def get_contours_from_mask(x_grid, y_grid, mask):
fig, ax = plt.subplots()
with warnings.catch_warnings():
warnings.simplefilter("ignore", UserWarning)
cs = ax.contour(x_grid, y_grid, mask, [0])
contours = [path.vertices for path in cs.collections[0].get_paths()]
plt.close(fig)
return contours
def read_dicom3D(direc, i_option):
# item = 0
for subdir, dirs, files in os.walk(direc): # pylint: disable = unused-variable
k = 0
for file in tqdm(sorted(files)):
# print('filename=', file)
if os.path.splitext(file)[1] == ".dcm":
dataset = pydicom.dcmread(direc + file)
if k == 0:
ArrayDicom = np.zeros(
(dataset.Rows, dataset.Columns, 0),
dtype=dataset.pixel_array.dtype,
)
tmp_array = dataset.pixel_array
if i_option.startswith(("y", "yeah", "yes")):
max_val = np.amax(tmp_array)
tmp_array = tmp_array / max_val
min_val = np.amin(tmp_array)
tmp_array = tmp_array - min_val
tmp_array = 1 - tmp_array # inverting the range
# min_val = np.amin(tmp_array) # normalizing
# tmp_array = tmp_array - min_val
# tmp_array = tmp_array / (np.amax(tmp_array))
tmp_array = u.norm01(tmp_array)
else:
# min_val = np.amin(tmp_array)
# tmp_array = tmp_array - min_val
# tmp_array = tmp_array / (np.amax(tmp_array))
tmp_array = u.norm01(tmp_array) # just normalize
ArrayDicom = np.dstack((ArrayDicom, tmp_array))
# print("item thickness [mm]=", dataset.SliceThickness)
SID = dataset.RTImageSID
dx = 1 / (SID * (1 / dataset.ImagePlanePixelSpacing[0]) / 1000)
dy = 1 / (SID * (1 / dataset.ImagePlanePixelSpacing[1]) / 1000)
print("pixel spacing row [mm]=", dx)
print("pixel spacing col [mm]=", dy)
else:
tmp_array = dataset.pixel_array
if i_option.startswith(("y", "yeah", "yes")):
max_val = np.amax(tmp_array)
tmp_array = tmp_array / max_val
min_val = np.amin(tmp_array)
tmp_array = tmp_array - min_val
tmp_array = 1 - tmp_array # inverting the range
# min_val = np.amin(tmp_array) # normalizing
# tmp_array = tmp_array - min_val
# tmp_array = tmp_array / (np.amax(tmp_array))
tmp_array = u.norm01(tmp_array)
else:
# min_val = np.amin(tmp_array)
# tmp_array = tmp_array - min_val
# tmp_array = tmp_array / (np.amax(tmp_array)) # just normalize
tmp_array = u.norm01(tmp_array)
ArrayDicom = np.dstack((ArrayDicom, tmp_array))
k = k + 1
xfield, yfield, rotfield = image_analyze(ArrayDicom, i_option)
multi_slice_viewer(ArrayDicom, dx, dy)
if np.shape(xfield)[2] == 2:
fig, peak_figs, junctions_figs = merge_view_vert(xfield, dx, dy)
with PdfPages(direc + "jaws_X_report.pdf") as pdf:
pdf.savefig(fig)
# for i in range(0, len(peak_figs)):
for _, f in enumerate(peak_figs):
pdf.savefig(f)
# for i in range(0, len(junctions_figs)):
for _, f in enumerate(junctions_figs):
pdf.savefig(f)
plt.close()
else:
print(
"X jaws data analysis not completed please verify that you have two X jaws images. For more information see manual."
)
if np.shape(yfield)[2] == 4:
fig, peak_figs, junctions_figs = merge_view_horz(yfield, dx, dy)
# print('peak_figs********************************************************=', len(peak_figs),peak_figs)
with PdfPages(direc + "jaws_Y_report.pdf") as pdf:
pdf.savefig(fig)
# for i in range(0, len(peak_figs)):
for _, f in enumerate(peak_figs):
pdf.savefig(f)
for _, f in enumerate(junctions_figs):
pdf.savefig(f)
plt.close()
else:
print(
"Y jaws data analysis not completed please verify that you have four Y jaws images. For more information see manual."
)
if np.shape(rotfield)[2] == 4:
fig, peak_figs, junctions_figs = merge_view_filtrot(rotfield, dx, dy)
with PdfPages(direc + "jaws_FR_report.pdf") as pdf:
pdf.savefig(fig)
for _, f in enumerate(peak_figs):
pdf.savefig(f)
for _, f in enumerate(junctions_figs):
pdf.savefig(f)
plt.close()
else:
print(
"Field rotation data analysis not completed please verify that you have four field rotation images. For more information see manual."
)