In Python, image processing unlocks powerful capabilities for computer vision, data augmentation, and automation—master these techniques to excel in ML engineering interviews and real-world applications! 🖼️

# PIL/Pillow Basics - The essential image library
from PIL import Image

# Open and display image
img = Image.open("input.jpg")
img.show()

# Convert formats
img.save("output.png")
img.convert("L").save("grayscale.jpg")  # RGB to grayscale

# Basic transformations
img.rotate(90).save("rotated.jpg")
img.resize((300, 300)).save("resized.jpg")
img.transpose(Image.FLIP_LEFT_RIGHT).save("mirrored.jpg")

# Advanced Manipulation - Professional editing
from PIL import ImageEnhance, ImageFilter

# Adjust brightness/contrast
enhancer = ImageEnhance.Brightness(img)
bright_img = enhancer.enhance(1.5)  # 50% brighter

# Apply filters
blurred = img.filter(ImageFilter.BLUR)
sharpened = img.filter(ImageFilter.SHARPEN)
edges = img.filter(ImageFilter.FIND_EDGES)

# Color manipulation
color_enhancer = ImageEnhance.Color(img)
color_enhancer.enhance(2.0).save("vibrant.jpg")  # Double saturation

# OpenCV Integration - Computer vision powerhouse
import cv2
import numpy as np

# Read and convert color spaces
cv_img = cv2.imread("input.jpg")
rgb_img = cv2.cvtColor(cv_img, cv2.COLOR_BGR2RGB)
hsv_img = cv2.cvtColor(cv_img, cv2.COLOR_BGR2HSV)

# Edge detection (Canny algorithm)
edges = cv2.Canny(cv_img, 100, 200)
cv2.imwrite("edges.jpg", edges)

# Face detection (interview favorite)
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
faces = face_cascade.detectMultiScale(rgb_img, 1.3, 5)
for (x, y, w, h) in faces:
    cv2.rectangle(cv_img, (x, y), (x+w, y+h), (255, 0, 0), 2)
cv2.imwrite("faces.jpg", cv_img)

# Batch Processing - Production automation
import os
from PIL import Image

def process_images(input_dir, output_dir):
    os.makedirs(output_dir, exist_ok=True)
    for filename in os.listdir(input_dir):
        if filename.lower().endswith(('.png', '.jpg', '.jpeg')):
            with Image.open(os.path.join(input_dir, filename)) as img:
                # Resize while maintaining aspect ratio
                img.thumbnail((800, 800))
                # Apply watermark
                watermark = Image.open("watermark.png")
                img.paste(watermark, (img.width - watermark.width, img.height - watermark.height), watermark)
                img.save(os.path.join(output_dir, filename))

process_images("raw_photos", "processed")

# Image Augmentation - Deep learning preparation
from torchvision import transforms

transform = transforms.Compose([
    transforms.RandomHorizontalFlip(),
    transforms.ColorJitter(brightness=0.2, contrast=0.2),
    transforms.RandomRotation(15),
    transforms.Resize((224, 224)),
    transforms.ToTensor()
])

# Apply to dataset
augmented_img = transform(img)

# EXIF Data Handling - Privacy/security critical
from PIL import Image

img = Image.open("photo_with_gps.jpg")

# Strip metadata (security interview question)
data = list(img.getdata())
clean_img = Image.new(img.mode, img.size)
clean_img.putdata(data)
clean_img.save("clean.jpg", "JPEG", exif=b"")

# Read specific metadata
exif = img.getexif()
if 36867 in exif:  # DateTimeOriginal
    print(exif[36867])

# Image Segmentation - Advanced computer vision
import numpy as np
import cv2

img = cv2.imread('input.jpg')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
_, thresh = cv2.threshold(gray, 180, 255, cv2.THRESH_BINARY_INV)

# Morphological operations
kernel = np.ones((2,2), np.uint8)
dilated = cv2.dilate(thresh, kernel, iterations=1)

# Find contours
contours, _ = cv2.findContours(dilated, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
for cnt in contours:
    area = cv2.contourArea(cnt)
    if area > 100:  # Filter small contours
        x, y, w, h = cv2.boundingRect(cnt)
        cv2.rectangle(img, (x, y), (x+w, y+h), (0, 255, 0), 2)

cv2.imwrite("segmented.jpg", img)

# Memory Optimization - Handle large images
from PIL import Image

# Process without loading entire image
with Image.open("huge_image.tiff") as img:
    # Work with tiles
    for i, (x, y, w, h) in enumerate(img.tile):
        tile = img.crop((x, y, x+w, y+h))
        # Process tile
        processed_tile = tile.filter(ImageFilter.SHARPEN)
        # Paste back
        img.paste(processed_tile, (x, y))
    img.save("optimized.tiff")

# Async Processing - Modern Python requirement
import asyncio
from PIL import Image

async def process_image_async(filename):
    loop = asyncio.get_running_loop()
    return await loop.run_in_executor(
        None, 
        lambda: Image.open(filename).resize((500, 500)).save(f"thumb_{filename}")
    )

async def main():
    tasks = [process_image_async(f) for f in ["img1.jpg", "img2.jpg", "img3.jpg"]]
    await asyncio.gather(*tasks)

asyncio.run(main())

# Cloud Integration - Production pipeline
from google.cloud import storage
from PIL import Image
import io

def process_gcs_image(bucket_name, source_blob, destination_blob):
    storage_client = storage.Client()
    bucket = storage_client.bucket(bucket_name)
    
    # Download from GCS
    blob = bucket.blob(source_blob)
    img_data = blob.download_as_bytes()
    img = Image.open(io.BytesIO(img_data))
    
    # Process image
    img = img.convert("RGB").resize((1024, 1024))
    
    # Upload back to GCS
    buffer = io.BytesIO()
    img.save(buffer, "JPEG")
    bucket.blob(destination_blob).upload_from_string(
        buffer.getvalue(), 
        content_type="image/jpeg"
    )

process_gcs_image("user-photos", "raw/photo.jpg", "processed/photo.jpg")

# Dockerized Service - Deployment pattern
# Dockerfile snippet:
# FROM python:3.10-slim
# RUN pip install pillow opencv-python
# COPY image_service.py /app/
# CMD ["python", "/app/image_service.py"]

# image_service.py
from http.server import HTTPServer, BaseHTTPRequestHandler
from PIL import Image
import io

class ImageHandler(BaseHTTPRequestHandler):
    def do_POST(self):
        content_len = int(self.headers['Content-Length'])
        img_data = self.rfile.read(content_len)
        
        # Process image
        img = Image.open(io.BytesIO(img_data))
        img = img.resize((800, 800))
        
        # Return processed image
        buffer = io.BytesIO()
        img.save(buffer, "JPEG")
        self.send_response(200)
        self.send_header("Content-Type", "image/jpeg")
        self.end_headers()
        self.wfile.write(buffer.getvalue())

HTTPServer(('', 8000), ImageHandler).serve_forever()

# Performance Benchmarking - Optimization proof
import time
from PIL import Image

# Compare resize methods
start = time.time()
for _ in range(100):
    Image.open("input.jpg").resize((500, 500), Image.LANCZOS)
lanczos_time = time.time() - start

start = time.time()
for _ in range(100):
    Image.open("input.jpg").resize((500, 500), Image.NEAREST)
nearest_time = time.time() - start

print(f"LANCZOS: {lanczos_time:.2f}s | NEAREST: {nearest_time:.2f}s")
# Output: LANCZOS: 4.20s | NEAREST: 1.80s (Quality vs Speed tradeoff)

# Image Hashing - Deduplication solution
import imagehash
from PIL import Image

def find_duplicates(image_dir, threshold=5):
    hashes = {}
    for filename in os.listdir(image_dir):
        img = Image.open(os.path.join(image_dir, filename))
        img_hash = imagehash.phash(img)
        
        # Find similar images
        for existing_hash, files in hashes.items():
            if img_hash - existing_hash < threshold:
                files.append(filename)
                break
        else:
            hashes[img_hash] = [filename]
    
    return [files for files in hashes.values() if len(files) > 1]

duplicates = find_duplicates("user_uploads")

# OCR Processing - Text extraction
import pytesseract
from PIL import Image

# Configure Tesseract path (if needed)
# pytesseract.pytesseract.tesseract_cmd = '/usr/bin/tesseract'

img = Image.open("document.jpg")
text = pytesseract.image_to_string(img, lang='eng')
print(text[:200])  # First 200 characters

# Get bounding boxes for text
data = pytesseract.image_to_data(img, output_type=pytesseract.Output.DICT)
for i, word in enumerate(data['text']):
    if word:
        x, y, w, h = data['left'][i], data['top'][i], data['width'][i], data['height'][i]
        print(f"Word: {word} | Position: ({x},{y}) Size: {w}x{h}")

# Generative Art - Creative application
from PIL import Image, ImageDraw
import random

img = Image.new('RGB', (800, 800), (255, 255, 255))
draw = ImageDraw.Draw(img)

# Generate random geometric pattern
for _ in range(1000):
    x1, y1 = random.randint(0, 800), random.randint(0, 800)
    x2, y2 = x1 + random.randint(-100, 100), y1 + random.randint(-100, 100)
    color = (random.randint(0, 255), random.randint(0, 255), random.randint(0, 255))
    draw.line([x1, y1, x2, y2], fill=color, width=random.randint(1, 5))

img.save("generative_art.jpg")

# Image Steganography - Security technique
def hide_message(image_path, message, output_path):
    img = Image.open(image_path)
    binary_message = ''.join(format(ord(c), '08b') for c in message) + '1111111111111110'
    
    pixels = list(img.getdata())
    new_pixels = []
    bit_index = 0
    
    for pixel in pixels:
        if bit_index < len(binary_message):
            r, g, b = pixel[:3]
            r = (r & ~1) | int(binary_message[bit_index])
            bit_index += 1
            new_pixels.append((r, g, b))
        else:
            new_pixels.append(pixel)
    
    img.putdata(new_pixels)
    img.save(output_path)

hide_message("cover.jpg", "Secret message", "stego.png")

# Interview Power Move: Custom Filter Implementation
import numpy as np
from PIL import Image

def apply_sepia(img):
    # Convert to numpy array
    img_array = np.array(img)
    
    # Sepia matrix (BT.709 coefficients)
    sepia_matrix = np.array([
        [0.393, 0.769, 0.189],
        [0.349, 0.686, 0.168],
        [0.272, 0.534, 0.131]
    ])
    
    # Apply matrix multiplication
    sepia_array = img_array @ sepia_matrix.T
    sepia_array = np.clip(sepia_array, 0, 255).astype(np.uint8)
    
    return Image.fromarray(sepia_array)

sepia_img = apply_sepia(Image.open("input.jpg"))
sepia_img.save("sepia.jpg")

# Pro Tip: Memory-Mapped Processing for Gigapixel Images
import numpy as np
from PIL import Image

def process_gigapixel(image_path):
    # Create memory-mapped array
    img = Image.open(image_path)
    mmap_file = np.memmap('temp.mmap', dtype='uint8', mode='w+', shape=img.size[::-1] + (3,))
    
    # Process in chunks
    chunk_size = (1000, 1000)
    for y in range(0, img.height, chunk_size[1]):
        for x in range(0, img.width, chunk_size[0]):
            box = (x, y, min(x+chunk_size[0], img.width), min(y+chunk_size[1], img.height))
            chunk = np.array(img.crop(box))
            mmap_file[y:box[3], x:box[2]] = chunk
    
    # Process entire image in memory-mapped array
    mmap_file = np.where(mmap_file > 128, 255, 0)  # Binarize
    Image.fromarray(mmap_file).save("processed.jpg")

process_gigapixel("gigapixel.tiff")

# Real-World Case Study: E-commerce Product Pipeline
import boto3
from PIL import Image
import io

def process_product_image(s3_bucket, s3_key):
    # 1. Download from S3
    s3 = boto3.client('s3')
    response = s3.get_object(Bucket=s3_bucket, Key=s3_key)
    img = Image.open(io.BytesIO(response['Body'].read()))
    
    # 2. Standardize dimensions
    img = img.convert("RGB")
    img = img.resize((1200, 1200), Image.LANCZOS)
    
    # 3. Remove background (simplified)
    # In practice: use rembg or AWS Rekognition
    img = remove_background(img)
    
    # 4. Generate variants
    variants = {
        "web": img.resize((800, 800)),
        "mobile": img.resize((400, 400)),
        "thumbnail": img.resize((100, 100))
    }
    
    # 5. Upload to CDN
    for name, variant in variants.items():
        buffer = io.BytesIO()
        variant.save(buffer, "JPEG", quality=95)
        s3.upload_fileobj(
            buffer, 
            "cdn-bucket", 
            f"products/{s3_key.split('/')[-1].split('.')[0]}_{name}.jpg",
            ExtraArgs={'ContentType': 'image/jpeg', 'CacheControl': 'max-age=31536000'}
        )
    
    # 6. Generate WebP version
    webp_buffer = io.BytesIO()
    img.save(webp_buffer, "WEBP", quality=85)
    s3.upload_fileobj(webp_buffer, "cdn-bucket", f"products/{s3_key.split('/')[-1].split('.')[0]}.webp")

process_product_image("user-uploads", "products/summer_dress.jpg")

By: @DataScienceM 👁️

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