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generate_styled_image.py
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generate_styled_image.py
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raw_image_dir = r'/media/MyDataStor2/chunweim/data/HVSMR/JPEGImages_no_style'
output_dir = r'/home/chunweim/chunweim/projects/StyleSegor/JPEGImages_styled'
mode = 'test'
print('mode:', mode)
from os import listdir
from os.path import isfile, join
raw_images = [f for f in listdir(raw_image_dir) if isfile(join(raw_image_dir, f))]
import timeit
import imageio
import os
#%% ========== ========== ========== ========== ========== ==========
# import library
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from PIL import Image
import matplotlib.pyplot as plt
import torchvision.transforms as transforms
import torchvision.models as models
from utils import *
from models import *
import numpy as np
#%matplotlib inline
#%config InlineBackend.figure_format = 'retina'
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print('device:', device)
#%% ========== ========== ========== ========== ========== ==========
def generate_styled_image(image_1, image_2, image_2_styled):
"""
parameters: full path
image_1 = 'training_axial_crop_pat0_v0_36.jpg'
image_2 = 'testing_axial_crop_pat15_v0_63.jpg'
image_2_styles = 'testing_axial_crop_pat15_v0_63_0.jpg' # using pat0 style
"""
width = 512
style_img = read_image(os.path.join(raw_image_dir, image_1), target_width=width).to(device)
content_img = read_image(os.path.join(raw_image_dir,image_2), target_width=width).to(device)
#%% ========== ========== ========== ========== ========== ==========
# build model
vgg16 = models.vgg16(pretrained=True)
vgg16 = VGG(vgg16.features[:23]).to(device).eval()
# compute feature
style_features = vgg16(style_img)
content_features = vgg16(content_img)
#%% ========== ========== ========== ========== ========== ==========
# compute Gram matrix
def gram_matrix(y):
(b, ch, h, w) = y.size()
features = y.view(b, ch, w * h)
features_t = features.transpose(1, 2)
gram = features.bmm(features_t) / (ch * h * w)
return gram
style_grams = [gram_matrix(x) for x in style_features]
#%% ========== ========== ========== ========== ========== ==========
# trining image
input_img = content_img.clone()
optimizer = optim.LBFGS([input_img.requires_grad_()])
style_weight = 1e6 # 1e6
content_weight = 1 # 1
run = [0]
while run[0] <= 50: # 300
def f():
optimizer.zero_grad()
features = vgg16(input_img)
content_loss = F.mse_loss(features[2], content_features[2]) * content_weight
style_loss = 0
grams = [gram_matrix(x) for x in features]
for a, b in zip(grams, style_grams):
style_loss += F.mse_loss(a, b) * style_weight
loss = style_loss + content_loss
if run[0] % 50 == 0:
print('Step {}: Style Loss: {:4f} Content Loss: {:4f}'.format(
run[0], style_loss.item(), content_loss.item()))
run[0] += 1
loss.backward()
return loss
optimizer.step(f)
#%% ========== ========== ========== ========== ========== ==========
img = input_img.cpu().detach().numpy()[0,:,:,:]
img = img.transpose((1, 2, 0))
imageio.imwrite(image_2_styled,img)
#%% ========== ========== ========== ========== ========== ==========
#%% ========== ========== ========== ========== ========== ==========
# load background lines:
import pickle
with open('id2y_background.pkl', "rb") as fi:
id2y_background = pickle.load(fi)
#%% ========== ========== ========== ========== ========== ==========
for k in range(len(raw_images)):
print('\n')
image_path = os.path.join(raw_image_dir, raw_images[k])
sample_name = raw_images[k].split('.')[0]
if mode == 'train':
if sample_name.startswith('test'):
print('skip:', raw_images[k])
continue
if mode == 'test':
if sample_name.startswith('train'):
print('skip:', raw_images[k])
continue
print('processing:', raw_images[k])
#%% ========== ========== ========== ========== ========== ==========
# we want to find 10 positions:
sample_no = int(sample_name.split('_')[3][3:])
view_index = int(sample_name.split('_')[4][1:])
slice_index = int(sample_name.split('_')[-1])
slice_percentage = id2y_background[sample_no][view_index][slice_index]
peak_x_this_sample = np.argmax(id2y_background[sample_no][view_index])
for i in range(10): # for each training slice, we only consider 10 testing style
# note: below is the new sample_no
if mode == 'train':
sample_no = i + 10
if mode == 'test':
sample_no = i
fixed_array = id2y_background[sample_no][view_index]
peak_x = np.argmax(fixed_array)
mapped_x_1 = np.argmin(np.abs(fixed_array[:peak_x] - slice_percentage))
mapped_x_2 = np.argmin(np.abs(fixed_array[peak_x:] - slice_percentage)) + peak_x
if slice_index > peak_x_this_sample:
mapped_x = mapped_x_2
else:
mapped_x = mapped_x_1
#%% ========== ========== ========== ========== ========== ==========
# now, we have this position: mapped_x
image_2 = image_path # current image is the content image
if mode == 'train':
# this is test image style, used for train set:
image_1_name = 'testing_axial_crop_pat'+str(sample_no)+'_v'+str(view_index)+'_'+str(mapped_x)+'.jpg'
if mode == 'test':
image_1_name = 'training_axial_crop_pat'+str(sample_no)+'_v'+str(view_index)+'_'+str(mapped_x)+'.jpg'
image_1 = os.path.join(raw_image_dir, image_1_name)
image_2_styled_name = sample_name.split('.')[0]+'_'+str(sample_no)+'.jpg'
image_2_styled = os.path.join(output_dir, image_2_styled_name)
print('content image:', image_2)
print('style image:', image_1)
print('content image output:', image_2_styled)
#%% ========== ========== ========== ========== ========== ==========
tic = timeit.default_timer()
generate_styled_image(image_1, image_2, image_2_styled)
toc = timeit.default_timer()
print('time:', toc-tic)
# break # only one image to see
#%% ========== ========== ========== ========== ========== ==========