import random

import numpy as np
from mmdet.datasets import PIPELINES
from mmdet.datasets.pipelines import RandomCrop


@PIPELINES.register_module()
class RelRandomCrop(RandomCrop):
    """Random crop the image & bboxes & masks & scene relations."""
    def _crop_data(self, results, crop_size, allow_negative_crop):
        """Function to randomly crop images, bounding boxes, masks, semantic
        segmentation maps, relations.

        Filter those incomplete relations and adjust annotations of the remaining relations.
        Args:
            results (dict): Result dict from loading pipeline.
            crop_size (tuple): Expected absolute size after cropping, (h, w).
            allow_negative_crop (bool): Whether to allow a crop that does not
                contain any bbox area. Default to False.
        Returns:
            dict: Randomly cropped results, 'img_shape' key in result dict is
                updated according to crop size.
        """
        assert crop_size[0] > 0 and crop_size[1] > 0
        for key in results.get('img_fields', ['img']):
            img = results[key]
            margin_h = max(img.shape[0] - crop_size[0], 0)
            margin_w = max(img.shape[1] - crop_size[1], 0)
            offset_h = np.random.randint(0, margin_h + 1)
            offset_w = np.random.randint(0, margin_w + 1)
            crop_y1, crop_y2 = offset_h, offset_h + crop_size[0]
            crop_x1, crop_x2 = offset_w, offset_w + crop_size[1]

            # crop the image
            img = img[crop_y1:crop_y2, crop_x1:crop_x2, ...]
            img_shape = img.shape
            results[key] = img
        results['img_shape'] = img_shape

        # crop bboxes accordingly and clip to the image boundary
        for key in results.get('bbox_fields', []):
            # e.g. gt_bboxes and gt_bboxes_ignore
            bbox_offset = np.array([offset_w, offset_h, offset_w, offset_h],
                                   dtype=np.float32)
            bboxes = results[key] - bbox_offset
            if self.bbox_clip_border:
                bboxes[:, 0::2] = np.clip(bboxes[:, 0::2], 0, img_shape[1])
                bboxes[:, 1::2] = np.clip(bboxes[:, 1::2], 0, img_shape[0])
            valid_inds = (bboxes[:, 2] > bboxes[:, 0]) & (bboxes[:, 3] >
                                                          bboxes[:, 1])
            if key == 'gt_bboxes':
                rels_left = []
                for rel in results.get('gt_rels', []):
                    if valid_inds[rel[0]] and valid_inds[rel[1]]:
                        rels_left.append([
                            sum(valid_inds[:rel[0]]),
                            sum(valid_inds[:rel[1]]), rel[2]
                        ])
                rels_left = np.array(rels_left)
                # If the crop does not contain any triplets and
                # allow_negative_crop is False, skip this image.
                if len(rels_left) == 0 and not allow_negative_crop:
                    return None
                results['gt_rels'] = rels_left
            results[key] = bboxes[valid_inds, :]
            # label fields. e.g. gt_labels and gt_labels_ignore
            label_key = self.bbox2label.get(key)
            if label_key in results:
                results[label_key] = results[label_key][valid_inds]

            # mask fields, e.g. gt_masks and gt_masks_ignore
            mask_key = self.bbox2mask.get(key)
            if mask_key in results:
                results[mask_key] = results[mask_key][
                    valid_inds.nonzero()[0]].crop(
                        np.asarray([crop_x1, crop_y1, crop_x2, crop_y2]))
                if self.recompute_bbox:
                    results[key] = results[mask_key].get_bboxes()

        # crop semantic seg
        for key in results.get('seg_fields', []):
            results[key] = results[key][crop_y1:crop_y2, crop_x1:crop_x2]

        return results
