def apply(self, sample, context):
        """
        Crop the image and modify bounding box.
        Operators:
            1. Scale the image width and height.
            2. Crop the image according to a radom sample.
            3. Rescale the bounding box.
            4. Determine if the new bbox is satisfied in the new image.
        Returns:
            sample: the image, bounding box are replaced.
        """
        assert 'image' in sample, "image data not found"
        im = sample['image']
        gt_bbox = sample['gt_bbox']
        gt_class = sample['gt_class']
        image_height, image_width = im.shape[:2]
        gt_bbox[:, 0] /= image_width
        gt_bbox[:, 1] /= image_height
        gt_bbox[:, 2] /= image_width
        gt_bbox[:, 3] /= image_height
        gt_score = None
        if 'gt_score' in sample:
            gt_score = sample['gt_score']
        sampled_bbox = []
        gt_bbox = gt_bbox.tolist()

        prob = np.random.uniform(0., 1.)
        if prob > self.sampling_prob:  # anchor sampling
            assert self.anchor_sampler
            for sampler in self.anchor_sampler:
                found = 0
                for i in range(sampler[1]):
                    if found >= sampler[0]:
                        break
                    sample_bbox = data_anchor_sampling(
                        gt_bbox, image_width, image_height,
                        self.das_anchor_scales, self.target_size)
                    if sample_bbox == 0:
                        break
                    if satisfy_sample_constraint_coverage(sampler, sample_bbox,
                                                          gt_bbox):
                        sampled_bbox.append(sample_bbox)
                        found = found + 1
            im = np.array(im)
            while sampled_bbox:
                idx = int(np.random.uniform(0, len(sampled_bbox)))
                sample_bbox = sampled_bbox.pop(idx)

                if 'gt_keypoint' in sample.keys():
                    keypoints = (sample['gt_keypoint'],
                                 sample['keypoint_ignore'])
                    crop_bbox, crop_class, crop_score, gt_keypoints = \
                        filter_and_process(sample_bbox, gt_bbox, gt_class,
                                scores=gt_score,
                                keypoints=keypoints)
                else:
                    crop_bbox, crop_class, crop_score = filter_and_process(
                        sample_bbox, gt_bbox, gt_class, scores=gt_score)
                crop_bbox, crop_class, crop_score = bbox_area_sampling(
                    crop_bbox, crop_class, crop_score, self.target_size,
                    self.min_size)

                if self.avoid_no_bbox:
                    if len(crop_bbox) < 1:
                        continue
                im = crop_image_sampling(im, sample_bbox, image_width,
                                         image_height, self.target_size)
                height, width = im.shape[:2]
                crop_bbox[:, 0] *= width
                crop_bbox[:, 1] *= height
                crop_bbox[:, 2] *= width
                crop_bbox[:, 3] *= height
                sample['image'] = im
                sample['gt_bbox'] = crop_bbox
                sample['gt_class'] = crop_class
                if 'gt_score' in sample:
                    sample['gt_score'] = crop_score
                if 'gt_keypoint' in sample.keys():
                    sample['gt_keypoint'] = gt_keypoints[0]
                    sample['keypoint_ignore'] = gt_keypoints[1]
                return sample
            return sample

        else:
            for sampler in self.batch_sampler:
                found = 0
                for i in range(sampler[1]):
                    if found >= sampler[0]:
                        break
                    sample_bbox = generate_sample_bbox_square(
                        sampler, image_width, image_height)
                    if satisfy_sample_constraint_coverage(sampler, sample_bbox,
                                                          gt_bbox):
                        sampled_bbox.append(sample_bbox)
                        found = found + 1
            im = np.array(im)
            while sampled_bbox:
                idx = int(np.random.uniform(0, len(sampled_bbox)))
                sample_bbox = sampled_bbox.pop(idx)
                sample_bbox = clip_bbox(sample_bbox)

                if 'gt_keypoint' in sample.keys():
                    keypoints = (sample['gt_keypoint'],
                                 sample['keypoint_ignore'])
                    crop_bbox, crop_class, crop_score, gt_keypoints = \
                        filter_and_process(sample_bbox, gt_bbox, gt_class,
                                scores=gt_score,
                                keypoints=keypoints)
                else:
                    crop_bbox, crop_class, crop_score = filter_and_process(
                        sample_bbox, gt_bbox, gt_class, scores=gt_score)
                # sampling bbox according the bbox area
                crop_bbox, crop_class, crop_score = bbox_area_sampling(
                    crop_bbox, crop_class, crop_score, self.target_size,
                    self.min_size)

                if self.avoid_no_bbox:
                    if len(crop_bbox) < 1:
                        continue
                xmin = int(sample_bbox[0] * image_width)
                xmax = int(sample_bbox[2] * image_width)
                ymin = int(sample_bbox[1] * image_height)
                ymax = int(sample_bbox[3] * image_height)
                im = im[ymin:ymax, xmin:xmax]
                height, width = im.shape[:2]
                crop_bbox[:, 0] *= width
                crop_bbox[:, 1] *= height
                crop_bbox[:, 2] *= width
                crop_bbox[:, 3] *= height
                sample['image'] = im
                sample['gt_bbox'] = crop_bbox
                sample['gt_class'] = crop_class
                if 'gt_score' in sample:
                    sample['gt_score'] = crop_score
                if 'gt_keypoint' in sample.keys():
                    sample['gt_keypoint'] = gt_keypoints[0]
                    sample['keypoint_ignore'] = gt_keypoints[1]
                return sample
            return sample

        Crop the image and modify bounding box.
        Operators:
            1. Scale the image width and height.
            2. Crop the image according to a radom sample.
            3. Rescale the bounding box.
            4. Determine if the new bbox is satisfied in the new image.
        Returns:
            sample: the image, bounding box are replaced.
        
ppdet/data/transform/operators.py