Can give the running demo

[wshert1688]

Can give the running demo

how to run decoder_A.h5 or encoder.h5

[Clorr]

To run the demo, you have to setup your environment (If you check my pull request, you will find a Dockerfile with the tools needed).

Once this is done, you will have to run python ./script.py from the code folder. This script will take all pics in data/trump and apply a new face on it. The resulting images will be in a folder named output

Note that my Dockerfile still has issues, and you will have to modify scandir and the mkdir parameters

[zaxs002]

import cv2
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]
    face = cv2.resize(face, (64, 64))
    face = numpy.expand_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 = cv2.resize(new_face, (160, 160))
    new_image = image.copy()
    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)