#!/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 [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 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)
    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)