diff --git a/mu_map/file/interfile.py b/mu_map/file/interfile.py
index b7202e8e199e1078b8e9e63475915ef389b5e9ea..85cef0ad767225bc3a2e84d31d4a2215caccd051 100644
--- a/mu_map/file/interfile.py
+++ b/mu_map/file/interfile.py
@@ -8,6 +8,9 @@ Interfile = Tuple[Dict[str, str], np.ndarray]
@dataclass
class _InterfileKeys:
+ """
+ Data class defining keys for an Interfile header.
+ """
placeholder: str = "{_}"
_dim: str = f"matrix size [{placeholder}]"
@@ -18,6 +21,9 @@ class _InterfileKeys:
bytes_per_pixel: str = "number of bytes per pixel"
number_format: str = "number format"
+ patient_orientation: str = "patient orientation"
+ patient_rotation: str = "supine"
+
def dim(self, index: int) -> str:
return self._dim.replace(self.placeholder, str(index))
@@ -26,22 +32,6 @@ class _InterfileKeys:
InterfileKeys = _InterfileKeys()
-"""
-Several keys defined in INTERFILE headers.
-"""
-KEY_DIM_1 = "matrix size [1]"
-KEY_DIM_2 = "matrix size [2]"
-KEY_DIM_3 = "matrix size [3]"
-KEY_SPACING_1 = "scaling factor (mm/pixel) [1]"
-KEY_SPACING_2 = "scaling factor (mm/pixel) [2]"
-KEY_SPACING_3 = "scaling factor (mm/pixel) [2]"
-KEY_NPROJECTIONS = "number of projections"
-
-KEY_DATA_FILE = "name of data file"
-
-KEY_BYTES_PER_PIXEL = "number of bytes per pixel"
-KEY_NUMBER_FORMAT = "number format"
-
"""
A template of an INTERFILE header.
@@ -55,6 +45,8 @@ GENERAL DATA :=
GENERAL IMAGE DATA :=
type of data := Tomographic
imagedata byte order := LITTLEENDIAN
+patient orientation := feet_in
+patient rotation := supine
isotope name := ^99m^Technetium
SPECT STUDY (General) :=
process status := Reconstructed
@@ -131,15 +123,15 @@ def load_interfile(filename: str) -> Tuple[Dict[str, str], np.ndarray]:
"""
header = parse_interfile_header(filename)
- dim_x = int(header[KEY_DIM_1])
- dim_y = int(header[KEY_DIM_2])
- dim_z = int(header[KEY_DIM_3]) if KEY_DIM_3 in header else int(header[KEY_NPROJECTIONS])
+ dim_x = int(header[InterfileKeys.dim(1)])
+ dim_y = int(header[InterfileKeys.dim(2)])
+ dim_z = int(header[InterfileKeys.dim(3)]) if InterfileKeys.dim(3) in header else int(header[InterfileKeys.n_projections])
- bytes_per_pixel = int(header[KEY_BYTES_PER_PIXEL])
- num_format = header[KEY_NUMBER_FORMAT]
+ bytes_per_pixel = int(header[InterfileKeys.bytes_per_pixel])
+ num_format = header[InterfileKeys.number_format]
dtype = type_by_format(num_format, bytes_per_pixel)
- data_file = os.path.join(os.path.dirname(filename), header[KEY_DATA_FILE])
+ data_file = os.path.join(os.path.dirname(filename), header[InterfileKeys.data_file])
with open(data_file, mode="rb") as f:
image = np.frombuffer(f.read(), dtype)
image = image.reshape((dim_z, dim_y, dim_x))
@@ -162,10 +154,10 @@ def write_interfile(filename: str, header: Dict[str, str], image: np.ndarray):
filename_data = f"{filename}.v"
filename_header = f"{filename}.hv"
- header[KEY_DATA_FILE] = os.path.basename(filename_data)
- header[KEY_DIM_3] = str(image.shape[0])
- header[KEY_DIM_2] = str(image.shape[1])
- header[KEY_DIM_1] = str(image.shape[2])
+ header[InterfileKeys.data_file] = os.path.basename(filename_data)
+ header[InterfileKeys.dim(3)] = str(image.shape[0])
+ header[InterfileKeys.dim(2)] = str(image.shape[1])
+ header[InterfileKeys.dim(1)] = str(image.shape[2])
image = image.astype(np.float32)
diff --git a/mu_map/recon/osem.py b/mu_map/recon/osem.py
index d4abd98a74695ce847e85a3c07e7462e4080cf1c..ef47a18ec186cc4af15ad44de6a7bb8df14d789e 100644
--- a/mu_map/recon/osem.py
+++ b/mu_map/recon/osem.py
@@ -10,10 +10,7 @@ from mu_map.file.interfile import (
parse_interfile_header_str,
load_interfile,
write_interfile,
- KEY_DIM_1,
- KEY_DIM_2,
- KEY_SPACING_1,
- KEY_SPACING_2,
+ InterfileKeys,
TEMPLATE_HEADER_IMAGE,
)
from mu_map.recon.filter import GaussianFilter
@@ -77,13 +74,13 @@ def uniform_estimate(projection: Tuple[Dict[str, str], np.ndarray]):
(image_proj.shape[1], image_proj.shape[2], image_proj.shape[2]), np.float32
)
- offset = -0.5 * image_proj.shape[2] * float(header_proj[KEY_SPACING_1])
+ offset = -0.5 * image_proj.shape[2] * float(header_proj[InterfileKeys.spacing(1)])
header = TEMPLATE_HEADER_IMAGE.replace("{ROWS}", str(image.shape[2]))
header = header.replace("{COLUMNS}", str(image.shape[1]))
header = header.replace("{SLICES}", str(image.shape[0]))
- header = header.replace("{SPACING_X}", header_proj[KEY_SPACING_1])
- header = header.replace("{SPACING_Y}", header_proj[KEY_SPACING_1])
- header = header.replace("{SPACING_Z}", header_proj[KEY_SPACING_2])
+ header = header.replace("{SPACING_X}", header_proj[InterfileKeys.spacing(1)])
+ header = header.replace("{SPACING_Y}", header_proj[InterfileKeys.spacing(1)])
+ header = header.replace("{SPACING_Z}", header_proj[InterfileKeys.spacing(1)])
header = header.replace("{OFFSET_X}", f"{offset:.4f}")
header = header.replace("{OFFSET_Y}", f"{offset:.4f}")
header = parse_interfile_header_str(header)
diff --git a/mu_map/recon/project.py b/mu_map/recon/project.py
index 5aaaa900a69717e7fd6bb9c206a46f212aab0e46..05cce8e703655bc36d27d6d006cc4d91ced820e3 100644
--- a/mu_map/recon/project.py
+++ b/mu_map/recon/project.py
@@ -9,8 +9,7 @@ import stir
from mu_map.file.interfile import (
load_interfile,
write_interfile,
- KEY_SPACING_1,
- KEY_SPACING_3,
+ InterfileKeys,
)
@@ -28,6 +27,8 @@ type of data := Tomographic
imagedata byte order := LITTLEENDIAN
number format := float
number of bytes per pixel := 4
+patient orientation := feet_in
+patient rotation := supine
SPECT STUDY (General) :=
;matrix axis label [2] := axial coordinate
@@ -88,8 +89,8 @@ def forward_project(
header_proj = TEMPLATE_HEADER_PROJ.replace("{DATA_FILE}", filename_proj_data)
header_proj = header_proj.replace("{SLICES}", str(n_slices))
header_proj = header_proj.replace("{BINS}", str(n_bins))
- header_proj = header_proj.replace("{SPACING_SLICES}", recon_header[KEY_SPACING_3])
- header_proj = header_proj.replace("{SPACING_BINS}", recon_header[KEY_SPACING_1])
+ header_proj = header_proj.replace("{SPACING_SLICES}", recon_header[InterfileKeys.spacing(3)])
+ header_proj = header_proj.replace("{SPACING_BINS}", recon_header[InterfileKeys.spacing(1)])
header_proj = header_proj.replace("{N_PROJECTIONS}", str(n_projections))
header_proj = header_proj.replace("{ROTATION}", str(rotation))
header_proj = header_proj.replace("{START_ANGLE}", str(start_angle))
diff --git a/mu_map/training/distance.py b/mu_map/training/distance.py
index d40ea9982fa85c01f1d384abcc65af7bd9e35078..3fdb79cf33d9e02918281ab874f08c51e89e653b 100644
--- a/mu_map/training/distance.py
+++ b/mu_map/training/distance.py
@@ -53,7 +53,6 @@ if __name__ == "__main__":
MaxNormTransform,
GaussianNormTransform,
)
- from mu_map.dataset.transform import ScaleTransform
from mu_map.logging import add_logging_args, get_logger_by_args
from mu_map.models.unet import UNet
diff --git a/mu_map/vis/slices.py b/mu_map/vis/slices.py
index 15c89159bfad693f75ac96491784aaeffbfcb7ab..ce36eaa715551574fab2fb73d6974a387ce08eb1 100644
--- a/mu_map/vis/slices.py
+++ b/mu_map/vis/slices.py
@@ -2,6 +2,7 @@ from typing import List
import numpy as np
+from mu_map.dataset.util import align_images
from mu_map.file.dicom import load_dcm_img
from mu_map.file.interfile import load_interfile_img
@@ -27,20 +28,23 @@ def load_image(filename: str) -> np.ndarray:
raise ValueError(f"Could not load {filename} as DICOM or INTERFILE image!")
-def join_images(images: List[np.ndarray], separator: np.ndarray) -> List[np.ndarray]:
+def join_images(
+ images: List[np.ndarray], separator: np.ndarray, vertical: bool = False
+) -> List[np.ndarray]:
"""
Create a new image by joining all input images along a separator image.
- Note that the images are joined horizontally. Thus, their shape along
- the first axis must be equal.
+ If joining horizontally, their shape must be equal along their first axis.
+ If joining vertically, their shape must be equal along their second axis.
:param images: a list of images to join
:param separator: a separator image inserted between all images
+ :param vertical: join images vertically instead of horizontally
:return: a new image joined as described above
"""
res = []
for image in images:
res += [image, separator]
- return np.hstack(res[:-1])
+ return np.vstack(res[:-1]) if vertical else np.hstack(res[:-1])
if __name__ == "__main__":
@@ -51,14 +55,26 @@ if __name__ == "__main__":
from mu_map.util import to_grayscale, COLOR_WHITE
parser = argparse.ArgumentParser(
- description="Visualize 3D Volumes as a video of their slices"
+ description="Visualize 3D Volumes as a video of their slices",
+ formatter_class=argparse.ArgumentDefaultsHelpFormatter,
)
parser.add_argument("images", type=str, nargs="+", help="the images to visualize")
parser.add_argument(
"--resize", type=int, default=512, help="resize images to this size"
)
parser.add_argument(
- "--fps", type=int, default=25, help="frames (slices) to show per second"
+ "--fps", type=int, default=10, help="frames (slices) to show per second"
+ )
+ parser.add_argument("--align", action="store_true", help="center align images")
+ parser.add_argument(
+ "--shared_range",
+ action="store_true",
+ help="normalize all images to the same value range",
+ )
+ parser.add_argument(
+ "--vertical",
+ action="store_true",
+ help="join images vertically instead of horizontally",
)
parser.add_argument(
"--window_name", type=str, default="Slices", help="name of the displayed window"
@@ -66,9 +82,33 @@ if __name__ == "__main__":
args = parser.parse_args()
images = list(map(load_image, args.images))
- scales = list(map(lambda image: args.resize / image.shape[1], images))
+
+ if args.align:
+ image_with_least_slices = sorted(images, key=lambda image: image.shape[0])[0]
+ images = list(
+ map(lambda image: align_images(image, image_with_least_slices)[0], images)
+ )
+
+ scales = list(
+ map(
+ lambda image: args.resize / image.shape[2]
+ if args.vertical
+ else args.resize / image.shape[1],
+ images,
+ )
+ )
slices = [0] * len(images)
- space = np.full((args.resize, 10), 239, np.uint8)
+ space = (
+ np.full((10, args.resize), 239, np.uint8)
+ if args.vertical
+ else np.full((args.resize, 10), 239, np.uint8)
+ )
+
+ min_vals = list(map(lambda image: image.min(), images))
+ max_vals = list(map(lambda image: image.max(), images))
+ if args.shared_range:
+ min_vals = [min(min_vals)] * len(images)
+ max_vals = [max(max_vals)] * len(images)
cv.namedWindow(args.window_name, cv.WINDOW_NORMAL)
cv.resizeWindow(args.window_name, 1600, 900)
@@ -77,16 +117,24 @@ if __name__ == "__main__":
current_timeout = timeout
while True:
_images = []
- for i, (image, _slice, scale) in enumerate(zip(images, slices, scales)):
- _image = to_grayscale(image[_slice], max_val=image.max(), min_val=image.min())
+ for i, (image, _slice, scale, min_val, max_val) in enumerate(
+ zip(images, slices, scales, min_vals, max_vals)
+ ):
+ _image = to_grayscale(image[_slice], min_val=min_val, max_val=max_val)
_image = cv.resize(_image, None, fx=scale, fy=scale)
_image = cv.putText(
- _image, str(_slice + 1), (0, 30), cv.FONT_HERSHEY_SIMPLEX, 1, COLOR_WHITE, 3
+ _image,
+ str(_slice + 1),
+ (0, 30),
+ cv.FONT_HERSHEY_SIMPLEX,
+ 1,
+ COLOR_WHITE,
+ 3,
)
_images.append(_image)
slices[i] = (_slice + 1) % image.shape[0]
- image = join_images(_images, space)
+ image = join_images(_images, space, vertical=args.vertical)
cv.imshow(args.window_name, image)
key = cv.waitKey(current_timeout)