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import cv2 |
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import numpy as np |
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import pyclipper |
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from shapely.geometry import Polygon |
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from collections import namedtuple |
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import torch |
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import warnings |
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warnings.filterwarnings('ignore') |
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def iou_rotate(box_a, box_b, method='union'): |
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rect_a = cv2.minAreaRect(box_a) |
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rect_b = cv2.minAreaRect(box_b) |
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r1 = cv2.rotatedRectangleIntersection(rect_a, rect_b) |
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if r1[0] == 0: |
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return 0 |
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else: |
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inter_area = cv2.contourArea(r1[1]) |
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area_a = cv2.contourArea(box_a) |
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area_b = cv2.contourArea(box_b) |
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union_area = area_a + area_b - inter_area |
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if union_area == 0 or inter_area == 0: |
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return 0 |
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if method == 'union': |
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iou = inter_area / union_area |
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elif method == 'intersection': |
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iou = inter_area / min(area_a, area_b) |
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else: |
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raise NotImplementedError |
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return iou |
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class SegDetectorRepresenter(): |
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def __init__(self, thresh=0.3, box_thresh=0.7, max_candidates=1000, unclip_ratio=1.5): |
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self.min_size = 3 |
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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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def __call__(self, batch, pred, is_output_polygon=False, height=None, width=None): |
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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, H, W) |
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thresh: [if exists] thresh hold prediction with shape (N, H, W) |
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thresh_binary: [if exists] binarized with threshhold, (N, H, W) |
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''' |
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pred = pred[:, 0, :, :] |
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segmentation = self.binarize(pred) |
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boxes_batch = [] |
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scores_batch = [] |
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batch_size = pred.size(0) if isinstance(pred, torch.Tensor) else pred.shape[0] |
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if height is None: |
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height = pred.shape[1] |
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if width is None: |
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width = pred.shape[2] |
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for batch_index in range(batch_size): |
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if is_output_polygon: |
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boxes, scores = self.polygons_from_bitmap(pred[batch_index], segmentation[batch_index], width, height) |
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else: |
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boxes, scores = self.boxes_from_bitmap(pred[batch_index], 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 (H, W), |
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whose values are binarized as {0, 1} |
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''' |
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assert len(_bitmap.shape) == 2 |
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bitmap = _bitmap.cpu().numpy() |
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pred = pred.cpu().detach().numpy() |
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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((bitmap * 255).astype(np.uint8), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE) |
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for contour in contours[:self.max_candidates]: |
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epsilon = 0.005 * 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, contour.squeeze(1)) |
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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=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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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(np.round(box[:, 0] / width * dest_width), 0, dest_width) |
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box[:, 1] = np.clip(np.round(box[:, 1] / height * dest_height), 0, dest_height) |
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boxes.append(box) |
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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 (H, W), |
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whose values are binarized as {0, 1} |
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''' |
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assert len(_bitmap.shape) == 2 |
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if isinstance(pred, torch.Tensor): |
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bitmap = _bitmap.cpu().numpy() |
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pred = pred.cpu().detach().numpy() |
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else: |
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bitmap = _bitmap |
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height, width = bitmap.shape |
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contours, _ = cv2.findContours((bitmap * 255).astype(np.uint8), 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.int64) |
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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].squeeze(1) |
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points, sside = self.get_mini_boxes(contour) |
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if sside < 2: |
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continue |
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points = np.array(points) |
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score = self.box_score_fast(pred, contour) |
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box = self.unclip(points, unclip_ratio=self.unclip_ratio).reshape(-1, 1, 2) |
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box, sside = self.get_mini_boxes(box) |
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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(np.round(box[:, 0] / width * dest_width), 0, dest_width) |
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box[:, 1] = np.clip(np.round(box[:, 1] / height * dest_height), 0, dest_height) |
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boxes[index, :, :] = box.astype(np.int64) |
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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], 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.int64), 0, w - 1) |
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xmax = np.clip(np.ceil(box[:, 0].max()).astype(np.int64), 0, w - 1) |
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ymin = np.clip(np.floor(box[:, 1].min()).astype(np.int64), 0, h - 1) |
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ymax = np.clip(np.ceil(box[:, 1].max()).astype(np.int64), 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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if bitmap.dtype == np.float16: |
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bitmap = bitmap.astype(np.float32) |
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return cv2.mean(bitmap[ymin:ymax + 1, xmin:xmax + 1], mask)[0] |
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class AverageMeter(object): |
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"""Computes and stores the average and current value""" |
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def __init__(self): |
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self.reset() |
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def reset(self): |
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self.val = 0 |
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self.avg = 0 |
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self.sum = 0 |
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self.count = 0 |
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def update(self, val, n=1): |
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self.val = val |
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self.sum += val * n |
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self.count += n |
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self.avg = self.sum / self.count |
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return self |
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class DetectionIoUEvaluator(object): |
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def __init__(self, is_output_polygon=False, iou_constraint=0.5, area_precision_constraint=0.5): |
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self.is_output_polygon = is_output_polygon |
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self.iou_constraint = iou_constraint |
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self.area_precision_constraint = area_precision_constraint |
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def evaluate_image(self, gt, pred): |
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def get_union(pD, pG): |
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return Polygon(pD).union(Polygon(pG)).area |
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def get_intersection_over_union(pD, pG): |
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return get_intersection(pD, pG) / get_union(pD, pG) |
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def get_intersection(pD, pG): |
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return Polygon(pD).intersection(Polygon(pG)).area |
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def compute_ap(confList, matchList, numGtCare): |
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correct = 0 |
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AP = 0 |
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if len(confList) > 0: |
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confList = np.array(confList) |
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matchList = np.array(matchList) |
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sorted_ind = np.argsort(-confList) |
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confList = confList[sorted_ind] |
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matchList = matchList[sorted_ind] |
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for n in range(len(confList)): |
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match = matchList[n] |
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if match: |
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correct += 1 |
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AP += float(correct) / (n + 1) |
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if numGtCare > 0: |
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AP /= numGtCare |
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return AP |
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perSampleMetrics = {} |
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matchedSum = 0 |
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Rectangle = namedtuple('Rectangle', 'xmin ymin xmax ymax') |
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numGlobalCareGt = 0 |
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numGlobalCareDet = 0 |
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arrGlobalConfidences = [] |
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arrGlobalMatches = [] |
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recall = 0 |
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precision = 0 |
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hmean = 0 |
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detMatched = 0 |
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iouMat = np.empty([1, 1]) |
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gtPols = [] |
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detPols = [] |
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gtPolPoints = [] |
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detPolPoints = [] |
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gtDontCarePolsNum = [] |
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detDontCarePolsNum = [] |
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pairs = [] |
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detMatchedNums = [] |
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arrSampleConfidences = [] |
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arrSampleMatch = [] |
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evaluationLog = "" |
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for n in range(len(gt)): |
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points = gt[n]['points'] |
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dontCare = gt[n]['ignore'] |
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if not Polygon(points).is_valid or not Polygon(points).is_simple: |
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continue |
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gtPol = points |
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gtPols.append(gtPol) |
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gtPolPoints.append(points) |
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if dontCare: |
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gtDontCarePolsNum.append(len(gtPols) - 1) |
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evaluationLog += "GT polygons: " + str(len(gtPols)) + (" (" + str(len( |
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gtDontCarePolsNum)) + " don't care)\n" if len(gtDontCarePolsNum) > 0 else "\n") |
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for n in range(len(pred)): |
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points = pred[n]['points'] |
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if not Polygon(points).is_valid or not Polygon(points).is_simple: |
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continue |
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detPol = points |
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detPols.append(detPol) |
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detPolPoints.append(points) |
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if len(gtDontCarePolsNum) > 0: |
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for dontCarePol in gtDontCarePolsNum: |
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dontCarePol = gtPols[dontCarePol] |
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intersected_area = get_intersection(dontCarePol, detPol) |
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pdDimensions = Polygon(detPol).area |
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precision = 0 if pdDimensions == 0 else intersected_area / pdDimensions |
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if (precision > self.area_precision_constraint): |
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detDontCarePolsNum.append(len(detPols) - 1) |
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break |
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evaluationLog += "DET polygons: " + str(len(detPols)) + (" (" + str(len( |
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detDontCarePolsNum)) + " don't care)\n" if len(detDontCarePolsNum) > 0 else "\n") |
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if len(gtPols) > 0 and len(detPols) > 0: |
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outputShape = [len(gtPols), len(detPols)] |
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iouMat = np.empty(outputShape) |
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gtRectMat = np.zeros(len(gtPols), np.int8) |
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detRectMat = np.zeros(len(detPols), np.int8) |
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if self.is_output_polygon: |
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for gtNum in range(len(gtPols)): |
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for detNum in range(len(detPols)): |
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pG = gtPols[gtNum] |
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pD = detPols[detNum] |
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iouMat[gtNum, detNum] = get_intersection_over_union(pD, pG) |
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else: |
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for gtNum in range(len(gtPols)): |
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for detNum in range(len(detPols)): |
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pG = np.float32(gtPols[gtNum]) |
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pD = np.float32(detPols[detNum]) |
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iouMat[gtNum, detNum] = iou_rotate(pD, pG) |
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for gtNum in range(len(gtPols)): |
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for detNum in range(len(detPols)): |
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if gtRectMat[gtNum] == 0 and detRectMat[ |
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detNum] == 0 and gtNum not in gtDontCarePolsNum and detNum not in detDontCarePolsNum: |
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if iouMat[gtNum, detNum] > self.iou_constraint: |
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gtRectMat[gtNum] = 1 |
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detRectMat[detNum] = 1 |
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detMatched += 1 |
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pairs.append({'gt': gtNum, 'det': detNum}) |
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detMatchedNums.append(detNum) |
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evaluationLog += "Match GT #" + \ |
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str(gtNum) + " with Det #" + str(detNum) + "\n" |
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numGtCare = (len(gtPols) - len(gtDontCarePolsNum)) |
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numDetCare = (len(detPols) - len(detDontCarePolsNum)) |
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if numGtCare == 0: |
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recall = float(1) |
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precision = float(0) if numDetCare > 0 else float(1) |
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else: |
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recall = float(detMatched) / numGtCare |
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precision = 0 if numDetCare == 0 else float( |
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detMatched) / numDetCare |
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hmean = 0 if (precision + recall) == 0 else 2.0 * \ |
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precision * recall / (precision + recall) |
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matchedSum += detMatched |
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numGlobalCareGt += numGtCare |
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numGlobalCareDet += numDetCare |
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perSampleMetrics = { |
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'precision': precision, |
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'recall': recall, |
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'hmean': hmean, |
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'pairs': pairs, |
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'iouMat': [] if len(detPols) > 100 else iouMat.tolist(), |
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'gtPolPoints': gtPolPoints, |
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'detPolPoints': detPolPoints, |
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'gtCare': numGtCare, |
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'detCare': numDetCare, |
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'gtDontCare': gtDontCarePolsNum, |
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'detDontCare': detDontCarePolsNum, |
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'detMatched': detMatched, |
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'evaluationLog': evaluationLog |
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} |
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return perSampleMetrics |
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|
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def combine_results(self, results): |
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numGlobalCareGt = 0 |
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numGlobalCareDet = 0 |
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matchedSum = 0 |
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for result in results: |
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numGlobalCareGt += result['gtCare'] |
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numGlobalCareDet += result['detCare'] |
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matchedSum += result['detMatched'] |
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methodRecall = 0 if numGlobalCareGt == 0 else float( |
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matchedSum) / numGlobalCareGt |
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methodPrecision = 0 if numGlobalCareDet == 0 else float( |
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matchedSum) / numGlobalCareDet |
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methodHmean = 0 if methodRecall + methodPrecision == 0 else 2 * \ |
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methodRecall * methodPrecision / ( |
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methodRecall + methodPrecision) |
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methodMetrics = {'precision': methodPrecision, |
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'recall': methodRecall, 'hmean': methodHmean} |
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return methodMetrics |
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class QuadMetric(): |
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def __init__(self, is_output_polygon=False): |
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self.is_output_polygon = is_output_polygon |
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self.evaluator = DetectionIoUEvaluator(is_output_polygon=is_output_polygon) |
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def measure(self, batch, output, box_thresh=0.6): |
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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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output: (polygons, ...) |
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''' |
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results = [] |
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gt_polyons_batch = batch['text_polys'] |
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ignore_tags_batch = batch['ignore_tags'] |
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pred_polygons_batch = np.array(output[0]) |
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pred_scores_batch = np.array(output[1]) |
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for polygons, pred_polygons, pred_scores, ignore_tags in zip(gt_polyons_batch, pred_polygons_batch, pred_scores_batch, ignore_tags_batch): |
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gt = [dict(points=np.int64(polygons[i]), ignore=ignore_tags[i]) for i in range(len(polygons))] |
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if self.is_output_polygon: |
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pred = [dict(points=pred_polygons[i]) for i in range(len(pred_polygons))] |
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else: |
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pred = [] |
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|
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for i in range(pred_polygons.shape[0]): |
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if pred_scores[i] >= box_thresh: |
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|
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pred.append(dict(points=pred_polygons[i, :, :].astype(np.int64))) |
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|
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results.append(self.evaluator.evaluate_image(gt, pred)) |
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return results |
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|
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def validate_measure(self, batch, output, box_thresh=0.6): |
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return self.measure(batch, output, box_thresh) |
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|
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def evaluate_measure(self, batch, output): |
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return self.measure(batch, output), np.linspace(0, batch['image'].shape[0]).tolist() |
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|
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def gather_measure(self, raw_metrics): |
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raw_metrics = [image_metrics |
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for batch_metrics in raw_metrics |
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for image_metrics in batch_metrics] |
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|
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result = self.evaluator.combine_results(raw_metrics) |
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|
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precision = AverageMeter() |
|
|
recall = AverageMeter() |
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fmeasure = AverageMeter() |
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|
|
|
|
precision.update(result['precision'], n=len(raw_metrics)) |
|
|
recall.update(result['recall'], n=len(raw_metrics)) |
|
|
fmeasure_score = 2 * precision.val * recall.val / (precision.val + recall.val + 1e-8) |
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fmeasure.update(fmeasure_score) |
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return { |
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'precision': precision, |
|
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'recall': recall, |
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'fmeasure': fmeasure |
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} |
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|
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def shrink_polygon_py(polygon, shrink_ratio): |
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|
""" |
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|
对框进行缩放，返回去的比例为1/shrink_ratio 即可 |
|
|
""" |
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|
cx = polygon[:, 0].mean() |
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|
cy = polygon[:, 1].mean() |
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|
polygon[:, 0] = cx + (polygon[:, 0] - cx) * shrink_ratio |
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polygon[:, 1] = cy + (polygon[:, 1] - cy) * shrink_ratio |
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return polygon |
|
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def shrink_polygon_pyclipper(polygon, shrink_ratio): |
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|
from shapely.geometry import Polygon |
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import pyclipper |
|
|
polygon_shape = Polygon(polygon) |
|
|
distance = polygon_shape.area * (1 - np.power(shrink_ratio, 2)) / polygon_shape.length |
|
|
subject = [tuple(l) for l in polygon] |
|
|
padding = pyclipper.PyclipperOffset() |
|
|
padding.AddPath(subject, pyclipper.JT_ROUND, pyclipper.ET_CLOSEDPOLYGON) |
|
|
shrinked = padding.Execute(-distance) |
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|
if shrinked == []: |
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|
shrinked = np.array(shrinked) |
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else: |
|
|
shrinked = np.array(shrinked[0]).reshape(-1, 2) |
|
|
return shrinked |
|
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|
|
|
class MakeShrinkMap(): |
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|
r''' |
|
|
Making binary mask from detection data with ICDAR format. |
|
|
Typically following the process of class `MakeICDARData`. |
|
|
''' |
|
|
|
|
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def __init__(self, min_text_size=4, shrink_ratio=0.4, shrink_type='pyclipper'): |
|
|
shrink_func_dict = {'py': shrink_polygon_py, 'pyclipper': shrink_polygon_pyclipper} |
|
|
self.shrink_func = shrink_func_dict[shrink_type] |
|
|
self.min_text_size = min_text_size |
|
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self.shrink_ratio = shrink_ratio |
|
|
|
|
|
def __call__(self, data: dict) -> dict: |
|
|
""" |
|
|
从scales中随机选择一个尺度，对图片和文本框进行缩放 |
|
|
:param data: {'imgs':,'text_polys':,'texts':,'ignore_tags':} |
|
|
:return: |
|
|
""" |
|
|
image = data['imgs'] |
|
|
text_polys = data['text_polys'] |
|
|
ignore_tags = data['ignore_tags'] |
|
|
|
|
|
h, w = image.shape[:2] |
|
|
text_polys, ignore_tags = self.validate_polygons(text_polys, ignore_tags, h, w) |
|
|
gt = np.zeros((h, w), dtype=np.float32) |
|
|
mask = np.ones((h, w), dtype=np.float32) |
|
|
for i in range(len(text_polys)): |
|
|
polygon = text_polys[i] |
|
|
height = max(polygon[:, 1]) - min(polygon[:, 1]) |
|
|
width = max(polygon[:, 0]) - min(polygon[:, 0]) |
|
|
if ignore_tags[i] or min(height, width) < self.min_text_size: |
|
|
cv2.fillPoly(mask, polygon.astype(np.int32)[np.newaxis, :, :], 0) |
|
|
ignore_tags[i] = True |
|
|
else: |
|
|
shrinked = self.shrink_func(polygon, self.shrink_ratio) |
|
|
if shrinked.size == 0: |
|
|
cv2.fillPoly(mask, polygon.astype(np.int32)[np.newaxis, :, :], 0) |
|
|
ignore_tags[i] = True |
|
|
continue |
|
|
cv2.fillPoly(gt, [shrinked.astype(np.int32)], 1) |
|
|
|
|
|
data['shrink_map'] = gt |
|
|
data['shrink_mask'] = mask |
|
|
return data |
|
|
|
|
|
def validate_polygons(self, polygons, ignore_tags, h, w): |
|
|
''' |
|
|
polygons (numpy.array, required): of shape (num_instances, num_points, 2) |
|
|
''' |
|
|
if len(polygons) == 0: |
|
|
return polygons, ignore_tags |
|
|
assert len(polygons) == len(ignore_tags) |
|
|
for polygon in polygons: |
|
|
polygon[:, 0] = np.clip(polygon[:, 0], 0, w - 1) |
|
|
polygon[:, 1] = np.clip(polygon[:, 1], 0, h - 1) |
|
|
|
|
|
for i in range(len(polygons)): |
|
|
area = self.polygon_area(polygons[i]) |
|
|
if abs(area) < 1: |
|
|
ignore_tags[i] = True |
|
|
if area > 0: |
|
|
polygons[i] = polygons[i][::-1, :] |
|
|
return polygons, ignore_tags |
|
|
|
|
|
def polygon_area(self, polygon): |
|
|
return cv2.contourArea(polygon) |
|
|
|
|
|
|
|
|
class MakeBorderMap(): |
|
|
def __init__(self, shrink_ratio=0.4, thresh_min=0.3, thresh_max=0.7): |
|
|
self.shrink_ratio = shrink_ratio |
|
|
self.thresh_min = thresh_min |
|
|
self.thresh_max = thresh_max |
|
|
|
|
|
def __call__(self, data: dict) -> dict: |
|
|
""" |
|
|
从scales中随机选择一个尺度，对图片和文本框进行缩放 |
|
|
:param data: {'imgs':,'text_polys':,'texts':,'ignore_tags':} |
|
|
:return: |
|
|
""" |
|
|
im = data['imgs'] |
|
|
text_polys = data['text_polys'] |
|
|
ignore_tags = data['ignore_tags'] |
|
|
|
|
|
canvas = np.zeros(im.shape[:2], dtype=np.float32) |
|
|
mask = np.zeros(im.shape[:2], dtype=np.float32) |
|
|
|
|
|
for i in range(len(text_polys)): |
|
|
if ignore_tags[i]: |
|
|
continue |
|
|
self.draw_border_map(text_polys[i], canvas, mask=mask) |
|
|
canvas = canvas * (self.thresh_max - self.thresh_min) + self.thresh_min |
|
|
|
|
|
data['threshold_map'] = canvas |
|
|
data['threshold_mask'] = mask |
|
|
return data |
|
|
|
|
|
def draw_border_map(self, polygon, canvas, mask): |
|
|
polygon = np.array(polygon) |
|
|
assert polygon.ndim == 2 |
|
|
assert polygon.shape[1] == 2 |
|
|
|
|
|
polygon_shape = Polygon(polygon) |
|
|
if polygon_shape.area <= 0: |
|
|
return |
|
|
distance = polygon_shape.area * (1 - np.power(self.shrink_ratio, 2)) / polygon_shape.length |
|
|
subject = [tuple(l) for l in polygon] |
|
|
padding = pyclipper.PyclipperOffset() |
|
|
padding.AddPath(subject, pyclipper.JT_ROUND, |
|
|
pyclipper.ET_CLOSEDPOLYGON) |
|
|
|
|
|
padded_polygon = np.array(padding.Execute(distance)[0]) |
|
|
cv2.fillPoly(mask, [padded_polygon.astype(np.int32)], 1.0) |
|
|
|
|
|
xmin = padded_polygon[:, 0].min() |
|
|
xmax = padded_polygon[:, 0].max() |
|
|
ymin = padded_polygon[:, 1].min() |
|
|
ymax = padded_polygon[:, 1].max() |
|
|
width = xmax - xmin + 1 |
|
|
height = ymax - ymin + 1 |
|
|
|
|
|
polygon[:, 0] = polygon[:, 0] - xmin |
|
|
polygon[:, 1] = polygon[:, 1] - ymin |
|
|
|
|
|
xs = np.broadcast_to( |
|
|
np.linspace(0, width - 1, num=width).reshape(1, width), (height, width)) |
|
|
ys = np.broadcast_to( |
|
|
np.linspace(0, height - 1, num=height).reshape(height, 1), (height, width)) |
|
|
|
|
|
distance_map = np.zeros( |
|
|
(polygon.shape[0], height, width), dtype=np.float32) |
|
|
for i in range(polygon.shape[0]): |
|
|
j = (i + 1) % polygon.shape[0] |
|
|
absolute_distance = self.distance(xs, ys, polygon[i], polygon[j]) |
|
|
distance_map[i] = np.clip(absolute_distance / distance, 0, 1) |
|
|
distance_map = distance_map.min(axis=0) |
|
|
|
|
|
xmin_valid = min(max(0, xmin), canvas.shape[1] - 1) |
|
|
xmax_valid = min(max(0, xmax), canvas.shape[1] - 1) |
|
|
ymin_valid = min(max(0, ymin), canvas.shape[0] - 1) |
|
|
ymax_valid = min(max(0, ymax), canvas.shape[0] - 1) |
|
|
canvas[ymin_valid:ymax_valid + 1, xmin_valid:xmax_valid + 1] = np.fmax( |
|
|
1 - distance_map[ |
|
|
ymin_valid - ymin:ymax_valid - ymax + height, |
|
|
xmin_valid - xmin:xmax_valid - xmax + width], |
|
|
canvas[ymin_valid:ymax_valid + 1, xmin_valid:xmax_valid + 1]) |
|
|
|
|
|
def distance(self, xs, ys, point_1, point_2): |
|
|
''' |
|
|
compute the distance from point to a line |
|
|
ys: coordinates in the first axis |
|
|
xs: coordinates in the second axis |
|
|
point_1, point_2: (x, y), the end of the line |
|
|
''' |
|
|
height, width = xs.shape[:2] |
|
|
square_distance_1 = np.square(xs - point_1[0]) + np.square(ys - point_1[1]) |
|
|
square_distance_2 = np.square(xs - point_2[0]) + np.square(ys - point_2[1]) |
|
|
square_distance = np.square(point_1[0] - point_2[0]) + np.square(point_1[1] - point_2[1]) |
|
|
|
|
|
cosin = (square_distance - square_distance_1 - square_distance_2) / (2 * np.sqrt(square_distance_1 * square_distance_2)) |
|
|
square_sin = 1 - np.square(cosin) |
|
|
square_sin = np.nan_to_num(square_sin) |
|
|
|
|
|
result = np.sqrt(square_distance_1 * square_distance_2 * square_sin / square_distance) |
|
|
result[cosin < 0] = np.sqrt(np.fmin(square_distance_1, square_distance_2))[cosin < 0] |
|
|
return result |
|
|
|
|
|
def extend_line(self, point_1, point_2, result): |
|
|
ex_point_1 = (int(round(point_1[0] + (point_1[0] - point_2[0]) * (1 + self.shrink_ratio))), |
|
|
int(round(point_1[1] + (point_1[1] - point_2[1]) * (1 + self.shrink_ratio)))) |
|
|
cv2.line(result, tuple(ex_point_1), tuple(point_1), 4096.0, 1, lineType=cv2.LINE_AA, shift=0) |
|
|
ex_point_2 = (int(round(point_2[0] + (point_2[0] - point_1[0]) * (1 + self.shrink_ratio))), |
|
|
int(round(point_2[1] + (point_2[1] - point_1[1]) * (1 + self.shrink_ratio)))) |
|
|
cv2.line(result, tuple(ex_point_2), tuple(point_2), 4096.0, 1, lineType=cv2.LINE_AA, shift=0) |
|
|
return ex_point_1, ex_point_2 |
