需要一張源臉和<= 5張目標臉照片 https://github.com/shaoanlu/fewshot-face-translation-GAN clone

git clone --recursive https://github.com/shaoanlu/fewshot-face-translation-GAN.git

需要套件

pip install Keras==2.2.4
pip install tensorflow==1.13.1
or
pip install tensorflow==1.12.0
or
pip install tensorflow-gpu==1.13.1

pip install opencv-python
pip install matplotlib

下載模型

https://drive.google.com/uc?id=1DUMmZGTGKMyEYSKy-w34IDHawVF24rIs
https://drive.google.com/uc?id=1xl8cg7xaRnMsyiODcXguJ83d5hwodckB
mkdir weights && \
mv decoder.h5 weights/decoder.h5 && \
mv encoder.h5 weights/encoder.h5

錯誤

model.load_weights AttributeError: 'str' object has no attribute 'decode'

解決

pip install h5py==2.10 -i https://pypi.doubanio.com/simple

測試code

import os
import warnings
warnings.filterwarnings("ignore")

from models import FaceTranslationGANInferenceModel
model = FaceTranslationGANInferenceModel()

from face_toolbox_keras.models.verifier.face_verifier import FaceVerifier
fv = FaceVerifier(classes=512)

from face_toolbox_keras.models.parser import face_parser
fp = face_parser.FaceParser()

from face_toolbox_keras.models.detector import face_detector
fd = face_detector.FaceAlignmentDetector()

from face_toolbox_keras.models.detector.iris_detector import IrisDetector
idet = IrisDetector()
#idet.set_detector(fd)
# Translate faces
import numpy as np
from utils import utils
from matplotlib import pyplot as plt
from PIL import Image


fn_src = ["data/go.jpg"]
if len(fn_src) >= 1:
    fn_src = fn_src[0]

# fns_tar = ["data/1.jpg","data/2.jpg","data/3.jpg","data/4.jpg","data/5.jpg"]
def conversion(fn_src,fns_tar,path):
    fns_file = []
    for file in fns_tar:
        if(file.lower().endswith(('.bmp', '.dib', '.png', '.jpg', '.jpeg', '.pbm', '.pgm', '.ppm', '.tif', '.tiff'))):
            fns_file.append(path+file)
        
    src, mask, aligned_im, (x0, y0, x1, y1), landmarks = utils.get_src_inputs(fn_src, fd, fp, idet)
    tar, emb_tar = utils.get_tar_inputs(fns_file, fd, fv)
    out = model.inference(src, mask, tar, emb_tar)
    result_face = np.squeeze(((out[0] + 1) * 255 / 2).astype(np.uint8))
    result_img = utils.post_process_result(fn_src, fd, result_face, aligned_im, src, x0, y0, x1, y1, landmarks)
    # im = Image.fromarray(result_img)
    # im.save("data/output.jpg")
    print("")
    save(src,path+"output/src.jpg")
    save(tar,path+"output/tar.jpg")
    save(result_face,path+"output/result_face.jpg")
    save(result_img,path+"output/result_img.jpg")

def save(img,name):
    im = Image.fromarray(img)
    im.save(name)

# david
fns_tar = os.listdir("data/adam")
conversion(fn_src,fns_tar,"data/adam/")
print("")
fn_src = "data/test.jpg"

源頭

https://paperswithcode.com/paper/few-shot-unsupervised-image-to-image