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import mmpose
import os
import glob
from mmpose.apis import MMPoseInferencer
import gradio as gr
import numpy as np
import cv2
print("[INFO]: Imported modules!")
human = MMPoseInferencer("human")
hand = MMPoseInferencer("hand")
human3d = MMPoseInferencer(pose3d="human3d")
inferencers = {"Estimate human 2d poses":human, "Estimate human 2d hand poses":hand, "Estimate human 3d poses":human3d}
# inferencer = MMPoseInferencer('human')
# inferencer = MMPoseInferencer(pose3d="human3d")
# https://github.com/open-mmlab/mmpose/tree/dev-1.x/configs/body_3d_keypoint/pose_lift
# motionbert_ft_h36m-d80af323_20230531.pth
# simple3Dbaseline_h36m-f0ad73a4_20210419.pth
# videopose_h36m_243frames_fullconv_supervised_cpn_ft-88f5abbb_20210527.pth
# videopose_h36m_81frames_fullconv_supervised-1f2d1104_20210527.pth
# videopose_h36m_27frames_fullconv_supervised-fe8fbba9_20210527.pth
# videopose_h36m_1frame_fullconv_supervised_cpn_ft-5c3afaed_20210527.pth
# https://github.com/open-mmlab/mmpose/blob/main/mmpose/apis/inferencers/pose3d_inferencer.py
print("[INFO]: Downloaded models!")
def poses(photo, check):
# Selecting the specific inferencer
inferencer = inferencers[check] # 'hand', 'human , device='cuda'
print("[INFO]: Running inference!")
#vis_out_dir
result_generator = inferencer(photo,
vis_out_dir =".",
return_vis=True,
thickness=2,
rebase_keypoint_height=True)
result = next(result_generator)
#for result in result_generator:
# print("[INFO] Result: ", result)
# # Prepare to save video
# output_file = os.path.join("output.mp4")
# fourcc = cv2.VideoWriter_fourcc(*"mp4v") # Codec for MP4 video
# fps = 32
# height = 480
# width = 640
# size = (width,height)
# out_writer = cv2.VideoWriter(output_file, fourcc, fps, size)
# for result in result_generator:
# print("[INFO] Result: ", result)
# frame = result["visualization"]
# out_writer.write(cv2.cvtColor(frame[0], cv2.COLOR_BGR2RGB))
# print(os.listdir())
# print("[INFO]: Visualizing results!")
# print(os.listdir())
# print()
# out_writer.release()
# cv2.destroyAllWindows() # Closing window
output_files = glob.glob(os.path.join(".", "*.mp4"))
print(output_files)
return output_files[0]
# # 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
available_methods = ["Estimate human 2d poses", "Estimate human 2d hand poses", "Estimate human 3d poses"]
webcam = gr.Interface(
fn=poses,
inputs= [gr.Video(source="webcam"),
gr.CheckboxGroup(available_methods, label="Methods", type="value", info="Select the model(s) you want")],
outputs = gr.Video(format='mp4'),
title = 'Pose estimation',
description = 'Pose estimation on video',
allow_flagging=False
)
file = gr.Interface(
poses,
inputs = [gr.Video(source="upload"),
gr.CheckboxGroup(available_methods, label="Methods", type="value", info="Select the model(s) you want")],
outputs = gr.Video(format='mp4'),
allow_flagging=False
)
demo = gr.TabbedInterface(
interface_list=[file, webcam],
tab_names=["From a File", "From your Webcam"]
)
demo.launch(server_name="0.0.0.0", server_port=7860)
if __name__ == "__main__":
run()
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