def transfer_image(image):
# image_tensor = test_transforms(image).float()
input = utils.load_image(image)
input = input.to(device)
output = model(input)
output_img = (utils.im_convert(output) * 255.0).astype(np.uint8)
return output_img
# the content loss
content_loss = torch.mean(
(target_features['conv4_2'] - content_features['conv4_2'])**2)
# the style loss
# initialize the style loss to 0
style_loss = 0
# then add to it for each layer's gram matrix loss
for layer in style_weights:
# get the "target" style representation for the layer
target_feature = target_features[layer]
target_gram = utils.gram_matrix(target_feature)
_, d, h, w = target_feature.shape
# get the "style" style representation
style_gram = style_grams[layer]
# the style loss for one layer, weighted appropriately
layer_style_loss = style_weights[layer] * torch.mean(
(target_gram - style_gram)**2)
# add to the style loss
style_loss += layer_style_loss / (d * h * w)
# calculate the *total* loss
total_loss = content_weight * content_loss + style_weight * style_loss
# update your target image
optimizer.zero_grad()
total_loss.backward()
optimizer.step()
cv2.imwrite('saved_images/corgi.png', utils.im_convert(target))
def main(args):
i_path = args.input_path
m_path = args.mask_path
bg_path = args.bg_path
np.random.seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
torch.backends.cudnn.deterministic = True
camouflage_dir = args.output_dir
os.makedirs(camouflage_dir, exist_ok=True)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
VGG = models.vgg19(pretrained=True).features
VGG.to(device)
for parameter in VGG.parameters():
parameter.requires_grad_(False)
style_net = HRNet.HRNet()
style_net.to(device)
transform = Compose([
Normalize(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225],
),
ToTensorV2(),
])
# try to give fore con_layers more weight so that can get more detail in output iamge
style_weights = args.style_weight_dic
mask = cv2.imread(m_path, 0)
mask = scaling(mask, scale=args.mask_scale)
if args.crop:
idx_y, idx_x = np.where(mask > 0)
x1_m, y1_m, x2_m, y2_m = np.min(idx_x), np.min(idx_y), np.max(
idx_x), np.max(idx_y)
else:
x1_m, y1_m = 0, 0
y2_m, x2_m = mask.shape
x2_m, y2_m = 8 * (x2_m // 8), 8 * (y2_m // 8)
x1_m = 8 * (x1_m // 8)
x2_m = 8 * (x2_m // 8)
y1_m = 8 * (y1_m // 8)
y2_m = 8 * (y2_m // 8)
fore_origin = cv2.cvtColor(cv2.imread(i_path), cv2.COLOR_BGR2RGB)
fore_origin = scaling(fore_origin, scale=args.mask_scale)
fore = fore_origin[y1_m:y2_m, x1_m:x2_m]
mask_crop = mask[y1_m:y2_m, x1_m:x2_m]
mask_crop = np.where(mask_crop > 0, 255, 0).astype(np.uint8)
kernel = np.ones((15, 15), np.uint8)
mask_dilated = cv2.dilate(mask_crop, kernel, iterations=1)
origin = cv2.cvtColor(cv2.imread(bg_path), cv2.COLOR_BGR2RGB)
h_origin, w_origin, _ = origin.shape
h, w = mask_dilated.shape
assert h < h_origin, "mask height must be smaller than bg height, and lower mask_scale parameter!!"
assert w < w_origin, "mask width must be smaller than bg width, and lower mask_scale parameter!!"
print("mask size,height:{},width:{}".format(h, w))
if args.hidden_selected is None:
y_start, x_start = recommend(origin, fore, mask_dilated)
else:
y_start, x_start = args.hidden_selected
x1, y1 = x_start + x1_m, y_start + y1_m
x2, y2 = x1 + w, y1 + h
if y2 > h_origin:
y1 -= (y2 - h_origin)
y2 = h_origin
if x2 > w_origin:
x1 -= (x2 - w_origin)
x2 = w_origin
print("hidden region...,height-{}:{},width-{}:{}".format(y1, y2, x1, x2))
mat_dilated = fore * np.expand_dims(
mask_crop / 255, axis=-1) + origin[y1:y2, x1:x2] * np.expand_dims(
(mask_dilated - mask_crop) / 255, axis=-1)
bg = origin.copy()
bg[y1:y2,
x1:x2] = fore * np.expand_dims(mask_crop / 255, axis=-1) + origin[
y1:y2, x1:x2] * np.expand_dims(1 - mask_crop / 255, axis=-1)
content_image = transform(image=mat_dilated)["image"].unsqueeze(0)
style_image = transform(image=origin[y1:y2, x1:x2])["image"].unsqueeze(0)
content_image = content_image.to(device)
style_image = style_image.to(device)
style_features = get_features(style_image, VGG, mode="style")
if args.style_all:
style_image_all = transform(
image=origin)["image"].unsqueeze(0).to(device)
style_features = get_features(style_image_all, VGG, mode="style")
style_gram_matrixs = {}
style_index = {}
for layer in style_features:
sf = style_features[layer]
_, _, h_sf, w_sf = sf.shape
mask_sf = (cv2.resize(mask_dilated, (w_sf, h_sf))).flatten()
sf_idxes = np.where(mask_sf > 0)[0]
gram_matrix = gram_matrix_slice(sf, sf_idxes)
style_gram_matrixs[layer] = gram_matrix
style_index[layer] = sf_idxes
target = content_image.clone().requires_grad_(True).to(device)
foreground_features = get_features(content_image, VGG, mode="camouflage")
target_features = foreground_features.copy()
attention_layers = [
"conv3_1",
"conv3_2",
"conv3_3",
"conv3_4",
"conv4_1",
"conv4_2",
"conv4_3",
"conv4_4",
]
for u, layer in enumerate(attention_layers):
target_feature = target_features[layer].detach().cpu().numpy(
) # output image's feature map after layer
attention = attention_map_cv(target_feature)
h, w = attention.shape
if "conv3" in layer:
attention = cv2.resize(attention, (w // 2, h // 2)) * 1 / 4
if u == 0:
all_attention = attention
else:
all_attention += attention
all_attention /= 5
max_att, min_att = np.max(all_attention), np.min(all_attention)
all_attention = (all_attention - min_att) / (max_att - min_att)
if args.erode_border:
h, w = all_attention.shape
mask_erode = cv2.erode(mask_crop, kernel, iterations=3)
mask_erode = cv2.resize(mask_erode, (w, h))
mask_erode = np.where(mask_erode > 0, 1, 0)
all_attention = all_attention * mask_erode
foreground_attention = torch.from_numpy(all_attention.astype(
np.float32)).clone().to(device).unsqueeze(0).unsqueeze(0)
b, ch, h, w = foreground_features["conv4_1"].shape
mask_f = cv2.resize(mask_dilated, (w, h)) / 255
idx = np.where(mask_f > 0)
size = len(idx[0])
mask_f = torch.from_numpy(mask_f.astype(
np.float32)).clone().to(device).unsqueeze(0).unsqueeze(0)
foreground_chi = foreground_features["conv4_1"] * foreground_attention
foreground_chi = foreground_chi.detach().cpu().numpy()[0].transpose(
1, 2, 0)
foreground_cosine = cosine_distances(foreground_chi[idx])
background_features = get_features(style_image, VGG, mode="camouflage")
idxes = np.where(mask_dilated > 0)
n_neighbors, n_jobs, reg = 7, None, 1e-3
nbrs = NearestNeighbors(n_neighbors=n_neighbors + 1, n_jobs=n_jobs)
X_origin = origin[y1:y2, x1:x2][idxes] / 255
nbrs.fit(X_origin)
X = nbrs._fit_X
Weight_Matrix = barycenter_kneighbors_graph(nbrs,
n_neighbors=n_neighbors,
reg=reg,
n_jobs=n_jobs)
idx_new = np.where(idxes[0] < (y2 - y1 - 1))
idxes_h = (idxes[0][idx_new], idxes[1][idx_new])
idx_new = np.where(idxes[1] < (x2 - x1 - 1))
idxes_w = (idxes[0][idx_new], idxes[1][idx_new])
mask_norm = mask_crop / 255.
mask_norm_torch = torch.from_numpy(
(mask_norm).astype(np.float32)).unsqueeze(0).unsqueeze(0).to(device)
boundary = (mask_dilated - mask_crop) / 255
boundary = torch.from_numpy(
(boundary).astype(np.float32)).unsqueeze(0).unsqueeze(0).to(device)
content_loss_epoch = []
style_loss_epoch = []
total_loss_epoch = []
time_start = datetime.datetime.now()
epoch = 0
show_every = args.show_every
optimizer = optim.Adam(style_net.parameters(), lr=args.lr)
steps = args.epoch
mse = nn.MSELoss()
while epoch <= steps:
#############################
### boundary conceal ########
#############################
target = style_net(content_image).to(device)
target = content_image * boundary + target * mask_norm_torch
target.requires_grad_(True)
target_features = get_features(
target, VGG) # extract output image's all feature maps
#############################
### content loss #########
#############################
target_features_content = get_features(target, VGG, mode="content")
content_loss = torch.sum((target_features_content['conv4_2'] -
foreground_features['conv4_2'])**2) / 2
content_loss *= args.lambda_weights["content"]
#############################
### style loss #########
#############################
style_loss = 0
# compute each layer's style loss and add them
for layer in style_weights:
target_feature = target_features[
layer] # output image's feature map after layer
#target_gram_matrix = get_gram_matrix(target_feature)
target_gram_matrix = gram_matrix_slice(target_feature,
style_index[layer])
style_gram_matrix = style_gram_matrixs[layer]
b, c, h, w = target_feature.shape
layer_style_loss = style_weights[layer] * torch.sum(
(target_gram_matrix - style_gram_matrix)**2) / (
(2 * c * w * h)**2)
#layer_style_loss = style_weights[layer] * torch.mean((target_gram_matrix - style_gram_matrix) ** 2)
style_loss += layer_style_loss
style_loss *= args.lambda_weights["style"]
#############################
### camouflage loss #########
#############################
target_chi = target_features["conv4_1"] * foreground_attention
target_chi = target_chi.detach().cpu().numpy()[0].transpose(1, 2, 0)
target_cosine = cosine_distances(target_chi[idx])
leave_loss = (np.mean(np.abs(target_cosine - foreground_cosine)) / 2)
leave_loss = torch.Tensor([leave_loss]).to(device)
remove_matrix = (1.0 - foreground_attention) * mask_f * (
target_features["conv4_1"] - background_features["conv4_1"])
r_min, r_max = torch.min(remove_matrix), torch.max(remove_matrix)
remove_matrix = (remove_matrix - r_min) / (r_max - r_min)
remove_loss = (torch.mean(remove_matrix**2) / 2).to(device)
camouflage_loss = leave_loss + args.mu * remove_loss
camouflage_loss *= args.lambda_weights["cam"]
#############################
### regularization loss #####
#############################
target_renormalize = target.detach().cpu().numpy()[0, :].transpose(
1, 2, 0)
target_renormalize = target_renormalize * np.array(
(0.229, 0.224, 0.225)) + np.array((0.485, 0.456, 0.406))
target_renormalize = target_renormalize.clip(0, 1)[idxes]
target_reconst = torch.from_numpy(
(Weight_Matrix * target_renormalize).astype(np.float32))
target_renormalize = torch.from_numpy(
target_renormalize.astype(np.float32))
reg_loss = mse(target_renormalize, target_reconst).to(device)
reg_loss *= args.lambda_weights["reg"]
#############################
### total variation loss ####
#############################
tv_h = torch.pow(target[:, :, 1:, :] - target[:, :, :-1, :],
2).detach().cpu().numpy()[0].transpose(1, 2, 0)
tv_w = torch.pow(target[:, :, :, 1:] - target[:, :, :, :-1],
2).detach().cpu().numpy()[0].transpose(1, 2, 0)
tv_h_mask = tv_h[:, :,
0][idxes_h] + tv_h[:, :,
1][idxes_h] + tv_h[:, :,
2][idxes_h]
tv_w_mask = tv_w[:, :,
0][idxes_w] + tv_w[:, :,
2][idxes_w] + tv_w[:, :,
2][idxes_w]
tv_loss = torch.from_numpy(
(np.array(np.mean(np.concatenate([tv_h_mask,
tv_w_mask]))))).to(device)
tv_loss *= args.lambda_weights["tv"]
total_loss = content_loss + style_loss + camouflage_loss + reg_loss + tv_loss
total_loss_epoch.append(total_loss)
style_loss_epoch.append(style_loss)
optimizer.zero_grad()
total_loss.backward()
optimizer.step()
if epoch % show_every == 0:
print("After %d criterions:" % epoch)
print('Total loss: ', total_loss.item())
print('Style loss: ', style_loss.item())
print('camouflage loss: ', camouflage_loss.item())
print('camouflage loss leave: ', leave_loss.item())
print('camouflage loss remove: ', remove_loss.item())
print('regularization loss: ', reg_loss.item())
print('total variation loss: ', tv_loss.item())
print('content loss: ', content_loss.item())
print("elapsed time:{}".format(datetime.datetime.now() -
time_start))
canvas = origin.copy()
fore_gen = im_convert(target) * 255.
sub_canvas = np.vstack(
[mat_dilated, fore_gen, origin[y1:y2, x1:x2]])
canvas[y1:y2, x1:x2] = fore_gen * np.expand_dims(
mask_norm, axis=-1) + origin[y1:y2, x1:x2] * np.expand_dims(
1.0 - mask_norm, axis=-1)
canvas = canvas.astype(np.uint8)
if args.save_process:
new_path = os.path.join(
camouflage_dir, "{}_epoch{}.png".format(args.name, epoch))
cv2.imwrite(new_path, cv2.cvtColor(canvas, cv2.COLOR_RGB2BGR))
cv2.rectangle(canvas, (x1, y1), (x2, y2), (255, 0, 0), 10)
cv2.rectangle(canvas, (x1 - x1_m, y1 - y1_m), (x2, y2),
(255, 255, 0), 10)
canvas = np.vstack([canvas, bg])
h_c, w_c, _ = canvas.shape
h_s, w_s, _ = sub_canvas.shape
sub_canvas = cv2.resize(sub_canvas, (int(w_s * (h_c / h_s)), h_c))
canvas = np.hstack([sub_canvas, canvas])
canvas = canvas.astype(np.uint8)
canvas = cv2.cvtColor(canvas, cv2.COLOR_RGB2BGR)
h_show, w_show, c = canvas.shape
cv2.imshow(
"now camouflage...",
cv2.resize(
canvas,
(w_show // args.show_comp, h_show // args.show_comp)))
epoch += 1
if cv2.waitKey(1) & 0xFF == ord('q'):
break
time_end = datetime.datetime.now()
print('totally cost:{}'.format(time_end - time_start))
new_path = os.path.join(camouflage_dir, "{}.png".format(args.name))
canvas = origin.copy()
fore_gen = im_convert(target) * 255.
canvas[y1:y2,
x1:x2] = fore_gen * np.expand_dims(mask_norm, axis=-1) + origin[
y1:y2, x1:x2] * np.expand_dims(1.0 - mask_norm, axis=-1)
canvas = canvas.astype(np.uint8)
canvas = cv2.cvtColor(canvas, cv2.COLOR_RGB2BGR)
cv2.imwrite(new_path, canvas)
import numpy as np
import matplotlib.pyplot as plt
from utils import load_dataset, im_convert
# data path
path = 'ants_and_bees'
# load_dataset
training_loader, validation_loader = load_dataset(path)
# classes
classes = ('ant', 'bee')
dataiter = iter(training_loader)
images, labels = dataiter.next()
fig = plt.figure(figsize=(25, 4))
for idx in np.arange(20):
ax = fig.add_subplot(2, 10, idx + 1, xticks=[], yticks=[])
plt.imshow(im_convert(images[idx]))
ax.set_title(classes[labels[idx].item()])
plt.show()
valid = Variable(Tensor(imgs.shape[0], 1).fill_(1.0), requires_grad=False)
fake = Variable(Tensor(imgs.shape[0], 1).fill_(0.0), requires_grad=False)
real_imgs = Variable(imgs.type(Tensor))
optimizer_G.zero_grad()
z = Variable(Tensor(np.random.normal(0, 1, (imgs.shape[0],100,1,1))))
gen_imgs = gen(z)
b = dis(gen_imgs)
g_loss = adversarial_loss(b, valid)
g_loss.backward()
optimizer_G.step()
optimizer_D.zero_grad()
real_loss = adversarial_loss(dis(real_imgs), valid)
fake_loss = adversarial_loss(dis(gen_imgs.detach()), fake)
d_loss = (real_loss + fake_loss) / 2
d_loss.backward()
optimizer_D.step()
if(epoch%1==0):
a = gen_imgs[0]
a = utils.im_convert(a)
plt.imshow(a)
plt.show()
g_losses.append(g_loss.item())
d_losses.append(d_loss.item())
print ("[Epoch %d/%d] [Batch %d/%d] [D loss: %f] [G loss: %f]" % (epoch, 1000, i, len(dataloader),
d_loss.item(), g_loss.item()))
error_plot(d_losses,g_losses)
import wget
from params import *
from model import Generator
from utils import view_samples, im_convert
import torch
import argparse
from torch.autograd import Variable
import numpy as np
n = 20
n_col = 5
n_row = 4
wget.download(trained_weights_url)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
#creating generator object
gen = Generator().to(device)
gen.load_state_dict(torch.load('./dcgan.h5', map_location=device))
#define number of images to generate
Tensor = torch.FloatTensor
if (torch.cuda.is_available()):
Tensor = torch.cuda.FloatTensor
z = Variable(Tensor(np.random.normal(0, 1, (n, 100, 1, 1))))
gen_imgs = gen(z)
a = []
for i in range(n):
b = im_convert(gen_imgs[i])
a.append(b)
#enter number of rows and columns in plot such that n_row*n_col=n
view_samples(n_row, n_col, a)
ax2.plot(epoch, style_loss_epoch)
ax2.set_title("Style loss")
ax2.set_xlabel("epoch")
ax2.set_ylabel("Style loss")
ax3.plot(epoch, content_loss_epoch)
ax3.set_title("Content loss")
ax3.set_xlabel("epoch")
ax3.set_ylabel("Content loss")
plt.show()
# display the raw images
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(20, 10))
#content and style ims side-by-side
ax1.imshow(utils.im_convert(content_image))
ax2.imshow(utils.im_convert(output_image))
plt.show()
image = utils.im_convert(output_image)
img2 = Image.open('style.png') #opening style.png image from original code
print(type(img2)) #class 'PIL.PngImagePlugin.PngImageFile'
print(type(image)) #class 'numpy.ndarray'
print(type(output_image)) #class 'torch.Tensor'
#img = Image.open(BytesIO(image))
#image = Image.fromarray(np.uint8(cm.gist_earth(image)*255))
#plt.imsave("./Datasets/STYLED_IMAGES/{}.jpg".format(steps),image,cmap = 'Greys')
#image.save("./Datasets/STYLED_IMAGES/{}.jpg".format(epoch))
#image.save("./Datasets/STYLED_IMAGES/{}.jpg".format(epoch))
#plt.close()
'''
checkpoint = {'model': style_net(),
def style_transfer(device, content_image_path, style_image_path,
output_image_path, max_size, content_weight, style_weight,
steps, show_every, conv_style_weights):
'''
Style transfer on a content image.
'''
# Extracting and fixing features of pretrained VGG19
vgg = models.vgg19(pretrained=True).features
# Preventing any change on the weights of the original model
for param in vgg.parameters():
param.requires_grad_(False)
# Move the model to the device requested
torch.cuda.empty_cache()
try:
device = torch.device(device)
print("The device used is: {}".format(device))
except BaseException:
print("Error, the required device is not available")
vgg.to(device)
# Load in content and style image
content = load_image(content_image_path, max_size).to(device)
# Resize style to match content
style = load_image(style_image_path, max_size,
shape=content.shape[-2:]).to(device)
# Content and style features before training
content_features = get_features(content, vgg)
style_features = get_features(style, vgg)
# Gram matrices for each layer of our style representation
style_grams = {
layer: gram_matrix(style_features[layer])
for layer in style_features
}
# Create a third "target" image and prep it for change
target = content.clone().requires_grad_(True).to(device)
# Iteration hyperparameters
optimizer = optim.Adam([target], lr=0.003)
for ii in range(1, steps + 1):
# Get the features from your target image
target_features = get_features(target, vgg)
# The content loss
content_loss = torch.mean(
(target_features['conv4_2'] - content_features['conv4_2'])**2)
# The style loss
style_loss = 0
# Add loss for each layer's gram matrix loss
for layer in conv_style_weights:
# Get the "target" style representation for the layer
target_feature = target_features[layer]
target_gram = gram_matrix(target_feature)
_, d, h, w = target_feature.shape
# Get the "style" style representation
style_gram = style_grams[layer]
# The style loss for one layer, weighted appropriately
layer_style_loss = (conv_style_weights[layer] * torch.mean(
(target_gram - style_gram)**2))
# Add to the style loss
style_loss += layer_style_loss / (d * h * w)
# Calculate the *total* loss
total_loss = content_weight * content_loss + style_weight * style_loss
# Update your target image
optimizer.zero_grad()
total_loss.backward()
optimizer.step()
# Display intermediate images and print the loss
if ii % show_every == 0:
print('Total loss: ', total_loss.item())
plt.imshow(im_convert(target))
plt.axis('off')
plt.savefig(output_image_path + "_{}.jpg".format(ii // show_every),
bbox_inches='tight',
pad_inches=0)
# plt.show()
plt.imshow(im_convert(target))
plt.axis('off')
plt.savefig(output_image_path + "_final.jpg",
bbox_inches='tight',
pad_inches=0)
torch.cuda.empty_cache()