Update app.py

app.py

@@ -21,17 +21,23 @@ def preprocess_gray_clahe(img):
     return clahe.apply(gray)
 
 def detect_and_match(img1_gray, img2_gray, method="SIFT", ratio_thresh=0.78):
-    if method=="SIFT": detector=cv2.SIFT_create(nfeatures=5000); matcher=cv2.BFMatcher(cv2.NORM_L2)
-    elif method=="ORB": detector=cv2.ORB_create(5000); matcher=cv2.BFMatcher(cv2.NORM_HAMMING)
-    elif method=="BRISK": detector=cv2.BRISK_create(); matcher=cv2.BFMatcher(cv2.NORM_HAMMING)
-    elif method=="KAZE": detector=cv2.KAZE_create(); matcher=cv2.BFMatcher(cv2.NORM_L2)
-    elif method=="AKAZE": detector=cv2.AKAZE_create(); matcher=cv2.BFMatcher(cv2.NORM_HAMMING)
-    else: return None,None,[]
-    
+    if method=="SIFT":
+        detector=cv2.SIFT_create(nfeatures=5000); matcher=cv2.BFMatcher(cv2.NORM_L2)
+    elif method=="ORB":
+        detector=cv2.ORB_create(5000); matcher=cv2.BFMatcher(cv2.NORM_HAMMING)
+    elif method=="BRISK":
+        detector=cv2.BRISK_create(); matcher=cv2.BFMatcher(cv2.NORM_HAMMING)
+    elif method=="KAZE":
+        detector=cv2.KAZE_create(); matcher=cv2.BFMatcher(cv2.NORM_L2)
+    elif method=="AKAZE":
+        detector=cv2.AKAZE_create(); matcher=cv2.BFMatcher(cv2.NORM_HAMMING)
+    else:
+        return None,None,[]
+
     kp1, des1 = detector.detectAndCompute(img1_gray,None)
     kp2, des2 = detector.detectAndCompute(img2_gray,None)
-    if des1 is None or des2 is None: return None,None,[]
-    
+    if des1 is None or des2 is None:
+        return None,None,[]
     raw_matches = matcher.knnMatch(des1,des2,k=2)
     good = [m for m,n in raw_matches if m.distance < ratio_thresh*n.distance]
     return kp1, kp2, good
@@ -51,16 +57,14 @@ def parse_xml_points(xml_file):
 # ---------------- Fit-to-Box Helper ----------------
 def fit_to_box(img, target_h=600, target_w=600):
     h, w = img.shape[:2]
-    scale = min(target_w/w, target_h/h)   # preserve aspect ratio
+    scale = min(target_w/w, target_h/h)  # preserve aspect ratio
     new_w, new_h = int(w*scale), int(h*scale)
     resized = cv2.resize(img, (new_w, new_h))
-
     # symmetric padding
     top = (target_h - new_h) // 2
     bottom = target_h - new_h - top
     left = (target_w - new_w) // 2
     right = target_w - new_w - left
-
     canvas = np.ones((target_h, target_w, 3), dtype=np.uint8) * 255
     canvas[top:top+new_h, left:left+new_w] = resized
     return canvas
@@ -71,14 +75,14 @@ def add_heading(img, text):
     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)
+    cv2.putText(canvas, text, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0,0,0), 2, cv2.LINE_AA)
     return canvas
 
 # ---------------- Main Function ----------------
-def homography_show_three(flat_file, persp_file, json_file, xml_file):
+def homography_all_detectors(flat_file, persp_file, json_file, xml_file):
     flat_img = cv2.imread(flat_file)
     persp_img = cv2.imread(persp_file)
+
     mockup = json.load(open(json_file.name))
     roi_data = mockup["printAreas"][0]["position"]
     roi_x, roi_y = roi_data["x"], roi_data["y"]
@@ -87,53 +91,67 @@ def homography_show_three(flat_file, persp_file, json_file, xml_file):
 
     flat_gray = preprocess_gray_clahe(flat_img)
     persp_gray = preprocess_gray_clahe(persp_img)
-    xml_points = parse_xml_points(xml_file.name)
-
-    # --- SIFT (only to get Homography) ---
-    kp1,kp2,good_matches = detect_and_match(flat_gray,persp_gray,"SIFT")
-    if kp1 is None or kp2 is None or len(good_matches)<4:
-        return ["Not enough matches found"], None, None, None
-
-    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)
 
-    # --- 1. Flat image with ROI from JSON ---
-    flat_with_roi = flat_img.copy()
-    roi_corners_flat = get_rotated_rect_corners(roi_x,roi_y,roi_w,roi_h,roi_rot_deg)
-    cv2.polylines(flat_with_roi,[roi_corners_flat.astype(int)],True,(0,255,0),2)
-    for px,py in roi_corners_flat: cv2.circle(flat_with_roi,(int(px),int(py)),5,(255,0,0),-1)
-
-    # --- 2. Perspective image with Homography ROI ---
-    roi_corners_persp = cv2.perspectiveTransform(roi_corners_flat.reshape(-1,1,2),H).reshape(-1,2)
-    persp_roi = persp_img.copy()
-    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)
-
-    # --- 3. Perspective image with Ground Truth ROI from XML ---
-    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)
-
-    # Resize + Headings
-    flat_rgb = add_heading(fit_to_box(cv2.cvtColor(flat_with_roi,cv2.COLOR_BGR2RGB),600,600), "Flat Image with JSON ROI")
-    roi_rgb  = add_heading(fit_to_box(cv2.cvtColor(persp_roi,cv2.COLOR_BGR2RGB),600,600), "Perspective Img with ROI(Homography)")
-    xml_rgb  = add_heading(fit_to_box(cv2.cvtColor(xml_gt_img,cv2.COLOR_BGR2RGB),600,600), "Perspective Img with GT ROI")
-
-    # Side-by-side 1 row
-    combined = np.hstack([flat_rgb, roi_rgb, xml_rgb])
-
-    base_name = os.path.splitext(os.path.basename(persp_file))[0]
-    file_name = f"{base_name}_result.png"
-    cv2.imwrite(file_name, cv2.cvtColor(combined,cv2.COLOR_RGB2BGR))
+    xml_points = parse_xml_points(xml_file.name)
 
-    return [file_name], file_name, None, None, None
+    methods = ["SIFT","ORB","BRISK","KAZE","AKAZE"]
+    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
+
+        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)
+        if H is None:
+            continue
+
+        # 1. Flat image with ROI (from JSON)
+        flat_with_roi = flat_img.copy()
+        roi_corners_flat = get_rotated_rect_corners(roi_x,roi_y,roi_w,roi_h,roi_rot_deg)
+        cv2.polylines(flat_with_roi,[roi_corners_flat.astype(int)],True,(0,255,0),2)
+        for px,py in roi_corners_flat:  
+            cv2.circle(flat_with_roi,(int(px),int(py)),5,(255,0,0),-1)
+
+        # 2. Perspective with Homography ROI
+        roi_corners_persp = cv2.perspectiveTransform(roi_corners_flat.reshape(-1,1,2),H).reshape(-1,2)
+        persp_roi = persp_img.copy()
+        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)
+
+        # 3. Perspective with GT ROI (from XML)
+        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 to RGB + resize + add headings
+        flat_rgb = add_heading(fit_to_box(cv2.cvtColor(flat_with_roi,cv2.COLOR_BGR2RGB),600,600), "Flat Image with ROI")
+        roi_rgb = add_heading(fit_to_box(cv2.cvtColor(persp_roi,cv2.COLOR_BGR2RGB),600,600), "Perspective Image with Homography ROI")
+        xml_rgb = add_heading(fit_to_box(cv2.cvtColor(xml_gt_img,cv2.COLOR_BGR2RGB),600,600), "Perspective GT ROI")
+
+        # Concatenate side by side
+        combined_row = np.hstack([flat_rgb, roi_rgb, xml_rgb])
+
+        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_row,cv2.COLOR_RGB2BGR))
+        gallery_paths.append(file_name)
+        download_files.append(file_name)
+
+    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_show_three,
+    fn=homography_all_detectors,
     inputs=[
         gr.Image(label="Upload Flat Image",type="filepath"),
         gr.Image(label="Upload Perspective Image",type="filepath"),
@@ -141,11 +159,15 @@ iface = gr.Interface(
         gr.File(label="Upload XML file",file_types=[".xml"])
     ],
     outputs=[
-        gr.Gallery(label="Final Result",show_label=True),
-        gr.File(label="Download Result")
+        gr.Gallery(label="Results per Detector",show_label=True),
+        gr.File(label="Download SIFT Result"),
+        gr.File(label="Download ORB Result"),
+        gr.File(label="Download BRISK Result"),
+        gr.File(label="Download KAZE Result"),
+        gr.File(label="Download AKAZE Result")
     ],
-    title="Homography ROI vs GT ROI",
-    description="Shows Flat image with JSON ROI, Perspective with Homography ROI, and Perspective with GT ROI in one row."
+    title="Homography ROI + XML GT",
+    description="Flat with ROI + Perspective ROI (Homography + GT). Aspect ratio preserved, images centered in uniform boxes, headings added."
 )
 
 iface.launch()