sdimg 0.2.2

Small, function-based image and mask processing library built on numpy

sdimg

Small, function-based image and mask processing library built on numpy.ndarray.

Install

pip install sdimg

Modules

• sdimg.image: hist_norm, clahe_norm, minmax_norm, zscore_norm, gaussian_blur, median_blur, denoise, sharpen, adjust_brightness_contrast, to_gray, to_rgb, to_uint8, is_image
• sdimg.mask: morphology, convex_hull, concave_hull, extract_edge, distance_transform, pick_largest, fill_holes, get_box_from_mask, get_box_from_coords, get_coords, get_centroid, get_roi_size, get_box_size, to_roi_box, to_mask, is_mask
• sdimg.spatial: resize, resize_keep_ratio, crop, pad_to_square, rotate, flip, split, merge
• sdimg.fusion: otsu_threshold, grabcut

Core Contracts

• Input arrays must be numpy.ndarray
• Images: shape (H, W) or (H, W, C) with C in 1..4
• Color channel order: RGB. Color images passed to sdimg must be in RGB order. cv2.imread returns BGR — callers using OpenCV I/O must convert with cv2.cvtColor(img, cv2.COLOR_BGR2RGB) before calling sdimg functions.
• Channel-count semantics:
  • C == 1: grayscale
  • C == 2: grayscale + alpha (alpha is ignored by to_gray/to_rgb)
  • C == 3: RGB
  • C == 4: RGBA (alpha is ignored by to_gray/to_rgb)
• Masks: shape (H, W), binary values (bool, {0,1}, {0,255})
• Output images are np.uint8
• Output masks are binary np.uint8 in {0, 1}
• BBox format: (wmin, hmin, wmax, hmax)
• Empty-mask returns None for:
  • to_roi_box
  • get_box_from_mask
  • get_box_from_coords
  • get_centroid

Error Policy

• TypeError: wrong input type (non-numpy.ndarray)
• ValueError: invalid shape, invalid params, invalid mask values, invalid bbox
• RuntimeError: wrapped lower-level failures (cv2, internal processing)

Quick Example

import numpy as np
from sdimg.image import hist_norm, gaussian_blur
from sdimg.mask import morphology, to_roi_box
from sdimg.fusion import grabcut

image = np.random.randint(0, 256, (128, 128, 3), dtype=np.uint8)
mask = np.zeros((128, 128), dtype=np.uint8)
mask[32:96, 40:88] = 1

image = hist_norm(image)
image = gaussian_blur(image, (5, 5), 1.2)
mask = morphology(mask, "open", (3, 3), 1)

roi_box = to_roi_box(mask)
if roi_box is not None:
    refined = grabcut(image=image, roi=roi_box["roi"], box=roi_box["box"])