Update app.py

app.py

@@ -1,83 +1,61 @@
 import streamlit as st
 import cv2
 import numpy as np
+import matplotlib.pyplot as plt
 from PIL import Image
 
-# Function to count circles in an image frame
-def count_circles(frame):
-    # Convert to grayscale and blur to remove noise
-    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
-    gray = cv2.medianBlur(gray, 5)
+# Function to detect circular pipes
+def detect_pipes(image):
+    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
     
-    # Hough Circle Transform to detect circles
+    # Apply GaussianBlur to reduce image noise and improve circle detection
+    blurred = cv2.GaussianBlur(gray, (15, 15), 0)
+    
+    # Detect circles using HoughCircles
     circles = cv2.HoughCircles(
-        gray, cv2.HOUGH_GRADIENT, dp=1.2, minDist=50,
-        param1=50, param2=30, minRadius=10, maxRadius=100
+        blurred, 
+        cv2.HOUGH_GRADIENT, 
+        dp=1.2, 
+        minDist=30, 
+        param1=50, 
+        param2=30, 
+        minRadius=20, 
+        maxRadius=150
     )
     
-    circle_count = 0
     if circles is not None:
-        circles = np.uint16(np.around(circles))
+        circles = np.uint16(np.around(circles))  # Convert float to integer
         for i in circles[0, :]:
-            # Draw the circle
-            cv2.circle(frame, (i[0], i[1]), i[2], (0, 255, 0), 3)
-            circle_count += 1
-            
-    return frame, circle_count
-
-# Streamlit UI
-def main():
-    st.title("Circle Counter with Camera Input")
-    st.write("Use your device camera to count and highlight circles in real-time or upload an image.")
-
-    # Option to choose between Camera and Image upload
-    option = st.radio("Choose Input Method:", ["Camera", "Upload Image"])
-
-    # If user selects Camera
-    if option == "Camera":
-        st.write("Starting Camera Stream...")
-        camera_stream = st.empty()  # Stream placeholder
-        stop_button = st.button("Stop Camera")
-
-        # Access camera using OpenCV
-        cap = cv2.VideoCapture(0)  # 0 is the default camera
+            center = (i[0], i[1])  # center of the circle
+            radius = i[2]  # radius of the circle
+            # Draw the circle center
+            cv2.circle(image, center, 1, (0, 100, 100), 3)
+            # Draw the circle perimeter
+            cv2.circle(image, center, radius, (255, 0, 255), 3)
         
-        while cap.isOpened() and not stop_button:
-            ret, frame = cap.read()
-            if not ret:
-                st.error("Failed to capture video!")
-                break
-
-            # Count circles in the frame
-            processed_frame, circle_count = count_circles(frame)
-            
-            # Convert the frame for display in Streamlit
-            frame_rgb = cv2.cvtColor(processed_frame, cv2.COLOR_BGR2RGB)
-            camera_stream.image(frame_rgb, caption=f"Circles Detected: {circle_count}", use_column_width=True)
-
-        cap.release()
-        st.write("Camera Stopped.")
+        return image, len(circles[0, :])  # Return image with circles drawn and the count
+    else:
+        return image, 0  # No circles found
 
-    # If user selects Upload Image
-    elif option == "Upload Image":
-        uploaded_file = st.file_uploader("Upload an image...", type=["jpg", "jpeg", "png"])
-
-        if uploaded_file is not None:
-            # Display uploaded image
-            image = Image.open(uploaded_file)
-            st.image(image, caption="Uploaded Image", use_column_width=True)
-
-            # Convert to OpenCV format
-            frame = np.array(image)
-            frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
-
-            # Count circles
-            processed_image, circle_count = count_circles(frame)
+# Streamlit UI
+st.title('Pipe Detection in Industrial Rack')
 
-            # Display processed image
-            processed_image_rgb = cv2.cvtColor(processed_image, cv2.COLOR_BGR2RGB)
-            st.image(processed_image_rgb, caption=f"Circles Detected: {circle_count}", use_column_width=True)
-            st.success(f"Number of circles detected: {circle_count}")
+# Image upload
+uploaded_file = st.file_uploader("Choose an image...", type="jpg")
 
-if __name__ == "__main__":
-    main()
+if uploaded_file is not None:
+    # Read the uploaded image
+    image = np.array(Image.open(uploaded_file))
+    
+    # Convert from RGB to BGR (OpenCV uses BGR by default)
+    image_bgr = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
+    
+    # Detect pipes
+    result_image, pipe_count = detect_pipes(image_bgr)
+    
+    # Convert the result image back to RGB for displaying in Streamlit
+    result_image_rgb = cv2.cvtColor(result_image, cv2.COLOR_BGR2RGB)
+    
+    # Display the result
+    st.image(result_image_rgb, channels="RGB", caption="Detected Pipes in Rack")
+    st.write(f"Total number of pipes detected: {pipe_count}")