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#!/usr/bin/env python
from __future__ import annotations
import argparse
import os
import pathlib
import subprocess
import sys
import tarfile
if os.getenv('SYSTEM') == 'spaces':
import mim
mim.uninstall('mmcv-full', confirm_yes=True)
mim.install('mmcv-full==1.5.0', is_yes=True)
subprocess.call('pip uninstall -y opencv-python'.split())
subprocess.call('pip uninstall -y opencv-python-headless'.split())
subprocess.call('pip install opencv-python-headless==4.5.5.64'.split())
import gradio as gr
import huggingface_hub
import numpy as np
import torch
import torch.nn as nn
sys.path.insert(0, 'ViTPose/')
from mmdet.apis import inference_detector, init_detector
from mmpose.apis import (inference_top_down_pose_model, init_pose_model,
process_mmdet_results, vis_pose_result)
TOKEN = os.environ['TOKEN']
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser()
parser.add_argument('--device', type=str, default='cpu')
parser.add_argument('--theme', type=str)
parser.add_argument('--share', action='store_true')
parser.add_argument('--port', type=int)
parser.add_argument('--disable-queue',
dest='enable_queue',
action='store_false')
return parser.parse_args()
class DetModel:
def __init__(self, device: str | torch.device):
self.device = torch.device(device)
self.models = self._load_models()
self.model_name = 'YOLOX-l'
def _load_models(self) -> dict[str, nn.Module]:
model_dict = {
'YOLOX-tiny': {
'config':
'mmdet_configs/configs/yolox/yolox_tiny_8x8_300e_coco.py',
'model':
'https://download.openmmlab.com/mmdetection/v2.0/yolox/yolox_tiny_8x8_300e_coco/yolox_tiny_8x8_300e_coco_20211124_171234-b4047906.pth',
},
'YOLOX-s': {
'config':
'mmdet_configs/configs/yolox/yolox_s_8x8_300e_coco.py',
'model':
'https://download.openmmlab.com/mmdetection/v2.0/yolox/yolox_s_8x8_300e_coco/yolox_s_8x8_300e_coco_20211121_095711-4592a793.pth',
},
'YOLOX-l': {
'config':
'mmdet_configs/configs/yolox/yolox_l_8x8_300e_coco.py',
'model':
'https://download.openmmlab.com/mmdetection/v2.0/yolox/yolox_l_8x8_300e_coco/yolox_l_8x8_300e_coco_20211126_140236-d3bd2b23.pth',
},
'YOLOX-x': {
'config':
'mmdet_configs/configs/yolox/yolox_x_8x8_300e_coco.py',
'model':
'https://download.openmmlab.com/mmdetection/v2.0/yolox/yolox_x_8x8_300e_coco/yolox_x_8x8_300e_coco_20211126_140254-1ef88d67.pth',
},
}
models = {
key: init_detector(dic['config'], dic['model'], device=self.device)
for key, dic in model_dict.items()
}
return models
def set_model_name(self, name: str) -> None:
self.model_name = name
def detect_and_visualize(
self, image: np.ndarray,
score_threshold: float) -> tuple[list[np.ndarray], np.ndarray]:
out = self.detect(image)
vis = self.visualize_detection_results(image, out, score_threshold)
return out, vis
def detect(self, image: np.ndarray) -> list[np.ndarray]:
image = image[:, :, ::-1] # RGB -> BGR
model = self.models[self.model_name]
out = inference_detector(model, image)
return out
def visualize_detection_results(
self,
image: np.ndarray,
detection_results: list[np.ndarray],
score_threshold: float = 0.3) -> np.ndarray:
person_det = [detection_results[0]] + [np.array([]).reshape(0, 5)]
image = image[:, :, ::-1] # RGB -> BGR
model = self.models[self.model_name]
vis = model.show_result(image,
person_det,
score_thr=score_threshold,
bbox_color=None,
text_color=(200, 200, 200),
mask_color=None)
return vis[:, :, ::-1] # BGR -> RGB
class PoseModel:
def __init__(self, device: str | torch.device):
self.device = torch.device(device)
self.models = self._load_models()
self.model_name = 'ViTPose-B (multi-task train, COCO)'
def _load_models(self) -> dict[str, nn.Module]:
model_dict = {
'ViTPose-B (single-task train)': {
'config':
'ViTPose/configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/ViTPose_base_coco_256x192.py',
'model': 'models/vitpose-b.pth',
},
'ViTPose-L (single-task train)': {
'config':
'ViTPose/configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/ViTPose_large_coco_256x192.py',
'model': 'models/vitpose-l.pth',
},
'ViTPose-B (multi-task train, COCO)': {
'config':
'ViTPose/configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/ViTPose_base_coco_256x192.py',
'model': 'models/vitpose-b-multi-coco.pth',
},
'ViTPose-L (multi-task train, COCO)': {
'config':
'ViTPose/configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/ViTPose_large_coco_256x192.py',
'model': 'models/vitpose-l-multi-coco.pth',
},
}
models = dict()
for key, dic in model_dict.items():
ckpt_path = huggingface_hub.hf_hub_download('hysts/ViTPose',
dic['model'],
use_auth_token=TOKEN)
model = init_pose_model(dic['config'],
ckpt_path,
device=self.device)
models[key] = model
return models
def set_model_name(self, name: str) -> None:
self.model_name = name
def predict_pose_and_visualize(
self,
image: np.ndarray,
det_results: list[np.ndarray],
box_score_threshold: float,
kpt_score_threshold: float,
vis_dot_radius: int,
vis_line_thickness: int,
) -> tuple[list[dict[str, np.ndarray]], np.ndarray]:
out = self.predict_pose(image, det_results, box_score_threshold)
vis = self.visualize_pose_results(image, out, kpt_score_threshold,
vis_dot_radius, vis_line_thickness)
return out, vis
def predict_pose(
self,
image: np.ndarray,
det_results: list[np.ndarray],
box_score_threshold: float = 0.5) -> list[dict[str, np.ndarray]]:
image = image[:, :, ::-1] # RGB -> BGR
model = self.models[self.model_name]
person_results = process_mmdet_results(det_results, 1)
out, _ = inference_top_down_pose_model(model,
image,
person_results=person_results,
bbox_thr=box_score_threshold,
format='xyxy')
return out
def visualize_pose_results(self,
image: np.ndarray,
pose_results: list[np.ndarray],
kpt_score_threshold: float = 0.3,
vis_dot_radius: int = 4,
vis_line_thickness: int = 1) -> np.ndarray:
image = image[:, :, ::-1] # RGB -> BGR
model = self.models[self.model_name]
vis = vis_pose_result(model,
image,
pose_results,
kpt_score_thr=kpt_score_threshold,
radius=vis_dot_radius,
thickness=vis_line_thickness)
return vis[:, :, ::-1] # BGR -> RGB
def set_example_image(example: list) -> dict:
return gr.Image.update(value=example[0])
def extract_tar() -> None:
if pathlib.Path('mmdet_configs/configs').exists():
return
with tarfile.open('mmdet_configs/configs.tar') as f:
f.extractall('mmdet_configs')
def main():
args = parse_args()
extract_tar()
det_model = DetModel(device=args.device)
pose_model = PoseModel(device=args.device)
css = '''
h1#title {
text-align: center;
}
'''
with gr.Blocks(theme=args.theme, css=css) as demo:
gr.Markdown('''<h1 id="title">ViTPose</h1>
This is an unofficial demo for [https://github.com/ViTAE-Transformer/ViTPose](https://github.com/ViTAE-Transformer/ViTPose).'''
)
with gr.Box():
gr.Markdown('## Step 1')
with gr.Row():
with gr.Column():
with gr.Row():
input_image = gr.Image(label='Input Image',
type='numpy')
with gr.Row():
detector_name = gr.Dropdown(list(
det_model.models.keys()),
value=det_model.model_name,
label='Detector')
with gr.Row():
detect_button = gr.Button(value='Detect')
det_preds = gr.Variable()
with gr.Column():
with gr.Row():
detection_visualization = gr.Image(
label='Detection Result', type='numpy')
with gr.Row():
vis_det_score_threshold = gr.Slider(
0,
1,
step=0.05,
value=0.5,
label='Visualization Score Threshold')
with gr.Row():
redraw_det_button = gr.Button(value='Redraw')
with gr.Row():
paths = sorted(pathlib.Path('images').rglob('*.jpg'))
example_images = gr.Dataset(components=[input_image],
samples=[[path.as_posix()]
for path in paths])
with gr.Box():
gr.Markdown('## Step 2')
with gr.Row():
with gr.Column():
with gr.Row():
pose_model_name = gr.Dropdown(
list(pose_model.models.keys()),
value=pose_model.model_name,
label='Pose Model')
det_score_threshold = gr.Slider(
0,
1,
step=0.05,
value=0.5,
label='Box Score Threshold')
with gr.Row():
predict_button = gr.Button(value='Predict')
pose_preds = gr.Variable()
with gr.Column():
with gr.Row():
pose_visualization = gr.Image(label='Result',
type='numpy')
with gr.Row():
vis_kpt_score_threshold = gr.Slider(
0,
1,
step=0.05,
value=0.3,
label='Visualization Score Threshold')
with gr.Row():
vis_dot_radius = gr.Slider(1,
10,
step=1,
value=4,
label='Dot Radius')
with gr.Row():
vis_line_thickness = gr.Slider(1,
10,
step=1,
value=2,
label='Line Thickness')
with gr.Row():
redraw_pose_button = gr.Button(value='Redraw')
gr.Markdown(
'<center><img src="https://visitor-badge.glitch.me/badge?page_id=hysts.vitpose" alt="visitor badge"/></center>'
)
detector_name.change(fn=det_model.set_model_name,
inputs=[
detector_name,
],
outputs=None)
detect_button.click(fn=det_model.detect_and_visualize,
inputs=[
input_image,
vis_det_score_threshold,
],
outputs=[
det_preds,
detection_visualization,
])
redraw_det_button.click(fn=det_model.visualize_detection_results,
inputs=[
input_image,
det_preds,
vis_det_score_threshold,
],
outputs=[
detection_visualization,
])
pose_model_name.change(fn=pose_model.set_model_name,
inputs=[
pose_model_name,
],
outputs=None)
predict_button.click(fn=pose_model.predict_pose_and_visualize,
inputs=[
input_image,
det_preds,
det_score_threshold,
vis_kpt_score_threshold,
vis_dot_radius,
vis_line_thickness,
],
outputs=[
pose_preds,
pose_visualization,
])
redraw_pose_button.click(fn=pose_model.visualize_pose_results,
inputs=[
input_image,
pose_preds,
vis_kpt_score_threshold,
vis_dot_radius,
vis_line_thickness,
],
outputs=[
pose_visualization,
])
example_images.click(fn=set_example_image,
inputs=[
example_images,
],
outputs=[
input_image,
])
demo.launch(
enable_queue=args.enable_queue,
server_port=args.port,
share=args.share,
)
if __name__ == '__main__':
main()
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