OpenCV实现人体动作识别

 

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