Image properties (shape, dtype, size) in Computer Vision

Introduction

Knowing image properties helps us understand the image's size, color format, and data type. This is important before using images in machine learning.

Checking the size of images before feeding them into a model.

Verifying the color channels (like RGB or grayscale) of images.

Ensuring the data type matches what the model expects (e.g., integers or floats).

Debugging image loading issues by inspecting image properties.

Preparing images for resizing or normalization steps.

Syntax

Computer Vision

image.shape
image.dtype
image.size

shape shows the dimensions of the image (height, width, channels).

dtype tells the type of data stored (like uint8 for 0-255 pixel values).

size gives the total number of elements (height x width x channels).

Examples

This prints the size and color channels of the image.

Computer Vision

print(image.shape)  # (height, width, channels)

This shows the type of numbers used to store pixel values.

Computer Vision

print(image.dtype)  # e.g., uint8

This gives the total count of all elements in the image array.

Computer Vision

print(image.size)  # total number of elements

Sample Model

This code creates a small random color image and prints its array, shape, data type, and size.

Computer Vision

import numpy as np
import cv2

# Create a simple 3x3 RGB image with random colors
image = np.random.randint(0, 256, (3, 3, 3), dtype=np.uint8)

print('Image array:')
print(image)
print('Shape:', image.shape)
print('Data type:', image.dtype)
print('Size:', image.size)

Important Notes

Images usually have 3 channels for color (Red, Green, Blue) or 1 for grayscale.

Data type uint8 means pixel values range from 0 to 255.

Shape order is usually (height, width, channels) in most libraries like OpenCV and NumPy.

Summary

Shape tells the image dimensions and color channels.

Data type shows how pixel values are stored.

Size is the total number of elements in the image array.

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