import tensorflow as tf
import cv2
from models.unet import Unet
from data_augmentation.data_augmentation import DataAugmentation
import numpy as np
gpus = tf.config.experimental.list_physical_devices('GPU')
tf.config.experimental.set_memory_growth(gpus[0], True)
# Initialize
IMAGE_PATH = "dataset/Original/Testing/"
MASK_PATH = "dataset/MASKS/Testing/"
IMAGE_FILE = "Frame00314-org"
model = Unet(input_shape=(224, 224, 1)).build()
model.load_weights("models/model_weight.h5")
model.summary()
print("yeah")
def convert_to_tensor(numpy_image):
numpy_image = np.expand_dims(numpy_image, axis=2)
numpy_image = np.expand_dims(numpy_image, axis=0)
tensor_image = tf.convert_to_tensor(numpy_image)
return tensor_image
def predict(image):
process_obj = DataAugmentation(input_size=224, output_size=224)
image_processed = process_obj.data_process_test(image)
tensor_image = convert_to_tensor(image_processed)
# EarlyStopping(monitor=f'val_{monitors}', patience =10, verbose =1 , mode ='min'),
ModelCheckpoint(os.path.join(SEGMENT_RESULT_PATH,
"checkpoint-{epoch:03d}.h5"),
monitor=f'val_{monitors}',
save_best_only=True,
mode='min'),
LearningRateScheduler(lr_scheduler, verbose=1),
HistoryCheckpoint(os.path.join(SEGMENT_RESULT_PATH, "checkpoint_hist.csv"),
monitors),
# SlackMessage(MY_SLACK_TOKEN,monitors)
]
try:
weight = last_cheackpoint(SEGMENT_RESULT_PATH)
init_epoch = int(os.path.basename(weight.split("-")[-1].split(".")[0]))
unet.load_weights(weight)
print(
f"*******************\ncheckpoint restored {weight}\n*******************"
)
except:
init_epoch = 0
print(
"*******************\nfailed to load checkpoint\n*******************")
train_options = {
"optimizer": get_config(optim),
"batchsize": BATCH_SIZE,
"loss_function": loss_func,
"input_shape": IMAGE_SHAPE,
"augmemtation": augm
}
