816ba43cd6
- engine/pipeline/preprocessing.py - engine/tests/test_preprocessing.py GSD-Task: S01/T02
130 lines
3.7 KiB
Python
130 lines
3.7 KiB
Python
"""OpenCV preprocessing pipeline for raster-to-vector conversion."""
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import cv2
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import numpy as np
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def decode_image(raw_bytes: bytes) -> np.ndarray:
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"""Decode raw image bytes into a BGR numpy array."""
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buf = np.frombuffer(raw_bytes, dtype=np.uint8)
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img = cv2.imdecode(buf, cv2.IMREAD_COLOR)
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if img is None:
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raise ValueError("Failed to decode image from provided bytes")
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return img
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def to_grayscale(img: np.ndarray) -> np.ndarray:
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"""Convert BGR image to single-channel grayscale."""
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if len(img.shape) == 2:
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return img
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return cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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def denoise(
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img: np.ndarray,
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d: int = 9,
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sigma_color: float = 75.0,
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sigma_space: float = 75.0,
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) -> np.ndarray:
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"""Apply bilateral filter for edge-preserving denoising."""
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return cv2.bilateralFilter(img, d, sigma_color, sigma_space)
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def enhance_contrast(
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img: np.ndarray,
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clip_limit: float = 2.0,
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tile_grid_size: tuple[int, int] = (8, 8),
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) -> np.ndarray:
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"""Apply CLAHE contrast enhancement."""
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clahe = cv2.createCLAHE(clipLimit=clip_limit, tileGridSize=tile_grid_size)
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return clahe.apply(img)
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def threshold(
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img: np.ndarray,
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manual_thresh: int | None = None,
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) -> np.ndarray:
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"""Apply thresholding — Otsu auto by default, manual override if provided."""
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if manual_thresh is not None:
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_, result = cv2.threshold(img, manual_thresh, 255, cv2.THRESH_BINARY)
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else:
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_, result = cv2.threshold(img, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
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return result
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def edge_detect(
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img: np.ndarray,
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low: int = 50,
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high: int = 150,
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) -> np.ndarray:
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"""Apply Canny edge detection."""
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return cv2.Canny(img, low, high)
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def morphological_ops(
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img: np.ndarray,
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kernel_size: int = 3,
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dilate_iterations: int = 1,
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erode_iterations: int = 1,
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) -> np.ndarray:
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"""Apply dilation then erosion (closing-style) to clean up binary image."""
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kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (kernel_size, kernel_size))
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result = cv2.dilate(img, kernel, iterations=dilate_iterations)
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result = cv2.erode(result, kernel, iterations=erode_iterations)
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return result
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def preprocess(
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raw_bytes: bytes,
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params: dict | None = None,
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) -> np.ndarray:
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"""Run the full preprocessing pipeline on raw image bytes.
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Stages: decode → grayscale → denoise → contrast → threshold → morphological ops.
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Edge detection is optional (enabled via params["edge_detect"] = True).
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All stage parameters are tunable via the params dict. Keys:
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denoise_d, denoise_sigma_color, denoise_sigma_space,
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clahe_clip_limit, clahe_tile_grid_size,
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threshold_manual,
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edge_detect (bool), edge_low, edge_high,
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morph_kernel_size, morph_dilate_iterations, morph_erode_iterations
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"""
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p = params or {}
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img = decode_image(raw_bytes)
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img = to_grayscale(img)
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img = denoise(
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img,
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d=p.get("denoise_d", 9),
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sigma_color=p.get("denoise_sigma_color", 75.0),
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sigma_space=p.get("denoise_sigma_space", 75.0),
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)
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img = enhance_contrast(
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img,
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clip_limit=p.get("clahe_clip_limit", 2.0),
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tile_grid_size=p.get("clahe_tile_grid_size", (8, 8)),
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)
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img = threshold(
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img,
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manual_thresh=p.get("threshold_manual"),
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)
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if p.get("edge_detect", False):
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img = edge_detect(
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img,
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low=p.get("edge_low", 50),
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high=p.get("edge_high", 150),
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)
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img = morphological_ops(
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img,
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kernel_size=p.get("morph_kernel_size", 3),
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dilate_iterations=p.get("morph_dilate_iterations", 1),
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erode_iterations=p.get("morph_erode_iterations", 1),
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)
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return img
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