Image Processing with Python: Pillow and OpenCV Guide

Processing images is a common task in Python, especially in computer vision, image processing, and data analysis, and the most widely used libraries are Pillow and OpenCV.

Using the Pillow library

from PIL import Image
import matplotlib.pyplot as plt
img = Image.open('image.jpg')
plt.imshow(img)
plt.show()

img = Image.open('image.jpg')
img.save('output_image.png', 'PNG')

img = Image.open('image.jpg')
resized_img = img.resize((800, 600))
resized_img.save('resized_image.jpg')

img = Image.open('image.jpg')
cropped_img = img.crop((50, 50, 300, 300))  # (left, upper, right, lower)
cropped_img.save('cropped_image.jpg')

img = Image.open('image.jpg')
rotated_img = img.rotate(90)
rotated_img.save('rotated_image.jpg')

img = Image.open('image.jpg').convert('L')
img.save('gray_image.jpg')

from PIL import Image, ImageDraw, ImageFont
img = Image.open('image.jpg')
draw = ImageDraw.Draw(img)
font = ImageFont.truetype("arial.ttf", 36)
draw.text((50, 50), "Hello, World!", fill="white", font=font)
img.save('text_image.jpg')

background = Image.open('background.jpg')
foreground = Image.open('foreground.png').convert('RGBA')
background.paste(foreground, (0, 0), foreground)
background.save('composite_image.jpg')

from PIL import Image, ImageFilter
img = Image.open('image.jpg')
blurred_img = img.filter(ImageFilter.BLUR)
blurred_img.save('blurred_image.jpg')

img = Image.open('image.jpg')
sharpened_img = img.filter(ImageFilter.SHARPEN)
sharpened_img.save('sharpened_image.jpg')

from PIL import Image, ImageEnhance
img = Image.open('image.jpg')
enhancer = ImageEnhance.Contrast(img)
enhanced_img = enhancer.enhance(1.5)
enhanced_img.save('enhanced_image.jpg')

img = Image.open('image.jpg')
print(f"Format: {img.format}")
print(f"Size: {img.size}")
print(f"Mode: {img.mode}")

Using the OpenCV library

import cv2
import matplotlib.pyplot as plt
img = cv2.imread('image.jpg')
img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
plt.imshow(img_rgb)
plt.show()

img = cv2.imread('image.jpg')
cv2.imwrite('output_image.png', img)

img = cv2.imread('image.jpg')
resized_img = cv2.resize(img, (800, 600))
cv2.imwrite('resized_image.jpg', resized_img)

img = cv2.imread('image.jpg')
cropped_img = img[50:300, 50:300]
cv2.imwrite('cropped_image.jpg', cropped_img)

img = cv2.imread('image.jpg')
(h, w) = img.shape[:2]
center = (w // 2, h // 2)
M = cv2.getRotationMatrix2D(center, 90, 1.0)
rotated_img = cv2.warpAffine(img, M, (w, h))
cv2.imwrite('rotated_image.jpg', rotated_img)

img = cv2.imread('image.jpg', cv2.IMREAD_GRAYSCALE)
cv2.imwrite('gray_image.jpg', img)

edges = cv2.Canny(img, 100, 200)
cv2.imwrite('edges_image.jpg', edges)

_, thresh_img = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY)
cv2.imwrite('threshold_image.jpg', thresh_img)