Update mdr_pdf_parser.py
Browse files- mdr_pdf_parser.py +3 -3
mdr_pdf_parser.py
CHANGED
@@ -1577,7 +1577,7 @@ def mdr_get_rotated_crop(img, points):
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h = int(max(np.linalg.norm(pts[0] - pts[3]), np.linalg.norm(pts[1] - pts[2])))
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std = np.float32([[0, 0], [w, 0], [w, h], [0, h]])
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M = cv2.getPerspectiveTransform(pts, std)
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dst = cv2.warpPerspective(img, M, (w, h), borderMode=cv2.
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dh, dw = dst.shape[0:2]
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if dh > 0 and dw > 0 and dh * 1.0 / dw >= 1.5:
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dst = cv2.rotate(dst, cv2.ROTATE_90_CLOCKWISE)
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@@ -2171,7 +2171,7 @@ class MDRLayoutReader:
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layoutreader_cache_dir = Path(self._model_path) # self._model_path is like "./mdr_models/layoutreader"
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mdr_ensure_directory(str(layoutreader_cache_dir)) # Ensure this specific directory exists
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-
name = "
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print(f"MDRLayoutReader: Attempting to load LayoutLMv3 model '{name}'. Cache dir: {layoutreader_cache_dir}")
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try:
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@@ -2822,7 +2822,7 @@ class MDRExtractionEngine:
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ori_im_cv = cv2.cvtColor(np.array(image.convert("RGB")), cv2.COLOR_RGB2BGR)
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gray_cv = cv2.cvtColor(ori_im_cv, cv2.COLOR_BGR2GRAY)
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clahe_obj = cv2.createCLAHE(clipLimit=
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enhanced_gray_cv = clahe_obj.apply(gray_cv)
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# Convert back to BGR for downstream components that might expect 3 channels
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# (even if they only use one, like the detector)
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h = int(max(np.linalg.norm(pts[0] - pts[3]), np.linalg.norm(pts[1] - pts[2])))
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std = np.float32([[0, 0], [w, 0], [w, h], [0, h]])
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M = cv2.getPerspectiveTransform(pts, std)
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+
dst = cv2.warpPerspective(img, M, (w, h), borderMode=cv2.BORDER_CONSTANT, borderValue=(128, 128, 128), flags=cv2.INTER_CUBIC)
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dh, dw = dst.shape[0:2]
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if dh > 0 and dw > 0 and dh * 1.0 / dw >= 1.5:
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dst = cv2.rotate(dst, cv2.ROTATE_90_CLOCKWISE)
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layoutreader_cache_dir = Path(self._model_path) # self._model_path is like "./mdr_models/layoutreader"
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mdr_ensure_directory(str(layoutreader_cache_dir)) # Ensure this specific directory exists
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+
name = "lakshya-rawat/document-qa-model"
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print(f"MDRLayoutReader: Attempting to load LayoutLMv3 model '{name}'. Cache dir: {layoutreader_cache_dir}")
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try:
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ori_im_cv = cv2.cvtColor(np.array(image.convert("RGB")), cv2.COLOR_RGB2BGR)
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gray_cv = cv2.cvtColor(ori_im_cv, cv2.COLOR_BGR2GRAY)
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+
clahe_obj = cv2.createCLAHE(clipLimit=1.0, tileGridSize=(8, 8))
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enhanced_gray_cv = clahe_obj.apply(gray_cv)
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# Convert back to BGR for downstream components that might expect 3 channels
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# (even if they only use one, like the detector)
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