main.py

import mmpose
import os
from mmpose.apis import MMPoseInferencer
print("[INFO]: Imported modules!")

import gradio as gr
import numpy as np
import cv2

inferencer = MMPoseInferencer('human')
print("[INFO]: Downloaded models!")


def poses(photo):
    result_generator = inferencer(photo, 
                                  vis_out_dir =".",
                                  return_vis=True,
                                  thickness=2)
    


    video = cv2.VideoCapture(photo)

    # Prepare to save video
    output_file = os.path.join("output.mp4")

    fourcc = cv2.VideoWriter_fourcc(*"mp4v")  # Codec for MP4 video
    fps = video.get(cv2.CAP_PROP_FPS)
    height = 480
    width = 640
    size = (width,height)

    out_writer = cv2.VideoWriter(output_file, fourcc, fps, size)

    for result in result_generator:
        frame = result["visualization"]
        print(frame)
        print(type(frame))
        out_writer.write(frame)

    print(os.listdir())
    print("[INFO]: Visualizing results!")
    print(os.listdir())
    print()

    out_writer.release()

    cv2.destroyAllWindows() # Closing window


    return output_file


# # specify detection model by alias
# # the available aliases include 'human', 'hand', 'face', 'animal',
# # as well as any additional aliases defined in mmdet
# inferencer = MMPoseInferencer(
#     # suppose the pose estimator is trained on custom dataset
#     pose2d='custom_human_pose_estimator.py',
#     pose2d_weights='custom_human_pose_estimator.pth',
#     det_model='human'
# )
    
def run():
    #https://github.com/open-mmlab/mmpose/blob/main/docs/en/user_guides/inference.md
    demo = gr.Interface(fn=poses, 
                        inputs=gr.Video(source="webcam"),
                        outputs=gr.Video())


    demo.launch(server_name="0.0.0.0", server_port=7860)


if __name__ == "__main__":
    run()
    print(os.listdir())