paddlex，2.1.0识别预测代码（包含视频的）

官方目前训练完自带的predict.py里面只有识别图片的&＃xff0c;没有识别视频的。

各种百度踩坑后&＃xff0c;今天写成&＃xff01;opencv-python 是 4.5.5.64版本的&＃xff0c;比较新&＃xff0c;所以语法有可能会不一样。请注意根据自己的版本修改&＃xff0c;或者直接升级opencv-python

# 脚本运行依赖paddlex
# pip install paddlex
import cv2
import os
import paddlex as pdx
import argparse
import jsondef pred(args):if(args.use_gpu &＃61;&＃61; False):predictor &＃61; pdx.deploy.Predictor(args.model_dir)else :predictor &＃61; pdx.deploy.Predictor(args.model_dir, use_gpu&＃61;True)if(args.img_file is not None):try:result &＃61; predictor.predict(img_file&＃61;args.img_file)print("result:" &＃43; json.dumps(result))pdx.det.visualize(args.img_file, result, threshold&＃61;0.5, save_dir&＃61;args.save_dir)except Exception as e :print(e) # 最好包一层 try catch 有时会识别不出来 会报 list index out rangeelif(args.video_file is not None):images_mats &＃61; video_to_image(args.video_file, args.fps)fourcc &＃61; cv2.VideoWriter_fourcc(&＃39;m&＃39;, &＃39;p&＃39;, &＃39;4&＃39;, &＃39;v&＃39;)videoWriter &＃61; cv2.VideoWriter(args.save_dir &＃43; getfilename(args.video_file), fourcc, int(args.fps), (int(images_mats[2]),int(images_mats[1])), True)for image in images_mats[0]:try:result &＃61; predictor.predict(img_file&＃61;image)print("result:" &＃43; json.dumps(result))image_res &＃61; pdx.det.visualize(image, result, threshold&＃61;0.5, save_dir&＃61;None)videoWriter.write(image_res)except Exception as e:videoWriter.write(image)print(e) # 最好包一层 try catch 有时会识别不出来 会报 list index out rangevideoWriter.release()return resultdef video_to_image(video_path, nfps):"""视频解析为图片到指定文件夹:param video_path:视频路径:param nfps:每秒多少张图片:return:"""# 加载视频文件camera &＃61; cv2.VideoCapture(video_path)# 帧数times &＃61; 0images &＃61; []# 帧率(frames per second) 原视频的fps &＃61; camera.get(cv2.CAP_PROP_FPS)# 总帧数(frames)frames &＃61; camera.get(cv2.CAP_PROP_FRAME_COUNT)# 视频高度frame_height &＃61; camera.get(cv2.CAP_PROP_FRAME_HEIGHT )# 视频宽带frame_width &＃61; camera.get(cv2.CAP_PROP_FRAME_WIDTH)# 求要取的帧数frame_frequency &＃61; int(fps/nfps)print("帧数&＃xff1a;"&＃43;str(fps))print("总帧数&＃xff1a;"&＃43;str(frames))print("视屏总时长&＃xff1a;"&＃43;"{0:.2f}".format(frames/fps)&＃43;"秒")while True:times &＃43;&＃61; 1res, image &＃61; camera.read()if not res:breakif times % frame_frequency &＃61;&＃61; 0:images.append(image)return images, frame_height, frame_widthdef getfilename(path):return os.path.basename(path) #输出为 1.mp4def parse_args():parser &＃61; argparse.ArgumentParser()parser.add_argument(&＃39;--use_gpu&＃39;, default&＃61;False, type&＃61;bool)parser.add_argument(&＃39;--model_dir&＃39;, default&＃61;&＃39;./inference_model&＃39;, type&＃61;str)parser.add_argument(&＃39;--gpu_id &＃39;, default&＃61;0, type&＃61;int)parser.add_argument(&＃39;--img_file&＃39;, default&＃61;None, type&＃61;str)parser.add_argument(&＃39;--video_file&＃39;, default&＃61;None, type&＃61;str)parser.add_argument(&＃39;--fps&＃39;, default&＃61;5, type&＃61;int) #识别后的视频的fps&＃xff0c;考虑到识别效率一般设置小一点的数parser.add_argument(&＃39;--save_dir&＃39;, default&＃61;&＃39;./output/&＃39;, type&＃61;str)args &＃61; parser.parse_args()args.video_file&＃61;"C:\\Users\\hurui\\Desktop\\1.mp4" # 方便vscode直接F5调试if (args.img_file is None and args.video_file is None): sys.exit(1)return argsif __name__ &＃61;&＃61; &＃39;__main__&＃39;:args &＃61; parse_args()result &＃61; pred(args)if result is not None:print(&＃39;Done!&＃39;)else:print(&＃39;Does not any objects!&＃39;)