Update angle_detection_app.py

angle_detection_app.py

@@ -1,10 +1,8 @@
 import gradio as gr
 import cv2
 import numpy as np
-import matplotlib.pyplot as plt
-from typing import Tuple, List
-import tempfile
 import os
+from typing import Tuple, List
 
 def detect_bends_and_angles(
     image,
@@ -21,32 +19,19 @@ def detect_bends_and_angles(
     """
     Detect bends and calculate angles relative to horizontal with configurable parameters.
     """
-    # Convert image to grayscale
     gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
-    
-    # Step 2: Apply Gaussian blur
-    # Ensure blur_kernel_size is odd and greater than 0
     if blur_kernel_size is None:
-        blur_kernel_size = 3  # Default to 3 if not provided
-    blur_kernel_size = max(3, blur_kernel_size | 1)  # Ensure it's an odd number
+        blur_kernel_size = 3
+    blur_kernel_size = max(3, blur_kernel_size | 1)  # Ensure it's odd
     blurred = cv2.GaussianBlur(gray, (blur_kernel_size, blur_kernel_size), 0)
-    
-    # Step 3: Perform edge detection
     edges = cv2.Canny(blurred, canny_threshold1, canny_threshold2)
-    
-    # Step 4: Dilate edges
     kernel = np.ones((dilation_kernel_size, dilation_kernel_size), np.uint8)
     dilated = cv2.dilate(edges, kernel, iterations=1)
     
-    # Step 5: Detect parallel lines and identify bends
     height, width = dilated.shape
     lines = cv2.HoughLinesP(
-        dilated, 
-        rho=1, 
-        theta=np.pi/180, 
-        threshold=hough_threshold, 
-        minLineLength=min_line_length, 
-        maxLineGap=max_line_gap
+        dilated, rho=1, theta=np.pi/180, threshold=hough_threshold,
+        minLineLength=min_line_length, maxLineGap=max_line_gap
     )
     
     bend_points = []
@@ -68,7 +53,6 @@ def detect_bends_and_angles(
             if abs(x1 - x1_next) < bend_threshold and abs(y1 - y1_next) < bend_threshold:
                 bend_points.append((x1, y1))
     
-    # Step 6: Calculate angles between bends
     angles = []
     for i in range(len(bend_points) - 1):
         x1, y1 = bend_points[i]
@@ -94,7 +78,6 @@ def process_image(
     """
     Process the image and return the visualization and angle measurements.
     """
-    # Convert Gradio image to numpy array if needed
     if isinstance(image, dict):
         image = image['image']
     if isinstance(image, str):
@@ -102,18 +85,10 @@ def process_image(
         image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
     
     bend_points, angles = detect_bends_and_angles(
-        image,
-        blur_kernel_size,
-        canny_threshold1,
-        canny_threshold2,
-        dilation_kernel_size,
-        hough_threshold,
-        min_line_length,
-        max_line_gap,
-        bend_threshold
+        image, blur_kernel_size, canny_threshold1, canny_threshold2, dilation_kernel_size,
+        hough_threshold, min_line_length, max_line_gap, bend_threshold
     )
     
-    # Create visualization
     result_img = image.copy()
     for i, (x, y) in enumerate(bend_points):
         cv2.circle(result_img, (x, y), 5, (0, 0, 255), -1)
@@ -127,7 +102,6 @@ def process_image(
             cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1
         )
     
-    # Create angle measurements text
     measurements = "Angle Measurements:\n"
     for i, ((x, y), angle) in enumerate(angles):
         measurements += f"Bend {chr(65 + i)} at ({x}, {y}): {angle:.1f}°\n"
@@ -144,38 +118,14 @@ def create_gradio_interface():
                 input_image = gr.Image(label="Input Image", type="numpy")
                 
                 with gr.Accordion("Algorithm Parameters", open=False):
-                    blur_kernel_size = gr.Slider(
-                        minimum=3, maximum=15, step=2,
-                        value=7, label="Blur Kernel Size"
-                    )
-                    canny_threshold1 = gr.Slider(
-                        minimum=0, maximum=100, step=10,
-                        value=30, label="Canny Threshold 1"
-                    )
-                    canny_threshold2 = gr.Slider(
-                        minimum=100, maximum=300, step=10,
-                        value=150, label="Canny Threshold 2"
-                    )
-                    dilation_kernel_size = gr.Slider(
-                        minimum=1, maximum=5, step=1,
-                        value=2, label="Dilation Kernel Size"
-                    )
-                    hough_threshold = gr.Slider(
-                        minimum=10, maximum=100, step=10,
-                        value=50, label="Hough Threshold"
-                    )
-                    min_line_length = gr.Slider(
-                        minimum=5, maximum=50, step=5,
-                        value=10, label="Minimum Line Length"
-                    )
-                    max_line_gap = gr.Slider(
-                        minimum=10, maximum=100, step=10,
-                        value=60, label="Maximum Line Gap"
-                    )
-                    bend_threshold = gr.Slider(
-                        minimum=5, maximum=30, step=5,
-                        value=15, label="Bend Threshold"
-                    )
+                    blur_kernel_size = gr.Slider(minimum=3, maximum=15, step=2, value=7, label="Blur Kernel Size")
+                    canny_threshold1 = gr.Slider(minimum=0, maximum=100, step=10, value=30, label="Canny Threshold 1")
+                    canny_threshold2 = gr.Slider(minimum=100, maximum=300, step=10, value=150, label="Canny Threshold 2")
+                    dilation_kernel_size = gr.Slider(minimum=1, maximum=5, step=1, value=2, label="Dilation Kernel Size")
+                    hough_threshold = gr.Slider(minimum=10, maximum=100, step=10, value=50, label="Hough Threshold")
+                    min_line_length = gr.Slider(minimum=5, maximum=50, step=5, value=10, label="Minimum Line Length")
+                    max_line_gap = gr.Slider(minimum=10, maximum=100, step=10, value=60, label="Maximum Line Gap")
+                    bend_threshold = gr.Slider(minimum=5, maximum=30, step=5, value=15, label="Bend Threshold")
                 
                 process_btn = gr.Button("Process Image", variant="primary")
             
@@ -185,45 +135,13 @@ def create_gradio_interface():
         
         process_btn.click(
             fn=process_image,
-            inputs=[
-                input_image,
-                blur_kernel_size,
-                canny_threshold1,
-                canny_threshold2,
-                dilation_kernel_size,
-                hough_threshold,
-                min_line_length,
-                max_line_gap,
-                bend_threshold
-            ],
+            inputs=[input_image, blur_kernel_size, canny_threshold1, canny_threshold2, dilation_kernel_size,
+                    hough_threshold, min_line_length, max_line_gap, bend_threshold],
             outputs=[output_image, output_text]
         )
         
-        
-    
     return interface
 
 if __name__ == "__main__":
     interface = create_gradio_interface()
-    # Get port from environment variable or use a different default port
-    port = int(os.environ.get("GRADIO_SERVER_PORT", 7861))
-    # Get host from environment variable or use default
-    host = os.environ.get("GRADIO_SERVER_NAME", "0.0.0.0")
-    # Launch the interface with error handling
-    try:
-        interface.launch(
-            server_name=host,
-            server_port=port,
-            share=True  # Set to True to create a public URL
-        )
-    except OSError as e:
-        print(f"Port {port} is in use. Trying next port...")
-        try:
-            interface.launch(
-                server_name=host,
-                server_port=port + 1,
-                share=True
-            )
-        except Exception as e:
-            print(f"Error launching the interface: {e}")
-            print("Please try a different port by setting the GRADIO_SERVER_PORT environment variable") 
+    interface.launch(share=False)  # Set share=False to avoid issues when deploying