Update app.py

app.py

@@ -6,51 +6,75 @@ import math
 
 
 
-# start webcam
-cap = cv2.VideoCapture(0)
-cap.set(3, 640)
-cap.set(4, 480)
+
+
+
+
+
+# Define fire color range (adjust based on your dataset)
+lower_red = np.array([0, 100, 100])
+upper_red = np.array([10, 255, 255])
 
 # Load the YOLO model
 model = YOLO("best.pt")  # Ensure the path to your model is correct
 
+def detect_fire(frame):
+    """Detects fire in an image using YOLOv8 and color thresholding.
+
+    Args:
+        frame: The image to be processed (OpenCV BGR format).
+
+    Returns:
+        bool: True if fire is detected, False otherwise.
+    """
+
+    # Convert image to RGB format for YOLOv8 compatibility
+    image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+
+    # Make predictions using YOLOv8 (modify class ID if needed)
+    results = model(image)
+
+    # Check for fire detections or color thresholding
+    fire_detected = False
+    for detection in results.pandas().xyxy[0]:  # Assuming single image inference
+        class_id = int(detection['name'])  # Get class ID (adjust based on your dataset)
+        if (class_id == 0 or  # Check for fire class (adjust for your model)
+            cv2.inRange(cv2.cvtColor(frame, cv2.COLOR_BGR2HSV), lower_red, upper_red).any()):  # Check for fire color range
+            fire_detected = True
+            break  # Stop iterating after finding fire
+
+    return fire_detected
 
-while True:
-    success, img = cap.read()
-    results = model(img, stream=True)
+def main():
+    """Captures video from webcam, detects fire, and displays results."""
 
-    # coordinates
-    for r in results:
-        boxes = r.boxes
+    cap = cv2.VideoCapture(0)  # 0 for default camera
 
-        for box in boxes:
-            # bounding box
-            x1, y1, x2, y2 = box.xyxy[0]
-            x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2) # convert to int values
+    while True:
+        ret, frame = cap.read()
 
-            # put box in cam
-            cv2.rectangle(img, (x1, y1), (x2, y2), (255, 0, 255), 3)
+        if not ret:
+            print("Error: Could not read frame.")
+            break
 
-            # confidence
-            confidence = math.ceil((box.conf[0]*100))/100
-            print("Confidence --->",confidence)
+        # Detect fire
+        fire_detected = detect_fire(frame)
 
-            # class name
-            cls = int(box.cls[0])
-            print("Class name -->", classNames[cls])
+        # Display results (modify text/color as needed)
+        if fire_detected:
+            cv2.putText(frame, "Fire Detected!", (10, 30), cv2.FONT_HERSHEY_SIMPLEX,
+                        1, (0, 0, 255), 2, cv2.LINE_AA)
+        else:
+            cv2.putText(frame, "No Fire Detected", (10, 30), cv2.FONT_HERSHEY_SIMPLEX,
+                        1, (0, 255, 0), 2, cv2.LINE_AA)
 
-            # object details
-            org = [x1, y1]
-            font = cv2.FONT_HERSHEY_SIMPLEX
-            fontScale = 1
-            color = (255, 0, 0)
-            thickness = 2
+        cv2.imshow('Fire Detection', frame)
 
-            cv2.putText(img, classNames[cls], org, font, fontScale, color, thickness)
+        if cv2.waitKey(1) & 0xFF == ord('q'):
+            break
 
-    cv2.imshow('Webcam', img)
-    if cv2.waitKey(1) == ord('q'):
-        break
+    cap.release()
+    cv2.destroyAllWindows()
 
-cap.release()
-cv2.destroyAllWindows()
+if __name__ == '__main__':
+    main()