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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) |