app.py

import gradio as gr
import cv2
import numpy as np

# 邊緣檢測函式
def edge_detection(image, threshold1, threshold2):
    gray_image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
    edges = cv2.Canny(gray_image, threshold1, threshold2)
    return edges

# 圖像分割函式
def image_segmentation(image, compactness):
    from skimage.segmentation import slic
    from skimage import color
    segments = slic(image, compactness=compactness, n_segments=200)
    return color.label2rgb(segments, image, kind='avg')

# 顏色範圍調整函式
def adjust_color(image, r_min, r_max, g_min, g_max, b_min, b_max):
    # 確保圖片格式為 RGB
    if image.shape[-1] == 4:  # 如果圖片有 alpha 通道
        image = cv2.cvtColor(image, cv2.COLOR_RGBA2RGB)
    
    # 調整 R、G、B 通道的範圍
    image = image.astype(np.float32)  # 將圖像轉為浮點數進行處理
    image[:, :, 0] = np.clip(image[:, :, 0], r_min, r_max)  # 調整 R 通道
    image[:, :, 1] = np.clip(image[:, :, 1], g_min, g_max)  # 調整 G 通道
    image[:, :, 2] = np.clip(image[:, :, 2], b_min, b_max)  # 調整 B 通道
    image = image.astype(np.uint8)  # 轉回整數型圖像
    
    return image

# 主應用程式
def app():
    with gr.Blocks() as demo:
        gr.Markdown("# 影像處理功能展示")
        gr.Markdown("本應用程式展示了使用 OpenCV 實現的影像處理功能，包括動態調整 RGB 範圍的功能。")
        
        with gr.Tab("邊緣檢測"):
            with gr.Row():
                input_image = gr.Image(label="輸入圖片")
                edge_result = gr.Image(label="邊緣檢測結果")
            with gr.Row():
                threshold1 = gr.Slider(0, 255, value=100, step=1, label="閾值1")
                threshold2 = gr.Slider(0, 255, value=200, step=1, label="閾值2")
            edge_button = gr.Button("執行邊緣檢測")
            edge_button.click(
                fn=edge_detection, 
                inputs=[input_image, threshold1, threshold2], 
                outputs=edge_result
            )
        
        with gr.Tab("圖像分割"):
            with gr.Row():
                input_image = gr.Image(label="輸入圖片")
                seg_result = gr.Image(label="分割結果")
            with gr.Row():
                compactness = gr.Slider(0.1, 100, value=10, step=0.1, label="分割緊湊度")
            seg_button = gr.Button("執行圖像分割")
            seg_button.click(
                fn=image_segmentation, 
                inputs=[input_image, compactness], 
                outputs=seg_result
            )
        
        with gr.Tab("顏色範圍調整"):
            with gr.Row():
                input_image = gr.Image(label="輸入圖片")
                adjusted_result = gr.Image(label="調整後的圖片")
            with gr.Row():
                r_min = gr.Slider(0, 255, value=0, step=1, label="R 最小值")
                r_max = gr.Slider(0, 255, value=255, step=1, label="R 最大值")
                g_min = gr.Slider(0, 255, value=0, step=1, label="G 最小值")
                g_max = gr.Slider(0, 255, value=255, step=1, label="G 最大值")
                b_min = gr.Slider(0, 255, value=0, step=1, label="B 最小值")
                b_max = gr.Slider(0, 255, value=255, step=1, label="B 最大值")
            adjust_button = gr.Button("調整顏色範圍")
            adjust_button.click(
                fn=adjust_color,
                inputs=[input_image, r_min, r_max, g_min, g_max, b_min, b_max],
                outputs=adjusted_result
            )
    
    return demo

# 啟動應用程式
if __name__ == "__main__":
    app().launch()