Cursor Practical Guide: Photography Post-Processing and AI Calibration

Cursor Practical Guide: Photography Post-Processing and AI Calibration

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Cursor Practical Guide: Photography Post-Processing and AI Calibration

As a photography enthusiast, I understand the importance of post-processing color correction. Today, I want to share a revolutionary tool – Cursor’s AI intelligent color correction feature!

A few days ago, Xiao Zhang came to me, looking worried, and said: “Niu Ge, I took a great composition of a landscape photo, but the colors just don’t seem right…”

I smiled and patted his shoulder: “Don’t worry, let me teach you how to use Cursor’s AI color correction feature, and it will solve your problem in no time!”

Cursor: The Intelligent Assistant for Post-Processing

Cursor is not just an editor for programmers; its AI color correction feature is simply a great assistant for photographers. It can:

  • • Intelligently analyze color distribution
  • • One-click optimize color temperature and tone
  • • Precisely correct exposure
  • • Automatically balance light and dark contrast

Practical Demonstration: Color Magic

Let’s first look at a segment of color processing code implemented in Python:

import cv2

import numpy as np

def enhance_colors(image_path):

    # Read the original image

    img = cv2.imread(image_path)
    
    # Convert to LAB color space

    lab = cv2.cvtColor(img, cv2.COLOR_BGR2LAB)
    
    # Split channels

    l, a, b = cv2.split(lab)
    
    # Apply CLAHE (Contrast Limited Adaptive Histogram Equalization)

    clahe = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(8,8))
    l = clahe.apply(l)
    
    # Merge channels

    enhanced_lab = cv2.merge([l, a, b])
    
    # Convert back to BGR

    enhanced_img = cv2.cvtColor(enhanced_lab, cv2.COLOR_LAB2BGR)
    
    # Increase color saturation

    hsv = cv2.cvtColor(enhanced_img, cv2.COLOR_BGR2HSV)
    h, s, v = cv2.split(hsv)
    s = cv2.multiply(s, 1.2)  # Increase saturation
    
    enhanced_hsv = cv2.merge([h, s, v])
    
    final_img = cv2.cvtColor(enhanced_hsv, cv2.COLOR_HSV2BGR)
    return final_img

def auto_white_balance(image):

    # Automatic white balance

    result = cv2.cvtColor(image, cv2.COLOR_BGR2LAB)
    avg_a = np.average(result[:, :, 1])
    avg_b = np.average(result[:, :, 2])
    
    result[:, :, 1] = result[:, :, 1] - ((avg_a - 128) * (result[:, :, 0] / 255.0) * 1.1)
    result[:, :, 2] = result[:, :, 2] - ((avg_b - 128) * (result[:, :, 0] / 255.0) * 1.1)
    
    return cv2.cvtColor(result, cv2.COLOR_LAB2BGR)

Four Great Tricks: Perfect Color Correction

1. Intelligent Color Temperature Calibration

Cursor can automatically analyze the scene’s light source and accurately adjust the color temperature, making the photo look more natural.

2. Dynamic Range Optimization

Through intelligent algorithms, it automatically balances highlight and shadow details, giving the photo richer layers.

3. Local Color Enhancement

Intelligently adjust saturation and contrast for different areas, making colors more vibrant.

4. Intelligent Skin Tone Beautification

A boon for portrait photography! Automatically identifies skin tone areas and accurately adjusts skin tone representation.

Practical Tips Galore

Color Correction Secrets for Landscape Photography:

def landscape_enhancement(image_path):

    img = cv2.imread(image_path)
    
    # Enhance blue skies and green fields

    hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
    h, s, v = cv2.split(hsv)
    
    # Enhance blue sky

    blue_mask = cv2.inRange(h, 100, 140)
    s[blue_mask > 0] = cv2.multiply(s[blue_mask > 0], 1.3)
    
    # Enhance green fields

    green_mask = cv2.inRange(h, 35, 85)
    s[green_mask > 0] = cv2.multiply(s[green_mask > 0], 1.2)
    
    enhanced = cv2.merge([h, s, v])
    return cv2.cvtColor(enhanced, cv2.COLOR_HSV2BGR)

Niu Ge’s Color Correction Insights

Color correction is not about creating false effects, but rather about:

  • • Restoring the real scene
  • • Highlighting the focal point of the image
  • • Enhancing visual impact
  • • Expressing photographic concepts

Practical Guide

  1. 1. First, use Cursor for basic calibration
  2. 2. Fine-tune parameters according to scene characteristics
  3. 3. Pay attention to maintaining overall balance in the image
  4. 4. Compare the effects before and after adjustments

Friendly Reminder: Good color correction should be natural; excessive adjustments can backfire.

Advice for beginners: First master basic parameters, then explore advanced techniques.

Now, it’s your turn to create stunning photos with Cursor! Go for it! 🚀

Remember what Niu Ge said: No matter how good the tool is, it requires an aesthetic eye and continuous practice. Let’s continue to move forward on the path of photography together!

— Niu Ge

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