版本：
注意：如果是opencv-python 3.3会报错，cv2.dnn 找不到 readNet()
对于识别的行为超过400种：
OpenCV官方示例的样本类别：https://github.com/opencv/opencv/blob/master/samples/data/dnn/action_recongnition_kinetics.txt
示例代码：https://github.com/opencv/opencv/blob/master/samples/dnn/action_recognition.py
项目目录结构：
完整代码：
# 执行以下命令： # python activity_recognition_demo.py --model resnet-34_kinetics.onnx --classes action_recognition_kinetics.txt --input videos/activities.mp4 from collections import deque import numpy as np import argparse import imutils import cv2 # 构造参数 ap = argparse.ArgumentParser() ap.add_argument( "-m", "--model", required=True, help="path to trained human activity recognition model") ap.add_argument( "-c", "--classes", required=True, help="path to class labels file") ap.add_argument( "-i", "--input", type=str, default="", help="optional path to video file") args = vars(ap.parse_args()) # 类别，样本持续时间（帧数），样本大小（空间尺寸） CLASSES = open(args["classes"]).read().strip().split("\n") SAMPLE_DURATION = 16 SAMPLE_SIZE = 112 print("处理中...") # 创建帧队列 frames = deque(maxlen=SAMPLE_DURATION) # 读取模型 net = cv2.dnn.readNet(args["model"]) # 待检测视频 vs = cv2.VideoCapture(args["input"] if args["input"] else 0) writer = None # 循环处理视频流 while True: # 读取每帧 (grabbed, frame) = vs.read() # 判断视频是否结束 if not grabbed: print("无视频读取...") break # 调整大小，放入队列中 frame = imutils.resize(frame, width=640) frames.append(frame) # 判断是否填充到最大帧数 if len(frames) < SAMPLE_DURATION: continue # 队列填充满后继续处理 blob = cv2.dnn.blobFromImages( frames, 1.0, (SAMPLE_SIZE, SAMPLE_SIZE), (114.7748, 107.7354, 99.4750), swapRB=True, crop=True) blob = np.transpose(blob, (1, 0, 2, 3)) blob = np.expand_dims(blob, axis=0) # 识别预测 net.setInput(blob) outputs = net.forward() label = CLASSES[np.argmax(outputs)] # 绘制框 cv2.rectangle(frame, (0, 0), (300, 40), (255, 0, 0), -1) cv2.putText(frame, label, (10, 25), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 0, 255), 2) # cv2.imshow("Activity Recognition", frame) # 检测是否保存 if writer is None: # 初始化视频写入器 # fourcc = cv2.VideoWriter_fourcc(*"MJPG") fourcc = cv2.VideoWriter_fourcc(*"mp4v") writer = cv2.VideoWriter( "videos\\test.mp4", fourcc, 30, (frame.shape[1], frame.shape[0]), True) writer.write(frame) # 按 q 键退出 # key = cv2.waitKey(1) & 0xFF # if key == ord("q"): # break print("结束...") writer.release() vs.release()
可能与我找的视频有关，有些测试效果不是很好。
测试结果：
模型下载地址：
链接：https://pan.baidu.com/s/17mQvUr6jsUyd2k0RrbrXaA 提取码：irho
