import importlib
import os
import gradio as gr

from annotator.util import resize_image, HWC3

config = {
    "canny": "CannyDetector",
    "hed": "HedDetector",
    "mlsd": "MLSDProcessor",
    "midas": "MidasProcessor",
    "openpose": "OpenposeDetector",
    "uniformer": "UniformerDetector"
}

package_annotator = "annotator"


def process_image(cls: str, img, res, *kwargs):
    img = resize_image(HWC3(img), res)
    # load_model()
    module_imp = importlib.import_module(package_annotator)
    model = getattr(module_imp, cls)
    image_processor = model()
    result = image_processor(img, *kwargs)
    if type(result) == tuple:
        return result
    return [result]


def process(cls):
    def process_fc(img, res, *args):
        return process_image(cls, img, res, *args)

    return process_fc


block = gr.Blocks().queue()
examples = [os.path.join(os.path.dirname(__file__), "examples/demo.jpeg")]
with block:
    with gr.Tab("Canny Edge"):
        with gr.Row():
            gr.Markdown("## Canny Edge")
        with gr.Row():
            with gr.Column():
                input_image = gr.Image(source='upload', type="numpy")
                low_threshold = gr.Slider(label="low_threshold", minimum=1, maximum=255, value=100, step=1)
                high_threshold = gr.Slider(label="high_threshold", minimum=1, maximum=255, value=200, step=1)
                resolution = gr.Slider(label="resolution", minimum=256, maximum=1024, value=512, step=64)
                run_button = gr.Button(label="Run")
                gr.Examples(examples, input_image)
            with gr.Column():
                gallery = gr.Gallery(label="Generated images", show_label=False).style(height="auto")
        run_button.click(fn=process(config["canny"]), inputs=[input_image, resolution, low_threshold, high_threshold],
                         outputs=[gallery])

    with gr.Tab("HED Edge"):
        with gr.Row():
            gr.Markdown("## HED Edge")
        with gr.Row():
            with gr.Column():
                input_image = gr.Image(source='upload', type="numpy")
                resolution = gr.Slider(label="resolution", minimum=256, maximum=1024, value=512, step=64)
                run_button = gr.Button(label="Run")
                gr.Examples(examples, input_image)
            with gr.Column():
                gallery = gr.Gallery(label="Generated images", show_label=False).style(height="auto")
        run_button.click(fn=process(config["hed"]), inputs=[input_image, resolution], outputs=[gallery])

    with gr.Tab("MLSD Edge"):
        with gr.Row():
            gr.Markdown("## MLSD Edge")
        with gr.Row():
            with gr.Column():
                input_image = gr.Image(source='upload', type="numpy")
                value_threshold = gr.Slider(label="value_threshold", minimum=0.01, maximum=2.0, value=0.1, step=0.01)
                distance_threshold = gr.Slider(label="distance_threshold", minimum=0.01, maximum=20.0, value=0.1,
                                               step=0.01)
                resolution = gr.Slider(label="resolution", minimum=256, maximum=1024, value=384, step=64)
                run_button = gr.Button(label="Run")
                gr.Examples(examples, input_image)
            with gr.Column():
                gallery = gr.Gallery(label="Generated images", show_label=False).style(height="auto")
        run_button.click(fn=process(config["mlsd"]),
                         inputs=[input_image, resolution, value_threshold, distance_threshold],
                         outputs=[gallery])

    with gr.Tab("MIDAS Depth and Normal"):
        with gr.Row():
            gr.Markdown("## MIDAS Depth and Normal")
        with gr.Row():
            with gr.Column():
                input_image = gr.Image(source='upload', type="numpy")
                alpha = gr.Slider(label="alpha", minimum=0.1, maximum=20.0, value=6.2, step=0.01)
                resolution = gr.Slider(label="resolution", minimum=256, maximum=1024, value=384, step=64)
                run_button = gr.Button(label="Run")
                gr.Examples(examples, input_image)
            with gr.Column():
                gallery = gr.Gallery(label="Generated images", show_label=False).style(height="auto")
        run_button.click(fn=process(config["midas"]), inputs=[input_image, resolution, alpha], outputs=[gallery])

    with gr.Tab("Openpose"):
        with gr.Row():
            gr.Markdown("## Openpose")
        with gr.Row():
            with gr.Column():
                input_image = gr.Image(source='upload', type="numpy")
                hand = gr.Checkbox(label='detect hand', value=False)
                resolution = gr.Slider(label="resolution", minimum=256, maximum=1024, value=512, step=64)
                run_button = gr.Button(label="Run")
                gr.Examples(examples, input_image)
            with gr.Column():
                gallery = gr.Gallery(label="Generated images", show_label=False).style(height="auto")
        run_button.click(fn=process(config["openpose"]), inputs=[input_image, resolution, hand], outputs=[gallery])

    with gr.Tab("Uniformer Segmentation"):
        with gr.Row():
            gr.Markdown("## Uniformer Segmentation")
        with gr.Row():
            with gr.Column():
                input_image = gr.Image(source='upload', type="numpy")
                resolution = gr.Slider(label="resolution", minimum=256, maximum=1024, value=512, step=64)
                run_button = gr.Button(label="Run")
                gr.Examples(examples, input_image)
            with gr.Column():
                gallery = gr.Gallery(label="Generated images", show_label=False).style(height="auto")
        run_button.click(fn=process(config["uniformer"]), inputs=[input_image, resolution], outputs=[gallery])

block.launch()