def __init__(self, input_path, output_path, output_size, bg):
src_img, img = initialize(src_path=input_path,
input_path=None,
size=output_size)
self.src_img = Image(src_img)
self.img = Image(img)
self.viewer = None
self.output_path = output_path
color_to_fill = self.src_img.get_average_color(
) if bg == 'None' else hex_to_rgb(bg)
self.img.fill_with(color_to_fill)
def generate_set(image_n, image_size, name):
def initialize():
X = np.zeros((image_n, image_size, image_size, 3))
Y = np.zeros((image_n, 9))
return X, Y
def rand():
return np.random.uniform(0., 1.), np.random.uniform(0., 1.), np.random.uniform(0., 1.), \
np.random.uniform(0., 1.), np.random.uniform(0., 1.), np.random.uniform(0., 1.), \
np.random.uniform(0., 1.), np.random.uniform(0., 1.), np.random.uniform(0., 1.)
X, Y = initialize()
image = Image(initializer.new_image(image_size, image_size))
white_image_np = np.ones((image_size, image_size, 3))
action = Environment.Action.LINE
for i in tqdm.tqdm(range(image_n), "Generating %s set" % name):
r, g, b, a, x1, y1, x2, y2, size = rand()
image.perform_action(action, (r, g, b, a, x1, y1, x2, y2, size))
array = image.array
X[i] = (array / 255.) - white_image_np
Y[i] = [r, g, b, a, x1, y1, x2, y2, size]
image.clear()
path = "./{}_{}.npy"
np.save(path.format(name, "X"), X)
np.save(path.format(name, "Y"), Y)
def undo(self):
self.successful_actions -= 1
self.img.delete()
self.img = Image(self.prev_img.img, self.prev_img.array)
self.reward = self.prev_reward
self.distance = self.prev_distance
self.undo_before_step = True
def generate_set(image_n, image_size, name, shape, without_rotation):
def initialize():
X = np.zeros((image_n, image_size, image_size, 3))
Y = np.zeros((image_n, 9))
return X, Y
def rand():
return np.random.uniform(0., 1.), np.random.uniform(0., 1.), np.random.uniform(0., 1.), \
np.random.uniform(0., 1.), np.random.uniform(0., 1.), np.random.uniform(0., 1.), \
np.random.uniform(0. + (1. / image_size), 1.), \
np.random.uniform(0. + (1. / image_size), 1.), np.random.uniform(0., 1.)
X, Y = initialize()
image = Image(initializer.new_image(image_size, image_size))
white_image_np = np.ones((image_size, image_size, 3))
action = Environment.Action.ELLIPSE if shape == 'ellipse' else Environment.Action.RECTANGLE
for i in tqdm.tqdm(range(image_n), "Generating %s set" % name):
r, g, b, a, x, y, w, h, rotation = rand()
w = min(w, 1 - x)
h = min(h, 1 - y)
rotation = 0.5 if without_rotation else rotation
image.perform_action(action, (r, g, b, a, x, y, w, h, rotation))
array = image.array
X[i] = (array / 255.) - white_image_np
Y[i] = [r, g, b, a, x, y, w, h, rotation]
image.clear()
path = "./{}_{}.npy"
np.save(path.format(name, "X"), X)
np.save(path.format(name, "Y"), Y)
def __init__(self, src_path, action_type, limit, output_size, diffs):
self.src_path = src_path
src_image, image = initialize_with_scaled_src(
self.src_path + "/src.jpg", output_size)
self.src_image = Image(src_image)
self.image = Image(image)
self.diffs = diffs
if self.diffs:
self.action_type_limited = True if action_type >= 0 else False
if self.action_type_limited:
self.action_type = action_type
self.action_number_limited = True if limit >= 0 else False
if self.action_number_limited:
self.limit = limit
self.out_path = None
self.img_src_out_path = None
self.abs_out_path = None
def __init__(self, src_path, acceptable_distance, input_path, actions,
size):
self.src_path = src_path
src_img, img = initializer.initialize(src_path, input_path, size)
self.src_img = Image(src_img)
self.img = Image(img)
self.prev_img = None
self.acceptable_dist = acceptable_distance
self.state = None
self.reward = 0
self.prev_reward = 0
self.distance = sum(abs(self.src_img.array - self.img.array))
self.prev_distance = 0
self.done = False
self.action_space = ToolSpace()
self.viewer = None
self.version_info = self.construct_version_info()
self.out_path = None
self.undo_before_step = False
self.action = None
self.args = None
self.successful_actions = 0
self.actions_before_success = 0
self.actions = actions
def generate_set(image_n, image_size, name):
def scale(x):
return x / float(image_size)
def initialize():
X = np.zeros((image_n, image_size, image_size))
Y = np.zeros((image_n, 9))
return X, Y
X, Y = initialize()
image = Image(initializer.new_image(image_size, image_size))
white_image_np = np.ones((image_size, image_size))
for i in tqdm.tqdm(range(image_n), "Generating %s set" % name):
square_size = np.random.randint(3, image_size)
x_coor = np.random.randint(0, image_size - square_size)
y_coor = np.random.randint(0, image_size - square_size)
image.perform_action(
RECTANGLE, (RED, GREEN, BLUE, ALPHA, scale(x_coor), scale(y_coor),
scale(square_size), scale(square_size), ROTATION))
array = np.sum(image.array, axis=2) / 3
X[i] = (array / 255.) - white_image_np
# draw white rectangle to "reset" image
image.perform_action_without_array_update(
RECTANGLE, (1., 1., 1., ALPHA, scale(x_coor), scale(y_coor),
scale(square_size), scale(square_size), ROTATION))
Y[i][0] = RED
Y[i][1] = GREEN
Y[i][2] = BLUE
Y[i][3] = ALPHA
Y[i][4] = scale(x_coor)
Y[i][5] = scale(y_coor)
Y[i][6] = scale(square_size)
Y[i][7] = scale(square_size)
Y[i][8] = ROTATION
path = "./{}_{}.npy"
np.save(path.format(name, "X"), X)
np.save(path.format(name, "Y"), Y)
def plugin_main(name, size, r, g, b, a, x1, y1, x2, y2, x3, y3):
image = Image(initializer.new_image(size, size))
action = Environment.Action.TRIANGLE
image.perform_action(action, (r, g, b, a, x1, y1, x2, y2, x3, y3))
image.save("%s/%s" % (PATH, name))
def plugin_main(name, size, r, g, b, a, x, y, w, h, rotation, shape):
image = Image(initializer.new_image(size, size))
action = Environment.Action.ELLIPSE if shape == 'ellipse' else Environment.Action.RECTANGLE
image.perform_action(action, (r, g, b, a, x, y, w, h, rotation))
image.save("%s/%s" % (PATH, name))
def generate_set(num_images, image_size, name, max_examples_per_part,
without_rotation):
def initialize(num_examples):
X = np.zeros((num_examples, image_size, image_size, 3))
Y = np.zeros((num_examples, 4))
return X, Y
def save(X, Y, p):
path = "./{}_{}_{}.npy"
np.save(path.format(name, "X", p), X)
np.save(path.format(name, "Y", p), Y)
def generate_args(a):
if a == 0 or a == 1:
return generate_args_for_selection_shape()
elif a == 2:
return generate_args_for_line()
else:
return generate_args_for_triangle()
def generate_args_for_selection_shape():
def rand():
return np.random.uniform(0., 1.), np.random.uniform(0., 1.), np.random.uniform(0., 1.), \
np.random.uniform(0., 1.), np.random.uniform(0., 1.), np.random.uniform(0., 1.), \
np.random.uniform(0. + (1. / image_size), 1.), \
np.random.uniform(0. + (1. / image_size), 1.), np.random.uniform(0., 1.)
r, g, b, a, x, y, w, h, rotation = rand()
w = min(w, 1 - x)
h = min(h, 1 - y)
rotation = 0.5 if without_rotation else rotation
return r, g, b, a, x, y, w, h, rotation
def generate_args_for_line():
def rand():
return np.random.uniform(0., 1.), np.random.uniform(0., 1.), np.random.uniform(0., 1.), \
np.random.uniform(0., 1.), np.random.uniform(0., 1.), np.random.uniform(0., 1.), \
np.random.uniform(0., 1.), np.random.uniform(0., 1.), np.random.uniform(0., 1.)
return rand()
def generate_args_for_triangle():
def rand():
return np.random.uniform(0., 1.), np.random.uniform(0., 1.), np.random.uniform(0., 1.), \
np.random.uniform(0., 1.), np.random.uniform(0., 1.), np.random.uniform(0., 1.), \
np.random.uniform(0., 1.), np.random.uniform(0., 1.), np.random.uniform(0., 1.), \
np.random.uniform(0., 1.)
return rand()
remaining_examples = num_images
part = 1
image = Image(initializer.new_image(image_size, image_size))
white_image_np = np.ones((image_size, image_size, 3))
while remaining_examples > 0:
examples_in_current_part = min(remaining_examples,
max_examples_per_part)
X, Y = initialize(examples_in_current_part)
for i in tqdm.tqdm(range(examples_in_current_part),
"Generating {} set, part {}".format(name, part)):
action = np.random.randint(0, 4)
args = generate_args(action)
image.perform_action(action, args)
array = image.array
X[i] = (array / 255.) - white_image_np
Y[i][action] = 1
image.clear()
save(X, Y, part)
remaining_examples -= examples_in_current_part
part += 1