Python CV ecosystem (OpenCV, PIL, torchvision) in Computer Vision - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to read an image using OpenCV.

Computer Vision

import cv2
image = cv2.[1]('image.jpg')

Drag options to blanks, or click blank then click option'

Aimread

Bread

Copen

Dload

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'cv2.read' instead of 'cv2.imread'.

Trying to use 'open' which is a Python built-in, not OpenCV.

The function cv2.imread is used to read an image file into an array.

2fill in blank

medium

Complete the code to convert a PIL image to a tensor using torchvision.

Computer Vision

from torchvision import transforms
transform = transforms.ToTensor()
tensor_image = transform([1])

Drag options to blanks, or click blank then click option'

Anumpy_array

Bimage_tensor

Ccv_image

Dpil_image

Attempts:

3 left

💡 Hint

Common Mistakes

Passing a numpy array instead of a PIL image.

Passing an OpenCV image which is a numpy array with BGR channels.

The ToTensor() transform converts a PIL Image or numpy.ndarray to a tensor. Here, the input must be a PIL image.

3fill in blank

hard

Fix the error in the code to convert an OpenCV image to RGB format.

Computer Vision

import cv2
image_bgr = cv2.imread('image.jpg')
image_rgb = cv2.[1](image_bgr, cv2.COLOR_BGR2RGB)

Drag options to blanks, or click blank then click option'

AconvertColor

BcvtColor

CchangeColor

DtransformColor

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'convertColor' which does not exist in OpenCV.

Using 'transformColor' which is invalid.

The correct OpenCV function to convert color spaces is cvtColor.

4fill in blank

hard

Fill both blanks to create a PIL image from a numpy array and save it.

Computer Vision

from PIL import Image
import numpy as np
array = np.zeros((100, 100, 3), dtype=np.uint8)
img = Image.[1](array)
img.[2]('output.png')

Drag options to blanks, or click blank then click option'

Afromarray

Bsave

Cshow

Dopen

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'Image.open' instead of 'Image.fromarray'.

Using 'img.show()' instead of 'img.save()' to save the image.

Image.fromarray creates a PIL image from a numpy array. save saves the image to a file.

5fill in blank

hard

Fill all three blanks to normalize a tensor image using torchvision transforms.

Computer Vision

from torchvision import transforms
normalize = transforms.Normalize(mean=[[1]], std=[[2]])
transform = transforms.Compose([transforms.ToTensor(), normalize])
# Apply transform to PIL image
normalized_tensor = transform(pil_image)
print(normalized_tensor.mean().item() [3] 0)

Drag options to blanks, or click blank then click option'

A0.485, 0.456, 0.406

B0.229, 0.224, 0.225

C>

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Swapping mean and std values.

Using '<' instead of '>' in the print statement.

The standard ImageNet mean and std values are used for normalization. After normalization, the mean of the tensor should be greater than 0.

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