OpenCV Python Tutorial: Complete Guide for Image Processing & Computer Vision Beginners

OpenCV Python Tutorial: Complete Guide for Beginners

OpenCV (Open Source Computer Vision Library) is one of the most powerful and widely used libraries for image processing and computer vision tasks. With Python support, it becomes extremely easy to build real-world applications like face detection, object tracking, image filtering, and video analysis.

In this tutorial, you will learn OpenCV in Python from the ground up with practical examples.

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1. What is OpenCV?

OpenCV is an open-source library designed for:

• Image processing
• Video analysis
• Object detection
• Face recognition
• Machine learning-based vision tasks

It is widely used in:

• AI applications
• Robotics
• Surveillance systems
• Self-driving cars
• Medical imaging

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2. Install OpenCV in Python

Before starting, install OpenCV using pip:

pip install opencv-python

For extra features (optional):

pip install opencv-contrib-python

Check installation:

import cv2
print(cv2.__version__)

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3. Reading and Displaying Images

Load an image

import cv2

img = cv2.imread("image.jpg")
cv2.imshow("Image Window", img)

cv2.waitKey(0)
cv2.destroyAllWindows()

Explanation:

• imread() → reads image
• imshow() → displays image
• waitKey(0) → waits for key press
• destroyAllWindows() → closes window

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4. Writing/Saving Images

import cv2

img = cv2.imread("image.jpg")
cv2.imwrite("output.jpg", img)

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5. Image Properties

print(img.shape)   # height, width, channels
print(img.size)    # total pixels
print(img.dtype)   # image data type

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6. Converting Color Spaces

Convert BGR to Grayscale

gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
cv2.imshow("Gray Image", gray)

Convert BGR to RGB

rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

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7. Image Resizing

resized = cv2.resize(img, (300, 300))
cv2.imshow("Resized Image", resized)

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8. Image Cropping

cropped = img[100:400, 200:500]
cv2.imshow("Cropped Image", cropped)

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9. Drawing Shapes on Images

Draw a line

cv2.line(img, (0,0), (200,200), (255,0,0), 3)

Draw rectangle

cv2.rectangle(img, (50,50), (200,200), (0,255,0), 2)

Draw circle

cv2.circle(img, (150,150), 50, (0,0,255), -1)

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10. Text on Image

cv2.putText(img, "Hello OpenCV", (50,50),
            cv2.FONT_HERSHEY_SIMPLEX, 1,
            (255,255,255), 2)

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11. Image Blurring

Gaussian Blur

blur = cv2.GaussianBlur(img, (15,15), 0)
cv2.imshow("Blurred Image", blur)

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12. Edge Detection (Canny)

edges = cv2.Canny(img, 100, 200)
cv2.imshow("Edges", edges)

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13. Image Thresholding

gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

_, thresh = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)
cv2.imshow("Threshold", thresh)

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14. Working with Video (Webcam)

Capture video from webcam

import cv2

cap = cv2.VideoCapture(0)

while True:
    ret, frame = cap.read()
    cv2.imshow("Webcam", frame)

    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()

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15. Saving Video from Webcam

import cv2

cap = cv2.VideoCapture(0)

fourcc = cv2.VideoWriter_fourcc(*'XVID')
out = cv2.VideoWriter('output.avi', fourcc, 20.0, (640, 480))

while True:
    ret, frame = cap.read()

    out.write(frame)
    cv2.imshow("Video", frame)

    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

cap.release()
out.release()
cv2.destroyAllWindows()

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16. Face Detection (Basic Example)

OpenCV includes pre-trained classifiers.

import cv2

face_cascade = cv2.CascadeClassifier(
    cv2.data.haarcascades + "haarcascade_frontalface_default.xml"
)

img = cv2.imread("face.jpg")
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

faces = face_cascade.detectMultiScale(gray, 1.1, 4)

for (x, y, w, h) in faces:
    cv2.rectangle(img, (x,y), (x+w, y+h), (255,0,0), 2)

cv2.imshow("Faces", img)
cv2.waitKey(0)
cv2.destroyAllWindows()

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17. Real-World OpenCV Projects You Can Build

Here are some ideas:

• Face detection system
• Motion detection camera
• Object tracking system
• Barcode scanner
• AI-based attendance system
• License plate recognition system

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18. Conclusion

OpenCV with Python is a powerful combination for building real-world computer vision applications. Once you master the basics like image processing, filtering, and video handling, you can move into advanced AI-based vision projects.

Start experimenting with images and videos today to strengthen your computer vision skills!