Image properties (shape, dtype, size) in Computer Vision - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the shape of the image array?

Given the following code that loads an image as a NumPy array, what is the shape of the array?

Computer Vision

import numpy as np
image = np.zeros((128, 256, 3), dtype=np.uint8)
print(image.shape)

A (128, 256, 3)

B (3, 128, 256)

C (256, 128, 3)

D (128, 256)

💡 Hint

Remember the shape format is (height, width, channels) for color images.

The shape (128, 256, 3) means the image has 128 pixels in height, 256 pixels in width, and 3 color channels (RGB).

❓ Predict Output

intermediate

What is the data type of the image array?

What will be printed by this code snippet?

Computer Vision

import numpy as np
image = np.ones((64, 64), dtype=np.float32)
print(image.dtype)

A float64

B float32

C uint8

D int64

💡 Hint

Check the dtype argument used when creating the array.

The dtype was set to np.float32, so the printed data type is float32.

❓ Metrics

advanced

Calculate the total number of pixels in a grayscale image

If an image has shape (512, 512), how many pixels does it contain?

A 512

B 1024

C 262144

D 1048576

💡 Hint

Multiply the height by the width to get total pixels.

512 * 512 = 262144 pixels in total.

❓ Model Choice

advanced

Choosing the correct image property for model input

You want to feed a color image into a neural network that expects input shape (batch_size, height, width, channels). Which property must you check before feeding the image?

A Data type of the image array

B Size of the image file on disk

C Number of unique colors in the image

D Shape of the image array

💡 Hint

The model expects a specific shape format.

The model input requires the image array shape to match (batch_size, height, width, channels).

🔧 Debug

expert

Why does this code raise an error when accessing image size?

Consider this code snippet: import numpy as np image = np.zeros((100, 100, 3), dtype=np.uint8) print(image.size())

Computer Vision

import numpy as np
image = np.zeros((100, 100, 3), dtype=np.uint8)
print(image.size())

A TypeError: 'numpy.int64' object is not callable

B Prints 30000

C Prints (100, 100, 3)

D SyntaxError due to missing colon

💡 Hint

Check if size is a method or a property.

The 'size' attribute is a property, not a method, so calling size() raises a TypeError.

Practice

1. What does the shape property of an image represent?

easy

A. The file size of the image in bytes

B. The data type of the pixel values

C. The dimensions and number of color channels of the image

D. The compression level of the image

Solution

1. Step 1: Understand what shape means in images

   The shape of an image is a tuple that shows its height, width, and number of color channels.

2. Step 2: Differentiate shape from other properties

   File size and data type are different properties; shape specifically refers to dimensions and channels.

3. Final Answer:

   The dimensions and number of color channels of the image -> Option C

4. Quick Check:

   Shape = dimensions + channels [OK]

Hint: Shape always shows height, width, and channels [OK]

Common Mistakes:

• Confusing shape with file size
• Mixing up data type with shape
• Thinking shape shows compression

2. Which of the following is the correct way to get the data type of an image stored in a NumPy array named img?

easy

A. img.dtype

B. img.type()

C. img.data_type

D. img.get_dtype()

Solution

1. Step 1: Recall NumPy syntax for data type

   In NumPy, the data type of an array is accessed using the dtype attribute.

2. Step 2: Check each option

   Only img.dtype is valid syntax; others are incorrect or do not exist.

3. Final Answer:

   img.dtype -> Option A

4. Quick Check:

   Use .dtype to get data type [OK]

Hint: Use .dtype attribute for NumPy array data type [OK]

Common Mistakes:

• Using parentheses like a function
• Trying non-existent attributes
• Confusing dtype with type() function

3. Given the following code:

import numpy as np
img = np.zeros((100, 200, 3), dtype=np.uint8)
print(img.size)

What will be the output?

medium

A. 3

B. 60000

C. 200

D. 100

Solution

1. Step 1: Understand the shape and size

   The image shape is (100, 200, 3). Size is total number of elements = 100 * 200 * 3 = 60000.

2. Step 2: Confirm what .size returns

   The size attribute returns total pixels including all channels.

3. Final Answer:

   60000 -> Option B

4. Quick Check:

   Size = height * width * channels = 60000 [OK]

Hint: Multiply all shape dimensions for size [OK]

Common Mistakes:

• Using only height or width as size
• Ignoring color channels in size
• Confusing size with shape

4. Consider this code snippet:

import numpy as np
img = np.array([[255, 128], [64, 0]])
print(img.shape)
print(img.dtype)

What is the error in this code if the goal is to represent a color image?

medium

A. The array values are out of range for images

B. The dtype should be float instead of int

C. The shape attribute is called incorrectly

D. The array shape lacks a color channel dimension

Solution

1. Step 1: Check the array shape

   The array shape is (2, 2), meaning 2 rows and 2 columns, no color channels.

2. Step 2: Understand color image requirements

   A color image needs 3 dimensions: height, width, and channels (usually 3 for RGB).

3. Final Answer:

   The array shape lacks a color channel dimension -> Option D

4. Quick Check:

   Color images need 3D shape [OK]

Hint: Color images need 3D shape (height, width, channels) [OK]

Common Mistakes:

• Thinking dtype must be float for images
• Assuming shape attribute is wrong
• Believing pixel values are out of range

5. You have a grayscale image loaded as a NumPy array with shape (256, 256) and dtype float32. You want to convert it to an 8-bit unsigned integer image suitable for display. Which code snippet correctly does this?

hard

A. img_uint8 = (img * 255).astype(np.uint8)

B. img_uint8 = img.astype(np.uint8)

C. img_uint8 = img / 255

D. img_uint8 = img.astype(np.float64)

Solution

1. Step 1: Understand dtype conversion needs

   Converting from float32 (0 to 1 range) to uint8 (0 to 255) requires scaling by 255.

2. Step 2: Check each option

   img_uint8 = (img * 255).astype(np.uint8) scales and converts correctly. img_uint8 = img.astype(np.uint8) converts without scaling, causing wrong values. Options A, B, and D do not convert to uint8 properly.

3. Final Answer:

   img_uint8 = (img * 255).astype(np.uint8) -> Option A

4. Quick Check:

   Scale float to 255 then convert to uint8 [OK]

Hint: Multiply floats by 255 before uint8 conversion [OK]

Common Mistakes:

• Skipping scaling before type conversion
• Using wrong dtype conversion
• Dividing instead of multiplying