Update app.py

app.py

@@ -5,7 +5,6 @@ import gradio as gr
 import os
 import xml.etree.ElementTree as ET
 
-# ---------------- Helper functions ----------------
 def get_rotated_rect_corners(x, y, w, h, rotation_deg):
     rot_rad = np.deg2rad(rotation_deg)
     cos_r, sin_r = np.cos(rot_rad), np.sin(rot_rad)
@@ -45,7 +44,6 @@ def parse_xml_points(xml_file):
         points.append([float(elem.get("x")), float(elem.get("y"))])
     return np.array(points,dtype=np.float32).reshape(-1,2)
 
-# ---------------- Main Function ----------------
 def homography_all_detectors(flat_file, persp_file, json_file, xml_file):
     flat_img = cv2.imread(flat_file)
     persp_img = cv2.imread(persp_file)
@@ -60,17 +58,15 @@ def homography_all_detectors(flat_file, persp_file, json_file, xml_file):
     xml_points = parse_xml_points(xml_file.name)
 
     methods = ["SIFT","ORB","BRISK","KAZE","AKAZE"]
-    gallery_images = []
+    gallery_paths = []
     download_files = []
 
     for method in methods:
         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
 
-        # Feature matching image
         match_img = cv2.drawMatches(flat_img,kp1,persp_img,kp2,good_matches,None,flags=2)
 
-        # Homography & ROI mapping
         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)
@@ -82,34 +78,25 @@ 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 on perspective
         xml_gt_img = persp_img.copy()
         xml_mapped = cv2.perspectiveTransform(xml_points.reshape(-1,1,2),H).reshape(-1,2)
         for px,py in xml_mapped: cv2.circle(xml_gt_img,(int(px),int(py)),5,(0,0,255),-1)
 
-        # Convert to RGB (original size kept)
-        flat_rgb = cv2.cvtColor(flat_img,cv2.COLOR_BGR2RGB)
-        match_rgb = cv2.cvtColor(match_img,cv2.COLOR_BGR2RGB)
-        roi_rgb = cv2.cvtColor(persp_roi,cv2.COLOR_BGR2RGB)
-        xml_rgb = cv2.cvtColor(xml_gt_img,cv2.COLOR_BGR2RGB)
-
-        # Merge 2x2 grid keeping original resolutions
-        top = np.hstack([flat_rgb, match_rgb])
-        bottom = np.hstack([roi_rgb, xml_rgb])
+        # Merge 2x2 grid (original size)
+        top = np.hstack([flat_img, match_img])
+        bottom = np.hstack([persp_roi, xml_gt_img])
         combined_grid = np.vstack([top, bottom])
 
-        gallery_images.append((combined_grid,f"{method} Detector"))
-
-        # Save combined grid for download
+        # Save combined 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))
+        cv2.imwrite(file_name, combined_grid)
+        gallery_paths.append(file_name)
         download_files.append(file_name)
 
     while len(download_files)<5: download_files.append(None)
-    return [gallery_images]+download_files[:5]
+    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,
     inputs=[
@@ -127,7 +114,7 @@ iface = gr.Interface(
         gr.File(label="Download AKAZE Result")
     ],
     title="Homography ROI Projection with Feature Matching & XML GT",
-    description="Flat + Perspective images with mockup.json & XML. Shows 4 views per detector: Flat, Feature Matches, ROI Projection, XML Ground Truth. Original resolution kept."
+    description="Flat + Perspective images with mockup.json & XML. Shows 4 views per detector. Original resolution kept."
 )
 
 iface.launch()