Dragness / app.py
Doron Adler
1024x1024, allow_flagging=False e56ef72
import os
os.system("pip install --upgrade pip")
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('dragness_p2s2p_torchscript_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=256):
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=256):
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 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, 256)
return out
title = "Dragness"
description = "Gradio demo for Drag finetuned Pixel2Style2Pixel. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below."
article = "<p style='text-align: center'><a href='https://github.com/justinpinkney/pixel2style2pixel/tree/nw' target='_blank'>Github Repo</a></p><p style='text-align: center'>samples: <img src='https://hf.space/gradioiframe/Norod78/Dragness/file/Sample00001.jpg' alt='Sample00001'/><img src='https://hf.space/gradioiframe/Norod78/Dragness/file/Sample00002.jpg' alt='Sample00002'/><img src='https://hf.space/gradioiframe/Norod78/Dragness/file/Sample00003.jpg' alt='Sample00003'/><img src='https://hf.space/gradioiframe/Norod78/Dragness/file/Sample00004.jpg' alt='Sample00004'/><img src='https://hf.space/gradioiframe/Norod78/Dragness/file/Sample00005.jpg' alt='Sample00005'/><img src='https://hf.space/gradioiframe/Norod78/Dragness/file/Sample00006.jpg' alt='Sample00006'/></p><p>Drag model was fine tuned by Doron Adler</p>"
examples=[['Example00001.jpg'],['Example00002.jpg'],['Example00003.jpg'],['Example00004.jpg'],['Example00005.jpg'],['Example00006.jpg'],['Example00007.jpg']]
gr.Interface(inference, gr.inputs.Image(type="pil",shape=(1024,1024)), gr.outputs.Image(type="pil"),title=title,description=description,article=article,examples=examples,enable_queue=True,allow_flagging=False).launch()