基于关键点检测的摔倒识别

该工程基于PaddleDetection实现人员摔倒识别。先通过特定命令预测视频关键点，得到json结果文件和可视化视频；再用source.py中代码判断摔倒，输出摔倒帧对应时间，还会在视频检测框左上角标注结果并保存为output.mp4，示例中检测到78帧摔倒。

基于PaddleDetection的人员摔倒识别

本工程是基于PaddleDetection人员关键点预测的摔倒识别案例

关于PaddleDetection的使用详情请参考github链接 PaddleDetection

1 获取视频关键点预测结果

在PaddleDetection下使用如下命令预测视频结果(相关模型可以从github中model_zoo获取):

python deploy/python/det_keypoint_unite_infer.py --det_model_dir=output_inference/ppyolo_r50vd_dcn_2x_coco/ --keypoint_model_dir=output_inference/hrnet_w32_256x192/ --video_file=./pose_demo/test.mp4  --device=gpu --save_res=True

预测完成后会在当前目录下得到预测结果json文件：det_keypoint_unite_video_results.json, 即此处使用的kpts_results.json文件.

在output目录下得到预测结果可视化文件：test.mp4, 即此处的video.mp4文件.

2 根据关键点结果判断是否摔倒

执行最下面命令行的命令，对关键点预测结果kpts_results.json做摔倒逻辑的判断，摔倒帧对应时间会打印显示。同时会在视频上对应时间的检测框左上角打印fall_down结果，结果视频保存为output.mp4。

import osimport sysimport cv2import numpy as npimport jsonimport collectionsfrom source import check_fall_down, videovis

#1）脚本第一个参数为关键点预测结果json文件jsonf = "kpts_results.json"with open(jsonf, "r") as rf:    kpts_data = json.load(rf)print("all data length: {}".format(len(kpts_data)))#2）如果需要视频打印摔倒文字，关键点可视化结果文件放在同路径videof = "video.mp4"#3）读取关键点结果后放入判断文件fallframes = check_fall_down(kpts_data)#4）根据检测的摔倒帧在视频显示videovis(videof, kpts_data, fallframes)

all data length: 468fall_down frames: 78time: 5.5s, fall down detectedtime: 6.0s, fall down detectedtime: 6.5s, fall down detectedtime: 7.0s, fall down detectedtime: 7.5s, fall down detectedtime: 8.0s, fall down detectedtime: 8.5s, fall down detectedtime: 9.0s, fall down detectedtime: 9.5s, fall down detectedtime: 10.0s, fall down detectedtime: 10.5s, fall down detectedtime: 11.0s, fall down detectedtime: 11.5s, fall down detectedtime: 12.0s, fall down detectedtime: 12.5s, fall down detectedtime: 18.0s, fall down detectedtime: 18.5s, fall down detectedtime: 19.5s, fall down detectedtime: 20.5s, fall down detectedtime: 21.0s, fall down detectedtime: 21.5s, fall down detectedtime: 23.5s, fall down detectedtime: 25.5s, fall down detectedtime: 26.0s, fall down detectedtime: 29.5s, fall down detectedtime: 30.0s, fall down detectedtime: 32.0s, fall down detectedtime: 32.5s, fall down detectedtime: 33.0s, fall down detectedtime: 35.0s, fall down detectedtime: 35.5s, fall down detectedtime: 36.0s, fall down detectedtime: 38.0s, fall down detectedtime: 38.5s, fall down detectedprint fall down result in video: video.mp4fps: 12, frame_count: 468

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