(args.save_dir / 'Visualizations').mkdir(exist_ok=True)
samples = os.listdir(str(args.mask_path))
#samples = [os.path.basename(x) for x in glob(str(args.mask_path / '*'))]
samples.sort()
for sample in tqdm(samples, 'Analysing thickness'):
# Sample path
thickness_path = base_path / sample / subdir
# Load image stacks
if thickness_path.exists():
data, files = load(str(thickness_path), rgb=False, n_jobs=args.n_threads)
else:
continue
# Visualize thickness map
print_orthogonal(data, savepath=str(args.save_dir / 'Visualizations' / ('CCTh_' + sample + '.png')))
# Histogram
data = data.flatten()
#data = data[data != 0] # Remove zeros
max_value = np.max(data)
hist, bin_edges = np.histogram(data * 3.2, bins=254, range=[1, max_value]) # Exclude background
plt.hist(data, bins=range(1, max_value))
plt.title(sample)
plt.savefig(str(args.save_dir / 'Visualizations' / ('histogram_' + sample + '.png')))
# Save most the frequent value
results['Most frequent thickness value'].append(bin_edges[np.argmax(hist)])
results['Sample'].append(sample)
mask_final,
dtype=args.dtype)
(args.save_dir.parent / 'probability').mkdir(exist_ok=True)
save(str(args.save_dir.parent / 'probability' / sample),
files,
mask_avg * 255,
dtype=args.dtype)
"""
render_volume(data_yz[:, :, :, 0] * mask_final,
savepath=str(args.save_dir / 'visualizations' / (sample + '_render' + args.dtype)),
white=True, use_outline=False)
"""
print_orthogonal(data_yz[:, :, :, 0],
invert=True,
res=3.2,
title=None,
cbar=True,
savepath=str(args.save_dir / 'visualizations' /
(sample + '_input.png')),
scale_factor=1000)
print_orthogonal(data_yz[:, :, :, 0],
mask=mask_final,
invert=True,
res=3.2,
title=None,
cbar=True,
savepath=str(args.save_dir / 'visualizations' /
(sample + '_prediction.png')),
scale_factor=1000)
#except Exception as e:
# print(f'Sample {sample} failed due to error:\n\n {e}\n\n.')
default='.bmp')
args = parser.parse_args()
# Loop for samples
args.save_dir.mkdir(exist_ok=True)
samples = os.listdir(str(args.data_path))
#samples = [os.path.basename(x) for x in glob(str(args.mask_path / '*.png'))]
samples.sort()
for sample in tqdm(samples, 'Smoothing'):
#try:
# Load image
_, data = load(str(args.data_path / sample), uCT=True)
data_scaled = map_uint16_to_uint8(data,
lower_bound=0,
upper_bound=40000)
print_orthogonal(data_scaled)
img = cv2.imread(str(args.mask_path / sample), cv2.IMREAD_GRAYSCALE)
if args.plot:
plt.imshow(img)
plt.title('Loaded image')
plt.show()
# Opening
kernel = cv2.getStructuringElement(shape=cv2.MORPH_ELLIPSE,
ksize=args.k_closing)
img = cv2.morphologyEx(img, cv2.MORPH_OPEN, kernel=kernel)
#plt.imshow(img); plt.title('Closing'); plt.show()
# Gaussian blur
img = cv2.GaussianBlur(img, ksize=args.k_gauss, sigmaX=0, sigmaY=0)
#plt.imshow(img); plt.title('Gaussian blur'); plt.show()
# Median filter (round kernel 7)
def evaluation_runner(args, config, save_dir):
"""
Calculates evaluation metrics on predicted masks against target.
:param args:
:param config:
:param save_dir:
:return:
"""
start_eval = time()
# Evaluation arguments
args.image_path = args.data_location / 'images'
args.mask_path = args.data_location / 'masks'
args.pred_path = args.data_location / 'predictions'
args.save_dir = args.data_location / 'evaluation'
args.save_dir.mkdir(exist_ok=True)
args.n_labels = 2
# Snapshots to be evaluated
if type(save_dir) != list:
save_dir = [save_dir]
# Iterate through snapshots
for snap in save_dir:
# Initialize results
results = {'Sample': [], 'Dice': [], 'IoU': [], 'Similarity': []}
# Loop for samples
(args.save_dir / ('visualizations_' + snap.name)).mkdir(exist_ok=True)
samples = os.listdir(str(args.mask_path))
samples.sort()
try:
for idx, sample in enumerate(samples):
print(
f'==> Processing sample {idx + 1} of {len(samples)}: {sample}'
)
# Load image stacks
if config['training']['experiment'] == '3D':
mask, files_mask = load(str(args.mask_path / sample),
axis=(0, 2, 1),
rgb=False,
n_jobs=args.n_threads)
pred, files_pred = load(str(args.pred_path / snap.name /
sample),
axis=(0, 2, 1),
rgb=False,
n_jobs=args.n_threads)
data, files_data = load(str(args.image_path / sample),
axis=(0, 2, 1),
rgb=False,
n_jobs=args.n_threads)
# Crop in case of inconsistency
crop = min(pred.shape, mask.shape)
mask = mask[:crop[0], :crop[1], :crop[2]]
pred = pred[:crop[0], :crop[1], :crop[2]]
else:
data = cv2.imread(str(args.image_path / sample))
mask = cv2.imread(str(args.mask_path / sample),
cv2.IMREAD_GRAYSCALE)
pred = cv2.imread(str(args.pred_path / snap.name / sample),
cv2.IMREAD_GRAYSCALE)
if pred is None:
sample = sample[:-4] + '.bmp'
pred = cv2.imread(
str(args.pred_path / snap.name / sample),
cv2.IMREAD_GRAYSCALE)
elif mask is None:
mask = cv2.imread(str(args.mask_path / sample),
cv2.IMREAD_GRAYSCALE)
# Crop in case of inconsistency
crop = min(pred.shape, mask.shape)
mask = mask[:crop[0], :crop[1]]
pred = pred[:crop[0], :crop[1]]
# Evaluate metrics
conf_matrix = calculate_conf(pred.astype(np.bool),
mask.astype(np.bool),
args.n_labels)
dice = calculate_dice(conf_matrix)[1]
iou = calculate_iou(conf_matrix)[1]
sim = calculate_volumetric_similarity(conf_matrix)[1]
print(
f'Sample {sample}: dice = {dice}, IoU = {iou}, similarity = {sim}'
)
# Save predicted full mask
if config['training']['experiment'] == '3D':
print_orthogonal(
data,
invert=False,
res=3.2,
cbar=True,
savepath=str(args.save_dir /
('visualizations_' + snap.name) /
(sample + '_input.png')),
scale_factor=1500)
print_orthogonal(
data,
mask=mask,
invert=False,
res=3.2,
cbar=True,
savepath=str(args.save_dir /
('visualizations_' + snap.name) /
(sample + '_reference.png')),
scale_factor=1500)
print_orthogonal(
data,
mask=pred,
invert=False,
res=3.2,
cbar=True,
savepath=str(args.save_dir /
('visualizations_' + snap.name) /
(sample + '_prediction.png')),
scale_factor=1500)
# Update results
results['Sample'].append(sample)
results['Dice'].append(dice)
results['IoU'].append(iou)
results['Similarity'].append(sim)
except AttributeError:
print(f'Sample {sample} failing. Skipping to next one.')
continue
# Add average value to
results['Sample'].append('Average values')
results['Dice'].append(np.average(results['Dice']))
results['IoU'].append(np.average(results['IoU']))
results['Similarity'].append(np.average(results['Similarity']))
# Write to excel
writer = pd.ExcelWriter(
str(args.save_dir / ('metrics_' + str(snap.name))) + '.xlsx')
df1 = pd.DataFrame(results)
df1.to_excel(writer, sheet_name='Metrics')
writer.save()
print(
f'Metrics evaluated in {(time() - start_eval) // 60} minutes, {(time() - start_eval) % 60} seconds.'
)
time_sample = time()
print(f'Processing sample {sample}')
# Load prediction
pred, files = load(str(args.masks / sample), axis=(
1,
2,
0,
))
# Downscale
#pred = (ndi.zoom(pred, 0.25) > 126).astype(np.bool)
if args.plot:
print_orthogonal(pred,
savepath=str(args.th_maps / 'visualization' /
(sample + '_pred.png')))
# Median filter
pred = ndi.median_filter(pred, size=args.median)
if args.plot:
print_orthogonal(pred,
savepath=str(args.th_maps / 'visualization' /
(sample + '_median.png')))
# Thickness analysis
# Create array of correct size
th_map = _local_thickness(pred,
mode=args.mode,
spacing_mm=args.resolution,
stack_axis=1,
# Crop in case of inconsistency
crop = min(pred.shape, mask.shape)
mask = mask[:crop[0], :crop[1], :crop[2]]
pred = pred[:crop[0], :crop[1], :crop[2]]
# Evaluate metrics
conf_matrix = calculate_conf(pred.astype(np.bool), mask.astype(np.bool), args.n_labels)
dice = calculate_dice(conf_matrix)[1]
iou = calculate_iou(conf_matrix)[1]
sim = calculate_volumetric_similarity(conf_matrix)[1]
print(f'Sample {sample}: dice = {dice}, IoU = {iou}, similarity = {sim}')
# Save predicted full mask
print_orthogonal(data, invert=False, res=3.2, cbar=True,
savepath=str(args.save_dir / ('visualizations_' + snap) / (sample + '_input.png')),
scale_factor=1500)
print_orthogonal(data, mask=mask, invert=False, res=3.2, cbar=True,
savepath=str(args.save_dir / ('visualizations_' + snap) / (sample + '_reference.png')),
scale_factor=1500)
print_orthogonal(data, mask=pred, invert=False, res=3.2, cbar=True,
savepath=str(args.save_dir / ('visualizations_' + snap) / (sample + '_prediction.png')),
scale_factor=1500)
# Update results
results['Sample'].append(sample)
results['Dice'].append(dice)
results['IoU'].append(iou)
results['Similarity'].append(sim)
# Add average value to