Image-Processing-using-AI/pred.py at master · Maximellerbach/Image-Processing-using-AI · GitHub

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import cv2
import numpy as np

from tqdm import tqdm
from tensorflow.keras.models import load_model
from glob import glob

model1 = load_model('upmodel1.h5')
model2 = load_model('upmodel2.h5')
model3 = load_model('debmodel1.h5')
model4 = load_model('debmodel2.h5')

model1.summary()
model2.summary()

# please enter the path of the file containing image
dos = glob('car_dataset\\*')
i = 0
for img_path in tqdm(dos):
    try:
        imgo = cv2.imread(img_path)
        i += 1

        w, h = imgo.shape[1], imgo.shape[0]

        total = w*h
        maximum = 1200*1200

        if total > maximum:  # if image is too big, divide image in multiple little image to pred and reconstruct it later
            ratio = maximum/total

            nb = 2

            while(ratio <= 0.50):
                ratio *= 2
                nb *= 2

            w = w//nb
            h = h//nb

            comp_img = []

            for xs in range(1, nb+1):
                for ys in range(1, nb+1):
                    img = imgo[int(h*ys-h):int(h*ys), int(w*xs-w):int(w*xs)]

                    pred = np.expand_dims(img/255, axis=0)

                    x = model1.predict(pred)
                    x = model2.predict(x)
                    x = model3.predict(x)
                    new_img = model4.predict(x)

                    comp_img.append(new_img[0])

            comp_img = np.array(comp_img)
            new_img = np.zeros((new_img.shape[1]*nb, new_img.shape[2]*nb, 3))

            h *= new_img.shape[0]//imgo.shape[0]
            w *= new_img.shape[1]//imgo.shape[1]
            n = 0

            # reconstruct image
            for xs in range(1, nb+1):
                for ys in range(1, nb+1):
                    new_img[h*ys-h:h*ys, w*xs-w:w*xs] = comp_img[n]
                    n += 1

            # please enter your output file
            cv2.imwrite('out\\'+str(i)+'.jpg', new_img*255)

        else:  # if image is little enought just pred the whole image

            pred = np.expand_dims(imgo, axis=0)

            x = model1.predict(pred/255)
            new_imgo = model2.predict(x)

            # please enter your output file
            cv2.imwrite('out\\'+str(i)+'.jpg', new_imgo[0]*255)

    except Exception as e:
        print(e)