Update app.py

app.py

@@ -63,20 +63,46 @@ def compute_discount(vehicle_type):
     return BASE_AMT
 
 def classify_plate_color(plate_img):
-    try:
-        img = np.array(plate_img)
-        hsv = cv2.cvtColor(img, cv2.COLOR_RGB2HSV)
-
-        green = np.sum(cv2.inRange(hsv, (35,40,40), (85,255,255)))
-        yellow = np.sum(cv2.inRange(hsv, (15,50,50), (35,255,255)))
-
-        if green > yellow:
-            return "EV"
-        elif yellow > green:
-            return "Commercial"
-        return "Personal"
-    except:
-        return "Unknown"
+     hsv = cv2.cvtColor(plate_img, cv2.COLOR_BGR2HSV)
+
+    # Masks for colors
+    masks = {}
+
+    # White
+    masks["white"] = cv2.inRange(hsv, np.array([0, 0, 180]), np.array([180, 60, 255]))
+
+    # Yellow
+    masks["yellow"] = cv2.inRange(hsv, np.array([15, 80, 80]), np.array([40, 255, 255]))
+
+    # Green
+    masks["green"] = cv2.inRange(hsv, np.array([35, 50, 50]), np.array([85, 255, 255]))
+
+    # Red
+    masks["red1"] = cv2.inRange(hsv, np.array([0, 70, 50]), np.array([10, 255, 255]))
+    masks["red2"] = cv2.inRange(hsv, np.array([170, 70, 50]), np.array([180, 255, 255]))
+    masks["red"] = masks["red1"] + masks["red2"]
+
+    # Blue
+    masks["blue"] = cv2.inRange(hsv, np.array([90, 50, 50]), np.array([130, 255, 255]))
+
+    # Count pixels
+    color_counts = {color: np.sum(mask) for color, mask in masks.items()}
+
+    dominant_color = max(color_counts, key=color_counts.get)
+
+    # Classification logic
+    if dominant_color == "white":
+        return "Private Vehicle"
+    elif dominant_color == "yellow":
+        return "Commercial Vehicle"
+    elif dominant_color == "green":
+        return "Electric Vehicle (EV)"
+    elif dominant_color == "red":
+        return "Temporary Registration Vehicle"
+    elif dominant_color == "blue":
+        return "Diplomatic Vehicle"
+    else:
+        return "Unknown Vehicle Type"
 
 def read_plate(plate_img):
     try: