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import os
os.system("pip install dlib")
import sys
import face_detection
import PIL
from PIL import Image, ImageOps
import numpy as np
import torch
torch.set_grad_enabled(False)
net = torch.jit.load('ComicsHeroesReduced_v2E03_Traced_Script_CPU.pt')
net.eval()
def tensor2im(var):
var = var.cpu().detach().transpose(0, 2).transpose(0, 1).numpy()
var = ((var + 1) / 2)
var[var < 0] = 0
var[var > 1] = 1
var = var * 255
return Image.fromarray(var.astype('uint8'))
def image_as_array(image_in):
im_array = np.array(image_in, np.float32)
im_array = (im_array/255)*2 - 1
im_array = np.transpose(im_array, (2, 0, 1))
im_array = np.expand_dims(im_array, 0)
return im_array
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:
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 face2drag(
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)
output = net(input)
output = tensor2im(output[0])
output = img_concat_h(tensor2im(torch.Tensor(aligned_img)[0]), output)
return output
import os
import collections
from typing import Union, List
import numpy as np
from PIL import Image
import PIL.Image
import PIL.ImageFile
import numpy as np
import scipy.ndimage
import requests
def inference(img):
out = face2drag(img, 512)
return out
title = "Comics hero"
description = "Make a \"Comics hero\"-like image in the spirit of an input face. Upload an image with a face, or click on one of the examples below. If a face could not be detected, A face will still be created based on the features of the input."
article = "<p style='text-align: center'><a href='https://github.com/justinpinkney/pix2pixHD' target='_blank'>Github Repo</a></p><p style='text-align: center'>samples: <img src='https://hf.space/gradioiframe/Norod78/ComicsHero/file/Sample00001.jpg' alt='Sample00001'/><img src='https://hf.space/gradioiframe/Norod78/ComicsHero/file/Sample00002.jpg' alt='Sample00002'/><img src='https://hf.space/gradioiframe/Norod78/ComicsHero/file/Sample00003.jpg' alt='Sample00003'/><img src='https://hf.space/gradioiframe/Norod78/ComicsHero/file/Sample00004.jpg' alt='Sample00004'/><img src='https://hf.space/gradioiframe/Norod78/ComicsHero/file/Sample00005.jpg' alt='Sample00005'/></p><p>The \"Comics Hero\" model was trained using Pix2PixHD by Doron Adler</p>"
examples=[['Example00001.jpg'],['Example00002.jpg'],['Example00003.jpg'],['Example00004.jpg'],['Example00005.jpg'], ['Example00006.jpg']]
gr.Interface(inference, gr.inputs.Image(type="pil",shape=(256,256)), gr.outputs.Image(type="pil"),title=title,description=description,article=article,examples=examples,enable_queue=True).launch()
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