'Grapefruit', 'Game board', 'Mop', 'Radish', 'Baozi', 'Target', 'French',
-        'Spring Rolls', 'Monkey', 'Rabbit', 'Pencil Case', 'Yak', 'Red Cabbage', 'Binoculars', 'Asparagus', 'Barbell',
-        'Scallop', 'Noddles', 'Comb', 'Dumpling', 'Oyster', 'Table Tennis paddle', 'Cosmetics Brush/Eyeliner Pencil',
-        'Chainsaw', 'Eraser', 'Lobster', 'Durian', 'Okra', 'Lipstick', 'Cosmetics Mirror', 'Curling', 'Table Tennis']
-
-
-# Download script/URL (optional) ---------------------------------------------------------------------------------------
-download: |
-  from pycocotools.coco import COCO
-  from tqdm import tqdm
-
-  from utils.general import Path, download, np, xyxy2xywhn
-
-  # 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)

src/Guide_stick_system/image_recognition/data/SKU-110K.yaml

@@ -1,52 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-# SKU-110K retail items dataset https://github.com/eg4000/SKU110K_CVPR19
-# Example usage: python train.py --data SKU-110K.yaml
-# parent
-# ├── yolov5
-# └── datasets
-#     └── SKU-110K  ← downloads here
-
-
-# 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
-nc: 1  # number of classes
-names: ['object']  # class names
-
-
-# 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

src/Guide_stick_system/image_recognition/data/VOC.yaml

@@ -1,80 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-# PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC
-# Example usage: python train.py --data VOC.yaml
-# parent
-# ├── yolov5
-# └── datasets
-#     └── VOC  ← downloads here
-
-
-# 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
-nc: 20  # number of classes
-names: ['aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog',
-        'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor']  # class names
-
-
-# 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)
-
-      for obj in root.iter('object'):
-          cls = obj.find('name').text
-          if cls in yaml['names'] and not 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 = yaml['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 = [url + 'VOCtrainval_06-Nov-2007.zip',  # 446MB, 5012 images
-          url + 'VOCtest_06-Nov-2007.zip',  # 438MB, 4953 images
-          url + 'VOCtrainval_11-May-2012.zip']  # 1.95GB, 17126 images
-  download(urls, dir=dir / 'images', delete=False)
-
-  # Convert
-  path = dir / f'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)
-
-      image_ids = open(path / f'VOC{year}/ImageSets/Main/{image_set}.txt').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

src/Guide_stick_system/image_recognition/data/VisDrone.yaml

@@ -1,61 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-# VisDrone2019-DET dataset https://github.com/VisDrone/VisDrone-Dataset
-# Example usage: python train.py --data VisDrone.yaml
-# parent
-# ├── yolov5
-# └── datasets
-#     └── VisDrone  ← downloads here
-
-
-# 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
-nc: 10  # number of classes
-names: ['pedestrian', 'people', 'bicycle', 'car', 'van', 'truck', 'tricycle', 'awning-tricycle', 'bus', '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)
-
-  # Convert
-  for d in 'VisDrone2019-DET-train', 'VisDrone2019-DET-val', 'VisDrone2019-DET-test-dev':
-      visdrone2yolo(dir / d)  # convert VisDrone annotations to YOLO labels

src/Guide_stick_system/image_recognition/data/coco.yaml

@@ -1,44 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-# COCO 2017 dataset http://cocodataset.org
-# Example usage: python train.py --data coco.yaml
-# parent
-# ├── yolov5
-# └── datasets
-#     └── coco  ← downloads here
-
-
-# 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  # train images (relative to 'path') 5000 images
-test: test-dev2017.txt  # 20288 of 40670 images, submit to https://competitions.codalab.org/competitions/20794
-
-# Classes
-nc: 80  # number of classes
-names: ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light',
-        'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
-        'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',
-        'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard',
-        'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
-        'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
-        'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone',
-        'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear',
-        'hair drier', 'toothbrush']  # class names
-
-
-# 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)

src/Guide_stick_system/image_recognition/data/coco128.yaml

@@ -1,30 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-# COCO128 dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017)
-# Example usage: python train.py --data coco128.yaml
-# parent
-# ├── yolov5
-# └── datasets
-#     └── coco128  ← downloads here
-
-
-# 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
-nc: 80  # number of classes
-names: ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light',
-        'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
-        'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',
-        'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard',
-        'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
-        'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
-        'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone',
-        'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear',
-        'hair drier', 'toothbrush']  # class names
-
-
-# Download script/URL (optional)
-download: https://ultralytics.com/assets/coco128.zip

src/Guide_stick_system/image_recognition/data/data.yaml

@@ -1,13 +0,0 @@
-# Custom data for safety helmet
-
-
-# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/]
-train: /home/data/yolo_format/images/train
-val: /home/data/yolo_format/images/val
-
-# number of classes
-nc: 2
-
-# class names
-names: ['phone', 'person']
- 

src/Guide_stick_system/image_recognition/data/hyps/hyp.finetune.yaml

@@ -1,39 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-# Hyperparameters for VOC finetuning
-# python train.py --batch 64 --weights yolov5m.pt --data VOC.yaml --img 512 --epochs 50
-# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials
-
-# Hyperparameter Evolution Results
-# Generations: 306
-#                   P         R     mAP.5 mAP.5:.95       box       obj       cls
-# Metrics:        0.6     0.936     0.896     0.684    0.0115   0.00805   0.00146
-
-lr0: 0.0032
-lrf: 0.12
-momentum: 0.843
-weight_decay: 0.00036
-warmup_epochs: 2.0
-warmup_momentum: 0.5
-warmup_bias_lr: 0.05
-box: 0.0296
-cls: 0.243
-cls_pw: 0.631
-obj: 0.301
-obj_pw: 0.911
-iou_t: 0.2
-anchor_t: 2.91
-# anchors: 3.63
-fl_gamma: 0.0
-hsv_h: 0.0138
-hsv_s: 0.664
-hsv_v: 0.464
-degrees: 0.373
-translate: 0.245
-scale: 0.898
-shear: 0.602
-perspective: 0.0
-flipud: 0.00856
-fliplr: 0.5
-mosaic: 1.0
-mixup: 0.243
-copy_paste: 0.0

src/Guide_stick_system/image_recognition/data/hyps/hyp.finetune_objects365.yaml

@@ -1,31 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-
-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

src/Guide_stick_system/image_recognition/data/hyps/hyp.scratch-high.yaml

@@ -1,34 +0,0 @@
-# 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: 0.2  # 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)

src/Guide_stick_system/image_recognition/data/hyps/hyp.scratch-low.yaml

@@ -1,34 +0,0 @@
-# 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)

src/Guide_stick_system/image_recognition/data/hyps/hyp.scratch-med.yaml

@@ -1,34 +0,0 @@
-# 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)

src/Guide_stick_system/image_recognition/data/hyps/hyp.scratch.yaml

@@ -1,34 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-# Hyperparameters for COCO training from scratch
-# python train.py --batch 40 --cfg yolov5m.yaml --weights '' --data coco.yaml --img 640 --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: 0.5  # 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)

src/Guide_stick_system/image_recognition/data/mask_data.yaml

@@ -1,12 +0,0 @@
-# Custom data for safety helmet
-
-
-# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/]
-train: F:/up/1212/YOLO_Mask/score/images/train
-val: F:/up/1212/YOLO_Mask/score/images/val
-
-# number of classes
-nc: 2
-
-# class names
-names: ['mask', 'face']

src/Guide_stick_system/image_recognition/data/scripts/download_weights.sh

@@ -1,20 +0,0 @@
-#!/bin/bash
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-# Download latest models from https://github.com/ultralytics/yolov5/releases
-# Example usage: bash path/to/download_weights.sh
-# parent
-# └── yolov5
-#     ├── yolov5s.pt  ← downloads here
-#     ├── yolov5m.pt
-#     └── ...
-
-python - <<EOF
-from utils.downloads import attempt_download
-
-models = ['n', 's', 'm', 'l', 'x']
-models.extend([x + '6' for x in models])  # add P6 models
-
-for x in models:
-    attempt_download(f'yolov5{x}.pt')
-
-EOF

src/Guide_stick_system/image_recognition/data/scripts/get_coco.sh

@@ -1,27 +0,0 @@
-#!/bin/bash
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-# Download COCO 2017 dataset http://cocodataset.org
-# Example usage: bash data/scripts/get_coco.sh
-# parent
-# ├── yolov5
-# └── datasets
-#     └── coco  ← downloads here
-
-# Download/unzip labels
-d='../datasets' # unzip directory
-url=https://github.com/ultralytics/yolov5/releases/download/v1.0/
-f='coco2017labels.zip' # or 'coco2017labels-segments.zip', 68 MB
-echo 'Downloading' $url$f ' ...'
-curl -L $url$f -o $f && unzip -q $f -d $d && rm $f &
-
-# Download/unzip images
-d='../datasets/coco/images' # unzip directory
-url=http://images.cocodataset.org/zips/
-f1='train2017.zip' # 19G, 118k images
-f2='val2017.zip'   # 1G, 5k images
-f3='test2017.zip'  # 7G, 41k images (optional)
-for f in $f1 $f2; do
-  echo 'Downloading' $url$f '...'
-  curl -L $url$f -o $f && unzip -q $f -d $d && rm $f &
-done
-wait # finish background tasks

src/Guide_stick_system/image_recognition/data/scripts/get_coco128.sh

@@ -1,17 +0,0 @@
-#!/bin/bash
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-# Download COCO128 dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017)
-# Example usage: bash data/scripts/get_coco128.sh
-# parent
-# ├── yolov5
-# └── datasets
-#     └── coco128  ← downloads here
-
-# Download/unzip images and labels
-d='../datasets' # unzip directory
-url=https://github.com/ultralytics/yolov5/releases/download/v1.0/
-f='coco128.zip' # or 'coco128-segments.zip', 68 MB
-echo 'Downloading' $url$f ' ...'
-curl -L $url$f -o $f && unzip -q $f -d $d && rm $f &
-
-wait # finish background tasks

src/Guide_stick_system/image_recognition/data/xView.yaml

@@ -1,102 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-# xView 2018 dataset https://challenge.xviewdataset.org
-# --------  DOWNLOAD DATA MANUALLY from URL above and unzip to 'datasets/xView' before running train command!  --------
-# Example usage: python train.py --data xView.yaml
-# parent
-# ├── yolov5
-# └── datasets
-#     └── xView  ← downloads here
-
-
-# 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/xView  # dataset root dir
-train: images/autosplit_train.txt  # train images (relative to 'path') 90% of 847 train images
-val: images/autosplit_val.txt  # train images (relative to 'path') 10% of 847 train images
-
-# Classes
-nc: 60  # number of classes
-names: ['Fixed-wing Aircraft', 'Small Aircraft', 'Cargo Plane', 'Helicopter', 'Passenger Vehicle', 'Small Car', 'Bus',
-        'Pickup Truck', 'Utility Truck', 'Truck', 'Cargo Truck', 'Truck w/Box', 'Truck Tractor', 'Trailer',
-        'Truck w/Flatbed', 'Truck w/Liquid', 'Crane Truck', 'Railway Vehicle', 'Passenger Car', 'Cargo Car',
-        'Flat Car', 'Tank car', 'Locomotive', 'Maritime Vessel', 'Motorboat', 'Sailboat', 'Tugboat', 'Barge',
-        'Fishing Vessel', 'Ferry', 'Yacht', 'Container Ship', 'Oil Tanker', 'Engineering Vehicle', 'Tower crane',
-        'Container Crane', 'Reach Stacker', 'Straddle Carrier', 'Mobile Crane', 'Dump Truck', 'Haul Truck',
-        'Scraper/Tractor', 'Front loader/Bulldozer', 'Excavator', 'Cement Mixer', 'Ground Grader', 'Hut/Tent', 'Shed',
-        'Building', 'Aircraft Hangar', 'Damaged Building', 'Facility', 'Construction Site', 'Vehicle Lot', 'Helipad',
-        'Storage Tank', 'Shipping container lot', 'Shipping Container', 'Pylon', 'Tower']  # class names
-
-
-# Download script/URL (optional) ---------------------------------------------------------------------------------------
-download: |
-  import json
-  import os
-  from pathlib import Path
-
-  import numpy as np
-  from PIL import Image
-  from tqdm import tqdm
-
-  from utils.datasets import autosplit
-  from utils.general import download, xyxy2xywhn
-
-
-  def convert_labels(fname=Path('xView/xView_train.geojson')):
-      # Convert xView geoJSON labels to YOLO format
-      path = fname.parent
-      with open(fname) as f:
-          print(f'Loading {fname}...')
-          data = json.load(f)
-
-      # Make dirs
-      labels = Path(path / 'labels' / 'train')
-      os.system(f'rm -rf {labels}')
-      labels.mkdir(parents=True, exist_ok=True)
-
-      # xView classes 11-94 to 0-59
-      xview_class2index = [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 0, 1, 2, -1, 3, -1, 4, 5, 6, 7, 8, -1, 9, 10, 11,
-                           12, 13, 14, 15, -1, -1, 16, 17, 18, 19, 20, 21, 22, -1, 23, 24, 25, -1, 26, 27, -1, 28, -1,
-                           29, 30, 31, 32, 33, 34, 35, 36, 37, -1, 38, 39, 40, 41, 42, 43, 44, 45, -1, -1, -1, -1, 46,
-                           47, 48, 49, -1, 50, 51, -1, 52, -1, -1, -1, 53, 54, -1, 55, -1, -1, 56, -1, 57, -1, 58, 59]
-
-      shapes = {}
-      for feature in tqdm(data['features'], desc=f'Converting {fname}'):
-          p = feature['properties']
-          if p['bounds_imcoords']:
-              id = p['image_id']
-              file = path / 'train_images' / id
-              if file.exists():  # 1395.tif missing
-                  try:
-                      box = np.array([int(num) for num in p['bounds_imcoords'].split(",")])
-                      assert box.shape[0] == 4, f'incorrect box shape {box.shape[0]}'
-                      cls = p['type_id']
-                      cls = xview_class2index[int(cls)]  # xView class to 0-60
-                      assert 59 >= cls >= 0, f'incorrect class index {cls}'
-
-                      # Write YOLO label
-                      if id not in shapes:
-                          shapes[id] = Image.open(file).size
-                      box = xyxy2xywhn(box[None].astype(np.float), w=shapes[id][0], h=shapes[id][1], clip=True)
-                      with open((labels / id).with_suffix('.txt'), 'a') as f:
-                          f.write(f"{cls} {' '.join(f'{x:.6f}' for x in box[0])}\n")  # write label.txt
-                  except Exception as e:
-                      print(f'WARNING: skipping one label for {file}: {e}')
-
-
-  # Download manually from https://challenge.xviewdataset.org
-  dir = Path(yaml['path'])  # dataset root dir
-  # urls = ['https://d307kc0mrhucc3.cloudfront.net/train_labels.zip',  # train labels
-  #         'https://d307kc0mrhucc3.cloudfront.net/train_images.zip',  # 15G, 847 train images
-  #         'https://d307kc0mrhucc3.cloudfront.net/val_images.zip']  # 5G, 282 val images (no labels)
-  # download(urls, dir=dir, delete=False)
-
-  # Convert labels
-  convert_labels(dir / 'xView_train.geojson')
-
-  # Move images
-  images = Path(dir / 'images')
-  images.mkdir(parents=True, exist_ok=True)
-  Path(dir / 'train_images').rename(dir / 'images' / 'train')
-  Path(dir / 'val_images').rename(dir / 'images' / 'val')
-
-  # Split
-  autosplit(dir / 'images' / 'train')

src/Guide_stick_system/image_recognition/data_gen.py

@@ -1,236 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
--------------------------------------------------
-Project Name: yolov5-jishi
-File Name: data_gen.py
-Author: chenming
-Create Date: 2021/11/8
-Description：
--------------------------------------------------
-"""
-# -*- coding: utf-8 -*-
-# @Time    : 20210610
-# @Author  : dejahu
-# @File    : gen_yolo_data.py
-# @Software: PyCharm
-# @Brief   : 生成测试、验证、训练的图片和标签
-
-import os
-import shutil
-from pathlib import Path
-from shutil import copyfile
- 
-import cv2
-from PIL import Image, ImageDraw
-from xml.dom.minidom import parse
-import numpy as np
-import os.path as osp
-import random
-
-# main，首先在当前目录生成数据的划分
-
-# 开始转化数据集
-
-# 更换代码中images替换的逻辑，和文件对应起来
-
-# todo 修改为你的数据的根目录
-FILE_ROOT = "/scm/data/xianyu/Mask/"
-IMAGE_SET_ROOT = FILE_ROOT + "VOC2021_Mask/ImageSets/Main"   # 图片区分文件的路径
-IMAGE_PATH = FILE_ROOT + "VOC2021_Mask/JPEGImages"  # 图片的位置
-ANNOTATIONS_PATH = FILE_ROOT + "VOC2021_Mask/Annotations"  # 数据集标签文件的位置
-LABELS_ROOT = FILE_ROOT + "VOC2021_Mask/Labels"  # 进行归一化之后的标签位置
-DEST_PPP = FILE_ROOT + "mask_yolo_format"
-DEST_IMAGES_PATH = "mask_yolo_format/images"  # 区分训练集、测试集、验证集的图片目标路径
-DEST_LABELS_PATH = "mask_yolo_format/labels"  # 区分训练集、测试集、验证集的标签文件目标路径
-
-if osp.isdir(LABELS_ROOT):
-    shutil.rmtree(LABELS_ROOT)
-    print("Labels存在，已删除")
-
-if osp.isdir(DEST_PPP):
-    shutil.rmtree(DEST_PPP)
-    print("Dest目录存在，已删除")
-# todo 修改为你数据集的标签名称
-label_names = ['face', 'face_mask']
-
-
-def cord_converter(size, box):
-    """
-    将标注的 xml 文件标注转换为 darknet 形的坐标
-    :param size: 图片的尺寸： [w,h]
-    :param box: anchor box 的坐标 [左上角x,左上角y,右下角x,右下角y,]
-    :return: 转换后的 [x,y,w,h]
-    """
-    x1 = int(box[0])
-    y1 = int(box[1])
-    x2 = int(box[2])
-    y2 = int(box[3])
-
-    dw = np.float32(1. / int(size[0]))
-    dh = np.float32(1. / int(size[1]))
-
-    w = x2 - x1
-    h = y2 - y1
-    x = x1 + (w / 2)
-    y = y1 + (h / 2)
-
-    x = x * dw
-    w = w * dw
-    y = y * dh
-    h = h * dh
-    return [x, y, w, h]
-
-
-def save_file(img_jpg_file_name, size, img_box):
-    # print("保存图片")
-    save_file_name = LABELS_ROOT + '/' + img_jpg_file_name + '.txt'
-    # print(save_file_name)
-    file_path = open(save_file_name, "a+")
-    # 默认给定的是id为0，防止错误数据的出现
-    for box in img_box:
-        box_name = box[0]
-        cls_num = 0
-        if box_name in label_names:
-            cls_num = label_names.index(box_name)
-        new_box = cord_converter(size, box[1:])
-        file_path.write(f"{cls_num} {new_box[0]} {new_box[1]} {new_box[2]} {new_box[3]}\n")
-    file_path.flush()
-    file_path.close()
-
-
-def test_dataset_box_feature(file_name, point_array):
-    """
-    使用样本数据测试数据集的建议框
-    :param image_name: 图片文件名
-    :param point_array: 全部的点 [建议框sx1,sy1,sx2,sy2]
-    :return: None
-    """
-    im = Image.open(rf"{IMAGE_PATH}\{file_name}")
-    imDraw = ImageDraw.Draw(im)
-    for box in point_array:
-        x1 = box[1]
-        y1 = box[2]
-        x2 = box[3]
-        y2 = box[4]
-        imDraw.rectangle((x1, y1, x2, y2), outline='red')
-    im.show()
-
-
-def get_xml_data(file_path, img_xml_file):
-    img_path = file_path + '/' + img_xml_file + '.xml'
-    # print(img_path)
-    dom = parse(img_path)
-    root = dom.documentElement
-    img_name = root.getElementsByTagName("filename")[0].childNodes[0].data
-    img_jpg_file_name = img_xml_file + '.jpg'
-    # print(img_jpg_file_name)
-    cv2.imread(img_jpg_file_name)
-    img_size = root.getElementsByTagName("size")[0]
-    if len(img_size) == 0:
-        img_h, img_w, c = cv2.imread(img_jpg_file_name).shape
-    else:
-        img_w = img_size.getElementsByTagName("width")[0].childNodes[0].data
-        img_h = img_size.getElementsByTagName("height")[0].childNodes[0].data
-        img_c = img_size.getElementsByTagName("depth")[0].childNodes[0].data
-    objects = root.getElementsByTagName("object")
-
-    img_box = []
-    for box in objects:
-        cls_name = box.getElementsByTagName("name")[0].childNodes[0].data
-        # todo 更换坐标点转换的逻辑
-        x1 = int(float(box.getElementsByTagName("xmin")[0].childNodes[0].data))
-        y1 = int(float(box.getElementsByTagName("ymin")[0].childNodes[0].data))
-        x2 = int(float(box.getElementsByTagName("xmax")[0].childNodes[0].data))
-        y2 = int(float(box.getElementsByTagName("ymax")[0].childNodes[0].data))
-        # print("box:(c,xmin,ymin,xmax,ymax)", cls_name, x1, y1, x2, y2)
-
-        img_box.append([cls_name, x1, y1, x2, y2])
-    # test_dataset_box_feature(img_jpg_file_name, img_box)
-    save_file(img_xml_file, [img_w, img_h], img_box)
-
-
-def copy_data(img_set_source, img_labels_root, imgs_source, type):
-    file_name = img_set_source + '/' + type + ".txt"
-    file = open(file_name)
-
-    # 判断文件夹是否存在，不存在则创建
-    root_file = Path(FILE_ROOT + DEST_IMAGES_PATH + '/' + type)
-    if not root_file.exists():
-        print(f"Path {root_file} is not exit")
-        os.makedirs(root_file)
-
-    root_file = Path(FILE_ROOT + DEST_LABELS_PATH + '/' + type)
-    if not root_file.exists():
-        print(f"Path {root_file} is not exit")
-        os.makedirs(root_file)
-
-    # 遍历文件夹
-    for line in file.readlines():
-        # print(line)
-        img_name = line.strip('\n')
-        img_sor_file = imgs_source + '/' + img_name + '.jpg'
-        label_sor_file = img_labels_root + '/' + img_name + '.txt'
-
-        # 复制图片
-        DICT_DIR = FILE_ROOT + DEST_IMAGES_PATH + '/' + type
-        img_dict_file = DICT_DIR + '/' + img_name + '.jpg'
-        copyfile(img_sor_file, img_dict_file)
-
-        # 复制 label
-        DICT_DIR = FILE_ROOT + DEST_LABELS_PATH + '/' + type
-        img_dict_file = DICT_DIR + '/' + img_name + '.txt'
-        copyfile(label_sor_file, img_dict_file)
-
-
-if __name__ == '__main__':
-    # 将文件进行 train 和 val 的区分
-    # 用于生成测试集使用，目前还在测试阶段，暂时不发布
-    img_set_root = IMAGE_SET_ROOT
-    imgs_root = IMAGE_PATH
-    img_labels_root = LABELS_ROOT
-    if osp.isdir(img_labels_root) == False:
-        os.makedirs(img_labels_root)
-    os.makedirs(DEST_PPP)
-    names = os.listdir(ANNOTATIONS_PATH)
-    # real_names = []
-    # for name in names:
-    #     if name.split(".")[-1] == "xml":
-    #         real_names.append(name.split(".")[0])
-    # # print(real_names)
-    # # print(real_names)
-    # random.shuffle(real_names)
-    # # print(real_names)
-    # length = len(real_names)
-    # split_point = int(length * 0.2)
-    #
-    # val_names = real_names[:split_point]
-    # train_names = real_names[split_point:]
-    #
-    # # 开始生成文件
-    # np.savetxt('data/val.txt', np.array(val_names), fmt="%s", delimiter="\n")
-    # np.savetxt('data/test.txt', np.array(val_names), fmt="%s", delimiter="\n")
-    # np.savetxt('data/train.txt', np.array(train_names), fmt="%s", delimiter="\n")
-    # print("txt文件生成完毕，请放在VOC2012的ImageSets/Main的目录下")
-    # 生成标签
-    root = ANNOTATIONS_PATH
-    files_all = os.listdir(root)
-    files = []
-    for file in files_all:
-        if file.split(".")[-1] == "xml":
-            files.append(file.split(".")[0])
-    # print(len(files))
-    for file in files:
-        # print("file name: ", file)
-        file_xml = file.split(".")
-        # try:
-        get_xml_data(root, file_xml[0])
-        # except:
-        #     print(file_xml[0])
-        # break
-    # copy_data(img_set_root, img_labels_root, imgs_root, "train")
-    # copy_data(img_set_root, img_labels_root, imgs_root, "val")
-    # copy_data(img_set_root, img_labels_root, imgs_root, "test")
-    # data = list(np.loadtxt("tt100k.txt", dtype=str))
-    # print(data)
-    # print(len(data))
-    print("数据转换已完成！")

src/Guide_stick_system/image_recognition/detect.py

@@ -1,246 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-Run inference on images, videos, directories, streams, etc.
-
-Usage:
-    $ python path/to/detect.py --weights yolov5s.pt --source 0  # webcam
-                                                             img.jpg  # image
-                                                             vid.mp4  # video
-                                                             path/  # directory
-                                                             path/*.jpg  # glob
-                                                             'https://youtu.be/Zgi9g1ksQHc'  # YouTube
-                                                             'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP stream
-"""
-
-import argparse
-import os
-import sys 
-from pathlib import Path
-
-import cv2
-import torch
-import torch.backends.cudnn as cudnn
-
-FILE = Path(__file__).resolve()
-ROOT = FILE.parents[0]  # YOLOv5 root directory
-if str(ROOT) not in sys.path:
-    sys.path.append(str(ROOT))  # add ROOT to PATH
-ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative
-
-from models.common import DetectMultiBackend
-from utils.datasets import IMG_FORMATS, VID_FORMATS, LoadImages, LoadStreams
-from utils.general import (LOGGER, check_file, check_img_size, check_imshow, check_requirements, colorstr,
-                           increment_path, non_max_suppression, print_args, scale_coords, strip_optimizer, xyxy2xywh)
-from utils.plots import Annotator, colors, save_one_box
-from utils.torch_utils import select_device, time_sync
-
-
-@torch.no_grad()
-def run(weights=ROOT / 'yolov5s.pt',  # model.pt path(s)
-        source=ROOT / 'data/images',  # file/dir/URL/glob, 0 for webcam
-        imgsz=640,  # inference size (pixels)
-        conf_thres=0.25,  # confidence threshold
-        iou_thres=0.45,  # NMS IOU threshold
-        max_det=1000,  # maximum detections per image
-        device='',  # cuda device, i.e. 0 or 0,1,2,3 or cpu
-        view_img=False,  # show results
-        save_txt=False,  # save results to *.txt
-        save_conf=False,  # save confidences in --save-txt labels
-        save_crop=False,  # save cropped prediction boxes
-        nosave=False,  # do not save images/videos
-        classes=None,  # filter by class: --class 0, or --class 0 2 3
-        agnostic_nms=False,  # class-agnostic NMS
-        augment=False,  # augmented inference
-        visualize=False,  # visualize features
-        update=False,  # update all models
-        project=ROOT / 'runs/detect',  # save results to project/name
-        name='exp',  # save results to project/name
-        exist_ok=False,  # existing project/name ok, do not increment
-        line_thickness=3,  # bounding box thickness (pixels)
-        hide_labels=False,  # hide labels
-        hide_conf=False,  # hide confidences
-        half=False,  # use FP16 half-precision inference
-        dnn=False,  # use OpenCV DNN for ONNX inference
-        ):
-    source = str(source)
-    save_img = not nosave and not source.endswith('.txt')  # save inference images
-    is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS)
-    is_url = source.lower().startswith(('rtsp://', 'rtmp://', 'http://', 'https://'))
-    webcam = source.isnumeric() or source.endswith('.txt') or (is_url and not is_file)
-    if is_url and is_file:
-        source = check_file(source)  # download
-
-    # Directories
-    save_dir = increment_path(Path(project) / name, exist_ok=exist_ok)  # increment run
-    (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True)  # make dir
-
-    # Load model
-    device = select_device(device)
-    model = DetectMultiBackend(weights, device=device, dnn=dnn)
-    stride, names, pt, jit, onnx = model.stride, model.names, model.pt, model.jit, model.onnx
-    imgsz = check_img_size(imgsz, s=stride)  # check image size
-
-    # Half
-    half &= pt and device.type != 'cpu'  # half precision only supported by PyTorch on CUDA
-    if pt:
-        model.model.half() if half else model.model.float()
-
-    # Dataloader
-    if webcam:
-        view_img = check_imshow()
-        cudnn.benchmark = True  # set True to speed up constant image size inference
-        dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt and not jit)
-        bs = len(dataset)  # batch_size
-    else:
-        dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt and not jit)
-        bs = 1  # batch_size
-    vid_path, vid_writer = [None] * bs, [None] * bs
-
-    # Run inference
-    if pt and device.type != 'cpu':
-        model(torch.zeros(1, 3, *imgsz).to(device).type_as(next(model.model.parameters())))  # warmup
-    dt, seen = [0.0, 0.0, 0.0], 0
-    for path, im, im0s, vid_cap, s in dataset:
-        t1 = time_sync()
-        im = torch.from_numpy(im).to(device)
-        im = im.half() if half 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
-        t2 = time_sync()
-        dt[0] += t2 - t1
-
-        # Inference
-        visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False
-        pred = model(im, augment=augment, visualize=visualize)
-        t3 = time_sync()
-        dt[1] += t3 - t2
-
-        # NMS
-        pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)
-        dt[2] += time_sync() - t3
-
-        # Second-stage classifier (optional)
-        # pred = utils.general.apply_classifier(pred, classifier_model, im, im0s)
-
-        # Process predictions
-        for i, det in enumerate(pred):  # per image
-            seen += 1
-            if webcam:  # batch_size >= 1
-                p, im0, frame = path[i], im0s[i].copy(), dataset.count
-                s += f'{i}: '
-            else:
-                p, im0, frame = path, im0s.copy(), getattr(dataset, 'frame', 0)
-
-            p = Path(p)  # to Path
-            save_path = str(save_dir / p.name)  # im.jpg
-            txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}')  # im.txt
-            s += '%gx%g ' % im.shape[2:]  # print string
-            gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]  # normalization gain whwh
-            imc = im0.copy() if save_crop else im0  # for save_crop
-            annotator = Annotator(im0, line_width=line_thickness, example=str(names))
-            if len(det):
-                # Rescale boxes from img_size to im0 size
-                det[:, :4] = scale_coords(im.shape[2:], det[:, :4], im0.shape).round()
-
-                # Print results
-                for c in det[:, -1].unique():
-                    n = (det[:, -1] == c).sum()  # detections per class
-                    s += f"{n} {names[int(c)]}{'s' * (n > 1)}, "  # add to string
-
-                # Write results
-                for *xyxy, conf, cls in reversed(det):
-                    if save_txt:  # Write to file
-                        xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywh
-                        line = (cls, *xywh, conf) if save_conf else (cls, *xywh)  # label format
-                        with open(txt_path + '.txt', 'a') as f:
-                            f.write(('%g ' * len(line)).rstrip() % line + '\n')
-
-                    if save_img or save_crop or view_img:  # Add bbox to image
-                        c = int(cls)  # integer class
-                        label = None if hide_labels else (names[c] if hide_conf else f'{names[c]} {conf:.2f}')
-                        annotator.box_label(xyxy, label, color=colors(c, True))
-                        if save_crop:
-                            save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True)
-
-            # Print time (inference-only)
-            LOGGER.info(f'{s}Done. ({t3 - t2:.3f}s)')
-
-            # Stream results
-            im0 = annotator.result()
-            if view_img:
-                cv2.imshow(str(p), im0)
-                cv2.waitKey(1)  # 1 millisecond
-
-            # Save results (image with detections)
-            if save_img:
-                if dataset.mode == 'image':
-                    cv2.imwrite(save_path, im0)
-                else:  # 'video' or 'stream'
-                    if vid_path[i] != save_path:  # new video
-                        vid_path[i] = save_path
-                        if isinstance(vid_writer[i], cv2.VideoWriter):
-                            vid_writer[i].release()  # release previous video writer
-                        if vid_cap:  # video
-                            fps = vid_cap.get(cv2.CAP_PROP_FPS)
-                            w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
-                            h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
-                        else:  # stream
-                            fps, w, h = 30, im0.shape[1], im0.shape[0]
-                            save_path += '.mp4'
-                        vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
-                    vid_writer[i].write(im0)
-
-    # Print results
-    t = tuple(x / seen * 1E3 for x in dt)  # speeds per image
-    LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}' % t)
-    if save_txt or save_img:
-        s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
-        LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}")
-    if update:
-        strip_optimizer(weights)  # update model (to fix SourceChangeWarning)
-
-
-def parse_opt():
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolov5s.pt', help='model path(s)')
-    parser.add_argument('--source', type=str, default=ROOT / 'data/images', help='file/dir/URL/glob, 0 for webcam')
-    parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640], help='inference size h,w')
-    parser.add_argument('--conf-thres', type=float, default=0.5, help='confidence threshold')
-    parser.add_argument('--iou-thres', type=float, default=0.45, help='NMS IoU threshold')
-    parser.add_argument('--max-det', type=int, default=1000, help='maximum detections per image')
-    parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
-    parser.add_argument('--view-img', action='store_true', help='show results')
-    parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
-    parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
-    parser.add_argument('--save-crop', action='store_true', help='save cropped prediction boxes')
-    parser.add_argument('--nosave', action='store_true', help='do not save images/videos')
-    parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --classes 0, or --classes 0 2 3')
-    parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')
-    parser.add_argument('--augment', action='store_true', help='augmented inference')
-    parser.add_argument('--visualize', action='store_true', help='visualize features')
-    parser.add_argument('--update', action='store_true', help='update all models')
-    parser.add_argument('--project', default=ROOT / 'runs/detect', help='save results to project/name')
-    parser.add_argument('--name', default='exp', help='save results to project/name')
-    parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
-    parser.add_argument('--line-thickness', default=3, type=int, help='bounding box thickness (pixels)')
-    parser.add_argument('--hide-labels', default=False, action='store_true', help='hide labels')
-    parser.add_argument('--hide-conf', default=False, action='store_true', help='hide confidences')
-    parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference')
-    parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference')
-    opt = parser.parse_args()
-    opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1  # expand
-    print_args(FILE.stem, opt)
-    return opt
-
-
-def main(opt):
-    check_requirements(exclude=('tensorboard', 'thop'))
-    run(**vars(opt))
-
-
-# 命令使用
-# python detect.py --weights runs/train/exp_yolov5s/weights/best.pt --source  data/images/fishman.jpg # webcam
-if __name__ == "__main__":
-    opt = parse_opt()
-    main(opt)

src/Guide_stick_system/image_recognition/docs/Dockerfile

@@ -1,61 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-
-# Start FROM Nvidia PyTorch image https://ngc.nvidia.com/catalog/containers/nvidia:pytorch
-FROM nvcr.io/nvidia/pytorch:21.10-py3
-
-# Install linux packages
-RUN apt update && apt install -y zip htop screen libgl1-mesa-glx
-
-# Install python dependencies
-COPY ../requirements.txt .
-RUN python -m pip install --upgrade pip
-RUN pip uninstall -y nvidia-tensorboard nvidia-tensorboard-plugin-dlprof
-RUN pip install --no-cache -r requirements.txt coremltools onnx gsutil notebook wandb>=0.12.2
-RUN pip install --no-cache -U torch torchvision numpy Pillow
-# RUN pip install --no-cache torch==1.10.0+cu113 torchvision==0.11.1+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html
-
-# Create working directory
-RUN mkdir -p /usr/src/app
-WORKDIR /usr/src/app
-
-# Copy contents
-COPY .. /usr/src/app
-
-# Downloads to user config dir
-ADD https://ultralytics.com/assets/Arial.ttf /root/.config/Ultralytics/
-
-# Set environment variables
-# ENV HOME=/usr/src/app
-
-
-# Usage Examples -------------------------------------------------------------------------------------------------------
-
-# Build and Push
-# t=ultralytics/yolov5:latest && sudo docker build -t $t . && sudo docker push $t
-
-# Pull and Run
-# t=ultralytics/yolov5:latest && sudo docker pull $t && sudo docker run -it --ipc=host --gpus all $t
-
-# Pull and Run with local directory access
-# t=ultralytics/yolov5:latest && sudo docker pull $t && sudo docker run -it --ipc=host --gpus all -v "$(pwd)"/datasets:/usr/src/datasets $t
-
-# Kill all
-# sudo docker kill $(sudo docker ps -q)
-
-# Kill all image-based
-# sudo docker kill $(sudo docker ps -qa --filter ancestor=ultralytics/yolov5:latest)
-
-# Bash into running container
-# sudo docker exec -it 5a9b5863d93d bash
-
-# Bash into stopped container
-# id=$(sudo docker ps -qa) && sudo docker start $id && sudo docker exec -it $id bash
-
-# Clean up
-# docker system prune -a --volumes
-
-# Update Ubuntu drivers
-# https://www.maketecheasier.com/install-nvidia-drivers-ubuntu/
-
-# DDP test
-# python -m torch.distributed.run --nproc_per_node 2 --master_port 1 train.py --epochs 3

src/Guide_stick_system/image_recognition/docs/README.md

@@ -1,292 +0,0 @@
-<div align="center">
-<p>
-   <a align="left" href="https://ultralytics.com/yolov5" target="_blank">
-   <img width="850" src="https://github.com/ultralytics/yolov5/releases/download/v1.0/splash.jpg"></a>
-</p>
-<br>
-<div>
-   <a href="https://github.com/ultralytics/yolov5/actions"><img src="https://github.com/ultralytics/yolov5/workflows/CI%20CPU%20testing/badge.svg" alt="CI CPU testing"></a>
-   <a href="https://zenodo.org/badge/latestdoi/264818686"><img src="https://zenodo.org/badge/264818686.svg" alt="YOLOv5 Citation"></a>
-   <a href="https://hub.docker.com/r/ultralytics/yolov5"><img src="https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker" alt="Docker Pulls"></a>
-   <br>
-   <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
-   <a href="https://www.kaggle.com/ultralytics/yolov5"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
-   <a href="https://join.slack.com/t/ultralytics/shared_invite/zt-w29ei8bp-jczz7QYUmDtgo6r6KcMIAg"><img src="https://img.shields.io/badge/Slack-Join_Forum-blue.svg?logo=slack" alt="Join Forum"></a>
-</div>
-<br>
-<div align="center">
-   <a href="https://github.com/ultralytics">
-   <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-github.png" width="2%"/>
-   </a>
-   <img width="2%" />
-   <a href="https://www.linkedin.com/company/ultralytics">
-   <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-linkedin.png" width="2%"/>
-   </a>
-   <img width="2%" />
-   <a href="https://twitter.com/ultralytics">
-   <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-twitter.png" width="2%"/>
-   </a>
-   <img width="2%" />
-   <a href="https://youtube.com/ultralytics">
-   <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-youtube.png" width="2%"/>
-   </a>
-   <img width="2%" />
-   <a href="https://www.facebook.com/ultralytics">
-   <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-facebook.png" width="2%"/>
-   </a>
-   <img width="2%" />
-   <a href="https://www.instagram.com/ultralytics/">
-   <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-instagram.png" width="2%"/>
-   </a>
-</div>
-
-<br>
-<p>
-YOLOv5 🚀 is a family of object detection architectures and models pretrained on the COCO dataset, and represents <a href="https://ultralytics.com">Ultralytics</a>
- open-source research into future vision AI methods, incorporating lessons learned and best practices evolved over thousands of hours of research and development.
-</p>
-
-<!--
-<a align="center" href="https://ultralytics.com/yolov5" target="_blank">
-<img width="800" src="https://github.com/ultralytics/yolov5/releases/download/v1.0/banner-api.png"></a>
--->
-
-</div>
-
-## <div align="center">Documentation</div>
-
-See the [YOLOv5 Docs](https://docs.ultralytics.com) for full documentation on training, testing and deployment.
-
-## <div align="center">Quick Start Examples</div>
-
-<details open>
-<summary>Install</summary>
-
-[**Python>=3.6.0**](https://www.python.org/) is required with all
-[requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) installed including
-[**PyTorch>=1.7**](https://pytorch.org/get-started/locally/):
-<!-- $ sudo apt update && apt install -y libgl1-mesa-glx libsm6 libxext6 libxrender-dev -->
-
-```bash
-$ git clone https://github.com/ultralytics/yolov5
-$ cd yolov5
-$ pip install -r requirements.txt
-```
-
-</details>
-
-<details open>
-<summary>Inference</summary>
-
-Inference with YOLOv5 and [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36). Models automatically download
-from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases).
-
-```python
-import torch
-
-# Model
-model = torch.hub.load('ultralytics/yolov5', 'yolov5s')  # or yolov5m, yolov5l, yolov5x, custom
-
-# Images
-img = 'https://ultralytics.com/images/zidane.jpg'  # or file, Path, PIL, OpenCV, numpy, list
-
-# Inference
-results = model(img)
-
-# Results
-results.print()  # or .show(), .save(), .crop(), .pandas(), etc.
-```
-
-</details>
-
-
-
-<details>
-<summary>Inference with detect.py</summary>
-
-`detect.py` runs inference on a variety of sources, downloading models automatically from
-the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases) and saving results to `runs/detect`.
-
-```bash
-$ python detect.py --source 0  # webcam
-                            img.jpg  # image
-                            vid.mp4  # video
-                            path/  # directory
-                            path/*.jpg  # glob
-                            'https://youtu.be/Zgi9g1ksQHc'  # YouTube
-                            'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP stream
-```
-
-</details>
-
-<details>
-<summary>Training</summary>
-
-Run commands below to reproduce results
-on [COCO](https://github.com/ultralytics/yolov5/blob/master/data/scripts/get_coco.sh) dataset (dataset auto-downloads on
-first use). Training times for YOLOv5s/m/l/x are 2/4/6/8 days on a single V100 (multi-GPU times faster). Use the
-largest `--batch-size` your GPU allows (batch sizes shown for 16 GB devices).
-
-```bash
-$ python train.py --data coco.yaml --cfg yolov5s.yaml --weights '' --batch-size 64
-                                         yolov5m                                40
-                                         yolov5l                                24
-                                         yolov5x                                16
-```
-
-<img width="800" src="https://user-images.githubusercontent.com/26833433/90222759-949d8800-ddc1-11ea-9fa1-1c97eed2b963.png">
-
-</details>
-
-<details open>
-<summary>Tutorials</summary>
-
-* [Train Custom Data](https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data)&nbsp; 🚀 RECOMMENDED
-* [Tips for Best Training Results](https://github.com/ultralytics/yolov5/wiki/Tips-for-Best-Training-Results)&nbsp; ☘️
-  RECOMMENDED
-* [Weights & Biases Logging](https://github.com/ultralytics/yolov5/issues/1289)&nbsp; 🌟 NEW
-* [Roboflow for Datasets, Labeling, and Active Learning](https://github.com/ultralytics/yolov5/issues/4975)&nbsp; 🌟 NEW
-* [Multi-GPU Training](https://github.com/ultralytics/yolov5/issues/475)
-* [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36)&nbsp; ⭐ NEW
-* [TorchScript, ONNX, CoreML Export](https://github.com/ultralytics/yolov5/issues/251) 🚀
-* [Test-Time Augmentation (TTA)](https://github.com/ultralytics/yolov5/issues/303)
-* [Model Ensembling](https://github.com/ultralytics/yolov5/issues/318)
-* [Model Pruning/Sparsity](https://github.com/ultralytics/yolov5/issues/304)
-* [Hyperparameter Evolution](https://github.com/ultralytics/yolov5/issues/607)
-* [Transfer Learning with Frozen Layers](https://github.com/ultralytics/yolov5/issues/1314)&nbsp; ⭐ NEW
-* [TensorRT Deployment](https://github.com/wang-xinyu/tensorrtx)
-
-</details>
-
-## <div align="center">Environments</div>
-
-Get started in seconds with our verified environments. Click each icon below for details.
-
-<div align="center">
-    <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb">
-        <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-colab-small.png" width="15%"/>
-    </a>
-    <a href="https://www.kaggle.com/ultralytics/yolov5">
-        <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-kaggle-small.png" width="15%"/>
-    </a>
-    <a href="https://hub.docker.com/r/ultralytics/yolov5">
-        <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-docker-small.png" width="15%"/>
-    </a>
-    <a href="https://github.com/ultralytics/yolov5/wiki/AWS-Quickstart">
-        <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-aws-small.png" width="15%"/>
-    </a>
-    <a href="https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart">
-        <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-gcp-small.png" width="15%"/>
-    </a>
-</div>
-
-## <div align="center">Integrations</div>
-
-<div align="center">
-    <a href="https://wandb.ai/site?utm_campaign=repo_yolo_readme">
-        <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-wb-long.png" width="49%"/>
-    </a>
-    <a href="https://roboflow.com/?ref=ultralytics">
-        <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-roboflow-long.png" width="49%"/>
-    </a>
-</div>
-
-|Weights and Biases|Roboflow ⭐ NEW|
-|:-:|:-:|
-|Automatically track and visualize all your YOLOv5 training runs in the cloud with [Weights & Biases](https://wandb.ai/site?utm_campaign=repo_yolo_readme)|Label and export your custom datasets directly to YOLOv5 for training with [Roboflow](https://roboflow.com/?ref=ultralytics) |
-
-
-<!-- ## <div align="center">Compete and Win</div>
-
-We are super excited about our first-ever Ultralytics YOLOv5 🚀 EXPORT Competition with **$10,000** in cash prizes!
-
-<p align="center">
-  <a href="https://github.com/ultralytics/yolov5/discussions/3213">
-  <img width="850" src="https://github.com/ultralytics/yolov5/releases/download/v1.0/banner-export-competition.png"></a>
-</p> -->
-
-## <div align="center">Why YOLOv5</div>
-
-<p align="left"><img width="800" src="https://user-images.githubusercontent.com/26833433/136901921-abcfcd9d-f978-4942-9b97-0e3f202907df.png"></p>
-<details>
-  <summary>YOLOv5-P5 640 Figure (click to expand)</summary>
-
-<p align="left"><img width="800" src="https://user-images.githubusercontent.com/26833433/136763877-b174052b-c12f-48d2-8bc4-545e3853398e.png"></p>
-</details>
-<details>
-  <summary>Figure Notes (click to expand)</summary>
-
-* **COCO AP val** denotes mAP@0.5:0.95 metric measured on the 5000-image [COCO val2017](http://cocodataset.org) dataset over various inference sizes from 256 to 1536.
-* **GPU Speed** measures average inference time per image on [COCO val2017](http://cocodataset.org) dataset using a [AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/) V100 instance at batch-size 32.
-* **EfficientDet** data from [google/automl](https://github.com/google/automl) at batch size 8.
-* **Reproduce** by `python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n6.pt yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt`
-</details>
-
-### Pretrained Checkpoints
-
-[assets]: https://github.com/ultralytics/yolov5/releases
-[TTA]: https://github.com/ultralytics/yolov5/issues/303
-
-|Model |size<br><sup>(pixels) |mAP<sup>val<br>0.5:0.95 |mAP<sup>val<br>0.5 |Speed<br><sup>CPU b1<br>(ms) |Speed<br><sup>V100 b1<br>(ms) |Speed<br><sup>V100 b32<br>(ms) |params<br><sup>(M) |FLOPs<br><sup>@640 (B)
-|---                    |---  |---    |---    |---    |---    |---    |---    |---
-|[YOLOv5n][assets]      |640  |28.4   |46.0   |**45** |**6.3**|**0.6**|**1.9**|**4.5**
-|[YOLOv5s][assets]      |640  |37.2   |56.0   |98     |6.4    |0.9    |7.2    |16.5
-|[YOLOv5m][assets]      |640  |45.2   |63.9   |224    |8.2    |1.7    |21.2   |49.0
-|[YOLOv5l][assets]      |640  |48.8   |67.2   |430    |10.1   |2.7    |46.5   |109.1
-|[YOLOv5x][assets]      |640  |50.7   |68.9   |766    |12.1   |4.8    |86.7   |205.7
-|                       |     |       |       |       |       |       |       |
-|[YOLOv5n6][assets]     |1280 |34.0   |50.7   |153    |8.1    |2.1    |3.2    |4.6
-|[YOLOv5s6][assets]     |1280 |44.5   |63.0   |385    |8.2    |3.6    |16.8   |12.6
-|[YOLOv5m6][assets]     |1280 |51.0   |69.0   |887    |11.1   |6.8    |35.7   |50.0
-|[YOLOv5l6][assets]     |1280 |53.6   |71.6   |1784   |15.8   |10.5   |76.8   |111.4
-|[YOLOv5x6][assets]<br>+ [TTA][TTA]|1280<br>1536 |54.7<br>**55.4** |**72.4**<br>72.3 |3136<br>- |26.2<br>- |19.4<br>- |140.7<br>- |209.8<br>-
-
-<details>
-  <summary>Table Notes (click to expand)</summary>
-
-* All checkpoints are trained to 300 epochs with default settings and hyperparameters.
-* **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](http://cocodataset.org) dataset.<br>Reproduce by `python val.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65`
-* **Speed** averaged over COCO val images using a [AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/) instance. NMS times (~1 ms/img) not included.<br>Reproduce by `python val.py --data coco.yaml --img 640 --conf 0.25 --iou 0.45`
-* **TTA** [Test Time Augmentation](https://github.com/ultralytics/yolov5/issues/303) includes reflection and scale augmentations.<br>Reproduce by `python val.py --data coco.yaml --img 1536 --iou 0.7 --augment`
-
-</details>
-
-## <div align="center">Contribute</div>
-
-We love your input! We want to make contributing to YOLOv5 as easy and transparent as possible. Please see our [Contributing Guide](CONTRIBUTING.md) to get started, and fill out the [YOLOv5 Survey](https://ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey) to send us feedback on your experiences. Thank you to all our contributors!
-
-<a href="https://github.com/ultralytics/yolov5/graphs/contributors"><img src="https://opencollective.com/ultralytics/contributors.svg?width=990" /></a>
-
-
-## <div align="center">Contact</div>
-
-For YOLOv5 bugs and feature requests please visit [GitHub Issues](https://github.com/ultralytics/yolov5/issues). For business inquiries or
-professional support requests please visit [https://ultralytics.com/contact](https://ultralytics.com/contact).
-
-<br>
-
-<div align="center">
-    <a href="https://github.com/ultralytics">
-        <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-github.png" width="3%"/>
-    </a>
-    <img width="3%" />
-    <a href="https://www.linkedin.com/company/ultralytics">
-        <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-linkedin.png" width="3%"/>
-    </a>
-    <img width="3%" />
-    <a href="https://twitter.com/ultralytics">
-        <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-twitter.png" width="3%"/>
-    </a>
-    <img width="3%" />
-    <a href="https://youtube.com/ultralytics">
-        <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-youtube.png" width="3%"/>
-    </a>
-    <img width="3%" />
-    <a href="https://www.facebook.com/ultralytics">
-        <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-facebook.png" width="3%"/>
-    </a>
-    <img width="3%" />
-    <a href="https://www.instagram.com/ultralytics/">
-        <img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-instagram.png" width="3%"/>
-    </a>
-</div>

src/Guide_stick_system/image_recognition/export.py

@@ -1,369 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-Export a YOLOv5 PyTorch model to TorchScript, ONNX, CoreML, TensorFlow (saved_model, pb, TFLite, TF.js,) formats
-TensorFlow exports authored by https://github.com/zldrobit
-
-Usage:
-    $ python path/to/export.py --weights yolov5s.pt --include torchscript onnx coreml saved_model pb tflite tfjs
-
-Inference:
-    $ python path/to/detect.py --weights yolov5s.pt
-                                         yolov5s.onnx  (must export with --dynamic)
-                                         yolov5s_saved_model
-                                         yolov5s.pb
-                                         yolov5s.tflite
-
-TensorFlow.js: 
-    $ cd .. && git clone https://github.com/zldrobit/tfjs-yolov5-example.git && cd tfjs-yolov5-example
-    $ npm install
-    $ ln -s ../../yolov5/yolov5s_web_model public/yolov5s_web_model
-    $ npm start
-"""
-
-import argparse
-import json
-import os
-import subprocess
-import sys
-import time
-from pathlib import Path
-
-import torch
-import torch.nn as nn
-from torch.utils.mobile_optimizer import optimize_for_mobile
-
-FILE = Path(__file__).resolve()
-ROOT = FILE.parents[0]  # YOLOv5 root directory
-if str(ROOT) not in sys.path:
-    sys.path.append(str(ROOT))  # add ROOT to PATH
-ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative
-
-from models.common import Conv
-from models.experimental import attempt_load
-from models.yolo import Detect
-from utils.activations import SiLU
-from utils.datasets import LoadImages
-from utils.general import (LOGGER, check_dataset, check_img_size, check_requirements, colorstr, file_size, print_args,
-                           url2file)
-from utils.torch_utils import select_device
-
-
-def export_torchscript(model, im, file, optimize, prefix=colorstr('TorchScript:')):
-    # YOLOv5 TorchScript model export
-    try:
-        LOGGER.info(f'\n{prefix} starting export with torch {torch.__version__}...')
-        f = file.with_suffix('.torchscript.pt')
-
-        ts = torch.jit.trace(model, im, strict=False)
-        d = {"shape": im.shape, "stride": int(max(model.stride)), "names": model.names}
-        extra_files = {'config.txt': json.dumps(d)}  # torch._C.ExtraFilesMap()
-        (optimize_for_mobile(ts) if optimize else ts).save(f, _extra_files=extra_files)
-
-        LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
-    except Exception as e:
-        LOGGER.info(f'{prefix} export failure: {e}')
-
-
-def export_onnx(model, im, file, opset, train, dynamic, simplify, prefix=colorstr('ONNX:')):
-    # YOLOv5 ONNX export
-    try:
-        check_requirements(('onnx',))
-        import onnx
-
-        LOGGER.info(f'\n{prefix} starting export with onnx {onnx.__version__}...')
-        f = file.with_suffix('.onnx')
-
-        torch.onnx.export(model, im, f, verbose=False, opset_version=opset,
-                          training=torch.onnx.TrainingMode.TRAINING if train else torch.onnx.TrainingMode.EVAL,
-                          do_constant_folding=not train,
-                          input_names=['images'],
-                          output_names=['output'],
-                          dynamic_axes={'images': {0: 'batch', 2: 'height', 3: 'width'},  # shape(1,3,640,640)
-                                        'output': {0: 'batch', 1: 'anchors'}  # shape(1,25200,85)
-                                        } if dynamic else None)
-
-        # Checks
-        model_onnx = onnx.load(f)  # load onnx model
-        onnx.checker.check_model(model_onnx)  # check onnx model
-        # LOGGER.info(onnx.helper.printable_graph(model_onnx.graph))  # print
-
-        # Simplify
-        if simplify:
-            try:
-                check_requirements(('onnx-simplifier',))
-                import onnxsim
-
-                LOGGER.info(f'{prefix} simplifying with onnx-simplifier {onnxsim.__version__}...')
-                model_onnx, check = onnxsim.simplify(
-                    model_onnx,
-                    dynamic_input_shape=dynamic,
-                    input_shapes={'images': list(im.shape)} if dynamic else None)
-                assert check, 'assert check failed'
-                onnx.save(model_onnx, f)
-            except Exception as e:
-                LOGGER.info(f'{prefix} simplifier failure: {e}')
-        LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
-        LOGGER.info(f"{prefix} run --dynamic ONNX model inference with: 'python detect.py --weights {f}'")
-    except Exception as e:
-        LOGGER.info(f'{prefix} export failure: {e}')
-
-
-def export_coreml(model, im, file, prefix=colorstr('CoreML:')):
-    # YOLOv5 CoreML export
-    ct_model = None
-    try:
-        check_requirements(('coremltools',))
-        import coremltools as ct
-
-        LOGGER.info(f'\n{prefix} starting export with coremltools {ct.__version__}...')
-        f = file.with_suffix('.mlmodel')
-
-        model.train()  # CoreML exports should be placed in model.train() mode
-        ts = torch.jit.trace(model, im, strict=False)  # TorchScript model
-        ct_model = ct.convert(ts, inputs=[ct.ImageType('image', shape=im.shape, scale=1 / 255, bias=[0, 0, 0])])
-        ct_model.save(f)
-
-        LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
-    except Exception as e:
-        LOGGER.info(f'\n{prefix} export failure: {e}')
-
-    return ct_model
-
-
-def export_saved_model(model, im, file, dynamic,
-                       tf_nms=False, agnostic_nms=False, topk_per_class=100, topk_all=100, iou_thres=0.45,
-                       conf_thres=0.25, prefix=colorstr('TensorFlow saved_model:')):
-    # YOLOv5 TensorFlow saved_model export
-    keras_model = None
-    try:
-        import tensorflow as tf
-        from tensorflow import keras
-
-        from models.tf import TFDetect, TFModel
-
-        LOGGER.info(f'\n{prefix} starting export with tensorflow {tf.__version__}...')
-        f = str(file).replace('.pt', '_saved_model')
-        batch_size, ch, *imgsz = list(im.shape)  # BCHW
-
-        tf_model = TFModel(cfg=model.yaml, model=model, nc=model.nc, imgsz=imgsz)
-        im = tf.zeros((batch_size, *imgsz, 3))  # BHWC order for TensorFlow
-        y = tf_model.predict(im, tf_nms, agnostic_nms, topk_per_class, topk_all, iou_thres, conf_thres)
-        inputs = keras.Input(shape=(*imgsz, 3), batch_size=None if dynamic else batch_size)
-        outputs = tf_model.predict(inputs, tf_nms, agnostic_nms, topk_per_class, topk_all, iou_thres, conf_thres)
-        keras_model = keras.Model(inputs=inputs, outputs=outputs)
-        keras_model.trainable = False
-        keras_model.summary()
-        keras_model.save(f, save_format='tf')
-
-        LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
-    except Exception as e:
-        LOGGER.info(f'\n{prefix} export failure: {e}')
-
-    return keras_model
-
-
-def export_pb(keras_model, im, file, prefix=colorstr('TensorFlow GraphDef:')):
-    # YOLOv5 TensorFlow GraphDef *.pb export https://github.com/leimao/Frozen_Graph_TensorFlow
-    try:
-        import tensorflow as tf
-        from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2
-
-        LOGGER.info(f'\n{prefix} starting export with tensorflow {tf.__version__}...')
-        f = file.with_suffix('.pb')
-
-        m = tf.function(lambda x: keras_model(x))  # full model
-        m = m.get_concrete_function(tf.TensorSpec(keras_model.inputs[0].shape, keras_model.inputs[0].dtype))
-        frozen_func = convert_variables_to_constants_v2(m)
-        frozen_func.graph.as_graph_def()
-        tf.io.write_graph(graph_or_graph_def=frozen_func.graph, logdir=str(f.parent), name=f.name, as_text=False)
-
-        LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
-    except Exception as e:
-        LOGGER.info(f'\n{prefix} export failure: {e}')
-
-
-def export_tflite(keras_model, im, file, int8, data, ncalib, prefix=colorstr('TensorFlow Lite:')):
-    # YOLOv5 TensorFlow Lite export
-    try:
-        import tensorflow as tf
-
-        from models.tf import representative_dataset_gen
-
-        LOGGER.info(f'\n{prefix} starting export with tensorflow {tf.__version__}...')
-        batch_size, ch, *imgsz = list(im.shape)  # BCHW
-        f = str(file).replace('.pt', '-fp16.tflite')
-
-        converter = tf.lite.TFLiteConverter.from_keras_model(keras_model)
-        converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS]
-        converter.target_spec.supported_types = [tf.float16]
-        converter.optimizations = [tf.lite.Optimize.DEFAULT]
-        if int8:
-            dataset = LoadImages(check_dataset(data)['train'], img_size=imgsz, auto=False)  # representative data
-            converter.representative_dataset = lambda: representative_dataset_gen(dataset, ncalib)
-            converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
-            converter.target_spec.supported_types = []
-            converter.inference_input_type = tf.uint8  # or tf.int8
-            converter.inference_output_type = tf.uint8  # or tf.int8
-            converter.experimental_new_quantizer = False
-            f = str(file).replace('.pt', '-int8.tflite')
-
-        tflite_model = converter.convert()
-        open(f, "wb").write(tflite_model)
-        LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
-
-    except Exception as e:
-        LOGGER.info(f'\n{prefix} export failure: {e}')
-
-
-def export_tfjs(keras_model, im, file, prefix=colorstr('TensorFlow.js:')):
-    # YOLOv5 TensorFlow.js export
-    try:
-        check_requirements(('tensorflowjs',))
-        import re
-
-        import tensorflowjs as tfjs
-
-        LOGGER.info(f'\n{prefix} starting export with tensorflowjs {tfjs.__version__}...')
-        f = str(file).replace('.pt', '_web_model')  # js dir
-        f_pb = file.with_suffix('.pb')  # *.pb path
-        f_json = f + '/model.json'  # *.json path
-
-        cmd = f"tensorflowjs_converter --input_format=tf_frozen_model " \
-              f"--output_node_names='Identity,Identity_1,Identity_2,Identity_3' {f_pb} {f}"
-        subprocess.run(cmd, shell=True)
-
-        json = open(f_json).read()
-        with open(f_json, 'w') as j:  # sort JSON Identity_* in ascending order
-            subst = re.sub(
-                r'{"outputs": {"Identity.?.?": {"name": "Identity.?.?"}, '
-                r'"Identity.?.?": {"name": "Identity.?.?"}, '
-                r'"Identity.?.?": {"name": "Identity.?.?"}, '
-                r'"Identity.?.?": {"name": "Identity.?.?"}}}',
-                r'{"outputs": {"Identity": {"name": "Identity"}, '
-                r'"Identity_1": {"name": "Identity_1"}, '
-                r'"Identity_2": {"name": "Identity_2"}, '
-                r'"Identity_3": {"name": "Identity_3"}}}',
-                json)
-            j.write(subst)
-
-        LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
-    except Exception as e:
-        LOGGER.info(f'\n{prefix} export failure: {e}')
-
-
-@torch.no_grad()
-def run(data=ROOT / 'data/coco128.yaml',  # 'dataset.yaml path'
-        weights=ROOT / 'yolov5s.pt',  # weights path
-        imgsz=(640, 640),  # image (height, width)
-        batch_size=1,  # batch size
-        device='cpu',  # cuda device, i.e. 0 or 0,1,2,3 or cpu
-        include=('torchscript', 'onnx', 'coreml'),  # include formats
-        half=False,  # FP16 half-precision export
-        inplace=False,  # set YOLOv5 Detect() inplace=True
-        train=False,  # model.train() mode
-        optimize=False,  # TorchScript: optimize for mobile
-        int8=False,  # CoreML/TF INT8 quantization
-        dynamic=False,  # ONNX/TF: dynamic axes
-        simplify=False,  # ONNX: simplify model
-        opset=12,  # ONNX: opset version
-        topk_per_class=100,  # TF.js NMS: topk per class to keep
-        topk_all=100,  # TF.js NMS: topk for all classes to keep
-        iou_thres=0.45,  # TF.js NMS: IoU threshold
-        conf_thres=0.25  # TF.js NMS: confidence threshold
-        ):
-    t = time.time()
-    include = [x.lower() for x in include]
-    tf_exports = list(x in include for x in ('saved_model', 'pb', 'tflite', 'tfjs'))  # TensorFlow exports
-    imgsz *= 2 if len(imgsz) == 1 else 1  # expand
-    file = Path(url2file(weights) if str(weights).startswith(('http:/', 'https:/')) else weights)
-
-    # Load PyTorch model
-    device = select_device(device)
-    assert not (device.type == 'cpu' and half), '--half only compatible with GPU export, i.e. use --device 0'
-    model = attempt_load(weights, map_location=device, inplace=True, fuse=True)  # load FP32 model
-    nc, names = model.nc, model.names  # number of classes, class names
-
-    # Input
-    gs = int(max(model.stride))  # grid size (max stride)
-    imgsz = [check_img_size(x, gs) for x in imgsz]  # verify img_size are gs-multiples
-    im = torch.zeros(batch_size, 3, *imgsz).to(device)  # image size(1,3,320,192) BCHW iDetection
-
-    # Update model
-    if half:
-        im, model = im.half(), model.half()  # to FP16
-    model.train() if train else model.eval()  # training mode = no Detect() layer grid construction
-    for k, m in model.named_modules():
-        if isinstance(m, Conv):  # assign export-friendly activations
-            if isinstance(m.act, nn.SiLU):
-                m.act = SiLU()
-        elif isinstance(m, Detect):
-            m.inplace = inplace
-            m.onnx_dynamic = dynamic
-            # m.forward = m.forward_export  # assign forward (optional)
-
-    for _ in range(2):
-        y = model(im)  # dry runs
-    LOGGER.info(f"\n{colorstr('PyTorch:')} starting from {file} ({file_size(file):.1f} MB)")
-
-    # Exports
-    if 'torchscript' in include:
-        export_torchscript(model, im, file, optimize)
-    if 'onnx' in include:
-        export_onnx(model, im, file, opset, train, dynamic, simplify)
-    if 'coreml' in include:
-        export_coreml(model, im, file)
-
-    # TensorFlow Exports
-    if any(tf_exports):
-        pb, tflite, tfjs = tf_exports[1:]
-        assert not (tflite and tfjs), 'TFLite and TF.js models must be exported separately, please pass only one type.'
-        model = export_saved_model(model, im, file, dynamic, tf_nms=tfjs, agnostic_nms=tfjs,
-                                   topk_per_class=topk_per_class, topk_all=topk_all, conf_thres=conf_thres,
-                                   iou_thres=iou_thres)  # keras model
-        if pb or tfjs:  # pb prerequisite to tfjs
-            export_pb(model, im, file)
-        if tflite:
-            export_tflite(model, im, file, int8=int8, data=data, ncalib=100)
-        if tfjs:
-            export_tfjs(model, im, file)
-
-    # Finish
-    LOGGER.info(f'\nExport complete ({time.time() - t:.2f}s)'
-                f"\nResults saved to {colorstr('bold', file.parent.resolve())}"
-                f'\nVisualize with https://netron.app')
-
-
-def parse_opt():
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='dataset.yaml path')
-    parser.add_argument('--weights', type=str, default=ROOT / 'yolov5s.pt', help='weights path')
-    parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640, 640], help='image (h, w)')
-    parser.add_argument('--batch-size', type=int, default=1, help='batch size')
-    parser.add_argument('--device', default='cpu', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
-    parser.add_argument('--half', action='store_true', help='FP16 half-precision export')
-    parser.add_argument('--inplace', action='store_true', help='set YOLOv5 Detect() inplace=True')
-    parser.add_argument('--train', action='store_true', help='model.train() mode')
-    parser.add_argument('--optimize', action='store_true', help='TorchScript: optimize for mobile')
-    parser.add_argument('--int8', action='store_true', help='CoreML/TF INT8 quantization')
-    parser.add_argument('--dynamic', action='store_true', help='ONNX/TF: dynamic axes')
-    parser.add_argument('--simplify', action='store_true', help='ONNX: simplify model')
-    parser.add_argument('--opset', type=int, default=13, help='ONNX: opset version')
-    parser.add_argument('--topk-per-class', type=int, default=100, help='TF.js NMS: topk per class to keep')
-    parser.add_argument('--topk-all', type=int, default=100, help='TF.js NMS: topk for all classes to keep')
-    parser.add_argument('--iou-thres', type=float, default=0.45, help='TF.js NMS: IoU threshold')
-    parser.add_argument('--conf-thres', type=float, default=0.25, help='TF.js NMS: confidence threshold')
-    parser.add_argument('--include', nargs='+',
-                        default=['torchscript', 'onnx'],
-                        help='available formats are (torchscript, onnx, coreml, saved_model, pb, tflite, tfjs)')
-    opt = parser.parse_args()
-    print_args(FILE.stem, opt)
-    return opt
-
-
-def main(opt):
-    run(**vars(opt))
-
-
-if __name__ == "__main__":
-    opt = parse_opt()
-    main(opt)

src/Guide_stick_system/image_recognition/hubconf.py

@@ -1,142 +0,0 @@
-# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
-"""
-PyTorch Hub models https://pytorch.org/hub/ultralytics_yolov5/
-
-Usage:
-    import torch
-    model = torch.hub.load('ultralytics/yolov5', 'yolov5s')
-""" 
-
-import torch
-
-
-def _create(name, pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
-    """Creates a specified YOLOv5 model
-
-    Arguments:
-        name (str): name of model, i.e. 'yolov5s'
-        pretrained (bool): load pretrained weights into the model
-        channels (int): number of input channels
-        classes (int): number of model classes
-        autoshape (bool): apply YOLOv5 .autoshape() wrapper to model
-        verbose (bool): print all information to screen
-        device (str, torch.device, None): device to use for model parameters
-
-    Returns:
-        YOLOv5 pytorch model
-    """
-    from pathlib import Path
-
-    from models.experimental import attempt_load
-    from models.yolo import Model
-    from utils.downloads import attempt_download
-    from utils.general import check_requirements, intersect_dicts, set_logging
-    from utils.torch_utils import select_device
-
-    file = Path(__file__).resolve()
-    check_requirements(exclude=('tensorboard', 'thop', 'opencv-python'))
-    set_logging(verbose=verbose)
-
-    save_dir = Path('') if str(name).endswith('.pt') else file.parent
-    path = (save_dir / name).with_suffix('.pt')  # checkpoint path
-    try:
-        device = select_device(('0' if torch.cuda.is_available() else 'cpu') if device is None else device)
-
-        if pretrained and channels == 3 and classes == 80:
-            model = attempt_load(path, map_location=device)  # download/load FP32 model
-        else:
-            cfg = list((Path(__file__).parent / 'models').rglob(f'{name}.yaml'))[0]  # model.yaml path
-            model = Model(cfg, channels, classes)  # create model
-            if pretrained:
-                ckpt = torch.load(attempt_download(path), map_location=device)  # load
-                csd = ckpt['model'].float().state_dict()  # checkpoint state_dict as FP32
-                csd = intersect_dicts(csd, model.state_dict(), exclude=['anchors'])  # intersect
-                model.load_state_dict(csd, strict=False)  # load
-                if len(ckpt['model'].names) == classes:
-                    model.names = ckpt['model'].names  # set class names attribute
-        if autoshape:
-            model = model.autoshape()  # for file/URI/PIL/cv2/np inputs and NMS
-        return model.to(device)
-
-    except Exception as e:
-        help_url = 'https://github.com/ultralytics/yolov5/issues/36'
-        s = 'Cache may be out of date, try `force_reload=True`. See %s for help.' % help_url
-        raise Exception(s) from e
-
-
-def custom(path='path/to/model.pt', autoshape=True, verbose=True, device=None):
-    # YOLOv5 custom or local model
-    return _create(path, autoshape=autoshape, verbose=verbose, device=device)
-
-
-def yolov5n(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
-    # YOLOv5-nano model https://github.com/ultralytics/yolov5
-    return _create('yolov5n', pretrained, channels, classes, autoshape, verbose, device)
-
-
-def yolov5s(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
-    # YOLOv5-small model https://github.com/ultralytics/yolov5
-    return _create('yolov5s', pretrained, channels, classes, autoshape, verbose, device)
-
-
-def yolov5m(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
-    # YOLOv5-medium model https://github.com/ultralytics/yolov5
-    return _create('yolov5m', pretrained, channels, classes, autoshape, verbose, device)
-
-
-def yolov5l(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
-    # YOLOv5-large model https://github.com/ultralytics/yolov5
-    return _create('yolov5l', pretrained, channels, classes, autoshape, verbose, device)
-
-
-def yolov5x(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
-    # YOLOv5-xlarge model https://github.com/ultralytics/yolov5
-    return _create('yolov5x', pretrained, channels, classes, autoshape, verbose, device)
-
-
-def yolov5n6(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
-    # YOLOv5-nano-P6 model https://github.com/ultralytics/yolov5
-    return _create('yolov5n6', pretrained, channels, classes, autoshape, verbose, device)
-
-
-def yolov5s6(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
-    # YOLOv5-small-P6 model https://github.com/ultralytics/yolov5
-    return _create('yolov5s6', pretrained, channels, classes, autoshape, verbose, device)
-
-
-def yolov5m6(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
-    # YOLOv5-medium-P6 model https://github.com/ultralytics/yolov5
-    return _create('yolov5m6', pretrained, channels, classes, autoshape, verbose, device)
-
-
-def yolov5l6(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
-    # YOLOv5-large-P6 model https://github.com/ultralytics/yolov5
-    return _create('yolov5l6', pretrained, channels, classes, autoshape, verbose, device)
-
-
-def yolov5x6(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
-    # YOLOv5-xlarge-P6 model https://github.com/ultralytics/yolov5
-    return _create('yolov5x6', pretrained, channels, classes, autoshape, verbose, device)
-
-
-if __name__ == '__main__':
-    model = _create(name='yolov5s', pretrained=True, channels=3, classes=80, autoshape=True, verbose=True)  # pretrained
-    # model = custom(path='path/to/model.pt')  # custom
-
-    # Verify inference
-    from pathlib import Path
-
-    import cv2
-    import numpy as np
-    from PIL import Image
-
-    imgs = ['data/images/zidane.jpg',  # filename
-            Path('data/images/zidane.jpg'),  # Path
-            'https://ultralytics.com/images/zidane.jpg',  # URI
-            cv2.imread('data/images/bus.jpg')[:, :, ::-1],  # OpenCV
-            Image.open('data/images/bus.jpg'),  # PIL
-            np.zeros((320, 640, 3))]  # numpy
-
-    results = model(imgs)  # batched inference
-    results.print()
-    results.save()

src/Guide_stick_system/image_recognition/manage/itt/ReadMe

@@ -1,2 +0,0 @@
-主要实现每1s读取一次img目录中的图片，并且识别的图片中的文字会存储到txt目录中，
-另外，txt目录中设置了当存储的备份文件即历史识别后结构仅仅保留最后1m以来做记录器。

src/Guide_stick_system/image_recognition/manage/itt/wenzhi.py

@@ -1,98 +0,0 @@
-import pytesseract  
-from PIL import Image  
-import sys  
-import os
-import time  
-import datetime 
-  
-
-def ensure_directories():  
-    # 确保存储输出文本的目录存在  
-    output_dir = 'txt'  
-    if not os.path.exists(output_dir):  
-        os.makedirs(output_dir)
-
-def ensure_pytesseract_installed():  
-    try:  
-        # 尝试导入pytesseract以确认它已安装  
-        import pytesseract  
-    except ImportError:  
-        print("pytesseract库未安装。请运行'pip install pytesseract'来安装它。")  
-        sys.exit(1)  
-  
-def ensure_tesseract_executable_configured():  
-    try:  
-        # 尝试获取Tesseract的路径以确认它已配置  
-        pytesseract.pytesseract.tesseract_cmd  
-    except AttributeError:  
-        print("未配置Tesseract可执行文件的路径。请在pytesseract中设置tesseract_cmd。")  
-        sys.exit(1)  
-  
-def image_to_text(image_path):  
-    try:  
-        # 打开图像文件  
-        img = Image.open(image_path)  
-    except IOError:  
-        print(f"无法打开图像文件：{image_path}")  
-        return None  
-  
-    try:  
-        # 使用pytesseract进行文字识别  
-        text = pytesseract.image_to_string(img, lang='eng')  
-    except Exception as e:  
-        print(f"文字识别过程中发生错误：{e}")  
-        return None  
-  
-    return text  
-  
-def main():  
-    # 确保pytesseract库已安装  
-    ensure_pytesseract_installed()  
-  
-    # 确保配置了Tesseract可执行文件的路径  
-    ensure_tesseract_executable_configured()
-    
-    # 确保输出目录存在  
-    ensure_directories()
-    
-    # 设置输出目录路径  
-    output_dir_path = 'txt'  
-  
-  
-    # 主循环，每3秒处理一张图片  
-    while True:  
-        # 获取当前时间，并格式化为文件名  
-        current_time = datetime.datetime.now().strftime("%Y%m%d%H%M%S")  
-        image_filename = f"{current_time}.jpg"  
-        image_path = os.path.join('img', image_filename)
-        output_file_path = os.path.join(output_dir_path, f"{current_time}.txt")
-  
-        # 检查图片是否存在  
-        if not os.path.exists(image_path):  
-            print(f"图片 {image_path} 不存在，等待下一张...")  
-            time.sleep(1)  
-            continue  
-  
-         # 调用image_to_text函数并将结果写入文件  
-        text = image_to_text(image_path)  
-        if text:  
-            with open(output_file_path, 'w', encoding='utf-8') as file:  
-                file.write(text)  
-            print(f"图片 {image_filename} 的识别结果已保存到 {output_file_path}")  
-        else:  
-            print(f"无法识别图片 {image_filename} 中的文字。")  
-            
-        # 检查txt文件夹中的文件数量，如果超过60个，删除最早的文件  
-        files = os.listdir(output_dir_path)  
-        if len(files) > 60:  
-            # 对文件进行排序，以便找到最早的文件  
-            files.sort()  
-            oldest_file_path = os.path.join(output_dir_path, files[0])  
-            os.remove(oldest_file_path)  
-            print(f"已删除最早的文件：{oldest_file_path}")  
-  
-        # 等待1秒  
-        time.sleep(1)  
-  
-if __name__ == "__main__":  
-    main()

src/Guide_stick_system/image_recognition/manage/log.txt

@@ -1,242 +0,0 @@
-2024年 07月 01日 星期一 21:13:13 CST: wenzhi.py 被停止, PID: 29387
-2024年 07月 01日 星期一 21:13:13 CST: tts.py 运行失败，退出状态码 1, PID: 29405
-2024年 07月 01日 星期一 21:13:18 CST: wenzhi.py 被停止, PID: 29408
-2024年 07月 01日 星期一 21:13:18 CST: tts.py 运行失败，退出状态码 1, PID: 29435
-2024年 07月 01日 星期一 21:13:23 CST: wenzhi.py 被停止, PID: 29456
-2024年 07月 01日 星期一 21:13:23 CST: tts.py 运行失败，退出状态码 1, PID: 29474
-2024年 07月 01日 星期一 21:14:26 CST: wenzhi.py 被停止, PID: 29817
-2024年 07月 01日 星期一 21:14:26 CST: tts.py 运行失败，退出状态码 1, PID: 29835
-2024年 07月 01日 星期一 21:14:31 CST: wenzhi.py 被停止, PID: 29838
-2024年 07月 01日 星期一 21:14:31 CST: tts.py 运行失败，退出状态码 1, PID: 29865
-2024年 07月 01日 星期一 21:16:08 CST: wenzhi.py 被停止, PID: 30373
-2024年 07月 01日 星期一 21:16:08 CST: tts.py 运行失败，退出状态码 1, PID: 30391
-2024年 07月 01日 星期一 21:16:13 CST: wenzhi.py 被停止, PID: 30412
-2024年 07月 01日 星期一 21:16:13 CST: tts.py 运行失败，退出状态码 1, PID: 30466
-2024年 07月 01日 星期一 21:16:18 CST: wenzhi.py 被停止, PID: 30469
-2024年 07月 01日 星期一 21:16:18 CST: tts.py 运行失败，退出状态码 1, PID: 30550
-2024年 07月 01日 星期一 21:16:42 CST: wenzhi.py 被停止, PID: 30664
-2024年 07月 01日 星期一 21:16:42 CST: tts.py 运行失败，退出状态码 1, PID: 30691
-2024年 07月 01日 星期一 21:16:47 CST: wenzhi.py 被停止, PID: 30694
-2024年 07月 01日 星期一 21:16:47 CST: tts.py 运行失败，退出状态码 1, PID: 30721
-2024年 07月 01日 星期一 21:16:52 CST: wenzhi.py 被停止, PID: 30733
-2024年 07月 01日 星期一 21:16:52 CST: tts.py 运行失败，退出状态码 1, PID: 30769
-2024年 07月 01日 星期一 21:16:57 CST: wenzhi.py 被停止, PID: 30781
-2024年 07月 01日 星期一 21:16:57 CST: tts.py 运行失败，退出状态码 1, PID: 30835
-2024年 07月 01日 星期一 21:17:02 CST: wenzhi.py 被停止, PID: 30838
-2024年 07月 01日 星期一 21:17:02 CST: tts.py 运行失败，退出状态码 1, PID: 30895
-2024年 07月 01日 星期一 21:19:05 CST: wenzhi.py 被停止, PID: 31525
-2024年 07月 01日 星期一 21:19:05 CST: tts.py 运行失败，退出状态码 1, PID: 31543
-2024年 07月 01日 星期一 21:19:10 CST: wenzhi.py 被停止, PID: 31546
-2024年 07月 01日 星期一 21:19:10 CST: tts.py 运行失败，退出状态码 1, PID: 31573
-2024年 07月 01日 星期一 21:20:16 CST: wenzhi.py 被停止, PID: 31924
-2024年 07月 01日 星期一 21:20:17 CST: tts.py 运行失败，退出状态码 1, PID: 31960
-2024年 07月 01日 星期一 21:20:22 CST: wenzhi.py 被停止, PID: 31981
-2024年 07月 01日 星期一 21:20:22 CST: tts.py 运行失败，退出状态码 1, PID: 32008
-2024年 07月 01日 星期一 21:20:27 CST: wenzhi.py 被停止, PID: 32029
-2024年 07月 01日 星期一 21:20:27 CST: tts.py 运行失败，退出状态码 1, PID: 32056
-2024年 07月 01日 星期一 21:20:32 CST: wenzhi.py 被停止, PID: 32059
-2024年 07月 01日 星期一 21:20:32 CST: tts.py 运行失败，退出状态码 1, PID: 32086
-2024年 07月 01日 星期一 21:20:37 CST: wenzhi.py 被停止, PID: 32125
-2024年 07月 01日 星期一 21:20:37 CST: tts.py 运行失败，退出状态码 1, PID: 32161
-2024年 07月 01日 星期一 21:20:42 CST: wenzhi.py 被停止, PID: 32191
-2024年 07月 01日 星期一 21:20:42 CST: tts.py 运行失败，退出状态码 1, PID: 32264
-2024年 07月 01日 星期一 21:20:47 CST: wenzhi.py 被停止, PID: 32267
-2024年 07月 01日 星期一 21:20:47 CST: tts.py 运行失败，退出状态码 1, PID: 32303
-2024年 07月 01日 星期一 21:20:52 CST: wenzhi.py 被停止, PID: 32306
-2024年 07月 01日 星期一 21:20:52 CST: tts.py 运行失败，退出状态码 1, PID: 32351
-2024年 07月 01日 星期一 21:20:57 CST: wenzhi.py 被停止, PID: 32354
-2024年 07月 01日 星期一 21:20:57 CST: tts.py 运行失败，退出状态码 1, PID: 32426
-2024年 07月 01日 星期一 23:05:26 CST: wenzhi.py 被停止, PID: 8301
-2024年 07月 01日 星期一 23:05:26 CST: tts.py 运行失败，退出状态码 1, PID: 8318
-2024年 07月 01日 星期一 23:05:28 CST: wenzhi.py 被停止, PID: 8322
-2024年 07月 01日 星期一 23:05:28 CST: tts.py 运行失败，退出状态码 1, PID: 8339
-2024年 07月 01日 星期一 23:05:30 CST: wenzhi.py 被停止, PID: 8352
-2024年 07月 01日 星期一 23:05:30 CST: tts.py 运行失败，退出状态码 1, PID: 8369
-2024年 07月 01日 星期一 23:05:32 CST: wenzhi.py 被停止, PID: 8373
-2024年 07月 01日 星期一 23:05:32 CST: tts.py 运行失败，退出状态码 1, PID: 8400
-2024年 07月 01日 星期一 23:05:34 CST: wenzhi.py 被停止, PID: 8422
-2024年 07月 01日 星期一 23:05:34 CST: tts.py 运行失败，退出状态码 1, PID: 8448
-2024年 07月 01日 星期一 23:09:24 CST: wenzhi.py 被停止, PID: 10042
-2024年 07月 01日 星期一 23:09:24 CST: tts.py 运行失败，退出状态码 1, PID: 10069
-2024年 07月 01日 星期一 23:09:26 CST: wenzhi.py 被停止, PID: 10073
-2024年 07月 01日 星期一 23:09:27 CST: tts.py 运行失败，退出状态码 1, PID: 10090
-2024年 07月 01日 星期一 23:09:29 CST: wenzhi.py 被停止, PID: 10121
-2024年 07月 01日 星期一 23:09:29 CST: tts.py 运行失败，退出状态码 1, PID: 10129
-2024年 07月 01日 星期一 23:09:31 CST: wenzhi.py 被停止, PID: 10178
-2024年 07月 01日 星期一 23:09:31 CST: tts.py 运行失败，退出状态码 1, PID: 10195
-2024年 07月 01日 星期一 23:09:33 CST: wenzhi.py 被停止, PID: 10208
-2024年 07月 01日 星期一 23:09:33 CST: tts.py 运行失败，退出状态码 1, PID: 10225
-2024年 07月 01日 星期一 23:09:35 CST: wenzhi.py 被停止, PID: 10229
-2024年 07月 01日 星期一 23:09:35 CST: tts.py 运行失败，退出状态码 1, PID: 10237
-2024年 07月 01日 星期一 23:09:37 CST: wenzhi.py 被停止, PID: 10241
-2024年 07月 01日 星期一 23:09:37 CST: tts.py 运行失败，退出状态码 1, PID: 10249
-2024年 07月 01日 星期一 23:09:39 CST: wenzhi.py 被停止, PID: 10289
-2024年 07月 01日 星期一 23:09:39 CST: tts.py 运行失败，退出状态码 1, PID: 10306
-2024年 07月 01日 星期一 23:09:41 CST: wenzhi.py 被停止, PID: 10310
-2024年 07月 01日 星期一 23:09:41 CST: tts.py 运行失败，退出状态码 1, PID: 10327
-2024年 07月 01日 星期一 23:09:43 CST: wenzhi.py 被停止, PID: 10340
-2024年 07月 01日 星期一 23:09:43 CST: tts.py 运行失败，退出状态码 1, PID: 10357
-2024年 07月 01日 星期一 23:09:45 CST: wenzhi.py 被停止, PID: 10379
-2024年 07月 01日 星期一 23:09:45 CST: tts.py 运行失败，退出状态码 1, PID: 10396
-2024年 07月 01日 星期一 23:13:21 CST: wenzhi.py 被停止, PID: 11686
-2024年 07月 01日 星期一 23:13:21 CST: tts.py 运行成功, PID: 11694
-2024年 07月 01日 星期一 23:13:23 CST: wenzhi.py 被停止, PID: 11698
-2024年 07月 01日 星期一 23:13:23 CST: tts.py 运行成功, PID: 11733
-2024年 07月 01日 星期一 23:14:23 CST: wenzhi.py 被停止, PID: 12133
-2024年 07月 01日 星期一 23:14:23 CST: tts.py 运行成功, PID: 12141
-2024年 07月 01日 星期一 23:14:25 CST: wenzhi.py 被停止, PID: 12154
-2024年 07月 01日 星期一 23:14:25 CST: tts.py 运行成功, PID: 12180
-2024年 07月 01日 星期一 23:16:13 CST: wenzhi.py 被停止, PID: 12910
-2024年 07月 01日 星期一 23:16:13 CST: tts.py 运行成功, PID: 12918
-2024年 07月 01日 星期一 23:16:15 CST: wenzhi.py 被停止, PID: 12940
-2024年 07月 01日 星期一 23:16:15 CST: tts.py 运行成功, PID: 12957
-2024年 07月 01日 星期一 23:16:17 CST: wenzhi.py 被停止, PID: 12961
-2024年 07月 01日 星期一 23:16:17 CST: tts.py 运行成功, PID: 12978
-2024年 07月 01日 星期一 23:16:19 CST: wenzhi.py 被停止, PID: 12982
-2024年 07月 01日 星期一 23:16:19 CST: tts.py 运行成功, PID: 12990
-2024年 07月 01日 星期一 23:16:21 CST: wenzhi.py 被停止, PID: 12994
-2024年 07月 01日 星期一 23:16:21 CST: tts.py 运行成功, PID: 13002
-2024年 07月 01日 星期一 23:16:23 CST: wenzhi.py 被停止, PID: 13024
-2024年 07月 01日 星期一 23:16:23 CST: tts.py 运行成功, PID: 13041
-2024年 07月 01日 星期一 23:16:25 CST: wenzhi.py 被停止, PID: 13054
-2024年 07月 01日 星期一 23:16:25 CST: tts.py 运行成功, PID: 13062
-2024年 07月 01日 星期一 23:16:27 CST: wenzhi.py 被停止, PID: 13075
-2024年 07月 01日 星期一 23:16:27 CST: tts.py 运行成功, PID: 13083
-2024年 07月 01日 星期一 23:17:10 CST: wenzhi.py 被停止, PID: 13388
-2024年 07月 01日 星期一 23:17:10 CST: tts.py 运行成功, PID: 13396
-2024年 07月 01日 星期一 23:17:12 CST: wenzhi.py 被停止, PID: 13400
-2024年 07月 01日 星期一 23:17:12 CST: tts.py 运行成功, PID: 13408
-2024年 07月 01日 星期一 23:17:23 CST: wenzhi.py 被停止, PID: 13502
-2024年 07月 01日 星期一 23:17:23 CST: tts.py 运行成功, PID: 13510
-2024年 07月 01日 星期一 23:17:25 CST: wenzhi.py 被停止, PID: 13514
-2024年 07月 01日 星期一 23:17:25 CST: tts.py 运行成功, PID: 13522
-2024年 07月 01日 星期一 23:17:27 CST: wenzhi.py 被停止, PID: 13526
-2024年 07月 01日 星期一 23:17:27 CST: tts.py 运行成功, PID: 13534
-2024年 07月 01日 星期一 23:17:59 CST: wenzhi.py 被停止, PID: 13762
-2024年 07月 01日 星期一 23:17:59 CST: tts.py 运行失败，退出状态码 1, PID: 13779
-2024年 07月 01日 星期一 23:18:01 CST: wenzhi.py 被停止, PID: 13783
-2024年 07月 01日 星期一 23:18:01 CST: tts.py 运行失败，退出状态码 1, PID: 13791
-2024年 07月 01日 星期一 23:18:03 CST: wenzhi.py 被停止, PID: 13795
-2024年 07月 01日 星期一 23:18:03 CST: tts.py 运行失败，退出状态码 1, PID: 13821
-2024年 07月 01日 星期一 23:18:05 CST: wenzhi.py 被停止, PID: 13825
-2024年 07月 01日 星期一 23:18:05 CST: tts.py 运行失败，退出状态码 1, PID: 13833
-2024年 07月 01日 星期一 23:18:07 CST: wenzhi.py 被停止, PID: 13837
-2024年 07月 01日 星期一 23:18:07 CST: tts.py 运行失败，退出状态码 1, PID: 13854
-2024年 07月 01日 星期一 23:18:09 CST: wenzhi.py 被停止, PID: 13867
-2024年 07月 01日 星期一 23:18:09 CST: tts.py 运行失败，退出状态码 1, PID: 13875
-2024年 07月 01日 星期一 23:18:11 CST: wenzhi.py 被停止, PID: 13879
-2024年 07月 01日 星期一 23:18:11 CST: tts.py 运行失败，退出状态码 1, PID: 13887
-2024年 07月 01日 星期一 23:18:13 CST: wenzhi.py 被停止, PID: 13891
-2024年 07月 01日 星期一 23:18:13 CST: tts.py 运行失败，退出状态码 1, PID: 13899
-2024年 07月 01日 星期一 23:18:15 CST: wenzhi.py 被停止, PID: 13903
-2024年 07月 01日 星期一 23:18:15 CST: tts.py 运行失败，退出状态码 1, PID: 13911
-2024年 07月 01日 星期一 23:18:17 CST: wenzhi.py 被停止, PID: 13915
-2024年 07月 01日 星期一 23:18:18 CST: tts.py 运行失败，退出状态码 1, PID: 13923
-2024年 07月 01日 星期一 23:18:20 CST: wenzhi.py 被停止, PID: 13927
-2024年 07月 01日 星期一 23:18:20 CST: tts.py 运行失败，退出状态码 1, PID: 13935
-2024年 07月 01日 星期一 23:18:22 CST: wenzhi.py 被停止, PID: 13939
-2024年 07月 01日 星期一 23:18:22 CST: tts.py 运行失败，退出状态码 1, PID: 13947
-2024年 07月 01日 星期一 23:18:24 CST: wenzhi.py 被停止, PID: 13960
-2024年 07月 01日 星期一 23:18:24 CST: tts.py 运行失败，退出状态码 1, PID: 13968
-2024年 07月 01日 星期一 23:18:26 CST: wenzhi.py 被停止, PID: 13972
-2024年 07月 01日 星期一 23:18:26 CST: tts.py 运行失败，退出状态码 1, PID: 13980
-2024年 07月 01日 星期一 23:18:28 CST: wenzhi.py 被停止, PID: 13984
-2024年 07月 01日 星期一 23:18:28 CST: tts.py 运行失败，退出状态码 1, PID: 13992
-2024年 07月 01日 星期一 23:18:30 CST: wenzhi.py 被停止, PID: 13996
-2024年 07月 01日 星期一 23:18:30 CST: tts.py 运行失败，退出状态码 1, PID: 14004
-2024年 07月 01日 星期一 23:18:32 CST: wenzhi.py 被停止, PID: 14017
-2024年 07月 01日 星期一 23:18:32 CST: tts.py 运行失败，退出状态码 1, PID: 14025
-2024年 07月 01日 星期一 23:18:34 CST: wenzhi.py 被停止, PID: 14029
-2024年 07月 01日 星期一 23:18:34 CST: tts.py 运行失败，退出状态码 1, PID: 14046
-2024年 07月 01日 星期一 23:18:36 CST: wenzhi.py 被停止, PID: 14050
-2024年 07月 01日 星期一 23:18:36 CST: tts.py 运行失败，退出状态码 1, PID: 14076
-2024年 07月 01日 星期一 23:18:38 CST: wenzhi.py 被停止, PID: 14080
-2024年 07月 01日 星期一 23:18:38 CST: tts.py 运行失败，退出状态码 1, PID: 14088
-2024年 07月 01日 星期一 23:18:40 CST: wenzhi.py 被停止, PID: 14092
-2024年 07月 01日 星期一 23:18:40 CST: tts.py 运行失败，退出状态码 1, PID: 14109
-2024年 07月 01日 星期一 23:18:42 CST: wenzhi.py 被停止, PID: 14113
-2024年 07月 01日 星期一 23:18:42 CST: tts.py 运行失败，退出状态码 1, PID: 14130
-2024年 07月 01日 星期一 23:18:44 CST: wenzhi.py 被停止, PID: 14134
-2024年 07月 01日 星期一 23:18:44 CST: tts.py 运行失败，退出状态码 1, PID: 14142
-2024年 07月 01日 星期一 23:18:46 CST: wenzhi.py 被停止, PID: 14146
-2024年 07月 01日 星期一 23:18:46 CST: tts.py 运行失败，退出状态码 1, PID: 14154
-2024年 07月 01日 星期一 23:18:48 CST: wenzhi.py 被停止, PID: 14167
-2024年 07月 01日 星期一 23:18:48 CST: tts.py 运行失败，退出状态码 1, PID: 14184
-2024年 07月 01日 星期一 23:18:50 CST: wenzhi.py 被停止, PID: 14197
-2024年 07月 01日 星期一 23:18:50 CST: tts.py 运行失败，退出状态码 1, PID: 14223
-2024年 07月 01日 星期一 23:18:52 CST: wenzhi.py 被停止, PID: 14227
-2024年 07月 01日 星期一 23:18:52 CST: tts.py 运行失败，退出状态码 1, PID: 14235
-2024年 07月 01日 星期一 23:18:54 CST: wenzhi.py 被停止, PID: 14248
-2024年 07月 01日 星期一 23:18:54 CST: tts.py 运行失败，退出状态码 1, PID: 14282
-2024年 07月 01日 星期一 23:18:56 CST: wenzhi.py 被停止, PID: 14296
-2024年 07月 01日 星期一 23:18:56 CST: tts.py 运行失败，退出状态码 1, PID: 14313
-2024年 07月 01日 星期一 23:18:58 CST: wenzhi.py 被停止, PID: 14334
-2024年 07月 01日 星期一 23:18:58 CST: tts.py 运行失败，退出状态码 1, PID: 14361
-2024年 07月 01日 星期一 23:19:00 CST: wenzhi.py 被停止, PID: 14374
-2024年 07月 01日 星期一 23:19:00 CST: tts.py 运行失败，退出状态码 1, PID: 14391
-2024年 07月 01日 星期一 23:19:02 CST: wenzhi.py 被停止, PID: 14395
-2024年 07月 01日 星期一 23:19:02 CST: tts.py 运行失败，退出状态码 1, PID: 14403
-2024年 07月 01日 星期一 23:19:04 CST: wenzhi.py 被停止, PID: 14425
-2024年 07月 01日 星期一 23:19:04 CST: tts.py 运行失败，退出状态码 1, PID: 14433
-2024年 07月 01日 星期一 23:19:06 CST: wenzhi.py 被停止, PID: 14446
-2024年 07月 01日 星期一 23:19:07 CST: tts.py 运行失败，退出状态码 1, PID: 14463
-2024年 07月 01日 星期一 23:19:09 CST: wenzhi.py 被停止, PID: 14485
-2024年 07月 01日 星期一 23:19:09 CST: tts.py 运行失败，退出状态码 1, PID: 14502
-2024年 07月 01日 星期一 23:19:11 CST: wenzhi.py 被停止, PID: 14515
-2024年 07月 01日 星期一 23:19:11 CST: tts.py 运行失败，退出状态码 1, PID: 14532
-2024年 07月 01日 星期一 23:19:13 CST: wenzhi.py 被停止, PID: 14536
-2024年 07月 01日 星期一 23:19:13 CST: tts.py 运行失败，退出状态码 1, PID: 14544
-2024年 07月 01日 星期一 23:19:15 CST: wenzhi.py 被停止, PID: 14548
-2024年 07月 01日 星期一 23:19:15 CST: tts.py 运行失败，退出状态码 1, PID: 14574
-2024年 07月 01日 星期一 23:19:17 CST: wenzhi.py 被停止, PID: 14578
-2024年 07月 01日 星期一 23:19:17 CST: tts.py 运行失败，退出状态码 1, PID: 14586
-2024年 07月 01日 星期一 23:19:19 CST: wenzhi.py 被停止, PID: 14599
-2024年 07月 01日 星期一 23:19:19 CST: tts.py 运行失败，退出状态码 1, PID: 14616
-2024年 07月 01日 星期一 23:19:21 CST: wenzhi.py 被停止, PID: 14620
-2024年 07月 01日 星期一 23:19:21 CST: tts.py 运行失败，退出状态码 1, PID: 14646
-2024年 07月 01日 星期一 23:19:23 CST: wenzhi.py 被停止, PID: 14650
-2024年 07月 01日 星期一 23:19:23 CST: tts.py 运行失败，退出状态码 1, PID: 14658
-2024年 07月 01日 星期一 23:19:25 CST: wenzhi.py 被停止, PID: 14671
-2024年 07月 01日 星期一 23:19:25 CST: tts.py 运行失败，退出状态码 1, PID: 14679
-2024年 07月 01日 星期一 23:19:27 CST: wenzhi.py 被停止, PID: 14692
-2024年 07月 01日 星期一 23:19:27 CST: tts.py 运行失败，退出状态码 1, PID: 14709
-2024年 07月 01日 星期一 23:19:29 CST: wenzhi.py 被停止, PID: 14713
-2024年 07月 01日 星期一 23:19:29 CST: tts.py 运行失败，退出状态码 1, PID: 14721
-2024年 07月 01日 星期一 23:19:31 CST: wenzhi.py 被停止, PID: 14735
-2024年 07月 01日 星期一 23:19:31 CST: tts.py 运行失败，退出状态码 1, PID: 14743
-2024年 07月 01日 星期一 23:19:33 CST: wenzhi.py 被停止, PID: 14756
-2024年 07月 01日 星期一 23:19:33 CST: tts.py 运行失败，退出状态码 1, PID: 14773
-2024年 07月 01日 星期一 23:19:35 CST: wenzhi.py 被停止, PID: 14777
-2024年 07月 01日 星期一 23:19:35 CST: tts.py 运行失败，退出状态码 1, PID: 14785
-2024年 07月 01日 星期一 23:19:37 CST: wenzhi.py 被停止, PID: 14798
-2024年 07月 01日 星期一 23:19:37 CST: tts.py 运行失败，退出状态码 1, PID: 14815
-2024年 07月 01日 星期一 23:19:39 CST: wenzhi.py 被停止, PID: 14819
-2024年 07月 01日 星期一 23:19:39 CST: tts.py 运行失败，退出状态码 1, PID: 14836
-2024年 07月 01日 星期一 23:19:41 CST: wenzhi.py 被停止, PID: 14840
-2024年 07月 01日 星期一 23:19:41 CST: tts.py 运行失败，退出状态码 1, PID: 14848
-2024年 07月 01日 星期一 23:19:43 CST: wenzhi.py 被停止, PID: 14852
-2024年 07月 01日 星期一 23:19:43 CST: tts.py 运行失败，退出状态码 1, PID: 14860
-2024年 07月 01日 星期一 23:19:45 CST: wenzhi.py 被停止, PID: 14864
-2024年 07月 01日 星期一 23:19:45 CST: tts.py 运行失败，退出状态码 1, PID: 14872
-2024年 07月 01日 星期一 23:19:47 CST: wenzhi.py 被停止, PID: 14885
-2024年 07月 01日 星期一 23:19:47 CST: tts.py 运行失败，退出状态码 1, PID: 14893
-2024年 07月 01日 星期一 23:19:49 CST: wenzhi.py 被停止, PID: 14897
-2024年 07月 01日 星期一 23:19:49 CST: tts.py 运行失败，退出状态码 1, PID: 14905
-2024年 07月 01日 星期一 23:19:51 CST: wenzhi.py 被停止, PID: 14909
-2024年 07月 01日 星期一 23:19:51 CST: tts.py 运行失败，退出状态码 1, PID: 14926
-2024年 07月 01日 星期一 23:19:53 CST: wenzhi.py 被停止, PID: 14948
-2024年 07月 01日 星期一 23:19:53 CST: tts.py 运行失败，退出状态码 1, PID: 14956
-2024年 07月 01日 星期一 23:19:55 CST: wenzhi.py 被停止, PID: 14969
-2024年 07月 01日 星期一 23:19:55 CST: tts.py 运行失败，退出状态码 1, PID: 14977
-2024年 07月 01日 星期一 23:19:58 CST: wenzhi.py 被停止, PID: 14981
-2024年 07月 01日 星期一 23:19:58 CST: tts.py 运行失败，退出状态码 1, PID: 15007
-2024年 07月 01日 星期一 23:20:00 CST: wenzhi.py 被停止, PID: 15020
-2024年 07月 01日 星期一 23:20:00 CST: tts.py 运行失败，退出状态码 1, PID: 15037
-2024年 07月 01日 星期一 23:20:02 CST: wenzhi.py 被停止, PID: 15041
-2024年 07月 01日 星期一 23:20:02 CST: tts.py 运行失败，退出状态码 1, PID: 15049
-2024年 07月 01日 星期一 23:20:04 CST: wenzhi.py 被停止, PID: 15053
-2024年 07月 01日 星期一 23:20:04 CST: tts.py 运行失败，退出状态码 1, PID: 15061
-2024年 07月 01日 星期一 23:20:06 CST: wenzhi.py 被停止, PID: 15067
-2024年 07月 01日 星期一 23:20:06 CST: tts.py 运行失败，退出状态码 1, PID: 15075
-2024年 07月 01日 星期一 23:20:08 CST: wenzhi.py 被停止, PID: 15079
-2024年 07月 01日 星期一 23:20:08 CST: tts.py 运行失败，退出状态码 1, PID: 15105

src/Guide_stick_system/image_recognition/manage/repted.sh

@@ -1,40 +0,0 @@
-#!/bin/bash  
-  
-# repted.sh  
-while true; do  
-    # 在后台运行itt目录中的wenzhi.py脚本并获取其PID  
-    python3 itt/wenzhi.py &  
-    wenzhi_pid=$!  
-  
-    # 等待一段时间，让wenzhi.py开始执行  
-    sleep 1
-  
-    # 检查wenzhi.py是否还在运行  
-    if kill -0 $wenzhi_pid 2>/dev/null; then  
-        # wenzhi.py仍在运行，尝试停止它  
-        kill $wenzhi_pid  
-        wait $wenzhi_pid  
-        echo "$(date): wenzhi.py 被停止, PID: $wenzhi_pid" >> log.txt  
-    else  
-        # wenzhi.py已经停止，记录日志  
-        echo "$(date): wenzhi.py 已经停止, PID: $wenzhi_pid" >> log.txt  
-    fi  
-  
-    # 在后台运行tts目录中的tts.py脚本并获取其PID  
-    python3 tts/tts.py &  
-    tts_pid=$!  
-  
-    # 等待tts.py脚本结束以获取其退出状态  
-    wait $tts_pid  
-    tts_exit_status=$?  
-  
-    # 检查tts.py脚本是否成功运行  
-    if [ $tts_exit_status -eq 0 ]; then  
-        echo "$(date): tts.py 运行成功, PID: $tts_pid" >> log.txt  
-    else  
-        echo "$(date): tts.py 运行失败，退出状态码 $tts_exit_status, PID: $tts_pid" >> log.txt  
-    fi  
-  
-    # 等待一段时间后再重复这个过程  
-    sleep 1
-done

src/Guide_stick_system/image_recognition/manage/tts/trans.py

@@ -1,69 +0,0 @@
-import numpy as np
-from sklearn.model_selection import train_test_split
-from sklearn.preprocessing import OneHotEncoder
-from keras.models import Sequential
-from keras.layers import Dense, Embedding, Flatten
-from keras.preprocessing.sequence import pad_sequences
-from keras.utils import to_categorical
-import re
-
-# 文本清洗函数
-def clean_text(text):
-    # 使用正则表达式去除非标准字符
-    cleaned_text = re.sub(r'[^a-zA-Z0-9\s]', '', text.lower())
-    return cleaned_text
-
-# 读取文本文件
-def read_text_file(file_path):
-    with open(file_path, 'r', encoding='utf-8') as f:
-        return f.read()
-
-# 数据准备
-def prepare_data(text, max_sequence_length=10):
-    words = set(clean_text(text).split())
-    word2index = {word: i for i, word in enumerate(words, 1)}
-    index2word = {i: word for word, i in word2index.items()}
-    
-    sentences = text.split('. ')
-    X, y = [], []
-    for sentence in sentences:
-        cleaned_sentence = clean_text(sentence)
-        tokens = [word2index[word] for word in cleaned_sentence.split() if word in word2index]
-        for i in range(1, len(tokens)):
-            X.append(tokens[:-i])
-            y.append(tokens[i])
-    
-    # 使用pad_sequences处理变长序列
-    X = pad_sequences(X, maxlen=max_sequence_length, padding='pre', truncating='post')
-    y = to_categorical(np.array(y), num_classes=len(word2index))
-    return X, y, word2index, index2word
-
-# 读取文本文件并准备数据
-text = read_text_file('input.txt')  # 假设输入文件名为input.txt
-X, y, word2index, index2word = prepare_data(text)
-
-# 划分训练集和测试集
-X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
-
-# 构建NNLM模型（这里使用Embedding层来捕获词汇之间的相似性）
-model = Sequential()
-model.add(Embedding(input_dim=len(word2index) + 1, output_dim=32, input_length=max_sequence_length))
-model.add(Flatten())
-model.add(Dense(128, activation='relu'))
-model.add(Dense(len(word2index), activation='softmax'))
-
-model.compile(optimizer='adam', loss='categorical_crossentropy')
-
-# 训练模型
-model.fit(np.array([x for x in X_train]), y_train, epochs=10, batch_size=32)
-
-# 预测
-predictions = model.predict([x for x in X_test])
-predicted_words = [np.argmax(pred) for pred in predictions]
-predicted_sentences = [' '.join(index2word[word] for word in [sentence[0]] + [predicted_words[i] for i, _ in enumerate(sentence[1:]) if i < len(predicted_words)]) 
-                       for sentence in X_test]
-
-# 将预测结果写入文本文件
-with open('output.txt', 'w', encoding='utf-8') as f:
-    for sentence in predicted_sentences:
-        f.write(sentence + '\n')

src/Guide_stick_system/image_recognition/manage/tts/tts.py

@@ -1,76 +0,0 @@
-import subprocess
-import os  
-import datetime
-from datetime import timedelta
-import re
-  
-def speak_text_from_file(file_path, voice='zh', speed=150, pitch=50, output_file=None):  
-    """  
-    从文件中读取文本并使用espeak将其转换为语音。  
-  
-    :param file_path: 文本文件的路径  
-    :param voice: 使用的声音（例如 'zh' 用于中文）  
-    :param speed: 语速（默认为 150）  
-    :param pitch: 音调（默认为 50）  
-    :param output_file: 如果指定，将语音输出保存为WAV文件  
-    """  
-    with open(file_path, 'r', encoding='utf-8') as file:  
-        text = file.read()  
-  
-    # 构建espeak命令  
-    cmd = ['espeak', '-v', voice, '-s', str(speed), '-p', str(pitch)]  
-    if output_file:  
-        cmd.extend(['-w', output_file])  
-    else:  
-        # 如果没有指定输出文件，则直接播放语音  
-        pass  # 这里可以添加其他选项，如音量调整等  
-  
-    # 使用stdin将文本传递给espeak  
-    try:  
-        proc = subprocess.Popen(cmd, stdin=subprocess.PIPE, stderr=subprocess.PIPE, encoding='utf-8')  
-        proc.communicate(input=text)  
-  
-        if proc.returncode != 0:  
-            # 获取并打印错误信息  
-            error_message = proc.stderr.read()  
-            print(f"Error executing espeak: {error_message}")  
-    except Exception as e:  
-        print(f"An error occurred: {e}")  
-
-# 获取当前文件夹路径  
-current_dir = os.getcwd()  
-  
-# 获取父文件夹路径  
-parent_dir = os.path.dirname(current_dir)
-
-specific_folder_name = "txt"
-
-specific_folder_path = os.path.join(current_dir, specific_folder_name)
-  
-# 列出当前文件夹中的所有txt文件  
-txt_files = [f for f in os.listdir(specific_folder_path) if f.endswith('.txt')]  
-  
-# 提取文件名中的数字，并找出最大的数字  
-max_number = -1  
-max_file = None  
-for txt_file in txt_files:  
-    # 使用正则表达式提取文件名中的数字  
-    match = re.search(r'^(\d+)\.txt$', txt_file)  
-    if match:  
-        file_number = int(match.group(1))  
-        if file_number > max_number:  
-            max_number = file_number  
-            max_file = txt_file  
-  
-# 检查是否找到了数字命名最大的txt文件  
-if max_file:  
-    file_path = os.path.join(current_dir, max_file)  
-    # 调用函数来朗读文本文件  
-    speak_text_from_file(file_path, voice='zh', speed=140, pitch=55)  
-  
-    # 如果你想要将语音保存为WAV文件，可以这样做：  
-    output_wav_file = 'output/output.wav'  # WAV文件的输出路径  
-    speak_text_from_file(file_path, voice='zh', speed=140, pitch=55, output_file=output_wav_file)  
-else:  
-    print("没有找到数字命名最大的txt文件。")
-    print(specific_folder_path)

src/Guide_stick_system/image_recognition/manage/txt/20240701231806.txt

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