labels = 2
if model_name == "fcn":
model = fcn_8s(input_shape=(256, 256, 7),
num_classes=labels,
lr_init=lr_init,
lr_decay=lr_decay,
vgg_weight_path=vgg_path)
elif model_name == "unet":
model = unet(input_shape=(256, 256, 7),
num_classes=labels,
lr_init=lr_init,
lr_decay=lr_decay,
vgg_weight_path=vgg_path)
elif model_name == "pspnet":
model = pspnet50(input_shape=(256, 256, 7),
num_classes=labels,
lr_init=lr_init,
lr_decay=lr_decay)
elif model_name == "deeplabv3p":
model = Deeplabv3(input_shape=(256, 256, 3), classes=labels)
elif model_name == "deeplabv3":
model = deeplabv3_plus(input_shape=(256, 256, 7), num_classes=labels)
elif model_name == "maskrcnn":
modelt = modellib.MaskRCNN(mode='training',
config=config,
model_dir=MODEL_DIR)
elif model_name == 'refinenet':
model = refinenet(input_shape=(256, 256, 5), num_classes=lebels)
#model =build_network_resnet101(inputHeight=256,inputWidth=256,n_classes=len(labels))
#model = build_network_resnet101_stack(inputHeight=256,inputWidth=256,n_classes=len(labels),nStack=2)
elif model_name == "segnet":
model = SegNet(input_shape=(256, 256, 5), classes=labels)
channels = 3
# Create model
print('Creating network...\n')
if model_name == "fcn":
model = fcn_8s(input_shape=(img_height, img_width, channels), num_classes=nb_class,
lr_init=1e-3, lr_decay=5e-4, vgg_weight_path=vgg_path)
elif model_name == "unet":
model = unet(input_shape=(img_height, img_width, channels), num_classes=nb_class,
lr_init=1e-3, lr_decay=5e-4, vgg_weight_path=vgg_path)
elif model_name == "fuzzyunet":
model = fuzzy_unet(input_shape=(img_height, img_width, channels), num_classes=nb_class,
lr_init=1e-3, lr_decay=5e-4, vgg_weight_path=vgg_path)
elif model_name == "pspnet":
model = pspnet50(input_shape=(img_height, img_width, channels), num_classes=nb_class, lr_init=1e-3, lr_decay=5e-4)
# load weights
try:
model.load_weights(model_name + '_model_weight.h5')
except:
print("You must train model and get weight before test.")
# Palette, used to show the result
palette = VOCPalette(nb_class=nb_class)
# print the testing image's name
print(img_path)
# read testing image
imgorg = Image.open(img_path)
# read label
imglab = Image.open(label_path)
# Choose model to train
if model_name == "fcn":
model = fcn_8s(input_shape=(256, 512, 3),
num_classes=len(labels),
lr_init=lr_init,
lr_decay=lr_decay,
vgg_weight_path=vgg_path)
elif model_name == "unet":
model = unet(input_shape=(256, 512, 3),
num_classes=len(labels),
lr_init=lr_init,
lr_decay=lr_decay,
vgg_weight_path=vgg_path)
elif model_name == "pspnet":
model = pspnet50(input_shape=(256, 512, 3),
num_classes=len(labels),
lr_init=lr_init,
lr_decay=lr_decay)
# Define callbacks
checkpoint = ModelCheckpoint(filepath=model_name + '_model_weight.h5',
monitor='val_dice_coef',
save_best_only=True,
save_weights_only=True)
train_check = TrainCheck(output_path='./img', model_name=model_name)
#early_stopping = EarlyStopping(monitor='val_dice_coef', patience=10)
# training
history = model.fit_generator(data_generator('dataset_parser/data.h5',
TRAIN_BATCH, 'train'),
steps_per_epoch=3475 // TRAIN_BATCH,
validation_data=data_generator(
elif model_name == "unet_gau":
model = unet_gau(input_shape=(512, 512, 3), num_classes=len(labels), lr_init=1e-3, lr_decay=5e-4)
elif model_name == "unet_fpagau":
model = unet_fpagau(input_shape=(512, 512, 3), num_classes=len(labels), lr_init=1e-3, lr_decay=5e-4)
elif model_name == "unet_att":
model = unet_att(input_shape=(512, 512, 3), num_classes=len(labels), lr_init=1e-3, lr_decay=5e-4)
elif model_name == "unet_se":
model = unet_se(input_shape=(512, 512, 3), num_classes=len(labels), lr_init=1e-3, lr_decay=5e-4)
elif model_name == "unet_se_gau":
model = unet_se_gau(input_shape=(512, 512, 3), num_classes=len(labels), lr_init=1e-3, lr_decay=5e-4)
elif model_name == "unet_se_gau_fpa":
model = unet_se_gau_fpa(input_shape=(512, 512, 3), num_classes=len(labels), lr_init=1e-3, lr_decay=5e-4)
elif model_name == "unet_atts":
model = unet_atts(input_shape=(512, 512, 3), num_classes=len(labels), lr_init=1e-3, lr_decay=5e-4)
elif model_name == "pspnet":
model = pspnet50(input_shape=(512, 512, 3), num_classes=len(labels), lr_init=1e-3, lr_decay=5e-4)
try:
model.load_weights('/media/ding/Study/First_next_term/lake segmentation project/Fifth/img/experiments/11/0.0001/unet_fpagau_model_weight.h5')
except:
print("You must train model and get weight before test.")
x_img = cv2.imread(img_path)
cv2.imshow('x_img', x_img)
x_img = cv2.cvtColor(x_img, cv2.COLOR_BGR2RGB)
x_img = x_img / 127.5 - 1
x_img = np.expand_dims(x_img, 0)
pred = model.predict(x_img)
res = result_map_to_img(pred[0])
cv2.imwrite('./img/test.png',res)
# cv2.imshow('res', res)
img_path = args.img_path
labels = 2
if model_name == "fcn":
model = fcn_8s(input_shape=(256, 256, 5),
num_classes=labels,
lr_init=1e-3,
lr_decay=5e-4)
elif model_name == "unet":
model = unet(input_shape=(256, 256, 7),
num_classes=labels,
lr_init=1e-3,
lr_decay=5e-4)
elif model_name == "pspnet":
model = pspnet50(input_shape=(256, 256, 5),
num_classes=labels,
lr_init=1e-3,
lr_decay=5e-4)
elif model_name == 'deeplabv3p':
model = Deeplabv3(input_shape=(256, 256, 5), classes=labels)
elif model_name == "deeplabv3":
model = deeplabv3_plus(input_shape=(256, 256, 5), num_classes=labels)
elif model_name == "segnet":
model = SegNet(input_shape=(256, 256, 5), classes=labels)
elif model_name == "refinenet":
model = refinenet(input_shape=(256, 256, 5), num_classes=labels)
model.load_weights("h5File/unet_model_weight.h5")
#model.load_weights("h5File/"+model_name+'_model_weight.h5')
print("load model successfully")
x_img = tifffile.imread("/data/test_h/15out.tif") / 255