Python DATA Examples

Example #1

File: main-checkpoint.py Project: rajatkb/Deep-Super-Resolution-Research

from keras.layers import Input, MaxPool2D, Deconvolution2D, Convolution2D, Add, Dense, AveragePooling2D, UpSampling2D, Reshape, Flatten, Subtract, Concatenate
from keras.optimizers import Adam
from keras.callbacks import LearningRateScheduler
from keras import backend as k

from keras.utils import multi_gpu_model

import tensorflow as tf
from keras.utils import plot_model
from subpixel import Subpixel
from PIL import Image
import cv2

folder = '../Data'
CHANNEL = 3
DATA = DATA(folder=folder)

out_patch_size = DATA.patch_size
inp_patch_size = int(out_patch_size / 2)
DATA.load_data(folder=folder)

print("Training data Y:", DATA.training_patches_Y.shape)
print("Training data X:", DATA.training_patches_2x.shape)
print(" Max data: ", DATA.training_patches_2x.max())


def PSNRLossnp(y_true, y_pred):
    return 10 * np.log(255 * 2 / (np.mean(np.square(y_pred - y_true))))


def SSIM(y_true, y_pred):

Example #2

File: main.py Project: rajatkb/Deep-Super-Resolution-Research

from keras.layers import Input, MaxPool2D, Deconvolution2D, Convolution2D, Add, Dense, AveragePooling2D, UpSampling2D, Reshape, Flatten, Subtract, Concatenate, Lambda
from keras.optimizers import Adam
from keras.callbacks import LearningRateScheduler
from keras import backend as k
from keras.applications import vgg16
from keras.utils import multi_gpu_model
from keras.applications.imagenet_utils import preprocess_input
import tensorflow as tf
from keras.utils import plot_model
from subpixel import Subpixel
from PIL import Image
import cv2

CHANNEL = 3

DATA = DATA(folder=folder, patch_size=int(scale * 32))

out_patch_size = DATA.patch_size
inp_patch_size = int(out_patch_size / scale)
if not test_only:
    DATA.load_data(folder=folder)
    if scale == 2:
        x = DATA.training_patches_2x
    elif scale == 4:
        x = DATA.training_patches_4x
    elif scale == 8:
        x = DATA.training_patches_8x


def PSNRLossnp(y_true, y_pred):
    return 10 * np.log(255 * 2 / (np.mean(np.square(y_pred - y_true))))

Example #3

File: main2.py Project: rajatkb/Deep-Super-Resolution-Research

from keras.optimizers import Adam
from keras.callbacks import LearningRateScheduler
from keras import backend as k

from keras.utils import multi_gpu_model

import tensorflow as tf
from keras.utils import plot_model
from subpixel import Subpixel
from PIL import Image
import cv2


folder = '../BSDS200 processed'
CHANNEL = 3
DATA = DATA(folder = folder)

out_patch_size =  DATA.patch_size 
inp_patch_size = int(out_patch_size/ 2)
DATA.load_data(folder=folder)

print("Training data Y:" , DATA.training_patches_Y.shape)
print("Training data X:" , DATA.training_patches_2x.shape)


def PSNRLossnp(y_true,y_pred):
        return 10* np.log(255*2 / np.mean(np.square(y_pred - y_true)))

def SSIM( y_true,y_pred):
    u_true = k.mean(y_true)
    u_pred = k.mean(y_pred)

Example #4

        #     if j > epochs:
        #         break
    def predict(self, p):
        p = p / 255
        g = self.get_model().predict(np.array(p))
        g[0] = np.array(g[0] * 255, np.uint8)
        g[1] = np.array(g[1] * 255, np.uint8)
        g[2] = np.array(g[2] * 255, np.uint8)
        return g


if __name__ == '__main__':
    CHANNEL = 3
    out_patch_size = 128
    inp_patch_size = 16
    DATA = DATA(folder=folder, patch_size=out_patch_size)
    if not test_only:
        DATA.load_data(folder=folder)
        x = DATA.training_patches_8x
        x2 = DATA.training_patches_4x
        x4 = DATA.training_patches_2x
        x8 = DATA.training_patches_Y

    net = SRResnet(lr=learning_rate,
                   chk=chk,
                   test=test_only,
                   content_lambda=content_lambda,
                   mse_lambda=mse_lambda,
                   adv_lambda=adv_lambda,
                   patch_size=inp_patch_size,
                   visualize=not test_only)