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import os
import gradio as gr
import numpy as np
import spaces
import supervision as sv
import torch
from render import draw_links, draw_points, keypoint_colors, link_colors
from tqdm import tqdm
from transformers import (
AutoProcessor,
RTDetrForObjectDetection,
VitPoseForPoseEstimation,
)
css = """
.feedback textarea {font-size: 24px !important}
"""
device = "cuda"
def calculate_end_frame_index(source_video_path):
video_info = sv.VideoInfo.from_video_path(source_video_path)
return video_info.total_frames
@spaces.GPU
def process_image(
input_image,
model_variant,
progress=gr.Progress(track_tqdm=True),
):
# You can choose detector by your choice
person_image_processor = AutoProcessor.from_pretrained(
"PekingU/rtdetr_r50vd_coco_o365"
)
person_model = RTDetrForObjectDetection.from_pretrained(
"PekingU/rtdetr_r50vd_coco_o365", device_map=device
)
if model_variant == "Base":
model_name = "yonigozlan/synthpose-vitpose-base-hf"
else:
model_name = "yonigozlan/synthpose-vitpose-huge-hf"
image_processor = AutoProcessor.from_pretrained(model_name)
model = VitPoseForPoseEstimation.from_pretrained(model_name, device_map=device)
keypoint_edges = model.config.edges
frame = np.array(input_image)
inputs = person_image_processor(images=frame, return_tensors="pt").to(device)
with torch.no_grad():
outputs = person_model(**inputs)
results = person_image_processor.post_process_object_detection(
outputs,
target_sizes=torch.tensor([(frame.shape[0], frame.shape[1])]),
threshold=0.4,
)
result = results[0] # take first image results
# Human label refers 0 index in COCO dataset
person_boxes = result["boxes"][result["labels"] == 0]
person_boxes = person_boxes.cpu().numpy()
# Convert boxes from VOC (x1, y1, x2, y2) to COCO (x1, y1, w, h) format
person_boxes[:, 2] = person_boxes[:, 2] - person_boxes[:, 0]
person_boxes[:, 3] = person_boxes[:, 3] - person_boxes[:, 1]
# ------------------------------------------------------------------------
# Stage 2. Detect keypoints for each person found
# ------------------------------------------------------------------------
inputs = image_processor(frame, boxes=[person_boxes], return_tensors="pt").to(
device
)
with torch.no_grad():
outputs = model(**inputs)
pose_results = image_processor.post_process_pose_estimation(
outputs, boxes=[person_boxes]
)
image_pose_result = pose_results[0] # results for first image
for pose_result in image_pose_result:
scores = np.array(pose_result["scores"])
keypoints = np.array(pose_result["keypoints"])
# draw each point on image
draw_points(
frame,
keypoints,
scores,
keypoint_colors,
keypoint_score_threshold=0.3,
radius=max(2, int(max(frame.shape[0], frame.shape[1]) / 500)),
show_keypoint_weight=False,
)
# draw links
draw_links(
frame,
keypoints,
scores,
keypoint_edges,
link_colors,
keypoint_score_threshold=0.3,
thickness=max(2, int(max(frame.shape[0], frame.shape[1]) / 1000)),
show_keypoint_weight=False,
)
return frame
@spaces.GPU
def process_video(
input_video,
model_variant,
progress=gr.Progress(track_tqdm=True),
):
video_info = sv.VideoInfo.from_video_path(input_video)
total = calculate_end_frame_index(input_video)
frame_generator = sv.get_video_frames_generator(source_path=input_video, end=total)
result_file_name = "output.mp4"
result_file_path = os.path.join(os.getcwd(), result_file_name)
# You can choose detector by your choice
person_image_processor = AutoProcessor.from_pretrained(
"PekingU/rtdetr_r50vd_coco_o365"
)
person_model = RTDetrForObjectDetection.from_pretrained(
"PekingU/rtdetr_r50vd_coco_o365", device_map=device
)
if model_variant == "Base":
model_name = "yonigozlan/synthpose-vitpose-base-hf"
else:
model_name = "yonigozlan/synthpose-vitpose-huge-hf"
image_processor = AutoProcessor.from_pretrained(model_name)
model = VitPoseForPoseEstimation.from_pretrained(model_name, device_map=device)
keypoint_edges = model.config.edges
with sv.VideoSink(result_file_path, video_info=video_info) as sink:
for _ in tqdm(range(total), desc="Processing video.."):
try:
frame = next(frame_generator)
except StopIteration:
break
# ------------------------------------------------------------------------
# Stage 1. Detect humans on the image
# ------------------------------------------------------------------------
inputs = person_image_processor(images=frame, return_tensors="pt").to(
device
)
with torch.no_grad():
outputs = person_model(**inputs)
results = person_image_processor.post_process_object_detection(
outputs,
target_sizes=torch.tensor([(frame.shape[0], frame.shape[1])]),
threshold=0.4,
)
result = results[0] # take first image results
# Human label refers 0 index in COCO dataset
person_boxes = result["boxes"][result["labels"] == 0]
person_boxes = person_boxes.cpu().numpy()
# Convert boxes from VOC (x1, y1, x2, y2) to COCO (x1, y1, w, h) format
person_boxes[:, 2] = person_boxes[:, 2] - person_boxes[:, 0]
person_boxes[:, 3] = person_boxes[:, 3] - person_boxes[:, 1]
# ------------------------------------------------------------------------
# Stage 2. Detect keypoints for each person found
# ------------------------------------------------------------------------
if len(person_boxes) == 0:
sink.write_frame(frame)
continue
inputs = image_processor(
frame, boxes=[person_boxes], return_tensors="pt"
).to(device)
with torch.no_grad():
outputs = model(**inputs)
pose_results = image_processor.post_process_pose_estimation(
outputs, boxes=[person_boxes]
)
image_pose_result = pose_results[0] # results for first image
for pose_result in image_pose_result:
scores = np.array(pose_result["scores"])
keypoints = np.array(pose_result["keypoints"])
# draw each point on image
draw_points(
frame,
keypoints,
scores,
keypoint_colors,
keypoint_score_threshold=0.3,
radius=max(2, int(frame.shape[0] / 500)),
show_keypoint_weight=False,
)
# draw links
draw_links(
frame,
keypoints,
scores,
keypoint_edges,
link_colors,
keypoint_score_threshold=0.3,
thickness=max(1, int(frame.shape[0] / 1000)),
show_keypoint_weight=False,
)
sink.write_frame(frame)
return result_file_path
with gr.Blocks(theme=gr.themes.Soft(), css=css) as demo:
gr.Markdown("## Markerless Motion Capture with SynthPose")
gr.Markdown(
"""
SynthPose is a new approach that enables finetuning of pre-trained 2D human pose models to predict an arbitrarily denser set of keypoints for accurate kinematic analysis through the use of synthetic data.
More details are available in [OpenCapBench: A Benchmark to Bridge Pose Estimation and Biomechanics](https://arxiv.org/abs/2406.09788).<br />
This particular variant was finetuned on a set of keypoints usually found on motion capture setups, and include coco keypoints as well.<br />
The keypoints part of the skeleton are the COCO keypoints, and the pink ones the anatomical markers.
"""
)
gr.Markdown(
"Simply upload a video, and press run to start the inference! You can also try the examples below. 👇"
)
with gr.Tabs():
with gr.Tab("Video"):
with gr.Row():
with gr.Column():
model_variant = gr.Radio(
["Base", "Huge"],
label="Model Variant",
value="Base",
interactive=True,
)
input_video = gr.Video(label="Input Video")
with gr.Column():
output_video = gr.Video(label="Output Video")
with gr.Row():
submit_video = gr.Button(variant="primary")
example = gr.Examples(
examples=[
["./tennis.mp4"],
["./football.mp4"],
["./basket.mp4"],
["./hurdles.mp4"],
],
inputs=[input_video],
outputs=output_video,
)
submit_video.click(
fn=process_video,
inputs=[input_video, model_variant],
outputs=[output_video],
)
with gr.Tab("Image"):
with gr.Row():
with gr.Column():
model_variant = gr.Radio(
["Base", "Huge"],
label="Model Variant",
value="Base",
interactive=True,
)
input_image = gr.Image(label="Input Image")
with gr.Column():
output_image = gr.Image(label="Output Image")
with gr.Row():
submit_image = gr.Button(variant="primary")
example_image = gr.Examples(
examples=[
["demo.jpeg"],
],
inputs=[input_image],
outputs=output_image,
)
submit_image.click(
fn=process_image,
inputs=[input_image, model_variant],
outputs=[output_image],
)
if __name__ == "__main__":
demo.launch(show_error=True)
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