def main():
project_dir = "/home/sk/senior_project"
dataset_dir = project_dir + "/dataset"
img_dir = dataset_dir + "/images_pool"
target_dir = dataset_dir + "/groundTruth_seg_pool_train"
test_dir = dataset_dir + "/groundTruth_seg_pool_test"
result_dir = project_dir + "/cnn_ae_" + str(time.time()).split(".")[0]
model_dir = result_dir + "/model"
pred_dir = result_dir + "/predict_result"
if not os.path.exists(result_dir):
os.mkdir(result_dir)
os.mkdir(model_dir)
os.mkdir(pred_dir)
TRAIN_IMAGES = []
TRAIN_TARGET_IMAGES = get_file_path(target_dir)
VAL_IMAGES = []
VAL_TARGET_IMAGES = get_file_path(test_dir)
for train_target_path in TRAIN_TARGET_IMAGES:
name = get_file_name(train_target_path)
img_path = img_dir + "/" + name + ".jpg"
if not os.path.exists(img_path):
continue
TRAIN_IMAGES.append(img_path)
for val_target_path in VAL_TARGET_IMAGES:
name = get_file_name(val_target_path)
img_path = img_dir + "/" + name + ".jpg"
if not os.path.exists(img_path):
continue
VAL_IMAGES.append(img_path)
img_cols = 256
img_rows = 256
img_cols_result = 484
img_rows_result = 304
x_train = load_image(TRAIN_IMAGES)
y_train = load_image(TRAIN_TARGET_IMAGES)
x_val = load_image(VAL_IMAGES)
y_val = load_image(VAL_TARGET_IMAGES)
ae = Autoencoder(model_dir=model_dir, pred_dir=pred_dir)
ae.train_model(x_train, y_train, x_val, y_val, epochs=2000, batch_size=10)
def main():
project_dir = r"E:\Onedrive\KSIP\MachineLearning"
dataset_dir = project_dir + "/dataset"
result_dir = project_dir + "/cnn_ae_" + str(time.time()).replace(".","")
model_dir = result_dir + "/model"
pred_dir = result_dir + "/predict_result"
for p in [result_dir, model_dir, pred_dir]:
if not os.path.exists(p):
os.mkdir(p)
INPUT_TRAIN_IMAGES = get_file_path(PATH_INPUT_TRAIN_STFT)[:10000]
INPUT_VAL_IMAGES = get_file_path(PATH_INPUT_VALID_STFT)[:1000]
TARGET_TRAIN_IMAGES = []
TARGET_VAL_IMAGES = []
for fpath in INPUT_TRAIN_IMAGES:
path = fpath.replace(PATH_INPUT_TRAIN_STFT, PATH_TARGET_SEG_STFT)
TARGET_TRAIN_IMAGES.append(path)
for fpath in INPUT_VAL_IMAGES:
path = fpath.replace(PATH_INPUT_VALID_STFT, PATH_TARGET_SEG_STFT)
TARGET_VAL_IMAGES.append(path)
img_cols = 64
img_rows = 64
img_cols_result = 64
img_rows_result = 64
print_debug("Initial model")
x_train = load_image(INPUT_TRAIN_IMAGES)
print_debug("Load image traning input done")
y_train = load_image(TARGET_TRAIN_IMAGES)
print_debug("Load image target input done")
x_val = load_image(INPUT_VAL_IMAGES)
print_debug("Load image training validation done")
y_val = load_image(TARGET_VAL_IMAGES)
print_debug("Load image target validation input done")
ae = Autoencoder(model_dir=model_dir, pred_dir=pred_dir)
ae.train_model(x_train, y_train, x_val, y_val, epochs=1000, batch_size=50)