test.py

import keras
from keras.applications import VGG16
from keras import layers
from keras.models import Model
from keras.preprocessing.image import ImageDataGenerator
from keras import optimizers
import numpy as np


np.random.seed(5)
RN = 0.25
ME = 0.5

def clean_img(img):
    img = np.array(img)
    img = img.astype('float') / 255.0;

    return img

def add_noise(img):
    rn = RN
    me = ME

    img = np.array(img)

    img = img.astype('float') / 255.0;

    s = (np.shape(img))
    mimg = np.zeros((s[0], s[1]))

    mimg[0::2, 0::2] = img[0::2, 0::2, 0];
    mimg[1::2, 0::2] = img[1::2, 0::2, 1];
    mimg[0::2, 1::2] = img[0::2, 1::2, 1];
    mimg[1::2, 1::2] = img[1::2, 1::2, 2];

    alpha = me / np.mean(mimg);

    img = alpha * mimg;

    nimg = np.random.poisson(img) + np.random.normal(0, rn, (s[0], s[1]))

    nimg = np.round(nimg) / alpha

    nimg[nimg < 0] = 0
    img[nimg > 31] = 31 #5 bits


    bimg = np.zeros((s[0], s[1], 1))

    bimg[:, :, 0] = nimg

    return bimg

input1 = layers.Input((256,256, 1))

demosai = layers.Conv2D(32, (5, 5), activation='relu', padding='same')(input1)
demosai = layers.Conv2D(3, (5, 5), activation='relu', padding='same')(demosai)


val_datagen = ImageDataGenerator(preprocessing_function=clean_img )

def generate_generator_multiple(generator, dir, batch_size, img_height, img_width):
    genX1 = generator.flow_from_directory(
        dir,
        target_size=(img_height, img_width),
        batch_size=batch_size,
        class_mode='categorical',
        shuffle=False)

    while True:
        X1i = genX1.next()

        img = X1i[0] * 255.0;
        s = np.shape(img)
        IMG = np.zeros((s[0], s[1], s[2], 1))
        l = np.shape(img)
        for i in range(0, l[0]):
            temp = np.zeros((img_height, img_width, 3));
            temp[:, :, :] = img[i, :, :, :];

            temp1 = add_noise(temp)

            IMG[i, :, :, :] = temp1


        yield IMG, X1i[1]


def custom_objective(y_true, y_pred):
    sum2 = keras.losses.categorical_crossentropy(y_true, y_pred)
    return sum2


def custom_metric(y_true, y_pred):
    sum3 = keras.metrics.categorical_accuracy(y_true, y_pred)

    return sum3



test_generator = generate_generator_multiple(generator=val_datagen,
                                                 dir='data/clean/new_hard/test',
                                                 batch_size=24,
                                                 img_height=256,
                                                 img_width=256)

conv_base1 = VGG16(weights='imagenet',
                      include_top=False,
                      input_shape=(256, 256, 3))


cb1 = Model(inputs=conv_base1.input, outputs=[conv_base1.output])

cb1= cb1(demosai)

flat2 = layers.Flatten()(cb1)

dense1 = layers.Dense(512, activation='relu')(flat2)
dense1 = layers.Dense(512, activation='relu')(dense1)
dense2 = layers.Dense(10, activation='softmax')(dense1)
model = Model(inputs=[input1], outputs=[dense2])

model.load_weights('model_zoo/QIS_Me_0.5.h5')

model.compile(loss=[custom_objective],
              loss_weights=[1],
              optimizer=optimizers.RMSprop(lr=1e-6, decay=4e-4),
              metrics={(dense2.name).split('/')[0]: [custom_metric]})


vv = model.evaluate_generator(test_generator, steps=20)

acc = vv[1]

print("Accuracy : ", acc*100,"%")