Create app.py

app.py

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+import gradio as gr
+from skimage import io, filters, color
+import cv2
+import numpy as np
+from skimage.restoration import inpaint
+
+def edge_detection(image, threshold1, threshold2):
+    gray_image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
+    edges = cv2.Canny(gray_image, threshold1, threshold2)
+    return edges
+
+def image_segmentation(image, compactness):
+    from skimage.segmentation import slic
+    segments = slic(image, compactness=compactness, n_segments=200)
+    return color.label2rgb(segments, image, kind='avg')
+
+def inpaint_image(image, mask_threshold):
+    gray_image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
+    _, mask = cv2.threshold(gray_image, mask_threshold, 255, cv2.THRESH_BINARY)
+    mask = mask.astype(bool)
+    inpainted = inpaint.inpaint_biharmonic(image, mask, multichannel=True)
+    return inpainted
+
+def app():
+    with gr.Blocks() as demo:
+        gr.Markdown("# 计算机视觉功能展示")
+        gr.Markdown("使用 Scikit-Image 和 OpenCV 实现的一些基本功能")
+        with gr.Tab("边缘检测"):
+            with gr.Row():
+                input_image = gr.Image(label="输入图像")
+                edge_result = gr.Image(label="检测结果")
+            with gr.Row():
+                threshold1 = gr.Slider(0, 255, value=100, step=1, label="阈值1")
+                threshold2 = gr.Slider(0, 255, value=200, step=1, label="阈值2")
+            edge_button = gr.Button("运行边缘检测")
+            edge_button.click(edge_detection, inputs=[input_image, threshold1, threshold2], outputs=edge_result)
+        with gr.Tab("图像分割"):
+            with gr.Row():
+                input_image = gr.Image(label="输入图像")
+                seg_result = gr.Image(label="分割结果")
+            with gr.Row():
+                compactness = gr.Slider(0.1, 100, value=10, step=0.1, label="紧凑度")
+            seg_button = gr.Button("运行图像分割")
+            seg_button.click(image_segmentation, inputs=[input_image, compactness], outputs=seg_result)
+        with gr.Tab("图像修复"):
+            with gr.Row():
+                input_image = gr.Image(label="输入图像")
+                inpaint_result = gr.Image(label="修复结果")
+            with gr.Row():
+                mask_threshold = gr.Slider(0, 255, value=128, step=1, label="遮罩阈值")
+            inpaint_button = gr.Button("运行图像修复")
+            inpaint_button.click(inpaint_image, inputs=[input_image, mask_threshold], outputs=inpaint_result)
+    return demo
+
+if __name__ == "__main__":
+    app().launch()