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evaluate.py
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evaluate.py
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import numpy as np
import skimage.measure
from feature_matching import Projection_Preprocessing
import re
from skimage.metrics import structural_similarity,normalized_root_mse
import skimage.measure
import skimage.morphology
import cv2
import cal
import os
import SimpleITK as sitk
import scipy.ndimage
import i0_data
import load_data
import utils
import config
margin=0
def evalNeedleArea(img, img2, projname, name):
if False and projname in ("genA", "201020"):
if img.shape[0] > 500:
pos = [408, 472-margin, 498-margin]
mult = 2
else:
pos = [204, 236-margin, 249-margin]
mult = 1
elif projname in ("genB", "2010201"):
if img.shape[0] > 500:
pos = [465, 514-margin, 525-margin]
mult = 2
else:
pos = [232, 257-margin, 262-margin]
pos = [199, 258-margin, 257-margin]
mult = 1
elif projname in ("genC", ""):
if img.shape[0] > 500:
pos = [465, 514-margin, 525-margin]
mult = 2
else:
#pos = [232, 257-margin, 262-margin]
pos = [169, 158, 157]
mult = 1
else:
return 0
mask = np.zeros_like(img, dtype=bool)
mask[pos[0],pos[1]-2:pos[1]+2,pos[2]-2:pos[2]+2] = True
#rec = sitk.GetImageFromArray(1.0*mask)
#rec.SetOrigin(out_rec_meta[0])
#out_spacing = (out_rec_meta[2][0]/mult,out_rec_meta[2][1]/mult,out_rec_meta[2][2]/mult)
#rec.SetSpacing(out_spacing)
#sitk.WriteImage(rec, os.path.join("recos", "area_"+name+"_input1.nrrd"), True)
#lines = img[mask].reshape((1,4,4))
lines = img*mask
maxpos = np.argmax(lines)
maxpos = np.unravel_index(maxpos, lines.shape)
maxval = lines[maxpos]
mask = np.zeros_like(img, dtype=bool)
mask[pos[0]-10:pos[0]+10] = True
mask = skimage.morphology.binary_opening(mask*(img>(maxval*0.5)), skimage.morphology.cube(2))
#rec = sitk.GetImageFromArray(mask*1.0)
#rec.SetOrigin(out_rec_meta[0])
#out_spacing = (out_rec_meta[2][0]/mult,out_rec_meta[2][1]/mult,out_rec_meta[2][2]/mult)
#rec.SetSpacing(out_spacing)
#sitk.WriteImage(rec, os.path.join("recos", "area_"+name+"_input2.nrrd"), True)
labels = skimage.measure.label(mask)
mask1 = labels==labels[maxpos]
mask = np.zeros_like(img2, dtype=bool)
mask[pos[0],pos[1]-2:pos[1]+2,pos[2]-2:pos[2]+2] = True
lines = img2*mask
maxpos = np.argmax(lines)
maxpos = np.unravel_index(maxpos, lines.shape)
maxval = lines[maxpos]
mask = np.zeros_like(img2, dtype=bool)
mask[pos[0]-10:pos[0]+10] = True
mask = skimage.morphology.binary_opening(mask*(img2>(maxval*0.5)), skimage.morphology.cube(2))
labels = skimage.measure.label(mask)
mask2 = labels==labels[maxpos]
im_sum = np.sum(mask1) + np.sum(mask2)
intersection = np.logical_and(mask1, mask2)
dice = 2. * np.sum(intersection) / im_sum
#rec = sitk.GetImageFromArray(mask1*1.0)
#rec.SetOrigin(out_rec_meta[0])
#out_spacing = (out_rec_meta[2][0]/mult,out_rec_meta[2][1]/mult,out_rec_meta[2][2]/mult)
#rec.SetSpacing(out_spacing)
#sitk.WriteImage(rec, os.path.join("recos", "area_"+name+"_input1.nrrd"), True)
#rec = sitk.GetImageFromArray(mask2*1.0)
#rec.SetOrigin(out_rec_meta[0])
#out_spacing = (out_rec_meta[2][0]/mult,out_rec_meta[2][1]/mult,out_rec_meta[2][2]/mult)
#rec.SetSpacing(out_spacing)
#sitk.WriteImage(rec, os.path.join("recos", "area_"+name+"_input2.nrrd"), True)
#dice2 = distance.dice(mask1, mask2)
#print(dice, dice2)
return dice
#return np.count_nonzero(mask) * out_spacing[2] * out_spacing[2]
#def evalFWHM(img, name, pos = [241,261,265]):
def evalFWHM(img, name, pos = [232, 262, 257]):
mult = 1
if name in ("genA", "201020"):
if img.shape[0] > 500:
pos = [408, 472, 498]
mult = 2
else:
pos = [204, 236, 249]
mult = 1
elif name in ("genB", "2010201"):
if img.shape[0] > 500:
pos = [465, 514, 525]
mult = 2
else:
pos = [232, 257, 262]
mult = 1
else:
return 0
mask = np.zeros_like(img, dtype=bool)
mask[pos[0],pos[1]-2:pos[1]+2,pos[2]-2:pos[2]+2] = True
#rec = sitk.GetImageFromArray(img*mask)
#rec.SetOrigin(out_rec_meta[0])
#out_spacing = (out_rec_meta[2][0]/mult,out_rec_meta[2][1]/mult,out_rec_meta[2][2]/mult)
#rec.SetSpacing(out_spacing)
#sitk.WriteImage(rec, os.path.join("recos", "fwhm_"+name+"_input1.nrrd"), True)
lines = img[mask].reshape((1,4,4))
maxpos = np.argmax(lines)
maxpos = np.unravel_index(maxpos, lines.shape)
mask = np.zeros_like(img, dtype=bool)
mask[maxpos[0]+pos[0],maxpos[1]+pos[1]-2,:] = True
#rec = sitk.GetImageFromArray(img*mask)
#rec.SetOrigin(out_rec_meta[0])
out_spacing = (out_rec_meta[2][0]/mult,out_rec_meta[2][1]/mult,out_rec_meta[2][2]/mult)
#rec.SetSpacing(out_spacing)
#sitk.WriteImage(rec, os.path.join("recos", "fwhm_"+name+"_input2.nrrd"), True)
line = img[mask]
maxpos = maxpos[2]+pos[2]-2
maxval = line[maxpos]
i = maxpos
left = 0
right = len(line)-1
while i > 0:
if line[i] == 0.5*maxval:
left = i
break
if line[i] < 0.5*maxval:
left = i + (maxval-line[i]) / (line[i+1]-line[i])
break
i -= 1
i = maxpos
while i < len(line):
if line[i] == 0.5*maxval:
right = i
break
if line[i] < 0.5*maxval:
right = i-1 + (maxval-line[i]) / (line[i-1]-line[i])
break
i += 1
fwhm = (right-left) * out_spacing[2]
print("FWHM: ", fwhm)
return fwhm
def evalPerformance(output_in, real_in, runtime, name, stats_file='stats.csv', real_fwhm=None, real_area=None, gi_config={"GIoldold":[None], "absp1":[None], "p1":[None]}):
if runtime == 0:
output = output_in
real = real_in
if runtime == 1:
output = Projection_Preprocessing(output_in)
real = Projection_Preprocessing(real_in)
if runtime == 2:
output = (output_in-np.mean(output_in))/(np.std(output_in))
real = (real_in-np.mean(real_in))/(np.std(real_in))
if True or runtime == 3:
output = (output_in-np.min(output_in))/(np.max(output_in)-np.min(output_in))
real = (real_in-np.min(real_in))/(np.max(real_in)-np.min(real_in))
if runtime == 4:
output = (output_in-np.min(output_in))/(np.max(output_in)-np.min(output_in))
output = (output-np.mean(output))/np.std(output)
real = (real_in-np.min(real_in))/(np.max(real_in)-np.min(real_in))
real = (real-np.mean(real))/np.std(real)
if runtime == 5:
output = (output_in-np.mean(output_in))/(np.max(output_in)-np.min(output_in))
real = (real_in-np.mean(real_in))/(np.max(real_in)-np.min(real_in))
#print(name, output.shape, real.shape)
#name = name + str(runtime)
#sitk.WriteImage(sitk.GetImageFromArray(output), name + "_out.nrrd")
#sitk.WriteImage(sitk.GetImageFromArray(real), name + "_real.nrrd")
if np.size(output[0]) != np.size(real[0]):
print(name, np.size(output[0]), output.shape, np.size(real[0]), real.shape)
return
vals = []
#for i in range(len(real)):
if "rec" in stats_file:
vals.append(1.0/cal.calcGIObjective(real, output, 0, None, {"GIoldold":[None], "absp1":[None], "p1":[None]}))
else:
for i in range(real.shape[0]):
v = 1.0/cal.calcGIObjective(real[i], output[i], 0, None, {"GIoldold":[None], "absp1":[None], "p1":[None]})
if not v!=v:
vals.append(v)
print("NGI: ", np.mean(vals), np.median(vals))
vals1 = []
#for i in range(len(real)):
# vals1.append(np.mean(cv2.matchTemplate(real[i], output[i], cv2.TM_CCORR_NORMED)))
vals2 = []
#for i in range(len(real)):
# vals2.append(np.mean(cv2.matchTemplate(real[i], output[i], cv2.TM_CCOEFF_NORMED)))
vals3 = []
vals4 = []
for i in range(len(real)):
break
a = real[i].flatten()
b = output[i].flatten()
a = (a - np.mean(a)) / (np.std(a) * len(a))
b = (b - np.mean(b)) / (np.std(b))
vals3.append(np.mean(cv2.matchTemplate(a, b, cv2.TM_CCORR)))
vals4.append(np.mean(cv2.matchTemplate(a, b, cv2.TM_CCOEFF)))
vals5 = []
#for i in range(len(real)):
#vals5.append(mutual_information_2d(real, output, normalized=True))
vals6 = []
#for i in range(len(real)):
#print(real.dtype, output.dtype)
if "rec" in stats_file:
vals6.append(structural_similarity(real, output))
else:
for i in range(real.shape[0]):
vals6.append(structural_similarity(real[i], output[i]))
print("SSIM: ", np.mean(vals6), np.median(vals6))
vals7 = []
#for i in range(len(real)):
if "rec" in stats_file:
vals7.append(normalized_root_mse(real, output))
else:
for i in range(real.shape[0]):
v = normalized_root_mse(real[i], output[i])
if not v!=v:
vals7.append(v)
print("NRMSE: ", np.mean(vals7), np.median(vals7))
vals8 = []
#if "rec" in stats_file:
#vals8.append(evalFWHM(output, "genB"))
#if real_fwhm is None:
# real_fwhm = evalFWHM(real, "genB")
#vals8.append(vals8[-1]-real_fwhm)
vals9 = []
if "rec" in stats_file and "proj" not in stats_file:
vals9.append(evalNeedleArea(output, real, "genC", name))
#if real_area is None:
# real_area = evalNeedleArea(real, "genB", name)
#vals9.append(vals9[-1]-real_area)
print("Area: ", vals9[-1])
with open(stats_file, "a") as f:
#f.write("{0};NGI;{1};=MIN(OFFSET(INDIRECT(ADDRESS(ROW(),COLUMN())), 0, 4, 1, {2}));=MAX(OFFSET(INDIRECT(ADDRESS(ROW(),COLUMN())), 0, 3, 1, {2}));=AVERAGE(OFFSET(INDIRECT(ADDRESS(ROW(),COLUMN())), 0, 2, 1, {2}));=STDEV.P(OFFSET(INDIRECT(ADDRESS(ROW(),COLUMN())), 0, 1, 1, {2}));".format(name, runtime/(24*60*60), len(vals)) + ";".join([str(v) for v in vals]) + "\n")
#f.write("{0};CCORR_NORM;{1};=MIN(OFFSET(INDIRECT(ADDRESS(ROW(),COLUMN())), 0, 4, 1, {2}));=MAX(OFFSET(INDIRECT(ADDRESS(ROW(),COLUMN())), 0, 3, 1, {2}));=AVERAGE(OFFSET(INDIRECT(ADDRESS(ROW(),COLUMN())), 0, 2, 1, {2}));=STDEV.P(OFFSET(INDIRECT(ADDRESS(ROW(),COLUMN())), 0, 1, 1, {2}));".format(name, runtime/(24*60*60), len(vals)) + ";".join([str(v) for v in vals1]) + "\n")
#f.write("{0};CCOEFF_NORM;{1};=MIN(OFFSET(INDIRECT(ADDRESS(ROW(),COLUMN())), 0, 4, 1, {2}));=MAX(OFFSET(INDIRECT(ADDRESS(ROW(),COLUMN())), 0, 3, 1, {2}));=AVERAGE(OFFSET(INDIRECT(ADDRESS(ROW(),COLUMN())), 0, 2, 1, {2}));=STDEV.P(OFFSET(INDIRECT(ADDRESS(ROW(),COLUMN())), 0, 1, 1, {2}));".format(name, runtime/(24*60*60), len(vals)) + ";".join([str(v) for v in vals2]) + "\n")
#f.write("{0};NCCORR;{1};=MIN(OFFSET(INDIRECT(ADDRESS(ROW(),COLUMN())), 0, 4, 1, {2}));=MAX(OFFSET(INDIRECT(ADDRESS(ROW(),COLUMN())), 0, 3, 1, {2}));=AVERAGE(OFFSET(INDIRECT(ADDRESS(ROW(),COLUMN())), 0, 2, 1, {2}));=MEDIAN(OFFSET(INDIRECT(ADDRESS(ROW(),COLUMN())), 0, 1, 1, {2}));".format(name, runtime/(24*60*60), len(vals)) + ";".join([str(v) for v in vals3]) + "\n")
#f.write("{0};NCCOEFF;{1};=MIN(OFFSET(INDIRECT(ADDRESS(ROW(),COLUMN())), 0, 4, 1, {2}));=MAX(OFFSET(INDIRECT(ADDRESS(ROW(),COLUMN())), 0, 3, 1, {2}));=AVERAGE(OFFSET(INDIRECT(ADDRESS(ROW(),COLUMN())), 0, 2, 1, {2}));=MEDIAN(OFFSET(INDIRECT(ADDRESS(ROW(),COLUMN())), 0, 1, 1, {2}));".format(name, runtime/(24*60*60), len(vals)) + ";".join([str(v) for v in vals4]) + "\n")
#f.write("{0};NMI;{1};=MIN(OFFSET(INDIRECT(ADDRESS(ROW(),COLUMN())), 0, 4, 1, {2}));=MAX(OFFSET(INDIRECT(ADDRESS(ROW(),COLUMN())), 0, 3, 1, {2}));=AVERAGE(OFFSET(INDIRECT(ADDRESS(ROW(),COLUMN())), 0, 2, 1, {2}));=STDEV.P(OFFSET(INDIRECT(ADDRESS(ROW(),COLUMN())), 0, 1, 1, {2}));".format(name, runtime/(24*60*60), len(vals)) + ";".join([str(v) for v in vals5]) + "\n")
#f.write("{0};SSIM;{1};=MIN(OFFSET(INDIRECT(ADDRESS(ROW(),COLUMN())), 0, 4, 1, {2}));=MAX(OFFSET(INDIRECT(ADDRESS(ROW(),COLUMN())), 0, 3, 1, {2}));=AVERAGE(OFFSET(INDIRECT(ADDRESS(ROW(),COLUMN())), 0, 2, 1, {2}));=STDEV.P(OFFSET(INDIRECT(ADDRESS(ROW(),COLUMN())), 0, 1, 1, {2}));".format(name, runtime/(24*60*60), len(vals)) + ";".join([str(v) for v in vals6]) + "\n")
#f.write("{0};NRMSE;{1};=MIN(OFFSET(INDIRECT(ADDRESS(ROW(),COLUMN())), 0, 4, 1, {2}));=MAX(OFFSET(INDIRECT(ADDRESS(ROW(),COLUMN())), 0, 3, 1, {2}));=AVERAGE(OFFSET(INDIRECT(ADDRESS(ROW(),COLUMN())), 0, 2, 1, {2}));=STDEV.P(OFFSET(INDIRECT(ADDRESS(ROW(),COLUMN())), 0, 1, 1, {2}));".format(name, runtime/(24*60*60), len(vals)) + ";".join([str(v) for v in vals7]) + "\n")
#if "rec" in stats_file:
# f.write("{0};FWHM;{1};=MIN(OFFSET(INDIRECT(ADDRESS(ROW(),COLUMN())), 0, 4, 1, {2}));=MAX(OFFSET(INDIRECT(ADDRESS(ROW(),COLUMN())), 0, 3, 1, {2}));=AVERAGE(OFFSET(INDIRECT(ADDRESS(ROW(),COLUMN())), 0, 2, 1, {2}));=STDEV.P(OFFSET(INDIRECT(ADDRESS(ROW(),COLUMN())), 0, 1, 1, {2}));".format(name, runtime/(24*60*60), len(vals)) + ";".join([str(v) for v in vals8]) + "\n")
if "rec" in stats_file:
# f.write("{0};Area;{1};=MIN(OFFSET(INDIRECT(ADDRESS(ROW(),COLUMN())), 0, 4, 1, {2}));=MAX(OFFSET(INDIRECT(ADDRESS(ROW(),COLUMN())), 0, 3, 1, {2}));=AVERAGE(OFFSET(INDIRECT(ADDRESS(ROW(),COLUMN())), 0, 2, 1, {2}));=STDEV.P(OFFSET(INDIRECT(ADDRESS(ROW(),COLUMN())), 0, 1, 1, {2}));".format(name, runtime/(24*60*60), len(vals)) + ";".join([str(v) for v in vals9]) + "\n")
print(stats_file, "bytes written", f.write(" & ".join([str(d) for d in [name, runtime/(24*60*60), np.mean(vals), np.mean(vals6), np.mean(vals7), np.mean(vals9)]]) + "\n"))
else:
print(name, len(vals), len(vals6), len(vals7))
print(stats_file, "bytes written", f.write(" & ".join([str(d) for d in [name, runtime/(24*60*60), np.mean(vals), np.mean(vals6), np.mean(vals7)]]) + "\n"))
def evalSinoResults(out_path, in_path, projname, methods=None):
ims, ims_un, mas, kvs, angles, coord_systems, sids, sods = load_data.read_dicoms(in_path)
detector_shape = np.array((1920,2480))
detector_mult = int(np.floor(detector_shape[0] / ims_un.shape[1]))
detector_edges = int(detector_shape[0] / detector_mult - ims_un.shape[1]), int(detector_shape[1] / detector_mult - ims_un.shape[2])
i0_ims, i0_mas, i0_kvs = i0_data.i0_data(detector_mult, ims_un.shape[1])
res = np.mean( np.mean(i0_ims, axis=(1,2))[:,np.newaxis,np.newaxis] / i0_ims, axis=0)
i0s = i0_data.i0_interpol(i0_ims, i0_mas, np.mean(mas))
i0s[i0s==0] = 1e-8
ims_un[ims_un==0] = 1e-8
#print(detector_shape, detector_mult, detector_edges, ims_un.shape, i0_ims.shape, i0s.shape, np.count_nonzero(ims_un==0))
ims_norm = (np.array(-np.log(ims_un/i0s), dtype=np.float32))
#print(np.count_nonzero(~np.isfinite(ims_norm)),np.count_nonzero(~np.isfinite(ims_un)),np.count_nonzero(~np.isfinite(i0s)))
#print(ims_norm.flatten()[np.isnan(ims_norm).flatten()][:10], ims_un.flatten()[np.isnan(ims_norm).flatten()][:10], i0s.flatten()[np.isnan(np.sum(ims_norm,axis=0)).flatten()][:10])
ims_norm[np.isnan(ims_norm)] = 0
print("forcast_"+projname+"4_sino-output", out_path)
for filename in os.listdir(out_path):
#if re.fullmatch("forcast_"+projname+"4_sino-output", filename) != None:
if "forcast_"+projname+"4_sino-output" in filename:
#if re.fullmatch("target_sino.nrrd", filename) != None:
img = sitk.ReadImage(os.path.join(out_path, filename))
proj = (sitk.GetArrayFromImage(img))
if proj.dtype != np.float32:
proj = np.array(proj, dtype=np.float32)
ims_norm2 = np.swapaxes(proj, 0, 1)
def sino_data():
input_sino = True
for filename in os.listdir(out_path):
if re.fullmatch("forcast_(.+?)_sino-output.nrrd", filename) != None and projname in filename and float(filename.split("_")[4]) in methods:
img = sitk.ReadImage(os.path.join(out_path, filename))
proj = (sitk.GetArrayFromImage(img))
if proj.dtype != np.float32:
proj = np.array(proj, dtype=np.float32)
img = sitk.ReadImage(os.path.join(out_path, filename.replace("sino-output", "projs-input")))
target = (sitk.GetArrayFromImage(img))
if target.dtype != np.float32:
target = np.array(target, dtype=np.float32)
#projs.append(proj)
#names.append("_".join(filename.split('_')[1:-1]))
yield "_".join(filename.split('_')[1:-1]), proj, target
if re.fullmatch("forcast_(.+?)_sino-input.nrrd", filename) != None and projname in filename and input_sino == False:
input_sino = True
img = sitk.ReadImage(os.path.join(out_path, filename))
proj = (sitk.GetArrayFromImage(img))
if proj.dtype != np.float32:
proj = np.array(proj, dtype=np.float32)
#projs.append(proj)
#names.append(projname+"_input")
yield projname+"_input", proj
if re.fullmatch("trajtomo_reco_matlab_(.+?)_output.nrrd", filename) != None and projname in filename:
img = sitk.ReadImage(os.path.join(out_path, filename))
proj = (sitk.GetArrayFromImage(img))
if proj.dtype != np.float32:
proj = np.array(proj, dtype=np.float32)
#projs.append(proj)
#names.append("_".join(filename.split('_')[3:-1])+"_matlab")
yield "_".join(filename.split('_')[3:-1])+"_matlab", proj
for name, proj, target in sino_data():
print(name)
skip = int(np.ceil(ims_un.shape[0]/proj.shape[1]))
#i0s = [i0_est(ims_un[::skip][i], proj[:,i]) for i in range(proj.shape[1])]
#ims = np.array(-np.log(ims_un[::skip]/np.mean(i0s)), dtype=np.float32)
#i0s = i0_interpol(i0_ims, i0_mas, np.mean(mas))
#ims = -np.log(ims/i0s)[::skip]
ims = ims_norm[::skip]
ims2 = ims_norm2[::skip]
i0s = np.array([i0_data.i0_est(ims_un[i], proj[:,i])*res for i in range(ims_un.shape[0])])
i0s = np.mean(i0s, axis=0)
i0s[i0s==0] = 1e-8
ims_norm3 = (np.array(-np.log(ims_un/i0s), dtype=np.float32))
ims3 = ims_norm3[::skip]
try:
#evalPerformance(np.swapaxes(proj, 0, 1), ims, 0, name, 'stats_proj.csv')
#evalPerformance(np.swapaxes(proj, 0, 1), ims2, 0, name+"sim", 'stats_proj.csv')
#evalPerformance(np.swapaxes(proj, 0, 1), ims, 1, name, 'stats_proj.csv')
#evalPerformance(np.swapaxes(proj, 0, 1), ims2, 1, name+"sim", 'stats_proj.csv')
#evalPerformance(np.swapaxes(proj, 0, 1), ims, 2, name, 'stats_proj.csv')
#evalPerformance(np.swapaxes(proj, 0, 1), ims2, 2, name+"sim", 'stats_proj.csv')
evalPerformance(np.swapaxes(proj, 0, 1), ims, 0, name, 'stats_proj.csv')
evalPerformance(np.swapaxes(proj, 0, 1), ims2, 0, name+"sim", 'stats_proj.csv')
#evalPerformance(np.swapaxes(proj, 0, 1), ims, 0, name, 'stats_proj_rec.csv')
#evalPerformance(np.swapaxes(proj, 0, 1), ims2, 0, name+"sim", 'stats_proj_rec.csv')
#evalPerformance(np.swapaxes(proj, 0, 1), ims, 4, name, 'stats_proj.csv')
#evalPerformance(np.swapaxes(proj, 0, 1), ims2, 4, name+"sim", 'stats_proj.csv')
#evalPerformance(np.swapaxes(proj, 0, 1), ims, 5, name, 'stats_proj.csv')
#evalPerformance(np.swapaxes(proj, 0, 1), ims2, 5, name+"sim", 'stats_proj.csv')
#evalPerformance(np.swapaxes(proj, 0, 1), ims3, 0, name+"i0", 'stats_proj.csv')
evalPerformance(np.swapaxes(proj, 0, 1), np.swapaxes(target,0,1), 0, name+"proj", 'stats_proj.csv')
#evalPerformance(Projection_Preprocessing(np.swapaxes(proj, 0, 1)), Projection_Preprocessing(ims), 0, name+"pre", 'stats_proj.csv')
except Exception as e:
print(e)
raise e
def evalRecoResults(out_path, in_path, projname, methods=None):
global out_rec_meta
input_recos = {}
input_gi_confs = {}
input_fwhm = {}
input_area = {}
metas = {}
for filename in os.listdir(out_path):
#if re.fullmatch("forcast_[^_]+_reco-input.nrrd", filename) != None:
if re.fullmatch("forcast_201020(1_imbu|_imbureg_noimbu|_imbu)_cbct_4_reco-output.nrrd", filename) != None:
#if re.fullmatch("target_reco.nrrd", filename) != None:
try:
img = sitk.ReadImage(os.path.join(out_path, filename))
real_img = np.array(sitk.GetArrayFromImage(img), dtype=np.float32)
#print(real_img.shape, end=" -> ")
#real_img = scipy.ndimage.zoom(real_img, 0.5, order=1)
#print(real_img.shape)
real_name = filename.split('_')[1]
#real_name = ""
if False and real_name == '201020':
continue
#real_name = "genB"
#real_name = real_name.replace('2010201', '201020')
metas[real_name] = (img.GetOrigin(), img.GetSize(), img.GetSpacing(), real_img.shape)
input_recos[real_name] = real_img
input_gi_confs[real_name] = {"GIoldold":[None], "absp1":[None], "p1":[None]}
out_rec_meta = metas[real_name]
print(real_name, filename, real_img.shape)
input_fwhm[real_name] = evalFWHM(real_img, real_name)
input_area[real_name] = evalNeedleArea(real_img, real_img, real_name, real_name)
if False and '2010201' in real_name:
oname = real_name.replace('2010201', '201020')
metas[oname] = metas[real_name]
input_recos[oname] = real_img
input_gi_confs[oname] = input_gi_confs[real_name]
out_rec_meta = metas[real_name]
input_fwhm[oname] = input_fwhm[real_name]
input_area[oname] = input_area[real_name]
evalPerformance(real_img[:,margin:-margin,margin:-margin], real_img[:,margin:-margin,margin:-margin], 0, real_name, 'stats_rec.csv', real_fwhm=input_fwhm[real_name], real_area=input_area[real_name], gi_config=input_gi_confs[real_name])
except Exception as e:
print(e)
if re.fullmatch("forcast_201020_imbu_cbct_4_reco-output.nrrd", filename) != None:
img = sitk.ReadImage(os.path.join(out_path, filename))
real_img = np.array(sitk.GetArrayFromImage(img), dtype=np.float32)
input_recos["2010202"] = real_img
def reco_data():
input_sino = False
for filename in os.listdir(out_path):
if re.fullmatch("forcast_matlab_(.+?)_reco-output.nrrd", filename) != None and projname in filename and float(filename.split("_")[-2]) in methods:
img = sitk.ReadImage(os.path.join(out_path, filename))
proj = sitk.GetArrayFromImage(img)
#projs.append(proj)
#print(filename, proj.shape)
#names.append("_".join(filename.split('_')[2:]))
yield "_".join(filename.split('_')[2:]), proj, filename
elif re.fullmatch("forcast_matlab_(.+?)_reco-output_sirt.nrrd", filename) != None and projname in filename and float(filename.split("_")[-3]) in methods:
img = sitk.ReadImage(os.path.join(out_path, filename))
proj = sitk.GetArrayFromImage(img)
#projs.append(proj)
#print(filename, proj.shape)
#names.append("_".join(filename.split('_')[2:]))
yield "_".join(filename.split('_')[2:]), proj, filename
elif re.fullmatch("forcast_matlab_(.+?)_reco-output_cgls.nrrd", filename) != None and projname in filename and float(filename.split("_")[-3]) in methods:
img = sitk.ReadImage(os.path.join(out_path, filename))
proj = sitk.GetArrayFromImage(img)
#projs.append(proj)
#print(filename, proj.shape)
#names.append("_".join(filename.split('_')[2:]))
yield "_".join(filename.split('_')[2:]), proj, filename
elif re.fullmatch("forcast_(.+?)_reco-output_sirt.nrrd", filename) != None and projname in filename and float(filename.split("_")[-3]) in methods:
img = sitk.ReadImage(os.path.join(out_path, filename))
proj = sitk.GetArrayFromImage(img)
#projs.append(proj)
#print(filename, proj.shape)
#names.append("_".join(filename.split('_')[1:]))
yield "_".join(filename.split('_')[1:]), proj, filename
elif re.fullmatch("forcast_(.+?)_reco-output_cgls.nrrd", filename) != None and projname in filename and float(filename.split("_")[-3]) in methods:
img = sitk.ReadImage(os.path.join(out_path, filename))
proj = sitk.GetArrayFromImage(img)
#projs.append(proj)
#print(filename, proj.shape)
#names.append("_".join(filename.split('_')[1:]))
yield "_".join(filename.split('_')[1:]), proj, filename
elif re.fullmatch("forcast_(.+?)_reco-output.nrrd", filename) != None and projname in filename and float(filename.split("_")[-2]) in methods:
img = sitk.ReadImage(os.path.join(out_path, filename))
proj = sitk.GetArrayFromImage(img)
#projs.append(proj)
#print(filename, proj.shape)
#names.append("_".join(filename.split('_')[1:]))
yield "_".join(filename.split('_')[1:]), proj, filename
elif re.fullmatch("trajtomo_reco_matlab_"+projname+"_reg\.nrrd", filename) != None and projname in filename:
img = sitk.ReadImage(os.path.join(out_path, filename))
proj = sitk.GetArrayFromImage(img)
#projs.append(proj)
#print(filename, proj.shape)
#names.append("_".join(filename.split('_')[3:])+"_matlab")
yield "_".join(filename.split('_')[3:])+"_matlab", proj, filename
elif re.fullmatch("forcast_"+projname.split("_")[0]+"_reco-input.nrrd", filename) != None:# and projname in filename and input_sino == False:
continue
input_sino = True
img = sitk.ReadImage(os.path.join(out_path, filename))
proj = sitk.GetArrayFromImage(img)
#projs.append(proj)
#names.append(projname+"_input")
yield projname+"_input", proj, filename
#elif re.fullmatch("forcast_[^_]+_reco-input.nrrd", filename) != None:
# img = sitk.ReadImage(os.path.join(out_path, filename))
# input_recos[filename.split('_')[1]] = sitk.GetArrayFromImage(img)
for name, proj, filename in reco_data():
print(filename, name)
real_name = name.split('_')[0].replace('A', 'B')
#real_name = "2010201"
ims = np.array(input_recos[real_name])
#if (np.array(proj.shape)!=np.array(ims.shape)).any():
# ims = np.array(scipy.ndimage.zoom(ims, np.array(proj.shape)/np.array(ims.shape), order=1), dtype=np.float32)
# proj = np.array(proj, dtype=np.float32)
if (np.array(proj.shape)!=np.array(ims.shape)).any():
proj = np.array(scipy.ndimage.zoom(proj, np.array(ims.shape)/np.array(proj.shape), order=1), dtype=ims.dtype)
#if "input" not in name:
# continue
if ims.shape == proj.shape:
out_rec_meta = metas[real_name]
try:
mask = utils.create_circular_mask(proj.shape, radius_off=margin)
#write_images(1.0*mask, "mask.nrrd")
output = (proj*mask)[30:-30,margin:-margin,margin:-margin]
real = (ims*mask)[30:-30,margin:-margin,margin:-margin]
#print(output.shape, real.shape)
input_gi_confs[real_name] = {"GIoldold":[None], "absp1":[None], "p1":[None]}
#evalPerformance(output, real, 0, name, 'stats_rec.csv', real_fwhm=input_fwhm[real_name], real_area=input_area[real_name], gi_config=input_gi_confs[real_name])
evalPerformance(Projection_Preprocessing(output), Projection_Preprocessing(real), 0, name+"pre", 'stats_rec.csv', real_fwhm=input_fwhm[real_name], real_area=input_area[real_name], gi_config=input_gi_confs[real_name])
#if "_0_" in name:
# diff = proj-input_recos["201020"]
# write_images(-diff, os.path.join(out_path, "diff_" + name))
# diff = proj-input_recos["2010201"]
# write_images(-diff, os.path.join(out_path, "diff2_" + name))
# diff = proj-input_recos["2010202"]
# write_images(-diff, os.path.join(out_path, "diff3_" + name))
#else:
# diff = proj-input_recos["201020"]
# write_images(diff, os.path.join(out_path, "diff_" + name))
# diff = proj-input_recos["2010201"]
# write_images(diff, os.path.join(out_path, "diff2_" + name))
# diff = proj-input_recos["2010202"]
# write_images(diff, os.path.join(out_path, "diff3_" + name))
except Exception as ex:
print(name, "failed", ex)
raise
def evalAllResults(evalSino=True, evalReco=True, outpath="recos"):
projs = config.get_proj_paths()
if evalSino:
with open("stats_proj.csv", "w") as f:
f.truncate()
f.write(" & ".join(["Name", "Runtime", "NGI", "SSIM", "NRMSE"]) + "\\\\\n")
with open("stats_proj_rec.csv", "w") as f:
f.truncate()
f.write(" & ".join(["Name", "Runtime", "NGI", "SSIM", "NRMSE", "Dice"]) + "\\\\\n")
for name, proj_path, _, methods in projs:
print(proj_path)
evalSinoResults(outpath, proj_path, name, methods)
if evalReco:
with open("stats_rec.csv", "w") as f:
f.truncate()
f.write(" & ".join(["Name", "Runtime", "NGI", "SSIM", "NRMSE", "Area"]) + "\\\\\n")
for name, proj_path, _, methods in projs:
print(proj_path)
evalRecoResults(outpath, proj_path, name, methods)