llowcv 0.1.1

Lightweight cv2-like image processing library powered by Pillow

llowCV

A lightweight, cv2-like image processing library powered by Pillow.

OpenCV + NumPy is too heavy for edge devices and PyInstaller executables. llowCV uses only Pillow as its core dependency and mirrors cv2's API names, argument order, and coordinate system — minimizing migration cost.

import llowcv as lcv

img = lcv.imread("input.jpg")
img = lcv.resize(img, (640, 480))
img = lcv.put_text(img, "Hello World", (10, 30), font="Arial.ttf", size=24, color=(255, 255, 255))
lcv.imwrite("out.jpg", img)

Why llowCV?

	OpenCV	llowCV
Core dependency	OpenCV + NumPy	Pillow only
Install size	~100 MB	~10 MB
PyInstaller exe	Painful	Easy
Raspberry Pi / Edge	Heavy	Lightweight
cv2-compatible API	—	✅
Unicode text rendering	Requires config	Just pass a TTF path
Real-time / Video	✅	Out of scope

Installation

pip install llowcv

For Matplotlib display support:

pip install llowcv[mpl]

For NumPy / OpenCV interop:

pip install llowcv[cv2]

Color Formats

llowCV uses RGB as its internal format — the opposite of cv2's BGR.

mode	Use case	Channel order
"RGB"	Color image (default)	R, G, B
"RGBA"	Color with transparency	R, G, B, A
"L"	Grayscale	Luminance only

img = lcv.imread("input.jpg")               # → mode='RGB'
img = lcv.imread("input.png", mode="RGBA")  # → mode='RGBA' (transparency preserved)
img = lcv.imread("input.jpg", mode="L")     # → mode='L'   (grayscale)

BGR and BGRA are not directly supported. Use the interop API to exchange data with cv2:

# cv2 (BGR ndarray) → llowcv (RGB PIL.Image)
img = lcv.from_cv2(cv2_bgr_array)   # BGR → RGB conversion happens automatically

# llowcv (RGB PIL.Image) → cv2 (BGR ndarray)
arr = lcv.as_cv2(img)               # RGB → BGR conversion happens automatically

alpha_composite requires both images to be mode='RGBA'. blend and composite expect mode='RGB'.

Channel operations

# Swap R and B channels (fix a BGR-ordered PIL.Image, or convert back)
img = lcv.bgr2rgb(img)    # equivalent to cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = lcv.rgb2bgr(img)    # same operation (alias)

# Split and merge channels
r, g, b = lcv.split(img)                   # like cv2.split — each channel is mode='L'
img      = lcv.merge([r, g, b])            # like cv2.merge
img      = lcv.merge([r, g, b, a], mode="RGBA")  # works for RGBA too

# Extract a single channel
r_channel = lcv.split(img)[0]              # R channel only, mode='L'

API Reference

Image I/O

img = lcv.imread("input.jpg")               # Read as RGB
img = lcv.imread("input.jpg", mode="L")     # Read as grayscale
lcv.imwrite("out.png", img)                 # Format inferred from extension
lcv.imshow(img)                             # Open with OS default viewer
lcv.imshow(img, backend="mpl")              # Display via Matplotlib (pip install llowcv[mpl])
lcv.imshow(img, backend="mpl", block=False) # Non-blocking display (mpl only)

Transforms

img = lcv.resize(img, (640, 480))                              # Resize
img = lcv.resize(img, (640, 480), interpolation="cubic")       # With interpolation
img = lcv.resize(img, img.size, silent=True)                   # Suppress same-size warning
img = lcv.crop(img, (x, y, w, h))                              # Crop (cv2-style xywh)
img = lcv.rotate(img, 90)                                      # Rotate counter-clockwise
img = lcv.rotate(img, 45, expand=True)                         # Expand canvas to fit
img = lcv.flip(img, 0)                                         # Flip vertical
img = lcv.flip(img, 1)                                         # Flip horizontal
img = lcv.flip(img, -1)                                        # Flip both axes

Interpolation options: "nearest" / "linear" (default) / "bilinear" / "cubic" / "bicubic" / "lanczos"

Filters & Color

img = lcv.blur(img, (5, 5))               # Box blur
img = lcv.blur(img, (1, 1))               # radius=0 — no-op, raises UserWarning
img = lcv.blur(img, (1, 1), silent=True)  # Suppress the warning
img = lcv.sharpen(img, amount=1.5)        # Sharpen (1.0 = no change)
img = lcv.to_gray(img)                    # Convert to grayscale (mode='L')

Text Drawing

img = lcv.put_text(
    img,
    text="Hello, World!",
    org=(10, 40),              # Bottom-left origin (cv2-compatible)
    font="path/to/font.ttf",
    size=32,
    color=(255, 255, 255),
)

org uses the same bottom-left origin as cv2.putText. font accepts any TTF or OTF file path — Unicode text (CJK, emoji, etc.) works out of the box.

Compositing

out = lcv.blend(img1, img2, alpha=0.5)        # Alpha blend (0.0=img1, 1.0=img2)
out = lcv.composite(img1, img2, mask)          # Mask composite (mask=0→img1, 255→img2)
out = lcv.alpha_composite(dst, src)            # RGBA alpha compositing

NumPy / OpenCV Interop (opt-in)

Available after pip install llowcv[cv2]:

arr = lcv.as_cv2(img)    # PIL.Image (RGB) → ndarray (BGR)
img = lcv.from_cv2(arr)  # ndarray (BGR) → PIL.Image (RGB)
arr = lcv.as_numpy(img)  # PIL.Image → RGB ndarray
arr = lcv.to_bgr(arr)    # RGB ndarray → BGR ndarray

Camera

with lcv.Camera(index=0) as cam:
    frame = cam.capture()          # Returns PIL.Image (RGB)
    lcv.imwrite("frame.jpg", frame)

Uses Windows Media Foundation on Windows and v4l2 on Linux.

Global Configuration

# Suppress all no-op warnings at once
lcv.config.warn_noop = False

# Or control via environment variable (0 / false / no / off to disable)
# LLOWCV_WARN_NOOP=0 python main.py

When warn_noop is enabled, a UserWarning is raised for:

• resize: dsize matches the input image size
• blur: ksize results in no blurring (e.g. (1, 1))
• imshow: block=False used with the pillow backend

Design Principles

• Pillow-only core — NumPy and OpenCV are strictly opt-in
• Immutable returns — every API returns a new PIL.Image; no in-place mutation, no thread-safety concerns
• cv2-compatible API — same names, argument order, and coordinate conventions as OpenCV
• No Video / GUI / ML — for real-time processing, use OpenCV

Migrating from cv2

# Before (cv2)
import cv2
img = cv2.imread("input.jpg")
img = cv2.resize(img, (640, 480))
cv2.imwrite("out.jpg", img)

# After (llowcv)
import llowcv as lcv
img = lcv.imread("input.jpg")
img = lcv.resize(img, (640, 480))
lcv.imwrite("out.jpg", img)

Key differences:

	cv2	llowcv
Image type	numpy.ndarray (BGR)	PIL.Image (RGB)
In-place ops	Common	None — always returns new image
waitKey / GUI loop	Required	Not needed

Requirements

• Python >= 3.10
• Pillow (core)
• matplotlib ([mpl] optional)
• numpy + opencv-python ([cv2] optional)
• numpy + opencv-python ([cv2] extra, optional)

License

MIT License