Essential Python Libraries for Computer Vision and Video Processing

Essential Python Libraries for Computer Vision and Video Processing

Python has become a leading language for machine learning and also research in computer vision and video processing due to its versatility and extensive ecosystem of powerful libraries. I believe that the main reason for Python's popularity in computer vision applications is the existence of machine learning frameworks like TensorFlow or PyTorch. My gut feeling still tends to assign computer vision more toward high-performing languages like C and C++, but Python takes more space, especially during the prototyping phase. Then if the business use case makes sense and there is a clear demand to lower the cost of production. The production can be implemented in high-performance languages and specific inference engines. 

Below is an organized overview of the most popular libraries categorized by practical use cases, accompanied by module highlights, typical applications, and illustrative code examples. All you need to start prototyping your next computer vision project.

Complete List of Computer Vision Modules

• OpenCV – A powerful open-source library for real-time computer vision, providing image processing, object detection, and video analysis capabilities.

• Scikit-Image – A collection of image processing algorithms for scientific applications, focusing on filtering, segmentation, and morphology.

• PyTorch – A deep learning framework widely used for developing and training machine learning models for image classification, object detection, and segmentation.

• MoviePy – A video editing library for Python that enables cutting, concatenating, and applying effects to video clips.

• FFmpeg-Python – A wrapper for FFmpeg, enabling video transcoding, streaming, and processing through Python scripting.

• MediaPipe – A framework for real-time perception AI, supporting hand tracking, face detection, and pose estimation.

• Pillow – A Python Imaging Library (PIL) fork that provides image opening, manipulation, and conversion capabilities.

• Matplotlib – A visualization library that allows for rendering images and plotting graphs, commonly used in image analysis.

• NumPy – A numerical computing library that provides multi-dimensional array operations, essential for image processing and matrix computations.

• SciPy – A scientific computing library that extends NumPy, offering additional functionality for image filtering and mathematical transformations.

1. Image Processing & Basic Computer Vision

OpenCV (Open Source Computer Vision Library)

Core Module Overview:

• core – Basic functions (data structures, arithmetic operations).
• imgproc – Image processing (filtering, transformations).
• highgui – GUI window handling and video I/O.
• video – Motion analysis and tracking.
• calib3d – Camera calibration and 3D reconstruction.
• features2d – Feature detection and description (SIFT, ORB).
• objdetect – Object detection (face, eyes, pedestrians).
• ml – Machine learning algorithms.
• photo – Computational photography (denoising, HDR).
• videoio – Video input/output operations.
• dnn – Deep neural networks module for inference.
• text – Scene text detection and recognition.

• Real-time object detection.
• Image transformations and enhancements.
• Video IO and streaming
• Video processing.

Example:

import cv2

image = cv2.imread('image.jpg')
gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
cv2.imshow('Gray Image', gray_image)
cv2.waitKey(0)
cv2.destroyAllWindows()

2. Advanced Image Analysis and ML Integration

Scikit-Image

Module Overview:

• Image filtering, segmentation, morphology.
• Feature extraction.

Use Cases:

• Medical imaging.
• Scientific image analysis.

Example:

from skimage import io, filters

image = io.imread('cells.png', as_gray=True)
edges = filters.sobel(image)
io.imshow(edges)
io.show()

PyTorch

Module Overview:

• Deep learning framework for computer vision.
• Provides pre-trained models and easy integration for custom models.

Use Cases:

• Image classification.
• Object detection and segmentation.

Example:

import torch
import torchvision.transforms as transforms
from torchvision.models.detection import fasterrcnn_resnet50_fpn
from PIL import Image, ImageDraw, ImageFont

# Load pre-trained object detection model
model = fasterrcnn_resnet50_fpn(pretrained=True)
model.eval()

# Load and preprocess the image
img = Image.open('image.jpg')
transform = transforms.Compose([
    transforms.ToTensor()
])
input_tensor = transform(img).unsqueeze(0)

# Perform inference
with torch.no_grad():
    outputs = model(input_tensor)

# Extract detected bounding boxes and labels
labels = outputs[0]['labels'].numpy()
scores = outputs[0]['scores'].numpy()
bboxes = outputs[0]['boxes'].numpy()

# Define label map (COCO dataset labels)
from torchvision.models.detection.anchors import AnchorGenerator
LABELS = {1: 'person', 2: 'bicycle', 3: 'car', 4: 'motorcycle', 5: 'airplane', 6: 'bus', 7: 'train', 8: 'truck', 9: 'boat'}

# Draw bounding boxes on the image
draw = ImageDraw.Draw(img)
font = ImageFont.load_default()
for i in range(len(labels)):
    if scores[i] > 0.5:  # Filter based on confidence threshold
        label = LABELS.get(labels[i], 'Unknown')
        box = bboxes[i]
        draw.rectangle([box[0], box[1], box[2], box[3]], outline='red', width=3)
        draw.text((box[0], box[1] - 10), label, fill='red', font=font)

# Show the image with detections
img.show()

3. Real-Time Video and Stream Processing

MoviePy

Module Overview:

• Video editing, compositing, effects.

Use Cases:

• Automating video editing tasks.
• Dynamic content creation.

Example:

from moviepy.editor import VideoFileClip

clip = VideoFileClip('video.mp4').subclip(10, 20)
clip.preview()

FFmpeg-Python

Module Overview:

• Bindings to FFmpeg for video encoding/decoding.

Use Cases:

• Transcoding and streaming.
• Batch video processing.

Example:

import ffmpeg

ffmpeg.input('video.mp4').output('output.avi').run()

4. Specialized Vision Tasks

MediaPipe

Module Overview:

• Real-time tracking (hands, face, pose).

Use Cases:

• Gesture recognition.
• Augmented reality.

Example:

import cv2
import mediapipe as mp

mp_drawing = mp.solutions.drawing_utils
mp_pose = mp.solutions.pose

with mp_pose.Pose() as pose:
    img = cv2.imread('c:/pose/2.jpg')
    results = pose.process(img)
    mp_drawing.draw_landmarks(img, results.pose_landmarks, mp_pose.POSE_CONNECTIONS)
    cv2.imshow('Pose Tracking', img)
    cv2.waitKey(0)

Conclusion

Python's extensive set of libraries provides powerful and flexible tools for diverse computer vision and video delivery tasks, ranging from basic image processing to sophisticated deep learning models and real-time interactive applications. By leveraging these libraries, developers and researchers can efficiently build, experiment, and deploy advanced visual computing applications.

Let me know your next computer vision project.