from ultralytics import YOLO
import gradio as gr
import torch
from tools import fast_process, format_results, box_prompt, point_prompt
from PIL import ImageDraw
import numpy as np

# Load the pre-trained model
model = YOLO('checkpoints/FastSAM.pt')

device = 'cuda' if torch.cuda.is_available() else 'cpu'

# Description
title = "<center><strong><font size='8'>🏃 Fast Segment Anything 🤗</font></strong></center>"

news = """ # 📖 News

        🔥 2023/06/24: Add the 'Advanced options" in Everything mode to get a more detailed adjustment.
        
        🔥 2023/06/26: Support the points mode. (Better and faster interaction will come soon!)
        
        """

         

description_e = """This is a demo on Github project 🏃 [Fast Segment Anything Model](https://github.com/CASIA-IVA-Lab/FastSAM).
                
                🎯 Upload an Image, segment it with Fast Segment Anything (Everything mode). The other modes will come soon.
                
                ⌛️ It takes about 6~ seconds to generate segment results. The concurrency_count of queue is 1, please wait for a moment when it is crowded.
                
                🚀 To get faster results, you can use a smaller input size and leave high_visual_quality unchecked.
                
                📣 You can also obtain the segmentation results of any Image through this Colab: [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1oX14f6IneGGw612WgVlAiy91UHwFAvr9?usp=sharing)
                
                😚 A huge thanks goes out to the @HuggingFace Team for supporting us with GPU grant.
                
                🏠 Check out our [Model Card 🏃](https://huggingface.co/An-619/FastSAM)
                
              """

description_p = """This is a demo on Github project 🏃 [Fast Segment Anything Model](https://github.com/CASIA-IVA-Lab/FastSAM).
                
                🎯 Upload an Image, add points and segment it with Fast Segment Anything (Points mode).
                
                ⌛️ It takes about 6~ seconds to generate segment results. The concurrency_count of queue is 1, please wait for a moment when it is crowded.
                
                🚀 To get faster results, you can use a smaller input size and leave high_visual_quality unchecked.
                
                📣 You can also obtain the segmentation results of any Image through this Colab: [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1oX14f6IneGGw612WgVlAiy91UHwFAvr9?usp=sharing)
                
                😚 A huge thanks goes out to the @HuggingFace Team for supporting us with GPU grant.
                
                🏠 Check out our [Model Card 🏃](https://huggingface.co/An-619/FastSAM)
                
              """

examples = [["assets/sa_8776.jpg"], ["assets/sa_414.jpg"], ["assets/sa_1309.jpg"], ["assets/sa_11025.jpg"],
            ["assets/sa_561.jpg"], ["assets/sa_192.jpg"], ["assets/sa_10039.jpg"], ["assets/sa_862.jpg"]]

default_example = examples[0]

css = "h1 { text-align: center } .about { text-align: justify; padding-left: 10%; padding-right: 10%; }"


def segment_everything(
    input,
    input_size=1024, 
    iou_threshold=0.7,
    conf_threshold=0.25,
    better_quality=False,
    withContours=True,
    mask_random_color=True,
    use_retina=True,
    ):
    input_size = int(input_size)  # 确保 imgsz 是整数

    # Thanks for the suggestion by hysts in HuggingFace.
    w, h = input.size
    scale = input_size / max(w, h)
    new_w = int(w * scale)
    new_h = int(h * scale)
    input = input.resize((new_w, new_h))

    results = model(input,
                    device=device,
                    retina_masks=True,
                    iou=iou_threshold,
                    conf=conf_threshold,
                    imgsz=input_size,)
    
    fig = fast_process(annotations=results[0].masks.data,
                        image=input,
                        device=device,
                        scale=(1024 // input_size),
                        better_quality=better_quality,
                        mask_random_color=mask_random_color,
                        bbox=None,
                        use_retina=use_retina,
                        withContours=withContours,)
    return fig

def segment_with_points(
    input,
    input_size=1024, 
    iou_threshold=0.7,
    conf_threshold=0.25,
    better_quality=False,
    withContours=True,
    mask_random_color=True,
    use_retina=True,
    ):    
    global global_points
    global global_point_label
    
    input_size = int(input_size)  # 确保 imgsz 是整数
    # Thanks for the suggestion by hysts in HuggingFace.
    w, h = input.size
    scale = input_size / max(w, h)
    new_w = int(w * scale)
    new_h = int(h * scale)
    input = input.resize((new_w, new_h))
    
    scaled_points = [[int(x * scale) for x in point] for point in global_points]

    results = model(input,
                    device=device,
                    retina_masks=True,
                    iou=iou_threshold,
                    conf=conf_threshold,
                    imgsz=input_size,)
    
    results = format_results(results[0], 0)
    
    annotations, _ = point_prompt(results, scaled_points, global_point_label, new_h, new_w)
    annotations = np.array([annotations])
        
    fig = fast_process(annotations=annotations,
                        image=input,
                        device=device,
                        scale=(1024 // input_size),
                        better_quality=better_quality,
                        mask_random_color=mask_random_color,
                        bbox=None,
                        use_retina=use_retina,
                        withContours=withContours,)
    global_points = []
    global_point_label = []
    return fig, None

def get_points_with_draw(image, label, evt: gr.SelectData):
    x, y = evt.index[0], evt.index[1]
    point_radius, point_color = 15, (255, 255, 0) if label == 'Add Mask' else (255, 0, 255)
    global global_points
    global global_point_label
    print((x, y))
    global_points.append([x, y])
    global_point_label.append(1 if label == 'Add Mask' else 0)
    
    # 创建一个可以在图像上绘图的对象
    draw = ImageDraw.Draw(image)
    draw.ellipse([(x - point_radius, y - point_radius), (x + point_radius, y + point_radius)], fill=point_color)
    return image
    

# input_size=1024
# high_quality_visual=True
# inp = 'assets/sa_192.jpg'
# input = Image.open(inp)
# device = 'cuda' if torch.cuda.is_available() else 'cpu'
# input_size = int(input_size)  # 确保 imgsz 是整数
# results = model(input, device=device, retina_masks=True, iou=0.7, conf=0.25, imgsz=input_size)
# pil_image = fast_process(annotations=results[0].masks.data,
#                             image=input, high_quality=high_quality_visual, device=device)

cond_img_e = gr.Image(label="Input", value=default_example[0], type='pil')
cond_img_p = gr.Image(label="Input with points", value=default_example[0], type='pil')

segm_img_e = gr.Image(label="Segmented Image", interactive=False, type='pil')
segm_img_p = gr.Image(label="Segmented Image with points", interactive=False, type='pil')

global_points = []
global_point_label = [] # TODO:Clear points each image

input_size_slider = gr.components.Slider(minimum=512,
                                         maximum=1024,
                                         value=1024,
                                         step=64,
                                         label='Input_size',
                                         info='Our model was trained on a size of 1024')

with gr.Blocks(css=css, title='Fast Segment Anything') as demo:
    with gr.Row():
            with gr.Column(scale=1):
                # Title
                gr.Markdown(title)
        
            with gr.Column(scale=1):
                # News
                gr.Markdown(news)
                
    with gr.Tab("Everything mode"):
        # Images
        with gr.Row(variant="panel"):
            with gr.Column(scale=1):
                cond_img_e.render()

            with gr.Column(scale=1):
                segm_img_e.render()

        # Submit & Clear
        with gr.Row():
            with gr.Column():
                input_size_slider.render()

                with gr.Row():
                    contour_check = gr.Checkbox(value=True, label='withContours', info='draw the edges of the masks')

                    with gr.Column():
                        segment_btn_e = gr.Button("Segment Everything", variant='primary')
                        clear_btn_e = gr.Button("Clear", variant="secondary")

                gr.Markdown("Try some of the examples below ⬇️")
                gr.Examples(examples=examples,
                            inputs=[cond_img_e],
                            outputs=segm_img_e,
                            fn=segment_everything,
                            cache_examples=True,
                            examples_per_page=4)

            with gr.Column():
                with gr.Accordion("Advanced options", open=False):
                    iou_threshold = gr.Slider(0.1, 0.9, 0.7, step=0.1, label='iou', info='iou threshold for filtering the annotations')
                    conf_threshold = gr.Slider(0.1, 0.9, 0.25, step=0.05, label='conf', info='object confidence threshold')
                    with gr.Row():
                        mor_check = gr.Checkbox(value=False, label='better_visual_quality', info='better quality using morphologyEx')
                        with gr.Column():
                            retina_check = gr.Checkbox(value=True, label='use_retina', info='draw high-resolution segmentation masks')
                    
                # Description
                gr.Markdown(description_e)

    with gr.Tab("Points mode"):
        # Images
        with gr.Row(variant="panel"):
            with gr.Column(scale=1):
                cond_img_p.render()

            with gr.Column(scale=1):
                segm_img_p.render()
                
        # Submit & Clear
        with gr.Row():
            with gr.Column():
                with gr.Row():
                    add_or_remove = gr.Radio(["Add Mask", "Remove Area"], value="Add Mask", label="Point_label (foreground/background)")

                    with gr.Column():
                        segment_btn_p = gr.Button("Segment with points prompt", variant='primary')
                        clear_btn_p = gr.Button("Clear points", variant='secondary')

                gr.Markdown("Try some of the examples below ⬇️")
                gr.Examples(examples=examples,
                            inputs=[cond_img_p],
                            outputs=segm_img_p,
                            fn=segment_with_points,
                            # cache_examples=True,
                            examples_per_page=4)

            with gr.Column():
                # Description
                gr.Markdown(description_p)
        
    cond_img_p.select(get_points_with_draw, [cond_img_p, add_or_remove], cond_img_p)

    segment_btn_e.click(segment_everything,
                    inputs=[cond_img_e, input_size_slider, iou_threshold, conf_threshold, mor_check, contour_check, retina_check],
                    outputs=segm_img_e)
    
    segment_btn_p.click(segment_with_points,
                    inputs=[cond_img_p],
                    outputs=[segm_img_p, cond_img_p])
    
    def clear():
        return None, None
    
    clear_btn_e.click(clear, outputs=[cond_img_e, segm_img_e])
    clear_btn_p.click(clear, outputs=[cond_img_p, segm_img_p])

demo.queue()
demo.launch()