app.py

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)