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']
        im_height, im_width = im.shape[:2]
        gt_score = None
        if 'gt_score' in sample:
            gt_score = sample['gt_score']
        sampled_bbox = []
        gt_bbox = gt_bbox.tolist()
        for sampler in self.batch_sampler:
            found = 0
            for i in range(sampler[1]):
                if found >= sampler[0]:
                    break
                sample_bbox = generate_sample_bbox(sampler)
                if satisfy_sample_constraint(sampler, sample_bbox, gt_bbox,
                                             self.satisfy_all):
                    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)
            crop_bbox, crop_class, crop_score = \
                filter_and_process(sample_bbox, gt_bbox, gt_class, scores=gt_score)
            if self.avoid_no_bbox:
                if len(crop_bbox) < 1:
                    continue
            xmin = int(sample_bbox[0] * im_width)
            xmax = int(sample_bbox[2] * im_width)
            ymin = int(sample_bbox[1] * im_height)
            ymax = int(sample_bbox[3] * im_height)
            im = im[ymin:ymax, xmin:xmax]
            sample['image'] = im
            sample['gt_bbox'] = crop_bbox
            sample['gt_class'] = crop_class
            sample['gt_score'] = crop_score
            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