Topic: Python OpenCV – Part 1: Introduction, Image Reading, and Basic Operations

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What is OpenCV?

• OpenCV (Open Source Computer Vision Library) is a powerful computer vision and image processing library.

• It supports image and video capture, analysis, object detection, face recognition, and much more.

• Commonly used with Python, C++, and machine learning pipelines.

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Installing OpenCV

pip install opencv-python

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1. Reading and Displaying Images

import cv2

# Read the image
image = cv2.imread('image.jpg')

# Display the image in a window
cv2.imshow('My Image', image)
cv2.waitKey(0)  # Wait for any key to close the window
cv2.destroyAllWindows()

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2. Image Shape and Type

print(image.shape)  # (height, width, channels)
print(image.dtype)  # uint8 (8-bit integers for each channel)

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3. Converting Color Spaces

# Convert BGR to Grayscale
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# Convert BGR to RGB (for matplotlib or image correction)
rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

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4. Saving an Image

cv2.imwrite('gray_image.png', gray)

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5. Drawing Shapes

# Draw a red rectangle
cv2.rectangle(image, (50, 50), (200, 200), (0, 0, 255), 2)

# Draw a filled circle
cv2.circle(image, (150, 150), 40, (255, 0, 0), -1)

# Draw text
cv2.putText(image, 'OpenCV!', (40, 300), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)

cv2.imshow('Drawn Image', image)
cv2.waitKey(0)
cv2.destroyAllWindows()

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6. Resize and Flip

# Resize image
resized = cv2.resize(image, (300, 300))

# Flip image horizontally
flipped = cv2.flip(image, 1)

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Summary

• OpenCV allows you to read, display, modify, and save images easily.

• You can perform basic tasks like drawing, resizing, flipping, and color transformations.

• These operations are the building blocks for image analysis, preprocessing, and machine vision applications.

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Exercise

• Write a program that:

1. Loads an image.
2. Converts it to grayscale.
3. Draws a blue circle in the center.
4. Saves the new image to disk.

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#Python #OpenCV #ImageProcessing #ComputerVision #Beginners

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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

https://t.iss.one/DataScience4

Topic: 20 Important Python OpenCV Interview Questions with Brief Answers

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1. What is OpenCV?
OpenCV is an open-source library for computer vision, image processing, and machine learning.

2. How do you read and display an image using OpenCV?
Use cv2.imread() to read and cv2.imshow() to display images.

3. What color format does OpenCV use by default?
OpenCV uses BGR format instead of RGB.

4. How to convert an image from BGR to Grayscale?
Use cv2.cvtColor(image, cv2.COLOR_BGR2GRAY).

5. What is the difference between `cv2.imshow()` and matplotlib’s `imshow()`?
cv2.imshow() uses BGR and OpenCV windows; matplotlib uses RGB and inline plotting.

6. How do you write/save an image to disk?
Use cv2.imwrite('filename.jpg', image).

7. What are image thresholds?
Techniques to segment images into binary images based on pixel intensity.

8. What is Gaussian Blur, and why is it used?
A smoothing filter to reduce image noise and detail.

9. Explain the Canny Edge Detection process.
It detects edges using gradients, non-maximum suppression, and hysteresis thresholding.

10. How do you capture video from a webcam using OpenCV?
Use cv2.VideoCapture(0) and read frames in a loop.

11. What are contours in OpenCV?
Curves joining continuous points with the same intensity, used for shape detection.

12. How do you find and draw contours?
Use cv2.findContours() and cv2.drawContours().

13. What are morphological operations?
Operations like erosion and dilation that process shapes in binary images.

14. What is the difference between erosion and dilation?
Erosion removes pixels on object edges; dilation adds pixels.

15. How to perform image masking in OpenCV?
Use cv2.bitwise_and() with an image and a mask.

16. What is the use of `cv2.waitKey()`?
Waits for a key event for a specified time; needed to display images properly.

17. How do you resize an image?
Use cv2.resize(image, (width, height)).

18. Explain how to save a video using OpenCV.
Use cv2.VideoWriter() with codec, fps, and frame size.

19. How to convert an image from OpenCV BGR format to RGB for matplotlib?
Use cv2.cvtColor(image, cv2.COLOR_BGR2RGB).

20. What is the difference between `cv2.threshold()` and `cv2.adaptiveThreshold()`?
cv2.threshold() applies global threshold; adaptiveThreshold() applies varying thresholds locally.

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Summary

These questions cover basic to intermediate OpenCV concepts essential for interviews and practical use.

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#Python #OpenCV #InterviewQuestions #ComputerVision #ImageProcessing

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#YOLOv8 #ComputerVision #FireDetection #Python #AI #Safety

Lesson: Real-Time Fire Detection in an Industrial Facility with YOLOv8 and Alarm System

This tutorial guides you through building a computer vision project from scratch. We will use the YOLOv8 model to detect fire in a video feed from an industrial setting and trigger an alarm sound upon detection.

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#Step 1: Project Setup and Dependencies

First, we need to install the necessary Python libraries. We'll use ultralytics for the YOLOv8 model, opencv-python for video processing, and playsound to trigger our alarm. Open your terminal or command prompt and run the following command:

pip install ultralytics opencv-python playsound

After installation, create a Python file (e.g., fire_detector.py) and import these libraries.

import cv2
from ultralytics import YOLO
from playsound import playsound
import threading

# Hashtags: #Setup #Python #OpenCV #YOLOv8

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#Step 2: Load the Model and Prepare the Alarm System

We will load a pre-trained YOLOv8 model. For a real-world application, you must train a custom model on a dataset of fire and smoke images. For this example, we will write the code assuming you have a custom model named fire_model.pt that knows how to detect 'fire'.

You also need an alarm sound file (e.g., alarm.wav) in the same directory as your script.

# Load your custom-trained YOLOv8 model
# IMPORTANT: The standard YOLOv8 models do not detect 'fire'. 
# You must train your own model on a fire dataset.
model = YOLO('fire_model.pt') # Replace with your custom model path

# Path to your alarm sound file
ALARM_SOUND_PATH = "alarm.wav"

# A flag to ensure the alarm plays only once per detection event
alarm_on = False

def play_alarm():
    """Plays the alarm sound in a separate thread."""
    global alarm_on
    print("ALARM: Fire Detected!")
    playsound(ALARM_SOUND_PATH)
    alarm_on = False # Reset alarm flag after sound finishes

# Hashtags: #AIModel #AlarmSystem #SafetyFirst

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#Step 3: Main Loop for Video Processing and Detection

This is the core of our application. We will open a video file, read it frame by frame, and pass each frame to our YOLOv8 model for inference. If the model detects 'fire' with a certain confidence, we will draw a bounding box around it and trigger the alarm.

Create a video file named industrial_video.mp4 or use your own video source.