Update app.py

app.py

@@ -78,11 +78,21 @@ def remap_keypoints_to_box(kps, orig_shape, target_h=600, target_w=600):
         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
 
+# ---------------- Add Heading on Top ----------------
+def add_heading(img, text):
+    # add white band on top
+    h, w = img.shape[:2]
+    band_h = 40
+    canvas = np.ones((h+band_h, w, 3), dtype=np.uint8) * 255
+    canvas[band_h:] = img
+    cv2.putText(canvas, text, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 
+                1, (0,0,0), 2, cv2.LINE_AA)
+    return canvas
+
 # ---------------- Main Function ----------------
 def homography_all_detectors(flat_file, persp_file, json_file, xml_file):
     flat_img = cv2.imread(flat_file)
@@ -105,21 +115,14 @@ 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) ----------------
         flat_box = fit_to_box(flat_img, 600, 600)
         persp_box = fit_to_box(persp_img, 600, 600)
 
-        # 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
-        )
+        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)
@@ -131,25 +134,28 @@ 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_gt_img = persp_img.copy()
         ordered_pts = ['TopLeft', 'TopRight', 'BottomRight', 'BottomLeft']
         xml_polygon = [xml_points[pt] for pt in ordered_pts]
         pts = np.array(xml_polygon, np.int32).reshape((-1,1,2))
         cv2.polylines(xml_gt_img,[pts],isClosed=True,color=(255,0,0),thickness=3)
 
-        # Convert all to RGB & fit into boxes
         flat_rgb = fit_to_box(cv2.cvtColor(flat_img,cv2.COLOR_BGR2RGB),600,600)
         match_rgb = fit_to_box(cv2.cvtColor(match_img,cv2.COLOR_BGR2RGB),600,600)
         roi_rgb = fit_to_box(cv2.cvtColor(persp_roi,cv2.COLOR_BGR2RGB),600,600)
         xml_rgb = fit_to_box(cv2.cvtColor(xml_gt_img,cv2.COLOR_BGR2RGB),600,600)
 
+        # Add headings
+        flat_rgb = add_heading(flat_rgb, "Flat Image")
+        match_rgb = add_heading(match_rgb, "Flat -> Perspective Feature Matching")
+        roi_rgb = add_heading(roi_rgb, "Perspective Image with Homography ROI")
+        xml_rgb = add_heading(xml_rgb, "Perspective GT ROI")
+
         # Merge 2x2 grid
         top = np.hstack([flat_rgb, match_rgb])
         bottom = np.hstack([roi_rgb, xml_rgb])
         combined_grid = np.vstack([top, bottom])
 
-        # Save grid
         base_name = os.path.splitext(os.path.basename(persp_file))[0]
         file_name = f"{base_name}_{method.lower()}.png"
         cv2.imwrite(file_name, cv2.cvtColor(combined_grid,cv2.COLOR_RGB2BGR))
@@ -159,7 +165,6 @@ def homography_all_detectors(flat_file, persp_file, json_file, xml_file):
     while len(download_files)<5: download_files.append(None)
     return gallery_paths, download_files[0], download_files[1], download_files[2], download_files[3], download_files[4]
 
-    
 # ---------------- Gradio UI ----------------
 iface = gr.Interface(
     fn=homography_all_detectors,
@@ -178,7 +183,7 @@ iface = gr.Interface(
         gr.File(label="Download AKAZE Result")
     ],
     title="Homography ROI + Feature Matching + XML GT",
-    description="Flat + Perspective images with mockup.json & XML. Aspect ratio preserved, images centered in uniform boxes."
+    description="Flat + Perspective images with mockup.json & XML. Aspect ratio preserved, images centered in uniform boxes, headings added."
 )
 
 iface.launch()