修复测试snowboydecoder.py中的模拟对象创建问题

# Conflicts:
#	doc/软件开发项目的个人自评报告-智能导盲杖.xlsx

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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+GNU GENERAL PUBLIC LICENSE
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+PURPOSE.  THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
+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

@@ -0,0 +1,67 @@
+# 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

@@ -0,0 +1,53 @@
+# 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

@@ -0,0 +1,112 @@
+# 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

@@ -0,0 +1,52 @@
+# 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

@@ -0,0 +1,80 @@
+# 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

@@ -0,0 +1,61 @@
+# 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

@@ -0,0 +1,44 @@
+# 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

@@ -0,0 +1,30 @@
+# 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

@@ -0,0 +1,13 @@
+# 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

@@ -0,0 +1,39 @@
+# 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

@@ -0,0 +1,31 @@
+# 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

@@ -0,0 +1,34 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# Hyperparameters for high-augmentation COCO training from scratch
+# python train.py --batch 32 --cfg yolov5m6.yaml --weights '' --data coco.yaml --img 1280 --epochs 300
+# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials
+
+lr0: 0.01  # initial learning rate (SGD=1E-2, Adam=1E-3)
+lrf: 0.2  # 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

@@ -0,0 +1,34 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# Hyperparameters for low-augmentation COCO training from scratch
+# python train.py --batch 64 --cfg yolov5n6.yaml --weights '' --data coco.yaml --img 640 --epochs 300 --linear
+# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials
+
+lr0: 0.01  # initial learning rate (SGD=1E-2, Adam=1E-3)
+lrf: 0.01  # final OneCycleLR learning rate (lr0 * lrf)
+momentum: 0.937  # SGD momentum/Adam beta1
+weight_decay: 0.0005  # optimizer weight decay 5e-4
+warmup_epochs: 3.0  # warmup epochs (fractions ok)
+warmup_momentum: 0.8  # warmup initial momentum
+warmup_bias_lr: 0.1  # warmup initial bias lr
+box: 0.05  # box loss gain
+cls: 0.5  # cls loss gain
+cls_pw: 1.0  # cls BCELoss positive_weight
+obj: 1.0  # obj loss gain (scale with pixels)
+obj_pw: 1.0  # obj BCELoss positive_weight
+iou_t: 0.20  # IoU training threshold
+anchor_t: 4.0  # anchor-multiple threshold
+# anchors: 3  # anchors per output layer (0 to ignore)
+fl_gamma: 0.0  # focal loss gamma (efficientDet default gamma=1.5)
+hsv_h: 0.015  # image HSV-Hue augmentation (fraction)
+hsv_s: 0.7  # image HSV-Saturation augmentation (fraction)
+hsv_v: 0.4  # image HSV-Value augmentation (fraction)
+degrees: 0.0  # image rotation (+/- deg)
+translate: 0.1  # image translation (+/- fraction)
+scale: 0.5  # image scale (+/- gain)
+shear: 0.0  # image shear (+/- deg)
+perspective: 0.0  # image perspective (+/- fraction), range 0-0.001
+flipud: 0.0  # image flip up-down (probability)
+fliplr: 0.5  # image flip left-right (probability)
+mosaic: 1.0  # image mosaic (probability)
+mixup: 0.0  # image mixup (probability)
+copy_paste: 0.0  # segment copy-paste (probability)

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

@@ -0,0 +1,34 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# Hyperparameters for medium-augmentation COCO training from scratch
+# python train.py --batch 32 --cfg yolov5m6.yaml --weights '' --data coco.yaml --img 1280 --epochs 300
+# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials
+
+lr0: 0.01  # initial learning rate (SGD=1E-2, Adam=1E-3)
+lrf: 0.1  # final OneCycleLR learning rate (lr0 * lrf)
+momentum: 0.937  # SGD momentum/Adam beta1
+weight_decay: 0.0005  # optimizer weight decay 5e-4
+warmup_epochs: 3.0  # warmup epochs (fractions ok)
+warmup_momentum: 0.8  # warmup initial momentum
+warmup_bias_lr: 0.1  # warmup initial bias lr
+box: 0.05  # box loss gain
+cls: 0.3  # cls loss gain
+cls_pw: 1.0  # cls BCELoss positive_weight
+obj: 0.7  # obj loss gain (scale with pixels)
+obj_pw: 1.0  # obj BCELoss positive_weight
+iou_t: 0.20  # IoU training threshold
+anchor_t: 4.0  # anchor-multiple threshold
+# anchors: 3  # anchors per output layer (0 to ignore)
+fl_gamma: 0.0  # focal loss gamma (efficientDet default gamma=1.5)
+hsv_h: 0.015  # image HSV-Hue augmentation (fraction)
+hsv_s: 0.7  # image HSV-Saturation augmentation (fraction)
+hsv_v: 0.4  # image HSV-Value augmentation (fraction)
+degrees: 0.0  # image rotation (+/- deg)
+translate: 0.1  # image translation (+/- fraction)
+scale: 0.9  # image scale (+/- gain)
+shear: 0.0  # image shear (+/- deg)
+perspective: 0.0  # image perspective (+/- fraction), range 0-0.001
+flipud: 0.0  # image flip up-down (probability)
+fliplr: 0.5  # image flip left-right (probability)
+mosaic: 1.0  # image mosaic (probability)
+mixup: 0.1  # image mixup (probability)
+copy_paste: 0.0  # segment copy-paste (probability)

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

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

@@ -0,0 +1,12 @@
+# 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

@@ -0,0 +1,20 @@
+#!/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

@@ -0,0 +1,27 @@
+#!/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

@@ -0,0 +1,17 @@
+#!/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

@@ -0,0 +1,102 @@
+# 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

@@ -0,0 +1,236 @@
+# -*- 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

@@ -0,0 +1,246 @@
+# 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

@@ -0,0 +1,61 @@
+# 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

@@ -0,0 +1,292 @@
+<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

@@ -0,0 +1,369 @@
+# 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

@@ -0,0 +1,142 @@
+# 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

@@ -0,0 +1,2 @@
+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

@@ -0,0 +1,98 @@
+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

@@ -0,0 +1,242 @@
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+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

@@ -0,0 +1,40 @@
+#!/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

@@ -0,0 +1,69 @@
+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

@@ -0,0 +1,76 @@
+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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+X is
+

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+
+X is
+

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+X is
+

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+
+X is
+

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+
+X is
+

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+
+X is
+

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+
+X is
+

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+
+X is
+

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+
+X is
+

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+
+X is
+

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+X is
+

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+
+X is
+

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+
+X is
+

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+
+X is
+

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+
+X is
+

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+
+X is
+

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+
+X is
+

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+
+X is
+

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+
+X is
+

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+
+X is
+

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+X is
+

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+X is
+

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+X is
+

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+X is
+

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+X is
+

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+

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+

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+

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+

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+