Python Basic Red-Eye Removal in Photos

Red-eye is a common issue in photos. It happens when flash reflects off the retina. Python can help fix it easily.

This guide shows how to remove red-eye using OpenCV. It's perfect for beginners. No advanced skills are needed.

Understanding Red-Eye Effect

Red-eye appears when light hits blood vessels in the eye. It's more common in low light. The flash causes the red color.

To fix it, we detect red pixels in the eye area. Then we replace them with natural colors. Python automates this process.

Setting Up Your Environment

First, install OpenCV. Use pip for installation. Run this command in your terminal:

pip install opencv-python

You'll also need NumPy. It helps with array operations. Install it with pip:

pip install numpy

Loading the Image

Start by loading your image. Use cv2.imread for this. Here's how to do it:

import cv2

# Load the image
image = cv2.imread('photo_with_red_eye.jpg')

Always check if the image loaded correctly. Display it using cv2.imshow.

Detecting Red-Eye Regions

Red-eye appears as bright red circles. We'll detect these areas. Convert the image to HSV color space first.

# Convert to HSV
hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)

HSV makes color detection easier. Define a range for red colors. Then create a mask.

# Define red color range
lower_red = (0, 70, 50)
upper_red = (10, 255, 255)

# Create mask
mask = cv2.inRange(hsv, lower_red, upper_red)

Removing Red-Eye Effect

Now replace red pixels with darker ones. This simulates the natural eye color. Use the mask to find red areas.

# Apply the mask
image[mask > 0] = [0, 0, 0]  # Replace with black

For better results, blend the colors. This makes the fix look more natural.

Saving the Result

After processing, save your image. Use cv2.imwrite for this step.

# Save the result
cv2.imwrite('fixed_photo.jpg', image)

Complete Code Example

Here's the full code for red-eye removal. It combines all the steps above.

import cv2
import numpy as np

# Load image
image = cv2.imread('photo_with_red_eye.jpg')

# Convert to HSV
hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)

# Define red range and create mask
lower_red = (0, 70, 50)
upper_red = (10, 255, 255)
mask = cv2.inRange(hsv, lower_red, upper_red)

# Apply mask
image[mask > 0] = [0, 0, 0]

# Save result
cv2.imwrite('fixed_photo.jpg', image)

Tips for Better Results

Adjust the color range if needed. Different cameras produce slightly different reds.

For more image editing, see our Python Image Flipping Guide.

Learn about other techniques in our Python Image Analysis Guide.

Conclusion

Python makes red-eye removal simple. With OpenCV, you can automate the process. This saves time and improves photos.

For more image processing, check our Python Image Rescaling Guide.

Start experimenting with your photos today. The results will impress you!