The article describes how to perform face swapping using machine learning. The code provided in the article uses the dlib library to detect faces and facial landmarks in the source and target images. The code then computes an affine transformation matrix to align the source face with the target face. Finally, it masks the swapped face region and blends the swapped face with the target image to create the final result.

Face swapping is a computer vision technique that involves replacing a face in an image or video with another face from a different source. Here's an example of how face swapping can be done using machine learning:

import cv
import dlib
import numpy as np


# load the source and target images
source_image = cv2.imread('source_image.jpg')
target_image = cv2.imread('target_image.jpg')


# initialize face detector and landmarks predictor
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor('shape_predictor_68_face_landmarks.dat')


# detect faces and landmarks in the source image
source_gray = cv2.cvtColor(source_image, cv2.COLOR_BGR2GRAY)
source_rects = detector(source_gray, 1)
source_shape = predictor(source_gray, source_rects[0])
source_points = np.array([(p.x, p.y) for p in source_shape.parts()])


# detect faces and landmarks in the target image
target_gray = cv2.cvtColor(target_image, cv2.COLOR_BGR2GRAY)
target_rects = detector(target_gray, 1)
target_shape = predictor(target_gray, target_rects[0])
target_points = np.array([(p.x, p.y) for p in target_shape.parts()])


# compute the affine transformation matrix
M = cv2.estimateAffinePartial2D(source_points, target_points)[0]


# warp the source image to fit the target face
swapped_image = cv2.warpAffine(source_image, M, (target_image.shape[1], target_image.shape[0]), flags=cv2.INTER_LINEAR, borderMode=cv2.BORDER_REFLECT_101)


# mask the swapped face region
mask = np.zeros_like(target_gray)
cv2.fillConvexPoly(mask, target_points, (255, 255, 255), 16, 0)
mask = cv2.erode(mask, None, iterations=6)
mask = cv2.dilate(mask, None, iterations=12)
mask = cv2.GaussianBlur(mask, (21, 21), 0)


# blend the swapped face with the target image
swapped_image = swapped_image.astype(float)
target_image = target_image.astype(float)
alpha = mask.astype(float) / 255
alpha = cv2.merge([alpha, alpha, alpha])
swapped_image = cv2.multiply(alpha, swapped_image) + cv2.multiply(1 - alpha, target_image)
swapped_image = swapped_image.astype(np.uint8)


# display the result
cv2.imshow('Face Swapped', swapped_image)
cv2.waitKey(0)
cv2.destroyAllWindows()

This code uses the dlib library to detect faces and facial landmarks in the source and target images. It then computes an affine transformation matrix to align the source face with the target face. Finally, it masks the swapped face region and blends the swapped face with the target image to create the final result.