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| from __future__ import absolute_import |
| from __future__ import division |
| from __future__ import print_function |
|
|
| import os |
|
|
| from ppdet.data.source.voc import pascalvoc_label |
| from ppdet.data.source.widerface import widerface_label |
| from ppdet.utils.logger import setup_logger |
| logger = setup_logger(__name__) |
|
|
| __all__ = ['get_categories'] |
|
|
|
|
| def get_categories(metric_type, anno_file=None, arch=None): |
| """ |
| Get class id to category id map and category id |
| to category name map from annotation file. |
| |
| Args: |
| metric_type (str): metric type, currently support 'coco', 'voc', 'oid' |
| and 'widerface'. |
| anno_file (str): annotation file path |
| """ |
| if arch == 'keypoint_arch': |
| return (None, {'id': 'keypoint'}) |
|
|
| if anno_file == None or (not os.path.isfile(anno_file)): |
| logger.warning( |
| "anno_file '{}' is None or not set or not exist, " |
| "please recheck TrainDataset/EvalDataset/TestDataset.anno_path, " |
| "otherwise the default categories will be used by metric_type.". |
| format(anno_file)) |
|
|
| if metric_type.lower() == 'coco' or metric_type.lower( |
| ) == 'rbox' or metric_type.lower() == 'snipercoco': |
| if anno_file and os.path.isfile(anno_file): |
| if anno_file.endswith('json'): |
| |
| from pycocotools.coco import COCO |
| coco = COCO(anno_file) |
| cats = coco.loadCats(coco.getCatIds()) |
|
|
| clsid2catid = {i: cat['id'] for i, cat in enumerate(cats)} |
| catid2name = {cat['id']: cat['name'] for cat in cats} |
|
|
| elif anno_file.endswith('txt'): |
| cats = [] |
| with open(anno_file) as f: |
| for line in f.readlines(): |
| cats.append(line.strip()) |
| if cats[0] == 'background': cats = cats[1:] |
|
|
| clsid2catid = {i: i for i in range(len(cats))} |
| catid2name = {i: name for i, name in enumerate(cats)} |
|
|
| else: |
| raise ValueError("anno_file {} should be json or txt.".format( |
| anno_file)) |
| return clsid2catid, catid2name |
|
|
| |
| else: |
| if metric_type.lower() == 'rbox': |
| logger.warning( |
| "metric_type: {}, load default categories of DOTA.".format( |
| metric_type)) |
| return _dota_category() |
| logger.warning("metric_type: {}, load default categories of COCO.". |
| format(metric_type)) |
| return _coco17_category() |
|
|
| elif metric_type.lower() == 'voc': |
| if anno_file and os.path.isfile(anno_file): |
| cats = [] |
| with open(anno_file) as f: |
| for line in f.readlines(): |
| cats.append(line.strip()) |
|
|
| if cats[0] == 'background': |
| cats = cats[1:] |
|
|
| clsid2catid = {i: i for i in range(len(cats))} |
| catid2name = {i: name for i, name in enumerate(cats)} |
|
|
| return clsid2catid, catid2name |
|
|
| |
| |
| else: |
| logger.warning("metric_type: {}, load default categories of VOC.". |
| format(metric_type)) |
| return _vocall_category() |
|
|
| elif metric_type.lower() == 'oid': |
| if anno_file and os.path.isfile(anno_file): |
| logger.warning("only default categories support for OID19") |
| return _oid19_category() |
|
|
| elif metric_type.lower() == 'widerface': |
| return _widerface_category() |
|
|
| elif metric_type.lower() == 'keypointtopdowncocoeval' or metric_type.lower( |
| ) == 'keypointtopdownmpiieval': |
| return (None, {'id': 'keypoint'}) |
|
|
| elif metric_type.lower() == 'pose3deval': |
| return (None, {'id': 'pose3d'}) |
|
|
| elif metric_type.lower() in ['mot', 'motdet', 'reid']: |
| if anno_file and os.path.isfile(anno_file): |
| cats = [] |
| with open(anno_file) as f: |
| for line in f.readlines(): |
| cats.append(line.strip()) |
| if cats[0] == 'background': |
| cats = cats[1:] |
| clsid2catid = {i: i for i in range(len(cats))} |
| catid2name = {i: name for i, name in enumerate(cats)} |
| return clsid2catid, catid2name |
| |
| else: |
| logger.warning( |
| "metric_type: {}, load default categories of pedestrian MOT.". |
| format(metric_type)) |
| return _mot_category(category='pedestrian') |
|
|
| elif metric_type.lower() in ['kitti', 'bdd100kmot']: |
| return _mot_category(category='vehicle') |
|
|
| elif metric_type.lower() in ['mcmot']: |
| if anno_file and os.path.isfile(anno_file): |
| cats = [] |
| with open(anno_file) as f: |
| for line in f.readlines(): |
| cats.append(line.strip()) |
| if cats[0] == 'background': |
| cats = cats[1:] |
| clsid2catid = {i: i for i in range(len(cats))} |
| catid2name = {i: name for i, name in enumerate(cats)} |
| return clsid2catid, catid2name |
| |
| else: |
| logger.warning( |
| "metric_type: {}, load default categories of VisDrone.".format( |
| metric_type)) |
| return _visdrone_category() |
|
|
| else: |
| raise ValueError("unknown metric type {}".format(metric_type)) |
|
|
|
|
| def _mot_category(category='pedestrian'): |
| """ |
| Get class id to category id map and category id |
| to category name map of mot dataset |
| """ |
| label_map = {category: 0} |
| label_map = sorted(label_map.items(), key=lambda x: x[1]) |
| cats = [l[0] for l in label_map] |
|
|
| clsid2catid = {i: i for i in range(len(cats))} |
| catid2name = {i: name for i, name in enumerate(cats)} |
|
|
| return clsid2catid, catid2name |
|
|
|
|
| def _coco17_category(): |
| """ |
| Get class id to category id map and category id |
| to category name map of COCO2017 dataset |
| |
| """ |
| clsid2catid = { |
| 1: 1, |
| 2: 2, |
| 3: 3, |
| 4: 4, |
| 5: 5, |
| 6: 6, |
| 7: 7, |
| 8: 8, |
| 9: 9, |
| 10: 10, |
| 11: 11, |
| 12: 13, |
| 13: 14, |
| 14: 15, |
| 15: 16, |
| 16: 17, |
| 17: 18, |
| 18: 19, |
| 19: 20, |
| 20: 21, |
| 21: 22, |
| 22: 23, |
| 23: 24, |
| 24: 25, |
| 25: 27, |
| 26: 28, |
| 27: 31, |
| 28: 32, |
| 29: 33, |
| 30: 34, |
| 31: 35, |
| 32: 36, |
| 33: 37, |
| 34: 38, |
| 35: 39, |
| 36: 40, |
| 37: 41, |
| 38: 42, |
| 39: 43, |
| 40: 44, |
| 41: 46, |
| 42: 47, |
| 43: 48, |
| 44: 49, |
| 45: 50, |
| 46: 51, |
| 47: 52, |
| 48: 53, |
| 49: 54, |
| 50: 55, |
| 51: 56, |
| 52: 57, |
| 53: 58, |
| 54: 59, |
| 55: 60, |
| 56: 61, |
| 57: 62, |
| 58: 63, |
| 59: 64, |
| 60: 65, |
| 61: 67, |
| 62: 70, |
| 63: 72, |
| 64: 73, |
| 65: 74, |
| 66: 75, |
| 67: 76, |
| 68: 77, |
| 69: 78, |
| 70: 79, |
| 71: 80, |
| 72: 81, |
| 73: 82, |
| 74: 84, |
| 75: 85, |
| 76: 86, |
| 77: 87, |
| 78: 88, |
| 79: 89, |
| 80: 90 |
| } |
|
|
| catid2name = { |
| 0: 'background', |
| 1: 'person', |
| 2: 'bicycle', |
| 3: 'car', |
| 4: 'motorcycle', |
| 5: 'airplane', |
| 6: 'bus', |
| 7: 'train', |
| 8: 'truck', |
| 9: 'boat', |
| 10: 'traffic light', |
| 11: 'fire hydrant', |
| 13: 'stop sign', |
| 14: 'parking meter', |
| 15: 'bench', |
| 16: 'bird', |
| 17: 'cat', |
| 18: 'dog', |
| 19: 'horse', |
| 20: 'sheep', |
| 21: 'cow', |
| 22: 'elephant', |
| 23: 'bear', |
| 24: 'zebra', |
| 25: 'giraffe', |
| 27: 'backpack', |
| 28: 'umbrella', |
| 31: 'handbag', |
| 32: 'tie', |
| 33: 'suitcase', |
| 34: 'frisbee', |
| 35: 'skis', |
| 36: 'snowboard', |
| 37: 'sports ball', |
| 38: 'kite', |
| 39: 'baseball bat', |
| 40: 'baseball glove', |
| 41: 'skateboard', |
| 42: 'surfboard', |
| 43: 'tennis racket', |
| 44: 'bottle', |
| 46: 'wine glass', |
| 47: 'cup', |
| 48: 'fork', |
| 49: 'knife', |
| 50: 'spoon', |
| 51: 'bowl', |
| 52: 'banana', |
| 53: 'apple', |
| 54: 'sandwich', |
| 55: 'orange', |
| 56: 'broccoli', |
| 57: 'carrot', |
| 58: 'hot dog', |
| 59: 'pizza', |
| 60: 'donut', |
| 61: 'cake', |
| 62: 'chair', |
| 63: 'couch', |
| 64: 'potted plant', |
| 65: 'bed', |
| 67: 'dining table', |
| 70: 'toilet', |
| 72: 'tv', |
| 73: 'laptop', |
| 74: 'mouse', |
| 75: 'remote', |
| 76: 'keyboard', |
| 77: 'cell phone', |
| 78: 'microwave', |
| 79: 'oven', |
| 80: 'toaster', |
| 81: 'sink', |
| 82: 'refrigerator', |
| 84: 'book', |
| 85: 'clock', |
| 86: 'vase', |
| 87: 'scissors', |
| 88: 'teddy bear', |
| 89: 'hair drier', |
| 90: 'toothbrush' |
| } |
|
|
| clsid2catid = {k - 1: v for k, v in clsid2catid.items()} |
| catid2name.pop(0) |
|
|
| return clsid2catid, catid2name |
|
|
|
|
| def _dota_category(): |
| """ |
| Get class id to category id map and category id |
| to category name map of dota dataset |
| """ |
| catid2name = { |
| 0: 'background', |
| 1: 'plane', |
| 2: 'baseball-diamond', |
| 3: 'bridge', |
| 4: 'ground-track-field', |
| 5: 'small-vehicle', |
| 6: 'large-vehicle', |
| 7: 'ship', |
| 8: 'tennis-court', |
| 9: 'basketball-court', |
| 10: 'storage-tank', |
| 11: 'soccer-ball-field', |
| 12: 'roundabout', |
| 13: 'harbor', |
| 14: 'swimming-pool', |
| 15: 'helicopter' |
| } |
| catid2name.pop(0) |
| clsid2catid = {i: i + 1 for i in range(len(catid2name))} |
| return clsid2catid, catid2name |
|
|
|
|
| def _vocall_category(): |
| """ |
| Get class id to category id map and category id |
| to category name map of mixup voc dataset |
| |
| """ |
| label_map = pascalvoc_label() |
| label_map = sorted(label_map.items(), key=lambda x: x[1]) |
| cats = [l[0] for l in label_map] |
|
|
| clsid2catid = {i: i for i in range(len(cats))} |
| catid2name = {i: name for i, name in enumerate(cats)} |
|
|
| return clsid2catid, catid2name |
|
|
|
|
| def _widerface_category(): |
| label_map = widerface_label() |
| label_map = sorted(label_map.items(), key=lambda x: x[1]) |
| cats = [l[0] for l in label_map] |
| clsid2catid = {i: i for i in range(len(cats))} |
| catid2name = {i: name for i, name in enumerate(cats)} |
|
|
| return clsid2catid, catid2name |
|
|
|
|
| def _oid19_category(): |
| clsid2catid = {k: k + 1 for k in range(500)} |
|
|
| catid2name = { |
| 0: "background", |
| 1: "Infant bed", |
| 2: "Rose", |
| 3: "Flag", |
| 4: "Flashlight", |
| 5: "Sea turtle", |
| 6: "Camera", |
| 7: "Animal", |
| 8: "Glove", |
| 9: "Crocodile", |
| 10: "Cattle", |
| 11: "House", |
| 12: "Guacamole", |
| 13: "Penguin", |
| 14: "Vehicle registration plate", |
| 15: "Bench", |
| 16: "Ladybug", |
| 17: "Human nose", |
| 18: "Watermelon", |
| 19: "Flute", |
| 20: "Butterfly", |
| 21: "Washing machine", |
| 22: "Raccoon", |
| 23: "Segway", |
| 24: "Taco", |
| 25: "Jellyfish", |
| 26: "Cake", |
| 27: "Pen", |
| 28: "Cannon", |
| 29: "Bread", |
| 30: "Tree", |
| 31: "Shellfish", |
| 32: "Bed", |
| 33: "Hamster", |
| 34: "Hat", |
| 35: "Toaster", |
| 36: "Sombrero", |
| 37: "Tiara", |
| 38: "Bowl", |
| 39: "Dragonfly", |
| 40: "Moths and butterflies", |
| 41: "Antelope", |
| 42: "Vegetable", |
| 43: "Torch", |
| 44: "Building", |
| 45: "Power plugs and sockets", |
| 46: "Blender", |
| 47: "Billiard table", |
| 48: "Cutting board", |
| 49: "Bronze sculpture", |
| 50: "Turtle", |
| 51: "Broccoli", |
| 52: "Tiger", |
| 53: "Mirror", |
| 54: "Bear", |
| 55: "Zucchini", |
| 56: "Dress", |
| 57: "Volleyball", |
| 58: "Guitar", |
| 59: "Reptile", |
| 60: "Golf cart", |
| 61: "Tart", |
| 62: "Fedora", |
| 63: "Carnivore", |
| 64: "Car", |
| 65: "Lighthouse", |
| 66: "Coffeemaker", |
| 67: "Food processor", |
| 68: "Truck", |
| 69: "Bookcase", |
| 70: "Surfboard", |
| 71: "Footwear", |
| 72: "Bench", |
| 73: "Necklace", |
| 74: "Flower", |
| 75: "Radish", |
| 76: "Marine mammal", |
| 77: "Frying pan", |
| 78: "Tap", |
| 79: "Peach", |
| 80: "Knife", |
| 81: "Handbag", |
| 82: "Laptop", |
| 83: "Tent", |
| 84: "Ambulance", |
| 85: "Christmas tree", |
| 86: "Eagle", |
| 87: "Limousine", |
| 88: "Kitchen & dining room table", |
| 89: "Polar bear", |
| 90: "Tower", |
| 91: "Football", |
| 92: "Willow", |
| 93: "Human head", |
| 94: "Stop sign", |
| 95: "Banana", |
| 96: "Mixer", |
| 97: "Binoculars", |
| 98: "Dessert", |
| 99: "Bee", |
| 100: "Chair", |
| 101: "Wood-burning stove", |
| 102: "Flowerpot", |
| 103: "Beaker", |
| 104: "Oyster", |
| 105: "Woodpecker", |
| 106: "Harp", |
| 107: "Bathtub", |
| 108: "Wall clock", |
| 109: "Sports uniform", |
| 110: "Rhinoceros", |
| 111: "Beehive", |
| 112: "Cupboard", |
| 113: "Chicken", |
| 114: "Man", |
| 115: "Blue jay", |
| 116: "Cucumber", |
| 117: "Balloon", |
| 118: "Kite", |
| 119: "Fireplace", |
| 120: "Lantern", |
| 121: "Missile", |
| 122: "Book", |
| 123: "Spoon", |
| 124: "Grapefruit", |
| 125: "Squirrel", |
| 126: "Orange", |
| 127: "Coat", |
| 128: "Punching bag", |
| 129: "Zebra", |
| 130: "Billboard", |
| 131: "Bicycle", |
| 132: "Door handle", |
| 133: "Mechanical fan", |
| 134: "Ring binder", |
| 135: "Table", |
| 136: "Parrot", |
| 137: "Sock", |
| 138: "Vase", |
| 139: "Weapon", |
| 140: "Shotgun", |
| 141: "Glasses", |
| 142: "Seahorse", |
| 143: "Belt", |
| 144: "Watercraft", |
| 145: "Window", |
| 146: "Giraffe", |
| 147: "Lion", |
| 148: "Tire", |
| 149: "Vehicle", |
| 150: "Canoe", |
| 151: "Tie", |
| 152: "Shelf", |
| 153: "Picture frame", |
| 154: "Printer", |
| 155: "Human leg", |
| 156: "Boat", |
| 157: "Slow cooker", |
| 158: "Croissant", |
| 159: "Candle", |
| 160: "Pancake", |
| 161: "Pillow", |
| 162: "Coin", |
| 163: "Stretcher", |
| 164: "Sandal", |
| 165: "Woman", |
| 166: "Stairs", |
| 167: "Harpsichord", |
| 168: "Stool", |
| 169: "Bus", |
| 170: "Suitcase", |
| 171: "Human mouth", |
| 172: "Juice", |
| 173: "Skull", |
| 174: "Door", |
| 175: "Violin", |
| 176: "Chopsticks", |
| 177: "Digital clock", |
| 178: "Sunflower", |
| 179: "Leopard", |
| 180: "Bell pepper", |
| 181: "Harbor seal", |
| 182: "Snake", |
| 183: "Sewing machine", |
| 184: "Goose", |
| 185: "Helicopter", |
| 186: "Seat belt", |
| 187: "Coffee cup", |
| 188: "Microwave oven", |
| 189: "Hot dog", |
| 190: "Countertop", |
| 191: "Serving tray", |
| 192: "Dog bed", |
| 193: "Beer", |
| 194: "Sunglasses", |
| 195: "Golf ball", |
| 196: "Waffle", |
| 197: "Palm tree", |
| 198: "Trumpet", |
| 199: "Ruler", |
| 200: "Helmet", |
| 201: "Ladder", |
| 202: "Office building", |
| 203: "Tablet computer", |
| 204: "Toilet paper", |
| 205: "Pomegranate", |
| 206: "Skirt", |
| 207: "Gas stove", |
| 208: "Cookie", |
| 209: "Cart", |
| 210: "Raven", |
| 211: "Egg", |
| 212: "Burrito", |
| 213: "Goat", |
| 214: "Kitchen knife", |
| 215: "Skateboard", |
| 216: "Salt and pepper shakers", |
| 217: "Lynx", |
| 218: "Boot", |
| 219: "Platter", |
| 220: "Ski", |
| 221: "Swimwear", |
| 222: "Swimming pool", |
| 223: "Drinking straw", |
| 224: "Wrench", |
| 225: "Drum", |
| 226: "Ant", |
| 227: "Human ear", |
| 228: "Headphones", |
| 229: "Fountain", |
| 230: "Bird", |
| 231: "Jeans", |
| 232: "Television", |
| 233: "Crab", |
| 234: "Microphone", |
| 235: "Home appliance", |
| 236: "Snowplow", |
| 237: "Beetle", |
| 238: "Artichoke", |
| 239: "Jet ski", |
| 240: "Stationary bicycle", |
| 241: "Human hair", |
| 242: "Brown bear", |
| 243: "Starfish", |
| 244: "Fork", |
| 245: "Lobster", |
| 246: "Corded phone", |
| 247: "Drink", |
| 248: "Saucer", |
| 249: "Carrot", |
| 250: "Insect", |
| 251: "Clock", |
| 252: "Castle", |
| 253: "Tennis racket", |
| 254: "Ceiling fan", |
| 255: "Asparagus", |
| 256: "Jaguar", |
| 257: "Musical instrument", |
| 258: "Train", |
| 259: "Cat", |
| 260: "Rifle", |
| 261: "Dumbbell", |
| 262: "Mobile phone", |
| 263: "Taxi", |
| 264: "Shower", |
| 265: "Pitcher", |
| 266: "Lemon", |
| 267: "Invertebrate", |
| 268: "Turkey", |
| 269: "High heels", |
| 270: "Bust", |
| 271: "Elephant", |
| 272: "Scarf", |
| 273: "Barrel", |
| 274: "Trombone", |
| 275: "Pumpkin", |
| 276: "Box", |
| 277: "Tomato", |
| 278: "Frog", |
| 279: "Bidet", |
| 280: "Human face", |
| 281: "Houseplant", |
| 282: "Van", |
| 283: "Shark", |
| 284: "Ice cream", |
| 285: "Swim cap", |
| 286: "Falcon", |
| 287: "Ostrich", |
| 288: "Handgun", |
| 289: "Whiteboard", |
| 290: "Lizard", |
| 291: "Pasta", |
| 292: "Snowmobile", |
| 293: "Light bulb", |
| 294: "Window blind", |
| 295: "Muffin", |
| 296: "Pretzel", |
| 297: "Computer monitor", |
| 298: "Horn", |
| 299: "Furniture", |
| 300: "Sandwich", |
| 301: "Fox", |
| 302: "Convenience store", |
| 303: "Fish", |
| 304: "Fruit", |
| 305: "Earrings", |
| 306: "Curtain", |
| 307: "Grape", |
| 308: "Sofa bed", |
| 309: "Horse", |
| 310: "Luggage and bags", |
| 311: "Desk", |
| 312: "Crutch", |
| 313: "Bicycle helmet", |
| 314: "Tick", |
| 315: "Airplane", |
| 316: "Canary", |
| 317: "Spatula", |
| 318: "Watch", |
| 319: "Lily", |
| 320: "Kitchen appliance", |
| 321: "Filing cabinet", |
| 322: "Aircraft", |
| 323: "Cake stand", |
| 324: "Candy", |
| 325: "Sink", |
| 326: "Mouse", |
| 327: "Wine", |
| 328: "Wheelchair", |
| 329: "Goldfish", |
| 330: "Refrigerator", |
| 331: "French fries", |
| 332: "Drawer", |
| 333: "Treadmill", |
| 334: "Picnic basket", |
| 335: "Dice", |
| 336: "Cabbage", |
| 337: "Football helmet", |
| 338: "Pig", |
| 339: "Person", |
| 340: "Shorts", |
| 341: "Gondola", |
| 342: "Honeycomb", |
| 343: "Doughnut", |
| 344: "Chest of drawers", |
| 345: "Land vehicle", |
| 346: "Bat", |
| 347: "Monkey", |
| 348: "Dagger", |
| 349: "Tableware", |
| 350: "Human foot", |
| 351: "Mug", |
| 352: "Alarm clock", |
| 353: "Pressure cooker", |
| 354: "Human hand", |
| 355: "Tortoise", |
| 356: "Baseball glove", |
| 357: "Sword", |
| 358: "Pear", |
| 359: "Miniskirt", |
| 360: "Traffic sign", |
| 361: "Girl", |
| 362: "Roller skates", |
| 363: "Dinosaur", |
| 364: "Porch", |
| 365: "Human beard", |
| 366: "Submarine sandwich", |
| 367: "Screwdriver", |
| 368: "Strawberry", |
| 369: "Wine glass", |
| 370: "Seafood", |
| 371: "Racket", |
| 372: "Wheel", |
| 373: "Sea lion", |
| 374: "Toy", |
| 375: "Tea", |
| 376: "Tennis ball", |
| 377: "Waste container", |
| 378: "Mule", |
| 379: "Cricket ball", |
| 380: "Pineapple", |
| 381: "Coconut", |
| 382: "Doll", |
| 383: "Coffee table", |
| 384: "Snowman", |
| 385: "Lavender", |
| 386: "Shrimp", |
| 387: "Maple", |
| 388: "Cowboy hat", |
| 389: "Goggles", |
| 390: "Rugby ball", |
| 391: "Caterpillar", |
| 392: "Poster", |
| 393: "Rocket", |
| 394: "Organ", |
| 395: "Saxophone", |
| 396: "Traffic light", |
| 397: "Cocktail", |
| 398: "Plastic bag", |
| 399: "Squash", |
| 400: "Mushroom", |
| 401: "Hamburger", |
| 402: "Light switch", |
| 403: "Parachute", |
| 404: "Teddy bear", |
| 405: "Winter melon", |
| 406: "Deer", |
| 407: "Musical keyboard", |
| 408: "Plumbing fixture", |
| 409: "Scoreboard", |
| 410: "Baseball bat", |
| 411: "Envelope", |
| 412: "Adhesive tape", |
| 413: "Briefcase", |
| 414: "Paddle", |
| 415: "Bow and arrow", |
| 416: "Telephone", |
| 417: "Sheep", |
| 418: "Jacket", |
| 419: "Boy", |
| 420: "Pizza", |
| 421: "Otter", |
| 422: "Office supplies", |
| 423: "Couch", |
| 424: "Cello", |
| 425: "Bull", |
| 426: "Camel", |
| 427: "Ball", |
| 428: "Duck", |
| 429: "Whale", |
| 430: "Shirt", |
| 431: "Tank", |
| 432: "Motorcycle", |
| 433: "Accordion", |
| 434: "Owl", |
| 435: "Porcupine", |
| 436: "Sun hat", |
| 437: "Nail", |
| 438: "Scissors", |
| 439: "Swan", |
| 440: "Lamp", |
| 441: "Crown", |
| 442: "Piano", |
| 443: "Sculpture", |
| 444: "Cheetah", |
| 445: "Oboe", |
| 446: "Tin can", |
| 447: "Mango", |
| 448: "Tripod", |
| 449: "Oven", |
| 450: "Mouse", |
| 451: "Barge", |
| 452: "Coffee", |
| 453: "Snowboard", |
| 454: "Common fig", |
| 455: "Salad", |
| 456: "Marine invertebrates", |
| 457: "Umbrella", |
| 458: "Kangaroo", |
| 459: "Human arm", |
| 460: "Measuring cup", |
| 461: "Snail", |
| 462: "Loveseat", |
| 463: "Suit", |
| 464: "Teapot", |
| 465: "Bottle", |
| 466: "Alpaca", |
| 467: "Kettle", |
| 468: "Trousers", |
| 469: "Popcorn", |
| 470: "Centipede", |
| 471: "Spider", |
| 472: "Sparrow", |
| 473: "Plate", |
| 474: "Bagel", |
| 475: "Personal care", |
| 476: "Apple", |
| 477: "Brassiere", |
| 478: "Bathroom cabinet", |
| 479: "studio couch", |
| 480: "Computer keyboard", |
| 481: "Table tennis racket", |
| 482: "Sushi", |
| 483: "Cabinetry", |
| 484: "Street light", |
| 485: "Towel", |
| 486: "Nightstand", |
| 487: "Rabbit", |
| 488: "Dolphin", |
| 489: "Dog", |
| 490: "Jug", |
| 491: "Wok", |
| 492: "Fire hydrant", |
| 493: "Human eye", |
| 494: "Skyscraper", |
| 495: "Backpack", |
| 496: "Potato", |
| 497: "Paper towel", |
| 498: "Lifejacket", |
| 499: "Bicycle wheel", |
| 500: "Toilet", |
| } |
|
|
| return clsid2catid, catid2name |
|
|
|
|
| def _visdrone_category(): |
| clsid2catid = {i: i for i in range(10)} |
|
|
| catid2name = { |
| 0: 'pedestrian', |
| 1: 'people', |
| 2: 'bicycle', |
| 3: 'car', |
| 4: 'van', |
| 5: 'truck', |
| 6: 'tricycle', |
| 7: 'awning-tricycle', |
| 8: 'bus', |
| 9: 'motor' |
| } |
| return clsid2catid, catid2name |
|
|
