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# Pose inferencing
import mmpose
from mmpose.apis import MMPoseInferencer
# Ultralytics
from ultralytics import YOLO
import torch
# Gradio
import gradio as gr
# System and files
import os
import glob
import uuid
# Image manipulation
import numpy as np
import cv2
print("[INFO]: Imported modules!")
human = MMPoseInferencer("human")
hand = MMPoseInferencer("hand") #kpt_thr (float) – The threshold to visualize the keypoints. Defaults to 0.3
human3d = MMPoseInferencer(pose3d="human3d")
track_model = YOLO('yolov8n.pt') # Load an official Detect model
# ultraltics
# [INFO] VIDEO INPUT: /tmp/gradio/927601b660ec45919366ce37df1ed004a1fcffab/sample_flip.webm
# Defining inferencer models to lookup in function
inferencers = {"Estimate human 2d poses":human, "Estimate human 2d hand poses":hand, "Estimate human 3d poses":human3d, "Detect and track":track_model}
print("[INFO]: Downloaded models!")
def tracking(video, model, boxes=True):
print("[INFO] Loading model...")
# Load an official or custom model
# Perform tracking with the model
print("[INFO] Starting tracking!")
# https://docs.ultralytics.com/modes/predict/
annotated_frame = model(video, boxes=boxes)
return annotated_frame
def show_tracking(video_content, vis_out_dir, model):
video = cv2.VideoCapture(video_content)
# Track
video_track = tracking(video_content, model.track)
# Prepare to save video
#out_file = os.path.join(vis_out_dir, "track.mp4")
out_file = "track.mp4"
print("[INFO]: TRACK", out_file)
fourcc = cv2.VideoWriter_fourcc(*"mp4v") # Codec for MP4 video
fps = video.get(cv2.CAP_PROP_FPS)
height, width, _ = video_track[0][0].orig_img.shape
size = (width,height)
out_track = cv2.VideoWriter(out_file, fourcc, fps, size)
# Go through frames and write them
for frame_track in video_track:
result_track = frame_track[0].plot() # plot a BGR numpy array of predictions
out_track.write(result_track)
print("[INFO] Done with frames")
#print(type(result_pose)) numpy ndarray
out_track.release()
video.release()
cv2.destroyAllWindows() # Closing window
return out_file
def poses(inferencer, video, vis_out_dir, kpt_thr):
print("[INFO] VIDEO INPUT: ", video)
result_generator = inferencer(video,
vis_out_dir = vis_out_dir,
return_vis=True,
thickness=2,
rebase_keypoint_height=True,
#kpt_thr=kpt_thr,
device="cuda"
)
result = [result for result in result_generator] #next(result_generator)
out_file = glob.glob(os.path.join(vis_out_dir, "*.mp4"))
return out_file
def infer(video, check, kpt_thr, webcam=True):
print("[INFO] VIDEO INPUT: ", video)
# Selecting the specific inferencer
out_files=[]
for i in check:
# Create out directory
vis_out_dir = str(uuid.uuid4())
inferencer = inferencers[i] # 'hand', 'human , device='cuda'
if i == "Detect and track":
#continue
trackfile = show_tracking(video, vis_out_dir, inferencer)
else:
if webcam==True:
print("WEBCAM")
add_dir = str(uuid.uuid4())
vidname = video.split("/")[-1]
vis_out_dir_web = "/".join(["/".join(video.split("/")[:-1]), add_dir])
out_file = poses(inferencer, video, vis_out_dir_web, kpt_thr)
fullname = os.path.join(vis_out_dir_web, vidname)
#if i == "Estimate human 3d poses":
# fullname = fullname[:-4]+"mp4" #Change to .mp4
# out_files.append(fullname)
#else:
out_files.append(fullname)
else:
out_files.extend(out_file)
print(out_files)
return "track.mp4", out_files[0], out_files[1], out_files[2] # out_files[3]
def run():
#https://github.com/open-mmlab/mmpose/blob/main/docs/en/user_guides/inference.md
check_web = gr.CheckboxGroup(choices = ["Detect and track", "Estimate human 2d poses", "Estimate human 2d hand poses", "Estimate human 3d poses"], label="Methods", type="value", info="Select the model(s) you want")
check_file = gr.CheckboxGroup(choices = ["Detect and track", "Estimate human 2d poses", "Estimate human 2d hand poses", "Estimate human 3d poses"], label="Methods", type="value", info="Select the model(s) you want")
description = """
\n\nHere you can upload videos or record one with your webcam and track objects or detect bodyposes in 2d and 3d.
"""
# Insert slider with kpt_thr
web_kpthr = gr.Slider(0, 1, value=0.3)
file_kpthr = gr.Slider(0, 1, value=0.3)
webcam = gr.Interface(
fn=infer,
inputs= [gr.Video(source="webcam", height=512), check_web, web_kpthr], # /tmp/gradio/927601b660ec45919366ce37df1ed004a1fcffab/sample_flip.webm
outputs = [gr.Video(format='mp4', height=512, label="Detect and track", show_label=True), gr.PlayableVideo(height=512, label = "Estimate human 2d poses", show_label=True), gr.PlayableVideo(height=512, label = "Estimate human 2d hand poses", show_label=True), gr.PlayableVideo(height=512, label = "Estimate human 3d poses", show_label=True)],
title = 'Tracking and pose estimation',
description = description,
allow_flagging=False
)
file = gr.Interface(
infer,
inputs = [gr.Video(source="upload", height=512), check_file, file_kpthr],
outputs = [gr.Video(format='mp4', height=512, label="Detect and track", show_label=True), gr.PlayableVideo(height=512, label = "Estimate human 2d poses", show_label=True), gr.PlayableVideo(height=512, label = "Estimate human 2d hand poses", show_label=True), gr.PlayableVideo(height=512, label = "Estimate human 3d poses", show_label=True)],
title = 'Tracking and pose estimation',
description = description,
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()
# 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
# 00000.mp4
# 000000.mp4
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