# -*- coding: UTF-8 -*- import argparse import time import os import cv2 import torch from numpy import random import copy import numpy as np from algorithm.Car_recognition.plate_recognition.plate_rec import get_plate_result,allFilePath,init_model,cv_imread # from plate_recognition.plate_cls import cv_imread from algorithm.Car_recognition.plate_recognition.double_plate_split_merge import get_split_merge from algorithm.Car_recognition.plate_recognition.color_rec import plate_color_rec,init_color_model from algorithm.Car_recognition.car_recognition.car_rec import init_car_rec_model,get_color_and_score from algorithm.Car_recognition.utils.datasets import letterbox from algorithm.Car_recognition.utils.general import check_img_size, non_max_suppression_face, scale_coords from algorithm.Car_recognition.utils.cv_puttext import cv2ImgAddText from read_data import LoadImages, LoadStreams import torch.backends.cudnn as cudnn clors = [(255,0,0),(0,255,0),(0,0,255),(255,255,0),(0,255,255)] danger=['危','险'] object_color=[(0,255,255),(0,255,0),(255,255,0)] class_type=['单层车牌','双层车牌','汽车'] class CarDetection(): def __init__(self, video_path=None): # self.detect_model = detect_model # self.plate_rec_model = plate_rec_model # self.car_rec_model = car_rec_model self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") self.detect_model =torch.load('weight/traffic/best.pt', map_location=self.device)['model'].float().fuse() # self.detect_model = load_model((os.getcwd()) + "/weight/traffic/detect.pt") #初始化检测模型 self.plate_rec_model= init_model((os.getcwd()) + "/weight/traffic/plate_rec_color.pth") #初始化识别模型 self.car_rec_model = init_car_rec_model((os.getcwd()) + "/weight/traffic/car_rec_color.pth") #初始化车辆识别模型 time_all = 0 time_begin=time.time() # self.frame = [None] if video_path is not None: self.video_name = video_path else: self.video_name = 'vid2.mp4' # A default video file self.imgsz = 384 self.dataset = LoadImages(self.video_name,self.imgsz) def use_webcam(self, source): source = source cudnn.benchmark = True self.dataset = LoadStreams(source, img_size=self.imgsz) def get_frame(self): for im0s in self.dataset: # print(self.dataset.mode) # print(self.dataset) if self.dataset.mode == 'stream': img = im0s[0].copy() else: img = im0s.copy() dict_list=detect_Recognition_plate(self.detect_model, img, self.device, self.plate_rec_model, car_rec_model=self.car_rec_model) ori_img=draw_result(img,dict_list) ret, jpeg = cv2.imencode(".jpg", ori_img) txt = str(dict_list) return jpeg.tobytes(), txt def order_points(pts): #四个点安好左上 右上 右下 左下排列 rect = np.zeros((4, 2), dtype = "float32") s = pts.sum(axis = 1) rect[0] = pts[np.argmin(s)] rect[2] = pts[np.argmax(s)] diff = np.diff(pts, axis = 1) rect[1] = pts[np.argmin(diff)] rect[3] = pts[np.argmax(diff)] return rect def four_point_transform(image, pts): #透视变换得到车牌小图 rect = order_points(pts) (tl, tr, br, bl) = rect widthA = np.sqrt(((br[0] - bl[0]) ** 2) + ((br[1] - bl[1]) ** 2)) widthB = np.sqrt(((tr[0] - tl[0]) ** 2) + ((tr[1] - tl[1]) ** 2)) maxWidth = max(int(widthA), int(widthB)) heightA = np.sqrt(((tr[0] - br[0]) ** 2) + ((tr[1] - br[1]) ** 2)) heightB = np.sqrt(((tl[0] - bl[0]) ** 2) + ((tl[1] - bl[1]) ** 2)) maxHeight = max(int(heightA), int(heightB)) dst = np.array([ [0, 0], [maxWidth - 1, 0], [maxWidth - 1, maxHeight - 1], [0, maxHeight - 1]], dtype = "float32") M = cv2.getPerspectiveTransform(rect, dst) warped = cv2.warpPerspective(image, M, (maxWidth, maxHeight)) return warped def scale_coords_landmarks(img1_shape, coords, img0_shape, ratio_pad=None): #返回到原图坐标 # Rescale coords (xyxy) from img1_shape to img0_shape if ratio_pad is None: # calculate from img0_shape gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1]) # gain = old / new pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2 # wh padding else: gain = ratio_pad[0][0] pad = ratio_pad[1] coords[:, [0, 2, 4, 6]] -= pad[0] # x padding coords[:, [1, 3, 5, 7]] -= pad[1] # y padding coords[:, :8] /= gain #clip_coords(coords, img0_shape) coords[:, 0].clamp_(0, img0_shape[1]) # x1 coords[:, 1].clamp_(0, img0_shape[0]) # y1 coords[:, 2].clamp_(0, img0_shape[1]) # x2 coords[:, 3].clamp_(0, img0_shape[0]) # y2 coords[:, 4].clamp_(0, img0_shape[1]) # x3 coords[:, 5].clamp_(0, img0_shape[0]) # y3 coords[:, 6].clamp_(0, img0_shape[1]) # x4 coords[:, 7].clamp_(0, img0_shape[0]) # y4 # coords[:, 8].clamp_(0, img0_shape[1]) # x5 # coords[:, 9].clamp_(0, img0_shape[0]) # y5 return coords def get_plate_rec_landmark(img, xyxy, conf, landmarks, class_num,device,plate_rec_model,car_rec_model): h,w,c = img.shape result_dict={} x1 = int(xyxy[0]) y1 = int(xyxy[1]) x2 = int(xyxy[2]) y2 = int(xyxy[3]) landmarks_np=np.zeros((4,2)) rect=[x1,y1,x2,y2] if int(class_num) ==2: car_roi_img = img[y1:y2,x1:x2] car_color,color_conf=get_color_and_score(car_rec_model,car_roi_img,device) result_dict['class_type']=class_type[int(class_num)] result_dict['rect']=rect #车辆roi result_dict['score']=conf #车牌区域检测得分 result_dict['object_no']=int(class_num) result_dict['car_color']=car_color result_dict['color_conf']=color_conf return result_dict for i in range(4): point_x = int(landmarks[2 * i]) point_y = int(landmarks[2 * i + 1]) landmarks_np[i]=np.array([point_x,point_y]) class_label= int(class_num) #车牌的的类型0代表单牌，1代表双层车牌 roi_img = four_point_transform(img,landmarks_np) #透视变换得到车牌小图 if class_label: #判断是否是双层车牌，是双牌的话进行分割后然后拼接 roi_img=get_split_merge(roi_img) plate_number ,plate_color= get_plate_result(roi_img,device,plate_rec_model) #对车牌小图进行识别,得到颜色和车牌号 for dan in danger: #只要出现‘危’或者‘险’就是危险品车牌 if dan in plate_number: plate_number='危险品' # cv2.imwrite("roi.jpg",roi_img) result_dict['class_type']=class_type[class_label] result_dict['rect']=rect #车牌roi区域 result_dict['landmarks']=landmarks_np.tolist() #车牌角点坐标 result_dict['plate_no']=plate_number #车牌号 result_dict['roi_height']=roi_img.shape[0] #车牌高度 result_dict['plate_color']=plate_color #车牌颜色 result_dict['object_no']=class_label #单双层 0单层 1双层 result_dict['score']=conf #车牌区域检测得分 return result_dict def detect_Recognition_plate(model, orgimg, device,plate_rec_model,car_rec_model=None): # Load model conf_thres = 0.3 iou_thres = 0.5 dict_list=[] # orgimg = cv2.imread(image_path) # BGR img0 = copy.deepcopy(orgimg) img0 = np.transpose(img0, (2, 0, 1)) img = torch.from_numpy(img0) assert orgimg is not None, 'Image Not Found ' # print(model) model.to(device) img.to(device) img = img.float() # uint8 to fp16/32 img /= 255.0 # 0 - 255 to 0.0 - 1.0 if img.ndimension() == 3: img = img.unsqueeze(0) # Inference pred = model(img)[0] # Apply NMS pred = non_max_suppression_face(pred, conf_thres, iou_thres) # Process detections for i, det in enumerate(pred): # detections per image if len(det): # Rescale boxes from img_size to im0 size det[:, :4] = scale_coords(img.shape[2:], det[:, :4], orgimg.shape).round() # Print results for c in det[:, -1].unique(): n = (det[:, -1] == c).sum() # detections per class det[:, 5:13] = scale_coords_landmarks(img.shape[2:], det[:, 5:13], orgimg.shape).round() for j in range(det.size()[0]): xyxy = det[j, :4].view(-1).tolist() conf = det[j, 4].cpu().numpy() landmarks = det[j, 5:13].view(-1).tolist() class_num = det[j, 13].cpu().numpy() result_dict = get_plate_rec_landmark(orgimg, xyxy, conf, landmarks, class_num,device,plate_rec_model,car_rec_model) dict_list.append(result_dict) return dict_list # cv2.imwrite('result.jpg', orgimg) def draw_result(orgimg,dict_list): result_str ="" for result in dict_list: rect_area = result['rect'] object_no = result['object_no'] if not object_no==2: x,y,w,h = rect_area[0],rect_area[1],rect_area[2]-rect_area[0],rect_area[3]-rect_area[1] padding_w = 0.05*w padding_h = 0.11*h rect_area[0]=max(0,int(x-padding_w)) rect_area[1]=max(0,int(y-padding_h)) rect_area[2]=min(orgimg.shape[1],int(rect_area[2]+padding_w)) rect_area[3]=min(orgimg.shape[0],int(rect_area[3]+padding_h)) height_area = int(result['roi_height']/2) landmarks=result['landmarks'] result_p = result['plate_no'] if result['object_no']==0:#单层 result_p+=" "+result['plate_color'] else: #双层 result_p+=" "+result['plate_color']+"双层" result_str+=result_p+" " for i in range(4): #关键点 cv2.circle(orgimg, (int(landmarks[i][0]), int(landmarks[i][1])), 5, clors[i], -1) if len(result)>=1: if "危险品" in result_p: #如果是危险品车牌，文字就画在下面 orgimg=cv2ImgAddText(orgimg,result_p,rect_area[0],rect_area[3],(0,255,0),height_area) else: orgimg=cv2ImgAddText(orgimg,result_p,rect_area[0]-height_area,rect_area[1]-height_area-10,(0,255,0),height_area) else: height_area=int((rect_area[3]-rect_area[1])/20) car_color = result['car_color'] car_color_str="车辆颜色:" car_color_str+=car_color orgimg=cv2ImgAddText(orgimg,car_color_str,rect_area[0],rect_area[1],(0,255,0),height_area) cv2.rectangle(orgimg,(rect_area[0],rect_area[1]),(rect_area[2],rect_area[3]),object_color[object_no],2) #画框 # print(result_str) return orgimg def get_second(capture): if capture.isOpened(): rate = capture.get(5) # 帧速率 FrameNumber = capture.get(7) # 视频文件的帧数 duration = FrameNumber/rate # 帧速率/视频总帧数 是时间，除以60之后单位是分钟 return int(rate),int(FrameNumber),int(duration)