Update app.py

app.py

@@ -27,6 +27,40 @@ midas_transforms = torch.hub.load('intel-isl/MiDaS', 'transforms', trust_repo=Tr
 transform = midas_transforms.dpt_transform
 print("All models loaded!")
 
+# ================================================
+# ROAD IMAGE CHECK
+# ================================================
+def is_road_image(img_rgb):
+    """
+    Returns True only if the image looks like a road/ground surface.
+    Checks for dark/grey ground tones typical of asphalt.
+    """
+    gray = cv2.cvtColor(img_rgb, cv2.COLOR_RGB2GRAY)
+    h, w = gray.shape
+
+    # Check bottom half of image (road is usually at the bottom)
+    bottom = img_rgb[h//2:, :, :]
+    r = bottom[:,:,0].astype(float)
+    g = bottom[:,:,1].astype(float)
+    b = bottom[:,:,2].astype(float)
+
+    # Asphalt/road pixels are dark grey: low brightness, low saturation
+    brightness   = np.mean(gray)
+    mean_r       = np.mean(r)
+    mean_g       = np.mean(g)
+    mean_b       = np.mean(b)
+
+    # Channel difference — road pixels have similar R,G,B (grey)
+    channel_diff = np.mean(np.abs(r - g) + np.abs(g - b) + np.abs(r - b))
+
+    # Road: moderate brightness, low channel variance, greyish
+    is_grey_enough  = channel_diff < 60      # road is grey (not colorful)
+    is_dark_enough  = brightness < 180       # road is not very bright
+    is_not_sky      = mean_b < 160          # sky images have high blue
+    is_not_logo     = brightness > 20        # logos/black images excluded
+
+    return is_grey_enough and is_dark_enough and is_not_sky and is_not_logo
+
 # ================================================
 # WEATHER DETECTION
 # ================================================
@@ -80,11 +114,27 @@ def detect_potholes(input_image):
     img_bgr = cv2.cvtColor(img_rgb, cv2.COLOR_RGB2BGR)
     H, W    = img_rgb.shape[:2]
 
+    # ---- ROAD CHECK ----
+    if not is_road_image(img_rgb):
+        fig = plt.figure(figsize=(8, 4))
+        fig.patch.set_facecolor('#0d1117')
+        ax = fig.add_subplot(111)
+        ax.set_facecolor('#161b22')
+        ax.imshow(img_rgb)
+        ax.axis('off')
+        ax.set_title(
+            'NOT A ROAD IMAGE — Please upload a road/street photo',
+            color='#ff4444', fontsize=13, fontweight='bold')
+        plt.tight_layout()
+        return fig, "This does not appear to be a road image.\nPlease upload a photo of a road or street for pothole detection."
+
     weather_label, weather_warning = detect_weather(img_rgb)
 
     tmp_path = '/tmp/input_image.png'
     cv2.imwrite(tmp_path, img_bgr)
-    results = rtdetr_model(tmp_path, verbose=False)
+
+    # ---- CONFIDENCE THRESHOLD = 0.5 ----
+    results = rtdetr_model(tmp_path, verbose=False, conf=0.5)
     boxes   = results[0].boxes
 
     inp = transform(img_rgb).to(device)
@@ -102,7 +152,12 @@ def detect_potholes(input_image):
     if boxes is not None and len(boxes) > 0:
         for i, box in enumerate(boxes.xyxy):
             x1, y1, x2, y2 = map(int, box.tolist())
-            conf       = float(boxes.conf[i])
+            conf = float(boxes.conf[i])
+
+            # ---- SKIP LOW CONFIDENCE ----
+            if conf < 0.5:
+                continue
+
             bw, bh     = x2-x1, y2-y1
             area_ratio = (bw * bh) / (H * W)
             bw_cm      = px_to_cm(bw, W)
@@ -114,7 +169,7 @@ def detect_potholes(input_image):
             severity_counts[sev] += 1
             cnorm = tuple(c/255 for c in crgb)
             pothole_data.append(dict(
-                id=i+1, severity=sev,
+                id=len(pothole_data)+1, severity=sev,
                 confidence=conf, width_cm=bw_cm, height_cm=bh_cm,
                 area_cm2=bw_cm*bh_cm, area_pct=area_ratio*100,
                 depth_cm=depth_cm, risk=risk, color=cnorm,