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utils.py
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utils.py
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import scipy.stats as st
import tensorflow as tf
import numpy as np
import sys
from functools import reduce
def log10(x):
numerator = tf.compat.v1.log(x)
denominator = tf.compat.v1.log(tf.constant(10, dtype=numerator.dtype))
return numerator / denominator
def _tensor_size(tensor):
from operator import mul
return reduce(mul, (d.value for d in tensor.get_shape()[1:]), 1)
def gauss_kernel(kernlen=21, nsig=3, channels=1):
interval = (2*nsig+1.)/(kernlen)
x = np.linspace(-nsig-interval/2., nsig+interval/2., kernlen+1)
kern1d = np.diff(st.norm.cdf(x))
kernel_raw = np.sqrt(np.outer(kern1d, kern1d))
kernel = kernel_raw/kernel_raw.sum()
out_filter = np.array(kernel, dtype = np.float32)
out_filter = out_filter.reshape((kernlen, kernlen, 1, 1))
out_filter = np.repeat(out_filter, channels, axis = 2)
return out_filter
def blur(x):
kernel_var = gauss_kernel(21, 3, 3)
return tf.nn.depthwise_conv2d(x, kernel_var, [1, 1, 1, 1], padding='SAME')
def process_command_args(arguments):
# specifying default parameters
batch_size = 50
train_size = 30000
learning_rate = 5e-4
num_train_iters = 20000
w_content = 10
w_color = 0.5
w_texture = 1
w_tv = 2000
dped_dir = 'dped/'
vgg_dir = 'vgg_pretrained/imagenet-vgg-verydeep-19.mat'
eval_step = 1000
phone = ""
for args in arguments:
if args.startswith("model"):
phone = args.split("=")[1]
if args.startswith("batch_size"):
batch_size = int(args.split("=")[1])
if args.startswith("train_size"):
train_size = int(args.split("=")[1])
if args.startswith("learning_rate"):
learning_rate = float(args.split("=")[1])
if args.startswith("num_train_iters"):
num_train_iters = int(args.split("=")[1])
# -----------------------------------
if args.startswith("w_content"):
w_content = float(args.split("=")[1])
if args.startswith("w_color"):
w_color = float(args.split("=")[1])
if args.startswith("w_texture"):
w_texture = float(args.split("=")[1])
if args.startswith("w_tv"):
w_tv = float(args.split("=")[1])
# -----------------------------------
if args.startswith("dped_dir"):
dped_dir = args.split("=")[1]
if args.startswith("vgg_dir"):
vgg_dir = args.split("=")[1]
if args.startswith("eval_step"):
eval_step = int(args.split("=")[1])
if phone == "":
print("\nPlease specify the camera model by running the script with the following parameter:\n")
print("python train_model.py model={iphone,blackberry,sony}\n")
sys.exit()
if phone not in ["iphone", "sony", "blackberry"]:
print("\nPlease specify the correct camera model:\n")
print("python train_model.py model={iphone,blackberry,sony}\n")
sys.exit()
print("\nThe following parameters will be applied for CNN training:\n")
print("Phone model:", phone)
print("Batch size:", batch_size)
print("Learning rate:", learning_rate)
print("Training iterations:", str(num_train_iters))
print()
print("Content loss:", w_content)
print("Color loss:", w_color)
print("Texture loss:", w_texture)
print("Total variation loss:", str(w_tv))
print()
print("Path to DPED dataset:", dped_dir)
print("Path to VGG-19 network:", vgg_dir)
print("Evaluation step:", str(eval_step))
print()
return phone, batch_size, train_size, learning_rate, num_train_iters, \
w_content, w_color, w_texture, w_tv,\
dped_dir, vgg_dir, eval_step
def process_test_model_args(arguments):
phone = ""
dped_dir = 'dped/'
test_subset = "small"
iteration = "all"
resolution = "orig"
use_gpu = "true"
for args in arguments:
if args.startswith("model"):
phone = args.split("=")[1]
if args.startswith("dped_dir"):
dped_dir = args.split("=")[1]
if args.startswith("test_subset"):
test_subset = args.split("=")[1]
if args.startswith("iteration"):
iteration = args.split("=")[1]
if args.startswith("resolution"):
resolution = args.split("=")[1]
if args.startswith("use_gpu"):
use_gpu = args.split("=")[1]
if phone == "":
print("\nPlease specify the model by running the script with the following parameter:\n")
print("python test_model.py model={iphone,blackberry,sony,iphone_orig,blackberry_orig,sony_orig}\n")
sys.exit()
return phone, dped_dir, test_subset, iteration, resolution, use_gpu
def get_resolutions():
# IMAGE_HEIGHT, IMAGE_WIDTH
res_sizes = {}
res_sizes["iphone"] = [1536, 2048]
res_sizes["iphone_orig"] = [1536, 2048]
res_sizes["blackberry"] = [1560, 2080]
res_sizes["blackberry_orig"] = [1560, 2080]
res_sizes["sony"] = [1944, 2592]
res_sizes["sony_orig"] = [1944, 2592]
res_sizes["high"] = [1260, 1680]
res_sizes["medium"] = [1024, 1366]
res_sizes["small"] = [768, 1024]
res_sizes["tiny"] = [600, 800]
return res_sizes
def get_specified_res(res_sizes, phone, resolution):
if resolution == "orig":
IMAGE_HEIGHT = res_sizes[phone][0]
IMAGE_WIDTH = res_sizes[phone][1]
else:
IMAGE_HEIGHT = res_sizes[resolution][0]
IMAGE_WIDTH = res_sizes[resolution][1]
IMAGE_SIZE = IMAGE_WIDTH * IMAGE_HEIGHT * 3
return IMAGE_HEIGHT, IMAGE_WIDTH, IMAGE_SIZE
def extract_crop(image, resolution, phone, res_sizes):
if resolution == "orig":
return image
else:
x_up = int((res_sizes[phone][1] - res_sizes[resolution][1]) / 2)
y_up = int((res_sizes[phone][0] - res_sizes[resolution][0]) / 2)
x_down = x_up + res_sizes[resolution][1]
y_down = y_up + res_sizes[resolution][0]
return image[y_up : y_down, x_up : x_down, :]