default=None,
help="Pretrained vgg16 weight path.")
args = parser.parse_args()
model_name = args.model
TRAIN_BATCH = args.train_batch
VAL_BATCH = args.val_batch
lr_init = args.lr_init
lr_decay = args.lr_decay
vgg_path = args.vgg
config = Config()
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)
# testing image's path
img_path = './BUS/data2/wavelet/Case81.png'
# label path
label_path = './BUS/data2/GT/Case81.png'
# input size
img_width = 128
img_height = 128
nb_class = 2
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.")
argmax_idx = np.argmax(res_map, axis=2)
# For np.where calculation.
lake = (argmax_idx == 1)
img[:, :, 0] = np.where(lake, 255, 0)
return img
model_name = 'fcn'
img_path = './img/test-512.tif'
# Use only 2 classes.
labels = ['lake','_background_']
# Choose model to train
if model_name == "fcn":
model = fcn_8s(input_shape=(512, 512, 3), num_classes=len(labels), lr_init=1e-3, lr_decay=5e-4)
elif model_name == "unet":
model = unet(input_shape=(512, 512, 3), num_classes=len(labels), lr_init=1e-3, lr_decay=5e-4)
elif model_name == "unet_fpa":
model = unet_fpa(input_shape=(512, 512, 3), num_classes=len(labels), lr_init=1e-3, lr_decay=5e-4)
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":
import cv2
import numpy as np
from keras import backend as K
from model.unet import unet
from model.fcn import fcn_8s
# Use only 3 classes.
labels = ['background', 'person', 'car', 'road']
model = fcn_8s(input_shape=(256, 512, 3),
num_classes=len(labels),
init_lr=5e-3)
model.load_weights('./model_weight.h5')
def result_map_to_img(res_map):
img = np.zeros((256, 512, 3), dtype=np.uint8)
res_map = np.squeeze(res_map)
argmax_idx = np.argmax(res_map, axis=2)
# For np.where calculation.
person = (argmax_idx == 1)
car = (argmax_idx == 2)
road = (argmax_idx == 3)
img[:, :, 0] = np.where(person, 255, 0)
img[:, :, 1] = np.where(car, 255, 0)
img[:, :, 2] = np.where(road, 255, 0)
args = parser.parse_args()
model_name = args.model
TRAIN_BATCH = args.train_batch
VAL_BATCH = args.val_batch
lr_init = args.lr_init
lr_decay = args.lr_decay
vgg_path = args.vgg
# Use only 3 classes.
labels = ['background', 'person', 'car', 'road']
# 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
'segnet'
],
help="Model to test. 'fcn', 'unet', 'pspnet' is available.")
parser.add_argument("-P",
"--img_path",
required=True,
help="The image path you want to test")
args = parser.parse_args()
model_name = args.model
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)
