Top 10 Python Libraries Every Computer Vision Engineer Should Know

1. PIL/Pillow

Pillow is a user‑friendly Python library that supports opening, manipulating, and saving many image formats, offering basic operations such as cropping, resizing, rotating, color changes, and the ability to add text or shapes, making it a convenient choice for image preprocessing in computer‑vision pipelines.

2. OpenCV (Open Source Computer Vision Library)

OpenCV, originally developed by Intel, is the most popular image‑processing library, providing a vast collection of algorithms for vision and machine‑learning tasks, optimized for real‑time applications like video surveillance, autonomous driving, and robotics. It is faster than Pillow for many operations, but reads images in BGR order, requiring conversion to RGB when mixed with other libraries.

cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

3. Mahotas

Mahotas offers a set of high‑performance image‑processing functions implemented in C++ with multithreading, delivering speed comparable to OpenCV while focusing on core image‑analysis tasks such as morphological operations, binary segmentation, and denoising.

Example

The following example uses Mahotas' demo image, computes an Otsu threshold, labels the binary mask, and applies a seeded watershed segmentation.

# import using ``mh`` abbreviation which is common:
import mahotas as mh

# Load one of the demo images
im = mh.demos.load('nuclear')

# Automatically compute a threshold
T_otsu = mh.thresholding.otsu(im)

# Label the thresholded image (thresholding is done with numpy operations)
seeds, nr_regions = mh.label(im > T_otsu)

# Call seeded watershed to expand the threshold
labeled = mh.cwatershed(im.max() - im, seeds)

A simple distance‑map example demonstrates Mahotas' ability to compute distance transforms on binary masks.

import pylab as p
import numpy as np
import mahotas as mh

f = np.ones((256, 256), bool)
f[200:, 240:] = False
f[128:144, 32:48] = False

dmap = mh.distance(f)
p.imshow(dmap)
p.show()

4. Scikit‑Image

Built on top of NumPy, SciPy, and scikit‑learn, scikit‑image provides a comprehensive suite of algorithms for image segmentation, geometric transformations, color‑space conversions, and filtering, with native support for multi‑dimensional data useful in video or medical imaging.

from skimage import data, io, filters
image = data.coins()
edges = filters.sobel(image)
io.imshow(edges)
io.show()

5. TensorFlow Image

TensorFlow Image is a TensorFlow module that handles image decoding, encoding, cropping, resizing, and format conversion, leveraging GPU acceleration for large datasets. It integrates with Keras utilities such as tf.keras.utils.image_dataset_from_directory to build training pipelines.

batch_size = 32
img_height = 180
img_width = 180

train_ds = tf.keras.utils.image_dataset_from_directory(
    data_dir,
    validation_split=0.2,
    subset="training",
    seed=123,
    image_size=(img_height, img_width),
    batch_size=batch_size)

6. PyTorch Vision

PyTorch Vision is the image‑processing companion of the PyTorch ecosystem, offering datasets, transforms, and utilities for video handling.

import torchvision
video_path = "path to a test video"
reader = torchvision.io.VideoReader(video_path, "video")
reader_md = reader.get_metadata()
print(reader_md["video"]["fps"])
reader.set_current_stream("video:0")

7. SimpleCV

SimpleCV abstracts OpenCV, PIL, and NumPy behind a beginner‑friendly API, allowing rapid prototyping of common vision tasks. However, the project receives limited maintenance and may become obsolete.

import SimpleCV
camera = SimpleCV.Camera()
image = camera.getImage()
image.show()

8. Imageio

Imageio provides a simple API for reading and writing a wide range of image and video formats, supporting NumPy arrays, PIL images, or raw byte strings, and includes convenient frame‑by‑frame video handling.

import imageio.v3 as iio
im = iio.imread('imageio:chelsea.png')  # read a standard image
print(im.shape)  # (300, 451, 3)
iio.imwrite('chelsea.jpg', im)  # convert to jpg

9. Albumentations

Albumentations is a fast, flexible library for image and mask augmentation, widely used in deep‑learning pipelines. It supports random crops, flips, brightness/contrast adjustments, and works seamlessly with OpenCV‑loaded images.

import albumentations as A
import cv2

transform = A.Compose([
    A.RandomCrop(width=256, height=256),
    A.HorizontalFlip(p=0.5),
    A.RandomBrightnessContrast(p=0.2),
])

image = cv2.imread("image.jpg")
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
transformed = transform(image=image)
transformed_image = transformed["image"]

10. timm

timm is a PyTorch model library that supplies a large collection of pretrained vision models, such as ResNet, EfficientNet, and many recent architectures. It is now a Hugging Face sub‑project, ensuring ongoing support.

import timm
import torch
model = timm.create_model('resnet34')
x = torch.randn(1, 3, 224, 224)
print(model(x).shape)

Conclusion

Whether you are just starting with basic image manipulation or exploring advanced deep‑learning models, these libraries collectively provide the essential tools for a wide range of computer‑vision tasks.