Update app.py

app.py

@@ -84,28 +84,20 @@ def fig2img(fig):
     return img
 
 
-# ---------------- Plate Color Classification ----------------
-
-def classify_plate_color(crop_img):
-    img = cv2.cvtColor(np.array(crop_img), cv2.COLOR_RGB2BGR)
-    hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
-    h, s, v = cv2.split(hsv)
-
-    avg_h, avg_s, avg_v = np.mean(h), np.mean(s), np.mean(v)
-
-    # Heuristic thresholds (India-style)
-    if avg_v < 80:
-        return "Black Plate (Commercial)"
-    if avg_s < 40 and avg_v > 180:
-        return "White Plate (Private)"
-    if 15 < avg_h < 35 and avg_s > 80:
-        return "Yellow Plate (Commercial)"
-    if 80 < avg_h < 130:
-        return "Blue Plate (Diplomatic)"
-    if 35 < avg_h < 85:
-        return "Green Plate (Electric)"
-
-    return "Unknown Plate"
+def classify_plate_color(plate_img):
+    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)))
+    white = np.sum(cv2.inRange(hsv, (0, 0, 200), (180, 30, 255)))
+
+    if green > yellow and green > white:
+        return "EV"
+    elif yellow > green and yellow > white:
+        return "Commercial"
+    else:
+        return "Personal"
 
 
 # ---------------- Visualization ----------------
@@ -246,8 +238,7 @@ Supports:
 """
 
 models = [
-    "nickmuchi/yolos-small-finetuned-license-plate-detection",
-    "nickmuchi/detr-resnet50-license-plate-detection"
+    "nickmuchi/yolos-small-finetuned-license-plate-detection"
 ]
 
 css = '''