【部分原创】python实现视频内的faceswap（换脸）1.准备⼯作，按博主的环境为准
Python 3.5
Opencv 3
Tensorflow 1.3.1linux查看网络状态
Keras 2
cudnn和CUDA，如果你的GPU⾜够厉害并且⽀持的话，可以选择安装
那就先安装起来，有兴趣的朋友给我个暗⽰，好让我有动⼒写下去，想实现整套的功能还是有点复杂的
第⼀部分，数据采集，及视频内⼈物脸
import cv2
save_path = 'your save path'
cascade = cv2.CascadeClassifier('haarcascade_l path')
cap = cv2.VideoCapture('your video path')
i = 0
while True:
ret,frame = ad()
gray = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)
rect = cascade.detectMultiScale(gray,scaleFactor=1.3,minNeighbors=9,minSize=(50,50),flags = cv2.CASCADE_SCALE_IMAGE)
print ("rect",rect)
if not rect is ():
for x,y,z,w in rect:
roiImg = frame[y:y+w,x:x+z]
cv2.imwrite(save_path+str(i)+'.jpg',roiImg)
i +=1
cv2.imshow('frame',frame)
if cv2.waitKey(1) &0xFF == ord('q'):
break
cv2.destroyAllWindows()
第⼆部分，国外⼤神开源代码，⽤于模型训练
import cv2
webassembly使用import numpy
from utils import get_image_paths, load_images, stack_images
from training_data import get_training_data
from model import autoencoder_A
from model import autoencoder_B
from model import encoder, decoder_A, decoder_B
try:
encoder  .load_weights( "models/encoder.h5"  )
decoder_A.load_weights( "models/decoder_A.h5" )
decoder_B.load_weights( "models/decoder_B.h5" )
except:
pass
def save_model_weights():
encoder  .save_weights( "models/encoder.h5"  )
decoder_A.save_weights( "models/decoder_A.h5" )
decoder_B.save_weights( "models/decoder_B.h5" )
print( "save model weights" )
images_A = get_image_paths( "data/trump" )
images_B = get_image_paths( "data/cage"  )
images_A = load_images( images_A ) / 255.0
images_B = load_images( images_B ) / 255.0
images_A += an( axis=(0,1,2) ) - an( axis=(0,1,2) )
print( "press 'q' to stop training and save model" )
for epoch in range(1000000):
batch_size = 64
warped_A, target_A = get_training_data( images_A, batch_size )
warped_B, target_B = get_training_data( images_B, batch_size )
loss_A = ain_on_batch( warped_A, target_A )
loss_B = ain_on_batch( warped_B, target_B )
print( loss_A, loss_B )
if epoch % 100 == 0:
save_model_weights()
test_A = target_A[0:14]
test_B = target_B[0:14]
figure_A = numpy.stack([
test_A,
autoencoder_A.predict( test_A ),
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autoencoder_B.predict( test_A ),
lodash安装], axis=1 )
figure_B = numpy.stack([
test_B,
autoencoder_B.predict( test_B ),
autoencoder_A.predict( test_B ),
], axis=1 )
figure = atenate( [ figure_A, figure_B ], axis=0 )
figure = shape( (4,7) + figure.shape[1:] )
figure = stack_images( figure )
figure = numpy.clip( figure * 255, 0, 255 ).astype('uint8')
cv2.imshow( "", figure )
key = cv2.waitKey(1)
if key == ord('q'):
save_model_weights()
exit()
第三部分，国外⼤神开源代码，⼈脸输出
import cv2rd client
import numpy
from pathlib import Path
from utils import get_image_paths
from model import autoencoder_A
from model import autoencoder_B
from model import encoder, decoder_A, decoder_B
encoder  .load_weights( "models/encoder.h5"  )
decoder_A.load_weights( "models/decoder_A.h5" )
decoder_B.load_weights( "models/decoder_B.h5" )
images_A = get_image_paths( "data/trump" )
images_B = get_image_paths( "data/cage" )
def convert_one_image( autoencoder, image ):
assert image.shape == (256,256,3)
crop = slice(48,208)
face = image[crop,crop]
python入门教程视屏face = size( face, (64,64) )
face = pand_dims( face, 0 )
new_face = autoencoder.predict( face / 255.0 )[0]
new_face = numpy.clip( new_face * 255, 0, 255 ).astype( image.dtype )    new_face = size( new_face, (160,160) )
new_image = py()
new_image[crop,crop] = new_face
return new_image
output_dir = Path( 'output' )
output_dir.mkdir( parents=True, exist_ok=True )
for fn in images_A:
image = cv2.imread(fn)
new_image = convert_one_image( autoencoder_B, image )
output_file = output_dir / Path(fn).name
cv2.imwrite( str(output_file), new_image )
第四部分，⼈脸替换
#import necessary libraries
import cv2
import glob as gb
# import numpy
#capture video from the webcam
cap = cv2.VideoCapture('your video path')
fourcc = cv2.VideoWriter_fourcc(*'XVID')
out = cv2.VideoWriter('your output video path', fourcc, 20.0, (1920, 1080))
#load the face finder
face_cascade = cv2.CascadeClassifier('haarcascade_l path') #load the face that will be swapped in
img_path = gb.glob("your image path")
#start loop
for path in img_path:
face_img = cv2.imread(path)
while True:
ret, img = ad()  # read image
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, 1.3, 3)  # find faces # for all the faces found in the frame
for (x, y, w, h) in faces:
# resize and blend the face to be swapped in
face = size(face_img, (h, w), interpolation=cv2.INTER_CUBIC)            face = cv2.addWeighted(img[y:y + h, x:x + w], .5, face, .5, 1) # swap faces
img[y:y + h, x:x + w] = face
out.write(img)
# show the image
cv2.imshow('img', img)
key = cv2.waitKey(1)
if key == ord('q'):
exit()
cv2.destroyAllWindows()
最后放⼀张训练⼀⼩时后的视频截图，⽤的是尼古拉斯凯奇的脸

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