Create app.py

app.py

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+import gradio as gr
+import cv2
+import numpy as np
+
+def edge_detection(image, threshold1, threshold2):
+    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+    edges = cv2.Canny(gray, threshold1, threshold2)
+    return edges
+
+def image_segmentation(image):
+    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+    _, segmented = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
+    return segmented
+
+def image_inpainting(image, mask):  
+    if mask.shape[:2] != image.shape[:2]:
+        mask = cv2.resize(mask, (image.shape[1], image.shape[0]), interpolation=cv2.INTER_NEAREST)
+    
+    if len(mask.shape) == 3:
+        mask = cv2.cvtColor(mask, cv2.COLOR_BGR2GRAY)
+    mask = (mask > 0).astype(np.uint8) * 255
+    inpainted = cv2.inpaint(image, mask, 3, cv2.INPAINT_TELEA)
+    return inpainted
+
+with gr.Blocks() as demo:
+    gr.Markdown("Computer Vision")
+    with gr.Tab("Edge Detection"):
+        inputs = [
+                gr.Image(type="numpy", label="Upload Image", default="/home/teng_aicv/Desktop/182862.jpg"),
+                gr.Slider(0, 255, value=50, step=1, label="Threshold 1"),
+                gr.Slider(0, 255, value=100, step=1, label="Threshold 2"),
+            ]
+        output = gr.Image(type="numpy", label="Edge Image") 
+        gr.Interface(
+            fn=edge_detection,
+            inputs=inputs,
+            outputs=output,
+            description="Upload an image and adjust the thresholds to perform edge detection"
+        )
+    with gr.Tab("Image Segmentation"):
+        inputs = [
+                gr.Image(type="numpy", label="Upload Image")
+            ]
+        output = gr.Image(type="numpy", label="Segmented Image") 
+        gr.Interface(
+            fn=image_segmentation,
+            inputs=inputs,
+            outputs=output,
+            description="Upload an image to perform image segmentation"
+        )
+    with gr.Tab("Image Inpainting"):
+        inputs = [
+                gr.Image(type="numpy", label="Upload Image"),
+                gr.Image(type="numpy", label="Upload Mask")
+            ]
+        output = gr.Image(type="numpy", label="Inpainted Image") 
+        gr.Interface(
+            fn=image_inpainting,
+            inputs=inputs,
+            outputs=output,
+            description="Upload an image and adjust the thresholds to perform image inpainting",
+            allow_flagging='never'
+        )
+
+if __name__ == "__main__":
+    demo.launch()