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

def edge_detection(image, threshold1, threshold2):
    """邊緣檢測"""
    ###先將圖片轉成灰度
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    ###再用Canny算法檢測邊緣
    edges = cv2.Canny(gray, threshold1, threshold2)
    return edges

def line_detection(image, threshold, min_line_length, max_line_gap):
    """直線檢測"""
    ##前面先做邊緣檢測的部分
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    edges = cv2.Canny(gray, 50, 150)
    ##用Hough找出直線
    lines = cv2.HoughLinesP(edges, 1, np.pi / 180, threshold, minLineLength=min_line_length, maxLineGap=max_line_gap)
    line_image = np.zeros_like(image)
    ##之後再化回原圖
    if lines is not None:
        for line in lines:
            x1, y1, x2, y2 = line[0]
            cv2.line(line_image, (x1, y1), (x2, y2), (0, 255, 0), 2)
    combined = cv2.addWeighted(image, 0.8, line_image, 1, 0)
    return combined

def image_inpainting(image, mask_radius):
    """圖像修復"""
    mask = np.zeros(image.shape[:2], dtype=np.uint8)
    cv2.circle(mask, (image.shape[1]//2, image.shape[0]//2), mask_radius, 255, -1)
    inpainted = cv2.inpaint(image, mask, 3, cv2.INPAINT_TELEA)
    return inpainted

# Example images for easy testing
example_images = [
    ("Edge Image 1", "https://www.sanrio.com.tw/wp-content/uploads/2018/09/25.%E6%98%8E%E6%98%9F%E4%BB%8B%E7%B4%B9KU-02.png"),
    ("Edge Image 2", "https://web.hocom.tw/Uploads/Product/21380_470807_ji9n9psb.jpg"),
    ("Line Image 1", "https://p3-sdbk2-media.byteimg.com/tos-cn-i-xv4ileqgde/5cc733d480b64417a7b9a5adc53e4f72~tplv-xv4ileqgde-resize-w:750.image"),
    ("Line Image 2", "https://imgs.699pic.com/images/321/926/661.jpg!detail.v1"),
    ("Inpaint Image 1","https://img95.699pic.com/xsj/0v/ig/gj.jpg!/fh/300"),
    ("Inpaint Image 2","https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcS0bxiS49IMEuZs7JjZx54JPw8x6s-YaONtPQ&s")
]   

# Gradio interface
with gr.Blocks() as demo:
    gr.Markdown("## Computer Vision Web Application")
    
    with gr.Tab("Edge Detection"):
        with gr.Row():
            with gr.Column():
                img_input = gr.Image(type="numpy", label="Input Image")
                threshold1 = gr.Slider(0, 255, 50, step=1, label="Threshold 1")
                threshold2 = gr.Slider(0, 255, 150, step=1, label="Threshold 2")
            with gr.Column():
                edge_output = gr.Image(label="Edge Detection Result")
        edge_button = gr.Button("Run Edge Detection")
        edge_button.click(edge_detection, inputs=[img_input, threshold1, threshold2], outputs=edge_output)
    
    with gr.Tab("Line Detection"):
        with gr.Row():
            with gr.Column():
                img_input_line = gr.Image(type="numpy", label="Input Image")
                threshold = gr.Slider(1, 100, 50, step=1, label="Threshold")
                min_line_length = gr.Slider(10, 200, 50, step=1, label="Min Line Length")
                max_line_gap = gr.Slider(1, 50, 10, step=1, label="Max Line Gap")
            with gr.Column():
                line_output = gr.Image(label="Line Detection Result")
        line_button = gr.Button("Run Line Detection")
        line_button.click(line_detection, inputs=[img_input_line, threshold, min_line_length, max_line_gap], outputs=line_output)
    
    with gr.Tab("Image Inpainting"):
        with gr.Row():
            with gr.Column():
                img_input_inpaint = gr.Image(type="numpy", label="Input Image")
                mask_radius = gr.Slider(10, 100, 30, step=1, label="Mask Radius")
            with gr.Column():
                inpaint_output = gr.Image(label="Inpainting Result")
        inpaint_button = gr.Button("Run Inpainting")
        inpaint_button.click(image_inpainting, inputs=[img_input_inpaint, mask_radius], outputs=inpaint_output)

    gr.Markdown("### Example Images")
    for label, url in example_images:
        gr.Markdown(f"[{label}]({url})")

# Launch Gradio app
demo.launch()