In this article, I will show you how to write a short (200 lines) Python script that automatically replaces the face of one image with the face of another. This process is divided into four main steps: detecting facial landmarks, aligning the second image to match the first, adjusting the color balance, and finally blending the features of the second image into the first.
1. **Use dlib to extract facial markers**
The script uses dlib's Python binding to detect and extract facial landmarks from an image. The algorithm used is based on Vahid Kazemi and Josephine Sullivan’s paper "Using Regression Tree One Millisecond Face Alignment." While the underlying mathematics is complex, the dlib interface is straightforward. Here's an example of how it works:
```python
PREDICTOR_PATH = "/home/matt/dlib-18.16/shape_predictor_68_face_landmarks.dat"
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor(PREDICTOR_PATH)
def get_landmarks(im):
rects = detector(im, 1)
if len(rects) > 1:
raise TooManyFaces
if len(rects) == 0:
raise NoFaces
return numpy.matrix([[px, py] for pin predictor(im, rects[0]).parts()])
```
This function returns a matrix of 68 points, each representing a specific facial feature such as the eyes, nose, and mouth.
2. **Analyze the face using Procrustes analysis**
Once we have the landmark coordinates for both images, we need to find a transformation that aligns the second image with the first. This involves rotation, scaling, and translation. The solution to this problem is known as the Procrustes analysis. Here's how it works in code:
```python
def transformation_from_points(points1, points2):
# Implementation of Procrustes analysis
# ...
return transformation_matrix
```
This transformation is then applied to the second image using OpenCV's `warpAffine` function to align it with the first.
3. **Correct the color of the second image**
After alignment, the colors may not match between the two images, leading to visible seams. To fix this, we use a Gaussian blur to create a local color correction. The idea is to scale the colors of the second image based on the first, ensuring a more natural blend.
```python
def correct_colours(im1, im2, landmarks1):
# Calculate blur amount based on eye positions
# Apply Gaussian blur and adjust colors
# ...
return corrected_image
```
4. **Mix the features of the second image into the first**
Finally, we use a mask to determine which parts of the second image should replace the first. This mask is created by drawing convex hulls around key facial features, and then applying a Gaussian blur to smooth the edges.
```python
def get_face_mask(im, landmarks):
# Create a mask for the face
# ...
return mask
```
By combining the masks from both images and applying them to the final output, we achieve a seamless face swap.
The complete code includes all these steps and can be found at the end of the article. It uses libraries like OpenCV, dlib, and NumPy to perform the face swapping efficiently. This approach provides a simple yet effective way to create realistic face swaps with minimal effort.
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