j1dZddlZddlZddlmZddlZddlZgdZ dZ dZ dZ dd Z Gd d Zd Zd ZdZdZedkrdZeedSdS)a LayoutProbAnalyzer Purpose: Analyze per-class layout probability maps from a PDF page using an ONNX model. The underlying network accepts a grayscale page image and returns a combined output tensor containing internal feature channels followed by segmentation logit channels. This class extracts only the segmentation head output (the last num_classes channels) and converts it to per-pixel probability maps. Usage: analyzer = LayoutProbAnalyzer() # uses default ONNX path analyzer = LayoutProbAnalyzer("custom.onnx") # explicit path prob_map = analyzer.get_layout_prob(page) # (H_page, W_page, num_classes) N)Path) backgroundtexttitlepicturetablez list-itemz page-headerz page-footerzsection-headerfootnotecaptionformulahotc6|jdd\}}|jdk}|r|ddddtjf}tj|dz||z zdz }tj|dz||z zdz }tjtj|tjd|dz }tj|dzd|dz } tjtj|tjd|dz } tj| dzd|dz } ||z tj ddtjf} || z tj tjddf} | dddtj }|dd|ddfdddd| fd| z z|dd|ddfdddd| f| zz}|dd| ddfdddd| fd| z z|dd| ddfdddd| f| zz}|d| z z|| zz ddd}|r|dddddf}|S)ad Resize a 2-D or 3-D numpy array to (target_h, target_w) using bilinear interpolation. No external libraries required. Args: img: np.ndarray, shape (H, W) or (H, W, C) target_h: int target_w: int Returns: np.ndarray of shape (target_h, target_w) or (target_h, target_w, C), dtype float32. Ng?r?) shapendimnpnewaxisarangeclipfloorastypeint32float32 transpose)imgtarget_htarget_wsrc_hsrc_wis_2d row_coords col_coordsrow0row1col0col1drdcimg_chwtopbottomouts /Users/tedpeters/Documents/Projects/Claude Projects/Misc Tools/notename-sheets/.venv/lib/python3.11/site-packages/pymupdf/layout/onnx/LayoutProbAnalyzer.py_resize_bilinearr0&s9RaR=LE5 HME $!!!QQQ "#)H%%+0@ACGJ)H%%+0@ACGJ 728J''..rx88!UQY G GD 74!8!UQY G GD 728J''..rx88!UQY G GD 74!8!UQY G GD t  # #BJ / /2: >B t  # #BJ / / AAA >BmmAq!$$++BJ77G QQQaaaZ AAAt ,b 9 QQQaaaZ AAAt ,r 12C QQQaaaZ AAAt ,b 9 QQQaaaZ AAAt ,r 12F #( frk ) 4 4Q1 = =C !!!QQQ'l Jrc||z }|dd}tj||z }|dd}||z tjS)a Apply temperature-scaled softmax across the class axis (axis 0). The input is expected to be raw segmentation logits, which may contain negative values. No ReLU masking is applied; standard numerically stable softmax is used directly. Temperature controls the sharpness of the distribution per pixel: T < 1 : sharper (winner-takes-all strengthened) T = 1 : standard softmax T > 1 : smoother (probability spread more evenly across classes) Args: seg_logits: np.ndarray, shape (num_classes, H, W), raw logits (may be negative) temperature: float, must be > 0 (default 1.0) Returns: np.ndarray, shape (num_classes, H, W), float32, values in [0, 1], summing to 1 along axis 0 at every pixel. rT)axiskeepdims)maxrexpsumrr) seg_logits temperaturescaledsafe_maxr6denoms r/_softmax_over_classesr=Rsh*+ %Fzzq4z00Hvfx'((CwwAw--E %K   + ++r1c eZdZdZddZdZdS)LayoutProbAnalyzeraI Loads an ONNX segmentation model and produces per-class probability maps for a given PDF page. The network output is a combined tensor of internal feature channels and segmentation logit channels. Only the last num_classes channels (the segmentation head output) are used; all preceding feature channels are discarded. Per-pixel probabilities are computed via temperature-scaled softmax across the class axis, so all classes compete and probabilities sum to 1 per pixel. A higher temperature smooths the distribution (less winner-takes-all). Nrc@|5ttjj}|d}t |d|_|jdj}|d|_|d|_ ||_ t|_ dS)aF Args: onnx_path: str or None. If None, defaults to /resources/onnx/feature_imf1.onnx where is two directories above this file. temperature: float > 0 (default 1.0). Controls softmax sharpness across classes per pixel. T > 1 spreads probability more evenly (less winner-takes-all). T = 1 is standard softmax. T < 1 sharpens the distribution. Nz!/resources/onnx/feature_imf1.onnxrr) r__file__resolveparent make_session_session get_inputsr _target_h _target_w _temperature _CLASS_NAMES class_names)self onnx_pathr9 script_dir input_shapes r/__init__zLayoutProbAnalyzer.__init__s  h//118?J&IIII(D99  M4466q9? 'N'N'(r1c|}tj|jtj}||j|j|j}|j dd\}}|j ddkr|dddddf tj |dddddf tj z|dddddf tj zdz }n,|dddddf tj }t||j |j}||} } | | kr || z | | z z }n tj|tj }|tjtjddddf} |jdj} |jd| | id} t/t0}| d| dddddf}t3||j}tj|d}t|||}|S) a Compute per-class layout probability maps for a PyMuPDF page. Pipeline: 1. Render the page to a numpy array at its default resolution. 2. Convert to grayscale and resize to the model input resolution using numpy-only bilinear interpolation. 3. Normalize the grayscale image to [0, 1]. 4. Run ONNX inference; slice the last num_classes channels from the combined output tensor to obtain segmentation logits. 5. Apply temperature-scaled softmax to convert logits to per-pixel class probabilities. 6. Resize the probability map back to the original page resolution. Args: page: fitz.Page (PyMuPDF page object) Returns: np.ndarray of shape (H_page, W_page, num_classes), float32. The last axis is aligned with self.class_names. dtypeNrrArrg@)rrr) get_pixmapr frombuffersamplesuint8reshapeheightwidthnrrrr0rHrIminr5 zeros_likerrFrGnamerunlenrKr=rJr)rMpagepix bytes_datapage_imgpage_hpage_wgray gray_resizedlohinn_input input_nameraw_out n_classesr8prob_map prob_map_hwc prob_resizeds r/get_layout_probz"LayoutProbAnalyzer.get_layout_probsX.__&&]3;bh??? 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