/Pose_Estimation

Code for running OpenPose code for human pose estimation using deep learning in OpenCV

Primary LanguagePython

Code for running OpenPose code for human pose estimation using deep learning in OpenCV.

First step is download the pretrained models:

Download the models.

bash get_models.sh

Resize a Video

ffmpeg -i input.avi -s 1280x720 -aspect 4:3 -vcodec libx264 video_1280x720.avi

Testing with Webcam:

python3 test.py --proto pose/mpi/pose_deploy_linevec_faster_4_stages.prototxt --model pose/mpi/pose_iter_160000.caffemodel --dataset MPI

Custom Command

python3 video_pose.py --input video_720x1280.avi --proto pose/mpi/pose_deploy_linevec_faster_4_stages.prototxt --model pose/mpi/pose_iter_160000.caffemodel --dataset MPI

Run the Standard tests

To Run MPI pretrained model on an image sample.jpg: python3 run_pose.py --input sample.jpg --proto pose/mpi/pose_deploy_linevec_faster_4_stages.prototxt --model pose/mpi/pose_iter_160000.caffemodel --dataset MPI

To Run Body_25 pretrained model on an image sample.jpg: python3 run_pose.py --input sample.jpg --proto pose/body_25/body_25_deploy.prototxt --model pose/body_25/pose_iter_584000.caffemodel

To Run COCO pretrained model on an image sample.jpg: python3 run_pose.py --input sample.jpg --proto pose/coco/deploy_coco.prototxt --model pose/coco/pose_iter_440000.caffemodel --dataset COCO

cv2.imshow("frame",frame) if cv2.waitKey(0) &0xFF == ord('q'): exit