Added canny edges tab

app.py

@@ -368,7 +368,49 @@ with gr.Blocks() as beta:
 
 
 
+
+
+# ----- Canny Edge Tab -----------------------------------------------------------------
+
+from PIL import Image
+# import gradio as gr
+import numpy as np
+import cv2
+
+# Define a function to process the uploaded image
+def canny_process_image(input_image, input_low_threshold, input_high_threshold, input_invert):
+    # Convert the input image to a NumPy array
+    np_image = np.array(input_image)
+
+    output_image = input_image  # For example, just return the input image
+    numpy_image = np.array(output_image)
+    # Return the processed image
+
+    low_threshold = 100
+    high_threshold = 200
+    canny_1 = cv2.Canny(numpy_image, input_low_threshold, input_high_threshold)
+    canny_1 = canny_1[:, :, None]
+    canny_1 = np.concatenate([canny_1, canny_1, canny_1], axis=2)
+    if input_invert:
+      canny_1 = 255 - canny_1
+    canny_2 = Image.fromarray(canny_1)
+
+    return np.array(canny_2)
+
+# Define the input and output interfaces
+canny_input_image = gr.inputs.Image()
+canny_input_low_threshold = gr.inputs.Slider(minimum=0, maximum=1000, step=1, label="Lower Threshold:", default=100)
+canny_input_high_threshold = gr.inputs.Slider(minimum=0, maximum=1000, step=1, label="Upper Threshold:", default=200)
+canny_input_invert = gr.inputs.Checkbox(label="Invert Image")
+
+canny_outputs = gr.outputs.Image(type="numpy")
+
+# Create the Gradio interface
+canny_interface = gr.Interface(fn=canny_process_image, inputs=[canny_input_image, canny_input_low_threshold, canny_input_high_threshold, canny_input_invert], outputs=canny_outputs, title='Canny Edge Tracing', allow_flagging='never')
+
+
+
 # ----- Launch Tabs -----------------------------------------------------------------
 
-tabbed_interface = gr.TabbedInterface([new_welcome, advanced_tab, beta], ["Welcome", "Advanced", "Beta"])
+tabbed_interface = gr.TabbedInterface([new_welcome, advanced_tab, beta, canny_interface], ["Welcome", "Advanced", "Beta", "Edges"])
 tabbed_interface.launch()