# 耗时最短代码 import numpy as np import cv2,time,math import matplotlib.pyplot as plt def get_ms(time2, time1): return (time2-time1)*1000.0 # 计算一点到二次函数曲线的距离，二次函数的表达式为x = a*(y**2) + b*y + c def point2QF(a, b, c, y, x): # 坐标点(y, x) distance = abs(x - a*(y**2) - b*y - c) / math.sqrt(1 + ((2*a*y + b)**2)) return distance # 存储所有speedRoad的contours def storageRoad(contours, pars): allRoadCnt = [] # 存储所有speedRoad的contours for cnt in contours: # 道路 if len(cnt) >= 6: rect = cv2.minAreaRect(cnt) if rect[1][0] * rect[1][1] > pars['RoadArea']: # 过滤掉面积小于阈值的speedRoad allRoadCnt.append(cnt) return allRoadCnt # 返回符合标准的lane的个数及contours def storageLane(contours, pars): """ contours：lane分割后的原始contours newLaneContours：符合标准的lane的contours laneNumber：符合标准的lane的个数 符合标准的lane定义如下： （1）contours中的坐标点个数不小于6 （2）lane最小外接矩形的面积大于阈值laneArea （3）lane最小外接矩形的最短边与最长边的比值小于等于阈值roundness """ laneNumber = 0 newLaneContours = () for cnt in contours: if len(cnt) >= 6: rect = cv2.minAreaRect(cnt) if rect[1][0] * rect[1][1] > pars['laneArea'] and min(rect[1]) / max(rect[1]) <= pars['roundness']: laneNumber += 1 newLaneContours = newLaneContours + (cnt, ) return laneNumber, newLaneContours # 将contours中顶点数大于等于6的车辆信息(合格vehicle)和顶点数小于6的车辆信息(不合格vehicle)分别保存起来 def vehicleDivide(contours, vehicleBD, normVehicle, dets, count, i, unnormVehicle, normVehicleCOOR, centerCOOR): if len(contours) >= 6: vehicleBD.append(contours) normVehicle.append(dets[count]) normVehicleCOOR.append(centerCOOR) else: dets[int(i / 2)].append(0) dets[int(i / 2)].append(0) unnormVehicle.append(dets[int(i / 2)]) return vehicleBD, normVehicle, unnormVehicle, normVehicleCOOR # 存储所有vehicle的信息 def storageVehicle(pars, imgVehicle, dets): """ 输入 pars：字典名 imgVehicle：分割图，只包含vehicle和背景 dets：是一个list，其中存储检测得到的各vehicle的信息，即[[x0, y0, x1, y1, 车辆得分, cls], ...] 输出 dets：存储合格vehicle的信息，即[x0, y0, x1, y1, 车辆得分, cls] vehicleBD：存储合格vehicle的contours unnormVehicle：存储不合格vehicle的信息，即[x0, y0, x1, y1, 车辆得分, cls] normVehicleCOOR：存储合格vehicle的中心点坐标 说明 合格vehicle：contours中的顶点数大于等于6 不合格vehicle：contours中的顶点数小于6 """ vehicleBD = [] # 存储一副图像中vehicles的contours normVehicle = [] # 将合格vehicle的信息存储在normVehicle中 unnormVehicle = [] # 将不合格vehicle的信息存储在unnormVehicle中 normVehicleCOOR = [] # 存储合格vehicle的中心点坐标 img = cv2.cvtColor(imgVehicle, cv2.COLOR_BGR2GRAY) count = 0 for i in range(0, len(pars['vehicleCOOR']), 2): y1 = int(pars['vehicleCOOR'][i][1] * pars['ZoomFactor']['y']) y2 = int(pars['vehicleCOOR'][i + 1][1] * pars['ZoomFactor']['y']) x1 = int(pars['vehicleCOOR'][i][0] * pars['ZoomFactor']['x']) x2 = int(pars['vehicleCOOR'][i + 1][0] * pars['ZoomFactor']['x']) if y1 >= 2: y1 = y1 - 2 if y2 <= (pars['modelSize'][1] - 2): y2 = y2 + 2 if x1 >= 2: x1 = x1 - 2 if x2 <= (pars['modelSize'][0] - 2): x2 = x2 + 2 centerCOOR = (int((x1 + x2) / 2), int((y1 + y2) / 2)) img1 = img[y1:y2, x1:x2] up = np.zeros((20, (x2 - x1)), dtype='uint8') left = np.zeros(((40 + y2 - y1), 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] vehicleBD, normVehicle, unnormVehicle, normVehicleCOOR = vehicleDivide(maxAreaContours, vehicleBD, normVehicle, dets, count, i, unnormVehicle, normVehicleCOOR, centerCOOR) elif len(contours2) == 1: vehicleBD, normVehicle, unnormVehicle, normVehicleCOOR = vehicleDivide(contours2[0], vehicleBD, normVehicle, dets, count, i, unnormVehicle, normVehicleCOOR, centerCOOR) else: dets[int(i / 2)].append(0) dets[int(i / 2)].append(0) unnormVehicle.append(dets[int(i / 2)]) count += 1 dets = normVehicle return dets, vehicleBD, unnormVehicle, normVehicleCOOR # 计算违停得分 def IllegalParkScore1(vehicleBD, allRoadCnt, dets, unnormVehicle, normVehicleCOOR, a_l, b_l, c_l, a_r, b_r, c_r): """ 对vehicle是否在speedRoad上进行判断，并计算违章得分 输出targetList 其格式为：[[cls, x0, y0, x1, y1, score, 违章得分, 违章类别], ...] """ if len(vehicleBD) != 0: for i in range(len(vehicleBD)): rect = cv2.minAreaRect(vehicleBD[i]) center = normVehicleCOOR[i] # vehicle的中心点坐标 if len(allRoadCnt) != 0: # 当车道线个数至少有两条时，才计算违章得分 for j in range(len(allRoadCnt)): # 判断车辆矩形框的中心点坐标是否在道路矩形框的范围内 flag = cv2.pointPolygonTest(allRoadCnt[j], center, False) if flag >= 0: dets[i].append(0) # 给违章得分占位 dets[i].append(0) # 给违章类别占位 if center[0] < predict(a_l, b_l, c_l, center[1]): distance = point2QF(a_l, b_l, c_l, center[1], center[0]) if distance >= min(rect[1]) / 2: dets[i][6], dets[i][7] = 1, 1 else: dets[i][6], dets[i][7] = distance / (min(rect[1]) / 2), 1 elif center[0] > predict(a_r, b_r, c_r, center[1]): distance = point2QF(a_r, b_r, c_r, center[1], center[0]) if distance >= min(rect[1]) / 2: dets[i][6], dets[i][7] = 1, 1 else: dets[i][6], dets[i][7] = distance / (min(rect[1]) / 2), 1 else: dets[i][6], dets[i][7] = 0, 0 break # 如果分割图像中不存在speedRoad，则无法进行违章判定，将所有车辆的违章类别设为0，即没有违章 if len(dets[i]) < 8: dets[i].append(0) # 违章得分为0 dets[i].append(0) # 0表示没有违章 targetList = dets if len(unnormVehicle) != 0: for i in range(len(unnormVehicle)): targetList.append(unnormVehicle[i]) # 将所有车辆的信息合并到一起 else: targetList = unnormVehicle return targetList # 计算违停得分 def IllegalParkScore2(vehicleBD, dets, unnormVehicle): """ 计算违章得分 输出targetList 其格式为：[[cls, x0, y0, x1, y1, score, 违章得分, 违章类别], ...] """ if len(vehicleBD) != 0: for i in range(len(vehicleBD)): if len(dets[i]) < 8: dets[i].append(0) # 违章得分为0 dets[i].append(0) # 0表示没有违章 targetList = dets if len(unnormVehicle) != 0: for i in range(len(unnormVehicle)): targetList.append(unnormVehicle[i]) # 将所有车辆的信息合并到一起 else: targetList = unnormVehicle return targetList # 找最左侧lane时，将要删除的右侧lane的序号存储在delRightLane。找最右侧lane时，将要删除的左侧lane的序号存储在delLeftLane。 def devideLane(laneInfo, i, m, delRightLane, delLeftLane, y): index1 = np.where(laneInfo[i][3] == y) index1 = index1[0].tolist() index1.sort() x_1 = laneInfo[i][5][index1[0]][0] index2 = np.where(laneInfo[m][3] == y) index2 = index2[0].tolist() index2.sort() x_2 = laneInfo[m][5][index2[0]][0] if x_1 < x_2: if i not in delLeftLane: delLeftLane.append(i) # 保留右侧lane if m not in delRightLane: delRightLane.append(m) else: if m not in delLeftLane: delLeftLane.append(m) if i not in delRightLane: delRightLane.append(i) return delRightLane, delLeftLane # 确定最左侧和最右侧的lane簇 def detLine(contours): """ 输入 contours：各lane的contours 输出 laneInfo：存储各lane的信息，每条lane的信息为：[contours, y坐标范围, lane序号, arr_y, y坐标范围的长度, cnt] delRightLane：在确定最左侧lane时，其存储需要删除的lane的序号 delLeftLane：在确定最右侧lane时，其存储需要删除的lane的序号 """ mergList = [] for i in range(len(contours)): cnt = np.squeeze(contours[i], 1) arr_y = cnt[:, 1] arrList = list(set(arr_y)) cnt_y = np.sort(np.array(arrList)) mergList.append([contours[i], cnt_y, i, arr_y, len(cnt_y), cnt]) laneInfo = sorted(mergList, key=(lambda x: x[4])) # [[contours[i], cnt_y, i, arr_y, len(cnt_y)],...] delRightLane = [] # 求最左侧lane delLeftLane = [] # 求最右侧lane laneInfoNew = [] for i in range(len(laneInfo)): laneInfoNew.append([laneInfo[i][1][0], laneInfo[i][1][-1], i]) # [[y_min, y_max, i],...] laneInfoNew = np.array(laneInfoNew) new1 = laneInfoNew[:, np.newaxis, :].repeat(laneInfoNew.shape[0], 1) new2 = laneInfoNew[np.newaxis, ...].repeat(laneInfoNew.shape[0], 0) new3 = np.concatenate((new1, new2), axis=2) y_i_min, y_i_max, y_m_min, y_m_max = new3[..., 0], new3[..., 1], new3[..., 3], new3[..., 4] mask1 = (y_i_min >= y_m_min) & (y_i_min <= y_m_max) & (y_i_max > y_m_max) mask2 = (y_i_max >= y_m_min) & (y_i_max <= y_m_max) & (y_i_min < y_m_min) mask3 = (y_i_min >= y_m_min) & (y_i_max <= y_m_max) mask4 = (y_i_min < y_m_min) & (y_i_max > y_m_max) if len(np.nonzero(mask1)[0]) != 0: mask1 = np.triu(mask1, k=1) serial_i = new3[mask1][..., 2] serial_m = new3[mask1][..., 5] for k in range(len(serial_i)): if (serial_m[k] not in delLeftLane) or (serial_m[k] not in delRightLane) or (serial_i[k] not in delLeftLane) or (serial_i[k] not in delRightLane): delRightLane, delLeftLane = devideLane(laneInfo, serial_i[k], serial_m[k], delRightLane, delLeftLane, laneInfo[serial_i[k]][1][0]) if len(np.nonzero(mask2)[0]) != 0: mask2 = np.triu(mask2, k=1) serial_i = new3[mask2][..., 2] serial_m = new3[mask2][..., 5] for k in range(len(serial_i)): if (serial_m[k] not in delLeftLane) or (serial_m[k] not in delRightLane) or (serial_i[k] not in delLeftLane) or (serial_i[k] not in delRightLane): delRightLane, delLeftLane = devideLane(laneInfo, serial_i[k], serial_m[k], delRightLane, delLeftLane, laneInfo[serial_i[k]][1][-1]) if len(np.nonzero(mask3)[0]) != 0: mask3 = np.triu(mask3, k=1) serial_i = new3[mask3][..., 2] serial_m = new3[mask3][..., 5] for k in range(len(serial_i)): if (serial_m[k] not in delLeftLane) or (serial_m[k] not in delRightLane) or (serial_i[k] not in delLeftLane) or (serial_i[k] not in delRightLane): delRightLane, delLeftLane = devideLane(laneInfo, serial_i[k], serial_m[k], delRightLane, delLeftLane, laneInfo[serial_i[k]][1][0]) if len(np.nonzero(mask4)[0]) != 0: mask4 = np.triu(mask4, k=1) serial_i = new3[mask4][..., 2] serial_m = new3[mask4][..., 5] for k in range(len(serial_i)): if (serial_m[k] not in delLeftLane) or (serial_m[k] not in delRightLane) or (serial_i[k] not in delLeftLane) or (serial_i[k] not in delRightLane): delRightLane, delLeftLane = devideLane(laneInfo, serial_i[k], serial_m[k], delRightLane, delLeftLane, laneInfo[serial_m[k]][1][0]) return laneInfo, delRightLane, delLeftLane # 对lane中的y值坐标进行下采样 def downSample(cnt_y): # number = len(cnt_y) * 0.0125 # cnt_y = np.random.choice(cnt_y, size=number, replace=False) if len(cnt_y) >= 1000: cnt_y = cnt_y[1::80] elif len(cnt_y) >= 900 and len(cnt_y) < 1000: cnt_y = cnt_y[1::75] elif len(cnt_y) >= 800 and len(cnt_y) < 900: cnt_y = cnt_y[1::70] elif len(cnt_y) >= 700 and len(cnt_y) < 800: cnt_y = cnt_y[1::65] elif len(cnt_y) >= 600 and len(cnt_y) < 700: cnt_y = cnt_y[1::60] elif len(cnt_y) >= 500 and len(cnt_y) < 600: cnt_y = cnt_y[1::55] elif len(cnt_y) >= 400 and len(cnt_y) < 500: cnt_y = cnt_y[1::40] elif len(cnt_y) >= 300 and len(cnt_y) < 400: cnt_y = cnt_y[1::45] elif len(cnt_y) >= 200 and len(cnt_y) < 300: cnt_y = cnt_y[1::40] elif len(cnt_y) >= 100 and len(cnt_y) < 200: cnt_y = cnt_y[1::35] elif len(cnt_y) >= 50 and len(cnt_y) < 100: cnt_y = cnt_y[1::20] elif len(cnt_y) >= 20 and len(cnt_y) < 50: cnt_y = cnt_y[1::6] else: cnt_y = cnt_y[1::5] return cnt_y # 求最左侧lane或最右侧lane中的各点坐标 def targetCOOR(laneInfo, delLane): """ 输入 laneInfo：存储各lane的信息，每条lane的信息为：[contours, y坐标范围, lane序号, arr_y, y坐标范围的长度, cnt] delLane：在确定最左侧lane或最右侧lane时，其存储需要删除的lane的序号。 输出 laneCOOR：存储最左侧或最右侧lane簇中各点的坐标 """ laneCOOR = [] # 存储lane中各点的坐标 centerSort = [] # 存储各lane按照中心点的y坐标排序后的结果 for j in range(len(laneInfo)): if j not in delLane: cnt = laneInfo[j][0] rect = cv2.minAreaRect(cnt) cnt = np.squeeze(cnt, 1) cnt_y = laneInfo[j][1] cnt_y = downSample(cnt_y) centerSort.append([rect[0][1], cnt_y, laneInfo[j][3], cnt, j]) centerSort = sorted(centerSort, key=(lambda x: x[0])) for i in range(len(centerSort)): centerCoordinate = [] for j in range(len(centerSort[i][1])): index = np.where(centerSort[i][2] == centerSort[i][1][j]) indexList = index[0].tolist() indexList.sort() x = (centerSort[i][3][indexList[0]][0] + centerSort[i][3][indexList[-1]][0]) / 2 y = (centerSort[i][3][indexList[0]][1] + centerSort[i][3][indexList[-1]][1]) / 2 centerCoordinate.append([x, y]) laneCOOR = laneCOOR + centerCoordinate return laneCOOR # 二次函数曲线表达式：x = a*(y**2) + b*y + c，根据图像中一点的y坐标求二次曲线中的x坐标 def predict(a, b, c, y): x = a * (y**2) + b * y + c return x def mixNoParking_road_postprocess(dets, mask, pars): """ 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 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：圆度 ,lane的长与宽的比率，作为判定是否为车道线的标准之一 laneArea：车道线的最小外接矩形的面积 ZoomFactor：图像在H和W方向上的缩放因子，其值小于1 fitOrder：多点拟合曲线的阶数 最终输出格式：[[x0, y0, x1, y1, 车辆得分, cls, 违章停车得分, 违章类别], ...] 违章类别：0表示正常车辆，1表示违章车辆 """ det_cors = [] for bb in dets: det_cors.append((int(bb[0]), int(bb[1]))) det_cors.append((int(bb[2]), int(bb[3]))) print('###line341:', det_cors) pars['vehicleCOOR'] = det_cors H, W = mask.shape[0:2] # mask的分辨率为360x640 scaleH = pars['modelSize'][1] / H # 自适应调整缩放比例 scaleW = pars['modelSize'][0] / W pars['ZoomFactor'] = {'x': scaleW, 'y': scaleH} 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() imgRoad = mask.copy() imgVehicle = mask.copy() lane_line = mask.copy() # 将vehicle和lane过滤掉，只包含背景和speedRoad imgRoad[imgRoad == 2] = 1 imgRoad[imgRoad == 3] = 1 # 将speedRoad和lane过滤掉，只保留vehicle和背景 imgVehicle[imgVehicle != 2] = 0 # 将speedRoad和vehicle过滤掉，只保留lane和背景 lane_line[lane_line < 3] = 0 imgRoad = cv2.cvtColor(np.uint8(imgRoad), cv2.COLOR_RGB2BGR) # 道路 imgVehicle = cv2.cvtColor(np.uint8(imgVehicle), cv2.COLOR_RGB2BGR) # 车辆 lane_line = cv2.cvtColor(np.uint8(lane_line), cv2.COLOR_RGB2BGR) # 对车道线进行膨胀操作 # kernel = np.ones((3, 3), np.uint8) # 膨胀范围 # lane_line = cv2.dilate(lane_line, kernel, iterations=2) # 迭代次数为2 t2 = time.time() img1 = cv2.cvtColor(imgRoad, cv2.COLOR_BGR2GRAY) roadContours, hierarchy = cv2.findContours(img1, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE) t3 = time.time() # 存储所有speedRoad的信息 allRoadCnt = storageRoad(roadContours, pars) t4 = time.time() img3 = cv2.cvtColor(lane_line, cv2.COLOR_BGR2GRAY) laneContours, hierarchy = cv2.findContours(img3, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE) # 存储所有lane的信息 laneNumber, newLaneContours = storageLane(laneContours, pars) t5 = time.time() if laneNumber >= 2: laneInfo, delRightLane, delLeftLane = detLine(newLaneContours) t6 = time.time() # 存储所有vehicle的信息 dets, vehicleBD, unnormVehicle, normVehicleCOOR = storageVehicle(pars, imgVehicle, dets) t7 = time.time() leftLaneCOOR = targetCOOR(laneInfo, delRightLane) rightLaneCOOR = targetCOOR(laneInfo, delLeftLane) rightLaneCOOR = np.array(rightLaneCOOR) rightX = rightLaneCOOR[:, 0] rightY = rightLaneCOOR[:, 1] leftLaneCOOR = np.array(leftLaneCOOR) leftX = leftLaneCOOR[:, 0] leftY = leftLaneCOOR[:, 1] # a_r，b_r，c_r分别是：最右侧车道线簇拟合的二次函数的二次项系数一次项系数，和常数项 a_r, b_r, c_r = np.polyfit(rightY, rightX, pars['fitOrder'])[0], np.polyfit(rightY, rightX, pars['fitOrder'])[1], np.polyfit(rightY, rightX, pars['fitOrder'])[2] # a_l，b_l，c_l分别是：最左侧车道线簇拟合的二次函数的二次项系数，一次项系数，和常数项 a_l, b_l, c_l = np.polyfit(leftY, leftX, pars['fitOrder'])[0], np.polyfit(leftY, leftX, pars['fitOrder'])[1], np.polyfit(leftY, leftX, pars['fitOrder'])[2] # """以下四行代码用于在后处理函数外画图""" # finalLane = [] # abc = [a_l, b_l, c_l, a_r, b_r, c_r] # abc中存储的是最左侧和最右侧二次函数的各项系数 # finalLane.append(rightLaneCOOR) # finalLane.append(leftLaneCOOR) # 计算违停得分 t8 = time.time() targetList = IllegalParkScore1(vehicleBD, allRoadCnt, dets, unnormVehicle, normVehicleCOOR, a_l, b_l, c_l, a_r, b_r, c_r) t9 = time.time() time_infos = 'postTime:%.2f(分割时间：%.2f, findContours:%.2f, ruleJudge:%.2f, storageRoad:%.2f, detLane:%.2f, storageLane:%.2f, storageVehicle:%.2f, fitLine:%.2f, IllegalParkScore1:%.2f)' % ( get_ms(t9, t1), get_ms(t2, t1), get_ms(t3, t2), get_ms(t9, t3), get_ms(t4, t3), get_ms(t6, t5), get_ms(t5, t4), get_ms(t7, t6), get_ms(t8, t7), get_ms(t9, t8)) # print('####line445:', targetList) # return targetList, time_infos, finalLane, lane_line, abc targetList = [ [ *b[0:4],b[6] if b[6]>0 else b[4], b[7] ] for b in targetList ] return targetList, time_infos else: dets, vehicleBD, unnormVehicle, normVehicleCOOR = storageVehicle(pars, imgVehicle, dets) t6 = time.time() targetList = IllegalParkScore2(vehicleBD, dets, unnormVehicle) t7 = time.time() time_infos = 'postTime:%.2f(分割时间：%.2f, findContours:%.2f, ruleJudge:%.2f, storageRoad:%.2f, storageLane:%.2f, storageVehicle:%.2f, IllegalParkScore2:%.2f)' % ( get_ms(t7, t1), get_ms(t2, t1), get_ms(t3, t2), get_ms(t7, t3), get_ms(t4, t3), get_ms(t5, t4), get_ms(t6, t5), get_ms(t7, t6)) # print('####line456:', targetList) targetList = [ [ *b[0:4],b[6] if b[6]>0 else b[4], b[7] ] for b in targetList ] return targetList, time_infos def mixNoParking_road_postprocess_N(predList, pars): dets, mask =predList[0:2] return mixNoParking_road_postprocess(dets, mask, pars)