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#!/usr/bin/env python
from __future__ import annotations
import os
import pathlib
import subprocess
import tarfile
import cv2
import gradio as gr
import numpy as np
from model import AppModel
DESCRIPTION = '''# MMDetection
This is an unofficial demo for [https://github.com/open-mmlab/mmdetection](https://github.com/open-mmlab/mmdetection).
<img id="overview" alt="overview" src="https://user-images.githubusercontent.com/12907710/137271636-56ba1cd2-b110-4812-8221-b4c120320aa9.png" />
'''
DEFAULT_MODEL_TYPE = 'detection'
DEFAULT_MODEL_NAMES = {
'detection': 'YOLOX-l',
'instance_segmentation': 'QueryInst (R-50-FPN)',
'panoptic_segmentation': 'MaskFormer (R-50)',
}
DEFAULT_MODEL_NAME = DEFAULT_MODEL_NAMES[DEFAULT_MODEL_TYPE]
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])
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)
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.queue().launch(show_api=False) |