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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
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 reshape(gray_img):
gray_img = np.asarray(gray_img)
gray_img = gray_img.reshape(256,256,1)
return gray_img
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
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)
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 result(Input,Choice,Step):
if Choice=="Indoor-Coloring":
if Step == 1.0:
pre_trained = tf.keras.models.load_model("indoor_1.h5")
if Step == 2.0:
pre_trained = tf.keras.models.load_model("indoor_2.h5")
if Step == 3.0:
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 == 1.0:
pre_trained = tf.keras.models.load_model("outdoor_1.h5")
if Step == 2.0:
pre_trained = tf.keras.models.load_model("outdoor_2.h5")
if Step == 3.0:
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":
if Step == 1.0 or Step == 2.0 or Step == 3.0:
pre_trained2 = tf.keras.models.load_model("generatorLR-HR_300.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_BGR2RGB)
Input = (Input/127.5) - 1
Input = Input.astype(np.float32)
Input = np.array(Input).reshape(1,256,256,3)
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)
Input = cv2.cvtColor(Input ,cv2.COLOR_BGR2RGB)
Psnr = 50
return Input, Psnr
#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", "Face-Coloring","Repair"]),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 Between Input and Output")],theme="grass", live=True
,css=""" body {background-color: rgba(127,191,63,0.48)} """,title="Colorization and Enhancement of Old Images",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=[["indoor.png","Indoor-Coloring",3.0],["indoor_10468.png","Indoor-Coloring",3.0],["outdoor_46.png","Outdoor-Coloring",3.0],["outdoor_1755.png","Outdoor-Coloring",3.0]])
iface.launch(debug="True",show_tips="True",inbrowser=True)