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import os
os.system("pip install dlib")
os.system("pip install gradio==2.5.3")
import sys
import face_detection
import PIL
from PIL import Image, ImageOps, ImageFile
import numpy as np
import torch
torch.set_grad_enabled(False)
model = torch.jit.load('u2net_bce_itr_18000_train_3.891670_tar_0.553700_512x_460x.jit.pt')
model.eval()
def normPRED(d):
ma = np.max(d)
mi = np.min(d)
dn = (d-mi)/(ma-mi)
return dn
def array_to_image(array_in):
array_in = normPRED(array_in)
array_in = np.squeeze(255.0*(array_in))
array_in = np.transpose(array_in, (1, 2, 0))
im = Image.fromarray(array_in.astype(np.uint8))
return im
def image_as_array(image_in):
image_in = np.array(image_in, np.float32)
tmpImg = np.zeros((image_in.shape[0],image_in.shape[1],3))
image_in = image_in/np.max(image_in)
if image_in.shape[2]==1:
tmpImg[:,:,0] = (image_in[:,:,0]-0.485)/0.229
tmpImg[:,:,1] = (image_in[:,:,0]-0.485)/0.229
tmpImg[:,:,2] = (image_in[:,:,0]-0.485)/0.229
else:
tmpImg[:,:,0] = (image_in[:,:,0]-0.485)/0.229
tmpImg[:,:,1] = (image_in[:,:,1]-0.456)/0.224
tmpImg[:,:,2] = (image_in[:,:,2]-0.406)/0.225
tmpImg = tmpImg.transpose((2, 0, 1))
image_out = np.expand_dims(tmpImg, 0)
return image_out
def find_aligned_face(image_in, size=512):
aligned_image, n_faces, quad = face_detection.align(image_in, face_index=0, output_size=size)
return aligned_image, n_faces, quad
def align_first_face(image_in, size=512):
aligned_image, n_faces, quad = find_aligned_face(image_in,size=size)
if n_faces == 0:
try:
image_in = ImageOps.exif_transpose(image_in)
except:
print("exif problem, not rotating")
image_in = image_in.resize((size, size))
im_array = image_as_array(image_in)
else:
im_array = image_as_array(aligned_image)
return im_array
def img_concat_h(im1, im2):
dst = Image.new('RGB', (im1.width + im2.width, im1.height))
dst.paste(im1, (0, 0))
dst.paste(im2, (im1.width, 0))
return dst
import gradio as gr
def face2doll(
img: Image.Image,
size: int
) -> Image.Image:
aligned_img = align_first_face(img)
if aligned_img is None:
output=None
else:
input = torch.Tensor(aligned_img)
results = model(input)
d2 = array_to_image(results[1].detach().numpy())
output = img_concat_h(array_to_image(aligned_img), d2)
del results
return output
def inference(img):
out = face2doll(img, 512)
return out
title = "Face2Doll U2Net"
description = "Style transfer a face into one of a \"Doll\". Upload an image with a face, or click on one of the examples below. If a face could not be detected, an image will still be created. Faces with glasses on, seem not to yield good results."
article = "<hr><p style='text-align: center'>See the <a href='https://github.com/Norod/U-2-Net-StyleTransfer' target='_blank'>Github Repo</a></p><p style='text-align: center'>samples: <img src='https://hf.space/gradioiframe/Norod78/Face2Doll/file/Sample00001.jpg' alt='Sample00001'/><img src='https://hf.space/gradioiframe/Norod78/Face2Doll/file/Sample00002.jpg' alt='Sample00002'/><img src='https://hf.space/gradioiframe/Norod78/Face2Doll/file/Sample00003.jpg' alt='Sample00003'/><img src='https://hf.space/gradioiframe/Norod78/Face2Doll/file/Sample00004.jpg' alt='Sample00004'/><img src='https://hf.space/gradioiframe/Norod78/Face2Doll/file/Sample00005.jpg' alt='Sample00005'/></p><p>The \"Face2Doll (U2Net)\" model was trained by <a href='https://linktr.ee/Norod78' target='_blank'>Doron Adler</a></p>"
examples=[['Example00001.jpg'],['Example00002.jpg'],['Example00003.jpg'],['Example00004.jpg'],['Example00005.jpg'], ['Example00006.jpg']]
gr.Interface(
inference,
gr.inputs.Image(type="pil", label="Input"),
gr.outputs.Image(type="pil", label="Output"),
title=title,
description=description,
article=article,
examples=examples,
enable_queue=True,
allow_flagging=False
).launch()
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