Image properties (shape, dtype, size) in Computer Vision - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to get the shape of the image array.

Computer Vision

image_shape = image.[1]

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

Adtype

Bshape

Csize

Dlen

Attempts:

3 left

💡 Hint

Common Mistakes

Using dtype instead of shape, which gives data type not dimensions.

Using size, which gives total number of elements, not the shape.

The shape property gives the dimensions of the image array, such as height, width, and channels.

2fill in blank

medium

Complete the code to find the data type of the image pixels.

Computer Vision

image_type = image.[1]

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

Ashape

Bsize

Cdtype

Dtype

Attempts:

3 left

💡 Hint

Common Mistakes

Using shape instead of dtype, which gives dimensions not data type.

Using type(), which returns the Python type of the object, not the array data type.

The dtype property tells you the data type of the image pixels, like uint8 or float32.

3fill in blank

hard

Fix the error in the code to get the total number of pixels in the image.

Computer Vision

total_pixels = image.[1]

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

Asize

Bshape

Cdtype

Dlen

Attempts:

3 left

💡 Hint

Common Mistakes

Using shape, which returns a tuple of dimensions, not a single number.

Using len, which only returns the size of the first dimension.

The size property returns the total number of elements (pixels) in the image array.

4fill in blank

hard

Fill both blanks to create a dictionary with image shape and data type.

Computer Vision

image_info = {'shape': image.[1], 'dtype': image.[2]

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

Ashape

Bdtype

Csize

Dtype

Attempts:

3 left

💡 Hint

Common Mistakes

Swapping shape and dtype properties.

Using size or type instead of dtype.

This dictionary stores the image's dimensions under 'shape' and the pixel data type under 'dtype'.

5fill in blank

hard

Fill all three blanks to create a summary string with image shape, data type, and total pixels.

Computer Vision

summary = f"Shape: {image.[1], Type: {image.[2], Pixels: {image.[3]"

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

Ashape

Bdtype

Csize

Dtype

Attempts:

3 left

💡 Hint

Common Mistakes

Using type() instead of dtype.

Mixing up size and shape.

This string shows the image's shape, data type, and total number of pixels for a quick overview.

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