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""" |
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This code is refered from: |
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https://github.com/WenmuZhou/DBNet.pytorch/blob/master/post_processing/seg_detector_representer.py |
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""" |
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from __future__ import absolute_import |
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from __future__ import division |
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from __future__ import print_function |
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|
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import numpy as np |
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import cv2 |
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import paddle |
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from shapely.geometry import Polygon |
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import pyclipper |
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class DBPostProcess(object): |
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""" |
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The post process for Differentiable Binarization (DB). |
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""" |
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|
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def __init__(self, |
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thresh=0.3, |
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box_thresh=0.7, |
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max_candidates=1000, |
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unclip_ratio=2.0, |
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use_dilation=False, |
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score_mode="fast", |
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box_type='quad', |
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**kwargs): |
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self.thresh = thresh |
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self.box_thresh = box_thresh |
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self.max_candidates = max_candidates |
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self.unclip_ratio = unclip_ratio |
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self.min_size = 3 |
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self.score_mode = score_mode |
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self.box_type = box_type |
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assert score_mode in [ |
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"slow", "fast" |
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], "Score mode must be in [slow, fast] but got: {}".format(score_mode) |
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self.dilation_kernel = None if not use_dilation else np.array( |
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[[1, 1], [1, 1]]) |
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|
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def polygons_from_bitmap(self, pred, _bitmap, dest_width, dest_height): |
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''' |
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_bitmap: single map with shape (1, H, W), |
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whose values are binarized as {0, 1} |
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''' |
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bitmap = _bitmap |
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height, width = bitmap.shape |
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boxes = [] |
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scores = [] |
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|
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contours, _ = cv2.findContours((bitmap * 255).astype(np.uint8), |
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cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE) |
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|
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for contour in contours[:self.max_candidates]: |
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epsilon = 0.002 * cv2.arcLength(contour, True) |
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approx = cv2.approxPolyDP(contour, epsilon, True) |
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points = approx.reshape((-1, 2)) |
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if points.shape[0] < 4: |
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continue |
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|
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score = self.box_score_fast(pred, points.reshape(-1, 2)) |
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if self.box_thresh > score: |
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continue |
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if points.shape[0] > 2: |
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box = self.unclip(points, self.unclip_ratio) |
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if len(box) > 1: |
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continue |
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else: |
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continue |
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box = box.reshape(-1, 2) |
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_, sside = self.get_mini_boxes(box.reshape((-1, 1, 2))) |
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if sside < self.min_size + 2: |
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continue |
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box = np.array(box) |
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box[:, 0] = np.clip( |
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np.round(box[:, 0] / width * dest_width), 0, dest_width) |
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box[:, 1] = np.clip( |
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np.round(box[:, 1] / height * dest_height), 0, dest_height) |
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boxes.append(box.tolist()) |
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scores.append(score) |
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return boxes, scores |
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|
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def boxes_from_bitmap(self, pred, _bitmap, dest_width, dest_height): |
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''' |
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_bitmap: single map with shape (1, H, W), |
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whose values are binarized as {0, 1} |
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''' |
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bitmap = _bitmap |
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height, width = bitmap.shape |
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outs = cv2.findContours((bitmap * 255).astype(np.uint8), cv2.RETR_LIST, |
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cv2.CHAIN_APPROX_SIMPLE) |
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if len(outs) == 3: |
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img, contours, _ = outs[0], outs[1], outs[2] |
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elif len(outs) == 2: |
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contours, _ = outs[0], outs[1] |
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num_contours = min(len(contours), self.max_candidates) |
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|
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boxes = [] |
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scores = [] |
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for index in range(num_contours): |
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contour = contours[index] |
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points, sside = self.get_mini_boxes(contour) |
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if sside < self.min_size: |
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continue |
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points = np.array(points) |
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if self.score_mode == "fast": |
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score = self.box_score_fast(pred, points.reshape(-1, 2)) |
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else: |
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score = self.box_score_slow(pred, contour) |
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if self.box_thresh > score: |
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continue |
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box = self.unclip(points, self.unclip_ratio).reshape(-1, 1, 2) |
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box, sside = self.get_mini_boxes(box) |
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if sside < self.min_size + 2: |
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continue |
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box = np.array(box) |
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|
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box[:, 0] = np.clip( |
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np.round(box[:, 0] / width * dest_width), 0, dest_width) |
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box[:, 1] = np.clip( |
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np.round(box[:, 1] / height * dest_height), 0, dest_height) |
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boxes.append(box.astype("int32")) |
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scores.append(score) |
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return np.array(boxes, dtype="int32"), scores |
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|
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def unclip(self, box, unclip_ratio): |
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poly = Polygon(box) |
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distance = poly.area * unclip_ratio / poly.length |
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offset = pyclipper.PyclipperOffset() |
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offset.AddPath(box, pyclipper.JT_ROUND, pyclipper.ET_CLOSEDPOLYGON) |
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expanded = np.array(offset.Execute(distance)) |
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return expanded |
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|
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def get_mini_boxes(self, contour): |
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bounding_box = cv2.minAreaRect(contour) |
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points = sorted(list(cv2.boxPoints(bounding_box)), key=lambda x: x[0]) |
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index_1, index_2, index_3, index_4 = 0, 1, 2, 3 |
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if points[1][1] > points[0][1]: |
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index_1 = 0 |
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index_4 = 1 |
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else: |
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index_1 = 1 |
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index_4 = 0 |
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if points[3][1] > points[2][1]: |
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index_2 = 2 |
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index_3 = 3 |
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else: |
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index_2 = 3 |
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index_3 = 2 |
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box = [ |
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points[index_1], points[index_2], points[index_3], points[index_4] |
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] |
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return box, min(bounding_box[1]) |
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|
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def box_score_fast(self, bitmap, _box): |
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''' |
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box_score_fast: use bbox mean score as the mean score |
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''' |
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h, w = bitmap.shape[:2] |
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box = _box.copy() |
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xmin = np.clip(np.floor(box[:, 0].min()).astype("int32"), 0, w - 1) |
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xmax = np.clip(np.ceil(box[:, 0].max()).astype("int32"), 0, w - 1) |
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ymin = np.clip(np.floor(box[:, 1].min()).astype("int32"), 0, h - 1) |
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ymax = np.clip(np.ceil(box[:, 1].max()).astype("int32"), 0, h - 1) |
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mask = np.zeros((ymax - ymin + 1, xmax - xmin + 1), dtype=np.uint8) |
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box[:, 0] = box[:, 0] - xmin |
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box[:, 1] = box[:, 1] - ymin |
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cv2.fillPoly(mask, box.reshape(1, -1, 2).astype("int32"), 1) |
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return cv2.mean(bitmap[ymin:ymax + 1, xmin:xmax + 1], mask)[0] |
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|
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def box_score_slow(self, bitmap, contour): |
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''' |
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box_score_slow: use polyon mean score as the mean score |
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''' |
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h, w = bitmap.shape[:2] |
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contour = contour.copy() |
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contour = np.reshape(contour, (-1, 2)) |
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|
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xmin = np.clip(np.min(contour[:, 0]), 0, w - 1) |
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xmax = np.clip(np.max(contour[:, 0]), 0, w - 1) |
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ymin = np.clip(np.min(contour[:, 1]), 0, h - 1) |
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ymax = np.clip(np.max(contour[:, 1]), 0, h - 1) |
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mask = np.zeros((ymax - ymin + 1, xmax - xmin + 1), dtype=np.uint8) |
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contour[:, 0] = contour[:, 0] - xmin |
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contour[:, 1] = contour[:, 1] - ymin |
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cv2.fillPoly(mask, contour.reshape(1, -1, 2).astype("int32"), 1) |
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return cv2.mean(bitmap[ymin:ymax + 1, xmin:xmax + 1], mask)[0] |
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|
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def __call__(self, outs_dict, shape_list): |
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pred = outs_dict['maps'] |
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if isinstance(pred, paddle.Tensor): |
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pred = pred.numpy() |
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pred = pred[:, 0, :, :] |
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segmentation = pred > self.thresh |
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|
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boxes_batch = [] |
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for batch_index in range(pred.shape[0]): |
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src_h, src_w, ratio_h, ratio_w = shape_list[batch_index] |
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if self.dilation_kernel is not None: |
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mask = cv2.dilate( |
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np.array(segmentation[batch_index]).astype(np.uint8), |
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self.dilation_kernel) |
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else: |
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mask = segmentation[batch_index] |
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if self.box_type == 'poly': |
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boxes, scores = self.polygons_from_bitmap(pred[batch_index], |
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mask, src_w, src_h) |
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elif self.box_type == 'quad': |
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boxes, scores = self.boxes_from_bitmap(pred[batch_index], mask, |
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src_w, src_h) |
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else: |
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raise ValueError("box_type can only be one of ['quad', 'poly']") |
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|
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boxes_batch.append({'points': boxes}) |
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return boxes_batch |
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|
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|
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class DistillationDBPostProcess(object): |
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def __init__(self, |
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model_name=["student"], |
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key=None, |
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thresh=0.3, |
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box_thresh=0.6, |
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max_candidates=1000, |
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unclip_ratio=1.5, |
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use_dilation=False, |
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score_mode="fast", |
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box_type='quad', |
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**kwargs): |
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self.model_name = model_name |
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self.key = key |
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self.post_process = DBPostProcess( |
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thresh=thresh, |
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box_thresh=box_thresh, |
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max_candidates=max_candidates, |
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unclip_ratio=unclip_ratio, |
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use_dilation=use_dilation, |
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score_mode=score_mode, |
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box_type=box_type) |
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|
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def __call__(self, predicts, shape_list): |
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results = {} |
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for k in self.model_name: |
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results[k] = self.post_process(predicts[k], shape_list=shape_list) |
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return results |
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