def catphan_helperf_analyze(args):
phantom = args["phantom"]
general_functions.set_configuration(args["config"]) # Transfer to this process
hu_tolerance = args["hu_tolerance"]
scaling_tolerance = args["scaling_tolerance"]
thickness_tolerance = args["thickness_tolerance"]
low_contrast_tolerance = args["low_contrast_tolerance"]
cnr_threshold = args["cnr_threshold"]
path = args["path"]
try:
if phantom == "Catphan 503":
mycbct_temp = pylinac_ct.CatPhan503(path)
elif phantom == "Catphan 504":
mycbct_temp = pylinac_ct.CatPhan504(path)
elif phantom == "Catphan 600":
mycbct_temp = pylinac_ct.CatPhan600(path)
elif phantom == "Catphan 604":
mycbct_temp = pylinac_ct.CatPhan604(path)
mycbct_temp.analyze(hu_tolerance=hu_tolerance, scaling_tolerance=scaling_tolerance,
thickness_tolerance=thickness_tolerance, low_contrast_tolerance=low_contrast_tolerance,
cnr_threshold=cnr_threshold)
except Exception as e:
return e
else:
return mycbct_temp
def fieldrot_helperf(args):
test_type = args["test_type"]
direction = args["direction"]
direction2 = args["direction2"]
number_samples = args["number_samples"]
margin = args["margin"]
clipbox = args["clipbox"]
invert = args["invert"]
w1 = args["w1"]
w2 = args["w2"]
colormap = args["colormap"]
med_filter = args["med_filter"]
general_functions.set_configuration(args["config"])
imgdescription = args["imgdescription"]
station = args["station"]
displayname = args["displayname"]
acquisition_datetime = args["acquisition_datetime"]
high_contrast = args["high_contrast"]
# Collect data for "save results"
dicomenergy = general_functions.get_energy_from_imgdescription(
imgdescription)
user_machine, user_energy = general_functions.get_user_machine_and_energy(
station, dicomenergy)
machines_and_energies = general_functions.get_machines_and_energies(
general_functions.get_treatmentunits_fieldrotation())
tolerances = general_functions.get_tolerance_user_machine_fieldrotation(
user_machine) # If user_machne has specific tolerance
if not tolerances:
tt = general_functions.get_settings_fieldrotation()
else:
tt = tolerances[0]
(tolerance_collabs, tolerance_collrel, tolerance_couchrel) = tt
tolerance_collabs = float(tolerance_collabs)
tolerance_collrel = float(tolerance_collrel)
tolerance_couchrel = float(tolerance_couchrel)
save_results = {
"user_machine": user_machine,
"user_energy": user_energy,
"machines_and_energies": machines_and_energies,
"displayname": displayname,
"nominal_angle": np.linspace(360, -360, 49).tolist()
}
try:
temp_folder1, file_path1 = RestToolbox.GetSingleDcm(
config.ORTHANC_URL, w1)
temp_folder2, file_path2 = RestToolbox.GetSingleDcm(
config.ORTHANC_URL, w2)
except:
return template("error_template",
{"error_message": "Cannot read images."})
# Load first image
try:
img1 = pylinac_image.DicomImage(file_path1)
# Here we force pixels to background outside of box:
if clipbox != 0:
try:
img1.check_inversion_by_histogram(percentiles=[
4, 50, 96
]) # Check inversion otherwise this might not work
general_functions.clip_around_image(img1, clipbox)
except Exception as e:
return template(
"error_template",
{"error_message": "Unable to apply clipbox. " + str(e)})
else:
img1.remove_edges(pixels=2)
if invert:
img1.invert()
else:
img1.check_inversion()
img1.flipud()
except:
return template("error_template",
{"error_message": "Cannot read image."})
try:
img2 = pylinac_image.DicomImage(file_path2)
if clipbox != 0:
try:
img2.check_inversion_by_histogram(percentiles=[
4, 50, 96
]) # Check inversion otherwise this might not work
general_functions.clip_around_image(img2, clipbox)
except Exception as e:
return template(
"error_template",
{"error_message": "Unable to apply clipbox. " + str(e)})
else:
img2.remove_edges(pixels=2)
if invert:
img2.invert()
else:
img2.check_inversion()
img2.flipud()
except:
return template("error_template",
{"error_message": "Cannot read image."})
# Apply some filtering
if med_filter > 0:
img1.filter(med_filter)
img2.filter(med_filter)
# Get radiation field box:
try:
center_cax1, rad_field_bounding_box1, field_corners1 = field_rotation._find_field_centroid(
img1)
center_cax2, rad_field_bounding_box2, field_corners2 = field_rotation._find_field_centroid(
img2)
except Exception as e:
return template("error_template", {"error_message": str(e)})
field_corners1 = field_corners1.astype(int)
field_corners2 = field_corners2.astype(int)
# Set colormap
cmap = matplotlib.cm.get_cmap(colormap)
if test_type == "Collimator absolute":
# Get BBs
try:
if high_contrast:
bbs1 = field_rotation._find_bb2(img1, rad_field_bounding_box1)
bbs2 = field_rotation._find_bb2(img2, rad_field_bounding_box2)
else:
bbs1 = field_rotation._find_bb(img1, rad_field_bounding_box1)
bbs2 = field_rotation._find_bb(img2, rad_field_bounding_box2)
except Exception as e:
return template("error_template", {"error_message": str(e)})
bb_coord1_1, bw_bb_im1_1 = bbs1[0]
bb_coord1_2, bw_bb_im1_2 = bbs1[1]
bb_coord2_1, bw_bb_im2_1 = bbs2[0]
bb_coord2_2, bw_bb_im2_2 = bbs2[1]
center1_1 = (bb_coord1_1[0], bb_coord1_1[1])
center1_2 = (bb_coord1_2[0], bb_coord1_2[1])
center2_1 = (bb_coord2_1[0], bb_coord2_1[1])
center2_2 = (bb_coord2_2[0], bb_coord2_2[1])
# Line between BBs:
bb_angle1 = np.arctan(np.inf if bb_coord1_1[0] - bb_coord1_2[0] ==
0 else (bb_coord1_1[1] - bb_coord1_2[1]) /
(bb_coord1_1[0] - bb_coord1_2[0])) * 180 / np.pi
bb_angle2 = np.arctan(np.inf if bb_coord2_1[0] - bb_coord2_2[0] ==
0 else (bb_coord2_1[1] - bb_coord2_2[1]) /
(bb_coord2_1[0] - bb_coord2_2[0])) * 180 / np.pi
img1_filled = np.copy(img1.array)
img2_filled = np.copy(img2.array)
# Fill BBs area with neighbouring values
field_rotation.fill_BB_hole(bb_coord1_1, bw_bb_im1_1, img1_filled)
field_rotation.fill_BB_hole(bb_coord1_2, bw_bb_im1_2, img1_filled)
field_rotation.fill_BB_hole(bb_coord2_1, bw_bb_im2_1, img2_filled)
field_rotation.fill_BB_hole(bb_coord2_2, bw_bb_im2_2, img2_filled)
# Get penumbra points
try:
samples_left1, samples_right1, p_left1, p_right1 = field_rotation.find_penumbra_points(
direction, number_samples, field_corners1, margin, img1_filled)
samples_left2, samples_right2, p_left2, p_right2 = field_rotation.find_penumbra_points(
direction2, number_samples, field_corners2, margin,
img2_filled)
except Exception as e:
return template("error_template", {"error_message": str(e)})
# Calculate field edge slopes
pmin = 0
pmax = np.max(
img1.shape) # Maksimum point for drawing regression lines
if direction == "X":
left_slope1, left_P1, left_err1 = field_rotation.calculate_regression(
p_left1, samples_left1, pmin, pmax)
right_slope1, right_P1, right_err1 = field_rotation.calculate_regression(
p_right1, samples_right1, pmin, pmax)
else:
left_slope1, left_P1, left_err1 = field_rotation.calculate_regression(
samples_left1, p_left1, pmin, pmax)
right_slope1, right_P1, right_err1 = field_rotation.calculate_regression(
samples_right1, p_right1, pmin, pmax)
if direction2 == "X":
left_slope2, left_P2, left_err2 = field_rotation.calculate_regression(
p_left2, samples_left2, pmin, pmax)
right_slope2, right_P2, right_err2 = field_rotation.calculate_regression(
p_right2, samples_right2, pmin, pmax)
else:
left_slope2, left_P2, left_err2 = field_rotation.calculate_regression(
samples_left2, p_left2, pmin, pmax)
right_slope2, right_P2, right_err2 = field_rotation.calculate_regression(
samples_right2, p_right2, pmin, pmax)
left_edge_angle1 = np.arctan(left_slope1) * 180 / np.pi
left_edge_angle2 = np.arctan(left_slope2) * 180 / np.pi
right_edge_angle1 = np.arctan(right_slope1) * 180 / np.pi
right_edge_angle2 = np.arctan(right_slope2) * 180 / np.pi
# First plot: field and penumbra points
fig1 = Figure(figsize=(10.5, 5.5), tight_layout={"w_pad": 3, "pad": 3})
ax1 = fig1.add_subplot(1, 2, 1)
ax2 = fig1.add_subplot(1, 2, 2)
# Plot error bars and goodness of fit
fig2 = Figure(figsize=(10, 4), tight_layout={"w_pad": 0, "pad": 1})
ax3 = fig2.add_subplot(1, 2, 1)
ax4 = fig2.add_subplot(1, 2, 2)
# Plot angled lines
fig3 = Figure(figsize=(10, 4), tight_layout={"w_pad": 0, "pad": 1})
ax5 = fig3.add_subplot(1, 2, 1)
ax6 = fig3.add_subplot(1, 2, 2)
ax1.imshow(img1.array,
cmap=cmap,
interpolation="none",
aspect="equal",
origin='lower')
ax1.set_title('Image 1')
ax1.axis('off')
ax2.imshow(img2.array,
cmap=cmap,
interpolation="none",
aspect="equal",
origin='lower')
ax2.set_title('Image 2')
ax2.axis('off')
#Plot field corners
ax1.plot(field_corners1[:, 1],
field_corners1[:, 0],
"mo",
markersize=5,
markeredgewidth=0)
ax2.plot(field_corners2[:, 1],
field_corners2[:, 0],
"mo",
markersize=5,
markeredgewidth=0)
# Plot penumbra points
if direction == "X":
ax1.plot(p_left1,
samples_left1,
"bx",
markersize=5,
markeredgewidth=2)
ax1.plot(p_right1,
samples_right1,
"yx",
markersize=5,
markeredgewidth=2)
else:
ax1.plot(samples_left1,
p_left1,
"bx",
markersize=5,
markeredgewidth=2)
ax1.plot(samples_right1,
p_right1,
"yx",
markersize=5,
markeredgewidth=2)
ax1.plot(left_P1[0], left_P1[1], "b--")
ax1.plot(right_P1[0], right_P1[1], "y--")
if direction2 == "X":
ax2.plot(p_left2,
samples_left2,
"bx",
markersize=5,
markeredgewidth=2)
ax2.plot(p_right2,
samples_right2,
"yx",
markersize=5,
markeredgewidth=2)
else:
ax2.plot(samples_left2,
p_left2,
"bx",
markersize=5,
markeredgewidth=2)
ax2.plot(samples_right2,
p_right2,
"yx",
markersize=5,
markeredgewidth=2)
ax2.plot(left_P2[0], left_P2[1], "b--")
ax2.plot(right_P2[0], right_P2[1], "y--")
# Plot errors:
ax3.plot(samples_left1, left_err1, "bx", markeredgewidth=2)
ax3.plot(samples_right1, right_err1, "yx", markeredgewidth=2)
ax4.plot(samples_left2, left_err2, "bx", markeredgewidth=2)
ax4.plot(samples_right2, right_err2, "yx", markeredgewidth=2)
limits_max = np.amax([
np.amax(left_err1),
np.amax(right_err1),
np.amax(left_err2),
np.amax(right_err2)
]) * 1.05
limits_min = np.amin([
np.amin(left_err1),
np.amin(right_err1),
np.amin(left_err2),
np.amin(right_err2)
]) * 0.95
limits = np.amax([abs(limits_max), abs(limits_min)])
limits = limits if limits > 1 else 1
ax3.set_ylim([-limits, limits])
ax4.set_ylim([-limits, limits])
ax3.set_ylabel("Deviation from fit [px]")
ax4.set_ylabel("Deviation from fit [px]")
ax3.set_xlabel("Field edge [px]")
ax4.set_xlabel("Field edge [px]")
ax3.set_title('Image 1 - Regression error')
ax4.set_title('Image 2 - Regression error')
# Plot angled lines:
# If angles are negative, convert to [pi, 2pi]
if abs(bb_angle1) > 80 and abs(bb_angle1) <= 90:
B1 = left_edge_angle1 if left_edge_angle1 >= 0 else 180 + left_edge_angle1
Y1 = right_edge_angle1 if right_edge_angle1 >= 0 else 180 + right_edge_angle1
BB1 = bb_angle1 if bb_angle1 >= 0 else 180 + bb_angle1
ref_angle_plot = PI / 2 # Reference angle for drawing (either 0 or 90)
else:
B1 = left_edge_angle1
Y1 = right_edge_angle1
BB1 = bb_angle1
ref_angle_plot = 0
if abs(bb_angle2) > 80 and abs(bb_angle2) <= 90:
B2 = left_edge_angle2 if left_edge_angle2 >= 0 else 180 + left_edge_angle2
Y2 = right_edge_angle2 if right_edge_angle2 >= 0 else 180 + right_edge_angle2
BB2 = bb_angle2 if bb_angle2 >= 0 else 180 + bb_angle2
else:
B2 = left_edge_angle2
Y2 = right_edge_angle2
BB2 = bb_angle2
a = 2
x_bb1 = [-a * np.cos(BB1 * PI / 180), a * np.cos(BB1 * PI / 180)]
y_bb1 = [-a * np.sin(BB1 * PI / 180), a * np.sin(BB1 * PI / 180)]
x_b1 = [-a * np.cos((B1) * PI / 180), a * np.cos((B1) * PI / 180)]
y_b1 = [-a * np.sin((B1) * PI / 180), a * np.sin((B1) * PI / 180)]
x_b2 = [
-a * np.cos((BB1 - (B2 - BB2)) * PI / 180), a * np.cos(
(BB1 - (B2 - BB2)) * PI / 180)
]
y_b2 = [
-a * np.sin((BB1 - (B2 - BB2)) * PI / 180), a * np.sin(
(BB1 - (B2 - BB2)) * PI / 180)
]
x_y1 = [-a * np.cos((Y1) * PI / 180), a * np.cos((Y1) * PI / 180)]
y_y1 = [-a * np.sin((Y1) * PI / 180), a * np.sin((Y1) * PI / 180)]
x_y2 = [
-a * np.cos((BB1 - (Y2 - BB2)) * PI / 180), a * np.cos(
(BB1 - (Y2 - BB2)) * PI / 180)
]
y_y2 = [
-a * np.sin((BB1 - (Y2 - BB2)) * PI / 180), a * np.sin(
(BB1 - (Y2 - BB2)) * PI / 180)
]
ax5.plot(x_bb1, y_bb1, "g-", label="BB")
ax5.plot(x_b1, y_b1, "b-", label="Gantry 0")
ax5.plot(x_b2, y_b2, "b--", label="Gantry 180")
ax6.plot(x_bb1, y_bb1, "g-", label="BB")
ax6.plot(x_y1, y_y1, "y-", label="Gantry 0")
ax6.plot(x_y2, y_y2, "y--", label="Gantry 180")
if ref_angle_plot == PI / 2:
max_xb = np.amax([np.abs(x_b1), np.abs(x_b2)])
max_xy = np.amax([np.abs(x_y1), np.abs(x_y2)])
ax5.set_xlim([-2 * max_xb, 2 * max_xb])
ax5.set_ylim([-1, 1])
ax6.set_xlim([-2 * max_xy, 2 * max_xy])
ax6.set_ylim([-1, 1])
else:
max_y = np.amax([np.abs(y_b1), np.abs(y_b2)])
max_yy = np.amax([np.abs(y_y1), np.abs(y_y2)])
ax5.set_ylim([-2 * max_y, 2 * max_y])
ax5.set_xlim([-1, 1])
ax6.set_ylim([-2 * max_yy, 2 * max_yy])
ax6.set_xlim([-1, 1])
ax5.legend(loc='upper right', fontsize=10, edgecolor="none")
ax5.set_title("Blue edge")
ax5.set_xlabel("LAT [px]")
ax5.set_ylabel("LONG [px]")
ax6.legend(loc='upper right', fontsize=10, edgecolor="none")
ax6.set_title("Yellow edge")
ax6.set_xlabel("LAT [px]")
ax6.set_ylabel("LONG [px]")
# Plot BB line and crosses
ax1.plot([bb_coord1_1[0], bb_coord1_2[0]],
[bb_coord1_1[1], bb_coord1_2[1]], "g-")
ax2.plot([bb_coord2_1[0], bb_coord2_2[0]],
[bb_coord2_1[1], bb_coord2_2[1]], "g-")
ax1.plot(center1_1[0],
center1_1[1],
'r+',
markersize=10,
markeredgewidth=2)
ax1.plot(center1_2[0],
center1_2[1],
'r+',
markersize=10,
markeredgewidth=2)
ax2.plot(center2_1[0],
center2_1[1],
'r+',
markersize=10,
markeredgewidth=2)
ax2.plot(center2_2[0],
center2_2[1],
'r+',
markersize=10,
markeredgewidth=2)
ax1.set_xlim([0, img1.shape[1]])
ax1.set_ylim([0, img1.shape[0]])
ax2.set_xlim([0, img2.shape[1]])
ax2.set_ylim([0, img2.shape[0]])
script1 = mpld3.fig_to_html(fig1, d3_url=D3_URL, mpld3_url=MPLD3_URL)
script2 = mpld3.fig_to_html(fig2, d3_url=D3_URL, mpld3_url=MPLD3_URL)
script3 = mpld3.fig_to_html(fig3, d3_url=D3_URL, mpld3_url=MPLD3_URL)
variables = {
"script1": script1,
"script2": script2,
"script3": script3,
"left_edge_angle1": left_edge_angle1,
"left_edge_angle2": left_edge_angle2,
"right_edge_angle1": right_edge_angle1,
"right_edge_angle2": right_edge_angle2,
"bb_angle1": bb_angle1,
"bb_angle2": bb_angle2,
"test_type": test_type,
"tolerance": tolerance_collabs
}
elif test_type == "Collimator relative":
# Get penumbra points
try:
samples_left1, samples_right1, p_left1, p_right1 = field_rotation.find_penumbra_points(
direction, number_samples, field_corners1, margin, img1.array)
samples_left2, samples_right2, p_left2, p_right2 = field_rotation.find_penumbra_points(
direction2, number_samples, field_corners2, margin, img2.array)
except Exception as e:
return template("error_template", {"error_message": str(e)})
# Calculate field edge slopes
pmin = 0
pmax = np.max(
img1.shape) # Maksimum point for drawing regression lines
if direction == "X":
left_slope1, left_P1, left_err1 = field_rotation.calculate_regression(
p_left1, samples_left1, pmin, pmax)
right_slope1, right_P1, right_err1 = field_rotation.calculate_regression(
p_right1, samples_right1, pmin, pmax)
else:
left_slope1, left_P1, left_err1 = field_rotation.calculate_regression(
samples_left1, p_left1, pmin, pmax)
right_slope1, right_P1, right_err1 = field_rotation.calculate_regression(
samples_right1, p_right1, pmin, pmax)
if direction2 == "X":
left_slope2, left_P2, left_err2 = field_rotation.calculate_regression(
p_left2, samples_left2, pmin, pmax)
right_slope2, right_P2, right_err2 = field_rotation.calculate_regression(
p_right2, samples_right2, pmin, pmax)
else:
left_slope2, left_P2, left_err2 = field_rotation.calculate_regression(
samples_left2, p_left2, pmin, pmax)
right_slope2, right_P2, right_err2 = field_rotation.calculate_regression(
samples_right2, p_right2, pmin, pmax)
left_edge_angle1 = np.arctan(left_slope1) * 180 / np.pi
left_edge_angle2 = np.arctan(left_slope2) * 180 / np.pi
right_edge_angle1 = np.arctan(right_slope1) * 180 / np.pi
right_edge_angle2 = np.arctan(right_slope2) * 180 / np.pi
# First plot: field and penumbra points
fig1 = Figure(figsize=(10.5, 5.5), tight_layout={"w_pad": 3, "pad": 3})
ax1 = fig1.add_subplot(1, 2, 1)
ax2 = fig1.add_subplot(1, 2, 2)
# Plot error bars and goodness of fit
fig2 = Figure(figsize=(10, 4), tight_layout={"w_pad": 0, "pad": 1})
ax3 = fig2.add_subplot(1, 2, 1)
ax4 = fig2.add_subplot(1, 2, 2)
ax1.imshow(img1.array,
cmap=cmap,
interpolation="none",
aspect="equal",
origin='lower')
ax1.set_title('Image 1')
ax1.axis('off')
ax2.imshow(img2.array,
cmap=cmap,
interpolation="none",
aspect="equal",
origin='lower')
ax2.set_title('Image 2')
ax2.axis('off')
#Plot field corners
ax1.plot(field_corners1[:, 1],
field_corners1[:, 0],
"mo",
markersize=5,
markeredgewidth=0)
ax2.plot(field_corners2[:, 1],
field_corners2[:, 0],
"mo",
markersize=5,
markeredgewidth=0)
# Plot penumbra points
if direction == "X":
ax1.plot(p_left1,
samples_left1,
"bx",
markersize=5,
markeredgewidth=2)
ax1.plot(p_right1,
samples_right1,
"yx",
markersize=5,
markeredgewidth=2)
else:
ax1.plot(samples_left1,
p_left1,
"bx",
markersize=5,
markeredgewidth=2)
ax1.plot(samples_right1,
p_right1,
"yx",
markersize=5,
markeredgewidth=2)
ax1.plot(left_P1[0], left_P1[1], "b--")
ax1.plot(right_P1[0], right_P1[1], "y--")
if direction2 == "X":
ax2.plot(p_left2,
samples_left2,
"bx",
markersize=5,
markeredgewidth=2)
ax2.plot(p_right2,
samples_right2,
"yx",
markersize=5,
markeredgewidth=2)
else:
ax2.plot(samples_left2,
p_left2,
"bx",
markersize=5,
markeredgewidth=2)
ax2.plot(samples_right2,
p_right2,
"yx",
markersize=5,
markeredgewidth=2)
ax2.plot(left_P2[0], left_P2[1], "b--")
ax2.plot(right_P2[0], right_P2[1], "y--")
ax1.set_xlim([0, img1.shape[1]])
ax1.set_ylim([0, img1.shape[0]])
ax2.set_xlim([0, img2.shape[1]])
ax2.set_ylim([0, img2.shape[0]])
# Plot errors:
ax3.plot(samples_left1, left_err1, "bx", markeredgewidth=2)
ax3.plot(samples_right1, right_err1, "yx", markeredgewidth=2)
ax4.plot(samples_left2, left_err2, "bx", markeredgewidth=2)
ax4.plot(samples_right2, right_err2, "yx", markeredgewidth=2)
limits_max = np.amax([
np.amax(left_err1),
np.amax(right_err1),
np.amax(left_err2),
np.amax(right_err2)
]) * 1.05
limits_min = np.amin([
np.amin(left_err1),
np.amin(right_err1),
np.amin(left_err2),
np.amin(right_err2)
]) * 0.95
limits = np.amax([abs(limits_max), abs(limits_min)])
limits = limits if limits > 1 else 1
ax3.set_ylim([-limits, limits])
ax4.set_ylim([-limits, limits])
ax3.set_ylabel("Deviation from fit [px]")
ax4.set_ylabel("Deviation from fit [px]")
ax3.set_xlabel("Field edge [px]")
ax4.set_xlabel("Field edge [px]")
ax3.set_title('Image 1 - Regression error')
ax4.set_title('Image 2 - Regression error')
script1 = mpld3.fig_to_html(fig1, d3_url=D3_URL, mpld3_url=MPLD3_URL)
script2 = mpld3.fig_to_html(fig2, d3_url=D3_URL, mpld3_url=MPLD3_URL)
variables = {
"script1": script1,
"script2": script2,
"script3": "",
"left_edge_angle1": left_edge_angle1,
"left_edge_angle2": left_edge_angle2,
"right_edge_angle1": right_edge_angle1,
"right_edge_angle2": right_edge_angle2,
"test_type": test_type,
"tolerance": tolerance_collrel
}
else: # Couch rotation
# Get BBs
try:
if high_contrast:
bbs1 = field_rotation._find_bb2(img1, rad_field_bounding_box1)
bbs2 = field_rotation._find_bb2(img2, rad_field_bounding_box2)
else:
bbs1 = field_rotation._find_bb(img1, rad_field_bounding_box1)
bbs2 = field_rotation._find_bb(img2, rad_field_bounding_box2)
except Exception as e:
return template("error_template", {"error_message": str(e)})
bb_coord1_1, bw_bb_im1_1 = bbs1[0]
bb_coord1_2, bw_bb_im1_2 = bbs1[1]
bb_coord2_1, bw_bb_im2_1 = bbs2[0]
bb_coord2_2, bw_bb_im2_2 = bbs2[1]
center1_1 = [bb_coord1_1[0], bb_coord1_1[1]]
center1_2 = [bb_coord1_2[0], bb_coord1_2[1]]
center2_1 = [bb_coord2_1[0], bb_coord2_1[1]]
center2_2 = [bb_coord2_2[0], bb_coord2_2[1]]
bb_line_center1 = [
np.average([center1_1[0], center1_2[0]]),
np.average([center1_1[1], center1_2[1]])
]
bb_line_center2 = [
np.average([center2_1[0], center2_2[0]]),
np.average([center2_1[1], center2_2[1]])
]
# Line between BBs:
bb_angle1 = np.arctan(np.inf if bb_coord1_1[0] - bb_coord1_2[0] ==
0 else (bb_coord1_1[1] - bb_coord1_2[1]) /
(bb_coord1_1[0] - bb_coord1_2[0])) * 180 / np.pi
bb_angle2 = np.arctan(np.inf if bb_coord2_1[0] - bb_coord2_2[0] ==
0 else (bb_coord2_1[1] - bb_coord2_2[1]) /
(bb_coord2_1[0] - bb_coord2_2[0])) * 180 / np.pi
fig1 = Figure(figsize=(10.5, 5.5), tight_layout={"w_pad": 3, "pad": 3})
ax1 = fig1.add_subplot(1, 2, 1)
ax2 = fig1.add_subplot(1, 2, 2)
ax1.imshow(img1.array,
cmap=cmap,
interpolation="none",
aspect="equal",
origin='lower')
ax1.set_title('Image 1')
ax1.axis('off')
ax2.imshow(img2.array,
cmap=cmap,
interpolation="none",
aspect="equal",
origin='lower')
ax2.set_title('Image 2')
ax2.axis('off')
# Plot BB line and crosses
ax1.plot([bb_coord1_1[0], bb_coord1_2[0]],
[bb_coord1_1[1], bb_coord1_2[1]], "g-")
ax2.plot([bb_coord2_1[0], bb_coord2_2[0]],
[bb_coord2_1[1], bb_coord2_2[1]], "g-")
ax1.plot(center1_1[0],
center1_1[1],
'r+',
markersize=10,
markeredgewidth=2)
ax1.plot(center1_2[0],
center1_2[1],
'r+',
markersize=10,
markeredgewidth=2)
ax2.plot(center2_1[0],
center2_1[1],
'r+',
markersize=10,
markeredgewidth=2)
ax2.plot(center2_2[0],
center2_2[1],
'r+',
markersize=10,
markeredgewidth=2)
ax1.set_xlim([0, img1.shape[1]])
ax1.set_ylim([0, img1.shape[0]])
ax2.set_xlim([0, img2.shape[1]])
ax2.set_ylim([0, img2.shape[0]])
script1 = mpld3.fig_to_html(fig1, d3_url=D3_URL, mpld3_url=MPLD3_URL)
variables = {
"script1": script1,
"script2": "",
"script3": "",
"bb_angle1": bb_angle1,
"bb_angle2": bb_angle2,
"bb_line_center1": bb_line_center1,
"bb_line_center2": bb_line_center2,
"test_type": test_type,
"tolerance": tolerance_couchrel
}
variables["acquisition_datetime"] = acquisition_datetime
variables["save_results"] = save_results
general_functions.delete_files_in_subfolders([temp_folder1, temp_folder2
]) # Delete image
return template("fieldrot_results", variables)
def flatsym_helper(args):
calc_definition = args["calc_definition"]
center_definition = args["center_definition"]
center_x = args["center_x"]
center_y = args["center_y"]
invert = args["invert"]
w = args["instance"]
station = args["station"]
imgdescription = args["imgdescription"]
displayname = args["displayname"]
acquisition_datetime = args["acquisition_datetime"]
general_functions.set_configuration(
args["config"]) # Transfer to this process
# Collect data for "save results"
dicomenergy = general_functions.get_energy_from_imgdescription(
imgdescription)
user_machine, user_energy = general_functions.get_user_machine_and_energy(
station, dicomenergy)
machines_and_energies = general_functions.get_machines_and_energies(
general_functions.get_treatmentunits_flatsym())
tolerances = general_functions.get_tolerance_user_machine_flatsym(
user_machine) # If user_machne has specific tolerance
if not tolerances:
tolerance_flat, tolerance_sym, pdf_report_enable = general_functions.get_settings_flatsym(
)
else:
tolerance_flat, tolerance_sym, pdf_report_enable = tolerances[0]
tolerance_flat = float(tolerance_flat)
tolerance_sym = float(tolerance_sym)
save_results = {
"user_machine": user_machine,
"user_energy": user_energy,
"machines_and_energies": machines_and_energies,
"displayname": displayname
}
temp_folder, file_path = RestToolbox.GetSingleDcm(config.ORTHANC_URL, w)
try:
flatsym = FlatSym(file_path)
except Exception as e:
return template("error_template", {
"error_message":
"The FlatSym module cannot calculate. " + str(e)
})
# Define the center pixel where to get profiles:
def find_field_centroid(img):
'''taken from pylinac WL module'''
min, max = np.percentile(img.image.array, [5, 99.9])
# min, max = np.amin(img.array), np.max(img.array)
threshold_img = img.image.as_binary((max - min) / 2 + min)
# clean single-pixel noise from outside field
coords = ndimage.measurements.center_of_mass(threshold_img)
return (int(coords[-1]), int(coords[0]))
flatsym.image.crop(pixels=2)
flatsym.image.check_inversion()
if invert:
flatsym.image.invert()
flatsym.image.array = np.flipud(flatsym.image.array)
vmax = flatsym.image.array.max()
if center_definition == "Automatic":
center_int = [
int(flatsym.image.array.shape[1] / 2),
int(flatsym.image.array.shape[0] / 2)
]
center = [0.5, 0.5]
elif center_definition == "CAX":
center_int = find_field_centroid(
flatsym) # Define as mechanical isocenter
center = [
int(center_int[0]) / flatsym.image.array.shape[1],
int(center_int[1]) / flatsym.image.array.shape[0]
]
else:
center_int = [int(center_x), int(center_y)]
center = [
int(center_x) / flatsym.image.array.shape[1],
int(center_y) / flatsym.image.array.shape[0]
]
fig = Figure(figsize=(9, 9), tight_layout={"w_pad": 1})
ax = fig.add_subplot(1, 1, 1)
ax.imshow(flatsym.image.array,
cmap=matplotlib.cm.jet,
interpolation="none",
vmin=0.9 * vmax,
vmax=vmax,
aspect="equal",
origin='lower')
ax.autoscale(tight=True)
# Plot lines along which the profiles are calculated:
ax.plot([0, flatsym.image.array.shape[1]], [center_int[1], center_int[1]],
"b-",
linewidth=2)
ax.plot([center_int[0], center_int[0]], [0, flatsym.image.array.shape[0]],
c="darkgreen",
linestyle="-",
linewidth=2)
ax.set_xlim([0, flatsym.image.array.shape[1]])
ax.set_ylim([0, flatsym.image.array.shape[0]])
# Get profiles
crossplane = PylinacSingleProfile(flatsym.image.array[center_int[1], :])
inplane = PylinacSingleProfile(flatsym.image.array[:, center_int[0]])
# Do some filtering:
crossplane.filter(kind='median', size=0.01)
inplane.filter(kind='median', size=0.01)
# Normalize profiles
norm_val_crossplane = crossplane.values[center_int[0]]
norm_val_inplane = inplane.values[center_int[1]]
crossplane.normalize(norm_val=norm_val_crossplane)
inplane.normalize(norm_val=norm_val_inplane)
# Get index of CAX of both profiles(different than center) to be used for mirroring:
fwhm_crossplane = crossplane.fwxm_center(interpolate=True)
fwhm_inplane = inplane.fwxm_center(interpolate=True)
# Plot profiles
divider = make_axes_locatable(ax)
ax_crossplane = divider.append_axes("bottom",
size="40%",
pad=0.25,
sharex=ax)
ax_crossplane.set_xlim([0, flatsym.image.array.shape[1]])
ax_crossplane.set_xticks([])
ax_crossplane.set_title("Crossplane")
ax_inplane = divider.append_axes("right", size="40%", pad=0.25, sharey=ax)
ax_inplane.set_ylim([0, flatsym.image.array.shape[0]])
ax_inplane.set_yticks([])
ax_inplane.set_title("Inplane")
ax_crossplane.plot(crossplane._indices, crossplane, "b-")
ax_crossplane.plot(2 * fwhm_crossplane - crossplane._indices, crossplane,
"b--")
ax_inplane.plot(inplane, inplane._indices, c="darkgreen", linestyle="-")
ax_inplane.plot(inplane,
2 * fwhm_inplane - inplane._indices,
c="darkgreen",
linestyle="--")
ax_inplane.grid(alpha=0.5)
ax_crossplane.grid(alpha=0.5)
mpld3.plugins.connect(fig,
mpld3.plugins.MousePosition(fontsize=14, fmt=".2f"))
script = mpld3.fig_to_html(fig, d3_url=D3_URL, mpld3_url=MPLD3_URL)
if calc_definition == "Elekta":
method = "elekta"
else:
method = "varian"
try:
flatsym.analyze(flatness_method=method,
symmetry_method=method,
vert_position=center[0],
horiz_position=center[1])
except Exception as e:
return template("error_template",
{"error_message": "Analysis failed. " + str(e)})
symmetry_hor = round(flatsym.symmetry['horizontal']['value'], 2)
symmetry_vrt = round(flatsym.symmetry['vertical']['value'], 2)
flatness_hor = round(flatsym.flatness['horizontal']['value'], 2)
flatness_vrt = round(flatsym.flatness['vertical']['value'], 2)
horizontal_width = round(
flatsym.symmetry['horizontal']['profile'].fwxm(interpolate=True) /
flatsym.image.dpmm, 2)
vertical_width = round(
flatsym.symmetry['vertical']['profile'].fwxm(interpolate=True) /
flatsym.image.dpmm, 2)
horizontal_penumbra_width = round(
flatsym.symmetry['horizontal']['profile'].penumbra_width(
interpolate=True) / flatsym.image.dpmm, 2)
vertical_penumbra_width = round(
flatsym.symmetry['vertical']['profile'].penumbra_width(
interpolate=True) / flatsym.image.dpmm, 2)
# Check if passed
if method == "varian":
if (flatness_hor <
tolerance_flat) and (flatness_vrt < tolerance_flat) and (
symmetry_hor < tolerance_sym) and (symmetry_vrt <
tolerance_sym):
passed = True
else:
passed = False
else:
if (abs(flatness_hor - 100) < tolerance_flat) and (
abs(flatness_vrt - 100) < tolerance_flat) and (
abs(symmetry_hor - 100) < tolerance_sym) and (
abs(symmetry_vrt - 100) < tolerance_sym):
passed = True
else:
passed = False
variables = {
"symmetry_hor": symmetry_hor,
"symmetry_vrt": symmetry_vrt,
"flatness_hor": flatness_hor,
"flatness_vrt": flatness_vrt,
"horizontal_width": horizontal_width,
"vertical_width": vertical_width,
"horizontal_penumbra_width": horizontal_penumbra_width,
"vertical_penumbra_width": vertical_penumbra_width,
"passed": passed,
"pdf_report_enable": pdf_report_enable,
"script": script,
"save_results": save_results,
"acquisition_datetime": acquisition_datetime,
"calc_definition": calc_definition
}
# Generate pylinac report:
if pdf_report_enable == "True":
pdf_file = tempfile.NamedTemporaryFile(delete=False,
prefix="FlatSym",
suffix=".pdf",
dir=config.PDF_REPORT_FOLDER)
flatsym.publish_pdf(pdf_file)
variables["pdf_report_filename"] = os.path.basename(pdf_file.name)
general_functions.delete_files_in_subfolders([temp_folder]) # Delete image
return template("flatsym_results", variables)
def catphan_calculate_helperf(args):
use_reference = args["use_reference"]
phantom = args["phantom"]
machine = args["machine"]
beam = args["beam"]
HU_delta = args["HU_delta"]
colormap = args["colormap"]
displayname = args["displayname"]
acquisition_datetime = args["acquisition_datetime"]
s = args["s"]
general_functions.set_configuration(args["config"]) # Transfer to this process
save_results = {
"machine": machine,
"beam": beam,
"phantom": phantom,
"displayname": displayname
}
# Set colormap
cmap = matplotlib.cm.get_cmap(colormap)
# Collect data for "save results"
tolerances = general_functions.get_tolerance_user_machine_catphan(machine, beam, phantom) # If user_machne has specific tolerance
if not tolerances:
hu, lcv, scaling, thickness, lowcontrast, cnr, mtf, uniformityidx, pdf_report_enable = "100", "2", "0.5", "0.25", "1", "10", "10", "3", "False"
else:
hu, lcv, scaling, thickness, lowcontrast, cnr, mtf, uniformityidx, pdf_report_enable = tolerances
hu_tolerance = float(hu)
lcv_tolerance = float(lcv)
scaling_tolerance = float(scaling)
thickness_tolerance = float(thickness)
low_contrast_tolerance = float(lowcontrast)
cnr_threshold = float(cnr)
mtf_tolerance = float(mtf)
uniformityidx_tolerance = float(uniformityidx)
ref_path = general_functions.get_referenceimagepath_catphan(machine, beam, phantom)
if ref_path is not None:
ref_path = os.path.join(config.REFERENCE_IMAGES_FOLDER, ref_path[0])
if os.path.exists(ref_path):
ref_exists = True
else:
ref_exists = False
else:
ref_exists = False
folder_path = RestToolbox.GetSeries2Folder2(config.ORTHANC_URL, s)
# Use two threads to speedup the calculation (if ref exists)
args_current = {"hu_tolerance": hu_tolerance, "scaling_tolerance": scaling_tolerance,
"thickness_tolerance": thickness_tolerance,
"cnr_threshold": cnr_threshold, "path": folder_path,
"phantom": phantom, "low_contrast_tolerance": low_contrast_tolerance,
"config": general_functions.get_configuration()}
args_ref = {"hu_tolerance": hu_tolerance, "scaling_tolerance": scaling_tolerance,
"thickness_tolerance": thickness_tolerance,
"cnr_threshold": cnr_threshold, "path": ref_path,
"phantom": phantom, "low_contrast_tolerance": low_contrast_tolerance,
"config": general_functions.get_configuration()}
if use_reference and ref_exists:
try:
p = ThreadPool(2)
[mycbct, mycbct_ref] = p.map(catphan_helperf_analyze, [args_current, args_ref])
finally:
p.close()
p.join()
else:
mycbct = catphan_helperf_analyze(args_current)
if use_reference and ref_exists:
if isinstance(mycbct_ref, Exception):
return template("error_template", {"error_message": "Unable to analyze reference image. " + str(mycbct_ref)
})
if isinstance(mycbct, Exception):
general_functions.delete_files_in_subfolders([folder_path]) # Delete temporary images
return template("error_template", {"error_message": "Unable to analyze image. " + str(mycbct)
})
try: # add this to prevent memory problems when threads with exceptions are still alive
# ######################### CTP528 - Resolution ###################################
fig_dcm = Figure(figsize=(10.5, 5), tight_layout={"w_pad":0, "pad": 1.5})
ax1 = fig_dcm.add_subplot(1,2,1)
ax2 = fig_dcm.add_subplot(1,2,2)
# Reference image array
if use_reference and ref_exists:
ax1.imshow(mycbct_ref.ctp528.image.array, cmap=cmap, interpolation="none", aspect="equal", origin='upper')
ax1.autoscale(enable=False)
else:
ax1.text(0.5, 0.5 ,"Reference image not available", horizontalalignment='center', verticalalignment='center')
# Analysed current array
ax2.imshow(mycbct.ctp528.image.array, cmap=cmap, interpolation="none", aspect="equal", origin='upper')
ax2.set_title('CTP528 current image')
ax1.set_title('CTP528 reference image')
ax2.autoscale(enable=False)
# Plot rMTF and gather some data
fig_mtf = Figure(figsize=(5, 5), tight_layout={"w_pad":2, "pad": 1})
ax_mtf = fig_mtf.add_subplot(1,1,1)
msize = 8
if use_reference and ref_exists:
ax_mtf.plot(list(mycbct_ref.ctp528.mtf.norm_mtfs.keys()), list(mycbct_ref.ctp528.mtf.norm_mtfs.values()), marker='o', color="blue",
markersize=msize, markerfacecolor="None", linestyle="--")
ax_mtf.plot(list(mycbct.ctp528.mtf.norm_mtfs.keys()), list(mycbct.ctp528.mtf.norm_mtfs.values()), marker='o', color="blue", markersize=msize)
ax_mtf.margins(0.05)
ax_mtf.grid('on')
ax_mtf.set_xlabel('Line pairs / mm')
ax_mtf.set_ylabel("Relative MTF")
ax_mtf.set_title('Modulation transfer function')
script_ctp528 = mpld3.fig_to_html(fig_dcm, d3_url=D3_URL, mpld3_url=MPLD3_URL)
script_ctp528mtf = mpld3.fig_to_html(fig_mtf, d3_url=D3_URL, mpld3_url=MPLD3_URL)
# Some data:
mtf30_ref = mycbct_ref.ctp528.mtf.relative_resolution(30) if use_reference and ref_exists else np.nan
mtf30 = mycbct.ctp528.mtf.relative_resolution(30)
mtf50_ref = mycbct_ref.ctp528.mtf.relative_resolution(50) if use_reference and ref_exists else np.nan
mtf50 = mycbct.ctp528.mtf.relative_resolution(50)
mtf80_ref = mycbct_ref.ctp528.mtf.relative_resolution(80) if use_reference and ref_exists else np.nan
mtf80 = mycbct.ctp528.mtf.relative_resolution(80)
if use_reference and ref_exists:
mtf_passing = True if abs(100*(mtf50-mtf50_ref)/mtf50_ref)<=mtf_tolerance else False
else:
mtf_passing = None
# ####################### CTP404 - GEOMETRY HU LINEARITY ####################
fig_404 = Figure(figsize=(10.5, 5), tight_layout={"w_pad":0, "pad": 1.5})
ax404_1 = fig_404.add_subplot(1,2,1)
ax404_2 = fig_404.add_subplot(1,2,2)
def ctp404_plotROI(mycbct, fig, axis):
# Plot lines and circles - taken from pylinac
# plot HU linearity ROIs
for roi in mycbct.ctp404.hu_rois.values():
axis.add_patch(matplotlib.patches.Circle((roi.center.x, roi.center.y), edgecolor=roi.plot_color, radius=roi.radius, fill=False))
for roi in mycbct.ctp404.bg_hu_rois.values():
axis.add_patch(matplotlib.patches.Circle((roi.center.x, roi.center.y), edgecolor='blue', radius=roi.radius, fill=False))
# plot thickness ROIs
for roi in mycbct.ctp404.thickness_rois.values():
axis.add_patch(matplotlib.patches.Rectangle((roi.bl_corner.x, roi.bl_corner.y), width=roi.width, height=roi.height,
angle=0, edgecolor="blue", alpha=1, facecolor="g", fill=False))
# plot geometry lines
for line in mycbct.ctp404.lines.values():
axis.plot((line.point1.x, line.point2.x), (line.point1.y, line.point2.y), linewidth=1, color=line.pass_fail_color)
# Plot tooltips for patches
names = []
hu_rois_centers_x = []
hu_rois_centers_y = []
hu_rois_radius = []
for name, roi in mycbct.ctp404.hu_rois.items():
names.append(name)
hu_rois_centers_x.append(roi.center.x)
hu_rois_centers_y.append(roi.center.y)
hu_rois_radius.append((roi.radius)**2)
hu_rois_ttip = axis.scatter(hu_rois_centers_x, hu_rois_centers_y, s = hu_rois_radius, alpha=0)
labels = [names[i] for i in range(len(names))]
tooltip = mpld3.plugins.PointLabelTooltip(hu_rois_ttip, labels=labels)
mpld3.plugins.connect(fig, tooltip)
# Add tooltips for 4 lines
inc = 1
for line in mycbct.ctp404.lines.values():
hu_lines_ttip = axis.plot((line.point1.x, line.point2.x), (line.point1.y, line.point2.y), alpha=0, lw=7)
tooltip2 = mpld3.plugins.LineLabelTooltip(hu_lines_ttip[0], label="Line "+str(inc))
mpld3.plugins.connect(fig, tooltip2)
inc += 1
# Reference image
if use_reference and ref_exists:
ax404_1.imshow(mycbct_ref.ctp404.image.array, cmap=cmap, interpolation="none", aspect="equal", origin='upper')
#mycbct_ref.ctp404.plot_rois(ax404_1)
ctp404_plotROI(mycbct_ref, fig_404, ax404_1) # alternative
ax404_1.autoscale(enable=False)
ax404_1.set_xlim([0, mycbct_ref.ctp404.image.shape[1]])
ax404_1.set_ylim([mycbct_ref.ctp404.image.shape[0], 0])
else:
ax404_1.text(0.5, 0.5 ,"Reference image not available", horizontalalignment='center', verticalalignment='center')
# Current image
ax404_2.imshow(mycbct.ctp404.image.array, cmap=cmap, interpolation="none", aspect="equal", origin='upper')
ctp404_plotROI(mycbct, fig_404, ax404_2) # alternative
#mycbct.ctp404.plot_rois(ax404_2)
ax404_2.set_xlim([0, mycbct.ctp404.image.shape[1]])
ax404_2.set_ylim([mycbct.ctp404.image.shape[0], 0])
ax404_1.set_title('CTP404 reference image')
ax404_2.set_title('CTP404 current image')
ax404_2.autoscale(enable=False)
# Draw HU linearity plot
def plot_linearity(mycbct, fig, axis, plot_delta):
'''Taken from pylinac'''
nominal_x_values = [roi.nominal_val for roi in mycbct.ctp404.hu_rois.values()]
actual_values = []
diff_values = []
if plot_delta:
values = []
names = []
for name, roi in mycbct.ctp404.hu_rois.items():
names.append(name)
values.append(roi.value_diff)
actual_values.append(roi.pixel_value)
diff_values.append(roi.value_diff)
nominal_measurements = [0]*len(values)
ylabel = 'HU Delta'
else:
values = []
names = []
for name, roi in mycbct.ctp404.hu_rois.items():
names.append(name)
values.append(roi.pixel_value)
actual_values.append(roi.pixel_value)
diff_values.append(roi.value_diff)
nominal_measurements = nominal_x_values
ylabel = 'Measured Values'
points = axis.plot(nominal_x_values, values, 'g+', markersize=15, mew=2)
axis.plot(nominal_x_values, nominal_measurements)
axis.plot(nominal_x_values, np.array(nominal_measurements) + mycbct.ctp404.hu_tolerance, 'r--')
axis.plot(nominal_x_values, np.array(nominal_measurements) - mycbct.ctp404.hu_tolerance, 'r--')
axis.margins(0.07)
axis.grid(True, alpha=0.35)
axis.set_xlabel("Nominal Values")
axis.set_ylabel(ylabel)
axis.set_title("HU linearity")
labels = [names[i]+" -- Nom.={:.1f}, Act.={:.1f}, Diff.={:.1f}".format(nominal_x_values[i], actual_values[i], diff_values[i]) for i in range(len(names))]
tooltip = mpld3.plugins.PointLabelTooltip(points[0], labels=labels, location="top right")
mpld3.plugins.connect(fig, tooltip)
fig_404_HU = Figure(figsize=(10.5, 5), tight_layout={"w_pad":1})
ax_HU_ref = fig_404_HU.add_subplot(1,2,1)
ax_HU = fig_404_HU.add_subplot(1,2,2)
# Reference HU linearity
if use_reference and ref_exists:
plot_linearity(mycbct_ref, fig_404_HU, ax_HU_ref, plot_delta=HU_delta)
else:
ax_HU_ref.text(0.5, 0.5 ,"Reference image not available", horizontalalignment='center', verticalalignment='center')
ax_HU_ref.set_title("HU linearity")
# Current HU linearity
plot_linearity(mycbct, fig_404_HU, ax_HU, plot_delta=HU_delta)
# Gather data from HU holes:
if use_reference and ref_exists:
HU_values_ref = []
HU_std_ref = []
HU_diff_ref = []
cnrs404_ref = []
for key, value in mycbct_ref.ctp404.hu_rois.items():
HU_values_ref.append(value.pixel_value)
HU_std_ref.append(round(value.std, 1))
HU_diff_ref.append(value.value_diff)
cnrs404_ref.append(round(value.cnr, 1))
# Background HU ROIs
for key, value in mycbct_ref.ctp404.bg_hu_rois.items():
HU_values_ref.append(value.pixel_value)
HU_std_ref.append(round(value.std, 1))
HU_diff_ref.append(np.nan)
cnrs404_ref.append(np.nan)
lcv_ref = round(mycbct_ref.ctp404.lcv, 2)
slice_thickness_ref = round(mycbct_ref.ctp404.meas_slice_thickness, 2)
lines_ref = [] # Line length
for l in mycbct_ref.ctp404.lines.values():
lines_ref.append(round(l.length_mm, 2))
lines_avg_ref = round(mycbct_ref.ctp404.avg_line_length, 2)
phantom_roll_ref = round(mycbct_ref.ctp404.catphan_roll, 2)
dicom_slice_thickness_ref = round(mycbct_ref.ctp404.slice_thickness, 2)
else:
length = len(list(mycbct.ctp404.hu_rois.values())+list(mycbct.ctp404.bg_hu_rois.values()))
HU_values_ref = [np.nan]*length
HU_std_ref = [np.nan]*length
HU_diff_ref = [np.nan]*length
cnrs404_ref = [np.nan]*length
lcv_ref = np.nan
slice_thickness_ref = np.nan
lines_ref = [np.nan]*len(mycbct.ctp404.lines.values())
lines_avg_ref = np.nan
phantom_roll_ref = np.nan
dicom_slice_thickness_ref = np.nan
HU_values = []
HU_std = []
HU_diff = []
HU_nominal = []
HU_names = []
cnrs404 = []
HU_CNR_values_dict = {}
for key, value in mycbct.ctp404.hu_rois.items():
HU_values.append(value.pixel_value)
HU_std.append(round(value.std, 1))
HU_diff.append(value.value_diff)
HU_nominal.append(value.nominal_val)
HU_names.append(key)
cnrs404.append(round(value.cnr, 1))
HU_CNR_values_dict[key] = [value.pixel_value, round(value.cnr, 1)]
# Background HU ROIs
for key, value in mycbct.ctp404.bg_hu_rois.items():
HU_values.append(value.pixel_value)
HU_std.append(round(value.std, 1))
HU_diff.append(np.nan)
HU_nominal.append(0)
HU_names.append("Background "+str(key))
cnrs404.append(np.nan)
HU_CNR_values_dict[key] = [value.pixel_value, "nan"]
# For easier acces of values in results
save_results["HU_CNR_values_dict"] = HU_CNR_values_dict
lcv = mycbct.ctp404.lcv
slice_thickness = round(mycbct.ctp404.meas_slice_thickness, 2)
phantom_roll = round(mycbct.ctp404.catphan_roll, 2)
dicom_slice_thickness = round(mycbct.ctp404.slice_thickness, 2)
# Get origin slice and phantom center and slice number of other modules
if use_reference and ref_exists:
mm_per_pixel_ref = round(mycbct_ref.mm_per_pixel, 2)
origin_slice_ref = mycbct_ref.origin_slice
ctp528_slice_ref = mycbct_ref.ctp528.slice_num
ctp486_slice_ref = mycbct_ref.ctp486.slice_num
ctp515_slice_ref = mycbct_ref.ctp515.slice_num if phantom != "Catphan 503" else np.nan
phantom_center_ref = [round(mycbct_ref.ctp404.phan_center.x, 2),
round(mycbct_ref.ctp404.phan_center.y, 2)]
else:
mm_per_pixel_ref = np.nan
origin_slice_ref = np.nan
ctp528_slice_ref = np.nan
ctp486_slice_ref = np.nan
ctp515_slice_ref = np.nan
phantom_center_ref = [np.nan, np.nan]
mm_per_pixel = round(mycbct.mm_per_pixel, 2)
origin_slice = mycbct.origin_slice
ctp528_slice = mycbct.ctp528.slice_num
ctp486_slice = mycbct.ctp486.slice_num
ctp515_slice = mycbct.ctp515.slice_num if phantom != "Catphan 503" else np.nan
phantom_center = [round(mycbct.ctp404.phan_center.x, 2),
round(mycbct.ctp404.phan_center.y, 2)]
lines = [] # Line length
for l in mycbct.ctp404.lines.values():
lines.append(round(l.length_mm, 2))
lines_avg = round(mycbct.ctp404.avg_line_length, 2)
passed_HU = mycbct.ctp404.passed_hu
passed_thickness = mycbct.ctp404.passed_thickness
passed_geometry = mycbct.ctp404.passed_geometry
passed_lcv = True if lcv >= lcv_tolerance else False
passed_404 = passed_HU and passed_thickness and passed_geometry and passed_lcv
script_404 = mpld3.fig_to_html(fig_404, d3_url=D3_URL, mpld3_url=MPLD3_URL)
script_404_HU = mpld3.fig_to_html(fig_404_HU, d3_url=D3_URL, mpld3_url=MPLD3_URL)
# ############################## CTP486 - UNIFORMITY ####################
fig_486 = Figure(figsize=(10.5, 5), tight_layout={"w_pad":0, "pad": 1.5})
ax486_1 = fig_486.add_subplot(1,2,1)
ax486_2 = fig_486.add_subplot(1,2,2)
if use_reference and ref_exists:
ax486_1.imshow(mycbct_ref.ctp486.image.array, cmap=cmap, interpolation="none", aspect="equal", origin='upper')
mycbct_ref.ctp486.plot_rois(ax486_1)
# Add text inside ROI for reference to uniformity index:
for ind, roi in enumerate(mycbct_ref.ctp486.rois.values()):
ax486_1.text(roi.center.x, roi.center.y, str(ind), horizontalalignment='center', verticalalignment='center')
else:
ax486_1.text(0.5, 0.5 ,"Reference image not available", horizontalalignment='center', verticalalignment='center')
ax486_2.imshow(mycbct.ctp486.image.array, cmap=cmap, interpolation="none", aspect="equal", origin='upper')
mycbct.ctp486.plot_rois(ax486_2)
# Add text inside ROI for reference to uniformity index:
for ind, roi in enumerate(mycbct.ctp486.rois.values()):
ax486_2.text(roi.center.x, roi.center.y, str(ind), horizontalalignment='center', verticalalignment='center')
ax486_1.set_title('CTP486 reference image')
ax486_1.autoscale(enable=False)
ax486_2.set_title('CTP486 current image')
ax486_2.autoscale(enable=False)
script_486 = mpld3.fig_to_html(fig_486, d3_url=D3_URL, mpld3_url=MPLD3_URL)
# Draw orthogonal profiles:
fig_486_profile = Figure(figsize=(10.5, 5), tight_layout={"w_pad":0, "pad": 1.5})
ax486_profile_ref = fig_486_profile.add_subplot(1,2,1)
ax486_profile = fig_486_profile.add_subplot(1,2,2)
if use_reference and ref_exists:
mycbct_ref.ctp486.plot_profiles(ax486_profile_ref)
else:
ax486_profile_ref.text(0.5, 0.5 ,"Reference image not available", horizontalalignment='center', verticalalignment='center')
mycbct.ctp486.plot_profiles(ax486_profile)
ax486_profile_ref.set_title('Reference uniformity profiles')
ax486_profile.set_title('Current uniformity profiles')
script_486_profile = mpld3.fig_to_html(fig_486_profile, d3_url=D3_URL, mpld3_url=MPLD3_URL)
# Get mean pixel values and uniformity index:
# take into account slope and intercept HU = slope * px + intercept
if use_reference and ref_exists:
hvalues_ref = [roi.pixel_value for roi in mycbct_ref.ctp486.rois.values()]
uidx_ref = round(mycbct_ref.ctp486.uniformity_index, 2)
else:
hvalues_ref = [np.nan]*len(mycbct.ctp486.rois.values())
uidx_ref = np.nan
hvalues = [roi.pixel_value for roi in mycbct.ctp486.rois.values()]
passed_uniformity = mycbct.ctp486.overall_passed
uidx = round(mycbct.ctp486.uniformity_index, 2)
passed_uniformity_index = True if abs(uidx)<=uniformityidx_tolerance else False
# ############################## CTP515 - LOW CONTRAST ####################
if phantom != "Catphan 503":
show_ctp515 = True
fig_515 = Figure(figsize=(10.5, 5), tight_layout={"w_pad":0, "pad": 1.5})
ax515_1 = fig_515.add_subplot(1,2,1)
ax515_2 = fig_515.add_subplot(1,2,2)
if use_reference and ref_exists:
ax515_1.imshow(mycbct_ref.ctp515.image.array, cmap=cmap, interpolation="none", aspect="equal", origin='upper')
mycbct_ref.ctp515.plot_rois(ax515_1)
else:
ax515_1.text(0.5, 0.5 ,"Reference image not available", horizontalalignment='center', verticalalignment='center')
ax515_2.imshow(mycbct.ctp515.image.array, cmap=cmap, interpolation="none", aspect="equal", origin='upper')
mycbct.ctp515.plot_rois(ax515_2)
ax515_1.set_title('CTP515 reference image')
ax515_1.autoscale(enable=False)
ax515_2.set_title('CTP515 current image')
ax515_2.autoscale(enable=False)
script_515 = mpld3.fig_to_html(fig_515, d3_url=D3_URL, mpld3_url=MPLD3_URL)
fig_515_contrast = Figure(figsize=(10, 5), tight_layout={"w_pad":1, "pad": 1})
ax515_contrast = fig_515_contrast.add_subplot(1,2,1)
ax515_cnr = fig_515_contrast.add_subplot(1,2,2)
cnrs_names = []
contrasts_515 = []
cnrs515 = []
for key, value in mycbct.ctp515.rois.items():
cnrs_names.append(key)
contrasts_515.append(value.contrast_constant)
cnrs515.append(round(value.cnr_constant, 2))
sizes_515 = np.array(cnrs_names, dtype=int)
ax515_contrast.plot(sizes_515, contrasts_515, marker='o', color="blue",
markersize=8, markerfacecolor="None", linestyle="-")
ax515_cnr.plot(sizes_515, cnrs515, marker='o', color="blue",
markersize=8, markerfacecolor="None", linestyle="-")
if use_reference and ref_exists:
contrasts_515_ref = []
cnrs515_ref = []
cnrs_names_ref = []
for key, value in mycbct_ref.ctp515.rois.items():
cnrs_names_ref.append(key)
contrasts_515_ref.append(value.contrast_constant)
cnrs515_ref.append(round(value.cnr_constant, 2))
sizes_515_ref = np.array(cnrs_names_ref, dtype=int)
ax515_contrast.plot(sizes_515_ref, contrasts_515_ref, marker='o', color="blue",
markersize=8, markerfacecolor="None", linestyle="--")
ax515_cnr.plot(sizes_515_ref, cnrs515_ref, marker='o', color="blue",
markersize=8, markerfacecolor="None", linestyle="--")
ctp515_visible_ref = mycbct_ref.ctp515.rois_visible
else:
ctp515_visible_ref = np.nan
cnrs515_ref = [np.nan]*len(mycbct.ctp515.rois.values())
ax515_contrast.margins(0.05)
ax515_contrast.grid(True)
ax515_contrast.set_xlabel('ROI size (mm)')
ax515_contrast.set_ylabel("Contrast * Diameter")
ax515_cnr.margins(0.05)
ax515_cnr.grid(True)
ax515_cnr.set_xlabel('ROI size (mm)')
ax515_cnr.set_ylabel("CNR * Diameter")
script_515_contrast = mpld3.fig_to_html(fig_515_contrast, d3_url=D3_URL, mpld3_url=MPLD3_URL)
ctp515_passed = mycbct.ctp515.overall_passed
#ctp515_passed = None
ctp515_visible = mycbct.ctp515.rois_visible
else:
show_ctp515 = False
script_515_contrast = None
script_515 = None
ctp515_visible_ref = np.nan
ctp515_passed = None
ctp515_visible = np.nan
cnrs515_ref = None
cnrs515 = None
cnrs_names = None
general_functions.delete_files_in_subfolders([folder_path]) # Delete temporary images
variables = {
"script_ctp528": script_ctp528,
"script_ctp528mtf": script_ctp528mtf,
"mtf30_ref": round(mtf30_ref, 2),
"mtf30": round(mtf30, 2),
"mtf50_ref": round(mtf50_ref, 2),
"mtf50": round(mtf50, 2),
"mtf80_ref": round(mtf80_ref, 2),
"mtf80": round(mtf80, 2),
"mtf_passing": mtf_passing,
"script_404": script_404,
"script_404_HU": script_404_HU,
"HU_values_ref": HU_values_ref,
"HU_std_ref": HU_std_ref,
"HU_values": HU_values,
"HU_std": HU_std,
"HU_nominal": HU_nominal,
"HU_names": HU_names,
"HU_diff_ref": HU_diff_ref,
"HU_diff": HU_diff,
"passed_HU": passed_HU,
"passed_thickness": passed_thickness,
"passed_geometry": passed_geometry,
"passed_lcv": passed_lcv,
"passed_404": passed_404,
"lcv_ref": lcv_ref,
"lcv": round(lcv, 2),
"slice_thickness": slice_thickness,
"slice_thickness_ref": slice_thickness_ref,
"dicom_slice_thickness": dicom_slice_thickness,
"dicom_slice_thickness_ref": dicom_slice_thickness_ref,
"lines_ref": lines_ref,
"lines": lines,
"lines_avg": lines_avg,
"lines_avg_ref": lines_avg_ref,
"phantom_roll": phantom_roll,
"phantom_roll_ref": phantom_roll_ref,
"origin_slice_ref": origin_slice_ref,
"origin_slice": origin_slice,
"ctp528_slice": ctp528_slice,
"ctp486_slice": ctp486_slice,
"ctp515_slice": ctp515_slice,
"ctp528_slice_ref": ctp528_slice_ref,
"ctp486_slice_ref": ctp486_slice_ref,
"ctp515_slice_ref": ctp515_slice_ref,
"phantom_center_ref": phantom_center_ref,
"phantom_center": phantom_center,
"mm_per_pixel": mm_per_pixel,
"mm_per_pixel_ref": mm_per_pixel_ref,
"cnrs404_ref" : cnrs404_ref,
"cnrs404": cnrs404,
"script_486": script_486,
"script_486_profile": script_486_profile,
"hvalues_ref": hvalues_ref,
"hvalues": hvalues,
"passed_uniformity": passed_uniformity,
"passed_uniformity_index": passed_uniformity_index,
"uidx": uidx,
"uidx_ref": uidx_ref,
"script_515": script_515,
"script_515_contrast": script_515_contrast,
"show_ctp515": show_ctp515,
"ctp515_passed": ctp515_passed,
"ctp515_visible": ctp515_visible,
"ctp515_visible_ref": ctp515_visible_ref,
"cnrs515_ref": cnrs515_ref,
"cnrs515": cnrs515,
"cnrs_names": cnrs_names,
"save_results": save_results,
"acquisition_datetime": acquisition_datetime,
"pdf_report_enable": pdf_report_enable
}
# Generate pylinac report:
if pdf_report_enable == "True":
pdf_file = tempfile.NamedTemporaryFile(delete=False, prefix="Catphan", suffix=".pdf", dir=config.PDF_REPORT_FOLDER)
mycbct.publish_pdf(pdf_file)
variables["pdf_report_filename"] = os.path.basename(pdf_file.name)
except Exception as e:
general_functions.delete_files_in_subfolders([folder_path]) # Delete temporary images
return template("error_template", {"error_message": "Cannot analyze image. "+str(e)
})
else:
return template("catphan_results", variables)
def planar_imaging_helperf(args):
# This function is used in order to prevent memory problems
clip_box = args["clip_box"]
phantom = args["phantom"]
machine = args["machine"]
beam = args["beam"]
leedsrot1 = args["leedsrot1"]
leedsrot2 = args["leedsrot2"]
inv = args["inv"]
bbox = args["bbox"]
w1 = args["w1"]
use_reference = args["use_reference"]
colormap = args["colormap"]
displayname = args["displayname"]
acquisition_datetime = args["acquisition_datetime"]
general_functions.set_configuration(
args["config"]) # Transfer to this process
# Collect data for "save results"
tolerances = general_functions.get_tolerance_user_machine_planarimaging(
machine, beam, phantom) # If user_machne has specific tolerance
if not tolerances:
lowtresh, hightresh, generate_pdf = 0.05, 0.1, "True"
else:
lowtresh, hightresh, generate_pdf = tolerances
lowtresh = float(lowtresh)
hightresh = float(hightresh)
# Set colormap
cmap = matplotlib.cm.get_cmap(colormap)
try:
temp_folder1, file_path1 = RestToolbox.GetSingleDcm(
config.ORTHANC_URL, w1)
except:
return template("error_template",
{"error_message": "Cannot read image."})
ref_path1 = general_functions.get_referenceimagepath_planarimaging(
machine, beam, phantom)
if ref_path1 is not None:
ref_path1 = os.path.join(config.REFERENCE_IMAGES_FOLDER, ref_path1[0])
if os.path.exists(ref_path1):
ref1_exists = True
else:
ref1_exists = False
else:
ref1_exists = False
if not use_reference:
ref1_exists = False
# First analyze first image
try:
if phantom == "QC3":
pi1 = StandardImagingQC3(file_path1)
elif phantom == "LeedsTOR":
pi1 = LeedsTOR(file_path1)
elif phantom == "Las Vegas":
pi1 = LasVegas(file_path1)
elif phantom == "DoselabMC2MV":
pi1 = DoselabMC2MV(file_path1)
else:
pi1 = DoselabMC2kV(file_path1)
if clip_box != 0:
try:
#pi1.image.check_inversion_by_histogram()
general_functions.clip_around_image(pi1.image, clip_box)
except Exception as e:
return template(
"error_template",
{"error_message": "Unable to apply clipbox. " + str(e)})
pi1.analyze(low_contrast_threshold=lowtresh,
high_contrast_threshold=hightresh,
invert=inv,
angle_override=None if leedsrot2 == 0 else leedsrot2)
except Exception as e:
return template("error_template",
{"error_message": "Cannot analyze image 1. " + str(e)})
# Analyze reference images if they exists
if ref1_exists:
try:
if phantom == "QC3":
ref1 = StandardImagingQC3(ref_path1)
elif phantom == "LeedsTOR":
ref1 = LeedsTOR(ref_path1)
elif phantom == "Las Vegas":
ref1 = LasVegas(ref_path1)
elif phantom == "DoselabMC2MV":
ref1 = DoselabMC2MV(ref_path1)
else:
ref1 = DoselabMC2kV(ref_path1)
if clip_box != 0:
try:
#ref1.image.check_inversion_by_histogram()
general_functions.clip_around_image(ref1.image, clip_box)
except Exception as e:
return template("error_template", {
"error_message":
"Unable to apply clipbox. " + str(e)
})
ref1.analyze(low_contrast_threshold=lowtresh,
high_contrast_threshold=hightresh,
invert=inv,
angle_override=None if leedsrot1 == 0 else leedsrot1)
except:
return template("error_template", {"error_message": "Cannot analyze reference image."\
" Check that the image in the database is valid."})
save_results = {
"machine": machine,
"beam": beam,
"phantom": phantom,
"displayname": displayname
}
fig = Figure(figsize=(10.5, 5), tight_layout={"w_pad": 0, "pad": 1.5})
ax_ref = fig.add_subplot(1, 2, 1)
ax_pi = fig.add_subplot(1, 2, 2)
# Plot reference image and regions
if phantom == "QC3":
low_contrast_rois_pi1 = pi1.low_contrast_rois[
1:] # Exclude first point which is background
else:
low_contrast_rois_pi1 = pi1.low_contrast_rois
if ref1_exists:
if phantom == "QC3":
low_contrast_rois_ref1 = ref1.low_contrast_rois[
1:] # Exclude first point which is background
else:
low_contrast_rois_ref1 = ref1.low_contrast_rois
if ref1_exists:
ax_ref.imshow(ref1.image.array,
cmap=cmap,
interpolation="none",
aspect="equal",
origin='upper')
ax_ref.set_title(phantom + ' Reference Image')
ax_ref.axis('off')
# plot the background ROIS
for roi in ref1.low_contrast_background_rois:
roi.plot2axes(ax_ref, edgecolor='yellow')
ax_ref.text(roi.center.x,
roi.center.y,
"B",
horizontalalignment='center',
verticalalignment='center')
# low contrast ROIs
for ind, roi in enumerate(low_contrast_rois_ref1):
roi.plot2axes(ax_ref, edgecolor=roi.plot_color)
ax_ref.text(roi.center.x,
roi.center.y,
str(ind),
horizontalalignment='center',
verticalalignment='center')
# plot the high-contrast ROIs
if phantom != "Las Vegas":
mtf_temp_ref = list(ref1.mtf.norm_mtfs.values())
for ind, roi in enumerate(ref1.high_contrast_rois):
color = 'b' if mtf_temp_ref[
ind] > ref1._high_contrast_threshold else 'r'
roi.plot2axes(ax_ref, edgecolor=color)
ax_ref.text(roi.center.x,
roi.center.y,
str(ind),
horizontalalignment='center',
verticalalignment='center')
else:
ax_ref.text(0.5,
0.5,
"Reference image not available",
horizontalalignment='center',
verticalalignment='center')
# Plot current image and regions
ax_pi.imshow(pi1.image.array,
cmap=cmap,
interpolation="none",
aspect="equal",
origin='upper')
ax_pi.axis('off')
ax_pi.set_title(phantom + ' Current Image')
# plot the background ROIS
for roi in pi1.low_contrast_background_rois:
roi.plot2axes(ax_pi, edgecolor='yellow')
ax_pi.text(roi.center.x,
roi.center.y,
"B",
horizontalalignment='center',
verticalalignment='center')
# low contrast ROIs
for ind, roi in enumerate(low_contrast_rois_pi1):
roi.plot2axes(ax_pi, edgecolor=roi.plot_color)
ax_pi.text(roi.center.x,
roi.center.y,
str(ind),
horizontalalignment='center',
verticalalignment='center')
# plot the high-contrast ROIs
if phantom != "Las Vegas":
mtf_temp_pi = list(pi1.mtf.norm_mtfs.values())
for ind, roi in enumerate(pi1.high_contrast_rois):
color = 'b' if mtf_temp_pi[
ind] > pi1._high_contrast_threshold else 'r'
roi.plot2axes(ax_pi, edgecolor=color)
ax_pi.text(roi.center.x,
roi.center.y,
str(ind),
horizontalalignment='center',
verticalalignment='center')
# Zoom on phantom if requested:
if bbox:
if phantom == "QC3" or phantom == "DoselabMC2MV" or phantom == "DoselabMC2kV":
pad = 15 # Additional space between cyan bbox and plot
if ref1_exists:
bounding_box_ref = ref1.phantom_ski_region.bbox
bbox_center_ref = ref1.phantom_center
if abs(bounding_box_ref[1] -
bounding_box_ref[3]) >= abs(bounding_box_ref[2] -
bounding_box_ref[0]):
dist = abs(bounding_box_ref[1] - bounding_box_ref[3]) / 2
ax_ref.set_ylim(bbox_center_ref.y + dist + pad,
bbox_center_ref.y - dist - pad)
ax_ref.set_xlim(bbox_center_ref.x - dist - pad,
bbox_center_ref.x + dist + pad)
else:
dist = abs(bounding_box_ref[2] - bounding_box_ref[0]) / 2
ax_ref.set_ylim(bbox_center_ref.y + dist + pad,
bbox_center_ref.y - dist - pad)
ax_ref.set_xlim(bbox_center_ref.x - dist - pad,
bbox_center_ref.x + dist + pad)
ax_ref.plot([
bounding_box_ref[1], bounding_box_ref[1],
bounding_box_ref[3], bounding_box_ref[3],
bounding_box_ref[1]
], [
bounding_box_ref[2], bounding_box_ref[0],
bounding_box_ref[0], bounding_box_ref[2],
bounding_box_ref[2]
],
c="cyan")
ax_ref.autoscale(False)
bounding_box_pi = pi1.phantom_ski_region.bbox
bbox_center_pi = pi1.phantom_center
if abs(bounding_box_pi[1] -
bounding_box_pi[3]) >= abs(bounding_box_pi[2] -
bounding_box_pi[0]):
dist = abs(bounding_box_pi[1] - bounding_box_pi[3]) / 2
ax_pi.set_ylim(bbox_center_pi.y + dist + pad,
bbox_center_pi.y - dist - pad)
ax_pi.set_xlim(bbox_center_pi.x - dist - pad,
bbox_center_pi.x + dist + pad)
else:
dist = abs(bounding_box_pi[2] - bounding_box_pi[0]) / 2
ax_pi.set_ylim(bbox_center_pi.y + dist + pad,
bbox_center_pi.y - dist - pad)
ax_pi.set_xlim(bbox_center_pi.x - dist - pad,
bbox_center_pi.x + dist + pad)
ax_pi.plot([
bounding_box_pi[1], bounding_box_pi[1], bounding_box_pi[3],
bounding_box_pi[3], bounding_box_pi[1]
], [
bounding_box_pi[2], bounding_box_pi[0], bounding_box_pi[0],
bounding_box_pi[2], bounding_box_pi[2]
],
c="cyan")
ax_pi.autoscale(False)
elif phantom == "Las Vegas": # For some reason phantom_ski_regio has an underscore
pad = 15 # Additional space between cyan bbox and plot
if ref1_exists:
bounding_box_ref = ref1._phantom_ski_region.bbox
bbox_center_ref = ref1.phantom_center
if abs(bounding_box_ref[1] -
bounding_box_ref[3]) >= abs(bounding_box_ref[2] -
bounding_box_ref[0]):
dist = abs(bounding_box_ref[1] - bounding_box_ref[3]) / 2
ax_ref.set_ylim(bbox_center_ref.y + dist + pad,
bbox_center_ref.y - dist - pad)
ax_ref.set_xlim(bbox_center_ref.x - dist - pad,
bbox_center_ref.x + dist + pad)
else:
dist = abs(bounding_box_ref[2] - bounding_box_ref[0]) / 2
ax_ref.set_ylim(bbox_center_ref.y + dist + pad,
bbox_center_ref.y - dist - pad)
ax_ref.set_xlim(bbox_center_ref.x - dist - pad,
bbox_center_ref.x + dist + pad)
ax_ref.plot([
bounding_box_ref[1], bounding_box_ref[1],
bounding_box_ref[3], bounding_box_ref[3],
bounding_box_ref[1]
], [
bounding_box_ref[2], bounding_box_ref[0],
bounding_box_ref[0], bounding_box_ref[2],
bounding_box_ref[2]
],
c="cyan")
ax_ref.autoscale(False)
bounding_box_pi = pi1._phantom_ski_region.bbox
bbox_center_pi = pi1.phantom_center
if abs(bounding_box_pi[1] -
bounding_box_pi[3]) >= abs(bounding_box_pi[2] -
bounding_box_pi[0]):
dist = abs(bounding_box_pi[1] - bounding_box_pi[3]) / 2
ax_pi.set_ylim(bbox_center_pi.y + dist + pad,
bbox_center_pi.y - dist - pad)
ax_pi.set_xlim(bbox_center_pi.x - dist - pad,
bbox_center_pi.x + dist + pad)
else:
dist = abs(bounding_box_pi[2] - bounding_box_pi[0]) / 2
ax_pi.set_ylim(bbox_center_pi.y + dist + pad,
bbox_center_pi.y - dist - pad)
ax_pi.set_xlim(bbox_center_pi.x - dist - pad,
bbox_center_pi.x + dist + pad)
ax_pi.plot([
bounding_box_pi[1], bounding_box_pi[1], bounding_box_pi[3],
bounding_box_pi[3], bounding_box_pi[1]
], [
bounding_box_pi[2], bounding_box_pi[0], bounding_box_pi[0],
bounding_box_pi[2], bounding_box_pi[2]
],
c="cyan")
ax_pi.autoscale(False)
elif phantom == "LeedsTOR":
pad = 15 # Additional space between cyan bbox and plot
if ref1_exists:
big_circle_idx = np.argsort([
ref1._regions[roi].major_axis_length for roi in ref1._blobs
])[-1]
circle_roi = ref1._regions[ref1._blobs[big_circle_idx]]
bounding_box_ref = circle_roi.bbox
bbox_center_ref = bbox_center(circle_roi)
max_xy = max([
abs(bounding_box_ref[1] - bounding_box_ref[3]) / 2,
abs(bounding_box_ref[0] - bounding_box_ref[2]) / 2
])
ax_ref.set_ylim(bbox_center_ref.y + max_xy + pad,
bbox_center_ref.y - max_xy - pad)
ax_ref.set_xlim(bbox_center_ref.x - max_xy - pad,
bbox_center_ref.x + max_xy + pad)
ax_ref.plot([
bounding_box_ref[1], bounding_box_ref[1],
bounding_box_ref[3], bounding_box_ref[3],
bounding_box_ref[1]
], [
bounding_box_ref[2], bounding_box_ref[0],
bounding_box_ref[0], bounding_box_ref[2],
bounding_box_ref[2]
],
c="cyan")
ax_ref.autoscale(False)
big_circle_idx = np.argsort([
pi1._regions[roi].major_axis_length for roi in pi1._blobs
])[-1]
circle_roi = pi1._regions[pi1._blobs[big_circle_idx]]
bounding_box_pi = circle_roi.bbox
bbox_center_pi = bbox_center(circle_roi)
max_xy = max([
abs(bounding_box_pi[1] - bounding_box_pi[3]) / 2,
abs(bounding_box_pi[0] - bounding_box_pi[2]) / 2
])
ax_pi.set_ylim(bbox_center_pi.y + max_xy + pad,
bbox_center_pi.y - max_xy - pad)
ax_pi.set_xlim(bbox_center_pi.x - max_xy - pad,
bbox_center_pi.x + max_xy + pad)
ax_pi.plot([
bounding_box_pi[1], bounding_box_pi[1], bounding_box_pi[3],
bounding_box_pi[3], bounding_box_pi[1]
], [
bounding_box_pi[2], bounding_box_pi[0], bounding_box_pi[0],
bounding_box_pi[2], bounding_box_pi[2]
],
c="cyan")
ax_pi.autoscale(False)
# Add phantom outline:
outline_obj_pi1, settings_pi1 = pi1._create_phantom_outline_object()
outline_obj_pi1.plot2axes(ax_pi, edgecolor='g', **settings_pi1)
if ref1_exists:
outline_obj_ref1, settings_ref1 = ref1._create_phantom_outline_object()
outline_obj_ref1.plot2axes(ax_ref, edgecolor='g', **settings_ref1)
# Plot low frequency contrast, CNR and rMTF
fig2 = Figure(figsize=(10.5, 10), tight_layout={"w_pad": 1})
ax_lfc = fig2.add_subplot(2, 2, 1)
ax_lfcnr = fig2.add_subplot(2, 2, 2)
ax_rmtf = fig2.add_subplot(2, 2, 3)
# lfc
ax_lfc.plot([abs(roi.contrast) for roi in low_contrast_rois_pi1],
marker='o',
markersize=8,
color='r')
if ref1_exists:
ax_lfc.plot([abs(roi.contrast) for roi in low_contrast_rois_ref1],
marker='o',
color='r',
markersize=8,
markerfacecolor="None",
linestyle="--")
ax_lfc.plot([], [], color='r', linestyle="--", label='Reference')
ax_lfc.plot([], [], color='r', label='Current')
ax_lfc.plot([0, len(low_contrast_rois_pi1) - 1], [lowtresh, lowtresh],
"-g")
ax_lfc.grid(True)
ax_lfc.set_title('Low-frequency Contrast')
ax_lfc.set_xlabel('ROI #')
ax_lfc.set_ylabel('Contrast')
ax_lfc.set_xticks(np.arange(0, len(low_contrast_rois_pi1), 1))
ax_lfc.legend(loc='upper right',
ncol=2,
columnspacing=0,
fontsize=12,
handletextpad=0)
ax_lfc.margins(0.05)
# CNR
ax_lfcnr.plot(
[abs(roi.contrast_to_noise) for roi in low_contrast_rois_pi1],
marker='^',
markersize=8,
color='r')
if ref1_exists:
ax_lfcnr.plot(
[abs(roi.contrast_to_noise) for roi in low_contrast_rois_ref1],
marker='^',
color='r',
markersize=8,
markerfacecolor="None",
linestyle="--")
ax_lfcnr.plot([], [], color='r', linestyle="--", label='Reference')
ax_lfcnr.plot([], [], color='r', label='Current')
ax_lfcnr.grid(True)
ax_lfcnr.set_title('Contrast-Noise Ratio')
ax_lfcnr.set_xlabel('ROI #')
ax_lfcnr.set_ylabel('CNR')
ax_lfcnr.set_xticks(np.arange(0, len(low_contrast_rois_pi1), 1))
ax_lfcnr.legend(loc='upper right',
ncol=2,
columnspacing=0,
fontsize=12,
handletextpad=0)
ax_lfcnr.margins(0.05)
# rMTF
if phantom != "Las Vegas":
mtfs_pi1 = list(pi1.mtf.norm_mtfs.values())
if ref1_exists:
mtfs_ref1 = list(ref1.mtf.norm_mtfs.values())
else:
mtfs_ref1 = [np.nan] * len(mtfs_pi1)
lppmm = pi1.mtf.spacings
ax_rmtf.plot(lppmm, mtfs_pi1, marker='D', markersize=8, color='b')
ax_rmtf.plot([min(lppmm), max(lppmm)], [hightresh, hightresh], "-g")
if ref1_exists:
ax_rmtf.plot(lppmm,
mtfs_ref1,
marker='D',
color='b',
markersize=8,
markerfacecolor="None",
linestyle="--")
ax_rmtf.plot([], [], color='b', linestyle="--", label='Reference')
ax_rmtf.plot([], [], color='b', label='Current')
ax_rmtf.grid(True)
ax_rmtf.set_title('High-frequency rMTF')
ax_rmtf.set_xlabel('Line pairs / mm')
ax_rmtf.set_ylabel('relative MTF')
ax_rmtf.legend(loc='upper right',
ncol=2,
columnspacing=0,
fontsize=12,
handletextpad=0)
ax_rmtf.margins(0.05)
f30 = [
ref1.mtf.relative_resolution(30) if ref1_exists else np.nan,
pi1.mtf.relative_resolution(30)
]
f40 = [
ref1.mtf.relative_resolution(40) if ref1_exists else np.nan,
pi1.mtf.relative_resolution(40)
]
f50 = [
ref1.mtf.relative_resolution(50) if ref1_exists else np.nan,
pi1.mtf.relative_resolution(50)
]
f80 = [
ref1.mtf.relative_resolution(80) if ref1_exists else np.nan,
pi1.mtf.relative_resolution(80)
]
else:
ax_rmtf.text(0.5,
0.5,
"MTF not available",
horizontalalignment='center',
verticalalignment='center')
f30 = [np.nan, np.nan]
f40 = [np.nan, np.nan]
f50 = [np.nan, np.nan]
f80 = [np.nan, np.nan]
if ref1_exists:
median_contrast = [
np.median([roi.contrast for roi in low_contrast_rois_ref1]),
np.median([roi.contrast for roi in low_contrast_rois_pi1])
]
median_CNR = [
np.median(
[roi.contrast_to_noise for roi in low_contrast_rois_ref1]),
np.median([roi.contrast_to_noise for roi in low_contrast_rois_pi1])
]
phantom_angle = [ref1.phantom_angle, pi1.phantom_angle]
else:
median_contrast = [
np.nan,
np.median([roi.contrast for roi in low_contrast_rois_pi1])
]
median_CNR = [
np.nan,
np.median([roi.contrast_to_noise for roi in low_contrast_rois_pi1])
]
phantom_angle = [np.nan, pi1.phantom_angle]
script = mpld3.fig_to_html(fig, d3_url=D3_URL, mpld3_url=MPLD3_URL)
script2 = mpld3.fig_to_html(fig2, d3_url=D3_URL, mpld3_url=MPLD3_URL)
variables = {
"script": script,
"script2": script2,
"f30": f30,
"f40": f40,
"f50": f50,
"f80": f80,
"median_contrast": median_contrast,
"median_CNR": median_CNR,
"pdf_report_enable": generate_pdf,
"save_results": save_results,
"acquisition_datetime": acquisition_datetime,
"phantom_angle": phantom_angle
}
if generate_pdf == "True":
pdf_file = tempfile.NamedTemporaryFile(delete=False,
prefix="PlanarImaging_",
suffix=".pdf",
dir=config.PDF_REPORT_FOLDER)
metadata = RestToolbox.GetInstances(config.ORTHANC_URL, [w1])
try:
patient = metadata[0]["PatientName"]
except:
patient = ""
try:
stationname = metadata[0]["StationName"]
except:
stationname = ""
try:
date_time = RestToolbox.get_datetime(metadata[0])
date_var = datetime.datetime.strptime(
date_time[0], "%Y%m%d").strftime("%d/%m/%Y")
except:
date_var = ""
pi1.publish_pdf(pdf_file,
notes=[
"Date = " + date_var, "Patient = " + patient,
"Station = " + stationname
])
variables["pdf_report_filename"] = os.path.basename(pdf_file.name)
general_functions.delete_figure([fig, fig2])
general_functions.delete_files_in_subfolders([temp_folder1
]) # Delete image
#gc.collect()
return template("planar_imaging_results", variables)
def starshot_helperf(args):
imgtype = args["imgtype"]
w = args["w"]
clip_box = args["clip_box"]
radius = args["radius"]
min_peak_height = args["min_peak_height"]
start_x = args["start_x"]
start_y = args["start_y"]
dpi = args["dpi"]
sid = args["sid"]
fwhm = args["fwhm"]
recursive = args["recursive"]
invert = args["invert"]
temp_folder = args["temp_folder"]
file_path = args["file_path"]
imgdescription = args["imgdescription"]
station = args["station"]
displayname = args["displayname"]
acquisition_datetime = args["acquisition_datetime"]
general_functions.set_configuration(args["config"])
# Collect data for "save results"
dicomenergy = general_functions.get_energy_from_imgdescription(imgdescription)
user_machine, user_energy = general_functions.get_user_machine_and_energy(station, dicomenergy)
machines_and_energies = general_functions.get_machines_and_energies(general_functions.get_treatmentunits_starshot())
tolerances = general_functions.get_tolerance_user_machine_starshot(user_machine) # If user_machne has specific tolerance
if not tolerances:
tolerance, pdf_report_enable = general_functions.get_settings_starshot()
else:
tolerance, pdf_report_enable = tolerances[0]
tolerance = float(tolerance) # If more than this, the test is "borderline", but not "failed"
save_results = {
"user_machine": user_machine,
"user_energy": user_energy,
"machines_and_energies": machines_and_energies,
"testtype": ["Collimator", "Couch", "Gantry"],
"displayname": displayname
}
if start_x==0 or start_y==0:
start_point=None
else:
start_point=(start_x, start_y)
if sid==0.0 and dpi==0:
try:
star = Starshot(file_path)
except Exception as e:
return template("error_template", {"error_message": "The Starshot module cannot calculate. "+str(e)})
elif sid==0.0 and dpi!=0:
try:
star = Starshot(file_path, dpi=dpi)
except Exception as e:
return template("error_template", {"error_message": "The Starshot module cannot calculate. "+str(e)})
elif sid!=0.0 and dpi==0:
try:
star = Starshot(file_path, sid=sid)
except Exception as e:
return template("error_template", {"error_message": "The Starshot module cannot calculate. "+str(e)})
else:
try:
star = Starshot(file_path, dpi=dpi, sid=sid)
except Exception as e:
return template("error_template", {"error_message": "The Starshot module cannot calculate. "+str(e)})
# Here we force pixels to background outside of box:
if clip_box != 0:
try:
star.image.check_inversion_by_histogram(percentiles=[4, 50, 96]) # Check inversion otherwise this might not work
general_functions.clip_around_image(star.image, clip_box)
except Exception as e:
return template("error_template", {"error_message": "Unable to apply clipbox. "+str(e)})
# If inversion is selected:
if invert:
star.image.invert()
# Now we try to analyse
try:
star.analyze(radius=radius, min_peak_height=min_peak_height, tolerance=tolerance,
start_point=start_point, fwhm=fwhm, recursive=recursive)
except Exception as e:
return template("error_template", {"error_message": "Module Starshot cannot calculate. "+str(e)})
fig_ss = Figure(figsize=(10, 6), tight_layout={"w_pad":4})
img_ax = fig_ss.add_subplot(1,2,1)
wobble_ax = fig_ss.add_subplot(1,2,2)
img_ax.imshow(star.image.array, cmap=matplotlib.cm.gray, interpolation="none", aspect="equal", origin='upper')
star.lines.plot(img_ax)
star.wobble.plot2axes(img_ax, edgecolor='green')
star.circle_profile.plot2axes(img_ax, edgecolor='green')
img_ax.axis('off')
img_ax.autoscale(tight=True)
img_ax.set_aspect(1)
img_ax.set_xticks([])
img_ax.set_yticks([])
star.lines.plot(wobble_ax)
star.wobble.plot2axes(wobble_ax, edgecolor='green')
star.circle_profile.plot2axes(wobble_ax, edgecolor='green')
wobble_ax.axis('off')
xlims = [star.wobble.center.x + star.wobble.diameter, star.wobble.center.x - star.wobble.diameter]
ylims = [star.wobble.center.y + star.wobble.diameter, star.wobble.center.y - star.wobble.diameter]
wobble_ax.set_xlim(xlims)
wobble_ax.set_ylim(ylims)
wobble_ax.axis('on')
wobble_ax.set_aspect(1)
script = mpld3.fig_to_html(fig_ss, d3_url=D3_URL, mpld3_url=MPLD3_URL)
variables = {
"script": script,
"passed": star.passed,
"radius": star.wobble.radius_mm,
"tolerance": star.tolerance,
"circle_center": star.wobble.center,
"pdf_report_enable": pdf_report_enable,
"save_results": save_results,
"acquisition_datetime": acquisition_datetime
}
# Generate pylinac report:
if pdf_report_enable == "True":
pdf_file = tempfile.NamedTemporaryFile(delete=False, prefix="Starshot_", suffix=".pdf", dir=config.PDF_REPORT_FOLDER)
if imgtype == "dicom":
metadata = RestToolbox.GetInstances(config.ORTHANC_URL, [w])
try:
patient = metadata[0]["PatientName"]
except:
patient = ""
try:
stationname = metadata[0]["StationName"]
except:
stationname = ""
try:
date_time = RestToolbox.get_datetime(metadata[0])
date_var = datetime.datetime.strptime(date_time[0], "%Y%m%d").strftime("%d/%m/%Y")
except:
date_var = ""
star.publish_pdf(pdf_file, notes=["Date = "+date_var, "Patient = "+patient, "Station = "+stationname])
else:
star.publish_pdf(pdf_file)
variables["pdf_report_filename"] = os.path.basename(pdf_file.name)
general_functions.delete_figure([fig_ss])
general_functions.delete_files_in_subfolders([temp_folder]) # Delete image
return template("starshot_results", variables)
def picket_fence_helperf(args):
'''This function is used in order to prevent memory problems'''
temp_folder = args["temp_folder"]
file_path = args["file_path"]
clip_box = args["clip_box"]
py_filter = args["py_filter"]
num_pickets = args["num_pickets"]
sag = args["sag"]
mlc = args["mlc"]
invert = args["invert"]
orientation = args["orientation"]
w = args["w"]
imgdescription = args["imgdescription"]
station = args["station"]
displayname = args["displayname"]
acquisition_datetime = args["acquisition_datetime"]
general_functions.set_configuration(
args["config"]) # Transfer to this process
# Chose module:
if mlc in ["Varian_80", "Elekta_80", "Elekta_160"]:
use_original_pylinac = "False"
else:
use_original_pylinac = "True"
# Collect data for "save results"
dicomenergy = general_functions.get_energy_from_imgdescription(
imgdescription)
user_machine, user_energy = general_functions.get_user_machine_and_energy(
station, dicomenergy)
machines_and_energies = general_functions.get_machines_and_energies(
general_functions.get_treatmentunits_picketfence())
tolerances = general_functions.get_tolerance_user_machine_picketfence(
user_machine) # If user_machne has specific tolerance
if not tolerances:
action_tolerance, tolerance, generate_pdf_report = general_functions.get_settings_picketfence(
)
else:
action_tolerance, tolerance, generate_pdf_report = tolerances[0]
tolerance = float(tolerance)
action_tol = float(action_tolerance)
save_results = {
"user_machine": user_machine,
"user_energy": user_energy,
"machines_and_energies": machines_and_energies,
"displayname": displayname
}
# Import either original pylinac module or the modified module
if use_original_pylinac == "True":
from pylinac import PicketFence as PicketFence # Original pylinac analysis
else:
if __name__ == '__main__' or parent_module.__name__ == '__main__':
from python_packages.pylinac.picketfence_modified import PicketFence as PicketFence
else:
from .python_packages.pylinac.picketfence_modified import PicketFence as PicketFence
try:
pf = PicketFence(file_path, filter=py_filter)
except Exception as e:
return template("error_template", {
"error_message":
"Module PicketFence cannot calculate. " + str(e)
})
# Here we force pixels to background outside of box:
if clip_box != 0:
try:
pf.image.check_inversion_by_histogram(percentiles=[
4, 50, 96
]) # Check inversion otherwise this might not work
general_functions.clip_around_image(pf.image, clip_box)
except Exception as e:
return template(
"error_template",
{"error_message": "Unable to apply clipbox. " + str(e)})
# Now invert if needed
if invert:
try:
pf.image.invert()
except Exception as e:
return template(
"error_template",
{"error_message": "Unable to invert the image. " + str(e)})
# Now analyze
try:
if use_original_pylinac == "True":
hdmlc = True if mlc == "Varian_120HD" else False
pf.analyze(tolerance=tolerance,
action_tolerance=action_tol,
hdmlc=hdmlc,
sag_adjustment=float(sag),
num_pickets=num_pickets,
orientation=orientation)
else:
pf.analyze(tolerance=tolerance,
action_tolerance=action_tol,
mlc_type=mlc,
sag_adjustment=float(sag),
num_pickets=num_pickets,
orientation=orientation)
except Exception as e:
return template(
"error_template",
{"error_message": "Picket fence module cannot analyze. " + str(e)})
# Added an if clause to tell if num of mlc's are not the same on all pickets:
num_mlcs = len(pf.pickets[0].mlc_meas)
for p in pf.pickets:
if len(p.mlc_meas) != num_mlcs:
return template(
"error_template", {
"error_message":
"Not all pickets have the same number of leaves. " +
"Probably your image si too skewed. Rotate your collimator a bit "
+
"and try again. Use the jaws perpendicular to MLCs to set the right "
+ "collimator angle."
})
error_array = np.array([])
max_error = []
max_error_leaf = []
passed_tol = []
picket_offsets = []
picket_nr = pf.num_pickets
for k in pf.pickets.pickets:
error_array = np.concatenate((error_array, k.error_array))
max_error.append(k.max_error)
max_err_leaf_ind = np.argmax(k.error_array)
max_error_leaf.append(max_err_leaf_ind)
passed_tol.append("Passed" if k.passed else "Failed")
picket_offsets.append(k.dist2cax)
# Plot images
if pf.settings.orientation == "Left-Right":
fig_pf = Figure(figsize=(9, 10), tight_layout={"w_pad": 0})
else:
fig_pf = Figure(figsize=(9.5, 7), tight_layout={"w_pad": 0})
img_ax = fig_pf.add_subplot(1, 1, 1)
img_ax.imshow(pf.image.array,
cmap=matplotlib.cm.gray,
interpolation="none",
aspect="equal",
origin='upper')
# Taken from pylinac: leaf_error_subplot:
tol_line_height = [pf.settings.tolerance, pf.settings.tolerance]
tol_line_width = [0, max(pf.image.shape)]
# make the new axis
divider = make_axes_locatable(img_ax)
if pf.settings.orientation == 'Up-Down':
axtop = divider.append_axes('right', 1.75, pad=0.2, sharey=img_ax)
else:
axtop = divider.append_axes('bottom', 1.75, pad=0.5, sharex=img_ax)
# get leaf positions, errors, standard deviation, and leaf numbers
pos, vals, err, leaf_nums = pf.pickets.error_hist()
# Changed leaf_nums to sequential numbers:
leaf_nums = list(np.arange(0, len(leaf_nums), 1))
# plot the leaf errors as a bar plot
if pf.settings.orientation == 'Up-Down':
axtop.barh(pos,
vals,
xerr=err,
height=pf.pickets[0].sample_width * 2,
alpha=0.4,
align='center')
# plot the tolerance line(s)
axtop.plot(tol_line_height, tol_line_width, 'r-', linewidth=3)
if pf.settings.action_tolerance is not None:
tol_line_height_action = [
pf.settings.action_tolerance, pf.settings.action_tolerance
]
tol_line_width_action = [0, max(pf.image.shape)]
axtop.plot(tol_line_height_action,
tol_line_width_action,
'y-',
linewidth=3)
# reset xlims to comfortably include the max error or tolerance value
axtop.set_xlim([0, max(max(vals), pf.settings.tolerance) + 0.1])
else:
axtop.bar(pos,
vals,
yerr=err,
width=pf.pickets[0].sample_width * 2,
alpha=0.4,
align='center')
axtop.plot(tol_line_width, tol_line_height, 'r-', linewidth=3)
if pf.settings.action_tolerance is not None:
tol_line_height_action = [
pf.settings.action_tolerance, pf.settings.action_tolerance
]
tol_line_width_action = [0, max(pf.image.shape)]
axtop.plot(tol_line_width_action,
tol_line_height_action,
'y-',
linewidth=3)
axtop.set_ylim([0, max(max(vals), pf.settings.tolerance) + 0.1])
# add formatting to axis
axtop.grid(True)
axtop.set_title("Average Error (mm)")
# add tooltips if interactive
# Copied this from previous version of pylinac
interactive = True
if interactive:
if pf.settings.orientation == 'Up-Down':
labels = [[
'Leaf pair: {0} <br> Avg Error: {1:3.3f} mm <br> Stdev: {2:3.3f} mm'
.format(leaf_num, err, std)
] for leaf_num, err, std in zip(leaf_nums, vals, err)]
voffset = 0
hoffset = 20
else:
labels = [[
'Leaf pair: {0}, Avg Error: {1:3.3f} mm, Stdev: {2:3.3f} mm'.
format(leaf_num, err, std)
] for leaf_num, err, std in zip(leaf_nums, vals, err)]
if pf.settings.orientation == 'Up-Down':
for num, patch in enumerate(axtop.axes.patches):
ttip = mpld3.plugins.PointHTMLTooltip(patch,
labels[num],
voffset=voffset,
hoffset=hoffset)
mpld3.plugins.connect(fig_pf, ttip)
mpld3.plugins.connect(fig_pf,
mpld3.plugins.MousePosition(fontsize=14))
else:
for num, patch in enumerate(axtop.axes.patches):
ttip = mpld3.plugins.PointLabelTooltip(patch,
labels[num],
location='top left')
mpld3.plugins.connect(fig_pf, ttip)
mpld3.plugins.connect(fig_pf,
mpld3.plugins.MousePosition(fontsize=14))
for p_num, picket in enumerate(pf.pickets):
picket.add_guards_to_axes(img_ax.axes)
for idx, mlc_meas in enumerate(picket.mlc_meas):
mlc_meas.plot2axes(img_ax.axes, width=1.5)
# plot CAX
img_ax.plot(pf.settings.image_center.x,
pf.settings.image_center.y,
'r+',
ms=12,
markeredgewidth=3)
# tighten up the plot view
img_ax.set_xlim([0, pf.image.shape[1]])
img_ax.set_ylim([pf.image.shape[0], 0])
img_ax.axis('off')
img_ax.set_xticks([])
img_ax.set_yticks([])
# Histogram of all errors and average profile plot
upper_bound = pf.settings.tolerance
upper_outliers = np.sum(error_array.flatten() >= upper_bound)
fig_pf2 = Figure(figsize=(10, 4), tight_layout={"w_pad": 2})
ax2 = fig_pf2.add_subplot(1, 2, 1)
ax3 = fig_pf2.add_subplot(1, 2, 2)
n, bins = np.histogram(error_array.flatten(),
density=False,
bins=10,
range=(0, upper_bound))
ax2.bar(bins[0:-1],
n,
width=np.diff(bins)[0],
facecolor='green',
alpha=0.75)
ax2.bar([upper_bound, upper_bound * 1.1],
upper_outliers,
width=0.1 * upper_bound,
facecolor='red',
alpha=0.75)
ax2.plot([pf.settings.action_tolerance, pf.settings.action_tolerance],
[0, max(n) / 2],
color="orange")
ax2.annotate("Action Tol.",
(pf.settings.action_tolerance, 1.05 * max(n) / 2),
color='black',
fontsize=6,
ha='center',
va='bottom')
ax2.plot([pf.settings.tolerance, pf.settings.tolerance], [0, max(n) / 2],
color="darkred")
ax2.annotate("Tol.", (pf.settings.tolerance, 1.05 * max(n) / 2),
color='black',
fontsize=6,
ha='center',
va='bottom')
# Plot mean inplane profile and calculate FWHM:
mlc_mean_profile = pf.pickets.image_mlc_inplane_mean_profile
ax3.plot(mlc_mean_profile.values, "b-")
picket_fwhm = []
fwhm_mean = 0
try:
peaks = mlc_mean_profile.find_peaks(max_number=picket_nr,
min_distance=0.02,
threshold=0.5)
peaks = np.sort(peaks)
ax3.plot(peaks, mlc_mean_profile[peaks], "ro")
separation = int(np.mean(np.diff(peaks)) / 3)
mmpd = 1 / pf.image.dpmm
# Get valleys
valleys = []
for p in np.arange(0, len(peaks) - 1, 1):
prof_partial = mlc_mean_profile[peaks[p]:peaks[p + 1]]
valleys.append(peaks[p] + np.argmin(prof_partial))
edge_points = [peaks[0] - separation
] + valleys + [peaks[-1] + separation]
ax3.plot(edge_points, mlc_mean_profile[edge_points], "yo")
for k in np.arange(0, len(edge_points) - 1, 1):
pr = PylinacSingleProfile(
mlc_mean_profile[edge_points[k]:edge_points[k + 1]])
left = pr[0]
right = pr[-1]
amplitude = mlc_mean_profile[peaks[k]]
if left < right:
x = 100 * ((amplitude - left) * 0.5 + left -
right) / (amplitude - right)
a = pr._penumbra_point(x=50, side="left", interpolate=True)
b = pr._penumbra_point(x=x, side="right", interpolate=True)
else:
x = 100 * ((amplitude - right) * 0.5 + right -
left) / (amplitude - left)
a = pr._penumbra_point(x=x, side="left", interpolate=True)
b = pr._penumbra_point(x=50, side="right", interpolate=True)
left_point = edge_points[k] + a
right_point = edge_points[k] + b
ax3.plot([left_point, right_point], [
np.interp(left_point,
np.arange(0, len(mlc_mean_profile.values), 1),
mlc_mean_profile.values),
np.interp(right_point,
np.arange(0, len(mlc_mean_profile.values), 1),
mlc_mean_profile.values)
],
"-k",
alpha=0.5)
picket_fwhm.append(np.abs(a - b) * mmpd)
fwhm_mean = np.mean(picket_fwhm)
except:
picket_fwhm = [np.nan] * picket_nr
fwhm_mean = np.nan
if len(picket_fwhm) != picket_nr:
fwhm_mean = np.mean(picket_fwhm)
picket_fwhm = [np.nan] * picket_nr
ax2.set_xlim([-0.025, pf.settings.tolerance * 1.15])
ax3.set_xlim([0, pf.image.shape[1]])
ax2.set_title("Leaf error")
ax3.set_title("MLC mean profile")
ax2.set_xlabel("Error [mm]")
ax2.set_ylabel("Counts")
ax3.set_xlabel("Pixel")
ax3.set_ylabel("Grey value")
passed = "Passed" if pf.passed else "Failed"
script = mpld3.fig_to_html(fig_pf, d3_url=D3_URL, mpld3_url=MPLD3_URL)
script2 = mpld3.fig_to_html(fig_pf2, d3_url=D3_URL, mpld3_url=MPLD3_URL)
variables = {
"script": script,
"script2": script2,
"passed": passed,
"max_error": max_error,
"max_error_leaf": max_error_leaf,
"passed_tol": passed_tol,
"picket_nr": picket_nr,
"tolerance": pf.settings.tolerance,
"perc_passing": pf.percent_passing,
"max_error_all": pf.max_error,
"max_error_picket_all": pf.max_error_picket,
"max_error_leaf_all": pf.max_error_leaf,
"median_error": pf.abs_median_error,
"spacing": pf.pickets.mean_spacing,
"picket_offsets": picket_offsets,
"fwhm_mean": fwhm_mean,
"picket_fwhm": picket_fwhm,
"pdf_report_enable": generate_pdf_report,
"save_results": save_results,
"acquisition_datetime": acquisition_datetime
}
# Generate pylinac report:
if generate_pdf_report == "True":
pdf_file = tempfile.NamedTemporaryFile(delete=False,
prefix="PicketFence_",
suffix=".pdf",
dir=config.PDF_REPORT_FOLDER)
metadata = RestToolbox.GetInstances(config.ORTHANC_URL, [w])
try:
patient = metadata[0]["PatientName"]
except:
patient = ""
try:
stationname = metadata[0]["StationName"]
except:
stationname = ""
try:
date_time = RestToolbox.get_datetime(metadata[0])
date_var = datetime.datetime.strptime(
date_time[0], "%Y%m%d").strftime("%d/%m/%Y")
except:
date_var = ""
pf.publish_pdf(pdf_file,
notes=[
"Date = " + date_var, "Patient = " + patient,
"Station = " + stationname
])
variables["pdf_report_filename"] = os.path.basename(pdf_file.name)
#gc.collect()
general_functions.delete_files_in_subfolders([temp_folder]) # Delete image
return template("picket_fence_results", variables)
def fieldsize_helperf(args):
mlc_type = args["mlc_type"]
iso_method = args["iso_method"]
mlc_direction = args["mlc_direction"]
mlc_points = args["mlc_points"]
jaw_points = args["jaw_points"]
mmpd = args["mmpd"]
cax_x = args["cax_x"]
cax_y = args["cax_y"]
clipbox = args["clipbox"]
invert = args["invert"]
imgdescription = args["imgdescription"]
station = args["station"]
displayname = args["displayname"]
acquisition_datetime = args["acquisition_datetime"]
w1 = args["w1"]
w2 = args["w2"]
general_functions.set_configuration(args["config"])
high_contrast = args["high_contrast"]
filter_size = args["filter_size"]
# Collect data for "save results"
dicomenergy = general_functions.get_energy_from_imgdescription(
imgdescription)
user_machine, user_energy = general_functions.get_user_machine_and_energy(
station, dicomenergy)
machines_and_energies = general_functions.get_machines_and_energies(
general_functions.get_treatmentunits_fieldsize())
tolerances = general_functions.get_tolerance_user_machine_fieldsize(
user_machine) # If user_machne has specific tolerance
if not tolerances:
tt = general_functions.get_settings_fieldsize()
else:
tt = tolerances[0]
(small_nominal, medium_nominal, large_nominal, small_exp_mlc,
medium_exp_mlc, large_exp_mlc, small_exp_jaw, medium_exp_jaw,
large_exp_jaw, tolerance_small_mlc, tolerance_medium_mlc,
tolerance_large_mlc, tolerance_small_jaw, tolerance_medium_jaw,
tolerance_large_jaw, tolerance_iso) = tt
small_exp_mlc = float(small_exp_mlc)
medium_exp_mlc = float(medium_exp_mlc)
large_exp_mlc = float(large_exp_mlc)
small_exp_jaw = float(small_exp_jaw)
medium_exp_jaw = float(medium_exp_jaw)
large_exp_jaw = float(large_exp_jaw)
tolerance_small_mlc = float(tolerance_small_mlc)
tolerance_medium_mlc = float(tolerance_medium_mlc)
tolerance_large_mlc = float(tolerance_large_mlc)
tolerance_small_jaw = float(tolerance_small_jaw)
tolerance_medium_jaw = float(tolerance_medium_jaw)
tolerance_large_jaw = float(tolerance_large_jaw)
tolerance_iso = float(tolerance_iso)
save_results = {
"user_machine": user_machine,
"user_energy": user_energy,
"machines_and_energies": machines_and_energies,
"displayname": displayname,
"testtype": ["MLC and Jaws", "Jaws only", "MLC only"]
}
try:
temp_folder1, file_path1 = RestToolbox.GetSingleDcm(
config.ORTHANC_URL, w1)
temp_folder2, file_path2 = RestToolbox.GetSingleDcm(
config.ORTHANC_URL, w2)
except:
return template("error_template",
{"error_message": "Cannot read images."})
# Load first image
try:
img1 = pylinac_image.DicomImage(file_path1)
# Here we force pixels to background outside of box:
if clipbox != 0:
try:
img1.check_inversion_by_histogram(percentiles=[
4, 50, 96
]) # Check inversion otherwise this might not work
general_functions.clip_around_image(img1, clipbox)
except Exception as e:
return template(
"error_template",
{"error_message": "Unable to apply clipbox. " + str(e)})
else:
img1.remove_edges(pixels=2)
if invert:
img1.invert()
else:
img1.check_inversion()
img1.flipud()
if filter_size != 0:
img1.filter(filter_size)
except:
return template("error_template",
{"error_message": "Cannot read image."})
try:
img2 = pylinac_image.DicomImage(file_path2)
if clipbox != 0:
try:
img2.check_inversion_by_histogram(percentiles=[
4, 50, 96
]) # Check inversion otherwise this might not work
general_functions.clip_around_image(img2, clipbox)
except Exception as e:
return template(
"error_template",
{"error_message": "Unable to apply clipbox. " + str(e)})
else:
img2.remove_edges(pixels=2)
if invert:
img2.invert()
else:
img2.check_inversion()
img2.flipud()
if filter_size != 0:
img1.filter(filter_size)
except:
return template("error_template",
{"error_message": "Cannot read image."})
# FIRST IMAGE (to get isocenter):
if iso_method == "Manual":
center = (float(cax_x), float(cax_y))
elif iso_method == "Plate (Elekta)":
img1_copy = copy.copy(
img1) # Copy because you will need the original later
reg = MLC_fieldsize._get_canny_regions(img1)
size = [r.area for r in reg]
sort = np.argsort(size)[::-1]
# Get the region with the right area:
for s in range(0, len(reg), 1):
if 0.9 < (reg[sort[s]].image.size /
(img1.dpmm * img1.dpmm) / 31420) < 1.1:
break
max_area_index = sort[s]
bb_box = reg[
max_area_index].bbox # (min_row, min_col, max_row, max_col)
margin = 15 # pixels
img1.array = img1.array[bb_box[0] + margin:bb_box[2] - margin,
bb_box[1] + margin:bb_box[3] - margin]
center = MLC_fieldsize._find_plate_center(
img1) # This is chosen as the mechanical center!!!
filter_size = 0.05 # Don't go higher!
sample_length = 15 # mm to both sides from the center!
sample_box = 5 # Half the number of lines per averaged profile minus one(must be odd number)
hor_margin = 50
vrt_margin = 30
# Vertical profiles (two regions):
samples_vertical = np.rint(center[0] - sample_length * img1.dpmm +
np.arange(1, 2 * sample_length * img1.dpmm +
1, 2 * sample_box +
1)).astype(int)
up_end = np.rint(center[1] - vrt_margin * img1.dpmm).astype(int)
down_start = np.rint(center[1] + vrt_margin * img1.dpmm).astype(int)
width_vert_up, fwhm_center_vert_up, width_vert_down, fwhm_center_vert_down = MLC_fieldsize.plate_ud_width(
img1, samples_vertical, filter_size, up_end, down_start,
sample_box)
# Horizontal profiles (two regions):
samples_horizontal = np.rint(
center[1] - sample_length * img1.dpmm +
np.arange(1, 2 * sample_length * img1.dpmm + 1, 2 * sample_box +
1)).astype(int)
left_end = np.rint(center[0] - hor_margin * img1.dpmm).astype(int)
right_start = np.rint(center[0] + hor_margin * img1.dpmm).astype(int)
width_hor_left, fwhm_center_hor_left, width_hor_right, fwhm_center_hor_right = MLC_fieldsize.plate_lr_width(
img1, samples_horizontal, filter_size, left_end, right_start,
sample_box)
vrt_px = (np.average(width_vert_up) + np.average(width_vert_down)) / 2
hor_px = (np.average(width_hor_left) + np.average(width_hor_right)) / 2
center_fwhm_x = (np.average(fwhm_center_hor_left) + right_start +
np.average(fwhm_center_hor_right)) / 2
center_fwhm_y = (np.average(fwhm_center_vert_up) + down_start +
np.average(fwhm_center_vert_down)) / 2
# Redefine center to the original image:
center_plate = [center_fwhm_x, center_fwhm_y]
center = (bb_box[1] + margin + center_plate[0],
bb_box[0] + margin + center_plate[1])
elif iso_method == "BB":
center, bb_box = MLC_fieldsize._find_field_centroid(img1)
if high_contrast:
bb_coord, bw_bb_im = MLC_fieldsize._find_bb2(
img1, bb_box) # Define as mechanical isocenter
else:
bb_coord, bw_bb_im = MLC_fieldsize._find_bb(
img1, bb_box) # Define as mechanical isocenter
center = (bb_coord[0], bb_coord[1])
elif iso_method == "CAX":
center, bb_box = MLC_fieldsize._find_field_centroid(
img1) # Define as mechanical isocenter
elif iso_method == "Opposing coll angle":
# Two fields with opposing collimator angles can be used to define the coll axis
center, bb_box = MLC_fieldsize._find_field_centroid(
img1) # Same as any field but now:
# Go to line below where center is redefined to account for image 2 CAX.
# Now calculate field size from second image
# Calculate mlc and jaw positions
# Define pixel size. Use either that from the dcm file or calculate it via Elekta plate:
if iso_method == "Plate (Elekta)":
dpmm = (vrt_px / 20.0 + hor_px / 20.0) / 2
else:
if mmpd != 0:
dpmm = 1.0 / mmpd
else:
dpmm = img2.dpmm
center_rad, bb_box2 = MLC_fieldsize._find_field_centroid(img2)
# If Opposing coll angle is used for isocenter definition, redefine center:
if iso_method == "Opposing coll angle":
center[0], center[1] = (center[0] + center_rad[0]) / 2.0, (
center[1] + center_rad[1]) / 2.0
marg_bb = 10 # This additional margin is included in the caluclation of bb_box2. Subtract it when needed!
nr_leaf_sample_points = int(mlc_points)
nr_jaw_sample_points = int(jaw_points)
leaf_scaling = dpmm
mlc_points = MLC_fieldsize.sample_points_mlc(nr_leaf_sample_points,
leaf_type=mlc_type)
# Sample mlc-s according o mlc_points, sample jaws equidistanly with center at rad center (derived from bb_box2)
if mlc_direction == "X":
# MLCS are horizontal
center_pixel_mlc = center[0]
center_pixel_jaws = center[1]
mlc_pixels = MLC_fieldsize.points_to_pixels_mlc(
leaf_scaling, mlc_points, bb_box2[0] + marg_bb,
bb_box2[1] - marg_bb, center_pixel_jaws)
jaw_pixels = MLC_fieldsize.sample_pixels_jaws(nr_jaw_sample_points,
bb_box2[2] + marg_bb,
bb_box2[3] - marg_bb)
else:
# MLCs are vertical
center_pixel_mlc = center[1]
center_pixel_jaws = center[0]
mlc_pixels = MLC_fieldsize.points_to_pixels_mlc(
leaf_scaling, mlc_points, bb_box2[2] + marg_bb,
bb_box2[3] - marg_bb, center_pixel_jaws)
jaw_pixels = MLC_fieldsize.sample_pixels_jaws(nr_jaw_sample_points,
bb_box2[0] + marg_bb,
bb_box2[1] - marg_bb)
penL, penR = MLC_fieldsize.calculate_penumbra_pixels_mlc(
img2, mlc_pixels, mlc_direction)
penL_abs = np.abs(penL - center_pixel_mlc)
penL_abs_avg = np.average(penL_abs, axis=1)
penR_abs = np.abs(penR - center_pixel_mlc)
penR_abs_avg = np.average(penR_abs, axis=1)
widths_mlc = np.abs(penL - penR)
widths_mlc_avg = np.average(widths_mlc, axis=1)
penL_jaw, penR_jaw = MLC_fieldsize.calculate_penumbra_pixels_jaws(
img2, jaw_pixels, mlc_direction)
penL_jaw_abs = np.abs(penL_jaw - center_pixel_jaws)
penR_jaw_abs = np.abs(penR_jaw - center_pixel_jaws)
widths_jaw = np.abs(penL_jaw - penR_jaw)
# Skewness of the rectangle (a measure of collimator angle):
# linear regression of mlc/jaw points (except the first and the last) y=mx+c
first_leaf = np.rint(
np.searchsorted((mlc_pixels - center_pixel_jaws).flatten(), 0) /
mlc_pixels.shape[1]).astype(int)
leaf_numbers = np.hstack(
(-np.arange(1, first_leaf + 1, 1)[::-1],
np.arange(1, mlc_pixels.shape[0] - first_leaf + 1, 1)))
temp_mlc = np.vstack([
(mlc_pixels[1:-1, :].flatten() - center_pixel_mlc) / dpmm,
np.ones(len(mlc_pixels[1:-1, :].flatten()))
]).T
m_mlcL, c_mlcL = np.linalg.lstsq(temp_mlc,
penL_abs[1:-1, :].flatten() / dpmm,
rcond=None)[0]
m_mlcR, c_mlcR = np.linalg.lstsq(temp_mlc,
penR_abs[1:-1, :].flatten() / dpmm,
rcond=None)[0]
temp_jaws = np.vstack([(jaw_pixels[1:-1] - center_pixel_jaws) / dpmm,
np.ones(len(jaw_pixels[1:-1]))]).T
m_jawL, c_jawL = np.linalg.lstsq(temp_jaws,
penL_jaw_abs[1:-1] / dpmm,
rcond=None)[0]
m_jawR, c_jawR = np.linalg.lstsq(temp_jaws,
penR_jaw_abs[1:-1] / dpmm,
rcond=None)[0]
# Not do some heavy plotting
size = 7
fig1 = Figure(figsize=(size, size), tight_layout={"w_pad": 1, "pad": 1})
ax1 = fig1.add_subplot(1, 1, 1)
if iso_method == "BB":
ax1.imshow(img1.array,
cmap=matplotlib.cm.Greys,
interpolation="none",
origin="lower",
extent=[0, img1.shape[1], 0, img1.shape[0]])
ax1.plot(center[0],
center[1],
'b+',
markersize=24,
markeredgewidth=3,
zorder=2)
border = np.average(np.percentile(bw_bb_im, [5, 99.9]))
ax1.contour(bw_bb_im, levels=[border], colors=["red"]) # BB
ax1.set_ylim(0, img1.shape[0])
ax1.set_xlim(0, img1.shape[1])
ax1.autoscale(enable=False)
if iso_method == "Plate (Elekta)":
ax1.imshow(img1_copy.array,
cmap=matplotlib.cm.prism_r,
interpolation="none",
origin="lower",
extent=[0, img1_copy.shape[1], 0, img1_copy.shape[0]])
ax1.plot(center[0],
center[1],
'b+',
markersize=24,
markeredgewidth=3,
zorder=2)
ax1.plot([left_end + center[0] - center_plate[0]] *
len(samples_horizontal),
samples_horizontal + center[1] - center_plate[1],
'wo',
markersize=3,
markeredgewidth=0,
zorder=2)
ax1.plot([right_start + center[0] - center_plate[0]] *
len(samples_horizontal),
samples_horizontal + center[1] - center_plate[1],
'wo',
markersize=3,
markeredgewidth=0,
zorder=2)
ax1.plot(samples_vertical + center[0] - center_plate[0],
[up_end + center[1] - center_plate[1]] *
len(samples_vertical),
'wo',
markersize=3,
markeredgewidth=0,
zorder=2)
ax1.plot(samples_vertical + center[0] - center_plate[0],
[down_start + center[1] - center_plate[1]] *
len(samples_vertical),
'wo',
markersize=3,
markeredgewidth=0,
zorder=2)
ax1.set_ylim(bb_box[0] + margin, bb_box[2] - margin)
ax1.set_xlim(bb_box[1] + margin, bb_box[3] - margin)
ax1.autoscale(enable=False)
if iso_method == "Manual" or iso_method == "CAX" or iso_method == "Opposing coll angle":
ax1.imshow(img1.array,
cmap=matplotlib.cm.prism_r,
interpolation="none",
origin="lower",
extent=[0, img1.shape[1], 0, img1.shape[0]])
ax1.plot(center[0],
center[1],
'b+',
markersize=24,
markeredgewidth=3,
zorder=2)
ax1.set_ylim(0, img1.shape[0])
ax1.set_xlim(0, img1.shape[1])
ax1.autoscale(enable=False)
mpld3.plugins.connect(fig1,
mpld3.plugins.MousePosition(fontsize=14, fmt=".1f"))
script1 = mpld3.fig_to_html(fig1, d3_url=D3_URL, mpld3_url=MPLD3_URL)
# Second plot
fig2 = Figure(figsize=(size, size), tight_layout={"w_pad": 1, "pad": 1})
ax2 = fig2.add_subplot(1, 1, 1)
ax2.imshow(img2.array,
cmap=matplotlib.cm.Greys,
interpolation="none",
origin="lower",
extent=[0, img2.shape[1], 0, img2.shape[0]])
ax2.plot(center[0],
center[1],
marker='+',
color="dodgerblue",
markersize=24,
markeredgewidth=3)
level = np.average(np.percentile(img2.array, [5, 99.9]))
ax2.contour(img2.array,
levels=[level],
colors=["magenta"],
linewidths=1,
alpha=0.7,
zorder=1)
ax2.plot(center_rad[0],
center_rad[1],
'm+',
markersize=24,
markeredgewidth=3,
zorder=2)
ax2.plot([None], [None],
marker='+',
color="dodgerblue",
ms=15,
mew=3,
label="Mechanical")
ax2.plot([None], [None], "m+", ms=15, mew=3, label="Radiation")
ax2.plot([0, img2.shape[1]], [center[1], center[1]],
linestyle="--",
color="dodgerblue",
alpha=0.5)
ax2.plot([center[0], center[0]], [0, img2.shape[0]],
linestyle="--",
color="dodgerblue",
alpha=0.5)
ax2.legend(framealpha=0, numpoints=1, ncol=2, loc='lower left', fontsize=8)
m1s = 5
ms2 = 6
if mlc_direction == "X":
m1 = ax2.plot(penL.flatten(),
mlc_pixels.flatten(),
'ro',
markersize=m1s,
markeredgewidth=0,
zorder=2)
m2 = ax2.plot(penR.flatten(),
mlc_pixels.flatten(),
'bo',
markersize=m1s,
markeredgewidth=0,
zorder=2)
j1 = ax2.plot(jaw_pixels,
penL_jaw,
'go',
markersize=ms2,
markeredgewidth=0,
zorder=2)
j2 = ax2.plot(jaw_pixels,
penR_jaw,
'yo',
markersize=ms2,
markeredgewidth=0,
zorder=2)
#ax2.plot(np.average((penL+penR)/2), np.average((penL_jaw+penR_jaw)/2), 'y+', markersize=24, markeredgewidth=3, zorder=2)
else:
m1 = ax2.plot(mlc_pixels.flatten(),
penL.flatten(),
'ro',
markersize=m1s,
markeredgewidth=0,
zorder=2)
m2 = ax2.plot(mlc_pixels.flatten(),
penR.flatten(),
'bo',
markersize=m1s,
markeredgewidth=0,
zorder=2)
j1 = ax2.plot(penL_jaw,
jaw_pixels,
'go',
markersize=ms2,
markeredgewidth=0,
zorder=2)
j2 = ax2.plot(penR_jaw,
jaw_pixels,
'yo',
markersize=ms2,
markeredgewidth=0,
zorder=2)
#ax2.plot(np.average((penL_jaw+penR_jaw)/2), np.average((penL+penR)/2), 'y+', markersize=24, markeredgewidth=3, zorder=2)
labels_m1 = [
"Distance from center = {:04.2f} mm, width = {:04.2f} mm".format(
penL_abs.flatten()[k] / dpmm,
widths_mlc.flatten()[k] / dpmm)
for k in range(0, len(penL_abs.flatten()), 1)
]
labels_m2 = [
"Distance from center = {:04.2f} mm, width = {:04.2f} mm".format(
penR_abs.flatten()[k] / dpmm,
widths_mlc.flatten()[k] / dpmm)
for k in range(0, len(penR_abs.flatten()), 1)
]
labels_j1 = [
"Distance from center = {:04.2f} mm, width = {:04.2f} mm".format(
penL_jaw_abs[k] / dpmm, widths_jaw[k] / dpmm)
for k in range(0, len(penL_jaw_abs), 1)
]
labels_j2 = [
"Distance from center = {:04.2f} mm, width = {:04.2f} mm".format(
penR_jaw_abs[k] / dpmm, widths_jaw[k] / dpmm)
for k in range(0, len(penR_jaw_abs), 1)
]
ttip1 = mpld3.plugins.PointLabelTooltip(m1[0],
labels_m1,
location='top left')
ttip2 = mpld3.plugins.PointLabelTooltip(m2[0],
labels_m2,
location='top left')
ttip3 = mpld3.plugins.PointLabelTooltip(j1[0],
labels_j1,
location='top left')
ttip4 = mpld3.plugins.PointLabelTooltip(j2[0],
labels_j2,
location='top left')
margin_imshow = 35
ax2.set_ylim(bb_box2[0] - margin_imshow, bb_box2[1] + margin_imshow)
ax2.set_xlim(bb_box2[2] - margin_imshow, bb_box2[3] + margin_imshow)
ax2.autoscale(enable=False)
mpld3.plugins.connect(fig2,
mpld3.plugins.MousePosition(fontsize=14, fmt=".1f"))
mpld3.plugins.connect(fig2, ttip1)
mpld3.plugins.connect(fig2, ttip2)
mpld3.plugins.connect(fig2, ttip3)
mpld3.plugins.connect(fig2, ttip4)
script2 = mpld3.fig_to_html(fig2, d3_url=D3_URL, mpld3_url=MPLD3_URL)
# Third plot
fig3 = Figure(figsize=(10, 9), tight_layout={"w_pad": 2, "pad": 2})
ax3 = fig3.add_subplot(3, 1, 1)
ax4 = fig3.add_subplot(3, 1, 2)
ax5 = fig3.add_subplot(3, 1, 3)
ax3.plot(np.arange(0, len(leaf_numbers), 1),
penR_abs_avg / dpmm,
"bo-",
linewidth=0.8)
ax3.set_xticks(np.arange(0, len(leaf_numbers), 1))
ax3.set_xticklabels([])
ax3.grid(linestyle='dotted', color="gray")
ax3.set_title("Right leaf distance from center [mm]")
ax3.margins(0.05)
ax4.plot(np.arange(0, len(leaf_numbers), 1),
-penL_abs_avg / dpmm,
"ro-",
linewidth=0.8)
ax4.set_xticks(np.arange(0, len(leaf_numbers), 1))
ax4.grid(linestyle='dotted', color="gray")
ax4.set_xticklabels([])
ax4.set_title("Left leaf distance from center [mm]")
ax4.margins(0.05)
ax5.plot(np.arange(0, len(leaf_numbers), 1),
widths_mlc_avg / dpmm,
"ko-",
linewidth=0.8)
ax5.set_xticks(np.arange(0, len(leaf_numbers), 1))
ax5.set_xticklabels(leaf_numbers)
ax5.grid(linestyle='dotted', color="gray")
ax5.set_xlabel("Leaf index")
ax5.set_title("Distance between leaves [mm]")
ax5.margins(0.05)
script3 = mpld3.fig_to_html(fig3, d3_url=D3_URL, mpld3_url=MPLD3_URL)
# Fourth plot
fig4 = Figure(figsize=(10, 9), tight_layout={"w_pad": 2, "pad": 2})
ax6 = fig4.add_subplot(3, 1, 1)
ax7 = fig4.add_subplot(3, 1, 2)
ax8 = fig4.add_subplot(3, 1, 3)
ax6.plot(np.arange(0, len(penR_jaw_abs), 1),
penR_jaw_abs / dpmm,
"yo-",
linewidth=0.8)
ax6.set_xticks(np.arange(0, len(penR_jaw_abs), 1))
ax6.set_xticklabels([])
ax6.grid(linestyle='dotted', color="gray")
ax6.set_title("Right jaw distance from center [mm]")
ax6.margins(0.05)
ax7.plot(np.arange(0, len(penL_jaw_abs), 1),
-penL_jaw_abs / dpmm,
"go-",
linewidth=0.8)
ax7.set_xticks(np.arange(0, len(penL_jaw_abs), 1))
ax7.grid(linestyle='dotted', color="gray")
ax7.set_xticklabels([])
ax7.set_title("Left jaw distance from center [mm]")
ax7.margins(0.05)
ax8.plot(np.arange(0, len(widths_jaw), 1),
widths_jaw / dpmm,
"ko-",
linewidth=0.8)
ax8.set_xticks(np.arange(0, len(widths_jaw), 1))
ax8.set_xticklabels(np.arange(1, len(widths_jaw) + 1, 1))
ax8.grid(linestyle='dotted', color="gray")
ax8.set_xlabel("Jaw sample point")
ax8.set_title("Distance between jaws [mm]")
ax8.margins(0.05)
script4 = mpld3.fig_to_html(fig4, d3_url=D3_URL, mpld3_url=MPLD3_URL)
# Caclculate stuff for the web interface
MLC_size_full = np.average(widths_mlc_avg[1:-1] / dpmm)
jaw_size_full = np.average(widths_jaw[1:-1] / dpmm)
#sizes_nominal = np.array([small_nominal, medium_nominal, large_nominal])
size_nominal_names = ["Small field", "Medium field", "Large field"]
sizes_exp_mlc = np.array([small_exp_mlc, medium_exp_mlc, large_exp_mlc
]) * 10.0
sizes_exp_jaw = np.array([small_exp_jaw, medium_exp_jaw, large_exp_jaw
]) * 10.0
tolerances_list_mlc = np.array([
tolerance_small_mlc, tolerance_medium_mlc, tolerance_large_mlc
]) * 10.0
tolerances_list_jaw = np.array([
tolerance_small_jaw, tolerance_medium_jaw, tolerance_large_jaw
]) * 10.0
# Guess which field was chosen
guess_ind_mlc = np.argmin(np.abs(sizes_exp_mlc - MLC_size_full))
guess_ind_jaw = np.argmin(np.abs(sizes_exp_jaw - jaw_size_full))
guessed_exp_mlc = sizes_exp_mlc[guess_ind_mlc]
guessed_exp_jaw = sizes_exp_jaw[guess_ind_jaw]
guessed_nominal_name = size_nominal_names[guess_ind_mlc]
tolerance_final_mlc = tolerances_list_mlc[guess_ind_mlc]
tolerance_final_jaw = tolerances_list_jaw[guess_ind_jaw]
tolerance_iso = tolerance_iso * 10.0 # Redefine to mm
if abs(guessed_exp_mlc - MLC_size_full) <= tolerance_final_mlc:
passed_mlc = True
else:
passed_mlc = False
if abs(guessed_exp_jaw - jaw_size_full) <= tolerance_final_jaw:
passed_jaw = True
else:
passed_jaw = False
if (center_rad[0] - center[0])**2 + (
center_rad[1] - center[1])**2 <= dpmm * dpmm * tolerance_iso**2:
passed_iso = True
else:
passed_iso = False
variables = {
"script1": script1,
"script2": script2,
"script3": script3,
"script4": script4,
"MLC_position_L": penL_abs_avg / dpmm,
"MLC_position_R": penR_abs_avg / dpmm,
"MLC_width": widths_mlc_avg / dpmm,
"jaw_position_L": penL_jaw_abs / dpmm,
"jaw_position_R": penR_jaw_abs / dpmm,
"jaw_width": widths_jaw / dpmm,
"leaf_numbers": leaf_numbers,
"angle_mlc_L": np.arctan(m_mlcL) * 180 / PI,
"angle_mlc_R": -np.arctan(m_mlcR) * 180 / PI, # changed sign!
"angle_jaw_L": -np.arctan(m_jawL) * 180 / PI, # changed sign!
"angle_jaw_R": np.arctan(m_jawR) * 180 / PI,
"MLC_size_L": np.average(penL_abs_avg[1:-1] / dpmm),
"MLC_size_R": np.average(penR_abs_avg[1:-1] / dpmm),
"MLC_size_full": MLC_size_full,
"jaw_size_L": np.average(penL_jaw_abs[1:-1] / dpmm),
"jaw_size_R": np.average(penR_jaw_abs[1:-1] / dpmm),
"jaw_size_full": jaw_size_full,
"center_offset_x": (center_rad[0] - center[0]) / dpmm,
"center_offset_y": (center_rad[1] - center[1]) / dpmm,
"dpmm": 1 / dpmm,
"center": center,
"mlc_direction": mlc_direction,
"center_rad": center_rad,
"save_results": save_results,
"passed_mlc": passed_mlc,
"passed_jaw": passed_jaw,
"passed_iso": passed_iso,
"tolerance_mlc": tolerance_final_mlc,
"tolerance_jaw": tolerance_final_jaw,
"tolerance_iso": tolerance_iso,
"expected_mlc": guessed_exp_mlc,
"expected_jaw": guessed_exp_jaw,
"guessed_fieldsize": guessed_nominal_name,
"acquisition_datetime": acquisition_datetime,
"iso_method": iso_method
}
general_functions.delete_files_in_subfolders([temp_folder1, temp_folder2
]) # Delete image
return template("fieldsize_results", variables)
def image_review_helperf(args):
converthu = args["converthu"]
invert = args["invert"]
calculate_histogram = args["calculate_histogram"]
colormap = args["colormap"]
w = args["w"]
general_functions.set_configuration(args["config"])
temp_folder, file_path = RestToolbox.GetSingleDcm(config.ORTHANC_URL, w)
try:
img = pylinac_image.DicomImage(file_path)
if invert:
img.invert()
if converthu: # Convert back to pixel values if needed.
img.array = (img.array - int(img.metadata.RescaleIntercept)) / int(img.metadata.RescaleSlope)
img_array = np.flipud(img.array)
except:
return template("error_template", {"error_message": "Cannot read image."})
size_x = img_array.shape[1]
size_y = img_array.shape[0]
img_min = np.min(img_array)
img_max = np.max(img_array)
x1 = np.arange(0, size_x, 1).tolist()
y1 = img_array[int(size_y//2), :].tolist()
y2 = np.arange(0, size_y, 1).tolist()
x2 = img_array[:, int(size_x//2)].tolist()
source = ColumnDataSource(data=dict(x1=x1, y1=y1)) # Bottom plot
source2 = ColumnDataSource(data=dict(x2=x2, y2=y2)) # Right plot
sourcebp = ColumnDataSource(data=dict(xbp=[], ybp=[]))
sourcerp = ColumnDataSource(data=dict(xrp=[], yrp=[]))
# Add table for pixel position and pixel value
hfmt = NumberFormatter(format="0.00")
source6 = ColumnDataSource(dict(
x=[],
y=[],
value=[]
))
columns2 = [TableColumn(field="x", title="x", formatter=hfmt),
TableColumn(field="y", title="y", formatter=hfmt),
TableColumn(field="value", title="value", formatter=hfmt)]
table2 = DataTable(source=source6, columns=columns2, editable=False, height=50, width = 220)
# Plotting profiles
callback = CustomJS(args=dict(source=source, source2=source2, sourcebp=sourcebp,
sourcerp=sourcerp, source6=source6), code="""
var geometry = cb_data['geometry'];
var x_data = parseInt(geometry.x); // current mouse x position in plot coordinates
var y_data = parseInt(geometry.y); // current mouse y position in plot coordinates
var data = source.data;
var data2 = source2.data;
var array = """+json.dumps(img_array.tolist())+""";
data['y1'] = array[y_data];
var column = [];
for(var i=0; i < array.length; i++){
column.push(array[i][x_data]);
}
data2['x2'] = column;
source.change.emit();
source2.change.emit();
var length_x = array[0].length;
var length_y = array.length;
if ((x_data<=length_x && x_data>=0) && (y_data<=length_y && y_data>=0)){
// Add points to plot:
sourcebp.data['xbp'] = [x_data];
sourcebp.data['ybp'] = [array[y_data][x_data]];
sourcerp.data['xrp'] = [array[y_data][x_data]];
sourcerp.data['yrp'] = [y_data];
// Get position and pixel value for table
source6.data["x"] = [geometry.x];
source6.data["y"] = [geometry.y];
source6.data["value"] = [array[y_data][x_data]];
sourcebp.change.emit();
sourcerp.change.emit();
source6.change.emit();
}
""")
# Add callback for calculating average value inside Rectangular ROI
x3 = np.arange(0, size_x, 1).tolist()
y3 = np.arange(0, size_y, 1).tolist()
source3 = ColumnDataSource(data=dict(x3=x3, y3=y3))
source4 = ColumnDataSource(data=dict(x4=[], y4=[], width4=[], height4=[]))
# Add table for mean and std
source5 = ColumnDataSource(dict(
mean=[],
median=[],
std=[],
minn=[],
maxx=[]
))
columns = [TableColumn(field="mean", title="mean", formatter=hfmt),
TableColumn(field="median", title="median", formatter=hfmt),
TableColumn(field="std", title="std", formatter=hfmt),
TableColumn(field="minn", title="min", formatter=hfmt),
TableColumn(field="maxx", title="max", formatter=hfmt)]
table = DataTable(source=source5, columns=columns, editable=False, height=50, width = 480)
# Calculate things within ROI
callback2 = CustomJS(args=dict(source3=source3, source4=source4, source5=source5), code="""
var geometry = cb_obj['geometry'];
var data3 = source3.data;
var data4 = source4.data;
var data5 = source5.data;
// Get data
var x0 = parseInt(geometry['x0']);
var x1 = parseInt(geometry['x1']);
var y0 = parseInt(geometry['y0']);
var y1 = parseInt(geometry['y1']);
// calculate Rect attributes
var width = x1 - x0;
var height = y1 - y0;
var x = x0 + width/2;
var y = y0 + height/2;
// update data source with new Rect attributes
data4['x4'] = [x];
data4['y4'] = [y];
data4['width4'] = [width];
data4['height4'] = [height];
// Get average value inside ROI
var array = """+json.dumps(img_array.tolist())+""";
var length_x = array[0].length;
var length_y = array.length;
if ((x0<=length_x && x0>=0) && (x1<=length_x && x1>=0) && (y0<=length_y && y0>=0) && (y1<=length_y && y1>=0)){
var avg_ROI = [];
for (var i=y0; i< y1; i++){
for (var j=x0; j<x1; j++){
avg_ROI.push(array[i][j]);
}
}
if (avg_ROI == undefined || avg_ROI.length==0){
data5["mean"] = [0];
data5["median"] = [0];
data5["std"] = [0];
}
else{
data5["mean"] = [math.mean(avg_ROI)];
data5["median"] = [math.median(avg_ROI)];
data5["std"] = [math.std(avg_ROI, 'uncorrected')];
data5["maxx"] = [math.max(avg_ROI)];
data5["minn"] = [math.min(avg_ROI)];
}
source4.change.emit();
source5.change.emit();
}
""")
plot_width = 500
plot_height = int((plot_width-20)*size_y/size_x)
fig = figure(x_range=[0, size_x], y_range=[0, size_y],
plot_width=plot_width, plot_height=plot_height, title="",
toolbar_location="right",
tools=["crosshair, wheel_zoom, pan, reset"])
fig_r = figure(x_range=[img_min, img_max], y_range=fig.y_range,
plot_width=250, plot_height=plot_height, title="",
toolbar_location="right",
tools=[])
fig_b = figure(x_range=fig.x_range, y_range=[img_min, img_max],
plot_width=plot_width, plot_height=200, title="",
toolbar_location="right",
tools=[])
# Define matplotlib palette and make it possible to be used dynamically.
cmap = matplotlib.cm.get_cmap(colormap) #chose any matplotlib colormap here
bokehpalette = [matplotlib.colors.rgb2hex(m) for m in cmap(np.arange(cmap.N)[::-1])] # Reversed direction of colormap
mapper = LinearColorMapper(palette=bokehpalette, low=np.min(img_array), high=np.max(img_array))
callback3 = CustomJS(args=dict(mapper=mapper, x_range=fig_r.x_range, y_range=fig_b.y_range), code="""
mapper.palette = """+json.dumps(bokehpalette)+""";
var start = cb_obj.value[0];
var end = cb_obj.value[1];
mapper.low = start;
mapper.high = end;
x_range.setv({"start": start, "end": end});
y_range.setv({"start": start, "end": end});
//mapper.change.emit();
""")
range_slider = RangeSlider(start=np.min(img_array), end=np.max(img_array), value=(np.min(img_array), np.max(img_array)), step=10, title="Level")
range_slider.js_on_change('value', callback3)
fig.image([img_array], x=0, y=0, dw=size_x, dh=size_y, color_mapper=mapper)
fig_b.line(x='x1', y='y1', source=source)
fig_r.line(x='x2', y='y2', source=source2)
fig_b.circle(x='xbp', y='ybp', source=sourcebp, size=7, fill_color="red", fill_alpha=0.5, line_color=None) # For point connected to crosshair
fig_r.circle(x='xrp', y='yrp', source=sourcerp, size=7, fill_color="red", fill_alpha=0.5, line_color=None) # For point connected to crosshair
fig.rect(x='x4', y='y4', width='width4', height='height4', source=source4, fill_alpha=0.3, fill_color='#009933')
fig.add_tools(HoverTool(tooltips=None, callback=callback))
fig.add_tools(BoxSelectTool())
fig.js_on_event(SelectionGeometry, callback2) # Add event_callback to Boxselecttool
grid = gridplot([[table, range_slider], [fig, fig_r], [fig_b, table2]])
script, div = components(grid)
# Calculate pixel value histogram
if calculate_histogram:
try:
bitsstored = int(img.metadata.BitsStored)
except:
bitsstored = 16
max_pixelvalue = 2**bitsstored-1
counts, bins = np.histogram(img_array.flatten(), density=False, range=(0, max_pixelvalue), bins=max_pixelvalue+1)
fig_hist = figure(x_range = [-100, max_pixelvalue*1.05], y_range=[0.5, np.max(counts)],
plot_width=750, plot_height=400, title="Pixel value histogram", y_axis_type="log",
toolbar_location="right", tools=["box_zoom, pan, reset"])
fig_hist.quad(top=counts, bottom=0.5, left=bins[:-1], right=bins[1:], alpha=0.5)
fig_hist.grid.visible = False
fig_hist.yaxis.axis_label = "Pixel Count"
fig_hist.xaxis.axis_label = "Pixel Value"
script_hist, div_hist = components(fig_hist)
else:
script_hist, div_hist = ["", ""]
variables = {"script": script,
"div": div,
"bokeh_file_css": BOKEH_FILE_CSS,
"bokeh_file_js": BOKEH_FILE_JS,
"bokeh_widgets_js": BOKEH_WIDGETS_JS,
"bokeh_tables_js": BOKEH_TABLES_JS,
"script_hist": script_hist,
"div_hist": div_hist
}
#gc.collect()
general_functions.delete_files_in_subfolders([temp_folder]) # Delete image
return template("image_review_results", variables)