db_postprocess_torch.py

import cv2
import paddle
import numpy as np
import pyclipper
from shapely.geometry import Polygon


class DBPostProcess():
    def __init__(self,
                 thresh=0.3,
                 box_thresh=0.7,
                 max_candidates=1000,
                 unclip_ratio=1.5):
        self.min_size = 3
        self.thresh = thresh
        self.box_thresh = box_thresh
        self.max_candidates = max_candidates
        self.unclip_ratio = unclip_ratio

    def __call__(self, pred, shape_list, is_output_polygon=False):
        '''
        batch: (image, polygons, ignore_tags
        h_w_list: 包含[h,w]的数组
        pred:
            binary: text region segmentation map, with shape (N, 1,H, W)
        '''
        if isinstance(pred, paddle.Tensor):
            pred = pred.numpy()
        pred = pred[:, 0, :, :]
        segmentation = self.binarize(pred)
        batch_out = []
        for batch_index in range(pred.shape[0]):
            height, width = shape_list[batch_index]
            boxes, scores = self.post_p(
                pred[batch_index],
                segmentation[batch_index],
                width,
                height,
                is_output_polygon=is_output_polygon)
            batch_out.append({"points": boxes})
        return batch_out

    def binarize(self, pred):
        return pred > self.thresh

    def post_p(self,
               pred,
               bitmap,
               dest_width,
               dest_height,
               is_output_polygon=True):
        '''
        _bitmap: single map with shape (H, W),
            whose values are binarized as {0, 1}
        '''
        height, width = pred.shape
        boxes = []
        new_scores = []
        contours, _ = cv2.findContours((bitmap * 255).astype(np.uint8),
                                       cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
        for contour in contours[:self.max_candidates]:
            epsilon = 0.005 * 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, unclip_ratio=self.unclip_ratio)
                if len(box) > 1 or len(box) == 0:
                    continue
            else:
                continue
            four_point_box, sside = self.get_mini_boxes(box.reshape((-1, 1, 2)))
            if sside < self.min_size + 2:
                continue

            if not is_output_polygon:
                box = np.array(four_point_box)
            else:
                box = box.reshape(-1, 2)
            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)
            new_scores.append(score)
        return boxes, new_scores

    def unclip(self, box, unclip_ratio=1.5):
        poly = Polygon(box)
        distance = poly.area * unclip_ratio / poly.length
        offset = pyclipper.PyclipperOffset()
        offset.AddPath(box, pyclipper.JT_ROUND, pyclipper.ET_CLOSEDPOLYGON)
        expanded = np.array(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):
        h, w = bitmap.shape[:2]
        box = _box.copy()
        xmin = np.clip(np.floor(box[:, 0].min()).astype(np.int), 0, w - 1)
        xmax = np.clip(np.ceil(box[:, 0].max()).astype(np.int), 0, w - 1)
        ymin = np.clip(np.floor(box[:, 1].min()).astype(np.int), 0, h - 1)
        ymax = np.clip(np.ceil(box[:, 1].max()).astype(np.int), 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(np.int32), 1)
        return cv2.mean(bitmap[ymin:ymax + 1, xmin:xmax + 1], mask)[0]