chore: import upstream snapshot with attribution

benchmark/PaddleOCR_DBNet/post_processing/__init__.py

@@ -0,0 +1,13 @@
+# -*- coding: utf-8 -*-
+# @Time    : 2019/12/5 15:17
+# @Author  : zhoujun
+
+from .seg_detector_representer import SegDetectorRepresenter
+
+
+def get_post_processing(config):
+    try:
+        cls = eval(config["type"])(**config["args"])
+        return cls
+    except:
+        return None

benchmark/PaddleOCR_DBNet/post_processing/seg_detector_representer.py

@@ -0,0 +1,191 @@
+import cv2
+import numpy as np
+import pyclipper
+import paddle
+from shapely.geometry import Polygon
+
+
+class SegDetectorRepresenter:
+    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, batch, pred, is_output_polygon=False):
+        """
+        batch: (image, polygons, ignore_tags
+        batch: a dict produced by dataloaders.
+            image: tensor of shape (N, C, H, W).
+            polygons: tensor of shape (N, K, 4, 2), the polygons of objective regions.
+            ignore_tags: tensor of shape (N, K), indicates whether a region is ignorable or not.
+            shape: the original shape of images.
+            filename: the original filenames of images.
+        pred:
+            binary: text region segmentation map, with shape (N, H, W)
+            thresh: [if exists] thresh hold prediction with shape (N, H, W)
+            thresh_binary: [if exists] binarized with threshold, (N, H, W)
+        """
+        if isinstance(pred, paddle.Tensor):
+            pred = pred.numpy()
+        pred = pred[:, 0, :, :]
+        segmentation = self.binarize(pred)
+        boxes_batch = []
+        scores_batch = []
+        for batch_index in range(pred.shape[0]):
+            height, width = batch["shape"][batch_index]
+            if is_output_polygon:
+                boxes, scores = self.polygons_from_bitmap(
+                    pred[batch_index], segmentation[batch_index], width, height
+                )
+            else:
+                boxes, scores = self.boxes_from_bitmap(
+                    pred[batch_index], segmentation[batch_index], width, height
+                )
+            boxes_batch.append(boxes)
+            scores_batch.append(scores)
+        return boxes_batch, scores_batch
+
+    def binarize(self, pred):
+        return pred > self.thresh
+
+    def polygons_from_bitmap(self, pred, _bitmap, dest_width, dest_height):
+        """
+        _bitmap: single map with shape (H, W),
+            whose values are binarized as {0, 1}
+        """
+
+        assert len(_bitmap.shape) == 2
+        bitmap = _bitmap  # The first channel
+        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 = 0.005 * cv2.arcLength(contour, True)
+            approx = cv2.approxPolyDP(contour, epsilon, True)
+            points = approx.reshape((-1, 2))
+            if points.shape[0] < 4:
+                continue
+            # _, sside = self.get_mini_boxes(contour)
+            # if sside < self.min_size:
+            #     continue
+            score = self.box_score_fast(pred, contour.squeeze(1))
+            if self.box_thresh > score:
+                continue
+
+            if points.shape[0] > 2:
+                box = self.unclip(points, unclip_ratio=self.unclip_ratio)
+                if len(box) > 1:
+                    continue
+            else:
+                continue
+            box = box.reshape(-1, 2)
+            _, sside = self.get_mini_boxes(box.reshape((-1, 1, 2)))
+            if sside < self.min_size + 2:
+                continue
+
+            if not isinstance(dest_width, int):
+                dest_width = dest_width.item()
+                dest_height = dest_height.item()
+
+            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)
+            scores.append(score)
+        return boxes, scores
+
+    def boxes_from_bitmap(self, pred, _bitmap, dest_width, dest_height):
+        """
+        _bitmap: single map with shape (H, W),
+            whose values are binarized as {0, 1}
+        """
+
+        assert len(_bitmap.shape) == 2
+        bitmap = _bitmap  # The first channel
+        height, width = bitmap.shape
+        contours, _ = cv2.findContours(
+            (bitmap * 255).astype(np.uint8), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE
+        )
+        num_contours = min(len(contours), self.max_candidates)
+        boxes = np.zeros((num_contours, 4, 2), dtype=np.int16)
+        scores = np.zeros((num_contours,), dtype=np.float32)
+
+        for index in range(num_contours):
+            contour = contours[index].squeeze(1)
+            points, sside = self.get_mini_boxes(contour)
+            if sside < self.min_size:
+                continue
+            points = np.array(points)
+            score = self.box_score_fast(pred, contour)
+            if self.box_thresh > score:
+                continue
+
+            box = self.unclip(points, unclip_ratio=self.unclip_ratio).reshape(-1, 1, 2)
+            box, sside = self.get_mini_boxes(box)
+            if sside < self.min_size + 2:
+                continue
+            box = np.array(box)
+            if not isinstance(dest_width, int):
+                dest_width = dest_width.item()
+                dest_height = dest_height.item()
+
+            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[index, :, :] = box.astype(np.int16)
+            scores[index] = score
+        return boxes, 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.int32), 0, w - 1)
+        xmax = np.clip(np.ceil(box[:, 0].max()).astype(np.int32), 0, w - 1)
+        ymin = np.clip(np.floor(box[:, 1].min()).astype(np.int32), 0, h - 1)
+        ymax = np.clip(np.ceil(box[:, 1].max()).astype(np.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(np.int32), 1)
+        return cv2.mean(bitmap[ymin : ymax + 1, xmin : xmax + 1], mask)[0]