import numpy as np
from ..img.image import Image, RGB
from .noise import Gauss as noise_Gauss
from .. import RANDOM_SEED
np.random.seed(RANDOM_SEED)
[docs]class Black(Image):
"""Create black Image using an Index table.
Args:
reqs[0] (Table): The Index table.
param["pitch"] (float): The pixel size in length_unit/pix.
param["interval"] (float, optional): The time interval in seconds.
param["img_size"] (list of int): The width and height of each image
(pixels).
param["type"] (str, optional): The value type of intensity. Defaults to
"uint8".
param["length_unit"] (str): The unit string for image size, such as
"um", "nm", "pix".
param["split_depth"] (int): The file split depth number.
Returns:
Image: The black image.
"""
[docs] def set_info(self, param={}):
"""Copy info from req[0] and add parameters."""
self.info.copy_req(0)
self.info.add_param("pitch", param["pitch"], param["length_unit"]
+ "/pix", "Pixel size")
if "interval" in param:
self.info.add_param("interval", param["interval"], "s/frame",
"Time interval")
if "type" not in param:
param["type"] = "uint8"
self.info.add_param("type", param["type"], "str",
"Value type of intensity")
self.info.add_column(
0, "intensity", param["type"], "a.u.", "Pixel intensity")
self.info.add_param("img_size", param["img_size"],
"pix", "Width and height of each image")
self.info.add_param("length_unit", param["length_unit"],
"str", "Unit of length")
self.info.set_split_depth(param["split_depth"])
[docs] @staticmethod
def process(reqs, param):
"""Create black Image using an Index table.
Args:
reqs[0] (pandas.DataFrame): The Index table.
param["img_size"] (list of int): The width and height of each image
(pixels).
Returns:
numpy.ndarray: The black image.
"""
df = reqs[0].copy()
return np.zeros([len(df), param["img_size"][1],
param["img_size"][0]])
[docs]class RandomRGB(RGB):
"""Create random RGB image using Index table.
Args:
reqs[0] (Table): Index table.
param["pitch"] (float): Pitch size in length_unit/pix.
param["interval"] (float, optional): Time interval in second.
param["img_size"] (list of int): Width and height of each image in
pixel.
param["split_depth"] (int): File split depth number.
param["length_unit"] (str): Unit string for column names such as "um",
"nm", "pix".
param["seed"] (int, optional): Random seed.
Returns:
Image: RGB Image
"""
[docs] def set_info(self, param={}):
self.info.copy_req()
self.info.add_param("pitch", param["pitch"], param["length_unit"]
+ "/pix", "Pixel size")
if "interval" in param:
self.info.add_param("interval", param["interval"], "s/frame",
"Time interval")
self.info.add_column(0, "intensity", "uint8",
"a.u.", "Pixel intensity")
self.info.add_param("img_size", param["img_size"], "pix",
"Width and height of each image")
self.info.add_param("length_unit", param["length_unit"],
"str", "Unit of length")
index_counts = self.reqs[0].info.get_param_value("index_counts")
index_counts.append(3)
self.info.add_param("index_counts", index_counts,
"list of int",
"Index counts including the color number")
if "seed" in param:
self.info.add_param("seed", param["seed"], "int", "Random seed")
np.random.seed(param["seed"])
self.info.set_split_depth(param["split_depth"])
[docs] @staticmethod
def process(reqs, param):
"""Create random RGB image.
Args:
reqs[0] (pandas.DataFrame): Index table.
param["img_size"] (list of int): Width and height of each image
(pix).
param["index_counts"] (list of int): Index counts ending with 3,
the color number.
Returns:
numpy.ndarray: RGB image
"""
n_img = np.prod(param["index_counts"])
return np.random.randint(0, 255, [n_img, param["img_size"][0],
param["img_size"][1]])
[docs]class CheckerBoard(Image):
"""Create a checkerboard image using an index table.
Args:
reqs[0] (Table): The index table.
param["pitch"] (float): The pixel size in length_unit/pixel.
param["interval"] (float, optional): The time interval in seconds.
param["img_size"] (list of int): The width and height of each image in
pixels.
param["box_size"] (list of int): The width and height of each
checkerboard box in pixels.
param["type"] (str, optional): The value type of intensity. Defaults to
"uint8".
param["length_unit"] (str): The unit string for column names such as
"um", "nm", or "pix".
param["intensity" (int or str): The degree of whiteness in each frame.
If "ascend" is specified, the degree of whiteness increases by one
in each subsequent frame.
param["split_depth"] (int): File split depth number.
Returns:
Image: Checkerboard image stack.
"""
[docs] def set_info(self, param={}):
"""Set info from reqs[0] and add image parameters."""
self.info.copy_req(0)
if "type" not in param:
param["type"] = "uint8"
self.info.add_param("type", param["type"], "str",
"Value type of intensity")
self.info.add_column(0, "intensity", param["type"],
"a.u.", "Pixel intensity")
self.info.add_param("pitch", param["pitch"], param["length_unit"]
+ "/pix", "Pixel size")
if "interval" in param:
self.info.add_param("interval", param["interval"], "s/frame",
"Time interval")
self.info.add_param("intensity", param.get("intensity", 1),
"float", "Intensity of white value")
self.info.add_param("img_size", param["img_size"], "pix",
"Width and height of each image")
self.info.add_param("box_size", param["box_size"], "pix",
"Width and height of checkerboard box")
self.info.add_param("length_unit", param["length_unit"],
"str", "Unit of length")
self.info.set_split_depth(param["split_depth"])
[docs] @staticmethod
def process(reqs, param):
"""Create checkerboard Image using Index table.
Args:
reqs[0] (pandas.DataFrame): Index table.
param["img_size"] (list of int): Width and height of each image
(pix).
param["box_size"] (list of int): The width and height of each
checkerboard box in pixels.
param["type"] (str): The value type of intensity.
param["intensity" (int or str): The degree of whiteness in each
frame. If "ascend" is specified, the degree of whiteness
increases by one in each subsequent frame.
Returns:
Image: Checkerboard image stack.
"""
df = reqs[0].copy()
h_img, w_img = param["img_size"]
h_box, w_box = param["box_size"]
zero = np.zeros([h_box, w_box])
one = np.ones([h_box, w_box])
check = np.vstack([np.hstack([one, zero]), np.hstack([zero, one])])
img = np.tile(check, (int(np.ceil(h_img / (h_box * 2))),
int(np.ceil(w_img / (w_box * 2)))))
img = np.flipud(img[:h_img, :w_img]).astype(param["type"])
stack = []
for i in range(len(df)):
if param["intensity"] == "ascend":
stack.append(img * (i + 1))
else:
stack.append(img * param["intensity"])
return np.array(stack)