Image Pre-processing for OCR / Template Matching

locate_text / ocr_read_structure and match_template feed the raw screen capture straight to the OCR engine or the matcher. Small UI text, dark themes, low contrast and a slightly rotated screenshot wreck both — and there was no preprocessing seam anywhere in the framework. This adds the standard pre-step pipeline — grayscale → upscale → binarize → deskew → denoise → CLAHE contrast — that multiplies the accuracy of the OCR and matching features you already use.

Every function runs on an injectable haystack image (ndarray / path / PIL, default: grab the screen / region) and returns a NumPy ndarray, so it is unit-testable on synthetic arrays. OpenCV + NumPy come in via je_open_cv. Imports no PySide6.

Headless API

from je_auto_control import preprocess_image, binarize, deskew, upscale

# One-shot pipeline, then OCR the cleaned image.
clean = preprocess_image("receipt.png", steps=("grayscale", "upscale",
                                                "deskew", "binarize"), scale=2.0)

# Or compose the individual steps.
bw = binarize("panel.png", method="adaptive_gaussian", block_size=41)
straight = deskew("scan.png", max_angle=10.0)
big = upscale("tiny_label.png", scale=3.0, interp="lanczos")

The building blocks are to_grayscale, upscale (scale / interp), binarize (method = otsu / adaptive_mean / adaptive_gaussian), denoise, enhance_contrast (CLAHE), deskew and detect_skew_angle (returns the measured text-skew in degrees, clamped to ±max_angle). preprocess_image chains any of the named steps — grayscale, upscale, binarize, denoise, deskew, contrast — in order; unknown step names raise ValueError.

Executor command

AC_preprocess_image runs the pipeline and writes the result to output_path (so it is usable from JSON / MCP / the builder): source is an image path (default: screen grab of region), steps an ordered list (or comma string), plus scale / block_size / c; it returns {path, width, height}. It is exposed as the MCP tool ac_preprocess_image and as a Script Builder command under Image.