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Advanced Image Processing Techniques Using OpenCV

In this lesson, we will cover advanced image processing techniques using OpenCV, such as edge detection, blurring, and object detection.


1. Edge Detection - Canny Edge Detection​

Edge detection is a technique used to identify boundaries within images.

Canny Edge Detection is one of the most widely used edge detection algorithms.

Canny Edge Detection Example
import cv2

# Load the image
image = cv2.imread("sample.jpg", cv2.IMREAD_GRAYSCALE)

# Apply Canny edge detection
edges = cv2.Canny(image, 100, 200)

cv2.imshow("Canny Edges", edges)
cv2.waitKey(0)
cv2.destroyAllWindows()

2. Blur and Noise Reduction​

To reduce noise or smooth (blur) the image, you can use GaussianBlur().

Gaussian Blur Example
blurred = cv2.GaussianBlur(image, (5, 5), 0)

cv2.imshow("Blurred Image", blurred)
cv2.waitKey(0)
cv2.destroyAllWindows()

Here, (5, 5) is the kernel size, and larger values result in a stronger blur effect.


3. Contour Detection​

Contours are used to find the boundaries of objects within images.

You can use OpenCV's cv2.findContours() to detect them.

Contour Detection Example
# Load and convert the image
image = cv2.imread("sample.jpg")
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
_, threshold = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)

# Detect contours
contours, _ = cv2.findContours(threshold, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

# Draw detected contours
cv2.drawContours(image, contours, -1, (0, 255, 0), 2)

cv2.imshow("Contours", image)
cv2.waitKey(0)
cv2.destroyAllWindows()

In this code, after thresholding, cv2.findContours() is used to detect contours, and cv2.drawContours() displays them in green.


4. Face and Object Detection (Haar Cascade)​

OpenCV allows you to detect faces using pre-trained Haar Cascade Classifiers.

Face Detection Example
# Load the Haar Cascade classifier
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")

# Load and convert the image
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# Detect faces
faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))

# Draw rectangles around detected faces
for (x, y, w, h) in faces:
    cv2.rectangle(image, (x, y), (x + w, y + h), (255, 0, 0), 2)

This code example detects faces and draws rectangles around them.


In this lesson, we covered advanced OpenCV features such as edge detection, blurring, contour detection, and face detection.

In the next lesson, we will solve a simple quiz based on what we have learned today!


Reference Materials​

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