import gradio as gr
import cv2
import numpy as np
from skimage import io, segmentation

# 功能函數
def image_segmentation(image):
    segments = segmentation.slic(image, n_segments=100, compactness=10)
    return segmentation.mark_boundaries(image, segments)

def edge_detection(image, threshold1, threshold2):
    edges = cv2.Canny(image, threshold1, threshold2)
    return edges

def image_inpainting(image, mask):
    # 確保影像和遮罩有相同的尺寸
    if image.shape[:2] != mask.shape[:2]:
        raise ValueError("影像和遮罩的尺寸不一致！")
    
    # 將遮罩轉換為單通道（灰度）
    if len(mask.shape) == 3:
        mask = cv2.cvtColor(mask, cv2.COLOR_BGR2GRAY)
    
    # 修復影像
    inpainted = cv2.inpaint(image, mask, inpaintRadius=3, flags=cv2.INPAINT_TELEA)
    return inpainted

def template_matching(image, template):
    result = cv2.matchTemplate(image, template, cv2.TM_CCOEFF_NORMED)
    min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(result)
    matched_image = image.copy()
    h, w = template.shape[:2]
    cv2.rectangle(matched_image, max_loc, (max_loc[0] + w, max_loc[1] + h), (0, 255, 0), 2)
    return matched_image

# Gradio 介面設置
def app_interface():
    with gr.Blocks() as demo:
        gr.Markdown("## 電腦視覺功能展示")

        with gr.Tabs():
            # 分割功能
            with gr.Tab("影像分割"):
                image_input = gr.Image(label="上傳影像", type="numpy")
                seg_output = gr.Image(label="分割後影像")
                seg_button = gr.Button("執行分割")
                seg_button.click(image_segmentation, inputs=[image_input], outputs=[seg_output])

            # 邊緣偵測
            with gr.Tab("邊緣偵測"):
                edge_input = gr.Image(label="上傳影像", type="numpy")
                thresh1 = gr.Slider(0, 255, value=100, step=1, label="閾值 1")
                thresh2 = gr.Slider(0, 255, value=200, step=1, label="閾值 2")
                edge_output = gr.Image(label="邊緣偵測結果")
                edge_button = gr.Button("執行邊緣偵測")
                edge_button.click(edge_detection, inputs=[edge_input, thresh1, thresh2], outputs=[edge_output])

            # 影像修復
            with gr.Tab("影像修復"):
                # 輸入影像與遮罩
                inpaint_input = gr.Image(label="上傳影像", type="numpy")
                mask_input = gr.Image(label="上傳遮罩 (白色為修復區域)", type="numpy")
                
                # 輸出修復後影像
                inpaint_output = gr.Image(label="修復後影像")
                
                # 按鈕操作
                inpaint_button = gr.Button("執行修復")
                
                # 綁定影像修復函數到按鈕
                inpaint_button.click(image_inpainting, inputs=[inpaint_input, mask_input], outputs=inpaint_output)

            # 模板匹配
            with gr.Tab("模板匹配"):
                match_input = gr.Image(label="上傳影像", type="numpy")
                template_input = gr.Image(label="上傳模板", type="numpy")
                match_output = gr.Image(label="模板匹配結果")
                match_button = gr.Button("執行匹配")
                match_button.click(template_matching, inputs=[match_input, template_input], outputs=[match_output])

    return demo

demo = app_interface()
demo.launch()