Spaces:
Paused
Paused
File size: 3,954 Bytes
5b2843c 0b40ce6 46bda0c 5b2843c 8b4ec72 69f6a0e 8b4ec72 5b2843c 4fe69a1 69f6a0e 5b2843c 4b36a0b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 |
import os
os.system("pip install --upgrade pip")
import onnxruntime as rt
import sys
import PIL
from PIL import Image, ImageOps, ImageFile
import numpy as np
from pathlib import Path
import collections
from typing import Union, List
import scipy.ndimage
import requests
MODEL_FILE = "comics-heroes_p2s2p_model_from_scripted-simp.quant.onnx"
so = rt.SessionOptions()
so.inter_op_num_threads = 1
so.intra_op_num_threads = 1
session = rt.InferenceSession(MODEL_FILE, sess_options=so)
input_name = session.get_inputs()[0].name
print("input_name = " + str(input_name))
output_name = session.get_outputs()[0].name
print("output_name = " + str(output_name))
os.system("pip install cmake")
os.system("pip install dlib")
import face_detection
def array_to_image(array_in):
array_in = np.squeeze(255*(array_in + 1)/2)
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):
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 face2hero(
img: Image.Image,
size: int
) -> Image.Image:
aligned_img = align_first_face(img)
if aligned_img is None:
output=None
else:
output = session.run([output_name], {input_name: aligned_img})[0]
output = array_to_image(output)
aligned_img = array_to_image(aligned_img).resize((output.width, output.height))
output = img_concat_h(aligned_img, output)
return output
def inference(img):
out = face2hero(img, 256)
return out
title = "Comics hero HD"
description = "Turn a face into the face of a \"Comics hero\". 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."
article = "<hr><p style='text-align: center'>See the <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/ComicsHeroHD/file/Sample00001.jpg' alt='Sample00001'/><img src='https://hf.space/gradioiframe/Norod78/ComicsHeroHD/file/Sample00002.jpg' alt='Sample00002'/><img src='https://hf.space/gradioiframe/Norod78/ComicsHeroHD/file/Sample00003.jpg' alt='Sample00003'/><img src='https://hf.space/gradioiframe/Norod78/ComicsHeroHD/file/Sample00004.jpg' alt='Sample00004'/><img src='https://hf.space/gradioiframe/Norod78/ComicsHeroHD/file/Sample00005.jpg' alt='Sample00005'/></p><p>The \"Comics Hero HD\" model was fine tuned on a pre-trained Pixel2Style2Pixel model 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",shape=(1024,1024)), gr.outputs.Image(type="pil"),title=title,description=description,article=article,examples=examples,enable_queue=True,allow_flagging=False).launch()
|