Python CV ecosystem (OpenCV, PIL, torchvision) in Computer Vision - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is OpenCV used for in Python?

OpenCV is a library used for computer vision tasks like image and video processing, object detection, and feature extraction. It helps computers understand visual data.

beginner

What does PIL stand for and what is its main purpose?

PIL stands for Python Imaging Library. It is mainly used for opening, manipulating, and saving many different image file formats easily.

intermediate

What is torchvision and how does it relate to PyTorch?

Torchvision is a package that works with PyTorch. It provides tools to load popular image datasets, pre-trained models, and image transformations to help build computer vision models.

intermediate

How does OpenCV differ from PIL in handling images?

OpenCV focuses on advanced computer vision tasks and works with images as arrays for fast processing. PIL is simpler and mainly used for basic image editing and format conversions.

intermediate

Name one common use case for torchvision in machine learning projects.

A common use case is loading and transforming datasets like CIFAR-10 or ImageNet, and using pre-trained models like ResNet to speed up training and improve accuracy.

Which library is best suited for real-time video processing in Python?

A OpenCV

B PIL

C torchvision

D NumPy

OpenCV is designed for real-time image and video processing, making it ideal for video tasks.

What is a primary feature of PIL?

A Loading pre-trained deep learning models

B Basic image editing and format conversion

C Video stream processing

D 3D image reconstruction

PIL is mainly used for simple image editing and converting between image formats.

Which package provides pre-trained models for image classification?

A OpenCV

B Matplotlib

C PIL

D torchvision

Torchvision offers pre-trained models like ResNet and VGG for image classification.

How does OpenCV represent images internally?

A As NumPy arrays

B As PIL Image objects

C As PyTorch tensors

D As JSON files

OpenCV uses NumPy arrays to represent images for fast numerical operations.

Which library would you use to apply transformations like random cropping or flipping to images in a PyTorch project?

A OpenCV

B PIL

C torchvision.transforms

D scikit-learn

Torchvision.transforms provides easy-to-use image transformation functions for PyTorch.

Explain the roles of OpenCV, PIL, and torchvision in the Python computer vision ecosystem.

Think about what each library is mainly used for and how they complement each other.

Describe how you would choose between OpenCV, PIL, and torchvision for a new computer vision project.

Consider the project needs: speed, complexity, or deep learning integration.

Practice

1. Which Python library is best known for fast image and video processing tasks?

easy

A. PIL (Pillow)

B. OpenCV

C. torchvision

D. matplotlib

Solution

1. Step 1: Understand library purposes

   OpenCV is designed for fast image and video processing, widely used in computer vision.

2. Step 2: Compare with other libraries

   PIL is mainly for image editing, torchvision is for ML image datasets, matplotlib is for plotting.

3. Final Answer:

   OpenCV -> Option B

4. Quick Check:

   Fast image/video processing = OpenCV [OK]

Hint: OpenCV = fast image/video tasks, PIL = editing, torchvision = ML prep [OK]

Common Mistakes:

• Confusing PIL as the fastest for video processing
• Thinking torchvision handles video processing
• Assuming matplotlib is for image processing

2. Which of the following is the correct way to read an image using OpenCV in Python?

easy

A. img = cv2.imread('image.jpg')

B. img = Image.open('image.jpg')

C. img = torchvision.io.read_image('image.jpg')

D. img = plt.imread('image.jpg')

Solution

1. Step 1: Identify OpenCV image reading syntax

   OpenCV uses cv2.imread() to load images from files.

2. Step 2: Differentiate from other libraries

   PIL uses Image.open(), torchvision uses torchvision.io.read_image(), matplotlib uses plt.imread().

3. Final Answer:

   img = cv2.imread('image.jpg') -> Option A

4. Quick Check:

   OpenCV image read = cv2.imread() [OK]

Hint: OpenCV reads images with cv2.imread() [OK]

Common Mistakes:

• Using Image.open() which is from PIL, not OpenCV
• Using plt.imread() which is for plotting, not OpenCV
• Confusing torchvision's read_image with OpenCV

3. What will be the shape and color format of the image loaded by this OpenCV code?

import cv2
img = cv2.imread('image.jpg')
print(img.shape)

medium

A. (height, width, 3) with RGB color order

B. (width, height, 3) with BGR color order

C. (width, height, 3) with RGB color order

D. (height, width, 3) with BGR color order

Solution

1. Step 1: Understand OpenCV image shape

   OpenCV loads images as NumPy arrays with shape (height, width, channels).

2. Step 2: Know OpenCV color format

   OpenCV uses BGR color order by default, not RGB.

3. Final Answer:

   (height, width, 3) with BGR color order -> Option D

4. Quick Check:

   OpenCV shape = (H, W, 3), color = BGR [OK]

Hint: OpenCV images: shape (H,W,3), color BGR [OK]

Common Mistakes:

• Assuming RGB color order instead of BGR
• Swapping width and height in shape
• Thinking OpenCV loads grayscale by default

4. This code tries to convert a PIL image to a NumPy array for OpenCV processing but causes an error:

from PIL import Image
import numpy as np
img_pil = Image.open('image.jpg')
img_cv = np.array(img_pil)

What is the likely cause and fix?

medium

A. PIL image must be converted to grayscale first

B. Color channels are in wrong order; convert RGB to BGR after np.array()

C. Use img_pil.convert('RGB') before np.array() to ensure 3 channels

D. No error; code works fine as is

Solution

1. Step 1: Identify PIL image mode issue

   PIL images may not be in RGB mode by default; could be 'P' or 'L' mode causing np.array to have unexpected shape.

2. Step 2: Fix by converting to RGB mode

   Use img_pil.convert('RGB') to ensure 3 color channels before converting to NumPy array.

3. Final Answer:

   Use img_pil.convert('RGB') before np.array() to ensure 3 channels -> Option C

4. Quick Check:

   PIL to NumPy needs RGB mode [OK]

Hint: Convert PIL image to RGB before np.array() [OK]

Common Mistakes:

• Assuming np.array always works without convert()
• Ignoring color channel order differences
• Trying to convert to grayscale unnecessarily

5. You want to prepare an image for a PyTorch model using torchvision transforms. Which sequence correctly converts a PIL image to a tensor normalized for pretrained models?

hard

A. Use torchvision.transforms.ToTensor() then torchvision.transforms.Normalize(mean, std)

B. Use cv2.imread() then convert to tensor manually

C. Use PIL.Image.open() then convert to NumPy array and normalize manually

D. Use torchvision.transforms.Normalize() only on PIL image

Solution

1. Step 1: Understand torchvision transform pipeline

   To prepare images for PyTorch models, convert PIL image to tensor with ToTensor(), which scales pixels to [0,1].

2. Step 2: Normalize tensor with mean and std

   Use Normalize() with pretrained model's mean and std to standardize input.

3. Final Answer:

   Use torchvision.transforms.ToTensor() then torchvision.transforms.Normalize(mean, std) -> Option A

4. Quick Check:

   ToTensor + Normalize = correct PyTorch prep [OK]

Hint: Use ToTensor() then Normalize() for PyTorch image prep [OK]

Common Mistakes:

• Trying to normalize PIL images directly
• Using OpenCV images without conversion
• Skipping normalization step