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utils.py
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utils.py
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import PIL.Image
import dnnlib
import dnnlib.tflib as tflib
import tensorflow as tf
import PIL.ImageFile
import scipy.ndimage
import numpy as np
import PIL.Image
import dnnlib
import dnnlib.tflib as tflib
import os
import re
import sys
import pretrained_networks
def Align_face_image(src_file, output_size=1024, transform_size=4096,
enable_padding=True):
print('aligning image...')
import dlib
img_ = dlib.load_rgb_image(src_file)
print("Image Shape :", img_.shape)
frontal_face = dlib.cnn_face_detection_model_v1("mmod_human_face_detector.dat") # cnn model
shape_ = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat") # same as ffhq dataset
dets = frontal_face(img_, 1)
for i, d in enumerate(dets):
print("Detection {}: Left: {} Top: {} Right: {} Bottom: {} Confidence: {}".format(i, d.rect.left(),
d.rect.top(),
d.rect.right(),
d.rect.bottom(),
d.confidence))
shape = shape_(img_, d.rect)
print("Part 0: {}, Part 1: {} ...".format(shape.part(0).x, shape.part(1)))
# Parse landmarks.
# pylint: disable=unused-variable
lm_chin = np.array([[shape.part(i).x, shape.part(i).y] for i in range(17)])
lm_eyebrow_left = np.array([[shape.part(i).x, shape.part(i).y] for i in range(17, 22)])
lm_eyebrow_right = np.array([[shape.part(i).x, shape.part(i).y] for i in range(22, 27)])
lm_nose = np.array([[shape.part(i).x, shape.part(i).y] for i in range(27, 31)])
lm_nostrils = np.array([[shape.part(i).x, shape.part(i).y] for i in range(31, 36)])
lm_eye_left = np.array([[shape.part(i).x, shape.part(i).y] for i in range(36, 42)])
lm_eye_right = np.array([[shape.part(i).x, shape.part(i).y] for i in range(42, 48)])
lm_mouth_outer = np.array([[shape.part(i).x, shape.part(i).y] for i in range(48, 60)])
lm_mouth_inner = np.array([[shape.part(i).x, shape.part(i).y] for i in range(60, 68)])
# Calculate auxiliary vectors.
eye_left = np.mean(lm_eye_left, axis=0)
eye_right = np.mean(lm_eye_right, axis=0)
eye_avg = (eye_left + eye_right) * 0.5
eye_to_eye = eye_right - eye_left
mouth_left = lm_mouth_outer[0]
mouth_right = lm_mouth_outer[6]
mouth_avg = (mouth_left + mouth_right) * 0.5
eye_to_mouth = mouth_avg - eye_avg
# Choose oriented crop rectangle.
x = eye_to_eye - np.flipud(eye_to_mouth) * [-1, 1]
x /= np.hypot(*x)
x *= max(np.hypot(*eye_to_eye) * 2.0, np.hypot(*eye_to_mouth) * 1.8)
y = np.flipud(x) * [-1, 1]
c = eye_avg + eye_to_mouth * 0.1
quad = np.stack([c - x - y, c - x + y, c + x + y, c + x - y])
qsize = np.hypot(*x) * 2
# Load in-the-wild image.
if not os.path.isfile(src_file):
print('\nCannot find source image. Please run "--wilds" before "--align".')
return
img = PIL.Image.open(src_file)
# Shrink.
shrink = int(np.floor(qsize / output_size * 0.5))
if shrink > 1:
rsize = (int(np.rint(float(img.size[0]) / shrink)), int(np.rint(float(img.size[1]) / shrink)))
img = img.resize(rsize, PIL.Image.ANTIALIAS)
quad /= shrink
qsize /= shrink
# Crop.
border = max(int(np.rint(qsize * 0.1)), 3)
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]))))
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]))
if crop[2] - crop[0] < img.size[0] or crop[3] - crop[1] < img.size[1]:
img = img.crop(crop)
quad -= crop[0:2]
# Pad.
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]))))
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))
if enable_padding and max(pad) > border - 4:
pad = np.maximum(pad, int(np.rint(qsize * 0.3)))
img = np.pad(np.float32(img), ((pad[1], pad[3]), (pad[0], pad[2]), (0, 0)), 'reflect')
h, w, _ = img.shape
y, x, _ = np.ogrid[:h, :w, :1]
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]))
blur = qsize * 0.02
img += (scipy.ndimage.gaussian_filter(img, [blur, blur, 0]) - img) * np.clip(mask * 3.0 + 1.0, 0.0, 1.0)
img += (np.median(img, axis=(0, 1)) - img) * np.clip(mask, 0.0, 1.0)
img = PIL.Image.fromarray(np.uint8(np.clip(np.rint(img), 0, 255)), 'RGB')
quad += pad[:2]
# Transform.
img = img.transform((transform_size, transform_size), PIL.Image.QUAD, (quad + 0.5).flatten(),
PIL.Image.BILINEAR)
if output_size < transform_size:
img = img.resize((output_size, output_size), PIL.Image.ANTIALIAS)
img.save(src_file)
def gram_matrix(input_tensor):
# We make the image channels first
channels = int(input_tensor.shape[-1])
a = tf.reshape(input_tensor, [-1, channels])
n = tf.shape(a)[0]
gram = tf.matmul(a, a, transpose_a=True)
return gram / tf.cast(n, tf.float32)
def get_style_loss(base_style, gram_target):
"""Expects two images of dimension h, w, c"""
# height, width, num filters of each laye
base_style = tf.reshape(base_style, [base_style.shape[1], base_style.shape[2], base_style.shape[3]])
height, width, channels = base_style.get_shape().as_list()
gram_style = gram_matrix(base_style)
return tf.reduce_mean(tf.square(gram_style - gram_target))
#----------------------------------------------------------------------------
def generate_im_official(network_pkl='gdrive:networks/stylegan2-ffhq-config-f.pkl', seeds=[22], truncation_psi=0.5):
print('Loading networks from "%s"...' % network_pkl)
_G, _D, Gs = pretrained_networks.load_networks(network_pkl)
noise_vars = [var for name, var in Gs.components.synthesis.vars.items() if name.startswith('noise')]
Gs_kwargs = dnnlib.EasyDict()
Gs_kwargs.output_transform = dict(func=tflib.convert_images_to_uint8, nchw_to_nhwc=True)
Gs_kwargs.randomize_noise = False
if truncation_psi is not None:
Gs_kwargs.truncation_psi = truncation_psi
for seed_idx, seed in enumerate(seeds):
print('Generating image for seed %d (%d/%d) ...' % (seed, seed_idx, len(seeds)))
rnd = np.random.RandomState(seed)
z = rnd.randn(1, *Gs.input_shape[1:]) # [minibatch, component]
tflib.set_vars({var: rnd.randn(*var.shape.as_list()) for var in noise_vars}) # [height, width]
images = Gs.run(z, None, **Gs_kwargs) # [minibatch, height, width, channel]
PIL.Image.fromarray(images[0], 'RGB').save(dnnlib.make_run_dir_path('seed%04d.png' % seed))
def generate_im_from_random_seed(Gs, seed=22, truncation_psi=0.5):
seeds = [seed]
noise_vars = [var for name, var in Gs.components.synthesis.vars.items() if name.startswith('noise')]
Gs_kwargs = dnnlib.EasyDict()
Gs_kwargs.output_transform = dict(func=tflib.convert_images_to_uint8, nchw_to_nhwc=True)
Gs_kwargs.randomize_noise = False
if truncation_psi is not None:
Gs_kwargs.truncation_psi = truncation_psi
for seed_idx, seed in enumerate(seeds):
print('Generating image for seed %d (%d/%d) ...' % (seed, seed_idx, len(seeds)))
rnd = np.random.RandomState(seed)
z = rnd.randn(1, *Gs.input_shape[1:]) # [minibatch, component]
tflib.set_vars({var: rnd.randn(*var.shape.as_list()) for var in noise_vars}) # [height, width]
images = Gs.run(z, None, **Gs_kwargs) # [minibatch, height, width, channel]
# PIL.Image.fromarray(images[0], 'RGB').save(dnnlib.make_run_dir_path('seed%04d.png' % seed))
return images
class Build_model:
def __init__(self, opt):
self.opt = opt
if os.path.exists("/usr/app/stylegan/stylegan2-ffhq-config-f.pkl"):
print("Found local StyleGan2 !")
network_pkl = "/usr/app/stylegan/stylegan2-ffhq-config-f.pkl" # Local load, avoiding to re-download 360Mb each time
else:
network_pkl = self.opt.network_pkl
print('Loading networks from "%s"...' % network_pkl)
_G, _D, Gs = pretrained_networks.load_networks(network_pkl)
self.Gs = Gs
self.Gs_syn_kwargs = dnnlib.EasyDict()
self.Gs_syn_kwargs.output_transform = dict(func=tflib.convert_images_to_uint8, nchw_to_nhwc=True)
self.Gs_syn_kwargs.randomize_noise = False
self.Gs_syn_kwargs.minibatch_size = 4
self.noise_vars = [var for name, var in Gs.components.synthesis.vars.items() if name.startswith('noise')]
rnd = np.random.RandomState(0)
tflib.set_vars({var: rnd.randn(*var.shape.as_list()) for var in self.noise_vars})
def generate_im_from_random_seed(self, seed=22, truncation_psi=0.5):
Gs = self.Gs
seeds = [seed]
noise_vars = [var for name, var in Gs.components.synthesis.vars.items() if name.startswith('noise')]
Gs_kwargs = dnnlib.EasyDict()
Gs_kwargs.output_transform = dict(func=tflib.convert_images_to_uint8, nchw_to_nhwc=True)
Gs_kwargs.randomize_noise = False
if truncation_psi is not None:
Gs_kwargs.truncation_psi = truncation_psi
for seed_idx, seed in enumerate(seeds):
print('Generating image for seed %d (%d/%d) ...' % (seed, seed_idx, len(seeds)))
rnd = np.random.RandomState(seed)
z = rnd.randn(1, *Gs.input_shape[1:]) # [minibatch, component]
tflib.set_vars({var: rnd.randn(*var.shape.as_list()) for var in noise_vars}) # [height, width]
images = Gs.run(z, None, **Gs_kwargs) # [minibatch, height, width, channel]
# PIL.Image.fromarray(images[0], 'RGB').save(dnnlib.make_run_dir_path('seed%04d.png' % seed))
return images
def generate_im_from_z_space(self, z, truncation_psi=0.5):
Gs = self.Gs
Gs_kwargs = dnnlib.EasyDict()
Gs_kwargs.output_transform = dict(func=tflib.convert_images_to_uint8, nchw_to_nhwc=True)
Gs_kwargs.randomize_noise = False
if truncation_psi is not None:
Gs_kwargs.truncation_psi = truncation_psi # [height, width]
images = Gs.run(z, None, **Gs_kwargs)
# PIL.Image.fromarray(images[0], 'RGB').save(dnnlib.make_run_dir_path('test_from_z.png'))
return images
def generate_im_from_w_space(self, w):
images = self.Gs.components.synthesis.run(w, **self.Gs_syn_kwargs)
# PIL.Image.fromarray(images[0], 'RGB').save(dnnlib.make_run_dir_path('test_from_w.png'))
return images
# def load_network(random_weights=False):
# URL_FFHQ = 'https://drive.google.com/uc?id=1MEGjdvVpUsu1jB4zrXZN7Y4kBBOzizDQ'
# tflib.init_tf()
#
# with dnnlib.util.open_url(URL_FFHQ, cache_dir=config.cache_dir) as f:
# G, D, Gs = pickle.load(f)
# if random_weights:
# Gs.reset_vars()
# return Gs
if __name__ == "__main__":
Our_model = Build_model()
# Our_model.generate_im_from_random_seed(10)
# Our_model.generate_im_from_random_seed(50)
rnd = np.random.RandomState(10)
# z = rnd.randn(1, *Our_model.Gs.input_shape[1:])
z = rnd.randn(2, 512)
w = Our_model.Gs.components.mapping.run(z, None)
w_avg = Our_model.Gs.get_var('dlatent_avg')
w = w_avg + (w - w_avg) * 0.5
Our_model.generate_im_from_w_space(w)