Update app.py

app.py

@@ -4,6 +4,7 @@ import json
 import gradio as gr
 import os
 import xml.etree.ElementTree as ET
+import traceback
 
 # ---------------- Helper functions ----------------
 def get_rotated_rect_corners(x, y, w, h, rotation_deg):
@@ -31,20 +32,15 @@ def preprocess_gray_clahe(img):
 
 def detect_and_match(img1_gray, img2_gray, method="SIFT", ratio_thresh=0.78):
     if method == "SIFT":
-        detector = cv2.SIFT_create(nfeatures=5000)
-        norm = cv2.NORM_L2
+        detector = cv2.SIFT_create(nfeatures=5000); norm = cv2.NORM_L2
     elif method == "ORB":
-        detector = cv2.ORB_create(5000)
-        norm = cv2.NORM_HAMMING
+        detector = cv2.ORB_create(5000); norm = cv2.NORM_HAMMING
     elif method == "BRISK":
-        detector = cv2.BRISK_create()
-        norm = cv2.NORM_HAMMING
+        detector = cv2.BRISK_create(); norm = cv2.NORM_HAMMING
     elif method == "KAZE":
-        detector = cv2.KAZE_create()
-        norm = cv2.NORM_L2
+        detector = cv2.KAZE_create(); norm = cv2.NORM_L2
     elif method == "AKAZE":
-        detector = cv2.AKAZE_create()
-        norm = cv2.NORM_HAMMING
+        detector = cv2.AKAZE_create(); norm = cv2.NORM_HAMMING
     else:
         return None, None, [], None
 
@@ -60,10 +56,8 @@ def detect_and_match(img1_gray, img2_gray, method="SIFT", ratio_thresh=0.78):
     matches_img = None
     if len(good) >= 4:
         matches_img = cv2.drawMatches(
-            cv2.cvtColor(img1_gray, cv2.COLOR_GRAY2BGR),
-            kp1,
-            cv2.cvtColor(img2_gray, cv2.COLOR_GRAY2BGR),
-            kp2,
+            cv2.cvtColor(img1_gray, cv2.COLOR_GRAY2BGR), kp1,
+            cv2.cvtColor(img2_gray, cv2.COLOR_GRAY2BGR), kp2,
             good, None,
             flags=cv2.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS
         )
@@ -77,107 +71,126 @@ def add_title(img_bgr, title):
     return np.vstack([bar, img_bgr])
 
 def parse_xml_roi_points(xml_path):
-    """Parse your XML structure, return list of polygons (Nx2 points)."""
-    if xml_path is None:
-        return None
-        
     polys = []
-    try:
-        tree = ET.parse(xml_path)
-        root = tree.getroot()
-        # Transform ROI points (FourPoint)
-        for tr in root.findall(".//transform"):
-            pts = []
-            for p in tr.findall("point"):
-                x = float(p.get("x")); y = float(p.get("y"))
-                pts.append([x, y])
-            if len(pts) >= 3:
-                polys.append(np.array(pts, dtype=np.float32))
-        # Overlay polygons
-        for ov in root.findall(".//overlay"):
-            pts = []
-            for p in ov.findall("point"):
-                x = float(p.get("x")); y = float(p.get("y"))
-                pts.append([x, y])
-            if len(pts) >= 3:
-                polys.append(np.array(pts, dtype=np.float32))
-    except Exception as e:
-        print("XML parse error:", e)
+    tree = ET.parse(xml_path)
+    root = tree.getroot()
+    for tr in root.findall(".//transform"):
+        pts = []
+        for p in tr.findall("point"):
+            x = float(p.get("x")); y = float(p.get("y"))
+            pts.append([x, y])
+        if len(pts) >= 3:
+            polys.append(np.array(pts, dtype=np.float32))
+    for ov in root.findall(".//overlay"):
+        pts = []
+        for p in ov.findall("point"):
+            x = float(p.get("x")); y = float(p.get("y"))
+            pts.append([x, y])
+        if len(pts) >= 3:
+            polys.append(np.array(pts, dtype=np.float32))
     return polys if polys else None
 
+def make_error_image(msg, width=800, height=600):
+    img = np.ones((height, width, 3), dtype=np.uint8)*255
+    y0 = 100
+    for i, line in enumerate(msg.split("\n")):
+        y = y0 + i*40
+        cv2.putText(img, line, (50, y), cv2.FONT_HERSHEY_SIMPLEX,
+                    0.8, (0,0,255), 2, cv2.LINE_AA)
+    return img
+
 # ---------------- 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)
-    
-    # FIX: Use the file paths directly (no .name needed)
-    mockup = json.load(open(json_file))
-    roi = mockup["printAreas"][0]
-    roi_x, roi_y = roi["position"]["x"], roi["position"]["y"]
-    roi_w, roi_h = roi["width"], roi["height"]
-    roi_rot_deg = roi["rotation"]
-
-    xml_polys = parse_xml_roi_points(xml_file) if xml_file else None
-
-    flat_gray = preprocess_gray_clahe(flat_img)
-    persp_gray = preprocess_gray_clahe(persp_img)
-
     results = []
     download_files = []
+    methods = ["SIFT","ORB","BRISK","KAZE","AKAZE"]
 
-    for method in ["SIFT","ORB","BRISK","KAZE","AKAZE"]:
-        kp1, kp2, good, matches_img = detect_and_match(flat_gray, persp_gray, method)
-        if kp1 is None or len(good) < 4:
-            continue
-
-        src_pts = np.float32([kp1[m.queryIdx].pt for m in good]).reshape(-1,1,2)
-        dst_pts = np.float32([kp2[m.trainIdx].pt for m in good]).reshape(-1,1,2)
-        H, _ = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 5.0)
-
-        # top-left = flat
-        top_left = add_title(flat_img.copy(), "Flat Image")
-
-        # top-right = feature matches
-        top_right = add_title(matches_img if matches_img is not None else flat_img.copy(),
-                              "Feature Matches (Flat→Perspective)")
-
-        # bottom-left = homography ROI
-        bottom_left = persp_img.copy()
-        if H is not None:
-            roi_corners = get_rotated_rect_corners(roi_x, roi_y, roi_w, roi_h, roi_rot_deg)
-            roi_persp = cv2.perspectiveTransform(roi_corners.reshape(-1,1,2), H).reshape(-1,2)
-            cv2.polylines(bottom_left, [roi_persp.astype(int)], True, (0,255,0), 3)
-        bottom_left = add_title(bottom_left, "ROI via Homography (from JSON)")
-
-        # bottom-right = XML ROI
-        bottom_right = persp_img.copy()
-        if xml_polys:
-            for poly in xml_polys:
-                cv2.polylines(bottom_right, [poly.astype(int)], True, (0,0,255), 3)
-        else:
-            cv2.putText(bottom_right, "No XML ROI", (50,100),
-                        cv2.FONT_HERSHEY_SIMPLEX, 2, (0,0,255), 3)
-        bottom_right = add_title(bottom_right, "Ground Truth ROI (XML)")
-
-        # stack horizontally
-        top = np.hstack([top_left, top_right])
-        bottom = np.hstack([bottom_left, bottom_right])
-        composite = np.vstack([top, bottom])
-
-        base_name = os.path.splitext(os.path.basename(persp_file))[0]
-        file_name = f"{base_name}_{method.lower()}_grid.png"
-        cv2.imwrite(file_name, composite)
-        results.append((cv2.cvtColor(composite, cv2.COLOR_BGR2RGB), f"{method} Grid"))
-        download_files.append(file_name)
+    try:
+        flat_img = cv2.imread(flat_file)
+        persp_img = cv2.imread(persp_file)
+        if flat_img is None or persp_img is None:
+            raise ValueError("Could not read input images")
+
+        mockup = json.load(open(json_file.name))
+        roi = mockup["printAreas"][0]
+        roi_x, roi_y = roi["position"]["x"], roi["position"]["y"]
+        roi_w, roi_h = roi["width"], roi["height"]
+        roi_rot_deg = roi["rotation"]
+
+        xml_polys = parse_xml_roi_points(xml_file.name) if xml_file else None
+
+        flat_gray = preprocess_gray_clahe(flat_img)
+        persp_gray = preprocess_gray_clahe(persp_img)
+
+        for method in methods:
+            try:
+                kp1, kp2, good, matches_img = detect_and_match(flat_gray, persp_gray, method)
+                if kp1 is None or len(good) < 4:
+                    raise ValueError(f"{method}: Not enough good matches")
+
+                src_pts = np.float32([kp1[m.queryIdx].pt for m in good]).reshape(-1,1,2)
+                dst_pts = np.float32([kp2[m.trainIdx].pt for m in good]).reshape(-1,1,2)
+                H, _ = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 5.0)
+                if H is None:
+                    raise ValueError(f"{method}: Homography could not be estimated")
+
+                # top-left = flat
+                top_left = add_title(flat_img.copy(), "Flat Image")
+                # top-right = matches
+                top_right = add_title(matches_img if matches_img is not None else flat_img.copy(),
+                                      "Feature Matches")
+                # bottom-left = homography ROI
+                bottom_left = persp_img.copy()
+                roi_corners = get_rotated_rect_corners(roi_x, roi_y, roi_w, roi_h, roi_rot_deg)
+                roi_persp = cv2.perspectiveTransform(roi_corners.reshape(-1,1,2), H).reshape(-1,2)
+                cv2.polylines(bottom_left, [roi_persp.astype(int)], True, (0,255,0), 3)
+                bottom_left = add_title(bottom_left, "ROI via Homography (JSON)")
+                # bottom-right = XML ROI
+                bottom_right = persp_img.copy()
+                if xml_polys:
+                    for poly in xml_polys:
+                        cv2.polylines(bottom_right, [poly.astype(int)], True, (0,0,255), 3)
+                else:
+                    cv2.putText(bottom_right, "No XML ROI", (50,100),
+                                cv2.FONT_HERSHEY_SIMPLEX, 2, (0,0,255), 3)
+                bottom_right = add_title(bottom_right, "Ground Truth ROI (XML)")
+
+                top = np.hstack([top_left, top_right])
+                bottom = np.hstack([bottom_left, bottom_right])
+                composite = np.vstack([top, bottom])
+
+                base_name = os.path.splitext(os.path.basename(persp_file))[0]
+                file_name = f"{base_name}_{method.lower()}_grid.png"
+                cv2.imwrite(file_name, composite)
+                results.append((cv2.cvtColor(composite, cv2.COLOR_BGR2RGB), f"{method} Grid"))
+                download_files.append(file_name)
+
+            except Exception as e:
+                err_msg = f"Error in {method}: {str(e)}"
+                print(err_msg)
+                tb = traceback.format_exc()
+                err_img = make_error_image(err_msg + "\n" + tb)
+                results.append((cv2.cvtColor(err_img, cv2.COLOR_BGR2RGB), f"{method} ERROR"))
+                # make txt file also
+                err_file = f"{method.lower()}_error.txt"
+                with open(err_file, "w") as f:
+                    f.write(err_msg + "\n\n" + tb)
+                download_files.append(err_file)
+
+    except Exception as e:
+        err_msg = f"Global Error: {str(e)}"
+        tb = traceback.format_exc()
+        err_img = make_error_image(err_msg + "\n" + tb)
+        results.append((cv2.cvtColor(err_img, cv2.COLOR_BGR2RGB), f"GLOBAL ERROR"))
+        err_file = "global_error.txt"
+        with open(err_file, "w") as f:
+            f.write(err_msg + "\n\n" + tb)
+        download_files = [err_file]*5
 
     while len(download_files) < 5:
         download_files.append(None)
-    
-    # Return the results in the correct format
-    gallery_output = results
-    file_outputs = download_files[:5]
-    
-    return [gallery_output] + file_outputs
+
+    return [results] + download_files[:5]
 
 # ---------------- Gradio UI ----------------
 iface = gr.Interface(
@@ -197,6 +210,7 @@ iface = gr.Interface(
         gr.File(label="Download AKAZE Grid")
     ],
     title="Homography ROI vs XML ROI",
-    description="Each detector produces one 2×2 grid: Flat, Matches, Homography ROI, Ground Truth ROI."
+    description="Each detector produces one 2×2 grid: Flat, Matches, Homography ROI, Ground Truth ROI. If any error occurs, it will be shown in the output image + downloadable txt log."
 )
-iface.launch()
+
+iface.launch()