Update app.py

app.py

@@ -3,7 +3,7 @@ import numpy as np
 import json
 import gradio as gr
 import os
-import xml.etree.ElementTree as ET
+from lxml import etree
 
 # ---------------- Helper functions ----------------
 def get_rotated_rect_corners(x, y, w, h, rotation_deg):
@@ -36,16 +36,30 @@ def detect_and_match(img1_gray, img2_gray, method="SIFT", ratio_thresh=0.78):
     good = [m for m,n in raw_matches if m.distance < ratio_thresh*n.distance]
     return kp1, kp2, good
 
-def parse_xml_points(xml_file):
-    tree = ET.parse(xml_file)
+# ---------------- XML overlay helper using lxml ----------------
+def extract_points_lxml(xml_path):
+    tree = etree.parse(xml_path)
     root = tree.getroot()
-    points=[]
-    for pt_type in ["TopLeft","TopRight","BottomLeft","BottomRight"]:
-        elem=root.find(f".//point[@type='{pt_type}']")
-        points.append([float(elem.get("x")), float(elem.get("y"))])
-    return np.array(points,dtype=np.float32).reshape(-1,2)
-
-# ---------------- Padding Helpers ----------------
+    transform = root.find('.//transform')
+    points = {}
+    for pt in transform.findall('.//point'):
+        pt_type = pt.attrib['type']
+        x = float(pt.attrib['x'])
+        y = float(pt.attrib['y'])
+        points[pt_type] = (x,y)
+    return points
+
+def draw_polygon_on_image(img, points_dict):
+    ordered_points = ['TopLeft','TopRight','BottomRight','BottomLeft']
+    polygon = [points_dict[pt] for pt in ordered_points]
+    pts = np.array(polygon, np.int32).reshape((-1,1,2))
+    img_copy = img.copy()
+    cv2.polylines(img_copy, [pts], isClosed=True, color=(0,0,255), thickness=3)
+    for px, py in polygon:
+        cv2.circle(img_copy, (int(px),int(py)), 5, (255,0,0), -1)
+    return img_copy
+
+# ---------------- Padding Helper ----------------
 def pad_to_size(img, target_h, target_w):
     h, w = img.shape[:2]
     canvas = np.ones((target_h, target_w,3), dtype=np.uint8)*255
@@ -77,7 +91,7 @@ def homography_all_detectors(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)
+    xml_points_dict = extract_points_lxml(xml_file.name)
 
     methods = ["SIFT","ORB","BRISK","KAZE","AKAZE"]
     gallery_paths = []
@@ -89,47 +103,42 @@ def homography_all_detectors(flat_file, persp_file, json_file, xml_file):
 
         match_img = cv2.drawMatches(flat_img,kp1,persp_img,kp2,good_matches,None,flags=2)
 
+        # ROI on perspective image
+        roi_corners_flat = get_rotated_rect_corners(roi_x,roi_y,roi_w,roi_h,roi_rot_deg)
         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_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)
+        if H is not None:
+            roi_corners_persp = cv2.perspectiveTransform(roi_corners_flat.reshape(-1,1,2),H).reshape(-1,2)
+            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_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)
+        # XML GT overlay on perspective
+        xml_gt_img = draw_polygon_on_image(persp_img, xml_points_dict)
 
         # Convert to RGB
         flat_rgb = cv2.cvtColor(flat_img,cv2.COLOR_BGR2RGB)
-        persp_rgb = cv2.cvtColor(persp_img,cv2.COLOR_BGR2RGB)
+        match_rgb = match_img_to_reference(cv2.cvtColor(match_img,cv2.COLOR_BGR2RGB),
+                                           flat_rgb.shape[0], flat_rgb.shape[1])
         roi_rgb = cv2.cvtColor(persp_roi,cv2.COLOR_BGR2RGB)
         xml_rgb = cv2.cvtColor(xml_gt_img,cv2.COLOR_BGR2RGB)
 
-        # Resize match_img to flat image width, maintain aspect ratio
-        match_rgb = match_img_to_reference(cv2.cvtColor(match_img, cv2.COLOR_BGR2RGB),
-                                           flat_rgb.shape[0], flat_rgb.shape[1])
-
-        # Determine max height and width for grid
+        # Determine max height and width
         max_h = max(flat_rgb.shape[0], match_rgb.shape[0], roi_rgb.shape[0], xml_rgb.shape[0])
         max_w = max(flat_rgb.shape[1], match_rgb.shape[1], roi_rgb.shape[1], xml_rgb.shape[1])
 
-        # Pad all images
+        # Pad images
         flat_pad = pad_to_size(flat_rgb, max_h, max_w)
         match_pad = pad_to_size(match_rgb, max_h, max_w)
         roi_pad = pad_to_size(roi_rgb, max_h, max_w)
         xml_pad = pad_to_size(xml_rgb, max_h, max_w)
 
-        # Merge 2x2 grid
+        # 2x2 grid
         top = np.hstack([flat_pad, match_pad])
         bottom = np.hstack([roi_pad, xml_pad])
         combined_grid = np.vstack([top, bottom])
 
-        # 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))
@@ -157,7 +166,7 @@ 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. Original resolution maintained. Grid aligned with white padding."
+    description="Flat + Perspective images with mockup.json & XML. Grid aligned. Perspective GT overlay now uses XML lxml parsing."
 )
 
 iface.launch()