Image Processing with OpenCV

Image Processing is a discipline focused on performing operations on an image to get an enhanced image or to extract some useful information from it. It's a type of signal processing where the input is an image (e.g., a photograph or video frame) and the output can be an image or a set of characteristics or parameters related to the image.

OpenCV (Open Source Computer Vision Library) is an open-source computer vision and machine learning software library. It was built to provide a common infrastructure for computer vision applications and to accelerate the use of machine perception in commercial products. Being a BSD-licensed product, OpenCV makes it easy for businesses to utilize and modify the code.

Key Features and Relationship:
- Comprehensive Library: OpenCV contains hundreds of optimized algorithms for image processing and computer vision. These algorithms can be used to detect and recognize faces, identify objects, classify human actions in videos, track camera movements, track moving objects, extract 3D models of objects, produce 3D point clouds from stereo cameras, stitch images together to produce a high-resolution image of an entire scene, find similar images from an image database, remove red eyes from images, follow eye movements, recognize scenery, and establish markers to overlay it with augmented reality, among other things.
- Platform Independence: It's available on Windows, Linux, OS X, Android, iOS, and can be integrated with various programming languages, most notably Python, C++, Java, and MATLAB.
- Efficiency: Many of its functions are optimized for performance, often taking advantage of multi-core processing.
- Integration: OpenCV serves as the primary toolset for applying various image processing techniques. From basic operations like reading, displaying, and saving images, to complex tasks such as feature detection, object recognition, and machine learning integration, OpenCV provides the necessary functions and infrastructure.

In essence, OpenCV is the robust toolkit that enables developers and researchers to implement and experiment with a wide array of image processing algorithms and computer vision applications.

import cv2
import numpy as np

 --- Step 1: Load an Image ---
 Make sure to have an image file (e.g., 'lena.jpg') in the same directory 
 or provide a full path to your image.
try:
     Attempt to load a sample image. If not found, create a dummy image.
    img = cv2.imread('lena.jpg')  Replace 'lena.jpg' with your image file name
    if img is None:
        raise FileNotFoundError("Image file 'lena.jpg' not found. Creating a dummy image for demonstration.")
except FileNotFoundError as e:
    print(e)
     Create a black dummy image if the actual image is not found
    img = np.zeros((400, 600, 3), dtype=np.uint8)
    cv2.putText(img, "Image Not Found!", (50, 150), cv2.FONT_HERSHEY_SIMPLEX, 1.2, (0, 0, 255), 2)
    cv2.putText(img, "Using a black dummy image for processing examples.", (50, 200), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 255), 2)

 Display the original image
cv2.imshow('Original Image', img)

 --- Step 2: Basic Image Processing Operations ---

 2.1 Convert to Grayscale
 Grayscale images are often used for simpler processing as they reduce complexity.
gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
cv2.imshow('Grayscale Image', gray_img)

 2.2 Apply Gaussian Blur
 Blurring helps to remove noise and smooth the image, often a pre-processing step.
 (kernel_width, kernel_height) must be positive and odd.
blurred_img = cv2.GaussianBlur(img, (5, 5), 0)
cv2.imshow('Blurred Image', blurred_img)

 2.3 Edge Detection using Canny Algorithm
 Canny is a popular edge detection algorithm. It often performs better on grayscale images.
 The arguments 100 and 200 are threshold1 and threshold2 for hysteresis procedure.
edges = cv2.Canny(gray_img, 100, 200)
cv2.imshow('Canny Edges', edges)

 2.4 Resizing an Image
 Resizing can be used to scale images up or down.
 Here, we resize to half its original width and height.
resized_img = cv2.resize(img, (int(img.shape[1] - 0.5), int(img.shape[0] - 0.5)))
cv2.imshow('Resized Image (50%)', resized_img)

 --- Step 3: Wait and Clean Up ---
 Wait indefinitely for a key press (0 means wait forever).
 After a key is pressed, all opened windows will be destroyed.
cv2.waitKey(0)
cv2.destroyAllWindows()