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f10f497 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 | """
Copyright © 2025 Howard Hughes Medical Institute, Authored by Carsen Stringer , Michael Rariden and Marius Pachitariu.
"""
import os
import numpy as np
import cv2
import tifffile
import logging
from tqdm import tqdm
import re
try:
import nd2
ND2 = True
except:
ND2 = False
try:
import nrrd
NRRD = True
except:
NRRD = False
io_logger = logging.getLogger(__name__)
def load_dax(filename):
### modified from ZhuangLab github:
### https://github.com/ZhuangLab/storm-analysis/blob/71ae493cbd17ddb97938d0ae2032d97a0eaa76b2/storm_analysis/sa_library/datareader.py#L156
inf_filename = os.path.splitext(filename)[0] + ".inf"
if not os.path.exists(inf_filename):
io_logger.critical(
f"ERROR: no inf file found for dax file {filename}, cannot load dax without it"
)
return None
### get metadata
image_height, image_width = None, None
# extract the movie information from the associated inf file
size_re = re.compile(r"frame dimensions = ([\d]+) x ([\d]+)")
length_re = re.compile(r"number of frames = ([\d]+)")
endian_re = re.compile(r" (big|little) endian")
with open(inf_filename, "r") as inf_file:
lines = inf_file.read().split("\n")
for line in lines:
m = size_re.match(line)
if m:
image_height = int(m.group(2))
image_width = int(m.group(1))
m = length_re.match(line)
if m:
number_frames = int(m.group(1))
m = endian_re.search(line)
if m:
if m.group(1) == "big":
bigendian = 1
else:
bigendian = 0
# set defaults, warn the user that they couldn"t be determined from the inf file.
if not image_height:
io_logger.warning("could not determine dax image size, assuming 256x256")
image_height = 256
image_width = 256
### load image
img = np.memmap(filename, dtype="uint16",
shape=(number_frames, image_height, image_width))
if bigendian:
img = img.byteswap()
img = np.array(img)
return img
def imread(filename):
"""
Read in an image file with tif or image file type supported by cv2.
Args:
filename (str): The path to the image file.
Returns:
numpy.ndarray: The image data as a NumPy array.
Raises:
None
Raises an error if the image file format is not supported.
Examples:
>>> img = imread("image.tif")
"""
# ensure that extension check is not case sensitive
ext = os.path.splitext(filename)[-1].lower()
if ext == ".tif" or ext == ".tiff" or ext == ".flex":
with tifffile.TiffFile(filename) as tif:
ltif = len(tif.pages)
try:
full_shape = tif.shaped_metadata[0]["shape"]
except:
try:
page = tif.series[0][0]
full_shape = tif.series[0].shape
except:
ltif = 0
if ltif < 10:
img = tif.asarray()
else:
page = tif.series[0][0]
shape, dtype = page.shape, page.dtype
ltif = int(np.prod(full_shape) / np.prod(shape))
io_logger.info(f"reading tiff with {ltif} planes")
img = np.zeros((ltif, *shape), dtype=dtype)
for i, page in enumerate(tqdm(tif.series[0])):
img[i] = page.asarray()
img = img.reshape(full_shape)
return img
elif ext == ".dax":
img = load_dax(filename)
return img
elif ext == ".nd2":
if not ND2:
io_logger.critical("ERROR: need to 'pip install nd2' to load in .nd2 file")
return None
elif ext == ".nrrd":
if not NRRD:
io_logger.critical(
"ERROR: need to 'pip install pynrrd' to load in .nrrd file")
return None
else:
img, metadata = nrrd.read(filename)
if img.ndim == 3:
img = img.transpose(2, 0, 1)
return img
elif ext != ".npy":
try:
img = cv2.imread(filename, -1) #cv2.LOAD_IMAGE_ANYDEPTH)
if img.ndim > 2:
img = img[..., [2, 1, 0]]
return img
except Exception as e:
io_logger.critical("ERROR: could not read file, %s" % e)
return None
else:
try:
dat = np.load(filename, allow_pickle=True).item()
masks = dat["masks"]
return masks
except Exception as e:
io_logger.critical("ERROR: could not read masks from file, %s" % e)
return None
def imsave(filename, arr):
"""
Saves an image array to a file.
Args:
filename (str): The name of the file to save the image to.
arr (numpy.ndarray): The image array to be saved.
Returns:
None
"""
ext = os.path.splitext(filename)[-1].lower()
if ext == ".tif" or ext == ".tiff":
tifffile.imwrite(filename, data=arr, compression="zlib")
else:
if len(arr.shape) > 2:
arr = cv2.cvtColor(arr, cv2.COLOR_BGR2RGB)
cv2.imwrite(filename, arr)
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