Update app.py

app.py

@@ -9,20 +9,16 @@ def get_rotated_rect_corners(x, y, w, h, rotation_deg):
     rot_rad = np.deg2rad(rotation_deg)
     cos_r = np.cos(rot_rad)
     sin_r = np.sin(rot_rad)
-
     cx = x + w/2
     cy = y + h/2
-
     local_corners = np.array([
         [-w/2, -h/2],
         [ w/2, -h/2],
         [ w/2,  h/2],
         [-w/2,  h/2]
     ])
-
     R = np.array([[cos_r, -sin_r],
                   [sin_r,  cos_r]])
-
     rotated_corners = np.dot(local_corners, R.T)
     corners = rotated_corners + np.array([cx, cy])
     return corners.astype(np.float32)
@@ -52,17 +48,12 @@ def detect_and_match(img1_gray, img2_gray, detector_name, ratio_thresh=0.78):
     detector = get_detector(detector_name)
     kp1, des1 = detector.detectAndCompute(img1_gray, None)
     kp2, des2 = detector.detectAndCompute(img2_gray, None)
-
     if detector_name in ["SIFT", "KAZE"]:
         matcher = cv2.BFMatcher(cv2.NORM_L2)
     else:
         matcher = cv2.BFMatcher(cv2.NORM_HAMMING)
-
     matches = matcher.knnMatch(des1, des2, k=2)
-    good = []
-    for m, n in matches:
-        if m.distance < ratio_thresh * n.distance:
-            good.append(m)
+    good = [m for m,n in matches if m.distance < ratio_thresh*n.distance]
     return kp1, kp2, good
 
 # === Main processing ===
@@ -106,19 +97,14 @@ def process_images(flat_img, persp_img, json_file):
         roi_corners_flat = get_rotated_rect_corners(roi_x, roi_y, roi_w, roi_h, roi_rot_deg)
         roi_corners_persp = cv2.perspectiveTransform(roi_corners_flat.reshape(-1,1,2), H).reshape(-1,2)
 
-        # Draw ROI & label
-        flat_out = flat_img.copy()
+        # Draw ROI & detector label on perspective image only
         persp_out = persp_img.copy()
-        cv2.polylines(flat_out, [roi_corners_flat.astype(int)], True, (255,0,0), 3)
         cv2.polylines(persp_out, [roi_corners_persp.astype(int)], True, (0,255,0), 3)
-        cv2.putText(flat_out, det, (10,30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0,255,255), 2)
         cv2.putText(persp_out, det, (10,30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0,255,255), 2)
 
         # Convert to RGB for Gradio
-        flat_rgb = cv2.cvtColor(flat_out, cv2.COLOR_BGR2RGB)
         persp_rgb = cv2.cvtColor(persp_out, cv2.COLOR_BGR2RGB)
-
-        gallery_images.extend([flat_rgb, persp_rgb])
+        gallery_images.append(persp_rgb)
 
     return gallery_images
 
@@ -131,10 +117,10 @@ iface = gr.Interface(
         gr.File(type="filepath", label="JSON File")
     ],
     outputs=[
-        gr.Gallery(label="Detector Results (Flat + Perspective per Detector)")
+        gr.Gallery(label="Detector Results (Perspective Images with ROI & Label)")
     ],
-    title="Feature Detection with ROI Projection",
-    description="Shows SIFT, ORB, BRISK, AKAZE, KAZE feature-based ROI projections. Each detector outputs Flat and Perspective images with labels."
+    title="Feature Detection ROI Projection",
+    description="Shows SIFT, ORB, BRISK, AKAZE, KAZE detector results on the Perspective image only, with ROI and detector label."
 )
 
 iface.launch()