YOLO-Tutorial-v2/yolo/dataset/voc.py

import cv2
import random
import numpy as np
import os.path as osp
import xml.etree.ElementTree as ET
import torch.utils.data as data

try:
    from .data_augment.strong_augment import MosaicAugment, MixupAugment
except:
    from  data_augment.strong_augment import MosaicAugment, MixupAugment


VOC_CLASSES = ('aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor')
voc_class_indexs = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]
voc_class_labels = ('aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor')


class VOCAnnotationTransform(object):
    def __init__(self, class_to_ind=None, keep_difficult=False):
        self.class_to_ind = class_to_ind or dict(
            zip(VOC_CLASSES, range(len(VOC_CLASSES))))
        self.keep_difficult = keep_difficult

    def __call__(self, target):
        res = []
        for obj in target.iter('object'):
            difficult = int(obj.find('difficult').text) == 1
            if not self.keep_difficult and difficult:
                continue
            name = obj.find('name').text.lower().strip()
            bbox = obj.find('bndbox')

            pts = ['xmin', 'ymin', 'xmax', 'ymax']
            bndbox = []
            for i, pt in enumerate(pts):
                cur_pt = int(bbox.find(pt).text) - 1
                bndbox.append(cur_pt)
            label_idx = self.class_to_ind[name]
            bndbox.append(label_idx)
            res += [bndbox]  # [x1, y1, x2, y2, label_ind]

        return res  # [[x1, y1, x2, y2, label_ind], ... ]


class VOCDataset(data.Dataset):
    def __init__(self, 
                 cfg,
                 data_dir   :str = None, 
                 image_set  = [('2007', 'trainval'), ('2012', 'trainval')],
                 transform  = None,
                 is_train   :bool =False,
                 ):
        # ----------- Basic parameters -----------
        self.image_set = image_set
        self.is_train  = is_train
        self.num_classes = 20
        # ----------- Path parameters -----------
        self.root = data_dir
        self._annopath = osp.join('%s', 'Annotations', '%s.xml')
        self._imgpath = osp.join('%s', 'JPEGImages', '%s.jpg')
        # ----------- Data parameters -----------
        self.ids = list()
        for (year, name) in image_set:
            rootpath = osp.join(self.root, 'VOC' + year)
            for line in open(osp.join(rootpath, 'ImageSets', 'Main', name + '.txt')):
                self.ids.append((rootpath, line.strip()))
        self.dataset_size = len(self.ids)
        self.class_labels = voc_class_labels
        self.class_indexs = voc_class_indexs
        # ----------- Transform parameters -----------
        self.target_transform = VOCAnnotationTransform()
        self.transform = transform
        if is_train:
            self.mosaic_prob = cfg.mosaic_prob
            self.mixup_prob  = cfg.mixup_prob
            self.copy_paste  = cfg.copy_paste
            self.mosaic_augment = None if cfg.mosaic_prob == 0. else MosaicAugment(cfg.train_img_size, cfg.affine_params, is_train)
            self.mixup_augment  = None if cfg.mixup_prob == 0. and cfg.copy_paste == 0.  else MixupAugment(cfg.train_img_size)
        else:
            self.mosaic_prob = 0.0
            self.mixup_prob  = 0.0
            self.copy_paste  = 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))
        print('use Copy-paste Augmentation: {}'.format(self.copy_paste))

    # ------------ Basic dataset function ------------
    def __getitem__(self, index):
        image, target, deltas = self.pull_item(index)
        return image, target, deltas

    def __len__(self):
        return self.dataset_size

    # ------------ 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, yolox_style=False):
        # ------------ Load a new image & target ------------
        if yolox_style:
            new_index = np.random.randint(0, len(self.ids))
            new_image, new_target = self.load_image_target(new_index)
        else:
            new_index = np.random.randint(0, len(self.ids))
            new_image, new_target = self.load_mosaic(new_index)
            
        # ------------ Mixup augmentation ------------
        image, target = self.mixup_augment(origin_image, origin_target, new_image, new_target, yolox_style)

        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

        # laod an annotation
        anno, _ = self.pull_anno(index)

        # guard against no boxes via resizing
        anno = np.array(anno).reshape(-1, 5)
        target = {
            "boxes": anno[:, :4],
            "labels": anno[:, 4],
            "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)

        # Yolov5-MixUp
        mixup = False
        if random.random() < self.mixup_prob:
            mixup = True
            image, target = self.load_mixup(image, target)

        # Copy-paste (use Yolox-Mixup to approximate copy-paste)
        if not mixup and random.random() < self.copy_paste:
            image, target = self.load_mixup(image, target, yolox_style=True)

        # augment
        image, target, deltas = self.transform(image, target, mosaic)

        return image, target, deltas

    def pull_image(self, index):
        img_id = self.ids[index]
        image = cv2.imread(self._imgpath % img_id, cv2.IMREAD_COLOR)

        return image, img_id

    def pull_anno(self, index):
        img_id = self.ids[index]
        anno = ET.parse(self._annopath % img_id).getroot()
        anno = self.target_transform(anno)

        return anno, img_id


if __name__ == "__main__":
    import time
    import argparse
    from build import build_transform
    
    parser = argparse.ArgumentParser(description='VOC-Dataset')

    # opt
    parser.add_argument('--root', default='D:/python_work/dataset/VOCdevkit/',
                        help='data root')
    parser.add_argument('--is_train', action="store_true", default=False,
                        help='train or not.')
    parser.add_argument('--aug_type', default="yolo", type=str, choices=["yolo", "ssd"],
                        help='yolo, ssd.')
    
    args = parser.parse_args()

    class YoloBaseConfig(object):
        def __init__(self) -> None:
            self.max_stride = 32
            # ---------------- Data process config ----------------
            self.box_format = 'xywh'
            self.normalize_coords = False
            self.mosaic_prob = 1.0
            self.mixup_prob  = 0.15
            self.copy_paste  = 0.3
            ## Pixel mean & std
            self.pixel_mean = [0., 0., 0.]
            self.pixel_std  = [255., 255., 255.]
            ## Transforms
            self.train_img_size = 640
            self.test_img_size  = 640
            self.use_ablu = True
            self.aug_type = 'yolo'
            self.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,
            }

    class SSDBaseConfig(object):
        def __init__(self) -> None:
            self.max_stride = 32
            # ---------------- Data process config ----------------
            self.box_format = 'xywh'
            self.normalize_coords = False
            self.mosaic_prob = 0.0
            self.mixup_prob  = 0.0
            self.copy_paste  = 0.0
            ## Pixel mean & std
            self.pixel_mean = [0., 0., 0.]
            self.pixel_std  = [255., 255., 255.]
            ## Transforms
            self.train_img_size = 640
            self.test_img_size  = 640
            self.aug_type = 'ssd'

    if args.aug_type == "yolo":
        cfg = YoloBaseConfig()
    elif args.aug_type == "ssd":
        cfg = SSDBaseConfig()

    transform = build_transform(cfg, args.is_train)
    dataset = VOCDataset(cfg, args.root, [('2007', 'test')], transform, 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 * cfg.pixel_std + cfg.pixel_mean

        # rgb -> bgr
        if transform.color_format == 'rgb':
            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):
            if cfg.box_format == 'xyxy':
                x1, y1, x2, y2 = box
            elif cfg.box_format == 'xywh':
                cx, cy, bw, bh = box
                x1 = cx - 0.5 * bw
                y1 = cy - 0.5 * bh
                x2 = cx + 0.5 * bw
                y2 = cy + 0.5 * bh
            
            if cfg.normalize_coords:
                x1 *= img_w
                y1 *= img_h
                x2 *= img_w
                y2 *= img_h

            cls_id = int(label)
            color = class_colors[cls_id]
            # class name
            label = voc_class_labels[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)