def patch_batch_classification_predict():
test_ids = [6]
input_shape = (256, 256, 3)
#class_mode = "binary";
class_mode = "categorical"
model_name = "refinenet"
#model_weights = "/media/fty/Windows/linux_data/weights/binary/pbic/unet_weights_epoch100.h5";
model_weights = "/media/fty/Windows/linux_data/weights/categorical/refinement/refinenet_weights_epoch300.h5"
plot = Vaihingen_class
active_positive_class = []
active_positive_class.append(Vaihingen_class.Building)
active_positive_class.append(Vaihingen_class.Tree)
active_positive_class.append(Vaihingen_class.Car)
active_positive_class.append(Vaihingen_class.Low_vegetation)
classes = len(active_positive_class) + 1
test_batch_size = 8
patch_based_dataset_test = Patch_based_dataset(tiff_path, label_path, plot,
active_positive_class)
patch_based_dataset_test.prepare_patch_based_dataset(
is_train=False,
load_ids=test_ids,
batch_size=test_batch_size,
class_mode=class_mode,
classes=classes,
is_augment=False,
model_input_pixel_size=(input_shape[0], input_shape[1]),
predict_center_pixel_size=(128, 128),
evaluated_path=evaluated_path)
model = ModelBuilder(PAI_FLAGS=None,
input_shape=input_shape,
classes=classes,
model_name=model_name,
load_weights=model_weights,
class_mode=class_mode)
class_result_pics = model.predict_and_evaluate(
test_dataset=patch_based_dataset_test,
steps_per_epoch=int(
math.ceil(patch_based_dataset_test.get_n_samples() /
float(test_batch_size))),
verbose=1,
pixel_based_evaluate=True,
show_class_result_pic=True)
for i in class_result_pics.keys():
plt.subplot(121)
plt.imshow(patch_based_dataset_test.evaluations[i][:, :, 0])
plt.subplot(122)
plt.imshow(class_result_pics[i])
plt.show()
def super_pixel_classification_predict():
test_ids = [6]
input_shape = (64, 64, 3)
n_segments = 20
model_name = "resnet"
model_weights = "/media/fty/Windows/linux_data/weights/categorical/resnet/resnet_super_pixel_weights_epoch450.h5"
plot = Vaihingen_class
active_positive_class = []
active_positive_class.append(Vaihingen_class.Building)
active_positive_class.append(Vaihingen_class.Tree)
active_positive_class.append(Vaihingen_class.Car)
active_positive_class.append(Vaihingen_class.Low_vegetation)
classes = len(active_positive_class) + 1
super_pixel_dataset_test = Super_pixel_seg_dataset(tiff_path, label_path,
plot,
active_positive_class)
test_batch_size = 100
super_pixel_dataset_test.prepare_superpixel_dataset(
is_train=False,
load_ids=test_ids,
n_segments=n_segments,
batch_size=test_batch_size,
is_augment=False,
model_input_pixel_size=(input_shape[0], input_shape[1]),
one_hot=False,
save_segments=True,
evaluated_path=evaluated_path,
exclude_boundary_objs=False)
model = ModelBuilder(PAI_FLAGS=None,
input_shape=input_shape,
classes=classes,
model_name=model_name,
load_weights=model_weights)
class_result_pics = model.predict_and_evaluate(
test_dataset=super_pixel_dataset_test,
steps_per_epoch=int(
math.ceil(super_pixel_dataset_test.get_n_samples() /
float(test_batch_size))),
verbose=1,
object_based_evaluate=True,
pixel_based_evaluate=True,
show_class_result_pic=True)
# print(class_result_pics);
# print(prob_result_pics);
# print(class_result_pics[6].shape);
# print(prob_result_pics[6].shape);
for i in class_result_pics.keys():
plt.subplot(121)
plt.imshow(super_pixel_dataset_test.evaluations[i][:, :, 0])
plt.subplot(122)
plt.imshow(class_result_pics[i])
plt.show()