Image properties (shape, dtype, size) in Computer Vision - ML Experiment: Train & Evaluate

Experiment - Image properties (shape, dtype, size)

Problem:You want to understand the basic properties of images used in machine learning, such as their shape, data type, and size in memory.

Current Metrics:N/A - This is a data exploration task without model training.

Issue:Beginners often do not know how to check image properties, which is important for preprocessing and debugging.

Your Task

Load an image using Python, then print its shape, data type, and size in bytes. Ensure the code runs without errors and outputs correct values.

Use only standard Python libraries and OpenCV (cv2) or PIL for image loading.

Do not use any deep learning frameworks for this task.

Hint 1

Hint 2

Hint 3

Solution

Computer Vision

import cv2

# Load image from file
image = cv2.imread('sample_image.jpg')  # Replace with your image path

# Check if image loaded successfully
if image is None:
    print('Error: Image not found or unable to load.')
else:
    # Get shape (height, width, channels)
    shape = image.shape
    # Get data type
    dtype = image.dtype
    # Calculate size in bytes
    size_bytes = image.size * image.itemsize

    print(f'Image shape: {shape}')
    print(f'Image data type: {dtype}')
    print(f'Image size in bytes: {size_bytes}')

Added code to load an image using OpenCV.

Printed image shape, data type, and calculated size in bytes.

Included error handling if image path is incorrect.

Results Interpretation

Before: No knowledge of image properties, which can cause errors in ML pipelines.

After: Able to print image shape (e.g., (480, 640, 3)), data type (e.g., uint8), and size in bytes (e.g., 921600).

Knowing image properties helps you prepare data correctly for machine learning models and avoid common errors.

Bonus Experiment

Try loading the same image using PIL (Pillow) and print its properties similarly.

💡 Hint

Use PIL.Image.open() and convert the image to a numpy array to access shape and dtype.

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