Update app.py

app.py

@@ -4,7 +4,6 @@ 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):
@@ -40,37 +39,24 @@ def detect_and_match(img1_gray, img2_gray, method="SIFT", ratio_thresh=0.78):
 def parse_xml_points(xml_file):
     tree = ET.parse(xml_file)
     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)
-
-def extract_four_points_from_xml(xml_path):
-    tree = etree.parse(xml_path)
-    root = tree.getroot()
     transform = root.find('.//transform')
     points = {}
     for pt in transform.findall('.//point'):
         pt_type = pt.attrib['type']
-        x = int(float(pt.attrib['x']))
-        y = int(float(pt.attrib['y']))
+        x = float(pt.attrib['x'])
+        y = float(pt.attrib['y'])
         points[pt_type] = (x, y)
     return points
 
-def draw_polygon_overlay(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_overlay = img.copy()
-    cv2.polylines(img_overlay, [pts], isClosed=True, color=(255,0,0), thickness=3)
-    return img_overlay
-
 # ---------------- Padding Helper ----------------
 def pad_to_size(img, target_h, target_w):
     h, w = img.shape[:2]
+    top_pad = 0
+    left_pad = 0
+    bottom_pad = target_h - h
+    right_pad = target_w - w
     canvas = np.ones((target_h, target_w,3), dtype=np.uint8)*255
-    canvas[:h, :w] = img
+    canvas[top_pad:top_pad+h, left_pad:left_pad+w] = img
     return canvas
 
 # ---------------- Main Function ----------------
@@ -86,7 +72,6 @@ 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_dict = extract_four_points_from_xml(xml_file.name)
 
     methods = ["SIFT","ORB","BRISK","KAZE","AKAZE"]
     gallery_paths = []
@@ -103,27 +88,31 @@ def homography_all_detectors(flat_file, persp_file, json_file, xml_file):
         H,_ = cv2.findHomography(src_pts,dst_pts,cv2.RANSAC,5.0)
         if H is None: continue
 
-        # Homography-based ROI overlay
+        # Homography ROI
         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 GT overlay
-        xml_gt_img = draw_polygon_overlay(persp_img, xml_dict)
+        # 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 to RGB
-        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)
+        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)
 
-        # Determine max height/width
+        # 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)
@@ -134,10 +123,10 @@ def homography_all_detectors(flat_file, persp_file, json_file, xml_file):
         bottom = np.hstack([roi_pad, xml_pad])
         combined_grid = np.vstack([top, bottom])
 
-        # Save
+        # 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))
+        cv2.imwrite(file_name, cv2.cvtColor(combined_grid,cv2.COLOR_RGB2BGR))
         gallery_paths.append(file_name)
         download_files.append(file_name)
 
@@ -162,7 +151,7 @@ iface = gr.Interface(
         gr.File(label="Download AKAZE Result")
     ],
     title="Homography ROI + Feature Matching + XML GT",
-    description="Shows Flat, Feature-Matched, Homography ROI (green), and XML Ground-Truth (red) overlay in a 2x2 grid with same size."
+    description="Flat + Perspective images with mockup.json & XML. Original resolution maintained. Grid aligned with white padding."
 )
 
 iface.launch()