Extracting Image Dimensions in Python: OpenCV Approach

Concepts involved:

• Python: The general-purpose programming language used for this code.
• OpenCV (cv2): A powerful library for computer vision tasks, including image processing.
• Image: A digital representation of a picture, typically stored as a two-dimensional array of pixels.
• Image size: The dimensions of the image, expressed as width and height (in pixels).

Code Breakdown:

import cv2

# Read the image using OpenCV's imread() function
img = cv2.imread('path/to/your/image.jpg')  # Replace with your image path

# Get the image size using the shape attribute of the NumPy array
height, width = img.shape[:2]

# Optional: Get the number of channels (color channels for color images)
channels = img.shape[2] if len(img.shape) == 3 else 1

print(f"Image size: Height = {height}, Width = {width}")

# Optional: Print the number of channels (if applicable)
if channels > 1:
    print(f"Number of channels: {channels}")

Explanation:

1. Import cv2: This line imports the OpenCV library, providing image processing functions.
2. Read the image: img = cv2.imread('path/to/your/image.jpg') reads the image from the specified file path and stores it as a NumPy array in the img variable. Make sure to replace the path with the actual location of your image.
3. Get image size: height, width = img.shape[:2] retrieves the image dimensions (height and width) from the shape attribute of the NumPy array. img.shape returns a tuple containing the number of rows (height), columns (width), and channels (for color images). Slicing with [:2] ensures we only get the first two elements (height and width).
4. Optional: Get number of channels: This code checks if the image has more than two dimensions (indicating color channels). If so, it extracts the number of channels using img.shape[2]. Otherwise, it assumes a grayscale image (one channel).
5. Print results: The code prints the image height and width in a formatted way. If the image has multiple channels, their number is printed as well.

Key Points:

• OpenCV represents images as NumPy arrays, making image manipulation efficient.
• The shape attribute of the NumPy array provides image dimensions.
• The number of channels indicates whether the image is grayscale (1 channel) or color (3 or 4 channels depending on encoding).

This code effectively retrieves the size of an image loaded using OpenCV in Python.

import cv2

# Replace 'path/to/your/image.jpg' with the actual path to your image
img = cv2.imread('path/to/your/image.jpg')

# Get image size
height, width = img.shape[:2]

# Optional: Get number of channels (if applicable)
channels = img.shape[2] if len(img.shape) == 3 else 1

print(f"Image size: Height = {height}, Width = {width}")

# Optional: Print the number of channels (if applicable)
if channels > 1:
    print(f"Number of channels: {channels}")

• The code first imports the cv2 library for OpenCV functionality.
• The height, width = img.shape[:2] line retrieves the image dimensions (height and width) from the shape attribute of the NumPy array representing the image.
• The optional section checks for and prints the number of channels (indicating color information) if the image has more than two dimensions.

Compiling and Running the Code:

1. Save the code: Create a new Python file (e.g., get_image_size.py) and paste the code into it.
2. Install OpenCV (if not already installed): Open a terminal or command prompt and run pip install opencv-python.
3. Run the code: Navigate to the directory where you saved the Python file (get_image_size.py) in your terminal and execute the script using python get_image_size.py.

Output:

The program will print the image size (height and width) on the console. If the image is a color image, the number of channels will be displayed as well.

This code provides a practical example of how to use OpenCV to determine the dimensions and color information of an image in Python.

Using Pillow (PIL Fork):

Pillow (PIL Fork) is another popular image processing library in Python. Here's how to use it:

from PIL import Image

# Replace 'path/to/your/image.jpg' with the actual path to your image
img = Image.open('path/to/your/image.jpg')

# Get image size
width, height = img.size

print(f"Image size: Height = {height}, Width = {width}")

• Import the Image class from Pillow.
• Open the image using Image.open('path/to/your/image.jpg').
• Access the image size directly using the size attribute, which returns a tuple containing width and height.

Using magic library (for basic size information):

The magic library can identify file types and sometimes extract basic information like image dimensions. It's a lightweight alternative if you only need the size and don't need full image processing capabilities.

Note: magic might not work for all image formats, especially JPEGs with more complex metadata.

import magic

# Replace 'path/to/your/image.jpg' with the actual path to your image
file_path = 'path/to/your/image.jpg'

try:
  with open(file_path, 'rb') as f:
    file_type = magic.from_file(f, mime=True)
    # Extract size information from the file type string (might be unreliable)
    size_info = re.search(r'(\d+) x (\d+)', file_type)
    if size_info:
      height, width = size_info.groups()
      print(f"Image size (estimated): Height = {height}, Width = {width}")
    else:
      print(f"Could not extract size information from {file_path}")
except FileNotFoundError:
  print(f"File '{file_path}' not found")

• Import the magic library.
• Open the image file in binary mode ('rb').
• Use magic.from_file to identify the file type and extract information as a MIME type string.
• Try to extract image dimensions using regular expressions (might be unreliable).
• Handle potential file not found errors.

Choosing the Right Method:

• If you need full image processing capabilities, OpenCV or Pillow are better choices.
• If you only need basic size information and want a lightweight solution, magic might be suitable (but consider its limitations).
• If performance is critical, benchmark different methods on your specific use case.