Basic image manipulation with arrays in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a 3x3 grayscale image filled with zeros.

NumPy

import numpy as np
image = np.[1]((3, 3))

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

Azeros

Bfull

Cempty

Dones

Attempts:

3 left

💡 Hint

Common Mistakes

Using np.ones creates an image filled with white pixels, not black.

Using np.empty creates an uninitialized array with random values.

Using np.zeros creates an array filled with zeros, which represents a black image in grayscale.

2fill in blank

medium

Complete the code to set the pixel at row 1, column 2 to white (255) in a grayscale image.

NumPy

image = np.zeros((3, 3), dtype=np.uint8)
image[1] = 255

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

A(1, 2)

B[1, 2]

C[2, 1]

D(2, 1)

Attempts:

3 left

💡 Hint

Common Mistakes

Swapping row and column indices.

Using square brackets inside the indexing which is invalid syntax.

In NumPy, the first index is the row and the second is the column. So image[1, 2] accesses row 1, column 2.

3fill in blank

hard

Fix the error in the code to invert a grayscale image (255 - pixel value).

NumPy

inverted = 255 - image[1]

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

A[]

B()

C.astype(int)

D.copy()

Attempts:

3 left

💡 Hint

Common Mistakes

Using parentheses or brackets after the array name causes syntax errors.

Not copying the array modifies the original image unintentionally.

Using .copy() creates a new array so the inversion does not affect the original image.

4fill in blank

hard

Fill both blanks to create a dictionary with pixel positions as keys and their values for pixels greater than 100.

NumPy

pixels = {(row, col): image[row, col] for row in range(image.shape[[1]]) for col in range(image.shape[[2]]) if image[row, col] > 100}

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

A0

B1

C2

D3

Attempts:

3 left

💡 Hint

Common Mistakes

Swapping the indices for rows and columns.

Using an index that is out of range for the shape tuple.

In NumPy, shape[0] is the number of rows and shape[1] is the number of columns.

5fill in blank

hard

Fill all three blanks to create a new image where pixels less than 50 are set to 0, others remain unchanged.

NumPy

new_image = np.array([[image[row, col] if image[row, col] [1] [2] else [3] for col in range(image.shape[1])] for row in range(image.shape[0])])

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

A>=

B50

C0

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' instead of '>=' reverses the condition.

Setting the else value incorrectly changes pixel values.

The condition keeps pixels greater or equal to 50 unchanged; pixels less than 50 are set to 0.

Practice

1. What does a 2D numpy array represent when used for a grayscale image?

easy

A. Pixel brightness values arranged in rows and columns

B. A list of image file names

C. The color channels of an image

D. The size of the image in bytes

Solution

1. Step 1: Understand image representation

   A grayscale image is made of pixels arranged in rows and columns, each pixel having a brightness value.

2. Step 2: Connect to numpy array structure

   A 2D numpy array stores these brightness values in a grid matching the image layout.

3. Final Answer:

   Pixel brightness values arranged in rows and columns -> Option A

4. Quick Check:

   Grayscale image = 2D pixel brightness array [OK]

Hint: Grayscale images use 2D arrays for pixel brightness [OK]

Common Mistakes:

• Confusing pixel values with file names
• Thinking 2D arrays store color channels
• Mixing image size with pixel data

2. Which numpy code correctly flips a 2D image array img vertically?

easy

A. np.fliplr(img)

B. np.flip(img, axis=1)

C. np.flip(img, axis=0)

D. np.flip(img)

Solution

1. Step 1: Understand vertical flip axis

   Vertical flip reverses rows, which corresponds to axis 0 in numpy arrays.

2. Step 2: Match numpy functions to axis

   np.flip(img, axis=0) flips vertically.

3. Final Answer:

   np.flip(img, axis=0) -> Option C

4. Quick Check:

   Vertical flip = axis 0 flip [OK]

Hint: Vertical flip uses axis=0 in np.flip [OK]

Common Mistakes:

• Using axis=1 flips horizontally
• np.flip(img) without axis reverses all axes
• Forgetting axis parameter in np.flip

3. Given this code:

import numpy as np
img = np.array([[10, 20], [30, 40]])
new_img = img + 5
print(new_img)

What is the output?

medium

A. [[10 20]
[30 40]]

B. [[5 15]
[25 35]]

C. [[50 60]
[70 80]]

D. [[15 25]
[35 45]]

Solution

1. Step 1: Understand numpy array addition

   Adding 5 to a numpy array adds 5 to every element (broadcasting).

2. Step 2: Calculate new array values

   Each element increases by 5: 10+5=15, 20+5=25, 30+5=35, 40+5=45.

3. Final Answer:

   [[15 25] [35 45]] -> Option D

4. Quick Check:

   Array + 5 adds 5 to all elements [OK]

Hint: Adding scalar adds to all pixels in array [OK]

Common Mistakes:

• Thinking addition changes shape
• Adding 5 only to first row or column
• Confusing addition with multiplication

4. What is wrong with this code to crop the top-left 2x2 part of a 3x3 image array img?:

cropped = img[0:2, 0:3]

medium

A. It crops 2 rows but 3 columns, not 2 columns

B. It causes an index error

C. It flips the image instead of cropping

D. It returns the whole image

Solution

1. Step 1: Analyze slicing indices

   The slice img[0:2, 0:3] selects rows 0 and 1 (2 rows) and columns 0,1,2 (3 columns).

2. Step 2: Compare to desired crop size

   We want a 2x2 crop, but this selects 2x3, so it includes one extra column.

3. Final Answer:

   It crops 2 rows but 3 columns, not 2 columns -> Option A

4. Quick Check:

   Slicing columns 0:3 selects 3 columns [OK]

Hint: Check slice ranges match desired rows and columns [OK]

Common Mistakes:

• Assuming slicing is exclusive of end index
• Expecting error from out-of-range slice
• Confusing rows and columns order

5. You have a color image as a 3D numpy array img with shape (100, 100, 3). How do you create a new image gray_img by averaging the color channels for each pixel?

hard

A. gray_img = img.sum(axis=0)

B. gray_img = img.mean(axis=2)

C. gray_img = img[:, :, 0]

D. gray_img = np.flip(img, axis=1)

Solution

1. Step 1: Understand image shape and channels

   The shape (100, 100, 3) means 100x100 pixels with 3 color channels (RGB) along axis 2.

2. Step 2: Average color channels per pixel

   Using img.mean(axis=2) calculates the average of the 3 channels for each pixel, producing a 2D grayscale image.

3. Final Answer:

   gray_img = img.mean(axis=2) -> Option B

4. Quick Check:

   Mean over axis 2 averages color channels [OK]

Hint: Use mean(axis=2) to average RGB channels [OK]

Common Mistakes:

• Summing over wrong axis
• Selecting only one color channel
• Using flip instead of averaging