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import tensorflow as tf
import os
import pathlib
import time
import datetime
from matplotlib import pyplot as plt
import numpy as np
from cv2 import cv2
import math
import keras
#tuz-karabiber gürültüsü
def saltpepperNoise(image):
row,col,ch = image.shape
s_vs_p = 0.5
amount = 0.004
out = image
# Salt mode
num_salt = np.ceil(amount * image.size * s_vs_p)
coords = [np.random.randint(0, i - 1, int(num_salt))
for i in image.shape]
out[coords] = 1
# Pepper mode
num_pepper = np.ceil(amount* image.size * (1. - s_vs_p))
coords = [np.random.randint(0, i - 1, int(num_pepper))
for i in image.shape]
out[coords] = 0
return out
def color_imread(path):
img = cv2.imread(path)
img = cv2.cvtColor(img , cv2.COLOR_BGR2RGB)
img = (img/127.5) - 1
img = img.astype(np.float32)
return img
def gray_imread(path):
img = cv2.imread(path)
img = cv2.cvtColor(img ,cv2.COLOR_BGR2GRAY)
img = img.astype(np.float32)
return img
def load():
input_paths = sorted('*.png')
real_paths = sorted('*.png')
input_images = []
real_images = []
for path in input_paths:
image = gray_imread(path)
input_images.append(image)
for path in real_paths:
image = color_imread(path)
real_images.append(image)
return input_images , real_images
def reshape(gray_img):
gray_img = np.asarray(gray_img)
gray_img = gray_img.reshape(256,256,1)
return gray_img
#input_images , real_images = load()
#test = gray_imread("/content/drive/MyDrive/ColabNotebooks/enhance/landscape.png")
#test = cv2.resize(test,(256,256))
#for i in range(len(input_images)):
# input_images[i] = reshape(input_images[i])
#test = reshape(test)
#print(np.asarray(test).shape)
array_Gen_loss=[]
def histogram_graphic(img):
hist,bins = np.histogram(img.flatten(),256,[0,256])
cdf = hist.cumsum()
cdf_normalized = cdf * float(hist.max()) / cdf.max()
plt.plot(cdf_normalized, color = 'b')
plt.hist(img.flatten(),256,[0,256], color = 'r')
plt.xlim([0, 230])
plt.legend(('cdf','histogram'), loc = 'upper left')
plt.show()
def preprocessing(path):
img = cv2.imread(path)
img = np.asarray(img).reshape(256,256,3)
#print(img.shape)
#cv2.imshow(img)
#cv2.imwrite("/content/drive/MyDrive/ColabNotebooks/enhance/Before_hist_equalizer.png",img)
#Işık ayarı
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV) #hsv formatında gerekiyor
hue, sat, val = cv2.split(hsv)
mid = 0.5
mean = np.mean(val)
gamma = math.log(mid*255)/math.log(mean)
#print("Gamma:",gamma)
#Çıkan gamma değerine göre ters işlem uygulayacak
#value kanalında gamma correction
#val_gamma = np.power(val, gamma).clip(0,255).astype(np.uint8)
# yeni value kanalı orijinal hue ve sat kanallarıyla birleştiriliyor
#hsv_gamma = cv2.merge([hue, sat, val_gamma])
#img_gamma = cv2.cvtColor(hsv_gamma, cv2.COLOR_HSV2BGR)
#cv2.imwrite("/content/drive/MyDrive/ColabNotebooks/img_gamma.png",img_gamma)
#cv2.imshow(img_gamma)
#Adaptive Histogram Equalization
#gamma_path = "/content/drive/MyDrive/ColabNotebooks/img_gamma.png"
#img2 = cv2.imread(gamma_path,0)
#img2 = np.asarray(img2).reshape(256,256,1)
#clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
#clipLimit -> Kontrast sınırı
#clahe_equ = clahe.apply(img2)
#cv2.imshow(clahe_equ)
#cv2.imwrite("/content/drive/MyDrive/ColabNotebooks/enhance/After_clahe_equalizer.png",clahe_equ)
#return clahe_equ
#preprocessing("/content/drive/MyDrive/ColabNotebooks/enhance/landscape.png")
def image_colorfulness(image):
# split the image into its respective RGB components
(B, G, R) = cv2.split(image.astype("float"))
# compute rg = R - G
rg = np.absolute(R - G)
# compute yb = 0.5 * (R + G) - B
yb = np.absolute(0.5 * (R + G) - B)
# compute the mean and standard deviation of both `rg` and `yb`
(rbMean, rbStd) = (np.mean(rg), np.std(rg))
(ybMean, ybStd) = (np.mean(yb), np.std(yb))
# combine the mean and standard deviations
stdRoot = np.sqrt((rbStd ** 2) + (ybStd ** 2))
meanRoot = np.sqrt((rbMean ** 2) + (ybMean ** 2))
# derive the "colorfulness" metric and return it
return stdRoot + (0.3 * meanRoot) # sınırı 24
from PIL import Image, ImageEnhance
def add_saturation(path):
clr = cv2.imread(path)
value = image_colorfulness(clr)
print(value)
img = Image.open(path)
enhanced_obj = ImageEnhance.Color(img)
if value<30 : #renk doygunluğu iyi durumda çıkanları da bir miktar arttırmak için sınırı 30 yapıyoruz
enhanced_obj.enhance((30-value)*0.1 + 0.75).save("enhance/deneme_sat.jpg")
#add_saturation("/content/drive/MyDrive/ColabNotebooks/enhance/cikti2.jpeg")
def unsharp_mask(image, kernel_size=(5, 5), sigma=1.0, amount=1.0, threshold=0):
"""Return a sharpened version of the image, using an unsharp mask."""
blurred = cv2.GaussianBlur(image, kernel_size, sigma)
sharpened = float(amount + 1) * image - float(amount) * blurred
sharpened = np.maximum(sharpened, np.zeros(sharpened.shape))
sharpened = np.minimum(sharpened, 255 * np.ones(sharpened.shape))
sharpened = sharpened.round().astype(np.uint8)
if threshold > 0:
low_contrast_mask = np.absolute(image - blurred) < threshold
np.copyto(sharpened, image, where=low_contrast_mask)
return sharpened
def example(image,name):
sharpened_image = unsharp_mask(image)
cv2.imwrite(name, sharpened_image)
#s_img= cv2.imread("/content/drive/MyDrive/ColabNotebooks/enhance/deneme.jpg")
#example(s_img,"/content/drive/MyDrive/ColabNotebooks/enhance/deneme_sharp.jpg")
#img2 = cv2.imread("/content/drive/MyDrive/ColabNotebooks/enhance/landscape.png")
#newimg2 = cv2.imread("/content/drive/MyDrive/ColabNotebooks/enhance/Output/nadam_image9.png")
#psnr(img2,newimg2)
#ssim(img2,newimg2)
import math
import cv2
import numpy as np
#original = cv2.imread("/content/drive/MyDrive/ColabNotebooks/enhance/landscape.png",0)
#contrast = cv2.imread("/content/drive/MyDrive/ColabNotebooks/enhance/After_clahe_equalizer_with_gamma.png",0)
#print(original.dtype)
#db = psnr(original, contrast)
#print(db)
OUTPUT_CHANNELS = 3
def downsample(filters, size, apply_batchnorm=True):
initializer = tf.random_normal_initializer(0., 0.02)
result = tf.keras.Sequential()
result.add(tf.keras.layers.Conv2D(filters, size, strides=2, padding='same',kernel_initializer=initializer, use_bias=False))
# Burada 2'ye bölüyoruz 256 --> 128
if apply_batchnorm:
result.add(tf.keras.layers.BatchNormalization())
result.add(tf.keras.layers.LeakyReLU())
return result
def upsample(filters, size, apply_dropout=False):
initializer = tf.random_normal_initializer(0., 0.02)
result = tf.keras.Sequential()
result.add(
tf.keras.layers.Conv2DTranspose(filters, size, strides=2,
padding='same',
kernel_initializer=initializer,
use_bias=False))
# burada da 2 kat arttırıyoruz
result.add(tf.keras.layers.BatchNormalization())
if apply_dropout:
result.add(tf.keras.layers.Dropout(0.5))
result.add(tf.keras.layers.ReLU())
return result
def Generator(tpu=False):
inputs = tf.keras.layers.Input(shape=[256, 256, 3])
down_stack = [
downsample(64, 4, apply_batchnorm=False), # (batch_size, 128, 128, 64)
downsample(128, 4), # (batch_size, 64, 64, 128)
downsample(256, 4), # (batch_size, 32, 32, 256)
downsample(512, 4), # (batch_size, 16, 16, 512)
downsample(512, 4), # (batch_size, 8, 8, 512)
downsample(512, 4), # (batch_size, 4, 4, 512)
downsample(512, 4), # (batch_size, 2, 2, 512)
downsample(512, 4), # (batch_size, 1, 1, 512)
]
up_stack = [
upsample(512, 4, apply_dropout=True), # (batch_size, 2, 2, 1024)
upsample(512, 4, apply_dropout=True), # (batch_size, 4, 4, 1024)
upsample(512, 4, apply_dropout=True), # (batch_size, 8, 8, 1024)
upsample(512, 4), # (batch_size, 16, 16, 1024)
upsample(256, 4), # (batch_size, 32, 32, 512)
upsample(128, 4), # (batch_size, 64, 64, 256)
upsample(64, 4), # (batch_size, 128, 128, 128)
]
initializer = tf.random_normal_initializer(0., 0.02)
last = tf.keras.layers.Conv2DTranspose(OUTPUT_CHANNELS, 4,
strides=2,
padding='same',
kernel_initializer=initializer,
activation='tanh') # (batch_size, 256, 256, 3)
# Build U-NET
x = inputs
# Downsampling through the model
skips = []
for down in down_stack:
x = down(x)
skips.append(x)
skips = reversed(skips[:-1]) # son elemani almadan terste yazdirir
# Upsampling and establishing the skip connections
for up, skip in zip(up_stack, skips):
x = up(x)
x = tf.keras.layers.Concatenate()([x, skip])
x = last(x)
model = tf.keras.Model(inputs=inputs, outputs=x)
return model
def Generator2(tpu=False):
inputs = tf.keras.layers.Input(shape=[256, 256, 3])
down_stack = [
downsample(64, 4, apply_batchnorm=False), # (batch_size, 128, 128, 64)
downsample(128, 4), # (batch_size, 64, 64, 128)
downsample(256, 4), # (batch_size, 32, 32, 256)
downsample(512, 4), # (batch_size, 16, 16, 512)
downsample(512, 4), # (batch_size, 8, 8, 512)
downsample(512, 4), # (batch_size, 4, 4, 512)
downsample(512, 4), # (batch_size, 2, 2, 512)
downsample(512, 4), # (batch_size, 1, 1, 512)
]
up_stack = [
upsample(512, 4, apply_dropout=True), # (batch_size, 2, 2, 1024)
upsample(512, 4, apply_dropout=True), # (batch_size, 4, 4, 1024)
upsample(512, 4, apply_dropout=True), # (batch_size, 8, 8, 1024)
upsample(512, 4), # (batch_size, 16, 16, 1024)
upsample(256, 4), # (batch_size, 32, 32, 512)
upsample(128, 4), # (batch_size, 64, 64, 256)
upsample(64, 4), # (batch_size, 128, 128, 128)
]
initializer = tf.random_normal_initializer(0., 0.02)
last = tf.keras.layers.Conv2DTranspose(OUTPUT_CHANNELS, 4,
strides=2,
padding='same',
kernel_initializer=initializer,
activation='tanh') # (batch_size, 256, 256, 3)
# Build U-NET
x = inputs
# Downsampling through the model
skips = []
for down in down_stack:
x = down(x)
skips.append(x)
skips = reversed(skips[:-1]) # son elemani almadan terste yazdirir
# Upsampling and establishing the skip connections
for up, skip in zip(up_stack, skips):
x = up(x)
x = tf.keras.layers.Concatenate()([x, skip])
x = last(x)
model = tf.keras.Model(inputs=inputs, outputs=x)
return model
#pre_trained = Generator()
#pre_trained.compile(optimizer='adam',loss=tf.keras.losses.BinaryCrossentropy(from_logits=True))
pre_trained2 = Generator2()
pre_trained2.compile(optimizer='adam',loss=tf.keras.losses.BinaryCrossentropy(from_logits=True))
from tensorflow.keras.models import *
from tensorflow.keras.layers import *
from tensorflow.keras.optimizers import *
#integer = (input_images[0]+1)*127.5
LAMBDA = 100
loss_object = tf.keras.losses.BinaryCrossentropy(from_logits=True)
def generator_loss(disc_generated_output, gen_output, target,total_loop):
gan_loss = loss_object(tf.ones_like(disc_generated_output), disc_generated_output)
# Mean absolute error
l1_loss = tf.reduce_mean(tf.abs(target - gen_output))
total_gen_loss = gan_loss + (LAMBDA * l1_loss)
if total_loop % 2 == 0:
array_Gen_loss.append(total_gen_loss)
return total_gen_loss, gan_loss, l1_loss
ssim_results = []
psnr_results = []
def ssim_psnr(pre,target):
ssim_res = ssim(pre,target)
psnr_res = psnr(pre,target)
ssim_results.append(ssim_res)
psnr_results.append(ssim_results)
def Discriminator():
initializer = tf.random_normal_initializer(0., 0.02)
inp = tf.keras.layers.Input(shape=[256, 256, 3], name='input_image') # lr
tar = tf.keras.layers.Input(shape=[256, 256, 3], name='target_image') # hr
x = tf.keras.layers.concatenate([inp, tar]) # (batch_size, 256, 256, channels*2)
down1 = downsample(64, 4, False)(x) # (batch_size, 128, 128, 64)
down2 = downsample(128, 4)(down1) # (batch_size, 64, 64, 128)
down3 = downsample(256, 4)(down2) # (batch_size, 32, 32, 256)
zero_pad1 = tf.keras.layers.ZeroPadding2D()(down3) # (batch_size, 34, 34, 256)
conv = tf.keras.layers.Conv2D(512, 4, strides=1,
kernel_initializer=initializer,
use_bias=False)(zero_pad1) # (batch_size, 31, 31, 512)
batchnorm1 = tf.keras.layers.BatchNormalization()(conv)
leaky_relu = tf.keras.layers.LeakyReLU()(batchnorm1)
zero_pad2 = tf.keras.layers.ZeroPadding2D()(leaky_relu) # (batch_size, 33, 33, 512)
last = tf.keras.layers.Conv2D(1, 4, strides=1,
kernel_initializer=initializer)(zero_pad2) # (batch_size, 30, 30, 1)
return tf.keras.Model(inputs=[inp, tar], outputs=last)
discriminator = Discriminator()
def discriminator_loss(disc_real_output, disc_generated_output):
real_loss = loss_object(tf.ones_like(disc_real_output), disc_real_output)
generated_loss = loss_object(tf.zeros_like(disc_generated_output), disc_generated_output)
total_disc_loss = real_loss + generated_loss # 0.5 ile de çarpabilirsin
return total_disc_loss
generator_optimizer = tf.keras.optimizers.Nadam(learning_rate=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-07, name="Nadam")
discriminator_optimizer = tf.keras.optimizers.Nadam(learning_rate=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-07, name="Nadam")
def generate_images(model, test_input,step):
if (step%1 == 0):
prediction = model(test_input, training=True)
pre = prediction[0]
pre = (pre+1)*127.5
pre = np.uint8(pre)
name = 'image{step}.png'.format(step=step)
plt.imsave(name,pre)
#test = np.array(test).reshape(1,256,256,1)
#input_images = np.array(input_images).reshape(-1,1,256,256,1)
#real_images = np.array(real_images).reshape(-1,1,256,256,3)
#print(real_images[0].shape)
def train_step(input_image, target, step):
with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:
gen_output = pre_trained(input_image, training=True)
disc_real_output = discriminator([input_image, target], training=True)
disc_generated_output = discriminator([input_image, gen_output], training=True)
gen_total_loss, gen_gan_loss, gen_l1_loss = generator_loss(disc_generated_output, gen_output, target,10)
disc_loss = discriminator_loss(disc_real_output, disc_generated_output)
generator_gradients = gen_tape.gradient(gen_total_loss,
pre_trained.trainable_variables)
discriminator_gradients = disc_tape.gradient(disc_loss,
discriminator.trainable_variables)
generator_optimizer.apply_gradients(zip(generator_gradients,
pre_trained.trainable_variables))
discriminator_optimizer.apply_gradients(zip(discriminator_gradients,
discriminator.trainable_variables))
def fit(input_images,real_images,test,steps):
example_input = test
start = time.time()
step = 0
i = 0
while step<steps:
print("Step = ",step)
while i < len(input_images):
train_step(input_images[i], real_images[i], step)
if (i%200 == 0):
print('i= ',i)
i +=1
generate_images(pre_trained, example_input,step)
step+=1
i = 0
generate_images(pre_trained, example_input,step)
#fit(input_images,real_images,test,10)
#pre_trained.save("enhance/pix2pix.h5")
a = array_Gen_loss
a = np.asarray(a)
plt.plot(a)
plt.ylabel('Loss Percent')
plt.xlabel('Epochs')
plt.show()
#pre_trained.summary()
#pre_trained.optimizer
#path2 = '/content/drive/MyDrive/ColabNotebooks/enhance/landscape.png'
#image = gray_imread(path2)
#image = saltpepperNoise(image)
#image = np.array(image).reshape(1,256,256,1)
#prediction = pre_trained(image,training=True)
#pre = prediction[0]
#pre = (pre+1)*127.5
#pre = np.uint8(pre)
#name = '/content/drive/MyDrive/ColabNotebooks/enhance/pre_trained.png'
#plt.imsave(name,pre)
#cv2.imshow(pre)
#def ssim(original,predict):
# ssim = tf.image.ssim(original, predict, max_val=1.0, filter_size=11, filter_sigma=1.5, k1=0.01, k2=0.03)
# return ssim
#def psnr(Input,Output,Choice):
# psnr = tf.image.psnr(Input, Output, max_val=255)
# return psnr
def result(Input,Choice,Step):
if Choice=="Indoor-Coloring":
if Step == 1:
pre_trained = tf.keras.models.load_model("indoor_1.h5")
if Step == 2:
pre_trained = tf.keras.models.load_model("indoor_2.h5")
if Step == 3:
pre_trained = tf.keras.models.load_model("indoor_3.h5")
size0 = Input.shape[0]
size1 = Input.shape[1]
start = Input
Input = cv2.resize(Input, (256,256), interpolation = cv2.INTER_AREA)
Input = cv2.cvtColor(Input , cv2.COLOR_BGR2GRAY)
Input = np.array(Input).reshape(1,256,256,1)
prediction = pre_trained(Input,training=True)
Input = prediction[0]
Input = (Input+1)*127.5
Input = np.uint8(Input)
Input = cv2.resize(Input, (size1,size0), interpolation = cv2.INTER_AREA)
finish = Input
mse = np.mean((start - finish) ** 2)
MAX = np.iinfo(start.dtype).max
if mse == 0:
Psnr = 100
else:
Psnr = 20 * math.log10(MAX / math.sqrt(mse))
return Input,Psnr
if Choice=="Outdoor-Coloring":
if Step == 1:
pre_trained = tf.keras.models.load_model("outdoor_1.h5")
if Step == 2:
pre_trained = tf.keras.models.load_model("outdoor_2.h5")
if Step == 3:
pre_trained = tf.keras.models.load_model("outdoor_3.h5")
size0 = Input.shape[0]
size1 = Input.shape[1]
start = Input
Input = cv2.resize(Input, (256,256), interpolation = cv2.INTER_AREA)
Input = cv2.cvtColor(Input , cv2.COLOR_BGR2GRAY)
Input = np.array(Input).reshape(1,256,256,1)
prediction = pre_trained(Input,training=True)
Input = prediction[0]
Input = (Input+1)*127.5
Input = np.uint8(Input)
Input = cv2.resize(Input, (size1,size0), interpolation = cv2.INTER_AREA)
finish = Input
mse = np.mean((start - finish) ** 2)
MAX = np.iinfo(start.dtype).max
if mse == 0:
Psnr = 100
else:
Psnr = 20 * math.log10(MAX / math.sqrt(mse))
return Input,Psnr
if Choice=="Enhancement":
pre_trained2 = tf.keras.models.load_model("gradio_pix2pix.h5")
size0 = Input.shape[0]
size1 = Input.shape[1]
Input = cv2.resize(Input, (256,256), interpolation = cv2.INTER_AREA)
Input = cv2.cvtColor(Input , cv2.COLOR_BGR2GRAY)
Input = np.array(Input).reshape(1,256,256,1)
prediction = pre_trained2(Input,training=True)
Input = prediction[0]
Input = (Input+1)*127.5
Input = np.uint8(Input)
Input = cv2.resize(Input, (size1,size0), interpolation = cv2.INTER_AREA)
return Input
#lst = cv2.imread('/content/drive/MyDrive/ColabNotebooks/enhance/low-sat.jpg')
#r = result(lst)
#cv2.imshow(r)
import gradio as gr
iface = gr.Interface(fn=result, inputs=[gr.inputs.Image(type="numpy",image_mode="RGB"),gr.inputs.Radio(["Indoor-Coloring","Outdoor-Coloring","Enhancement","Repair","Repair and Color"]),gr.inputs.Slider(minimum=1.0,maximum=3.0,default=3.0,step=1.0)], outputs=[gr.outputs.Image( type="auto", label="Output"),gr.outputs.Textbox(type="number",label="Psnr")],theme="grass",live=True
,css=""" body {background-color: rgba(127,191,63,0.48)} """,title="Image Enhancement",article=""" <a href="https://docs.google.com/document/d/19k6dyR5x_hd1M0yoU8i49dlDWvFmtnBT/edit?usp=sharing&ouid=115743073712072785012&rtpof=true&sd=true" download="example.docx"><img src="https://img.icons8.com/external-itim2101-lineal-color-itim2101/64/000000/external-article-blogger-and-influencer-itim2101-lineal-color-itim2101-1.png" alt="Article"></a>""",examples=[["dog.jpg","Indoor-Coloring"]])
iface.launch(debug="True",show_tips="True",inbrowser=True) |