Spaces:
Sleeping
Sleeping
| 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() | |