Introduction

OpenCV-Python is a powerful library for computer vision tasks such as image processing, object detection, and image enhancement. In this blog post, we will explore how to perform focus stacking using OpenCV-Python. Focus stacking is a technique used to combine multiple images with different focal points to create a final image with a greater depth of field.

Focus Stacking Algorithm

The focus stacking algorithm can be summarized in the following steps:

1. Capture a series of images with different focal points.
2. Align the images to correct any small misalignments caused by camera movement or subject motion.
3. Calculate a measure of focus for each pixel in the images.
4. Select the in-focus pixels from each image to create a final composite image.

Implementation

Let’s dive into the implementation of the focus stacking algorithm using the OpenCV-Python library. We will assume that you have already installed OpenCV-Python on your system.

Step 1: Capture Images

First, we need to capture a series of images with different focal points. This can be done by manually adjusting the focus of the camera or by using a focus stacking rail. Save the captured images in a folder on your computer.

Step 2: Align Images

To align the images, we can use the OpenCV-Python library’s feature matching capabilities. We will use the SIFT (Scale-Invariant Feature Transform) algorithm to detect and match keypoints in the images.

Here is an example code to align two images using SIFT:

import cv2

def align_images(image1, image2):
    sift = cv2.SIFT_create()
    
    keypoints1, descriptors1 = sift.detectAndCompute(image1, None)
    keypoints2, descriptors2 = sift.detectAndCompute(image2, None)
    
    matcher = cv2.BFMatcher()
    matches = matcher.knnMatch(descriptors1, descriptors2, k=2)
    
    good_matches = []
    for m, n in matches:
        if m.distance < 0.7 * n.distance:
            good_matches.append(m)
    
    src_pts = np.float32([keypoints1[m.queryIdx].pt for m in good_matches]).reshape(-1, 1, 2)
    dst_pts = np.float32([keypoints2[m.trainIdx].pt for m in good_matches]).reshape(-1, 1, 2)
    
    M, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 5.0)
    aligned_image = cv2.warpPerspective(image1, M, (image2.shape[1], image2.shape[0]))
    
    return aligned_image

Step 3: Calculate Focus Measure

To calculate the focus measure for each pixel in the aligned images, we can use the Laplacian operator. The Laplacian operator is a second-order derivative operator that highlights edges and high-frequency details in the image.

Here is an example code to calculate the focus measure of an image using the Laplacian operator:

import cv2

def calculate_focus_measure(image):
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    laplacian = cv2.Laplacian(gray, cv2.CV_64F)
    focus_measure = laplacian.var()
    
    return focus_measure

Step 4: Select In-Focus Pixels

Finally, we need to select the in-focus pixels from each image to create the final composite image. We can apply a simple thresholding technique to select the pixels with high focus measures.

Here is an example code to select the in-focus pixels from aligned images:

import cv2

def select_infocus_pixels(images, threshold):
    focus_measures = []
    for image in images:
        focus_measures.append(calculate_focus_measure(image))
    
    composite_image = np.zeros_like(images[0])
    
    for i in range(images[0].shape[0]):
        for j in range(images[0].shape[1]):
            pixel_focus_measures = [fm[i, j] for fm in focus_measures]
            max_focus_measure = max(pixel_focus_measures)
            
            if max_focus_measure > threshold:
                index = pixel_focus_measures.index(max_focus_measure)
                composite_image[i, j] = images[index][i, j]
    
    return composite_image

Conclusion

In this blog post, we have explored how to perform focus stacking using OpenCV-Python. By capturing multiple images with different focal points and aligning, calculating focus measures, and selecting in-focus pixels, we can create a final composite image with a greater depth of field.

OpenCV-Python provides a wide range of functionality for image processing tasks, and focus stacking is just one of many applications. Have fun experimenting with different algorithms and techniques to enhance your images using OpenCV-Python!