opendet/postprocess/db_postprocess.py

import numpy as np
import cv2
import torch
from shapely.geometry import Polygon
import pyclipper
"""
This code is refered from:
https://github.com/WenmuZhou/DBNet.pytorch/blob/master/post_processing/seg_detector_representer.py
"""


class DBPostProcess(object):
    """
    The post process for Differentiable Binarization (DB).
    """

    def __init__(
        self,
        thresh=0.3,
        box_thresh=0.7,
        max_candidates=1000,
        unclip_ratio=2.0,
        use_dilation=False,
        score_mode='fast',
        box_type='quad',
        **kwargs,
    ):
        self.thresh = thresh
        self.box_thresh = box_thresh
        self.max_candidates = max_candidates
        self.unclip_ratio = unclip_ratio
        self.min_size = 3
        self.score_mode = score_mode
        self.box_type = box_type
        assert score_mode in [
            'slow',
            'fast',
        ], 'Score mode must be in [slow, fast] but got: {}'.format(score_mode)

        self.dilation_kernel = None if not use_dilation else np.array([[1, 1],
                                                                       [1, 1]])

    def polygons_from_bitmap(self, pred, _bitmap, dest_width, dest_height):
        """
        _bitmap: single map with shape (1, H, W),
            whose values are binarized as {0, 1}
        """

        bitmap = _bitmap
        height, width = bitmap.shape

        boxes = []
        scores = []

        contours, _ = cv2.findContours((bitmap * 255).astype(np.uint8),
                                       cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)

        for contour in contours[:self.max_candidates]:
            epsilon = 0.002 * cv2.arcLength(contour, True)
            approx = cv2.approxPolyDP(contour, epsilon, True)
            points = approx.reshape((-1, 2))
            if points.shape[0] < 4:
                continue

            score = self.box_score_fast(pred, points.reshape(-1, 2))
            if self.box_thresh > score:
                continue

            if points.shape[0] > 2:
                box = self.unclip(points, self.unclip_ratio)
                if len(box) > 1:
                    continue
            else:
                continue
            box = np.array(box).reshape(-1, 2)
            if len(box) == 0:
                continue

            _, sside = self.get_mini_boxes(box.reshape((-1, 1, 2)))
            if sside < self.min_size + 2:
                continue

            box = np.array(box)
            box[:, 0] = np.clip(np.round(box[:, 0] / width * dest_width), 0,
                                dest_width)
            box[:, 1] = np.clip(np.round(box[:, 1] / height * dest_height), 0,
                                dest_height)
            boxes.append(box.tolist())
            scores.append(score)
        return boxes, scores

    def boxes_from_bitmap(self, pred, _bitmap, dest_width, dest_height):
        """
        _bitmap: single map with shape (1, H, W),
                whose values are binarized as {0, 1}
        """

        bitmap = _bitmap
        height, width = bitmap.shape

        outs = cv2.findContours((bitmap * 255).astype(np.uint8), cv2.RETR_LIST,
                                cv2.CHAIN_APPROX_SIMPLE)
        if len(outs) == 3:
            img, contours, _ = outs[0], outs[1], outs[2]
        elif len(outs) == 2:
            contours, _ = outs[0], outs[1]

        num_contours = min(len(contours), self.max_candidates)

        boxes = []
        scores = []
        for index in range(num_contours):
            contour = contours[index]
            points, sside = self.get_mini_boxes(contour)
            if sside < self.min_size:
                continue
            points = np.array(points)
            if self.score_mode == 'fast':
                score = self.box_score_fast(pred, points.reshape(-1, 2))
            else:
                score = self.box_score_slow(pred, contour)
            if self.box_thresh > score:
                continue

            box = self.unclip(points, self.unclip_ratio)
            if len(box) > 1:
                continue
            box = np.array(box).reshape(-1, 1, 2)
            box, sside = self.get_mini_boxes(box)
            if sside < self.min_size + 2:
                continue
            box = np.array(box)

            box[:, 0] = np.clip(np.round(box[:, 0] / width * dest_width), 0,
                                dest_width)
            box[:, 1] = np.clip(np.round(box[:, 1] / height * dest_height), 0,
                                dest_height)
            boxes.append(box.astype('int32'))
            scores.append(score)
        return np.array(boxes, dtype='int32'), scores

    def unclip(self, box, unclip_ratio):
        poly = Polygon(box)
        distance = poly.area * unclip_ratio / poly.length
        offset = pyclipper.PyclipperOffset()
        offset.AddPath(box, pyclipper.JT_ROUND, pyclipper.ET_CLOSEDPOLYGON)
        expanded = offset.Execute(distance)
        return expanded

    def get_mini_boxes(self, contour):
        bounding_box = cv2.minAreaRect(contour)
        points = sorted(list(cv2.boxPoints(bounding_box)), key=lambda x: x[0])

        index_1, index_2, index_3, index_4 = 0, 1, 2, 3
        if points[1][1] > points[0][1]:
            index_1 = 0
            index_4 = 1
        else:
            index_1 = 1
            index_4 = 0
        if points[3][1] > points[2][1]:
            index_2 = 2
            index_3 = 3
        else:
            index_2 = 3
            index_3 = 2

        box = [
            points[index_1], points[index_2], points[index_3], points[index_4]
        ]
        return box, min(bounding_box[1])

    def box_score_fast(self, bitmap, _box):
        """
        box_score_fast: use bbox mean score as the mean score
        """
        h, w = bitmap.shape[:2]
        box = _box.copy()
        xmin = np.clip(np.floor(box[:, 0].min()).astype('int32'), 0, w - 1)
        xmax = np.clip(np.ceil(box[:, 0].max()).astype('int32'), 0, w - 1)
        ymin = np.clip(np.floor(box[:, 1].min()).astype('int32'), 0, h - 1)
        ymax = np.clip(np.ceil(box[:, 1].max()).astype('int32'), 0, h - 1)

        mask = np.zeros((ymax - ymin + 1, xmax - xmin + 1), dtype=np.uint8)
        box[:, 0] = box[:, 0] - xmin
        box[:, 1] = box[:, 1] - ymin
        cv2.fillPoly(mask, box.reshape(1, -1, 2).astype('int32'), 1)
        return cv2.mean(bitmap[ymin:ymax + 1, xmin:xmax + 1], mask)[0]

    def box_score_slow(self, bitmap, contour):
        """
        box_score_slow: use polyon mean score as the mean score
        """
        h, w = bitmap.shape[:2]
        contour = contour.copy()
        contour = np.reshape(contour, (-1, 2))

        xmin = np.clip(np.min(contour[:, 0]), 0, w - 1)
        xmax = np.clip(np.max(contour[:, 0]), 0, w - 1)
        ymin = np.clip(np.min(contour[:, 1]), 0, h - 1)
        ymax = np.clip(np.max(contour[:, 1]), 0, h - 1)

        mask = np.zeros((ymax - ymin + 1, xmax - xmin + 1), dtype=np.uint8)

        contour[:, 0] = contour[:, 0] - xmin
        contour[:, 1] = contour[:, 1] - ymin

        cv2.fillPoly(mask, contour.reshape(1, -1, 2).astype('int32'), 1)
        return cv2.mean(bitmap[ymin:ymax + 1, xmin:xmax + 1], mask)[0]

    def __call__(self, outs_dict, shape_list, **kwargs):
        self.thresh= kwargs.get('thresh', self.thresh)
        self.box_thresh = kwargs.get('box_thresh', self.box_thresh)
        self.unclip_ratio = kwargs.get('unclip_ratio', self.unclip_ratio)
        self.box_type = kwargs.get('box_type', self.box_type)
        self.score_mode = kwargs.get('score_mode', self.score_mode)
        pred = outs_dict['maps']
        if isinstance(pred, torch.Tensor):
            pred = pred.detach().cpu().numpy()
        pred = pred[:, 0, :, :]
        segmentation = pred > self.thresh

        boxes_batch = []
        for batch_index in range(pred.shape[0]):
            src_h, src_w, ratio_h, ratio_w = shape_list[batch_index]
            if self.dilation_kernel is not None:
                mask = cv2.dilate(
                    np.array(segmentation[batch_index]).astype(np.uint8),
                    self.dilation_kernel,
                )
            else:
                mask = segmentation[batch_index]
            if self.box_type == 'poly':
                boxes, scores = self.polygons_from_bitmap(
                    pred[batch_index], mask, src_w, src_h)
            elif self.box_type == 'quad':
                boxes, scores = self.boxes_from_bitmap(pred[batch_index], mask,
                                                       src_w, src_h)
            else:
                raise ValueError(
                    "box_type can only be one of ['quad', 'poly']")

            boxes_batch.append({'points': boxes})
        return boxes_batch


class DistillationDBPostProcess(object):

    def __init__(
        self,
        model_name=['student'],
        key=None,
        thresh=0.3,
        box_thresh=0.6,
        max_candidates=1000,
        unclip_ratio=1.5,
        use_dilation=False,
        score_mode='fast',
        box_type='quad',
        **kwargs,
    ):
        self.model_name = model_name
        self.key = key
        self.post_process = DBPostProcess(
            thresh=thresh,
            box_thresh=box_thresh,
            max_candidates=max_candidates,
            unclip_ratio=unclip_ratio,
            use_dilation=use_dilation,
            score_mode=score_mode,
            box_type=box_type,
        )

    def __call__(self, predicts, shape_list):
        results = {}
        for k in self.model_name:
            results[k] = self.post_process(predicts[k], shape_list=shape_list)
        return results