import os
import sys
import torch
import torch.nn as nn
import torch.nn.functional as F
from PIL import Image,ImageFilter
import matplotlib.pyplot as plt
import numpy as np
import torchvision
from torchvision import transforms
from torchvision.transforms import ToTensor
from torchvision.transforms import ToPILImage
from torch.autograd import Variable
import math
import random
means = [0.485, 0.456, 0.406]
stds = [ 0.229, 0.224, 0.225]
pic_height = 512
pic_width = 512
opt = {
"pic_c":32, # middle convolution layers is 32
"pic_h":pic_height, # low responsity images' height is 48
"pic_w":pic_width,
"pic_up":2, # upscale
"heads":8,
"vec_dim":512,
"N":6,
"x_vocab_len":0,
"y_vocab_len":0,
"sentence_len":80,
"batchs":1000,
"batch_size":10,
"pad":0,
"sof":1,
"eof":2,
"cat_padding": 5,
}
def verse_normalize(img, means, stds):
means = torch.Tensor(means)
stds = torch.Tensor(stds)
for i in range(3):
img[:,i] = img[:,i] * stds[i] + means[i]
return img
"""
# errors
def psnr(img1, img2):
img1 = Variable(img1)
img2 = Variable(img2)
mse = ( (img1/1.0) - (img2/1.0) ) ** 2
mse = np.mean( np.array(mse) )
if mse < 1.0e-10:
return 100
return 10 * math.log10(255.0**2/mse)
"""
def psnr(img1, img2):
img1 = Variable(img1)
img2 = Variable(img2)
mse = ( (img1/255.0) - (img2/255.0) ) ** 2
mse = np.mean( np.array(mse) )
if mse < 1.0e-10:
return 100
return 10 * math.log10(1/math.sqrt(mse))
def rgb2ycbcr(rgb_image):
"""convert rgb into ycbcr"""
if len(rgb_image.shape)!=3 or rgb_image.shape[2]!=3:
return rgb_image
#raise ValueError("input image is not a rgb image")
rgb_image = rgb_image.astype(np.float32)
transform_matrix = np.array([[0.257, 0.564, 0.098],
[-0.148, -0.291, 0.439],
[0.439, -0.368, -0.071]])
shift_matrix = np.array([16, 128, 128])
ycbcr_image = np.zeros(shape=rgb_image.shape)
w, h, _ = rgb_image.shape
for i in range(w):
for j in range(h):
ycbcr_image[i, j, :] = np.dot(transform_matrix, rgb_image[i, j, :]) + shift_matrix
return ycbcr_image
def ycbcr2rgb(ycbcr_image):
"""convert ycbcr into rgb"""
if len(ycbcr_image.shape)!=3 or ycbcr_image.shape[2]!=3:
return ycbcr_image
#raise ValueError("input image is not a rgb image")
ycbcr_image = ycbcr_image.astype(np.float32)
transform_matrix = np.array([[0.257, 0.564, 0.098],
[-0.148, -0.291, 0.439],
[0.439, -0.368, -0.071]])
transform_matrix_inv = np.linalg.inv(transform_matrix)
shift_matrix = np.array([16, 128, 128])
rgb_image = np.zeros(shape=ycbcr_image.shape)
w, h, _ = ycbcr_image.shape
for i in range(w):
for j in range(h):
rgb_image[i, j, :] = np.dot(transform_matrix_inv, ycbcr_image[i, j, :]) - np.dot(transform_matrix_inv, shift_matrix)
return rgb_image.astype(np.uint8)
class dataloader:
def __init__(self, path, batchs, batch_size, test= False, test_offset = 0.9, order = False):
self.test = test
self.test_offset = test_offset
self.batchs_cnt = 0
self.batchs = batchs
self.batch_size = batch_size
self.path = path
self.file_list = os.listdir(self.path)
if order:
self.file_list = sorted(self.file_list)
else:
random.shuffle(self.file_list)
self.file_number = len(self.file_list)
self.file_start = 0
self.file_stop = self.file_number * self.test_offset - 1
if self.test:
self.file_start = self.file_number * self.test_offset
self.file_stop = self.file_number - 1
self.file_start = int(np.floor(self.file_start))
self.file_stop = int(np.floor(self.file_stop))
self.file_idx = self.file_start
self.downsample = transforms.Compose([
transforms.Resize( (int(pic_height/2),int(pic_width/2)), Image.BILINEAR),
#transforms.RandomResizedCrop( pic_height, scale=(0.08,1) ),
#transforms.Grayscale(num_output_channels = 3),
#transforms.ToTensor()
#transforms.Normalize(means, stds)
])
self.transform = transforms.Compose([
#transforms.Resize( (pic_height,pic_width), Image.BILINEAR),
transforms.RandomResizedCrop( pic_height, scale=(1,1) ),
#transforms.Grayscale(num_output_channels = 3),
transforms.ToTensor()
#transforms.Normalize(means, stds)
])
def get_len(self):
return self.file_stop - self.file_start
def __iter__(self):
return self
def __next__(self):
if (self.batchs_cnt >= self.batchs) & (self.batchs > 0):
self.batchs_cnt = 0
raise StopIteration
self.batchs_cnt += 1
X = []
Y = []
for i in range( self.batch_size):
X_, Y_ = self._next()
X.append(X_)
Y.append(Y_)
X = torch.stack(X, 0)
Y = torch.stack(Y, 0)
return X, Y
def _next(self):
if self.file_idx >= self.file_stop:
self.file_idx = self.file_start
file_path = self.path + '/' + self.file_list[self.file_idx]
self.file_idx += 1
Y = Image.open(file_path)
#print( "Y:", file_path, " (", Y.size )
if len(Y.getbands()) == 1:
Y = Y.convert("RGB")
#X = self.downsample(Y)
X = Y.filter(ImageFilter.BLUR)
X = self.transform(X)
Y = self.transform(Y)
return X,Y
class DRRN(nn.Module):
def __init__(self):
super(DRRN, self).__init__()
# 128 channels in hidden layers
c = 32
self.input = nn.Conv2d(in_channels=1, out_channels=c, kernel_size=3, stride=1, padding=1, bias=False)
self.conv1 = nn.Conv2d(in_channels=c, out_channels=c, kernel_size=3, stride=1, padding=1, bias=False)
self.conv2 = nn.Conv2d(in_channels=c, out_channels=c, kernel_size=3, stride=1, padding=1, bias=False)
self.output = nn.Conv2d(in_channels=c, out_channels=1, kernel_size=3, stride=1, padding=1, bias=False)
self.relu = nn.ReLU(inplace=True)
def init_weights(self):
print(" ****************************")
for m in self.modules():
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
def forward(self, x):
residual = x
inputs = self.input(self.relu(x))
out = inputs
for _ in range(2): #25
out = self.conv2(self.relu(self.conv1(self.relu(out))))
out = torch.add(out, inputs)
out = self.output(self.relu(out))
out = torch.add(out, residual)
return out
tensor_to_pil = ToPILImage()
def display_img(img1, img2):
plt.subplot(1, 2, 1)
plt.imshow(img1)
plt.subplot(1, 2, 2)
plt.imshow(img2)
plt.show()
'''
dataloader = dataloader('./dataset/Set14/image_SRF_2')
plt.figure("ddd")
plt.ion()
plt.cla()
for X,Y in dataloader:
plt.imshow(X)
plt.pause(0.1)
plt.ioff()
iii = tensor_to_pil(Y_[0])
plt.imshow(iii)
plt.show()
'''
model1 = DRRN()
model2 = DRRN()
model3 = DRRN()
loss = nn.MSELoss()
optimizer1 = torch.optim.Adam(model1.parameters(), lr = 0.001, betas = (0.9, 0.999))
optimizer2 = torch.optim.Adam(model2.parameters(), lr = 0.001, betas = (0.9, 0.999))
optimizer3 = torch.optim.Adam(model3.parameters(), lr = 0.001, betas = (0.9, 0.999))
dataloader_train = dataloader('/home/nicholas/Documents/dataset/imageNet', 10, 2)
#dataloader_test = dataloader('/home/nicholas/Documents/dataset/imageNet', 1, 2)
dataloader_test = dataloader('./dataset/Set14/image_SRF_3', 1, 2)
new_training = 1
def train():
global model1
global model2
global model3
if new_training == 1:
print(" new training ...")
model1.init_weights()
model2.init_weights()
model3.init_weights()
else:
print(" continue training ...")
model1= torch.load("./model/srcnn_l1.m")
model2= torch.load("./model/srcnn_l2.m")
model3= torch.load("./model/srcnn_l3.m")
running_loss = 0
plt.ion()
i = 0
for X,Y in dataloader_train:
#print(X.shape)
'''
Train
'''
model1.train()
model2.train()
model3.train()
_,c,h,w = X.shape
Y_1 = model1(X[:,0].reshape(-1,1,h,w))
optimizer1.zero_grad()
cost1 = loss(Y_1, Y[:,0].reshape(-1,1,h,w))
cost1.backward()
optimizer1.step()
Y_2 = model2(X[:,1].reshape(-1,1,h,w))
optimizer2.zero_grad()
cost2 = loss(Y_2, Y[:,1].reshape(-1,1,h,w))
cost2.backward()
optimizer2.step()
Y_3 = model3(X[:,2].reshape(-1,1,h,w))
optimizer3.zero_grad()
cost3 = loss(Y_3, Y[:,2].reshape(-1,1,h,w))
cost3.backward()
optimizer3.step()
#running_loss += cost.item()
print( "batch:{}, loss is {:.4f} {:.4f} {:.4f} ".format(i, cost1, cost2, cost3) )
i += 1
if i%1 == 0:
test(model1, model2, model3)
'''
Display images
'''
"""
Y_ = torch.stack( [Y_1[:,0], Y_2[:,0], Y_3[:,0] ], dim = 1)
iii = tensor_to_pil(Y_[0])
plt.subplot(1, 2, 1)
plt.imshow(iii)
iii = tensor_to_pil(X[0])
plt.subplot(1, 2, 2)
plt.imshow(iii)
plt.show()
plt.pause(0.1)
"""
plt.ioff()
torch.save(model1, "./model/srcnn_l1.m")
torch.save(model2, "./model/srcnn_l2.m")
torch.save(model3, "./model/srcnn_l3.m")
def test(model1, model2, model3):
running_loss = 0
#plt.ion()
for X,Y in dataloader_test:
#print(X.shape)
model1.eval()
model2.eval()
model3.eval()
_,c,h,w = X.shape
Y_1 = model1(X[:,0].reshape(-1,1,h,w))
Y_2 = model2(X[:,1].reshape(-1,1,h,w))
Y_3 = model3(X[:,2].reshape(-1,1,h,w))
print( c, h, w)
Y_ = torch.stack( [Y_1[:,0], Y_2[:,0], Y_3[:,0] ], dim = 1)
psnrv = psnr(Y_, Y)
print(" Psnr = ", psnr(Y_,Y), " psnr2 = ", psnr(X, Y) )
'''
Display images
'''
iii = tensor_to_pil(Y_[0])
plt.subplot(1, 2, 1)
plt.imshow(iii)
iii = tensor_to_pil(X[0])
plt.subplot(1, 2, 2)
plt.imshow(iii)
plt.show()
plt.pause(0.1)
#plt.ioff()
def run():
model1= torch.load("./model/srcnn_l1.m")
model2= torch.load("./model/srcnn_l2.m")
model3= torch.load("./model/srcnn_l3.m")
model1.eval()
model2.eval()
model3.eval()
path = "./dataset/Set5/image_SRF_2"
file_list = os.listdir(path)
#plt.ion()
# block size of LR images
lh = opt['pic_h']
lw = opt['pic_w']
# the stride of moving blocks processing
stride_h = lh - opt['cat_padding'] * 2
stride_w = lw - opt['cat_padding'] * 2
# the center sub block of model outputings
y_top = (opt['cat_padding'])
y_left = (opt['cat_padding'])
y_down = (lh - opt['cat_padding'])
y_right = (lw - opt['cat_padding'])
for img_name in file_list:
# get images
img = Image.open(path+'/'+img_name)
if len(img.getbands()) == 1:
img = img.convert("RGB")
c = 3
org_img = img
org_w, org_h = org_img.size
dst_w, dst_h = org_w * opt['pic_up'], org_h * opt['pic_up']
transform_up = transforms.Compose([
#transforms.RandomResizedCrop(pad_h, scale=(2.0,2.0)),
transforms.Resize( (dst_h, dst_w) ),
#transforms.CenterCrop( (pad_h, pad_w) ),
])
img = transform_up(img)
# padding the edge, turn ( h X w ) to ( uh X uw )
pad_h = dst_h + opt['cat_padding'] * 2
pad_w = dst_w + opt['cat_padding'] * 2
pad_h_off = ((pad_h - 2*opt['cat_padding']) % stride_h)
pad_w_off = ((pad_w - 2*opt['cat_padding']) % stride_w)
if pad_h_off != 0:
pad_h += stride_h - pad_h_off
if pad_w_off != 0:
pad_w += stride_w - pad_w_off
# pad_h must == pad_w
transform_pad = transforms.Compose([
#transforms.RandomResizedCrop(pad_h, scale=(2.0,2.0)),
transforms.CenterCrop( (pad_h, pad_w) ),
])
img = transform_pad(img)
w,h = img.size
# srcnn : preprocessing for images. So set size to sr_h, sr_w.
transform_mid = transforms.Compose([
#transforms.CenterCrop( (sr_h, sr_w) ),
transforms.ToTensor(),
#transforms.Normalize(means, stds)
])
transform_last = transforms.Compose([
transforms.CenterCrop( (dst_h, dst_w) ),
transforms.ToTensor(),
#transforms.Normalize(means, stds), as the Y_ has been verse normalized.
])
Y = torch.zeros( (1, 3, h, w) )
img_ = transform_mid(img)
top = 0
down = top + lh
while down <= h:
left = 0
right = left + lw
while right <= w:
X_ = img_[:, top : down, left : right].detach()
X_ = X_.reshape(-1, c, lh, lw)
Y_1 = model1(X_[:,0].reshape(-1,1,h,w))
Y_2 = model2(X_[:,1].reshape(-1,1,h,w))
Y_3 = model3(X_[:,2].reshape(-1,1,h,w))
Y_ = torch.stack( [Y_1[:,0], Y_2[:,0], Y_3[:,0] ], dim = 1)
cent_top = (top + opt['cat_padding'])
cent_left = (left + opt['cat_padding'])
cent_down = (down - opt['cat_padding'])
cent_right = (right - opt['cat_padding'])
Y[0,:, cent_top : cent_down, cent_left: cent_right] = Y_[0,:, y_top: y_down, y_left: y_right]
# move patchs to right
left += stride_w
right = left + lw
# move patchs to down
top += stride_h
down = top + lh
Y = verse_normalize(Y, means, stds)
Y = Y[0]
Y = tensor_to_pil(Y)
Y = transform_last(Y)
Y = tensor_to_pil(Y)
Y.save("./DRRN.png")
display_img( Y, org_img )
plt.pause(1)
plt.clf()
"""
For training run this command:
python face_cnn.py train
For testing fun this command:
python face_cnn.py test
"""
if __name__ == '__main__':
args = sys.argv[1:]
print( args, len(args))
if (len(args) == 1) & (args[0] == "train"):
train()
elif (len(args) == 1) & (args[0] == "run"):
run()
else:
test()