def resize_all(output_shape):
resizer_1 = ds.resize(u.mul_shape(output_shape, 4))
resizer_2 = ds.resize(u.mul_shape(output_shape, 2))
resizer_3 = ds.resize(output_shape)
def f(x):
x = resizer_1(x)
y = resizer_2(x)
z = resizer_3(y)
return x, y, z
return f
def prepare(dataset, epochs, batch_size, input_shape, output_shape):
stream = ds.epochs(dataset.image_ids, epochs)
# stream = ds.stream(check, stream)
stream = ds.stream(
lambda x: (dataset.load_image(x), dataset.load_output(x)), stream)
stream = ds.stream(ds.apply_to_x(ds.resize(input_shape)), stream)
stream = ds.stream(ds.apply_to_y(ds.resize(output_shape)), stream)
# stream = ds.stream(ds.apply_to_x(check), stream)
stream = ds.bufferize(stream, size=20)
batch = ds.stream_batch(stream, size=batch_size)
batch = ds.stream(
ds.apply_to_y(lambda x: ds.image2mask(x).reshape(x.shape + (1, ))),
batch)
return batch
def imageid2categoricalpixel(dataset, input_shape):
"""Convert image_id to categorical pixel."""
resizer = ds.resize(input_shape)
def f(image_id):
return to_regression(dataset, resizer, input_shape, image_id)
return f
def prepare(dataset, input_shape):
stream = ds.epochs(dataset.image_ids, epochs=1)
# stream = ds.stream(check, stream)
stream = ds.stream(
lambda x: (dataset._img_filenames[x], dataset.load_image(x)), stream)
stream = ds.stream(ds.apply_to_y(ds.resize(input_shape)), stream)
return stream
def resize_all(output_shape):
resizer_0 = ds.resize(u.mul_shape(output_shape, 16))
resizer_1 = ds.resize(u.mul_shape(output_shape, 8))
resizer_2 = ds.resize(u.mul_shape(output_shape, 4))
resizer_3 = ds.resize(u.mul_shape(output_shape, 2))
resizer_4 = ds.resize(output_shape)
def f(x):
w = resizer_0(x)
x = resizer_1(x)
y = resizer_2(x)
z = resizer_3(y)
v = resizer_4(x)
return w, x, y, z, v
return f
def prepare(dataset, input_shape, output_shape):
resizer = ds.resize(u.mul_shape(output_shape, 32))
stream = ds.epochs(dataset.image_ids, epochs=1)
stream = ds.stream(
lambda x: (dataset._img_filenames[x], resizer(dataset.load_image(x))),
stream)
return stream
def prepare(dataset, epochs, batch_size, input_shape, base_path):
lm = load_masks(dataset, base_path)
stream = ds.epochs(dataset.image_ids, epochs)
stream = ds.stream(lambda x: (dataset.load_image(x), lm(x)), stream)
stream = ds.stream(ds.apply_to_x(ds.resize(input_shape)), stream)
batch = ds.stream_batch(stream,
size=batch_size,
fun=lambda x, y: (np.array(x), fix_y(y)))
return batch
def resize_all(dataset, output_shape):
out_shape_1 = u.mul_shape(output_shape, 4)
resizer_1 = ds.resize(out_shape_1)
resizer_2 = ds.resize(u.mul_shape(output_shape, 2))
resizer_3 = ds.resize(output_shape)
def f((image_id, img)):
cat = dataset.to_categorical(image_id)
result = np.zeros(out_shape_1 + [cat.shape[2]])
for i in range(0, cat.shape[2]):
result[:, :, i] = resizer_1(cat[:, :, i])
x = resizer_1(img)
y = resizer_2(x)
z = resizer_3(y)
x = x.reshape(x.shape + (1, ))
y = y.reshape(y.shape + (1, ))
z = z.reshape(z.shape + (1, ))
return result, x, y, z
return f
def imageid2categoricalpixel(dataset, output_shape):
"""Convert image_id to categorical pixel."""
resizer = ds.resize(output_shape)
def f(image_id):
cat = dataset.to_categorical(image_id)
# expand values to be better separable after resizing
cat[cat > 0] = 255
#
class_ids = set(dataset.get_class_ids(image_id))
result = np.zeros(list(output_shape[:2]) + [cat.shape[2]])
for i in class_ids:
result[:, :, i] = resizer(cat[:, :, i])
# fix value generated from resizing
result[result <= 127] = 0
result[result > 127] = 1
return result
return f
def resize_all(output_shape):
resizer_orig = ds.resize(u.mul_shape(output_shape, 32))
# resizer_0 = ds.resize(u.mul_shape(output_shape, 16))
# resizer_1 = ds.resize(u.mul_shape(output_shape, 8))
# resizer_2 = ds.resize(u.mul_shape(output_shape, 4))
# resizer_3 = ds.resize(u.mul_shape(output_shape, 2))
# resizer_4 = ds.resize(output_shape)
def f(input_):
input_ = resizer_orig(input_)
# w = resizer_0(input_)
# x = resizer_1(w)
# y = resizer_2(x)
# z = resizer_3(y)
# v = resizer_4(z)
return input_, # , w, x, y, z, v
return f
output6 = layers.Conv2D(1, (1, 1), activation='sigmoid')(mul6)
return models.Model(inputs=model.inputs, outputs=[output6])
coco_path = '/media/hachreak/Magrathea/datasets/coco/size_320x320'
model_path = ''
epochs = 100
batch_size = 3
input_shape = (320, 320, 3)
output_shape = (10, 10)
action = 'train'
# action = 'evaluate'
resizer = ds.resize(input_shape[:2])
# validation dataset
dataset_val = u.get_dataset(coco_path, 'val')
gen_val = prepare(dataset_val,
epochs,
batch_size,
input_shape,
output_shape,
load_image=dataset_val.load_image,
resizer=resizer)
# img_o = load_augmented_image(dataset_val, ds.resize((320,320)), 0)
# fuu = next(gen_val)
# import ipdb; ipdb.set_trace()
