Lesson 9 · Python Libraries

Step-by-Step Guide to Setting Up the OpenAI API with Python for Beginners

OpenCV is a powerful open-source library for computer vision. It helps computers to understand images and videos. OpenCV is used for object detection, face…

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CoursePython Libraries
Lesson9 of 6
Video20 min
FormatJupyter notebook · 17 code cells

OpenCV NumPy Matplotlib

What you'll learn

Core Concepts in OpenCV
Reading and Displaying Images
Beginner Example: Drawing a Line
Intermediate Example: Image Thresholding
Advanced Example: Edge Detection
Error Handling in OpenCV
Best Practices with OpenCV
Mini-project: Make a Simple Stamped Badge

Data

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Introduction to OpenCV#

OpenCV is a powerful open-source library for computer vision.
It helps computers to understand images and videos.
OpenCV is used for object detection, face recognition, image editing, and more.
Real world uses include robotics, medical imaging, selfie filters, and video analysis.
In this lesson, you will learn key basics and see practical examples with OpenCV.

import warnings; warnings.filterwarnings("ignore")
import cv2
import numpy as np

# If you are on Windows and do not have OpenCV, run:
# pip install opencv-python

Core Concepts in OpenCV#

Images in OpenCV are numpy arrays.
You can load, display, and save images easily.
OpenCV works with color (BGR) and grayscale images.
Key functions: cv2.imread(), cv2.imshow(), cv2.imwrite(), cv2.cvtColor()
Images can be processed pixel by pixel.

# Let us create an image using numpy
img = np.zeros((100, 100, 3), dtype=np.uint8)
print(type(img))
print(img.shape)

<class 'numpy.ndarray'>
(100, 100, 3)

# Let us save the created image to disk.
cv2.imwrite("black_square.png", img)
print("Image saved as black_square.png")

Image saved as black_square.png

Reading and Displaying Images#

You can load images from files using cv2.imread().
Displaying images in Jupyter requires matplotlib.
Images can be displayed with colors or in grayscale.

import matplotlib.pyplot as plt
img2 = cv2.imread("Mathew.png")
plt.imshow(img2)
plt.title("Loaded Image")
plt.axis("off")
plt.show()

No description has been provided for this image

# Convert image to grayscale
gray_img = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)
plt.imshow(gray_img, cmap="gray")
plt.title("Grayscale Image")
plt.axis("off")
plt.show()

Beginner Example: Drawing a Line#

OpenCV lets you draw shapes on images.
You can draw lines, rectangles, circles, and add text.

img_line = img2.copy()
cv2.line(img_line, (10, 10), (2500, 2500), (255, 0, 0), 8)
plt.imshow(img_line)
plt.title("Line drawn")
plt.axis("off")
plt.show()

# Beginner Example: Drawing a rectangle
img_rect = img2.copy()
cv2.rectangle(img_rect, (300, 300), (1900, 1900), (0, 255, 0), 8)
plt.imshow(img_rect)
plt.title("Rectangle drawn")
plt.axis("off")
plt.show()

# Beginner Example: Adding Text
img_text = img2.copy()
cv2.putText(img_text, "Mathew Analytics", (650, 200), cv2.FONT_HERSHEY_SIMPLEX, 4, (255,255,255), 20)
plt.imshow(img_text)
plt.title("Text added")
plt.axis("off")
plt.show()

Intermediate Example: Image Thresholding#

Thresholding converts grayscale images to black and white.
It is used to separate objects from background.

_, thresh_img = cv2.threshold(gray_img, 50, 255, cv2.THRESH_BINARY)
plt.imshow(thresh_img, cmap="gray")
plt.title("Thresholded Image")
plt.axis("off")
plt.show()

# Intermediate Example: Blurring an Image
blur_img = cv2.GaussianBlur(img2, (7,7), 0)
plt.imshow(blur_img)
plt.title("Blurred Image")
plt.axis("off")
plt.show()

# Intermediate Example: Flipping an Image
flip_img = cv2.flip(img2, 1)
plt.imshow(flip_img)
plt.title("Image Flipped Horizontally")
plt.axis("off")
plt.show()

Advanced Example: Edge Detection#

Edge detection highlights borders in images.
OpenCV makes it easy with the Canny function.

edges = cv2.Canny(gray_img, 30, 100)
plt.imshow(edges, cmap="gray")
plt.title("Edges Detected")
plt.axis("off")
plt.show()

# Advanced Example: Contour Detection
contours, _ = cv2.findContours(edges, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
img_contour = img2.copy()
cv2.drawContours(img_contour, contours, -1, (0,255,255), 2)
plt.imshow(img_contour)
plt.title("Contours Detected")
plt.axis("off")
plt.show()

Error Handling in OpenCV#

Sometimes images may not be found or functions may fail.
Use try-except to catch errors and show messages.

try:
    missing_img = cv2.imread("non_existent_file.jpg")
    if missing_img is None:
        raise FileNotFoundError("Image file not found.")
    plt.imshow(missing_img)
except FileNotFoundError as e:
    print(e)

Image file not found.

# Debugging: Checking Image Properties
def check_image(img):
    if img is None:
        print("Image is None. Check file path or data.")
        return
    print("Image shape:", img.shape)
    print("Image data type:", img.dtype)

check_image(img2)

Image shape: (2208, 2208, 3)
Image data type: uint8

Best Practices with OpenCV#

Always check if images are loaded correctly.
Work with image copies to avoid losing original data.
Use clear variable names for readability.
Comment your code to explain steps.
Use cv2 functions for common tasks.

# Always check if the image is loaded, and use meaningful variables.
path = 'Mathew.png'
img_loaded = cv2.imread(path)
if img_loaded is not None:
    print(f'Loaded {path} successfully.')
else:
    print(f'Could not load {path}.')

Loaded Mathew.png successfully.

Mini-project: Make a Simple Stamped Badge#

Combine your skills to create an image with shapes and text.
You will draw a circle, rectangle, and add text to make a badge.
Save your badge image to disk.

badge = np.zeros((120, 120, 3), dtype=np.uint8)
cv2.circle(badge, (60, 60), 50, (200, 200, 0), -1)
cv2.rectangle(badge, (20, 90), (100,110), (255,0,0), -1)
cv2.putText(badge, "SUCCESS", (15, 105), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255,255,255), 2)
plt.imshow(badge)
plt.title("Your Badge")
plt.axis("off")
plt.show()
cv2.imwrite("my_badge.png", badge)

True

Keep Exploring OpenCV!#

There is a lot more you can do with images and videos.
Try loading your own photos and experimenting.
Check out the OpenCV documentation for more.

Thank you for learning OpenCV!#

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