Update app.py

app.py

@@ -1,7 +1,100 @@
+import numpy as np
 import gradio as gr
+import sys,  time
+import ipywidgets as widget
+from IPython.display import display
+import numpy as np
+import cv2
+from PIL import Image as PIL_Image
+from io import BytesIO
+import matplotlib.pyplot as plt
 
-def greet(name):
-    return "Hello " + name + "!!"
+# def flip_text(x):
+#     return x[::-1]
+# def flip_image(x):
+#     return np.fliplr(x)
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+def image_segmentation(x, threshold_factor):
+    cv2.startWindowThread()
+    def img_to_png(ima, cvt=None):
+        if cvt:
+            ima = cv2.cvtColor(ima, cvt)
+        im = PIL_Image.fromarray(ima)
+        bio = BytesIO()
+        im.save(bio, format='png')
+        return bio.getvalue()
+
+    # img = cv2.imread(x) # 改為使用Gradio傳入的圖片
+    img = x
+    # plt.imshow(img[..., ::-1])
+
+    gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
+    ret, thresh = cv2.threshold(gray,0,255,cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)
+    kernel = np.ones((3,3),np.uint8)
+    opening = cv2.morphologyEx(thresh,cv2.MORPH_OPEN,kernel, iterations = 2)
+
+    # sure background area
+    sure_bg = cv2.dilate(opening,kernel,iterations=3)
+    # Finding sure foreground area
+    dist_transform = cv2.distanceTransform(opening,cv2.DIST_L2,5)
+    ret, sure_fg = cv2.threshold(dist_transform,threshold_factor*dist_transform.max(),255,0)
+    # Finding unknown region
+    sure_fg = np.uint8(sure_fg)
+    unknown = cv2.subtract(sure_bg,sure_fg)
+    # Marker labelling
+    ret, markers0 = cv2.connectedComponents(sure_fg)
+    # Add one to all labels so that sure background is not 0, but 1
+    markers = markers0+1
+    # Now, mark the region of unknown with zero
+    markers[unknown==255] = 0
+    markers = cv2.watershed(img,markers)
+    img[markers == -1] = [255,0,0]
+
+    # plt.imshow(dist_transform)
+    # plt.imshow(sure_fg)
+    # plt.imshow(sure_bg, alpha=0.3)
+    # plt.imshow(markers)
+
+    # 將 markers 範圍縮放到 0-255 並轉換為 uint8 類型
+    markers_scaled = (markers - markers.min()) / (markers.max() - markers.min()) * 255
+    markers_scaled = markers_scaled.astype(np.uint8)
+
+    # 使用 applyColorMap 生成彩色圖像
+    markers_colored = cv2.applyColorMap(markers_scaled, cv2.COLORMAP_JET)
+
+    return markers_colored  # 返回彩色分割結果圖
+
+with gr.Blocks() as demo:
+    gr.Markdown("Image Segmentation using Gradio")
+    # with gr.Tab("Flip Text"):
+    #     text_input = gr.Textbox()
+    #     text_output = gr.Textbox()
+    #     text_button = gr.Button("Flip")
+    with gr.Tab("Image Segmentation"):
+        with gr.Row():
+            image_input = gr.Image()
+            image_output = gr.Image()
+        temp_slider = gr.Slider(
+            0, 1,
+            value=0.5,
+            step=0.01,
+            interactive=True,
+            label="Threshold Factor",
+        )
+        image_button = gr.Button("Flip")
+
+    # with gr.Accordion("Open for More!", open=False):
+    #     gr.Markdown("Look at me...")
+    #     temp_slider = gr.Slider(
+    #         0, 1,
+    #         value=0.1,
+    #         step=0.1,
+    #         interactive=True,
+    #         label="Slide me",
+    #     )
+
+    # text_button.click(flip_text, inputs=text_input, outputs=text_output)
+    image_button.click(image_segmentation, inputs=[image_input, temp_slider], outputs=image_output)
+
+if __name__ == "__main__":
+    demo.launch()