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#!/usr/bin/env python
from __future__ import annotations
import argparse
import os
import pathlib
import subprocess
import sys
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())
subprocess.call('git apply ../patch'.split(), cwd='CBNetV2')
subprocess.call('mv palette.py CBNetV2/mmdet/core/visualization/'.split())
import gradio as gr
import numpy as np
import torch
import torch.nn as nn
sys.path.insert(0, 'CBNetV2/')
from mmdet.apis import inference_detector, init_detector
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 Model:
def __init__(self, device: str | torch.device):
self.device = torch.device(device)
self.models = self._load_models()
self.model_name = 'Improved HTC (DB-Swin-B)'
def _load_models(self) -> dict[str, nn.Module]:
model_dict = {
'Faster R-CNN (DB-ResNet50)': {
'config':
'CBNetV2/configs/cbnet/faster_rcnn_cbv2d1_r50_fpn_1x_coco.py',
'model':
'https://github.com/CBNetwork/storage/releases/download/v1.0.0/faster_rcnn_cbv2d1_r50_fpn_1x_coco.pth.zip',
},
'Mask R-CNN (DB-Swin-T)': {
'config':
'CBNetV2/configs/cbnet/mask_rcnn_cbv2_swin_tiny_patch4_window7_mstrain_480-800_adamw_3x_coco.py',
'model':
'https://github.com/CBNetwork/storage/releases/download/v1.0.0/mask_rcnn_cbv2_swin_tiny_patch4_window7_mstrain_480-800_adamw_3x_coco.pth.zip',
},
# 'Cascade Mask R-CNN (DB-Swin-S)': {
# 'config':
# 'CBNetV2/configs/cbnet/cascade_mask_rcnn_cbv2_swin_small_patch4_window7_mstrain_400-1400_adamw_3x_coco.py',
# 'model':
# 'https://github.com/CBNetwork/storage/releases/download/v1.0.0/cascade_mask_rcnn_cbv2_swin_small_patch4_window7_mstrain_400-1400_adamw_3x_coco.pth.zip',
# },
'Improved HTC (DB-Swin-B)': {
'config':
'CBNetV2/configs/cbnet/htc_cbv2_swin_base_patch4_window7_mstrain_400-1400_giou_4conv1f_adamw_20e_coco.py',
'model':
'https://github.com/CBNetwork/storage/releases/download/v1.0.0/htc_cbv2_swin_base22k_patch4_window7_mstrain_400-1400_giou_4conv1f_adamw_20e_coco.pth.zip',
},
'Improved HTC (DB-Swin-L)': {
'config':
'CBNetV2/configs/cbnet/htc_cbv2_swin_large_patch4_window7_mstrain_400-1400_giou_4conv1f_adamw_1x_coco.py',
'model':
'https://github.com/CBNetwork/storage/releases/download/v1.0.0/htc_cbv2_swin_large22k_patch4_window7_mstrain_400-1400_giou_4conv1f_adamw_1x_coco.pth.zip',
},
'Improved HTC (DB-Swin-L (TTA))': {
'config':
'CBNetV2/configs/cbnet/htc_cbv2_swin_large_patch4_window7_mstrain_400-1400_giou_4conv1f_adamw_1x_coco.py',
'model':
'https://github.com/CBNetwork/storage/releases/download/v1.0.0/htc_cbv2_swin_large22k_patch4_window7_mstrain_400-1400_giou_4conv1f_adamw_1x_coco.pth.zip',
},
}
weight_dir = pathlib.Path('weights')
weight_dir.mkdir(exist_ok=True)
def _download(model_name: str, out_dir: pathlib.Path) -> None:
import zipfile
model_url = model_dict[model_name]['model']
zip_name = model_url.split('/')[-1]
out_path = out_dir / zip_name
if out_path.exists():
return
torch.hub.download_url_to_file(model_url, out_path)
with zipfile.ZipFile(out_path) as f:
f.extractall(out_dir)
def _get_model_path(model_name: str) -> str:
model_url = model_dict[model_name]['model']
model_name = model_url.split('/')[-1][:-4]
return (weight_dir / model_name).as_posix()
for model_name in model_dict:
_download(model_name, weight_dir)
models = {
key: init_detector(dic['config'],
_get_model_path(key),
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:
image = image[:, :, ::-1] # RGB -> BGR
model = self.models[self.model_name]
vis = model.show_result(image,
detection_results,
score_thr=score_threshold,
bbox_color=None,
text_color=(200, 200, 200),
mask_color=None)
return vis[:, :, ::-1] # BGR -> RGB
def set_example_image(example: list) -> dict:
return gr.Image.update(value=example[0])
def main():
args = parse_args()
model = Model(args.device)
css = '''
h1#title {
text-align: center;
}
'''
with gr.Blocks(theme=args.theme, css=css) as demo:
gr.Markdown('''<h1 id="title">VDIGPKU/CBNetV2</h1>
This is an unofficial demo for [https://github.com/VDIGPKU/CBNetV2](https://github.com/VDIGPKU/CBNetV2).'''
)
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(model.models.keys()),
value=model.model_name,
label='Detector')
with gr.Row():
detect_button = gr.Button(value='Detect')
detection_results = gr.Variable()
with gr.Column():
detection_visualization = gr.Image(label='Detection Result',
type='numpy')
visualization_score_threshold = gr.Slider(
0,
1,
step=0.05,
value=0.3,
label='Visualization Score Threshold')
redraw_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])
gr.Markdown(
'<center><img src="https://visitor-badge.glitch.me/badge?page_id=hysts.cbnetv2" alt="visitor badge"/></center>'
)
detector_name.change(fn=model.set_model_name,
inputs=[detector_name],
outputs=None)
detect_button.click(fn=model.detect_and_visualize,
inputs=[
input_image,
visualization_score_threshold,
],
outputs=[
detection_results,
detection_visualization,
])
redraw_button.click(fn=model.visualize_detection_results,
inputs=[
input_image,
detection_results,
visualization_score_threshold,
],
outputs=[detection_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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