import os import cv2 import time import random import numpy as np import torch from torch.utils.data import Dataset try: from pycocotools.coco import COCO except: print("It seems that the COCOAPI is not installed.") try: from .data_augment.yolov5_augment import yolov5_mosaic_augment, yolov5_mixup_augment, yolox_mixup_augment except: from data_augment.yolov5_augment import yolov5_mosaic_augment, yolov5_mixup_augment, yolox_mixup_augment coco_class_index = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 67, 70, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90] coco_class_labels = ('background', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light', 'fire hydrant', 'street sign', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'hat', 'backpack', 'umbrella', 'shoe', 'eye glasses', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 'tennis racket', 'bottle', 'plate', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed', 'mirror', 'dining table', 'window', 'desk', 'toilet', 'door', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'blender', 'book', 'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush') class COCODataset(Dataset): def __init__(self, img_size :int = 640, data_dir :str = None, image_set :str = 'train2017', trans_config = None, transform = None, is_train :bool =False, load_cache :bool = False, ): # ----------- Basic parameters ----------- self.img_size = img_size self.image_set = image_set self.is_train = is_train # ----------- Path parameters ----------- self.data_dir = data_dir if image_set == 'train2017': self.json_file='instances_train2017_clean.json' elif image_set == 'val2017': self.json_file='instances_val2017_clean.json' elif image_set == 'test2017': self.json_file='image_info_test-dev2017.json' else: raise NotImplementedError("Unknown json image set {}.".format(image_set)) # ----------- Data parameters ----------- self.coco = COCO(os.path.join(self.data_dir, 'annotations', self.json_file)) self.ids = self.coco.getImgIds() self.class_ids = sorted(self.coco.getCatIds()) self.dataset_size = len(self.ids) # ----------- Transform parameters ----------- self.transform = transform self.mosaic_prob = trans_config['mosaic_prob'] if trans_config else 0.0 self.mixup_prob = trans_config['mixup_prob'] if trans_config else 0.0 self.trans_config = trans_config print('==============================') print('use Mosaic Augmentation: {}'.format(self.mosaic_prob)) print('use Mixup Augmentation: {}'.format(self.mixup_prob)) print('==============================') # ----------- Cached data ----------- self.load_cache = load_cache self.cached_datas = None if self.load_cache: self.cached_datas = self._load_cache() # ------------ Basic dataset function ------------ def __len__(self): return len(self.ids) def __getitem__(self, index): return self.pull_item(index) def _load_cache(self): data_items = [] for idx in range(self.dataset_size): if idx % 2000 == 0: print("Caching images and targets : {} / {} ...".format(idx, self.dataset_size)) # load a data image, target = self.load_image_target(idx) orig_h, orig_w, _ = image.shape # resize image r = self.img_size / max(orig_h, orig_w) if r != 1: interp = cv2.INTER_LINEAR new_size = (int(orig_w * r), int(orig_h * r)) image = cv2.resize(image, new_size, interpolation=interp) img_h, img_w = image.shape[:2] # rescale bbox boxes = target["boxes"].copy() boxes[:, [0, 2]] = boxes[:, [0, 2]] / orig_w * img_w boxes[:, [1, 3]] = boxes[:, [1, 3]] / orig_h * img_h target["boxes"] = boxes dict_item = {} dict_item["image"] = image dict_item["target"] = target data_items.append(dict_item) return data_items # ------------ Mosaic & Mixup ------------ def load_mosaic(self, index): # load 4x mosaic image index_list = np.arange(index).tolist() + np.arange(index+1, len(self.ids)).tolist() id1 = index id2, id3, id4 = random.sample(index_list, 3) indexs = [id1, id2, id3, id4] # load images and targets image_list = [] target_list = [] for index in indexs: img_i, target_i = self.load_image_target(index) image_list.append(img_i) target_list.append(target_i) # Mosaic if self.trans_config['mosaic_type'] == 'yolov5_mosaic': image, target = yolov5_mosaic_augment( image_list, target_list, self.img_size, self.trans_config, self.trans_config['mosaic_keep_ratio'], self.is_train) return image, target def load_mixup(self, origin_image, origin_target): # YOLOv5 type Mixup if self.trans_config['mixup_type'] == 'yolov5_mixup': new_index = np.random.randint(0, len(self.ids)) new_image, new_target = self.load_mosaic(new_index) image, target = yolov5_mixup_augment( origin_image, origin_target, new_image, new_target) # YOLOX type Mixup elif self.trans_config['mixup_type'] == 'yolox_mixup': new_index = np.random.randint(0, len(self.ids)) new_image, new_target = self.load_image_target(new_index) image, target = yolox_mixup_augment( origin_image, origin_target, new_image, new_target, self.img_size, self.trans_config['mixup_scale']) return image, target # ------------ Load data function ------------ def load_image_target(self, index): # == Load a data from the cached data == if self.cached_datas is not None: # load a data data_item = self.cached_datas[index] image = data_item["image"] target = data_item["target"] # == Load a data from the local disk == else: # load an image image, _ = self.pull_image(index) height, width, channels = image.shape # load a target bboxes, labels = self.pull_anno(index) target = { "boxes": bboxes, "labels": labels, "orig_size": [height, width] } return image, target def pull_item(self, index): if random.random() < self.mosaic_prob: # load a mosaic image mosaic = True image, target = self.load_mosaic(index) else: mosaic = False # load an image and target image, target = self.load_image_target(index) # MixUp if random.random() < self.mixup_prob: image, target = self.load_mixup(image, target) # augment image, target, deltas = self.transform(image, target, mosaic) return image, target, deltas def pull_image(self, index): img_id = self.ids[index] img_file = os.path.join(self.data_dir, self.image_set, '{:012}'.format(img_id) + '.jpg') image = cv2.imread(img_file) if self.json_file == 'instances_val5k.json' and image is None: img_file = os.path.join(self.data_dir, 'train2017', '{:012}'.format(img_id) + '.jpg') image = cv2.imread(img_file) assert image is not None return image, img_id def pull_anno(self, index): img_id = self.ids[index] im_ann = self.coco.loadImgs(img_id)[0] anno_ids = self.coco.getAnnIds(imgIds=[int(img_id)], iscrowd=False) annotations = self.coco.loadAnns(anno_ids) # image infor width = im_ann['width'] height = im_ann['height'] #load a target bboxes = [] labels = [] for anno in annotations: if 'bbox' in anno and anno['area'] > 0: # bbox x1 = np.max((0, anno['bbox'][0])) y1 = np.max((0, anno['bbox'][1])) x2 = np.min((width - 1, x1 + np.max((0, anno['bbox'][2] - 1)))) y2 = np.min((height - 1, y1 + np.max((0, anno['bbox'][3] - 1)))) if x2 < x1 or y2 < y1: continue # class label cls_id = self.class_ids.index(anno['category_id']) bboxes.append([x1, y1, x2, y2]) labels.append(cls_id) # guard against no boxes via resizing bboxes = np.array(bboxes).reshape(-1, 4) labels = np.array(labels).reshape(-1) return bboxes, labels if __name__ == "__main__": import time import argparse from build import build_transform parser = argparse.ArgumentParser(description='COCO-Dataset') # opt parser.add_argument('--root', default='/Users/liuhaoran/Desktop/python_work/object-detection/dataset/COCO/', help='data root') parser.add_argument('--image_set', type=str, default='train2017', help='mixup augmentation.') parser.add_argument('-size', '--img_size', default=640, type=int, help='input image size.') parser.add_argument('--aug_type', type=str, default='ssd', help='augmentation type: ssd, yolov5, rtdetr.') parser.add_argument('--mosaic', default=0., type=float, help='mosaic augmentation.') parser.add_argument('--mixup', default=0., type=float, help='mixup augmentation.') parser.add_argument('--mixup_type', type=str, default='yolov5_mixup', help='mixup augmentation.') parser.add_argument('--is_train', action="store_true", default=False, help='mixup augmentation.') parser.add_argument('--load_cache', action="store_true", default=False, help='load cached data.') args = parser.parse_args() trans_config = { 'aug_type': args.aug_type, # optional: ssd, yolov5 'pixel_mean': [0., 0., 0.], 'pixel_std': [255., 255., 255.], # Basic Augment 'degrees': 0.0, 'translate': 0.2, 'scale': [0.1, 2.0], 'shear': 0.0, 'perspective': 0.0, 'hsv_h': 0.015, 'hsv_s': 0.7, 'hsv_v': 0.4, 'use_ablu': True, # Mosaic & Mixup 'mosaic_prob': args.mosaic, 'mixup_prob': args.mixup, 'mosaic_type': 'yolov5_mosaic', 'mixup_type': args.mixup_type, # optional: yolov5_mixup, yolox_mixup 'mosaic_keep_ratio': False, 'mixup_scale': [0.5, 1.5] } transform, trans_cfg = build_transform(args, trans_config, 32, args.is_train) pixel_mean = transform.pixel_mean pixel_std = transform.pixel_std color_format = transform.color_format dataset = COCODataset( img_size=args.img_size, data_dir=args.root, image_set='val2017', trans_config=trans_config, transform=transform, is_train=args.is_train, load_cache=args.load_cache ) np.random.seed(0) class_colors = [(np.random.randint(255), np.random.randint(255), np.random.randint(255)) for _ in range(80)] print('Data length: ', len(dataset)) for i in range(1000): t0 = time.time() image, target, deltas = dataset.pull_item(i) print("Load data: {} s".format(time.time() - t0)) # to numpy image = image.permute(1, 2, 0).numpy() # denormalize image = image * pixel_std + pixel_mean if color_format == 'rgb': # RGB to BGR image = image[..., (2, 1, 0)] # to uint8 image = image.astype(np.uint8) image = image.copy() img_h, img_w = image.shape[:2] boxes = target["boxes"] labels = target["labels"] for box, label in zip(boxes, labels): x1, y1, x2, y2 = box cls_id = int(label) color = class_colors[cls_id] # class name label = coco_class_labels[coco_class_index[cls_id]] image = cv2.rectangle(image, (int(x1), int(y1)), (int(x2), int(y2)), color, 2) # put the test on the bbox cv2.putText(image, label, (int(x1), int(y1 - 5)), 0, 0.5, color, 1, lineType=cv2.LINE_AA) cv2.imshow('gt', image) # cv2.imwrite(str(i)+'.jpg', img) cv2.waitKey(0)