utils/db_utils.py

import cv2
import numpy as np
import pyclipper
from shapely.geometry import Polygon
from collections import namedtuple
import torch
import warnings
warnings.filterwarnings('ignore')


def iou_rotate(box_a, box_b, method='union'):
    rect_a = cv2.minAreaRect(box_a)
    rect_b = cv2.minAreaRect(box_b)
    r1 = cv2.rotatedRectangleIntersection(rect_a, rect_b)
    if r1[0] == 0:
        return 0
    else:
        inter_area = cv2.contourArea(r1[1])
        area_a = cv2.contourArea(box_a)
        area_b = cv2.contourArea(box_b)
        union_area = area_a + area_b - inter_area
        if union_area == 0 or inter_area == 0:
            return 0
        if method == 'union':
            iou = inter_area / union_area
        elif method == 'intersection':
            iou = inter_area / min(area_a, area_b)
        else:
            raise NotImplementedError
        return iou

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)
        '''
        pred = pred[:, 0, :, :]
        segmentation = self.binarize(pred)
        boxes_batch = []
        scores_batch = []
        # print(pred.size())
        batch_size = pred.size(0) if isinstance(pred, torch.Tensor) else pred.shape[0]
        for batch_index in range(batch_size):
            # height, width = batch['shape'][batch_index]
            height, width = pred.shape[1], pred.shape[2]
            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.cpu().numpy()  # The first channel
        pred = pred.cpu().detach().numpy()
        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
        if isinstance(pred, torch.Tensor):
            bitmap = _bitmap.cpu().numpy()  # The first channel
            pred = pred.cpu().detach().numpy()
        else:
            bitmap = _bitmap
        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
            if sside < 2:
                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 < 5:
            #     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.int64), 0, w - 1)
        xmax = np.clip(np.ceil(box[:, 0].max()).astype(np.int64), 0, w - 1)
        ymin = np.clip(np.floor(box[:, 1].min()).astype(np.int64), 0, h - 1)
        ymax = np.clip(np.ceil(box[:, 1].max()).astype(np.int64), 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)
        if bitmap.dtype == np.float16:
            bitmap = bitmap.astype(np.float32)
        return cv2.mean(bitmap[ymin:ymax + 1, xmin:xmax + 1], mask)[0]

class AverageMeter(object):
    """Computes and stores the average and current value"""

    def __init__(self):
        self.reset()

    def reset(self):
        self.val = 0
        self.avg = 0
        self.sum = 0
        self.count = 0

    def update(self, val, n=1):
        self.val = val
        self.sum += val * n
        self.count += n
        self.avg = self.sum / self.count
        return self


class DetectionIoUEvaluator(object):
    def __init__(self, is_output_polygon=False, iou_constraint=0.5, area_precision_constraint=0.5):
        self.is_output_polygon = is_output_polygon
        self.iou_constraint = iou_constraint
        self.area_precision_constraint = area_precision_constraint

    def evaluate_image(self, gt, pred):

        def get_union(pD, pG):
            return Polygon(pD).union(Polygon(pG)).area

        def get_intersection_over_union(pD, pG):
            return get_intersection(pD, pG) / get_union(pD, pG)

        def get_intersection(pD, pG):
            return Polygon(pD).intersection(Polygon(pG)).area

        def compute_ap(confList, matchList, numGtCare):
            correct = 0
            AP = 0
            if len(confList) > 0:
                confList = np.array(confList)
                matchList = np.array(matchList)
                sorted_ind = np.argsort(-confList)
                confList = confList[sorted_ind]
                matchList = matchList[sorted_ind]
                for n in range(len(confList)):
                    match = matchList[n]
                    if match:
                        correct += 1
                        AP += float(correct) / (n + 1)

                if numGtCare > 0:
                    AP /= numGtCare

            return AP

        perSampleMetrics = {}

        matchedSum = 0

        Rectangle = namedtuple('Rectangle', 'xmin ymin xmax ymax')

        numGlobalCareGt = 0
        numGlobalCareDet = 0

        arrGlobalConfidences = []
        arrGlobalMatches = []

        recall = 0
        precision = 0
        hmean = 0

        detMatched = 0

        iouMat = np.empty([1, 1])

        gtPols = []
        detPols = []

        gtPolPoints = []
        detPolPoints = []

        # Array of Ground Truth Polygons' keys marked as don't Care
        gtDontCarePolsNum = []
        # Array of Detected Polygons' matched with a don't Care GT
        detDontCarePolsNum = []

        pairs = []
        detMatchedNums = []

        arrSampleConfidences = []
        arrSampleMatch = []

        evaluationLog = ""

        for n in range(len(gt)):
            points = gt[n]['points']
            # transcription = gt[n]['text']
            dontCare = gt[n]['ignore']

            if not Polygon(points).is_valid or not Polygon(points).is_simple:
                continue

            gtPol = points
            gtPols.append(gtPol)
            gtPolPoints.append(points)
            if dontCare:
                gtDontCarePolsNum.append(len(gtPols) - 1)

        evaluationLog += "GT polygons: " + str(len(gtPols)) + (" (" + str(len(
            gtDontCarePolsNum)) + " don't care)\n" if len(gtDontCarePolsNum) > 0 else "\n")

        for n in range(len(pred)):
            points = pred[n]['points']
            if not Polygon(points).is_valid or not Polygon(points).is_simple:
                continue

            detPol = points
            detPols.append(detPol)
            detPolPoints.append(points)
            if len(gtDontCarePolsNum) > 0:
                for dontCarePol in gtDontCarePolsNum:
                    dontCarePol = gtPols[dontCarePol]
                    intersected_area = get_intersection(dontCarePol, detPol)
                    pdDimensions = Polygon(detPol).area
                    precision = 0 if pdDimensions == 0 else intersected_area / pdDimensions
                    if (precision > self.area_precision_constraint):
                        detDontCarePolsNum.append(len(detPols) - 1)
                        break

        evaluationLog += "DET polygons: " + str(len(detPols)) + (" (" + str(len(
            detDontCarePolsNum)) + " don't care)\n" if len(detDontCarePolsNum) > 0 else "\n")

        if len(gtPols) > 0 and len(detPols) > 0:
            # Calculate IoU and precision matrixs
            outputShape = [len(gtPols), len(detPols)]
            iouMat = np.empty(outputShape)
            gtRectMat = np.zeros(len(gtPols), np.int8)
            detRectMat = np.zeros(len(detPols), np.int8)
            if self.is_output_polygon:
                for gtNum in range(len(gtPols)):
                    for detNum in range(len(detPols)):
                        pG = gtPols[gtNum]
                        pD = detPols[detNum]
                        iouMat[gtNum, detNum] = get_intersection_over_union(pD, pG)
            else:
                # gtPols = np.float32(gtPols)
                # detPols = np.float32(detPols)
                for gtNum in range(len(gtPols)):
                    for detNum in range(len(detPols)):
                        pG = np.float32(gtPols[gtNum])
                        pD = np.float32(detPols[detNum])
                        iouMat[gtNum, detNum] = iou_rotate(pD, pG)
            for gtNum in range(len(gtPols)):
                for detNum in range(len(detPols)):
                    if gtRectMat[gtNum] == 0 and detRectMat[
                        detNum] == 0 and gtNum not in gtDontCarePolsNum and detNum not in detDontCarePolsNum:
                        if iouMat[gtNum, detNum] > self.iou_constraint:
                            gtRectMat[gtNum] = 1
                            detRectMat[detNum] = 1
                            detMatched += 1
                            pairs.append({'gt': gtNum, 'det': detNum})
                            detMatchedNums.append(detNum)
                            evaluationLog += "Match GT #" + \
                                             str(gtNum) + " with Det #" + str(detNum) + "\n"

        numGtCare = (len(gtPols) - len(gtDontCarePolsNum))
        numDetCare = (len(detPols) - len(detDontCarePolsNum))
        if numGtCare == 0:
            recall = float(1)
            precision = float(0) if numDetCare > 0 else float(1)
        else:
            recall = float(detMatched) / numGtCare
            precision = 0 if numDetCare == 0 else float(
                detMatched) / numDetCare

        hmean = 0 if (precision + recall) == 0 else 2.0 * \
                                                    precision * recall / (precision + recall)

        matchedSum += detMatched
        numGlobalCareGt += numGtCare
        numGlobalCareDet += numDetCare

        perSampleMetrics = {
            'precision': precision,
            'recall': recall,
            'hmean': hmean,
            'pairs': pairs,
            'iouMat': [] if len(detPols) > 100 else iouMat.tolist(),
            'gtPolPoints': gtPolPoints,
            'detPolPoints': detPolPoints,
            'gtCare': numGtCare,
            'detCare': numDetCare,
            'gtDontCare': gtDontCarePolsNum,
            'detDontCare': detDontCarePolsNum,
            'detMatched': detMatched,
            'evaluationLog': evaluationLog
        }

        return perSampleMetrics

    def combine_results(self, results):
        numGlobalCareGt = 0
        numGlobalCareDet = 0
        matchedSum = 0
        for result in results:
            numGlobalCareGt += result['gtCare']
            numGlobalCareDet += result['detCare']
            matchedSum += result['detMatched']

        methodRecall = 0 if numGlobalCareGt == 0 else float(
            matchedSum) / numGlobalCareGt
        methodPrecision = 0 if numGlobalCareDet == 0 else float(
            matchedSum) / numGlobalCareDet
        methodHmean = 0 if methodRecall + methodPrecision == 0 else 2 * \
                                                                    methodRecall * methodPrecision / (
                                                                            methodRecall + methodPrecision)

        methodMetrics = {'precision': methodPrecision,
                         'recall': methodRecall, 'hmean': methodHmean}

        return methodMetrics

class QuadMetric():
    def __init__(self, is_output_polygon=False):
        self.is_output_polygon = is_output_polygon
        self.evaluator = DetectionIoUEvaluator(is_output_polygon=is_output_polygon)

    def measure(self, batch, output, box_thresh=0.6):
        '''
        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.
        output: (polygons, ...)
        '''
        results = []
        gt_polyons_batch = batch['text_polys']
        ignore_tags_batch = batch['ignore_tags']
        pred_polygons_batch = np.array(output[0])
        pred_scores_batch = np.array(output[1])
        for polygons, pred_polygons, pred_scores, ignore_tags in zip(gt_polyons_batch, pred_polygons_batch, pred_scores_batch, ignore_tags_batch):
            gt = [dict(points=np.int64(polygons[i]), ignore=ignore_tags[i]) for i in range(len(polygons))]
            if self.is_output_polygon:
                pred = [dict(points=pred_polygons[i]) for i in range(len(pred_polygons))]
            else:
                pred = []
                # print(pred_polygons.shape)
                for i in range(pred_polygons.shape[0]):
                    if pred_scores[i] >= box_thresh:
                        # print(pred_polygons[i,:,:].tolist())
                        pred.append(dict(points=pred_polygons[i, :, :].astype(np.int64)))
                # pred = [dict(points=pred_polygons[i,:,:].tolist()) if pred_scores[i] >= box_thresh for i in range(pred_polygons.shape[0])]
            results.append(self.evaluator.evaluate_image(gt, pred))
        return results

    def validate_measure(self, batch, output, box_thresh=0.6):
        return self.measure(batch, output, box_thresh)

    def evaluate_measure(self, batch, output):
        return self.measure(batch, output), np.linspace(0, batch['image'].shape[0]).tolist()

    def gather_measure(self, raw_metrics):
        raw_metrics = [image_metrics
                       for batch_metrics in raw_metrics
                       for image_metrics in batch_metrics]

        result = self.evaluator.combine_results(raw_metrics)

        precision = AverageMeter()
        recall = AverageMeter()
        fmeasure = AverageMeter()

        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)
        fmeasure.update(fmeasure_score)

        return {
            'precision': precision,
            'recall': recall,
            'fmeasure': fmeasure
        }

def shrink_polygon_py(polygon, shrink_ratio):
    """
    对框进行缩放，返回去的比例为1/shrink_ratio 即可
    """
    cx = polygon[:, 0].mean()
    cy = polygon[:, 1].mean()
    polygon[:, 0] = cx + (polygon[:, 0] - cx) * shrink_ratio
    polygon[:, 1] = cy + (polygon[:, 1] - cy) * shrink_ratio
    return polygon


def shrink_polygon_pyclipper(polygon, shrink_ratio):
    from shapely.geometry import Polygon
    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)
    shrunk = padding.Execute(-distance)
    if shrunk == []:
        shrunk = np.array(shrunk)
    else:
        shrunk = np.array(shrunk[0]).reshape(-1, 2)
    return shrunk

class MakeShrinkMap():
    r'''
    Making binary mask from detection data with ICDAR format.
    Typically following the process of class `MakeICDARData`.
    '''

    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
        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:
                shrunk = self.shrink_func(polygon, self.shrink_ratio)
                if shrunk.size == 0:
                    cv2.fillPoly(mask, polygon.astype(np.int32)[np.newaxis, :, :], 0)
                    ignore_tags[i] = True
                    continue
                cv2.fillPoly(gt, [shrunk.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