Doron Adler commited on
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How will your face look after the Apocalypsehas come? Upload an image with a face, or click one of the examples below. If a face could not be detected, the output box will remain empty.

Browse files
.gitattributes CHANGED
@@ -25,3 +25,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zstandard filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
 
 
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zstandard filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
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+ shape_predictor_5_face_landmarks.dat filter=lfs diff=lfs merge=lfs -text
Example00001.jpg ADDED
Example00002.jpg ADDED
Example00003.jpg ADDED
Example00004.jpg ADDED
Example00005.jpg ADDED
Example00007.jpg ADDED
README.md CHANGED
@@ -1,8 +1,8 @@
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  ---
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  title: Apocalyptify
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- emoji: πŸ“ˆ
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- colorFrom: purple
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- colorTo: gray
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  sdk: gradio
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  app_file: app.py
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  pinned: false
 
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  ---
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  title: Apocalyptify
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+ emoji: πŸ§Ÿβ€β™€οΈ
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+ colorFrom: yellow
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+ colorTo: green
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  sdk: gradio
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  app_file: app.py
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  pinned: false
Sample00001.jpg ADDED
Sample00002.jpg ADDED
Sample00003.jpg ADDED
Sample00004.jpg ADDED
Sample00005.jpg ADDED
apocalyptify_p2s2p_torchscript_cpu.pt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:ca926ecb07b64b668d31cf661c5b14b337899bbef79aa06111531846755ae9c7
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+ size 1202690451
app.py ADDED
@@ -0,0 +1,88 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ import os
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+ os.system("pip install dlib")
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+ import sys
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+ import face_detection
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+ import PIL
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+ from PIL import Image, ImageOps
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+ import numpy as np
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+
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+ import torch
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+ torch.set_grad_enabled(False)
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+ net = torch.jit.load('apocalyptify_p2s2p_torchscript_cpu.pt')
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+ net.eval()
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+
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+
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+ def tensor2im(var):
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+ var = var.cpu().detach().transpose(0, 2).transpose(0, 1).numpy()
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+ var = ((var + 1) / 2)
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+ var[var < 0] = 0
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+ var[var > 1] = 1
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+ var = var * 255
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+ return Image.fromarray(var.astype('uint8'))
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+
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+ def image_as_array(image_in):
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+ im_array = np.array(image_in, np.float32)
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+ im_array = (im_array/255)*2 - 1
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+ im_array = np.transpose(im_array, (2, 0, 1))
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+ im_array = np.expand_dims(im_array, 0)
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+ return im_array
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+
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+ def find_aligned_face(image_in, size=256):
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+ aligned_image, n_faces, quad = face_detection.align(image_in, face_index=0, output_size=size)
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+ return aligned_image, n_faces, quad
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+
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+ def align_first_face(image_in, size=256):
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+ aligned_image, n_faces, quad = find_aligned_face(image_in,size=size)
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+ if n_faces == 0:
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+ return None
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+ else:
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+ im_array = image_as_array(aligned_image)
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+
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+ return im_array
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+
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+ def img_concat_h(im1, im2):
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+ dst = Image.new('RGB', (im1.width + im2.width, im1.height))
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+ dst.paste(im1, (0, 0))
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+ dst.paste(im2, (im1.width, 0))
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+ return dst
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+
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+ import gradio as gr
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+
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+ def face2drag(
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+ img: Image.Image,
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+ size: int
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+ ) -> Image.Image:
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+
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+ aligned_img = align_first_face(img)
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+ if aligned_img is None:
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+ output=None
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+ else:
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+ input = torch.Tensor(aligned_img)
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+ output = net(input)
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+ output = tensor2im(output[0])
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+ output = img_concat_h(tensor2im(torch.Tensor(aligned_img)[0]), output)
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+
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+ return output
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+
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+ import os
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+ import collections
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+ from typing import Union, List
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+ import numpy as np
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+ from PIL import Image
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+ import PIL.Image
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+ import PIL.ImageFile
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+ import numpy as np
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+ import scipy.ndimage
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+ import requests
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+
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+ def inference(img):
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+ out = face2drag(img, 256)
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+ return out
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+
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+
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+ title = "Apocalyptify"
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+ description = "How will your face look after the Apocalypsehas come? Upload an image with a face, or click one of the examples below. If a face could not be detected, the output box will remain empty."
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+ 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/Apocalyptify/file/Sample00001.jpg' alt='Sample00001'/><img src='https://hf.space/gradioiframe/Norod78/Apocalyptify/file/Sample00002.jpg' alt='Sample00002'/><img src='https://hf.space/gradioiframe/Norod78/Apocalyptify/file/Sample00003.jpg' alt='Sample00003'/><img src='https://hf.space/gradioiframe/Norod78/Apocalyptify/file/Sample00004.jpg' alt='Sample00004'/><img src='https://hf.space/gradioiframe/Norod78/Apocalyptify/file/Sample00005.jpg' alt='Sample00005'/></p><p>The Apocalypse model was fine tuned on a pre-trained Pixel2Style2Pixel model by Doron Adler</p>"
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+
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+ examples=[['Example00001.jpg'],['Example00002.jpg'],['Example00003.jpg'],['Example00004.jpg'],['Example00005.jpg']]
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+ 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()
face_detection.py ADDED
@@ -0,0 +1,140 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ # Copyright (c) 2021 Justin Pinkney
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+
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+ import dlib
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+ import numpy as np
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+ import os
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+ from PIL import Image
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+ from PIL import ImageOps
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+ from scipy.ndimage import gaussian_filter
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+ import cv2
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+
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+
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+ MODEL_PATH = "shape_predictor_5_face_landmarks.dat"
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+ detector = dlib.get_frontal_face_detector()
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+
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+
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+ def align(image_in, face_index=0, output_size=256):
17
+ landmarks = list(get_landmarks(image_in))
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+ n_faces = len(landmarks)
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+ face_index = min(n_faces-1, face_index)
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+ if n_faces == 0:
21
+ aligned_image = image_in
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+ quad = None
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+ else:
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+ aligned_image, quad = image_align(image_in, landmarks[face_index], output_size=output_size)
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+
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+ return aligned_image, n_faces, quad
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+
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+
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+ def composite_images(quad, img, output):
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+ """Composite an image into and output canvas according to transformed co-ords"""
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+ output = output.convert("RGBA")
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+ img = img.convert("RGBA")
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+ input_size = img.size
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+ src = np.array(((0, 0), (0, input_size[1]), input_size, (input_size[0], 0)), dtype=np.float32)
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+ dst = np.float32(quad)
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+ mtx = cv2.getPerspectiveTransform(dst, src)
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+ img = img.transform(output.size, Image.PERSPECTIVE, mtx.flatten(), Image.BILINEAR)
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+ output.alpha_composite(img)
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+
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+ return output.convert("RGB")
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+
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+
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+ def get_landmarks(image):
44
+ """Get landmarks from PIL image"""
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+ shape_predictor = dlib.shape_predictor(MODEL_PATH)
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+
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+ max_size = max(image.size)
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+ reduction_scale = int(max_size/512)
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+ if reduction_scale == 0:
50
+ reduction_scale = 1
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+ downscaled = image.reduce(reduction_scale)
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+ img = np.array(downscaled)
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+ detections = detector(img, 0)
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+
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+ for detection in detections:
56
+ try:
57
+ face_landmarks = [(reduction_scale*item.x, reduction_scale*item.y) for item in shape_predictor(img, detection).parts()]
58
+ yield face_landmarks
59
+ except Exception as e:
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+ print(e)
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+
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+
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+ def image_align(src_img, face_landmarks, output_size=512, transform_size=2048, enable_padding=True, x_scale=1, y_scale=1, em_scale=0.1, alpha=False):
64
+ # Align function modified from ffhq-dataset
65
+ # See https://github.com/NVlabs/ffhq-dataset for license
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+
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+ lm = np.array(face_landmarks)
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+ lm_eye_left = lm[2:3] # left-clockwise
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+ lm_eye_right = lm[0:1] # left-clockwise
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+
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+ # Calculate auxiliary vectors.
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+ eye_left = np.mean(lm_eye_left, axis=0)
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+ eye_right = np.mean(lm_eye_right, axis=0)
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+ eye_avg = (eye_left + eye_right) * 0.5
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+ eye_to_eye = 0.71*(eye_right - eye_left)
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+ mouth_avg = lm[4]
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+ eye_to_mouth = 1.35*(mouth_avg - eye_avg)
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+
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+ # Choose oriented crop rectangle.
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+ x = eye_to_eye.copy()
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+ x /= np.hypot(*x)
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+ x *= max(np.hypot(*eye_to_eye) * 2.0, np.hypot(*eye_to_mouth) * 1.8)
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+ x *= x_scale
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+ y = np.flipud(x) * [-y_scale, y_scale]
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+ c = eye_avg + eye_to_mouth * em_scale
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+ quad = np.stack([c - x - y, c - x + y, c + x + y, c + x - y])
87
+ quad_orig = quad.copy()
88
+ qsize = np.hypot(*x) * 2
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+
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+ try:
91
+ src_img = ImageOps.exif_transpose(src_img)
92
+ except:
93
+ print("exif problem, not rotating")
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+
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+ img = src_img.convert('RGBA').convert('RGB')
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+
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+ # Shrink.
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+ shrink = int(np.floor(qsize / output_size * 0.5))
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+ if shrink > 1:
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+ rsize = (int(np.rint(float(img.size[0]) / shrink)), int(np.rint(float(img.size[1]) / shrink)))
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+ img = img.resize(rsize, Image.ANTIALIAS)
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+ quad /= shrink
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+ qsize /= shrink
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+
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+ # Crop.
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+ border = max(int(np.rint(qsize * 0.1)), 3)
107
+ crop = (int(np.floor(min(quad[:,0]))), int(np.floor(min(quad[:,1]))), int(np.ceil(max(quad[:,0]))), int(np.ceil(max(quad[:,1]))))
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+ crop = (max(crop[0] - border, 0), max(crop[1] - border, 0), min(crop[2] + border, img.size[0]), min(crop[3] + border, img.size[1]))
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+ if crop[2] - crop[0] < img.size[0] or crop[3] - crop[1] < img.size[1]:
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+ img = img.crop(crop)
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+ quad -= crop[0:2]
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+
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+ # Pad.
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+ pad = (int(np.floor(min(quad[:,0]))), int(np.floor(min(quad[:,1]))), int(np.ceil(max(quad[:,0]))), int(np.ceil(max(quad[:,1]))))
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+ pad = (max(-pad[0] + border, 0), max(-pad[1] + border, 0), max(pad[2] - img.size[0] + border, 0), max(pad[3] - img.size[1] + border, 0))
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+ if enable_padding and max(pad) > border - 4:
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+ pad = np.maximum(pad, int(np.rint(qsize * 0.3)))
118
+ img = np.pad(np.float32(img), ((pad[1], pad[3]), (pad[0], pad[2]), (0, 0)), 'reflect')
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+ h, w, _ = img.shape
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+ y, x, _ = np.ogrid[:h, :w, :1]
121
+ mask = np.maximum(1.0 - np.minimum(np.float32(x) / pad[0], np.float32(w-1-x) / pad[2]), 1.0 - np.minimum(np.float32(y) / pad[1], np.float32(h-1-y) / pad[3]))
122
+ blur = qsize * 0.02
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+ img += (gaussian_filter(img, [blur, blur, 0]) - img) * np.clip(mask * 3.0 + 1.0, 0.0, 1.0)
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+ img += (np.median(img, axis=(0,1)) - img) * np.clip(mask, 0.0, 1.0)
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+ img = np.uint8(np.clip(np.rint(img), 0, 255))
126
+ if alpha:
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+ mask = 1-np.clip(3.0 * mask, 0.0, 1.0)
128
+ mask = np.uint8(np.clip(np.rint(mask*255), 0, 255))
129
+ img = np.concatenate((img, mask), axis=2)
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+ img = Image.fromarray(img, 'RGBA')
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+ else:
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+ img = Image.fromarray(img, 'RGB')
133
+ quad += pad[:2]
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+
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+ # Transform.
136
+ img = img.transform((transform_size, transform_size), Image.QUAD, (quad + 0.5).flatten(), Image.BILINEAR)
137
+ if output_size < transform_size:
138
+ img = img.resize((output_size, output_size), Image.ANTIALIAS)
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+
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+ return img, quad_orig
requirements.txt ADDED
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+ numpy
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+ opencv-python-headless
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+ Pillow
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+ scikit-image
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+ torch
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+ torchvision
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+ ninja
8
+ scipy
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+ cmake
shape_predictor_5_face_landmarks.dat ADDED
@@ -0,0 +1,3 @@
 
 
 
 
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:c4b1e9804792707d3a405c2c16a80a20269e6675021f64a41d30fffafbc41888
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+ size 9150489