Image Processing Mini Project

import cv2
import numpy as np
import os

def full_processing_pipeline(input_path, output_dir="output"):
    """
    Apply a full suite of image processing operations.
    Saves each result for visual comparison.
    """
    os.makedirs(output_dir, exist_ok=True)

    image = cv2.imread(input_path)
    if image is None:
        print(f"Could not load: {input_path}")
        return

    print(f"Processing: {input_path}")
    print(f"Original size: {image.shape[1]}x{image.shape[0]}")

    # Resize to standard size
    resized = cv2.resize(image, (640, 480), interpolation=cv2.INTER_AREA)
    cv2.imwrite(f"{output_dir}/01_resized.jpg", resized)

    # Flip horizontally
    flipped = cv2.flip(resized, 1)
    cv2.imwrite(f"{output_dir}/02_flipped.jpg", flipped)

    # Brightness adjustments
    brighter = cv2.convertScaleAbs(resized, alpha=1.0, beta=60)
    darker   = cv2.convertScaleAbs(resized, alpha=1.0, beta=-60)
    cv2.imwrite(f"{output_dir}/03_brighter.jpg", brighter)
    cv2.imwrite(f"{output_dir}/04_darker.jpg", darker)

    # Convert to grayscale
    gray = cv2.cvtColor(resized, cv2.COLOR_BGR2GRAY)
    cv2.imwrite(f"{output_dir}/05_grayscale.jpg", gray)

    # Gaussian blur
    blurred = cv2.GaussianBlur(gray, (5, 5), 0)
    cv2.imwrite(f"{output_dir}/06_blurred.jpg", blurred)

    # Thresholding
    _, otsu = cv2.threshold(blurred, 0, 255,
                            cv2.THRESH_BINARY + cv2.THRESH_OTSU)
    cv2.imwrite(f"{output_dir}/07_threshold.jpg", otsu)

    # Sharpened
    kernel = np.array([[0,-1,0],[-1,5,-1],[0,-1,0]])
    sharpened = cv2.filter2D(resized, -1, kernel)
    cv2.imwrite(f"{output_dir}/08_sharpened.jpg", sharpened)

    print(f"Saved 8 processed images to: {output_dir}/")
    print("Operations: resize, flip, brightness, grayscale, blur, threshold, sharpen")

full_processing_pipeline("your_photo.jpg")