Update app.py

app.py

@@ -1,18 +1,67 @@
 import gradio as gr
 import cv2
 import numpy as np
+from PIL import Image
 
-def edge_detection(image, threshold1, threshold2):
+examples = [
+    '/home/teng_aicv/Desktop/176788.jpg',
+    '/home/teng_aicv/Desktop/182862.jpg'
+]
+masks = [
+    '/home/teng_aicv/Desktop/mask1.jpg',
+    '/home/teng_aicv/Desktop/mask2.jpg'
+]
+
+def edge_detection(mode_selector, upload_input, image_selector, image_selected, threshold1, threshold2):
+    if mode_selector == "Upload":
+        image = upload_input
+    else:
+        image = image_selected
     gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
     edges = cv2.Canny(gray, threshold1, threshold2)
     return edges
 
-def image_segmentation(image):
+def mode_selector_changed(mode_selector, image_selector):
+    if mode_selector == "Upload":
+        return gr.update(visible=True), gr.update(visible=False), gr.update(visible=False)
+    else:
+        return gr.update(visible=False), gr.update(visible=True), gr.update(visible=image_selector is not None, value=image_selector)
+
+def mode_selector_changed_inpainting(mode_selector, image_selector, mask_selector):
+    if mode_selector == "Upload":
+        return gr.update(visible=True), gr.update(visible=True), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)
+    else:
+        return gr.update(visible=False), gr.update(visible=False), gr.update(visible=True), gr.update(visible=image_selector is not None, value=image_selector), gr.update(visible=True), gr.update(visible=image_selector is not None, value=mask_selector)
+
+def close_selected_image(image_selected):
+    if image_selected is None:
+        return gr.update(visible=True, value=None), gr.update(visible=False, value=None)
+    else:
+        return gr.update(visible=True), gr.update(visible=True)
+    
+def close_selected_mask(mask_selected):
+    if mask_selected is None:
+        return gr.update(visible=True, value=None), gr.update(visible=False, value=None)
+    else:
+        return gr.update(visible=True), gr.update(visible=True)
+
+def image_segmentation(mode_selector, upload_input, image_selector, image_selected):
+    if mode_selector == "Upload":
+        image = upload_input
+    else:
+        image = image_selected
     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):  
+def image_inpainting(mode_selector, upload_input, upload_mask, image_selector, image_selected, mask_selector, mask_selected):  
+    if mode_selector == "Upload":
+        image = upload_input
+        mask = upload_mask
+    else:
+        image = image_selected
+        mask = mask_selected
+
     if mask.shape[:2] != image.shape[:2]:
         mask = cv2.resize(mask, (image.shape[1], image.shape[0]), interpolation=cv2.INTER_NEAREST)
     
@@ -22,54 +71,147 @@ def image_inpainting(image, mask):
     inpainted = cv2.inpaint(image, mask, 3, cv2.INPAINT_TELEA)
     return inpainted
 
-exmaples = [
-    '176788.jpg'
-    '182862.jpg'
-]
-masks = [
-    'mask1.jpg'
-    'mask2.jpg'
-]
-
 with gr.Blocks() as demo:
-    gr.Markdown("Computer Vision")
     with gr.Tab("Edge Detection"):
+        mode_selector = gr.Radio(["Upload", "Example"], value="Upload", label="Upload an image or using example image")
+
+        # Mode 1
+        upload_input = gr.Image(type="numpy", label="Upload Image", visible=True)
+
+        # Mode 2
+        image_selector = gr.Radio(examples, label="Select an example image", visible=False)
+        image_selected = gr.Image(None, label="Selected Image", visible=False, sources=[])
+
+        threshold1 = gr.Slider(0, 255, value=50, step=1, label="Threshold 1")
+        threshold2 = gr.Slider(0, 255, value=100, step=1, label="Threshold 2")
+
+        output = gr.Image(type="numpy", label="Edge Image")
         inputs = [
-                gr.Image(type="numpy", label="Upload Image", library=exmaples),
-                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") 
+            mode_selector,
+            upload_input,
+            image_selector,
+            image_selected,
+            threshold1,
+            threshold2
+        ]
+
+        mode_selector.change(
+            mode_selector_changed,
+            inputs=[mode_selector, image_selector],
+            outputs=[upload_input, image_selector, image_selected]
+        )
+
+        image_selector.change(
+            mode_selector_changed,
+            inputs=[mode_selector, image_selector],
+            outputs=[upload_input, image_selector, image_selected]
+        )
+
+        image_selected.change(
+            close_selected_image,
+            inputs=[image_selected],
+            outputs=[image_selector, image_selected]
+        )
+
         gr.Interface(
             fn=edge_detection,
             inputs=inputs,
             outputs=output,
-            description="Upload an image and adjust the thresholds to perform edge detection"
+            description="Upload an image and adjust the thresholds to perform edge detection",
+            flagging_mode='never'
         )
     with gr.Tab("Image Segmentation"):
+        mode_selector = gr.Radio(["Upload", "Example"], value="Upload", label="Upload an image or using example image")
+
+        # Mode 1
+        upload_input = gr.Image(type="numpy", label="Upload Image", visible=True)
+        # Mode 2
+        image_selector = gr.Radio(examples, label="Select an example image", visible=False)
+        image_selected = gr.Image(None, label="Selected Image", visible=False, sources=[])
+
+        output = gr.Image(type="numpy", label="Edge Image")
         inputs = [
-                gr.Image(type="numpy", label="Upload Image")
-            ]
-        output = gr.Image(type="numpy", label="Segmented Image") 
+            mode_selector,
+            upload_input,
+            image_selector,
+            image_selected,
+        ]
+        mode_selector.change(
+            mode_selector_changed,
+            inputs=[mode_selector, image_selector],
+            outputs=[upload_input, image_selector, image_selected]
+        )
+        image_selector.change(
+            mode_selector_changed,
+            inputs=[mode_selector, image_selector],
+            outputs=[upload_input, image_selector, image_selected]
+        )
+        image_selected.change(
+            close_selected_image,
+            inputs=[image_selected],
+            outputs=[image_selector, image_selected]
+        )
         gr.Interface(
             fn=image_segmentation,
             inputs=inputs,
             outputs=output,
-            description="Upload an image to perform image segmentation"
+            description="Upload an image and adjust the thresholds to perform edge detection",
+            flagging_mode='never'
         )
     with gr.Tab("Image Inpainting"):
+        mode_selector = gr.Radio(["Upload", "Example"], value="Upload", label="Upload an image or using example image")
+
+        # Mode 1
+        upload_input = gr.Image(type="numpy", label="Upload Image", visible=True)
+        upload_mask = gr.Image(type="numpy", label="Upload Mask", visible=True)
+        # Mode 2
+        image_selector = gr.Radio(examples, label="Select an example image", visible=False)
+        image_selected = gr.Image(None, label="Selected Image", visible=False, sources=[])
+        mask_selector = gr.Radio(masks, label="Select an example mask", visible=False)
+        mask_selected = gr.Image(None, label="Selected Mask", visible=False, sources=[])
+
+        output = gr.Image(type="numpy", label="Edge Image")
         inputs = [
-                gr.Image(type="numpy", label="Upload Image"),
-                gr.Image(type="numpy", label="Upload Mask")
-            ]
-        output = gr.Image(type="numpy", label="Inpainted Image") 
+            mode_selector,
+            upload_input,
+            upload_mask,
+            image_selector,
+            image_selected,
+            mask_selector,
+            mask_selected
+        ]
+        mode_selector.change(
+            mode_selector_changed_inpainting,
+            inputs=[mode_selector, image_selector, mask_selector],
+            outputs=[upload_input, upload_mask, image_selector, image_selected, mask_selector, mask_selected]
+        )
+        image_selector.change(
+            mode_selector_changed,
+            inputs=[mode_selector, image_selector],
+            outputs=[upload_input, image_selector, image_selected]
+        )
+        mask_selector.change(
+            mode_selector_changed,
+            inputs=[mode_selector, mask_selector],
+            outputs=[upload_mask, mask_selector, mask_selected]
+        )
+        image_selected.change(
+            close_selected_image,
+            inputs=[image_selected],
+            outputs=[image_selector, image_selected]
+        )
+        mask_selected.change(
+            close_selected_mask,
+            inputs=[mask_selected],
+            outputs=[mask_selector, mask_selected]
+        )
         gr.Interface(
             fn=image_inpainting,
             inputs=inputs,
             outputs=output,
-            description="Upload an image and adjust the thresholds to perform image inpainting",
+            description="Upload an image and adjust the thresholds to perform edge detection",
             flagging_mode='never'
         )
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch(allowed_paths=["/home/teng_aicv/Desktop/"])