# 设定开关，将最小外接矩形中心点间的距离作为vehicle之间的距离 import numpy as np import math, cv2, time from copy import deepcopy def xyxy_coordinate(boundbxs,contour): ''' 输入：两个对角坐标xyxy 输出：四个点位置 ''' x1 = boundbxs[0] y1 = boundbxs[1] x2 = boundbxs[2] y2 = boundbxs[3] for x in (x1,x2): for y in (y1,y2): flag = cv2.pointPolygonTest(contour, (int(x), int(y)), False) # 若为False，会找点是否在内，外，或轮廓上(相应返回+1, -1, 0)。 if flag == 1: return 1 return flag def get_ms(time2, time1): return (time2 - time1) * 1000.0 def two_points_distance(x1, y1, x2, y2): distance = math.sqrt((x1 - x2) ** 2 + (y1 - y2) ** 2) return distance # 保存正常vehicle和非正常vehicle的信息（当contours顶点数小于6时，无法拟合最小外接矩形，定义为非正常vehicle） def saveVehicle1(traffic_dict, contours, normVehicleBD, normVehicle, count, i, unnormVehicle, normVehicleCOOR): if len(contours) >= 6: normVehicleBD.append(contours) normVehicle.append(traffic_dict['det'][count]) rect = cv2.minAreaRect(contours) normVehicleCOOR.append(rect[0]) else: traffic_dict['det'][int(i / 2)] = traffic_dict['det'][int(i / 2)] + [0, 0.3, 999, -1, 3] unnormVehicle.append(traffic_dict['det'][int(i / 2)]) return normVehicleBD, normVehicle, unnormVehicle, normVehicleCOOR # saveVehicle2和saveVehicle1有区别 def saveVehicle2(traffic_dict, contours, normVehicleBD, normVehicle, count, i, unnormVehicle, normVehicleCOOR, centerCOOR): if len(contours) >= 6: normVehicleBD.append(contours) normVehicle.append(traffic_dict['det'][count]) normVehicleCOOR.append(centerCOOR) else: traffic_dict['det'][int(i / 2)] = traffic_dict['det'][int(i / 2)] + [0, 0.3, 999, -1, 3] unnormVehicle.append(traffic_dict['det'][int(i / 2)]) return normVehicleBD, normVehicle, unnormVehicle, normVehicleCOOR # 对于不在道路上的vehicle，将输出信息补全 def supplementInformation(traffic_dict, i, roundness, y_min, y_max, imgVehicle, rect): score = -1 traffic_dict['det'][i] = traffic_dict['det'][i] + [0, roundness, 999, [-1, -1, -1], 666] if y_min > 0 and y_max < imgVehicle.shape[0] and roundness > traffic_dict['roundness']: # 过滤掉上下方被speedRoad的边界截断的vehicle score = (min(rect[1]) - max(rect[1]) * traffic_dict['roundness']) / (max(rect[1]) * (1 - traffic_dict['roundness'])) return score # 判断交通事故类型 def judgeAccidentType(traffic_dict, b): if max(traffic_dict['det'][b][9]) == traffic_dict['det'][b][9][0] and traffic_dict['det'][b][9][0] != -1: return 0 elif max(traffic_dict['det'][b][9]) == traffic_dict['det'][b][9][1] and traffic_dict['det'][b][9][1] != -1: return 1 elif max(traffic_dict['det'][b][9]) == traffic_dict['det'][b][9][2] and traffic_dict['det'][b][9][2] != -1: return 2 else: return 3 # 计算距离得分 def distanceScore(vehicleWH, index1, index2, smallestDistance, traffic_dict): d1 = (min(vehicleWH[index1]) + min(vehicleWH[index2])) / 2 d2 = min(min(vehicleWH[index1]), min(vehicleWH[index2])) + max(min(vehicleWH[index1]), min(vehicleWH[index2])) / 2 if smallestDistance == d1: score1 = 1 traffic_dict['det'][index2][9][2] = score1 traffic_dict['det'][index2][10] = judgeAccidentType(traffic_dict, index2) elif smallestDistance < d2: score1 = 1 - (smallestDistance - d1) / (d2 - d1) if 0 < score1 < 1: traffic_dict['det'][index2][9][2] = score1 traffic_dict['det'][index2][10] = judgeAccidentType(traffic_dict, index2) else: traffic_dict['det'][index2][10] = judgeAccidentType(traffic_dict, index2) else: traffic_dict['det'][index2][10] = judgeAccidentType(traffic_dict, index2) return traffic_dict['det'] # 计算两个contours之间的最短距离 def array_distance(arr1, arr2): ''' 计算两个数组中，每任意两个点之间L2距离 arr1和arr2都必须是numpy数组 且维度分别是mx2，nx2 输出数组维度为mxn ''' m, _ = arr1.shape n, _ = arr2.shape arr1_power = np.power(arr1, 2) arr1_power_sum = arr1_power[:, 0] + arr1_power[:, 1] # 第1区域，x与y的平方和 arr1_power_sum = np.tile(arr1_power_sum, (n, 1)) # 将arr1_power_sum沿着y轴复制n倍，沿着x轴复制1倍，这里用于与arr2进行计算。 n x m 维度 arr1_power_sum = arr1_power_sum.T # 将arr1_power_sum进行转置 arr2_power = np.power(arr2, 2) arr2_power_sum = arr2_power[:, 0] + arr2_power[:, 1] # 第2区域，x与y的平方和 arr2_power_sum = np.tile(arr2_power_sum, (m, 1)) # 将arr1_power_sum沿着y轴复制m倍，沿着x轴复制1倍，这里用于与arr1进行计算。 m x n 维度 dis = arr1_power_sum + arr2_power_sum - (2 * np.dot(arr1, arr2.T)) # np.dot(arr1, arr2.T)矩阵相乘，得到xy的值。 dis = np.sqrt(dis) return dis # 存储所有道路的信息 def storageRoad(contours, allRoadContent, traffic_dict): speedRoadAngle = 0 for cnt in contours: # 道路 rect = cv2.minAreaRect(cnt) if rect[1][0] * rect[1][1] > traffic_dict['RoadArea']: # 过滤掉面积小于阈值的speedRoad if rect[1][0] <= rect[1][1]: if rect[2] >= 0 and rect[2] < 90: speedRoadAngle = rect[2] + 90 elif rect[2] == 90: speedRoadAngle = 0 else: if rect[2] >= 0 and rect[2] <= 90: speedRoadAngle = rect[2] allRoadContent.append([cnt, speedRoadAngle, rect[1]]) return allRoadContent # 存储所有vehicle的信息，方法1 def storageVehicle1(traffic_dict, normVehicleBD, normVehicle, unnormVehicle, normVehicleCOOR, imgVehicle): #输入： # #输出：traffic_dict['det'], normVehicleBD, unnormVehicle, normVehicleCOOR # traffic_dict['det']：resize缩小之后的坐标，类别，得分.[cls,x0,y0,x1,y1,score] # normVehicleBD : 正常车辆的contours。（正常车辆指的是countrous定点数>=6） # unnormVehicle : resize缩小之后的异常车辆坐标，类别，得分.[cls,x0,y0,x1,y1,score] count = 0 for i in range(0, len(traffic_dict['vehicleCOOR']), 2): mask = np.zeros(imgVehicle.shape[:2], dtype="uint8") x0 = int(traffic_dict['vehicleCOOR'][i][0] * traffic_dict['ZoomFactor']['y']) y0 = int(traffic_dict['vehicleCOOR'][i][1] * traffic_dict['ZoomFactor']['x']) x1 = int(traffic_dict['vehicleCOOR'][i + 1][0] * traffic_dict['ZoomFactor']['y']) y1 = int(traffic_dict['vehicleCOOR'][i + 1][1] * traffic_dict['ZoomFactor']['x']) cv2.rectangle(mask, (x0, y0), (x1, y1), 255, -1, lineType=cv2.LINE_AA) imgVehicle_masked = cv2.bitwise_and(imgVehicle, imgVehicle, mask=mask) img2 = cv2.cvtColor(imgVehicle_masked, cv2.COLOR_BGR2GRAY) contours2, hierarchy2 = cv2.findContours(img2, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE) if len(contours2) != 0: if len(contours2) > 1: # 这里我通过比较同一检测框内各个contours对应的最小外接矩形的面积，来剔除那些存在干扰的contours，最终只保留一个contours vehicleArea = [] # 存储vehicle的最小外接矩形的面积 for j in range(len(contours2)): rect = cv2.minAreaRect(contours2[j]) vehicleArea.append(rect[1][0] * rect[1][1]) maxAreaIndex = vehicleArea.index(max(vehicleArea)) maxAreaContours = contours2[maxAreaIndex] normVehicleBD, normVehicle, unnormVehicle, normVehicleCOOR = saveVehicle1(traffic_dict,maxAreaContours,normVehicleBD,normVehicle,count,i,unnormVehicle, normVehicleCOOR) elif len(contours2) == 1: normVehicleBD, normVehicle, unnormVehicle, normVehicleCOOR = saveVehicle1(traffic_dict,contours2[0],normVehicleBD,normVehicle,count,i,unnormVehicle, normVehicleCOOR) else: traffic_dict['det'][int(i / 2)] = traffic_dict['det'][int(i / 2)] + [0, 0.3, 999, -1, 3] unnormVehicle.append(traffic_dict['det'][int(i / 2)]) count += 1 traffic_dict['det'] = normVehicle return traffic_dict['det'], normVehicleBD, unnormVehicle, normVehicleCOOR # 存储所有vehicle的信息，方法2 def storageVehicle2(traffic_dict, normVehicleBD, normVehicle, unnormVehicle, normVehicleCOOR, imgVehicle): img = cv2.cvtColor(imgVehicle, cv2.COLOR_BGR2GRAY) count = 0 for i in range(0, len(traffic_dict['vehicleCOOR']), 2): row1 = int(traffic_dict['vehicleCOOR'][i][1] * traffic_dict['ZoomFactor']['x']) row2 = int(traffic_dict['vehicleCOOR'][i + 1][1] * traffic_dict['ZoomFactor']['x']) col1 = int(traffic_dict['vehicleCOOR'][i][0] * traffic_dict['ZoomFactor']['y']) col2 = int(traffic_dict['vehicleCOOR'][i + 1][0] * traffic_dict['ZoomFactor']['y']) if row1 >= 2: row1 = row1 - 2 if row2 <= (traffic_dict['modelSize'][1] - 2): row2 = row2 + 2 if col1 >= 2: col1 = col1 - 2 if col2 <= (traffic_dict['modelSize'][0] - 2): col2 = col2 + 2 centerCOOR = (int((col1 + col2) / 2), int((row1 + row2) / 2)) img1 = img[row1:row2, col1:col2] up = np.zeros((20, (col2 - col1)), dtype='uint8') left = np.zeros(((40 + row2 - row1), 20), dtype='uint8') img1 = np.concatenate((up, img1), axis=0) img1 = np.concatenate((img1, up), axis=0) img1 = np.concatenate((left, img1), axis=1) img2 = np.concatenate((img1, left), axis=1) contours2, hierarchy = cv2.findContours(img2, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE) if len(contours2) != 0: if len(contours2) > 1: vehicleArea = [] # 存储vehicle的最小外接矩形的面积 for j in range(len(contours2)): rect = cv2.minAreaRect(contours2[j]) vehicleArea.append(rect[1][0] * rect[1][1]) maxAreaIndex = vehicleArea.index(max(vehicleArea)) maxAreaContours = contours2[maxAreaIndex] normVehicleBD, normVehicle, unnormVehicle, normVehicleCOOR = saveVehicle2(traffic_dict,maxAreaContours,normVehicleBD,normVehicle,count,i,unnormVehicle,normVehicleCOOR,centerCOOR) elif len(contours2) == 1: normVehicleBD, normVehicle, unnormVehicle, normVehicleCOOR = saveVehicle2(traffic_dict,contours2[0],normVehicleBD,normVehicle,count,i,unnormVehicle,normVehicleCOOR,centerCOOR) else: traffic_dict['det'][int(i / 2)] = traffic_dict['det'][int(i / 2)] + [0, 0.3, 999, -1, 3] unnormVehicle.append(traffic_dict['det'][int(i / 2)]) count += 1 traffic_dict['det'] = normVehicle return traffic_dict['det'], normVehicleBD, unnormVehicle, normVehicleCOOR # 计算角度和长宽比得分 def angleRoundness(normVehicleBD, vehicleBox, vehicleWH, allRoadContent, traffic_dict, normVehicleCOOR, imgVehicle): ##输出：vehicleBox, vehicleWH, traffic_dict['det'] # vehicleBox--正常车辆通过contours得出的box,[ (x0,y0),(x1,y1),(x2,y2),(x3,y3)] # vehicleWH--正常车辆通过contours得出的box,[ (w,h)] # traffic_dict['det']--[[cls, x0, y0, x1, y1, score, 角度, 长宽比, 最小距离, max([角度得分, 长宽比得分, 最小距离得分]), 交通事故类别], ...] for i in range(len(normVehicleBD)): ellipse = cv2.fitEllipse(normVehicleBD[i]) vehicleAngle = 0 if ellipse[2] >= 0 and ellipse[2] < 90: vehicleAngle = 90 + ellipse[2] elif ellipse[2] >= 90 and ellipse[2] < 180: vehicleAngle = ellipse[2] - 90 elif ellipse[2] == 180: vehicleAngle = 90 rect = cv2.minAreaRect(normVehicleBD[i]) box = cv2.boxPoints(rect).astype(np.int32) center = normVehicleCOOR[i] vehicleBox.append(box) vehicleWH.append(rect[1]) roundness = min(rect[1]) / max(rect[1]) y_min = np.min(box[:, 1]) y_max = np.max(box[:, 1]) if len(allRoadContent) != 0: for j in range(len(allRoadContent)): flag = cv2.pointPolygonTest(allRoadContent[j][0], center, False) if flag >= 0: roadVehicleAngle = abs(vehicleAngle - allRoadContent[j][1]) traffic_dict['det'][i] = traffic_dict['det'][i] + [roadVehicleAngle, roundness, 999, [-1, -1, -1], 666] if y_min > 0 and y_max < imgVehicle.shape[0]: # 过滤掉上下方被speedRoad的边界截断的vehicle if roadVehicleAngle >= traffic_dict['roadVehicleAngle']: # 当道路同水平方向的夹角与车辆同水平方向的夹角的差值在15°和75°之间时，需要将车辆框出来 if roadVehicleAngle > 90: score1 = float((180 - roadVehicleAngle) / 90) else: score1 = float(roadVehicleAngle / 90) traffic_dict['det'][i][9][0] = score1 if roundness > traffic_dict['roundness']: score2 = (min(rect[1]) - max(rect[1]) * traffic_dict['roundness']) / (max(rect[1]) * (1 - traffic_dict['roundness'])) traffic_dict['det'][i][9][1] = score2 break else: j += 1 if len(traffic_dict['det'][i]) == 6: traffic_dict['det'][i][9][1] = supplementInformation(traffic_dict, i, roundness, y_min, y_max, imgVehicle, rect) else: traffic_dict['det'][i][9][1] = supplementInformation(traffic_dict, i, roundness, y_min, y_max, imgVehicle, rect) i += 1 return vehicleBox, vehicleWH, traffic_dict['det'] # 对于某一vehicle，以该vehicle的最小外接矩形的中心点为圆心O1，划定半径范围，求O1与半径范围内的其他vehicle的中心点之间的距离 def vehicleDistance1(normVehicleCOOR, normVehicleBD, traffic_dict, vehicleWH): if len(normVehicleCOOR) > 1: for b in range(len(normVehicleCOOR)): contoursMinDistance = [] # 存储contours之间的最短距离 tmp = normVehicleCOOR[b] normVehicleCOOR[b] = normVehicleCOOR[0] normVehicleCOOR[0] = tmp targetContours = [] # 存储目标vehicle和中心点同目标车辆中心点之间的距离小于traffic_dict['radius']的vehicle的box for c in range(1, len(normVehicleCOOR)): if two_points_distance(normVehicleCOOR[0][0], normVehicleCOOR[0][1], normVehicleCOOR[c][0], normVehicleCOOR[c][1]) <= traffic_dict['radius']: if normVehicleBD[b] not in targetContours: targetContours.append(normVehicleBD[b]) if c == b: targetContours.append(normVehicleBD[0]) else: targetContours.append(normVehicleBD[c]) if len(targetContours) != 0: goalVehicleContour = np.squeeze(targetContours[0], 1) for d in range(1, len(targetContours)): elseVehicleContour = np.squeeze(targetContours[d], 1) dist_arr = array_distance(goalVehicleContour, elseVehicleContour) min_dist = dist_arr[dist_arr > 0].min() contoursMinDistance.append(min_dist) traffic_dict['det'][b][8] = min(contoursMinDistance) if traffic_dict['det'][b][8] < min(vehicleWH[b]) * traffic_dict['vehicleFactor']: score1 = 1 - traffic_dict['det'][b][8] / (min(vehicleWH[b]) * traffic_dict['vehicleFactor']) traffic_dict['det'][b][9][2] = score1 traffic_dict['det'][b][10] = judgeAccidentType(traffic_dict, b) else: traffic_dict['det'][b][8] = 999 traffic_dict['det'][b][10] = judgeAccidentType(traffic_dict, b) tmp = normVehicleCOOR[b] normVehicleCOOR[b] = normVehicleCOOR[0] normVehicleCOOR[0] = tmp else: # 路上只有一辆车 if max(traffic_dict['det'][0][9]) == traffic_dict['det'][0][9][0] and traffic_dict['det'][0][9][0] != -1: traffic_dict['det'][0][10] = 0 elif max(traffic_dict['det'][0][9]) == traffic_dict['det'][0][9][1] and traffic_dict['det'][0][9][1] != -1: traffic_dict['det'][0][10] = 1 else: traffic_dict['det'][0][10] = 3 return traffic_dict['det'] # 计算vehicle的最小外接矩形中心点之间的距离和距离得分 def vehicleDistance2(normVehicleCOOR, traffic_dict, vehicleWH): if len(normVehicleCOOR) > 1: # 有多辆车 for b in range(len(normVehicleCOOR)): centerDistance = [] # 存储contours之间的最短距离 tmp = normVehicleCOOR[b] normVehicleCOOR[b] = normVehicleCOOR[0] normVehicleCOOR[0] = tmp for c in range(1, len(normVehicleCOOR)): centerDistance.append(two_points_distance(normVehicleCOOR[0][0], normVehicleCOOR[0][1], normVehicleCOOR[c][0], normVehicleCOOR[c][1])) smallestDistance = min(centerDistance) index = centerDistance.index(smallestDistance) traffic_dict['det'][b][8] = smallestDistance if index == b - 1: # 序号0和b对应的vehicle traffic_dict['det'] = distanceScore(vehicleWH, 0, b, smallestDistance, traffic_dict) else: traffic_dict['det'] = distanceScore(vehicleWH, index+1, b, smallestDistance, traffic_dict) tmp = normVehicleCOOR[b] normVehicleCOOR[b] = normVehicleCOOR[0] normVehicleCOOR[0] = tmp else: # 路上只有一辆车 if max(traffic_dict['det'][0][9]) == traffic_dict['det'][0][9][0] and traffic_dict['det'][0][9][0] != -1: traffic_dict['det'][0][10] = 0 elif max(traffic_dict['det'][0][9]) == traffic_dict['det'][0][9][1] and traffic_dict['det'][0][9][1] != -1: traffic_dict['det'][0][10] = 1 else: traffic_dict['det'][0][10] = 3 return traffic_dict['det'] def PostProcessing( traffic_dict): """ 对于字典traffic_dict中的各个键，说明如下： RoadArea：speedRoad的最小外接矩形的面积 roadVehicleAngle：判定发生交通事故的speedRoad与vehicle间的最小夹角 vehicleCOOR：是一个列表，用于存储被检测出的vehicle的坐标（vehicle检测模型） roundness：长宽比 ,vehicle的长与宽的比率，设置为0.7，若宽与长的比值大于0.7，则判定该vehicle发生交通事故 ZoomFactor：存储的是图像在H和W方向上的缩放因子，其值小于1 'cls':类别号 'vehicleFactor':两辆车之间的安全距离被定义为：min(车辆1的宽，车辆2的宽) * vehicleFactor 'radius'：半径，以某一vehicle的最小外接矩形的中点为圆心，以radius为半径，划定范围，过滤车辆 'distanceFlag'：开关。计算vehicle之间的距离时，可选择不同的函数 'vehicleFlag'：开关。存储vehicle的信息时，可选择不同的函数 未发生交通事故时，得分为-1，”事故类型“为3 最终输出格式：[[cls, x0, y0, x1, y1, score, 角度, 长宽比, 最小距离, max([角度得分, 长宽比得分, 最小距离得分]), 交通事故类别], ...] 交通事故类别：0表示角度，1表示长宽比，2表示最短距离，3表示未发生交通事故 """ det_cors = [] #print('###line338:', traffic_dict['det']) for bb in traffic_dict['det']: det_cors.append((int(bb[1]), int(bb[2]))) det_cors.append((int(bb[3]), int(bb[4]))) traffic_dict['vehicleCOOR'] = det_cors #testImageArray = testImageArray[:, :, 0] #H, W = testImageArray.shape[0:2] # sourceImage的分辨率为1080x1920 traffic_dict['modelSize']=[640,360] #traffic_dict['mask'] = cv2.resize(traffic_dict['mask'],(640,360)) mask = traffic_dict['mask'] H, W = mask.shape[0:2] #(640, 360) 720 1280 (720, 1280) ####line361: (1920, 1080) 720 1280 (720, 1280) ###line361: [640, 360] 360 640 (360, 640) #print('###line361:',traffic_dict['modelSize'], H,W ,mask.shape) scaleH = traffic_dict['modelSize'][1] / H # 自适应调整缩放比例 scaleW = traffic_dict['modelSize'][0] / W traffic_dict['ZoomFactor'] = {'x': scaleH, 'y': scaleW} new_hw = [int(H * scaleH), int(W * scaleW)] mask = cv2.resize(mask, (new_hw[1], new_hw[0])) if len(mask.shape) == 3: mask = mask[:, :, 0] t1 = time.time() normVehicleBD = [] # 存储一副图像中合格vehicle的contours,合格vehicle，即：contours中的顶点数大于等于6 imgRoad = mask.copy() imgVehicle = mask.copy() imgRoad[imgRoad == 2] = 0 # 将vehicle过滤掉，只包含背景和speedRoad imgVehicle[imgVehicle == 1] = 0 # 将speedRoad过滤掉，只包含背景和vehicle imgRoad = cv2.cvtColor(np.uint8(imgRoad), cv2.COLOR_RGB2BGR) # 道路 imgVehicle = cv2.cvtColor(np.uint8(imgVehicle), cv2.COLOR_RGB2BGR) # 车辆 t2 = time.time() img1 = cv2.cvtColor(imgRoad, cv2.COLOR_BGR2GRAY) contours, hierarchy = cv2.findContours(img1, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE) t3 = time.time() allRoadContent = [] # 存放所有的speedRoad信息，单个speedRoad的信息为：[cnt, speedRoadAngle, rect[1]] vehicleBox = [] # 存储合格vehicle的box参数，合格vehicle,即：contours顶点个数大于等于6 vehicleWH = [] # 存储合格vehicle的宽高 normVehicle = [] # 存储合格vehicle的信息 unnormVehicle = [] # 存储不合格vehicle的信息，不合格vehicle,即：contours顶点个数小于6 normVehicleCOOR = [] # 存储合格vehicle的中心点坐标 allRoadContent = storageRoad(contours, allRoadContent, traffic_dict) t4 = time.time() # 开关。存储vehicle的信息时，可选择不同的函数 if traffic_dict['vehicleFlag'] == True: traffic_dict['det'], normVehicleBD, unnormVehicle, normVehicleCOOR = storageVehicle1(traffic_dict, normVehicleBD, normVehicle, unnormVehicle, normVehicleCOOR, imgVehicle) #所有车辆的[cls,x0,y0,x1,y1,score] else: traffic_dict['det'], normVehicleBD, unnormVehicle, normVehicleCOOR = storageVehicle2(traffic_dict, normVehicleBD, normVehicle, unnormVehicle, normVehicleCOOR, imgVehicle) t5 = time.time() if len(normVehicleBD) != 0: t6 = time.time() vehicleBox, vehicleWH, traffic_dict['det'] = angleRoundness(normVehicleBD, vehicleBox, vehicleWH, allRoadContent, traffic_dict, normVehicleCOOR, imgVehicle) t7 = time.time() # 开关。计算vehicle之间的距离时，可选择不同的函数 if traffic_dict['distanceFlag'] == True: traffic_dict['det'] = vehicleDistance1(normVehicleCOOR, normVehicleBD, traffic_dict, vehicleWH) else: traffic_dict['det'] = vehicleDistance2(normVehicleCOOR, traffic_dict, vehicleWH) t8 = time.time() targetList = traffic_dict['det'] # print("line393", targetList) for i in range(len(targetList)): targetList[i][9] = max(targetList[i][9]) if len(unnormVehicle) != 0: targetList = targetList + unnormVehicle t9 = time.time() # print("line462", targetList) # 目标对象list, [[cls, x0, y0, x1, y1, score, 角度, 长宽比, 最小距离, max([角度得分, 长宽比得分, 最小距离得分]), 类别], ...] ruleJudge='angle-rundness-distance:%.1f'%( get_ms(t9,t6) ) else: targetList = unnormVehicle ruleJudge = 'No angle-rundness-distance judging' t10 = time.time() time_infos = '---test---nothing---' #time_infos = 'postTime:%.2f (分割时间：%.2f, findContours:%.2f, ruleJudge:%.2f, storageRoad:%.2f, storageVehicle:%.2f, angleRoundScore:%.2f, vehicleDistance:%.2f, mergeList:%.2f)' % ( # get_ms(t10, t1), get_ms(t2, t1), get_ms(t3, t2), get_ms(t10, t3), get_ms(t4, t3), get_ms(t5, t4), get_ms(t7, t6), get_ms(t8, t7), get_ms(t9, t8)) time_infos = 'postTime:%.2f , ( findContours:%.1f , carContourFilter:%.1f, %s )' %( get_ms(t10,t1), get_ms(t4,t1), get_ms(t5,t4),ruleJudge) return targetList, time_infos def TrafficPostProcessing(traffic_dict): """ 对于字典traffic_dict中的各个键，说明如下： RoadArea：speedRoad的最小外接矩形的面积 spillsCOOR：是一个列表，用于存储被检测出的spill的坐标（spill检测模型） ZoomFactor：存储的是图像在H和W方向上的缩放因子，其值小于1 'cls':类别号 """ traffic_dict['modelSize'] = [640, 360] mask = traffic_dict['mask'] H, W = mask.shape[0:2] scaleH = traffic_dict['modelSize'][1] / H # 自适应调整缩放比例 scaleW = traffic_dict['modelSize'][0] / W traffic_dict['ZoomFactor'] = {'x': scaleH, 'y': scaleW} new_hw = [int(H * scaleH), int(W * scaleW)] t0 = time.time() mask = cv2.resize(mask, (new_hw[1], new_hw[0])) if len(mask.shape) == 3: mask = mask[:, :, 0] imgRoad = mask.copy() imgRoad[imgRoad == 2] = 0 # 将vehicle过滤掉，只包含背景和speedRoad imgRoad = cv2.cvtColor(np.uint8(imgRoad), cv2.COLOR_RGB2BGR) # 道路 imgRoad = cv2.cvtColor(imgRoad, cv2.COLOR_BGR2GRAY) # contours, thresh = cv2.threshold(imgRoad, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU) # 寻找轮廓(多边界) contours, hierarchy = cv2.findContours(thresh, cv2.RETR_LIST, 2) contour_info = [] for c in contours: contour_info.append(( c, cv2.isContourConvex(c), cv2.contourArea(c), )) contour_info = sorted(contour_info, key=lambda c: c[2], reverse=True) t1 = time.time() '''新增模块：：如果路面为空，则返回原图、无抛洒物等。''' if contour_info == []: # final_img=_img_cv timeInfos = 'road is empty findContours:%.1f'%get_ms(t0,t1) return [], timeInfos else: # print(contour_info[0]) max_contour = contour_info[0][0] max_contour[:,:,0] = (max_contour[:,:,0] / scaleW).astype(np.int32) # contours恢复原图尺寸 max_contour[:,:,1] = (max_contour[:,:,1] / scaleH).astype(np.int32) # contours恢复原图尺寸 '''3、preds中spillage，通过1中路面过滤''' init_spillage_filterroad = traffic_dict['det'] final_spillage_filterroad = [] for i in range(len(init_spillage_filterroad)): flag = xyxy_coordinate(init_spillage_filterroad[i],max_contour) if flag == 1: final_spillage_filterroad.append(init_spillage_filterroad[i]) t2 = time.time() timeInfos = 'findContours:%.1f , carContourFilter:%.1f' % (get_ms(t0, t1), get_ms(t2, t1)) return final_spillage_filterroad, timeInfos # 返回最终绘制的结果图、最高速搞萨物（坐标、类别、置信度） def tracfficAccidentMixFunction(preds,seg_pred_mulcls,pars): tjime0=time.time() roadIou = pars['roadIou'] if 'roadIou' in pars.keys() else 0.5 preds = np.array(preds) #area_factors= np.array([np.sum(seg_pred_mulcls[int(x[2]):int(x[4]), int(x[1]):int(x[3])] )*1.0/(1.0*(x[3]-x[1])*(x[4]-x[2])+0.00001) for x in preds] ) area_factors= np.array([np.sum(seg_pred_mulcls[int(x[1]):int(x[3]), int(x[0]):int(x[2])] )*1.0/(1.0*(x[2]-x[0])*(x[3]-x[1])+0.00001) for x in preds] )#2023.08.03修改数据格式 water_flag = np.array(area_factors>roadIou) #print('##line936:',preds ) dets = preds[water_flag]##如果是水上目标，则需要与水的iou超过0.1；如果是岸坡目标，则直接保留。 dets = dets.tolist() #label_info = get_label_info(pars['label_csv']) imH,imW = seg_pred_mulcls.shape[0:2] seg_pred = cv2.resize(seg_pred_mulcls,( pars['modelSize'][0] , pars['modelSize'] [1]) ) mmH,mmW = seg_pred.shape[0:2] fx=mmW/imW;fy=mmH/imH det_coords=[] det_coords_original=[] for box in dets: #b_0 = box[1:5];b_0.insert(0,box[0]);b_0.append(box[5] ) b_0 = box[0:4];b_0.insert(0,box[5]);b_0.append(box[4]) det_coords_original.append( box ) if int(box[5]) != pars['CarId'] and int(box[5]) != pars['CthcId']: continue det_coords.append(b_0) #print('##line957:',det_coords_original ) pars['ZoomFactor']={'x':mmW/imW ,'y':mmH/imH} #pars['mask']=seg_pred; pars['mask']=seg_pred_mulcls; pars['det']=deepcopy(det_coords) #pars['label_info']=label_info tlist = list(pars.keys()); tlist.sort() if len(det_coords)> 0: #print('###line459:',pars['mask'].shape, pars['det']) list8,time_infos = PostProcessing(pars) #print('###line461:',list8 ) Accident_results = np.array(list8,dtype=object) acc_det=[] #[1.0, 1692.0, 169.0, 1803.0, 221.0, 0.494875431060791, 30, 0.5, 3.0, 0.3, 0] #[0 , 1 , 2 , 3 , 4 , 5 , 6, 7 , 8 , 9 , 10] for bpoints in list8: if bpoints[9]>pars['confThres']: xyxy=bpoints[1:5];xyxy=[int(x) for x in xyxy] cls=pars['cls'];conf=bpoints[9]; box_acc = [*xyxy,conf,cls] acc_det.append(box_acc) #if cls in allowedList: # p_result[1] = draw_painting_joint(xyxy,p_result[1],label_arraylist[int(cls)],score=conf,color=rainbows[int(cls)%20],font=font,socre_location="leftBottom") #print('###line475:',acc_det ) #去掉被定为事故的车辆 carCorslist = [ [ int(x[0]),int(x[1]), int(x[2]), int(x[3]) ] for x in det_coords_original ] #print('##line81:',det_coords_original ) accidentCarIndexs = [ carCorslist.index( [ int(x[0]),int(x[1]), int(x[2]), int(x[3]) ] ) for x in acc_det ] accidentCarIndexsKeep = set(list(range(len(det_coords_original)))) - set(accidentCarIndexs) det_coords_original_tmp = [ det_coords_original[x] for x in accidentCarIndexsKeep ] det_coords_original = det_coords_original_tmp #print('##line85:',det_coords_original ) det_coords_original.extend(acc_det) #4.0, 961.0, 275.0, 1047.0, 288.0, 0.26662659645080566, 0.0, 0.0 #0 , 1 , 2 , 3 , 4 , 5 , 6 , 7 #det_coords_original =[ [ *x[1:6], x[0],*x[6:8] ] for x in det_coords_original] else: time_infos=" no tracfficAccidentMix process" #p_result[2]= deepcopy(det_coords_original) return deepcopy(det_coords_original),time_infos def tracfficAccidentMixFunction_N(predList,pars): preds,seg_pred_mulcls = predList[0:2] return tracfficAccidentMixFunction(preds,seg_pred_mulcls,pars) def mixTraffic_postprocess(preds, seg_pred_mulcls,pars=None): '''输入：路面上的结果（类别+坐标）、原图、mask图像 过程：获得mask的轮廓，判断抛洒物是否在轮廓内。 在，则保留且绘制；不在，舍弃。 返回：最终绘制的结果图、最终路面上物体（坐标、类别、置信度）， ''' '''1、最大分隔路面作为判断依据''' roadIou = pars['roadIou'] if 'roadIou' in pars.keys() else 0.5 preds = np.array(preds) area_factors = np.array([np.sum(seg_pred_mulcls[int(x[1]):int(x[3]), int(x[0]):int(x[2])]) * 1.0 / ( 1.0 * (x[2] - x[0]) * (x[3] - x[1]) + 0.00001) for x in preds]) # 2023.08.03修改数据格式 water_flag = np.array(area_factors > roadIou) dets = preds[water_flag] ##如果是水上目标，则需要与水的iou超过0.1；如果是岸坡目标，则直接保留。 dets = dets.tolist() imH, imW = seg_pred_mulcls.shape[0:2] seg_pred = cv2.resize(seg_pred_mulcls, (pars['modelSize'][0], pars['modelSize'][1])) mmH, mmW = seg_pred.shape[0:2] fx = mmW / imW; fy = mmH / imH det_coords = [] for box in dets: if int(box[5]) != pars['cls']: continue det_coords.append(box) pars['ZoomFactor'] = {'x': mmW / imW, 'y': mmH / imH} pars['mask'] = seg_pred_mulcls; pars['det'] = deepcopy(det_coords) if len(det_coords) > 0: # print('###line459:',pars['mask'].shape, pars['det']) return TrafficPostProcessing(pars) else: return [], 'no spills find in road'