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.mp4from 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