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| import os | |
| 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 = "ffhq2pumpkinheads_pix2pixHD_e03_itr4500-simp.onnx" | |
| so = rt.SessionOptions() | |
| so.inter_op_num_threads = 4 | |
| so.intra_op_num_threads = 4 | |
| session = rt.InferenceSession(MODEL_FILE, sess_options=so,providers=['CPUExecutionProvider']) | |
| 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)) | |
| 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=1024): | |
| 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=1024): | |
| 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 face2vintage( | |
| 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 = face2vintage(img, 1024) | |
| return out | |
| title = "Pumpkin head Pix2PixHD" | |
| description = "Pumpkinize your head. 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/pix2pixHD' target='_blank'>Github Repo</a></p><p>The \"Pumpkin Head\" Pix2PixHD 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']] | |
| demo = gr.Interface( | |
| inference, | |
| inputs=[gr.Image(type="pil", label="Input")], | |
| outputs=[gr.Image(type="pil", label="Output")], | |
| title=title, | |
| description=description, | |
| article=article, | |
| examples=examples, | |
| allow_flagging=False | |
| ) | |
| demo.queue() | |
| demo.launch() |