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 }