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Update app.py
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app.py
CHANGED
@@ -77,7 +77,10 @@ def add_title(img_bgr, title):
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return np.vstack([bar, img_bgr])
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def parse_xml_roi_points(xml_path):
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"""Parse XML, return list of polygons (Nx2 points)."""
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polys = []
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try:
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tree = ET.parse(xml_path)
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@@ -106,13 +109,15 @@ def parse_xml_roi_points(xml_path):
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def homography_all_detectors(flat_file, persp_file, json_file, xml_file):
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flat_img = cv2.imread(flat_file)
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persp_img = cv2.imread(persp_file)
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roi = mockup["printAreas"][0]
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roi_x, roi_y = roi["position"]["x"], roi["position"]["y"]
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roi_w, roi_h = roi["width"], roi["height"]
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roi_rot_deg = roi["rotation"]
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xml_polys = parse_xml_roi_points(xml_file
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flat_gray = preprocess_gray_clahe(flat_img)
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persp_gray = preprocess_gray_clahe(persp_img)
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@@ -165,11 +170,14 @@ def homography_all_detectors(flat_file, persp_file, json_file, xml_file):
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results.append((cv2.cvtColor(composite, cv2.COLOR_BGR2RGB), f"{method} Grid"))
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download_files.append(file_name)
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# pad list with "" instead of None (to avoid Gradio error)
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while len(download_files) < 5:
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download_files.append(
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# ---------------- Gradio UI ----------------
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iface = gr.Interface(
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@@ -191,4 +199,4 @@ iface = gr.Interface(
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title="Homography ROI vs XML ROI",
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description="Each detector produces one 2×2 grid: Flat, Matches, Homography ROI, Ground Truth ROI."
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)
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iface.launch()
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return np.vstack([bar, img_bgr])
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def parse_xml_roi_points(xml_path):
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"""Parse your XML structure, return list of polygons (Nx2 points)."""
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if xml_path is None:
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return None
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polys = []
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try:
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tree = ET.parse(xml_path)
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def homography_all_detectors(flat_file, persp_file, json_file, xml_file):
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flat_img = cv2.imread(flat_file)
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persp_img = cv2.imread(persp_file)
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# FIX: Use the file paths directly (no .name needed)
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mockup = json.load(open(json_file))
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roi = mockup["printAreas"][0]
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roi_x, roi_y = roi["position"]["x"], roi["position"]["y"]
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roi_w, roi_h = roi["width"], roi["height"]
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roi_rot_deg = roi["rotation"]
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xml_polys = parse_xml_roi_points(xml_file) if xml_file else None
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flat_gray = preprocess_gray_clahe(flat_img)
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persp_gray = preprocess_gray_clahe(persp_img)
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results.append((cv2.cvtColor(composite, cv2.COLOR_BGR2RGB), f"{method} Grid"))
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download_files.append(file_name)
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while len(download_files) < 5:
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download_files.append(None)
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# Return the results in the correct format
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gallery_output = results
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file_outputs = download_files[:5]
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return [gallery_output] + file_outputs
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# ---------------- Gradio UI ----------------
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iface = gr.Interface(
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title="Homography ROI vs XML ROI",
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description="Each detector produces one 2×2 grid: Flat, Matches, Homography ROI, Ground Truth ROI."
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)
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iface.launch()
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