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import cv2 |
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import numpy as np |
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from shapely.geometry import Polygon |
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import pyclipper |
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from concern.config import Configurable, State |
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class SegDetectorRepresenter(Configurable): |
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thresh = State(default=0.3) |
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box_thresh = State(default=0.7) |
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max_candidates = State(default=100) |
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dest = State(default='binary') |
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def __init__(self, cmd={}, **kwargs): |
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self.load_all(**kwargs) |
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self.min_size = 3 |
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self.scale_ratio = 0.4 |
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if 'debug' in cmd: |
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self.debug = cmd['debug'] |
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if 'thresh' in cmd: |
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self.thresh = cmd['thresh'] |
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if 'box_thresh' in cmd: |
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self.box_thresh = cmd['box_thresh'] |
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if 'dest' in cmd: |
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self.dest = cmd['dest'] |
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def represent(self, batch, _pred, is_output_polygon=False): |
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''' |
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batch: (image, polygons, ignore_tags |
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batch: a dict produced by dataloaders. |
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image: tensor of shape (N, C, H, W). |
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polygons: tensor of shape (N, K, 4, 2), the polygons of objective regions. |
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ignore_tags: tensor of shape (N, K), indicates whether a region is ignorable or not. |
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shape: the original shape of images. |
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filename: the original filenames of images. |
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pred: |
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binary: text region segmentation map, with shape (N, 1, H, W) |
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thresh: [if exists] thresh hold prediction with shape (N, 1, H, W) |
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thresh_binary: [if exists] binarized with threshhold, (N, 1, H, W) |
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''' |
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images = batch['image'] |
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if isinstance(_pred, dict): |
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pred = _pred[self.dest] |
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else: |
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pred = _pred |
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segmentation = self.binarize(pred) |
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boxes_batch = [] |
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scores_batch = [] |
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for batch_index in range(images.size(0)): |
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height, width = batch['shape'][batch_index] |
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if is_output_polygon: |
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boxes, scores = self.polygons_from_bitmap( |
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pred[batch_index], |
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segmentation[batch_index], width, height) |
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else: |
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boxes, scores = self.boxes_from_bitmap( |
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pred[batch_index], |
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segmentation[batch_index], width, height) |
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boxes_batch.append(boxes) |
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scores_batch.append(scores) |
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return boxes_batch, scores_batch |
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def binarize(self, pred): |
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return pred > self.thresh |
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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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assert _bitmap.size(0) == 1 |
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bitmap = _bitmap.cpu().numpy()[0] |
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pred = pred.cpu().detach().numpy()[0] |
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height, width = bitmap.shape |
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boxes = [] |
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scores = [] |
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contours, _ = cv2.findContours( |
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(bitmap*255).astype(np.uint8), |
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cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE) |
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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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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, unclip_ratio=2.0) |
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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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if not isinstance(dest_width, int): |
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dest_width = dest_width.item() |
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dest_height = dest_height.item() |
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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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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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assert _bitmap.size(0) == 1 |
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bitmap = _bitmap.cpu().numpy()[0] |
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pred = pred.cpu().detach().numpy()[0] |
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height, width = bitmap.shape |
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contours, _ = cv2.findContours( |
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(bitmap*255).astype(np.uint8), |
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cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE) |
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num_contours = min(len(contours), self.max_candidates) |
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boxes = np.zeros((num_contours, 4, 2), dtype=np.int16) |
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scores = np.zeros((num_contours,), dtype=np.float32) |
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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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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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box = self.unclip(points).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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if not isinstance(dest_width, int): |
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dest_width = dest_width.item() |
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dest_height = dest_height.item() |
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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[index, :, :] = box.astype(np.int16) |
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scores[index] = score |
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return boxes, scores |
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def unclip(self, box, unclip_ratio=1.5): |
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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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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 = [points[index_1], points[index_2], |
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points[index_3], points[index_4]] |
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return box, min(bounding_box[1]) |
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def box_score_fast(self, bitmap, _box): |
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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(np.int32), 0, w - 1) |
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xmax = np.clip(np.ceil(box[:, 0].max()).astype(np.int32), 0, w - 1) |
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ymin = np.clip(np.floor(box[:, 1].min()).astype(np.int32), 0, h - 1) |
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ymax = np.clip(np.ceil(box[:, 1].max()).astype(np.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(np.int32), 1) |
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return cv2.mean(bitmap[ymin:ymax+1, xmin:xmax+1], mask)[0] |
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