删除 db torch后处理

ppocr/postprocess/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]
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