车牌识别实例化

1.在官网拉取最新代码,并创建检测py文件,并引入相应的库

import os,cv2,time
import numpy as np
import argparse
from deploy.python.infer import Detector
from deploy.pipeline.ppvehicle.vehicle_plate import PlateRecognizer
from deploy.python.visualize import visualize_box_mask,visualize_attr,visualize_vehicleplate
from deploy.pipeline.pipe_utils import crop_image_with_det
from deploy.pipeline.datacollector import Result
from deploy.python.preprocess import decode_image

2.加载目标检测模型以及OCR模型

class chepai_Detector():def __init__(self,args,cfg) -> None:self.det_predictor = Detector(cfg['obj_det_model_dir'], args.device)self.vehicleplate_detector = PlateRecognizer(args,cfg)

3.添加可视化以及推理代码

    def visualize_image(self,im_files, images, result,output_dir, thresh):start_idx, boxes_num_i = 0, 0det_res = result.get('det')human_attr_res = result.get('attr')vehicle_attr_res = result.get('vehicle_attr')vehicleplate_res = result.get('vehicleplate')for i, (im_file, im) in enumerate(zip(im_files, images)):if det_res is not None:det_res_i = {}boxes_num_i = det_res['boxes_num'][i]det_res_i['boxes'] = det_res['boxes'][start_idx:start_idx +boxes_num_i, :]im = visualize_box_mask(im,det_res_i,labels=['target'],threshold=thresh)im = np.ascontiguousarray(np.copy(im))im = cv2.cvtColor(im, cv2.COLOR_RGB2BGR)if human_attr_res is not None:human_attr_res_i = human_attr_res['output'][start_idx:start_idx+ boxes_num_i]im = visualize_attr(im, human_attr_res_i, det_res_i['boxes'])if vehicle_attr_res is not None:vehicle_attr_res_i = vehicle_attr_res['output'][start_idx:start_idx + boxes_num_i]im = visualize_attr(im, vehicle_attr_res_i, det_res_i['boxes'])if vehicleplate_res is not None:plates = vehicleplate_res['vehicleplate']det_res_i['boxes'][:, 4:6] = det_res_i['boxes'][:, 4:6] - det_res_i['boxes'][:, 2:4]im = visualize_vehicleplate(im, plates, det_res_i['boxes'])img_name = os.path.split(im_file)[-1]if not os.path.exists(output_dir):os.makedirs(output_dir)out_path = os.path.join(output_dir, img_name)cv2.imwrite(out_path, im)print("save result to: " + out_path)start_idx += boxes_num_idef detect(self,img_path,thresh,visual,output_dir):pipeline_res = Result()img = [decode_image(f, {})[0] for f in img_path]det_res = self.det_predictor.predict_image(img_path, visual=False)det_res = self.det_predictor.filter_box(det_res,thresh)pipeline_res.update(det_res, 'det')crop_inputs = crop_image_with_det(img, det_res)platelicenses = []for crop_input in crop_inputs:platelicense = self.vehicleplate_detector.get_platelicense(crop_input)platelicenses.extend(platelicense['plate'])vehicleplate_res = {'vehicleplate': platelicenses}pipeline_res.update(vehicleplate_res, 'vehicleplate')if visual:self.visualize_image(img_path, img, pipeline_res, thresh=thresh,output_dir=output_dir)

4.实例化上述类,并进行识别测试

if __name__ == '__main__':# 创建解析器parser = argparse.ArgumentParser(description='jiexicanshu')parser.add_argument('--device', type=str, default='gpu', help='选择使用GPU还是CPU')parser.add_argument('--enable_mkldnn', type=bool, default=False, help='')args = parser.parse_args()cfg = { 'det_model_dir': r'./output_inference/ch_PP-OCRv3_det_infer', 'det_limit_side_len': 736, 'det_limit_type': 'min', 'rec_model_dir': r'./output_inference/ch_PP-OCRv3_rec_infer', 'rec_image_shape': [3, 48, 320], 'rec_batch_num': 6, 'word_dict_path': 'deploy/pipeline/ppvehicle/rec_word_dict.txt', 'enable': True,'obj_det_model_dir': r'./output_inference/mot_ppyoloe_l_36e_ppvehicle'}img_path = [r"./test.jpg"] #待识别图片thresh = 0.5   #过滤阈值visual = True  #是否可视化output_dir = r'./test_detection'  #图片可视化后存放路径Detector_chepai = chepai_Detector(args=args,cfg=cfg)for i in range(100):a = time.time()Detector_chepai.detect(img_path,thresh,visual,output_dir)b = time.time()print(b-a)

需要注意的地方:
1.如果使用CPU推理,则将parser.add_argument(‘–device’, type=str, default=‘gpu’, help=‘选择使用GPU还是CPU’)中的default='gpu’改为default='cpu’即可.
2.下载目标检测模型(地址:https://bj.bcebos.com/v1/paddledet/models/pipeline/mot_ppyoloe_l_36e_ppvehicle.zip)以及OCR模型(OCR模型细分为检测模型(地址:https://bj.bcebos.com/v1/paddledet/models/pipeline/ch_PP-OCRv3_det_infer.tar.gz)和识别模型(地址:https://bj.bcebos.com/v1/paddledet/models/pipeline/ch_PP-OCRv3_rec_infer.tar.gz)),将以上压缩包下载到"自己的路径\PaddleDetection-release-2.4\output_inference"下并解压.
3.将img_path以及out_put_dir更改为自己待检测图片以及检测完后存储的路径即可.
4.改代码中默认将图片推理100次,若有需要,请自行更改

完成推理代码

import os,cv2,time
import numpy as np
import argparse
from deploy.python.infer import Detector
from deploy.pipeline.ppvehicle.vehicle_plate import PlateRecognizer
from deploy.python.visualize import visualize_box_mask,visualize_attr,visualize_vehicleplate
from deploy.pipeline.pipe_utils import crop_image_with_det
from deploy.pipeline.datacollector import Result
from deploy.python.preprocess import decode_image
class chepai_Detector():def __init__(self,args,cfg) -> None:self.det_predictor = Detector(cfg['obj_det_model_dir'], args.device)self.vehicleplate_detector = PlateRecognizer(args,cfg)def visualize_image(self,im_files, images, result,output_dir, thresh):start_idx, boxes_num_i = 0, 0det_res = result.get('det')human_attr_res = result.get('attr')vehicle_attr_res = result.get('vehicle_attr')vehicleplate_res = result.get('vehicleplate')for i, (im_file, im) in enumerate(zip(im_files, images)):if det_res is not None:det_res_i = {}boxes_num_i = det_res['boxes_num'][i]det_res_i['boxes'] = det_res['boxes'][start_idx:start_idx +boxes_num_i, :]im = visualize_box_mask(im,det_res_i,labels=['target'],threshold=thresh)im = np.ascontiguousarray(np.copy(im))im = cv2.cvtColor(im, cv2.COLOR_RGB2BGR)if human_attr_res is not None:human_attr_res_i = human_attr_res['output'][start_idx:start_idx+ boxes_num_i]im = visualize_attr(im, human_attr_res_i, det_res_i['boxes'])if vehicle_attr_res is not None:vehicle_attr_res_i = vehicle_attr_res['output'][start_idx:start_idx + boxes_num_i]im = visualize_attr(im, vehicle_attr_res_i, det_res_i['boxes'])if vehicleplate_res is not None:plates = vehicleplate_res['vehicleplate']det_res_i['boxes'][:, 4:6] = det_res_i['boxes'][:, 4:6] - det_res_i['boxes'][:, 2:4]im = visualize_vehicleplate(im, plates, det_res_i['boxes'])img_name = os.path.split(im_file)[-1]if not os.path.exists(output_dir):os.makedirs(output_dir)out_path = os.path.join(output_dir, img_name)cv2.imwrite(out_path, im)print("save result to: " + out_path)start_idx += boxes_num_idef detect(self,img_path,thresh,visual,output_dir):pipeline_res = Result()img = [decode_image(f, {})[0] for f in img_path]det_res = self.det_predictor.predict_image(img_path, visual=False)det_res = self.det_predictor.filter_box(det_res,thresh)pipeline_res.update(det_res, 'det')crop_inputs = crop_image_with_det(img, det_res)platelicenses = []for crop_input in crop_inputs:platelicense = self.vehicleplate_detector.get_platelicense(crop_input)platelicenses.extend(platelicense['plate'])vehicleplate_res = {'vehicleplate': platelicenses}pipeline_res.update(vehicleplate_res, 'vehicleplate')if visual:self.visualize_image(img_path, img, pipeline_res, thresh=thresh,output_dir=output_dir)if __name__ == '__main__':# 创建解析器parser = argparse.ArgumentParser(description='jiexicanshu')parser.add_argument('--device', type=str, default='gpu', help='选择使用GPU还是CPU')parser.add_argument('--enable_mkldnn', type=bool, default=False, help='')args = parser.parse_args()cfg = { 'det_model_dir': './output_inference/ch_PP-OCRv3_det_infer', 'det_limit_side_len': 736, 'det_limit_type': 'min', 'rec_model_dir': './output_inference/ch_PP-OCRv3_rec_infer', 'rec_image_shape': [3, 48, 320], 'rec_batch_num': 6, 'word_dict_path': 'deploy/pipeline/ppvehicle/rec_word_dict.txt', 'enable': True,'obj_det_model_dir': r'./output_inference/mot_ppyoloe_l_36e_ppvehicle'}img_path = [r"./test.jpg"]thresh = 0.5visual = Trueoutput_dir = r'./test_detection'Detector_chepai = chepai_Detector(args=args,cfg=cfg)for i in range(100):a = time.time()Detector_chepai.detect(img_path,thresh,visual,output_dir)b = time.time()print(b-a)

5.推理视频(不是官方手动,但是效果相似)

5.1首先将视频抽帧(即将视频变为多张图片)

def chouzhen(self,video_path,output_dir):cap = cv2.VideoCapture(video_path)  # 获取视频对象isOpened = cap.isOpened  # 判断是否打开# 视频信息获取fps = cap.get(cv2.CAP_PROP_FPS)imageNum = 0sum=0timef=1  #隔1帧保存一张图片while (isOpened):sum+=1(frameState, frame) = cap.read()  # 记录每帧及获取状态if frameState == True and (sum % timef==0):# 格式转变，BGRtoRGBframe = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)# 转变成Imageframe = Image.fromarray(np.uint8(frame))frame = np.array(frame)# RGBtoBGR满足opencv显示格式frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)imageNum = imageNum + 1fileName = os.path.join(output_dir,str(imageNum) + '.jpg' ) # 存储路径cv2.imwrite(fileName, frame, [cv2.IMWRITE_JPEG_QUALITY, 100])print(fileName + " successfully write in")  # 输出存储状态elif frameState == False:breakprint('finish!')cap.release()

参数介绍:
video_path:待检测的视频地址
output_dir:将视频转换为多张图片后的存储路径

5.2将检测完的众多图片再合成视频

def hebing(self,path, size):filelist = os.listdir(path)  # 获取该目录下的所有文件名filelist.sort(key=lambda x: int(x[:-4]))  ##文件名按数字排序'''fps:帧率：1秒钟有n张图片写进去[控制一张图片停留5秒钟，那就是帧率为1，重复播放这张图片5次] 如果文件夹下有50张 534*300的图片，这里设置1秒钟播放5张，那么这个视频的时长就是10秒'''fps = 16file_path =os.path.join(path , "test_video_1.mp4")  # 导出路径fourcc = cv2.VideoWriter_fourcc(*'mp4v')  # 不同视频编码对应不同视频格式（例：'I','4','2','0' 对应avi格式）video = cv2.VideoWriter(file_path, fourcc, fps, size)for item in filelist:print(item)if item.endswith('.jpg'):  # 判断图片后缀是否是.jpgitem = path + '/' + itemimg = cv2.imread(item)  # 使用opencv读取图像，直接返回numpy.ndarray 对象，通道顺序为BGR ，注意是BGR，通道值默认范围0-255。video.write(img)  # 把图片写进视频video.release()  # 释放

参数介绍:
path:检测完后的图片存储路径
size:检测完后图片的宽度和高度(tuple类型,example:(1920,1440))

5.3大致流程

 Detector_chepai = chepai_Detector(args,cfg)thresh = 0.5visual = Truevideo_path = r''            #待检测视频的路径output_dir = r''            #将待检测视频抽帧之后图片的存储路径output_dir_detect = r''     #图片检测完后的存储路径size = (480,270)            #合并图片的宽度和高度Detector_chepai.chouzhen(video_path,output_dir)img_list = os.listdir(output_dir)for i in img_list:Detector_chepai.detect([os.path.join(output_dir,i)],thresh,visual,output_dir_detect)Detector_chepai.hebing(output_dir_detect,size)

实例化检测类,给定各种参数,先将视频进行抽帧,将图片保存至output_dir路径下;之后用os.listdir(output_dir)遍历图片,使用detect()进行推理,推理完的图片将保存至output_dir_detect;最后使用hebiong()函数将output_dir_detect路径下的所有图片进行合并,并将合并后的视频输出到output_dir_detect文件夹下.

整体项目代码下载地址: https://download.csdn.net/download/qq_44836316/87362475

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