Update app.py

app.py

@@ -9,56 +9,32 @@ model = YOLO("best.pt")  # Ensure the path to your model is correct
 # Set the title of the app
 st.title("Live Fire Detection App")
 
-# Create a placeholder for the video stream
-video_placeholder = st.empty()
+# Define color ranges for fire detection
+lower_red = np.array([0, 100, 100])
+upper_red = np.array([10, 255, 255])
 
-# Function to perform live fire detection
-def detect_fire():
-    # Open a connection to the webcam (0 is the default camera)
-    cap = cv2.VideoCapture(0)
+# Create a video capture object
+cap = cv2.VideoCapture(0)  # 0 for default camera, change as needed
 
-    while True:
-        ret, frame = cap.read()
-        if not ret:
-            st.error("Failed to capture video.")
-            break
+while True:
+    ret, frame = cap.read()
 
-        # Convert the frame to RGB (as YOLO expects RGB input)
-        rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+    # Convert BGR to HSV color space
+    hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
 
-        # Make predictions on the current frame
-        results = model.predict(source=rgb_frame, conf=0.5)
+    # Create a mask for the fire color range
+    mask = cv2.inRange(hsv, lower_red, upper_red)
 
-        # Draw bounding boxes on the frame
-        fire_detected = False
-        for result in results:
-            boxes = result.boxes.xyxy  # Bounding boxes
-            for box in boxes:
-                x1, y1, x2, y2 = box[:4].astype(int)
-                rgb_frame = cv2.rectangle(rgb_frame, (x1, y1), (x2, y2), (0, 255, 0), 2)
+    # Apply the mask to the original frame
+    res = cv2.bitwise_and(frame, frame, mask=mask)
 
-                # Check if the detected class is "fire" (adjust based on your model's class mapping)
-                class_id = int(box[5])  # Assuming class ID is at the 6th position
-                if class_id == 0:  # Replace 0 with the actual class ID for fire if different
-                    fire_detected = True
+    # Display the resulting frame
+    cv2.imshow('Fire Detection', res)
 
-        # Display the frame in the Streamlit app
-        video_placeholder.image(rgb_frame, channels="RGB", use_column_width=True)
+    # Exit if 'q' is pressed
+    if cv2.waitKey(1) & 0xFF == ord('q'):
+        break
 
-        # Display message based on fire detection
-        if fire_detected:
-            st.success("🔥 Fire Detected!")
-        else:
-            st.warning("No Fire Detected.")
-
-        # Break the loop if the user presses 'q'
-        if cv2.waitKey(1) & 0xFF == ord('q'):
-            break
-
-    # Release the webcam and close windows
-    cap.release()
-    cv2.destroyAllWindows()
-
-# Start the detection process
-if st.button("Start Live Detection"):
-    detect_fire()
+# Release the capture and close windows
+cap.release()
+cv2.destroyAllWindows() 1