Update

.pre-commit-config.yaml

@@ -1,4 +1,4 @@
-exclude: ^(CBNetV2/|patch)
+exclude: ^patch
 repos:
 - repo: https://github.com/pre-commit/pre-commit-hooks
   rev: v4.2.0
@@ -21,26 +21,17 @@ repos:
   - id: docformatter
     args: ['--in-place']
 - repo: https://github.com/pycqa/isort
-  rev: 5.10.1
+  rev: 5.12.0
   hooks:
     - id: isort
 - repo: https://github.com/pre-commit/mirrors-mypy
-  rev: v0.812
+  rev: v0.991
   hooks:
     - id: mypy
       args: ['--ignore-missing-imports']
+      additional_dependencies: ['types-python-slugify']
 - repo: https://github.com/google/yapf
   rev: v0.32.0
   hooks:
   - id: yapf
     args: ['--parallel', '--in-place']
-- repo: https://github.com/kynan/nbstripout
-  rev: 0.5.0
-  hooks:
-    - id: nbstripout
-      args: ['--extra-keys', 'metadata.interpreter metadata.kernelspec cell.metadata.pycharm']
-- repo: https://github.com/nbQA-dev/nbQA
-  rev: 1.3.1
-  hooks:
-    - id: nbqa-isort
-    - id: nbqa-yapf

README.md

@@ -4,9 +4,12 @@ emoji: 📉
 colorFrom: gray
 colorTo: green
 sdk: gradio
-sdk_version: 3.0.17
+sdk_version: 3.35.2
 app_file: app.py
 pinned: false
+suggested_hardware: t4-small
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
+
+https://arxiv.org/abs/2107.00420

app.py

@@ -2,104 +2,66 @@
 
 from __future__ import annotations
 
-import argparse
 import pathlib
 
 import gradio as gr
 
 from model import Model
 
-DESCRIPTION = '''# CBNetV2
-
-This is an unofficial demo for [https://github.com/VDIGPKU/CBNetV2](https://github.com/VDIGPKU/CBNetV2).'''
-FOOTER = '<img id="visitor-badge" alt="visitor badge" src="https://visitor-badge.glitch.me/badge?page_id=hysts.cbnetv2" />'
-
-
-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()
-
-
-def set_example_image(example: list) -> dict:
-    return gr.Image.update(value=example[0])
-
-
-def main():
-    args = parse_args()
-    model = Model(args.device)
-
-    with gr.Blocks(theme=args.theme, css='style.css') as demo:
-        gr.Markdown(DESCRIPTION)
-
-        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():
-                with gr.Row():
-                    detection_visualization = gr.Image(
-                        label='Detection Result', type='numpy')
-                with gr.Row():
-                    visualization_score_threshold = gr.Slider(
-                        0,
-                        1,
-                        step=0.05,
-                        value=0.3,
-                        label='Visualization Score Threshold')
-                with gr.Row():
-                    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(FOOTER)
-
-        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()
+DESCRIPTION = '# [CBNetV2](https://github.com/VDIGPKU/CBNetV2)'
+
+model = Model()
+
+with gr.Blocks(css='style.css') as demo:
+    gr.Markdown(DESCRIPTION)
+
+    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(label='Detector',
+                                            choices=list(model.models.keys()),
+                                            value=model.model_name)
+            with gr.Row():
+                detect_button = gr.Button('Detect')
+                detection_results = gr.Variable()
+        with gr.Column():
+            with gr.Row():
+                detection_visualization = gr.Image(label='Detection Result',
+                                                   type='numpy')
+            with gr.Row():
+                visualization_score_threshold = gr.Slider(
+                    label='Visualization Score Threshold',
+                    minimum=0,
+                    maximum=1,
+                    step=0.05,
+                    value=0.3)
+            with gr.Row():
+                redraw_button = gr.Button('Redraw')
+
+    with gr.Row():
+        paths = sorted(pathlib.Path('images').rglob('*.jpg'))
+        gr.Examples(examples=[[path.as_posix()] for path in paths],
+                    inputs=input_image)
+
+    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])
+demo.queue(max_size=10).launch()

model.py

@@ -2,6 +2,7 @@ from __future__ import annotations
 
 import os
 import pathlib
+import shlex
 import subprocess
 import sys
 
@@ -11,12 +12,12 @@ if os.getenv('SYSTEM') == 'spaces':
     mim.uninstall('mmcv-full', confirm_yes=True)
     mim.install('mmcv-full==1.5.0', is_yes=True)
 
-    subprocess.run('pip uninstall -y opencv-python'.split())
-    subprocess.run('pip uninstall -y opencv-python-headless'.split())
-    subprocess.run('pip install opencv-python-headless==4.5.5.64'.split())
+    subprocess.run(shlex.split('pip uninstall -y opencv-python'))
+    subprocess.run(shlex.split('pip uninstall -y opencv-python-headless'))
+    subprocess.run(shlex.split('pip install opencv-python-headless==4.8.0.74'))
 
     with open('patch') as f:
-        subprocess.run('patch -p1'.split(), cwd='CBNetV2', stdin=f)
+        subprocess.run(shlex.split('patch -p1'), cwd='CBNetV2', stdin=f)
     subprocess.run('mv palette.py CBNetV2/mmdet/core/visualization/'.split())
 
 import numpy as np
@@ -31,8 +32,9 @@ from mmdet.apis import inference_detector, init_detector
 
 
 class Model:
-    def __init__(self, device: str | torch.device):
-        self.device = torch.device(device)
+    def __init__(self):
+        self.device = torch.device(
+            'cuda:0' if torch.cuda.is_available() else 'cpu')
         self.models = self._load_models()
         self.model_name = 'Improved HTC (DB-Swin-B)'
 

requirements.txt

@@ -1,7 +1,7 @@
 mmcv-full==1.5.0
 mmdet==2.24.1
 numpy==1.22.4
-opencv-python-headless==4.5.5.64
+opencv-python-headless==4.8.0.74
 openmim==0.1.5
 timm==0.5.4
 torch==1.11.0