def get_images(directory):
imgs = ImageDataGenerator().flow_from_directory(
directory,
color_mode='rgb',
target_size=(image_size_used, image_size_used),
class_mode=None,
batch_size=(28733))
imgs = imgs.next()
imgs = imgs.astype('float32')
ret = imgs / 255
ret = np.array(ret)
return ret
def load_real_samples(self):
# should later change so that we only load one batch into directory
X_train = ImageDataGenerator().flow_from_directory(
'train',
color_mode='rgb',
target_size=(self.img_rows, self.img_cols),
class_mode=None,
batch_size=1858)
X_train = X_train.next()
X = X_train.astype('float32')
X = (X - 127.5) / 127.5
# print(X)
return X
logical_gpus = tf.config.experimental.list_logical_devices('GPU')
print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPUs")
except RuntimeError as e:
# Memory growth must be set before GPUs have been initialized
print(e)
#load model
autoencoder = load_model('prevautoencoders/shrooms_ae_filter6_1.h5')
test_imgs_clean = []
test_imgs_masked = []
img_to_use = ImageDataGenerator().flow_from_directory("shrooms", color_mode='rgb', target_size = (128, 128), class_mode=None, batch_size=1)
img_to_use = img_to_use.next()
img_to_use = img_to_use.astype('float32')
img_to_use = img_to_use / 255
img_to_use = img_to_use[0]
current_mask_size = 25
mask = np.ones((128, 128, 3))
for i in range(20):
min_x = 50 if 50+current_mask_size < 127 else 127-current_mask_size
x = random.randint(min_x, 127-current_mask_size)
y = random.randint(1, 127-current_mask_size)
mask[x:x + current_mask_size,y:y + current_mask_size,:] = 0.0
masked_img = np.multiply(img_to_use, mask)
test_imgs_clean.append(img_to_use)
test_imgs_masked.append(masked_img)
test_imgs_clean = np.array(test_imgs_clean)
