Image Processing Useful code snippets

ImageProcessingUsefulCode.md

Image Processing useful code snippets

1. Pillow

Installation

pip install Pillow

Open an image

from PIL import Image

image = Image.open("path/to/image.jpg")

Display an image

image.show()

Resize an image

resized_image = image.resize((width, height))

Rotate an image

rotated_image = image.rotate(angle)

Save an image

image.save("path/to/image.jpg")

2. OpenCV

Installation

pip install opencv-python

Read and display an image

import cv2

# Read an image
img = cv2.imread("image.jpg")

# Display the image
cv2.imshow("Image", img)
cv2.waitKey(0)
cv2.destroyAllWindows()

Convert to grayscale

import cv2

# Read an image
img = cv2.imread("image.jpg")

# Convert to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# Display the grayscale image
cv2.imshow("Gray Image", gray)
cv2.waitKey(0)
cv2.destroyAllWindows()

Resize an image

import cv2

# Read an image
img = cv2.imread("image.jpg")

# Resize the image
resized = cv2.resize(img, (200, 200))

# Display the resized image
cv2.imshow("Resized Image", resized)
cv2.waitKey(0)
cv2.destroyAllWindows()

Treshold an image

import cv2

# Read an image
img = cv2.imread("image.jpg")

# Convert to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# Threshold the image
_, thresh = cv2.threshold(gray, 128, 255, cv2.THRESH_BINARY)

# Display the thresholded image
cv2.imshow("Thresholded Image", thresh)
cv2.waitKey(0)
cv2.destroyAllWindows()

3. Scikit-Image

Installation

pip install scikit-image

Load an image

from skimage import io

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

Display an image

import matplotlib.pyplot as plt

plt.imshow(image)
plt.show()

Resize an image

from skimage import transform

resized_image = transform.resize(image, (200, 200))

Grayscale conversion

from skimage import color

gray_image = color.rgb2gray(image)

Edge detection

from skimage import filters

edges = filters.sobel(gray_image)

Thresholding

from skimage import filters

threshold_value = filters.threshold_otsu(gray_image)
binary_image = gray_image > threshold_value