Spaces:
Runtime error
Runtime error
File size: 5,255 Bytes
5c24fa5 2dc39d3 2d73c11 5c24fa5 1fd5d6c 5c24fa5 c71f536 5c24fa5 1fd5d6c 5c24fa5 cb17e57 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 |
#!/usr/bin/env python
from __future__ import annotations
import os
import pathlib
import subprocess
import tarfile
if os.getenv('SYSTEM') == 'spaces':
import mim
mim.uninstall('mmcv-full', confirm_yes=True)
mim.install('mmcv-full==1.5.2', 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 cv2
import gradio as gr
import numpy as np
from model import AppModel
DESCRIPTION = '''# MMDetection
This is an unofficial demo for Object detection on olfactory objects.
<img id="overview" alt="overview" src="https://images.rkd.nl/rkd/thumb/650x650/7a734fa6-0a2b-aac0-8f2f-d3063de3a176.jpg" />
'''
DEFAULT_MODEL_TYPE = 'detection'
DEFAULT_MODEL_NAMES = {
'detection': 'Faster R-CNN (R-50-FPN)'
}
DEFAULT_MODEL_NAME = DEFAULT_MODEL_NAMES[DEFAULT_MODEL_TYPE]
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 update_input_image(image: np.ndarray) -> dict:
if image is None:
return gr.Image.update(value=None)
scale = 1500 / max(image.shape[:2])
if scale < 1:
image = cv2.resize(image, None, fx=scale, fy=scale)
return gr.Image.update(value=image)
def update_model_name(model_type: str) -> dict:
model_dict = getattr(AppModel, f'{model_type.upper()}_MODEL_DICT')
model_names = list(model_dict.keys())
model_name = DEFAULT_MODEL_NAMES[model_type]
return gr.Dropdown.update(choices=model_names, value=model_name)
def update_visualization_score_threshold(model_type: str) -> dict:
return gr.Slider.update(visible=model_type != 'panoptic_segmentation')
def update_redraw_button(model_type: str) -> dict:
return gr.Button.update(visible=model_type != 'panoptic_segmentation')
def set_example_image(example: list) -> dict:
return gr.Image.update(value=example[0])
extract_tar()
model = AppModel(DEFAULT_MODEL_NAME)
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.Group():
with gr.Row():
model_type = gr.Radio(list(DEFAULT_MODEL_NAMES.keys()),
value=DEFAULT_MODEL_TYPE,
label='Model Type')
with gr.Row():
model_name = gr.Dropdown(list(
model.DETECTION_MODEL_DICT.keys()),
value=DEFAULT_MODEL_NAME,
label='Model')
with gr.Row():
run_button = gr.Button(value='Run')
prediction_results = gr.Variable()
with gr.Column():
with gr.Row():
visualization = gr.Image(label='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])
input_image.change(fn=update_input_image,
inputs=input_image,
outputs=input_image)
model_type.change(fn=update_model_name,
inputs=model_type,
outputs=model_name)
model_type.change(fn=update_visualization_score_threshold,
inputs=model_type,
outputs=visualization_score_threshold)
model_type.change(fn=update_redraw_button,
inputs=model_type,
outputs=redraw_button)
model_name.change(fn=model.set_model, inputs=model_name, outputs=None)
print(f"Here : {model_name}")
run_button.click(fn=model.run,
inputs=[
model_name,
input_image,
visualization_score_threshold,
],
outputs=[
prediction_results,
visualization,
])
redraw_button.click(fn=model.visualize_detection_results,
inputs=[
input_image,
prediction_results,
visualization_score_threshold,
],
outputs=visualization)
example_images.click(fn=set_example_image,
inputs=example_images,
outputs=input_image)
demo.launch(share=True)
|