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# Objects365 dataset https://www.objects365.org/ | ||
# Train command: python train.py --data objects365.yaml | ||
# Default dataset location is next to YOLOv5: | ||
# /parent_folder | ||
# /datasets/objects365 | ||
# /yolov5 | ||
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# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/] | ||
train: ../datasets/objects365/images/train # 1.7 Million images | ||
val: ../datasets/objects365/images/val # 5570 images | ||
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# number of classes | ||
nc: 365 | ||
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# class names | ||
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', 'Moniter/TV', | ||
'Backpack', 'Umbrella', 'Traffic Light', 'Speaker', 'Watch', 'Tie', 'Trash bin Can', 'Slippers', 'Bicycle', | ||
'Stool', 'Barrel/bucket', 'Van', 'Couch', 'Sandals', 'Bakset', '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', 'Ballon', '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', | ||
'Billards', 'Converter', 'Bathtub', 'Wheelchair', 'Golf Club', 'Briefcase', 'Cucumber', 'Cigar/Cigarette', | ||
'Paint Brush', 'Pear', 'Heavy Truck', 'Hamburger', 'Extractor', 'Extention 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', 'Hotair ballon', | ||
'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', 'Mushroon', 'Screwdriver', 'Soap', 'Recorder', | ||
'Bear', 'Eggplant', 'Board Eraser', 'Coconut', 'Tape Measur/ Ruler', 'Pig', 'Showerhead', 'Globe', 'Chips', | ||
'Steak', 'Crosswalk Sign', 'Stapler', 'Campel', 'Formula 1', 'Pomegranate', 'Dishwasher', 'Crab', | ||
'Hoverboard', 'Meat ball', 'Rice Cooker', 'Tuba', 'Calculator', 'Papaya', 'Antelope', 'Parrot', 'Seal', | ||
'Buttefly', '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 Teniis paddle', 'Cosmetics Brush/Eyeliner Pencil', | ||
'Chainsaw', 'Eraser', 'Lobster', 'Durian', 'Okra', 'Lipstick', 'Cosmetics Mirror', 'Curling', 'Table Tennis' ] | ||
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import cv2 | ||
from pycocotools.coco import COCO | ||
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# Create the following folder structure: | ||
# datasets/object365/images/train, datasets/object365/images/val, datasets/labels/train, dataset/labels/val | ||
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# Download Object 365 from the Object 365 website And unpack all images in datasets/object365/images/train, | ||
# Put The script and zhiyuan_objv2_train.json file in dataset/object365 | ||
# Execute the script in datasets/object365 path | ||
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coco = COCO("zhiyuan_objv2_train.json") | ||
cats = coco.loadCats(coco.getCatIds()) | ||
nms = [cat["name"] for cat in cats] | ||
print("COCO categories: \n{}\n".format(" ".join(nms))) | ||
cash = set() | ||
for categoryId, cat in enumerate(nms): | ||
catIds = coco.getCatIds(catNms=[cat]) | ||
imgIds = coco.getImgIds(catIds=catIds) | ||
print(cat) | ||
# Create a subfolder in this directory called "labels". This is where the annotations will be saved in YOLO format | ||
for im in coco.loadImgs(imgIds): | ||
width, height = im["width"], im["height"] | ||
path = im["file_name"].split("/")[-1] # image filename | ||
try: | ||
# Test image for missing images | ||
if path not in cash: | ||
img = cv2.cvtColor(cv2.imread(f"images/train/{path}"), cv2.COLOR_BGR2RGB) | ||
cash.add(path) | ||
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with open("labels/train/" + path.replace(".jpg", ".txt"), "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) | ||
x, y = x + w / 2, y + h / 2 # xy to center | ||
file.write(f"{categoryId} {x / width:.5f} {y / height:.5f} {w / width:.5f} {h / height:.5f}\n") | ||
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except Exception as e: | ||
print(e) |
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