💡 Top 50 Pillow Operations for Image Processing

I. File & Basic Operations

• Open an image file.

from PIL import Image
img = Image.open("image.jpg")

• Save an image.

img.save("new_image.png")

• Display an image (opens in default viewer).

img.show()

• Create a new blank image.

new_img = Image.new("RGB", (200, 100), "blue")

• Get image format (e.g., 'JPEG').

print(img.format)

• Get image dimensions as a (width, height) tuple.

width, height = img.size

• Get pixel format (e.g., 'RGB', 'L' for grayscale).

print(img.mode)

• Convert image mode.

grayscale_img = img.convert("L")

• Get a pixel's color value at (x, y).

r, g, b = img.getpixel((10, 20))

• Set a pixel's color value at (x, y).

img.putpixel((10, 20), (255, 0, 0))

II. Cropping, Resizing & Pasting

• Crop a rectangular region.

box = (100, 100, 400, 400)
cropped_img = img.crop(box)

• Resize an image to an exact size.

resized_img = img.resize((200, 200))

• Create a thumbnail (maintains aspect ratio).

img.thumbnail((128, 128))

• Paste one image onto another.

img.paste(another_img, (50, 50))

III. Rotation & Transformation

• Rotate an image (counter-clockwise).

rotated_img = img.rotate(45, expand=True)

• Flip an image horizontally.

flipped_img = img.transpose(Image.FLIP_LEFT_RIGHT)

• Flip an image vertically.

flipped_img = img.transpose(Image.FLIP_TOP_BOTTOM)

• Rotate by 90, 180, or 270 degrees.

img_90 = img.transpose(Image.ROTATE_90)

• Apply an affine transformation.

transformed = img.transform(img.size, Image.AFFINE, (1, 0.5, 0, 0, 1, 0))

IV. ImageOps Module Helpers

• Invert image colors.

from PIL import ImageOps
inverted_img = ImageOps.invert(img)

• Flip an image horizontally (mirror).

mirrored_img = ImageOps.mirror(img)

• Flip an image vertically.

flipped_v_img = ImageOps.flip(img)

• Convert to grayscale.

grayscale = ImageOps.grayscale(img)

• Colorize a grayscale image.

colorized = ImageOps.colorize(grayscale, black="blue", white="yellow")

• Reduce the number of bits for each color channel.

posterized = ImageOps.posterize(img, 4)

• Auto-adjust image contrast.

adjusted_img = ImageOps.autocontrast(img)

• Equalize the image histogram.

equalized_img = ImageOps.equalize(img)

• Add a border to an image.

bordered = ImageOps.expand(img, border=10, fill='black')

V. Color & Pixel Operations

• Split image into individual bands (e.g., R, G, B).

r, g, b = img.split()

• Merge bands back into an image.

merged_img = Image.merge("RGB", (r, g, b))

• Apply a function to each pixel.

brighter_img = img.point(lambda i: i * 1.2)

• Get a list of colors used in the image.

colors = img.getcolors(maxcolors=256)

• Blend two images with alpha compositing.

# Both images must be in RGBA mode
blended = Image.alpha_composite(img1_rgba, img2_rgba)

VI. Filters (ImageFilter)

• Apply a simple blur filter.

from PIL import ImageFilter
blurred_img = img.filter(ImageFilter.BLUR)

• Apply a box blur with a given radius.

box_blur = img.filter(ImageFilter.BoxBlur(5))

• Apply a Gaussian blur.

gaussian_blur = img.filter(ImageFilter.GaussianBlur(radius=2))

• Sharpen the image.

sharpened = img.filter(ImageFilter.SHARPEN)

• Find edges.

edges = img.filter(ImageFilter.FIND_EDGES)

• Enhance edges.

edge_enhanced = img.filter(ImageFilter.EDGE_ENHANCE)

• Emboss the image.

embossed = img.filter(ImageFilter.EMBOSS)

• Find contours.

contours = img.filter(ImageFilter.CONTOUR)

VII. Image Enhancement (ImageEnhance)

• Adjust color saturation.

from PIL import ImageEnhance
enhancer = ImageEnhance.Color(img)
vibrant_img = enhancer.enhance(2.0)

• Adjust brightness.

enhancer = ImageEnhance.Brightness(img)
bright_img = enhancer.enhance(1.5)

• Adjust contrast.

enhancer = ImageEnhance.Contrast(img)
contrast_img = enhancer.enhance(1.5)

• Adjust sharpness.

enhancer = ImageEnhance.Sharpness(img)
sharp_img = enhancer.enhance(2.0)

VIII. Drawing (ImageDraw & ImageFont)

• Draw text on an image.

from PIL import ImageDraw, ImageFont
draw = ImageDraw.Draw(img)
font = ImageFont.truetype("arial.ttf", 36)
draw.text((10, 10), "Hello", font=font, fill="red")

• Draw a line.

draw.line((0, 0, 100, 200), fill="blue", width=3)

• Draw a rectangle (outline).

draw.rectangle([10, 10, 90, 60], outline="green", width=2)

• Draw a filled ellipse.

draw.ellipse([100, 100, 180, 150], fill="yellow")

• Draw a polygon.

draw.polygon([(10,10), (20,50), (60,10)], fill="purple")

#Python #Pillow #ImageProcessing #PIL #CheatSheet

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By: @CodeProgrammer ✨

🏆 150 Python Clean Code Essentials

📢 Elevate your Python skills! Discover 150 essential Clean Code principles for writing readable, understandable, and maintainable code.

⚡ Tap to unlock the complete answer and gain instant insight.

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By: @CodeProgrammer ✨

This combination is perhaps as low as we can get to explain how the Transformer works

#Transformers #LLM #AI

https://news.1rj.ru/str/CodeProgrammer 👍

100 Python Interview Questions and Answers

This book is a practical guide to mastering Python interview preparation. It contains 100 carefully curated questions with clear, concise answers designed in a quick-reference style.

#Python #PythonTips #PythonProgramming

https://news.1rj.ru/str/CodeProgrammer

Python Interview Codes Cheatsheet

Python Interview Codes Cheatsheet

https://news.1rj.ru/str/CodeProgrammer

🏆 Python NumPy Tips

📢 Unlock the power of NumPy! Get essential Python tips for creating and manipulating arrays effectively for data analysis and scientific computing.

⚡ Tap to unlock the complete answer and gain instant insight.

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By: @CodeProgrammer ✨

🤖🧠 The Transformer Architecture: How Attention Revolutionized Deep Learning

🗓️ 11 Nov 2025
📚 AI News & Trends

The field of artificial intelligence has witnessed a remarkable evolution and at the heart of this transformation lies the Transformer architecture. Introduced by Vaswani et al. in 2017, the paper “Attention Is All You Need” redefined the foundations of natural language processing (NLP) and sequence modeling. Unlike its predecessors – recurrent and convolutional neural networks, ...

#TransformerArchitecture #AttentionMechanism #DeepLearning #NaturalLanguageProcessing #NLP #AIResearch

🏆 Streamline Your Email Sending

📢 Effortlessly send emails to large audiences! This guide unlocks the power of email automation for your information and promotional campaigns.

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By: @CodeProgrammer ✨

Tip for clean code in Python:

Use Dataclasses for classes that primarily store data. The @dataclass decorator automatically generates special methods like __init__(), __repr__(), and __eq__(), reducing boilerplate code and making your intent clearer.

from dataclasses import dataclass

# --- BEFORE: Using a standard class ---
# A lot of boilerplate code is needed for basic functionality.

class ProductOld:
    def __init__(self, name: str, price: float, sku: str):
        self.name = name
        self.price = price
        self.sku = sku

    def __repr__(self):
        return f"ProductOld(name='{self.name}', price={self.price}, sku='{self.sku}')"

    def __eq__(self, other):
        if not isinstance(other, ProductOld):
            return NotImplemented
        return (self.name, self.price, self.sku) == (other.name, other.price, other.sku)

# Example Usage
product_a = ProductOld("Laptop", 1200.00, "LP-123")
product_b = ProductOld("Laptop", 1200.00, "LP-123")

print(product_a)  # Output: ProductOld(name='Laptop', price=1200.0, sku='LP-123')
print(product_a == product_b)  # Output: True


# --- AFTER: Using a dataclass ---
# The code is concise, readable, and less error-prone.

@dataclass(frozen=True) # frozen=True makes instances immutable
class Product:
    name: str
    price: float
    sku: str

# Example Usage
product_c = Product("Laptop", 1200.00, "LP-123")
product_d = Product("Laptop", 1200.00, "LP-123")

print(product_c)  # Output: Product(name='Laptop', price=1200.0, sku='LP-123')
print(product_c == product_d)  # Output: True

#Python #CleanCode #ProgrammingTips #SoftwareDevelopment #Dataclasses #CodeQuality

━━━━━━━━━━━━━━━
By: @CodeProgrammer ✨

📌 PyTorch Tutorial for Beginners: Build a Multiple Regression Model from Scratch

🗂 Category: DEEP LEARNING

🕒 Date: 2025-11-19 | ⏱️ Read time: 14 min read

Dive into PyTorch with this hands-on tutorial for beginners. Learn to build a multiple regression model from the ground up using a 3-layer neural network. This guide provides a practical, step-by-step approach to machine learning with PyTorch, ideal for those new to the framework.

#PyTorch #MachineLearning #NeuralNetwork #Regression #Python

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