Update app.py

app.py

@@ -8,14 +8,12 @@ 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 = np.cos(rot_rad)
-    sin_r = np.sin(rot_rad)
+    cos_r, sin_r = np.cos(rot_rad), np.sin(rot_rad)
     R = np.array([[cos_r, -sin_r], [sin_r, cos_r]])
     cx, cy = x + w/2, y + h/2
-    local_corners = np.array([[-w/2, -h/2],[w/2,-h/2],[w/2,h/2],[-w/2,h/2]])
+    local_corners = np.array([[-w/2,-h/2],[w/2,-h/2],[w/2,h/2],[-w/2,h/2]])
     rotated_corners = np.dot(local_corners, R.T)
-    corners = rotated_corners + np.array([cx, cy])
-    return corners.astype(np.float32)
+    return (rotated_corners + np.array([cx,cy])).astype(np.float32)
 
 def preprocess_gray_clahe(img):
     gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
@@ -23,25 +21,18 @@ 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": sift=cv2.SIFT_create(nfeatures=5000); matcher=cv2.BFMatcher(cv2.NORM_L2)
-    elif method=="ORB": orb=cv2.ORB_create(5000); matcher=cv2.BFMatcher(cv2.NORM_HAMMING)
-    elif method=="BRISK": brisk=cv2.BRISK_create(); matcher=cv2.BFMatcher(cv2.NORM_HAMMING)
-    elif method=="KAZE": kaze=cv2.KAZE_create(); matcher=cv2.BFMatcher(cv2.NORM_L2)
-    elif method=="AKAZE": akaze=cv2.AKAZE_create(); matcher=cv2.BFMatcher(cv2.NORM_HAMMING)
+    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 = None, None
-    kp2, des2 = None, None
-
-    if method=="SIFT": kp1, des1 = sift.detectAndCompute(img1_gray,None); kp2, des2 = sift.detectAndCompute(img2_gray,None)
-    elif method=="ORB": kp1, des1 = orb.detectAndCompute(img1_gray,None); kp2, des2 = orb.detectAndCompute(img2_gray,None)
-    elif method=="BRISK": kp1, des1 = brisk.detectAndCompute(img1_gray,None); kp2, des2 = brisk.detectAndCompute(img2_gray,None)
-    elif method=="KAZE": kp1, des1 = kaze.detectAndCompute(img1_gray,None); kp2, des2 = kaze.detectAndCompute(img2_gray,None)
-    elif method=="AKAZE": kp1, des1 = akaze.detectAndCompute(img1_gray,None); kp2, des2 = akaze.detectAndCompute(img2_gray,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,[]
-
-    raw_matches = matcher.knnMatch(des1, des2, k=2)
+    
+    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
 
@@ -51,78 +42,80 @@ def parse_xml_points(xml_file):
     points=[]
     for pt_type in ["TopLeft","TopRight","BottomLeft","BottomRight"]:
         elem=root.find(f".//point[@type='{pt_type}']")
-        x=float(elem.get("x")); y=float(elem.get("y"))
-        points.extend([x,y])
+        points.append([float(elem.get("x")), float(elem.get("y"))])
     return np.array(points,dtype=np.float32).reshape(-1,2)
 
-# ---------------- Main Homography Function ----------------
+# ---------------- 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)
-    mockup=json.load(open(json_file.name))
-    roi_data=mockup["printAreas"][0]["position"]
-    roi_x, roi_y=roi_data["x"], roi_data["y"]
-    roi_w=mockup["printAreas"][0]["width"]
-    roi_h=mockup["printAreas"][0]["height"]
-    roi_rot_deg=mockup["printAreas"][0]["rotation"]
-
-    flat_gray=preprocess_gray_clahe(flat_img)
-    persp_gray=preprocess_gray_clahe(persp_img)
-    methods=["SIFT","ORB","BRISK","KAZE","AKAZE"]
-
-    gallery_images=[]
-    download_files=[]
-
-    xml_points=parse_xml_points(xml_file.name)
+    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"]
+    roi_w, roi_h = mockup["printAreas"][0]["width"], mockup["printAreas"][0]["height"]
+    roi_rot_deg = mockup["printAreas"][0]["rotation"]
+
+    flat_gray = preprocess_gray_clahe(flat_img)
+    persp_gray = preprocess_gray_clahe(persp_img)
+    xml_points = parse_xml_points(xml_file.name)
+
+    methods = ["SIFT","ORB","BRISK","KAZE","AKAZE"]
+    gallery_images = []
+    download_files = []
 
     for method in methods:
-        kp1,kp2,good_matches=detect_and_match(flat_gray,persp_gray,method=method)
+        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 lines
+        # Feature matching
         match_img = cv2.drawMatches(flat_img,kp1,persp_img,kp2,good_matches,None,flags=2)
 
-        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)
+        # 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)
         if H is None: continue
 
-        # ROI mapping
         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)
         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)
 
-        # XML ground truth mapping
+        # 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 all to RGB
-        flat_rgb=cv2.cvtColor(flat_img,cv2.COLOR_BGR2RGB)
-        persp_roi_rgb=cv2.cvtColor(persp_roi,cv2.COLOR_BGR2RGB)
-        match_rgb=cv2.cvtColor(match_img,cv2.COLOR_BGR2RGB)
-        xml_rgb=cv2.cvtColor(xml_gt_img,cv2.COLOR_BGR2RGB)
+        # Convert to RGB & resize
+        size=(256,256)
+        flat_rgb = cv2.cvtColor(flat_img,cv2.COLOR_BGR2RGB)
+        flat_rgb = cv2.resize(flat_rgb,size)
+        match_rgb = cv2.cvtColor(match_img,cv2.COLOR_BGR2RGB)
+        match_rgb = cv2.resize(match_rgb,size)
+        roi_rgb = cv2.cvtColor(persp_roi,cv2.COLOR_BGR2RGB)
+        roi_rgb = cv2.resize(roi_rgb,size)
+        xml_rgb = cv2.cvtColor(xml_gt_img,cv2.COLOR_BGR2RGB)
+        xml_rgb = cv2.resize(xml_rgb,size)
+
+        # Merge 2x2 grid
+        top = np.hstack([flat_rgb, match_rgb])
+        bottom = np.hstack([roi_rgb, xml_rgb])
+        combined_grid = np.vstack([top,bottom])
 
-        # Combine all 4 in one gallery list per detector
-        combined = np.hstack([cv2.resize(flat_rgb,(256,256)),
-                              cv2.resize(match_rgb,(256,256)),
-                              cv2.resize(persp_roi_rgb,(256,256)),
-                              cv2.resize(xml_rgb,(256,256))])
-        gallery_images.append((combined,f"{method} Detector"))
+        gallery_images.append((combined_grid,f"{method} Detector"))
 
         # Save for download
-        base_name=os.path.splitext(os.path.basename(persp_file))[0]
-        file_name=f"{base_name}_{method.lower()}.png"
-        cv2.imwrite(file_name,combined[:,:,::-1]) # RGB->BGR
+        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))
         download_files.append(file_name)
 
     while len(download_files)<5: download_files.append(None)
     return [gallery_images]+download_files[:5]
 
 # ---------------- Gradio UI ----------------
-iface=gr.Interface(
+iface = gr.Interface(
     fn=homography_all_detectors,
     inputs=[
         gr.Image(label="Upload Flat Image",type="filepath"),