Update app.py

app.py

@@ -65,6 +65,24 @@ def fit_to_box(img, target_h=600, target_w=600):
     canvas[top:top+new_h, left:left+new_w] = resized
     return canvas
 
+# ---------------- NEW: Remap keypoints to boxed image coords ----------------
+def remap_keypoints_to_box(kps, orig_shape, target_h=600, target_w=600):
+    h, w = orig_shape[:2]
+    scale = min(target_w / w, target_h / h)
+    new_w, new_h = int(w * scale), int(h * scale)
+    top = (target_h - new_h) // 2
+    left = (target_w - new_w) // 2
+
+    kps_new = []
+    for kp in kps:
+        x, y = kp.pt
+        x_new = x * scale + left
+        y_new = y * scale + top
+        # (x, y, size, angle, response, octave, class_id)
+        kps_new.append(cv2.KeyPoint(x_new, y_new, max(1.0, kp.size * scale),
+                                    kp.angle, kp.response, kp.octave, kp.class_id))
+    return kps_new
+
 # ---------------- Main Function ----------------
 def homography_all_detectors(flat_file, persp_file, json_file, xml_file):
     flat_img = cv2.imread(flat_file)
@@ -87,12 +105,21 @@ def homography_all_detectors(flat_file, persp_file, json_file, xml_file):
         kp1,kp2,good_matches = detect_and_match(flat_gray,persp_gray,method)
         if kp1 is None or kp2 is None or len(good_matches)<4: continue
 
-        # ---------------- Match Image (aligned sizes)
+        # ---------------- Match Image (aligned sizes) ----------------
         flat_box = fit_to_box(flat_img, 600, 600)
         persp_box = fit_to_box(persp_img, 600, 600)
-        match_img = cv2.drawMatches(flat_box,kp1,persp_box,kp2,good_matches,None,flags=2)
 
-        # ---------------- Homography ROI
+        # NEW: remap original keypoints -> boxed coords (order preserved)
+        kp1_box = remap_keypoints_to_box(kp1, flat_img.shape, 600, 600)
+        kp2_box = remap_keypoints_to_box(kp2, persp_img.shape, 600, 600)
+
+        match_img = cv2.drawMatches(
+            flat_box, kp1_box,
+            persp_box, kp2_box,
+            good_matches, None, flags=2
+        )
+
+        # ---------------- Homography ROI (still uses original coords) ----------------
         src_pts = np.float32([kp1[m.queryIdx].pt for m in good_matches]).reshape(-1,1,2)
         dst_pts = np.float32([kp2[m.trainIdx].pt for m in good_matches]).reshape(-1,1,2)
         H,_ = cv2.findHomography(src_pts,dst_pts,cv2.RANSAC,5.0)
@@ -104,7 +131,7 @@ def homography_all_detectors(flat_file, persp_file, json_file, xml_file):
         cv2.polylines(persp_roi,[roi_corners_persp.astype(int)],True,(0,255,0),2)
         for px,py in roi_corners_persp: cv2.circle(persp_roi,(int(px),int(py)),5,(255,0,0),-1)
 
-        # ---------------- XML Ground-Truth overlay
+        # ---------------- XML Ground-Truth overlay ----------------
         xml_gt_img = persp_img.copy()
         ordered_pts = ['TopLeft', 'TopRight', 'BottomRight', 'BottomLeft']
         xml_polygon = [xml_points[pt] for pt in ordered_pts]