Before you can start using the recipes in this article, you'll need to install a few software libraries. These libraries provide the necessary functionality for performing image processing and computer vision tasks.

The following libraries are required for all of the recipes in this article:

• OpenCV
• NumPy
• Matplotlib

You can install these libraries using the following commands:

Python Image Processing Cookbook: Over 60 recipes to help you perform complex image processing and computer vision tasks with ease

by Shannon Duffy

4.2 out of 5

Language	:	English
File size	:	86658 KB
Text-to-Speech	:	Enabled
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Paperback	:	37 pages
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pip install opencv-python pip install numpy pip install matplotlib

Once you have installed the required libraries, you can start using the recipes in this article.

The following recipes are divided into several categories:

• Basic Image Manipulation
• Advanced Image Processing
• Object Detection and Tracking
• Machine Learning for Image Processing

The following recipes cover basic image manipulation tasks, such as resizing, cropping, and rotating images.

• Resize an image:python import cv2

Read the image

image = cv2.imread("image.jpg")

Resize the image to half its original size

resized_image = cv2.resize(image, (0, 0),fx=0.5, fy=0.5)

Save the resized image

cv2.imwrite("resized_image.jpg", resized_image)

• Crop an image:python import cv2

Read the image

image = cv2.imread("image.jpg")

Crop the image to a square

cropped_image = image[0:512, 0:512]

Save the cropped image

cv2.imwrite("cropped_image.jpg", cropped_image)

• Rotate an image:python import cv2

Read the image

image = cv2.imread("image.jpg")

Rotate the image by 90 degrees

rotated_image = cv2.rotate(image, cv2.ROTATE_90_CLOCKWISE)

Save the rotated image

cv2.imwrite("rotated_image.jpg", rotated_image)

The following recipes cover more advanced image processing tasks, such as filtering, thresholding, and morphological operations.

• Apply a Gaussian blur to an image:python import cv2

Read the image

image = cv2.imread("image.jpg")

Apply a Gaussian blur to the image

blurred_image = cv2.GaussianBlur(image, (5, 5),0)

Save the blurred image

cv2.imwrite("blurred_image.jpg", blurred_image)

• Threshold an image:python import cv2

Read the image

image = cv2.imread("image.jpg")

Convert the image to grayscale

gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

Threshold the image

threshold_image = cv2.threshold(gray_image, 127, 255, cv2.THRESH_BINARY)[1]

Save the threshold image

cv2.imwrite("threshold_image.jpg", threshold_image)

• Perform a morphological operation on an image:python import cv2

Read the image

image = cv2.imread("image.jpg")

Convert the image to grayscale

gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

Perform a morphological operation on the image

morphological_image = cv2.morphologyEx(gray_image, cv2.MORPH_CLOSE, cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5)))

Save the morphological image

cv2.imwrite("morphological_image.jpg", morphological_image)

The following recipes cover object detection and tracking tasks, such as finding objects in an image and tracking them over time.

• Detect objects in an image:python import cv2

Read the image

image = cv2.imread("image.jpg")

Convert the image to grayscale

gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

Detect objects in the image

objects = cv2.findContours(gray_image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[0]

Draw the objects on the image

cv2.drawContours(image, objects, -1, (0, 255, 0),2)

Save the image with the objects

cv2.imwrite("objects_image.jpg", image)

• Track objects in a video:python import cv2

Create a video capture object

cap = cv2.VideoCapture("video.mp4")

Create an object tracker

Python Image Processing Cookbook: Over 60 recipes to help you perform complex image processing and computer vision tasks with ease

by Shannon Duffy

4.2 out of 5

Language	:	English
File size	:	86658 KB
Text-to-Speech	:	Enabled
Screen Reader	:	Supported
Enhanced typesetting	:	Enabled
Print length	:	438 pages
Paperback	:	37 pages
Item Weight	:	3.84 ounces
Dimensions	:	6 x 0.1 x 9 inches

FREE