Cropping images in Computer Vision - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does cropping an image mean?

Cropping an image means cutting out a part of the image to keep only the important area and remove the rest.

beginner

Why do we crop images in machine learning?

We crop images to focus on important parts, reduce noise, and make the data smaller and easier for models to learn from.

beginner

Which coordinates define the crop area in an image?

The crop area is defined by the top-left corner (x, y) and the width and height of the rectangle to keep.

intermediate

How does cropping affect the training of a computer vision model?

Cropping can improve training by removing irrelevant parts, helping the model focus on key features and reducing computation time.

beginner

What is a common Python library used for cropping images?

Pillow (PIL) is a common Python library used to crop images easily with simple commands.

What does cropping an image do?

A Changes the colors of the image

B Cuts out a part of the image to keep

C Blurs the image

D Rotates the image

Cropping means cutting out a part of the image to keep only the important area.

Which of these is needed to crop an image?

A Number of colors

B Image brightness

C Image file format

D Top-left corner coordinates and size

Cropping requires the position (top-left corner) and the size (width and height) of the area to keep.

Why crop images before training a model?

A To focus on important parts and reduce noise

B To add more colors

C To increase image size

D To change file format

Cropping helps the model by focusing on important parts and removing irrelevant areas.

Which Python library is commonly used for cropping images?

A Matplotlib

B NumPy

C Pillow

D Scikit-learn

Pillow is a popular library for image processing including cropping.

What happens if you crop an image too small?

A You might lose important details

B The image becomes brighter

C The image rotates

D The image file size increases

Cropping too small can remove important parts needed for analysis.

Explain how cropping images can help improve machine learning model training.

Think about what parts of the image the model really needs.

Describe the steps and information needed to crop an image programmatically.

Consider what details define the crop and how to apply it.

Practice

1. What does cropping an image do in computer vision?

easy

A. Increases the image resolution

B. Changes the color of the entire image

C. Cuts out a part of the image using row and column ranges

D. Rotates the image by 90 degrees

Solution

1. Step 1: Understand cropping concept

   Cropping means selecting a smaller part of the image by specifying rows and columns.

2. Step 2: Compare options with definition

   Only Cuts out a part of the image using row and column ranges describes cutting out part of the image using row and column ranges.

3. Final Answer:

   Cuts out a part of the image using row and column ranges -> Option C

4. Quick Check:

   Cropping = cutting part of image [OK]

Hint: Cropping means cutting out part of the image [OK]

Common Mistakes:

• Confusing cropping with resizing
• Thinking cropping changes colors
• Mixing cropping with rotation

2. Which of the following is the correct syntax to crop an image stored in variable img to rows 10 to 50 and columns 20 to 70 in Python?

easy

A. img[10:50, 20:70]

B. img[20:70, 10:50]

C. img[10:50][20:70]

D. img.crop(10,50,20,70)

Solution

1. Step 1: Recall slicing syntax for images

   Images are sliced as img[row_start:row_end, col_start:col_end].

2. Step 2: Match given ranges to syntax

   Rows 10 to 50 and columns 20 to 70 means img[10:50, 20:70].

3. Final Answer:

   img[10:50, 20:70] -> Option A

4. Quick Check:

   Rows first, columns second in slicing [OK]

Hint: Remember slicing is img[row_start:row_end, col_start:col_end] [OK]

Common Mistakes:

• Swapping row and column indices
• Using double brackets instead of comma
• Using a non-existent crop method

3. Given the code:

import numpy as np
img = np.arange(100).reshape(10,10)
cropped = img[2:5, 3:7]
print(cropped)

What is the output?

medium

A. [[3 4 5 6]
[13 14 15 16]
[23 24 25 26]]

B. [[23 24 25 26]
[33 34 35 36]
[43 44 45 46]]

C. [[23 24 25 26 27]
[33 34 35 36 37]
[43 44 45 46 47]]

D. [[32 33 34 35]
[42 43 44 45]
[52 53 54 55]]

Solution

1. Step 1: Understand the image array

   img is a 10x10 array with values from 0 to 99 arranged row-wise.

2. Step 2: Extract rows 2 to 4 and columns 3 to 6

   Rows 2,3,4 correspond to indices 2,3,4; columns 3,4,5,6 correspond to indices 3 to 6 exclusive of 7.

3. Step 3: Identify values in cropped

   Row 2: values 20 to 29, columns 3 to 6 are 23,24,25,26
   Row 3: 33,34,35,36
   Row 4: 43,44,45,46

4. Final Answer:

   [[23 24 25 26] [33 34 35 36] [43 44 45 46]] -> Option B

5. Quick Check:

   Slice rows 2-5 and cols 3-7 gives these values [OK]

Hint: Check array shape and slicing ranges carefully [OK]

Common Mistakes:

• Confusing row and column indices
• Including end index in slice
• Misreading array reshape order

4. You try to crop an image using cropped = img[50:100, 30:60] but get an IndexError. What is the likely cause?

medium

A. The image variable is not defined

B. The slicing syntax is incorrect

C. The image is grayscale, not color

D. The image has fewer than 100 rows

Solution

1. Step 1: Understand IndexError cause

   IndexError occurs when slicing beyond array dimensions.

2. Step 2: Analyze slicing indices

   Rows 50 to 100 means accessing rows starting at 50. If image has fewer rows, this causes error.

3. Final Answer:

   The image has fewer than 100 rows -> Option D

4. Quick Check:

   IndexError = slicing outside image size [OK]

Hint: Check image shape before slicing [OK]

Common Mistakes:

• Assuming syntax error causes IndexError
• Confusing color channels with rows
• Not checking if variable is defined

5. You have a 200x200 image and want to crop a centered square of size 100x100. Which code correctly crops this center square?

hard

A. img[50:150, 50:150]

B. img[0:100, 0:100]

C. img[100:200, 100:200]

D. img[25:125, 25:125]

Solution

1. Step 1: Calculate center start and end indices

   Center of 200x200 is at 100,100. Half of 100 size is 50.

2. Step 2: Determine crop range

   Start at 100-50=50, end at 100+50=150 for both rows and columns.

3. Final Answer:

   img[50:150, 50:150] -> Option A

4. Quick Check:

   Center crop = middle 100 pixels from 200 [OK]

Hint: Center crop start = center - half size [OK]

Common Mistakes:

• Starting crop at 0 instead of center
• Using wrong indices for center
• Cropping smaller or larger than requested