Topic: Python OpenCV – Part 2: Image Thresholding, Blurring, and Edge Detection

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1. Image Thresholding

• Converts grayscale images to binary (black & white) by setting a pixel value threshold.

import cv2

image = cv2.imread('image.jpg', 0)  # load as grayscale

_, binary = cv2.threshold(image, 127, 255, cv2.THRESH_BINARY)

cv2.imshow("Binary Image", binary)
cv2.waitKey(0)
cv2.destroyAllWindows()

• Common thresholding types:

* THRESH_BINARY
* THRESH_BINARY_INV
* THRESH_TRUNC
* THRESH_TOZERO
* THRESH_OTSU (automatic threshold)

_, otsu = cv2.threshold(image, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)

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2. Image Blurring (Smoothing)

• Helps reduce image noise and detail.

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

# Median Blur
median = cv2.medianBlur(image, 5)

# Bilateral Filter (preserves edges)
bilateral = cv2.bilateralFilter(image, 9, 75, 75)

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3. Edge Detection with Canny

• Canny edge detection is one of the most popular techniques for detecting edges in an image.

edges = cv2.Canny(image, 100, 200)

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

* The two thresholds (100 and 200) are used for hysteresis (strong vs. weak edges).

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4. Combining Blur + Edge Detection

blurred = cv2.GaussianBlur(image, (5, 5), 0)
edges = cv2.Canny(blurred, 50, 150)

• Blurring before edge detection reduces noise and improves accuracy.

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Summary

• Thresholding simplifies images to black & white based on intensity.

• Blurring smooths out images and reduces noise.

• Canny Edge Detection is a reliable method for identifying edges.

• These tools are fundamental for object detection, image segmentation, and OCR tasks.

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Exercise

• Write a program that:

1. Loads an image in grayscale.
2. Applies Gaussian blur.
3. Uses Canny to detect edges.
4. Saves the final edge-detected image to disk.

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#Python #OpenCV #ImageProcessing #EdgeDetection #ComputerVision

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