import os import cv2 import time import random import numpy as np from torch.utils.data import Dataset from pycocotools.coco import COCO try: from .data_augment.strong_augment import MosaicAugment, MixupAugment except: from data_augment.strong_augment import MosaicAugment, MixupAugment 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, ): # ----------- 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.trans_config = trans_config self.transform = transform # ----------- Strong augmentation ----------- if is_train: 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.mosaic_augment = MosaicAugment(img_size, trans_config, is_train) if self.mosaic_prob > 0. else None self.mixup_augment = MixupAugment(img_size, trans_config) if self.mixup_prob > 0. else None else: self.mosaic_prob = 0.0 self.mixup_prob = 0.0 self.mosaic_augment = None self.mixup_augment = None print('==============================') print('use Mosaic Augmentation: {}'.format(self.mosaic_prob)) print('use Mixup Augmentation: {}'.format(self.mixup_prob)) # ------------ Basic dataset function ------------ def __len__(self): return len(self.ids) def __getitem__(self, index): return self.pull_item(index) # ------------ Mosaic & Mixup ------------ def load_mosaic(self, index): # ------------ Prepare 4 indexes of images ------------ ## 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 augmentation ------------ image, target = self.mosaic_augment(image_list, target_list) return image, target def load_mixup(self, origin_image, origin_target): # ------------ Load a new image & target ------------ if self.mixup_augment.mixup_type == 'yolov5': new_index = np.random.randint(0, len(self.ids)) new_image, new_target = self.load_mosaic(new_index) elif self.mixup_augment.mixup_type == 'yolox': new_index = np.random.randint(0, len(self.ids)) new_image, new_target = self.load_image_target(new_index) # ------------ Mixup augmentation ------------ image, target = self.mixup_augment(origin_image, origin_target, new_image, new_target) return image, target # ------------ Load data function ------------ def load_image_target(self, index): # 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, yolo.') 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.') args = parser.parse_args() trans_config = { 'aug_type': args.aug_type, # optional: ssd, yolov5 'pixel_mean': [123.675, 116.28, 103.53], 'pixel_std': [58.395, 57.12, 57.375], 'use_ablu': True, # Basic Augment 'affine_params': { '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, }, # Mosaic & Mixup 'mosaic_keep_ratio': False, 'mosaic_prob': args.mosaic, 'mixup_prob': args.mixup, 'mosaic_type': 'yolov5', 'mixup_type': 'yolov5', '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, ) 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)