def dump_fp_bbox_from_prediction(label_dirs, pred_dir):
pred_dir = Path(pred_dir)
save_path = pred_dir.parent / "bboxes-{}.pkl".format(pred_dir.parent.name)
all_bboxes = {}
counter = 0
for pred_path in sorted(pred_dir.glob("prediction-*.nii.gz")):
print(pred_path.name)
lab_file = pred_path.stem.replace("prediction", "segmentation")
lab_path = misc.find_file(label_dirs, lab_file)
result = array_kits.merge_labels(
nii_kits.nii_reader(pred_path)[1], [0, 2])
reference = array_kits.merge_labels(
nii_kits.nii_reader(lab_path)[1], [0, 2])
fps, tps = array_kits.find_tp_and_fp(result, reference)
for x in fps:
print(x, [x[3] - x[0], x[4] - x[1], x[5] - x[2]])
print()
counter += len(fps)
for x in tps:
print(x, [x[3] - x[0], x[4] - x[1], x[5] - x[2]])
print("#" * 80)
all_bboxes[int(pred_path.stem.replace(".nii", "").split("-")[-1])] = {
"fps": fps,
"tps": tps
}
print("FPs: {}".format(counter))
with save_path.open("wb") as f:
pickle.dump(all_bboxes, f, pickle.HIGHEST_PROTOCOL)
def dump_all_tumor_bbox():
save_file = Path(__file__).parent.parent / "data/LiTS/tumor_summary.csv"
disc = ndi.generate_binary_structure(3, connectivity=1)
info = collections.defaultdict(list)
for lits in LiTS_ROOTS:
for mask_path in sorted(lits.glob("segmentation-*.nii")):
hdr, mask = nii_kits.nii_reader(mask_path)
sx, sy, sz = hdr["srow_x"][0], hdr["srow_y"][1], hdr["srow_z"][2]
voxel = abs(sx * sy * sz)
mask = array_kits.merge_labels(mask, [0, 2])
labeled, num_obj = ndi.label(mask, disc)
objs = ndi.find_objects(labeled)
for i, obj in enumerate(objs):
bbox = array_kits.slices_to_bbox(obj)
area = np.sum(labeled == i + 1) * voxel
info["PID"].append(mask_path.name)
info["TID"].append(i)
info["min_x"].append(bbox[2])
info["min_y"].append(bbox[1])
info["min_z"].append(bbox[0])
info["max_x"].append(bbox[5])
info["max_y"].append(bbox[4])
info["max_z"].append(bbox[3])
info["area/cc"].append(area)
info["num_slices"].append(bbox[3] - bbox[0])
print(mask_path.name, i, bbox, area)
pd.DataFrame(data=info).to_csv(str(save_file))
def check_tumor_hist(num=10,
xrng=(-200, 250),
bins=100,
yrng=(0, 0.02),
show=True,
save_path=None):
name = "volume-{}.nii".format(num)
image_path = misc.find_file(LiTS_ROOTS, name)
print(image_path)
mask_path = image_path.parent / image_path.name.replace(
"volume", "segmentation")
_, image = nii_kits.nii_reader(image_path)
_, mask = nii_kits.nii_reader(mask_path)
analysis_kits.compute_liver_tumor_hist(
image,
mask,
1,
2,
"{} - total".format(image_path.stem),
xrng=xrng,
bins=bins,
yrng=yrng,
show=show,
save_path=Path(save_path or "") / "0-total.png")
# For each tumor
disc = ndi.generate_binary_structure(3, connectivity=1)
labeled, num_obj = ndi.label(array_kits.merge_labels(mask, [0, 2]), disc)
mask = np.clip(mask, 0, 1) + labeled
for i in range(num_obj):
z, y, x = np.mean(np.array(np.where(mask == i + 2)),
axis=1).astype(np.int32)
analysis_kits.compute_liver_tumor_hist(
image,
mask,
1,
2 + i,
"{} - tumor {} xyz: ({}, {}, {})".format(image_path.stem, i + 1, x,
y, z),
xrng=xrng,
bins=bins,
yrng=yrng,
show=show,
save_path=Path(save_path or "") / "{}-tumor.png".format(i + 1))
def dump_all_liver_tumor_hist():
if "Windows" in platform.system():
save_dir = Path(r"D:\DataSet\LiTS\hist")
elif "Linux" in platform.system():
save_dir = Path(__file__).parent.parent / "data/LiTS/hist"
for lits in LiTS_ROOTS:
for image_path in lits.glob("volume-*.nii"):
print(image_path)
mask_path = image_path.parent / image_path.name.replace(
"volume", "segmentation")
_, image = nii_kits.nii_reader(image_path)
_, mask = nii_kits.nii_reader(mask_path)
analysis_kits.compute_liver_tumor_hist(
image,
mask,
1,
2,
image_path.stem,
show=False,
save_path=save_dir / image_path.with_suffix(".png").name)
def run(mgr):
_, temp = nii_kits.nii_reader(
Path(__file__).parent / "model_dir/016_osmn_in_noise"
"/prediction/prediction-113.nii.gz")
temp = arr_ops.merge_labels(temp, [0, 2])
temp = temp[arr_ops.bbox_to_slices(mgr.bbox)]
temp = ndi.zoom(
temp,
[1, mgr.shape[0] / temp.shape[1], mgr.shape[1] / temp.shape[2]],
order=0)[n:]
for x in np.concatenate((temp, np.flip(temp, axis=0)), axis=0):
yield x
def dump_all_tumor_det_metrics():
pred_dir = Path(
__file__).parent.parent / "model_dir/004_triplet/prediction"
save_file = Path(
__file__).parent.parent / "model_dir/004_triplet/det_met.txt"
with save_file.open("w") as f:
metrics = []
for res_path in pred_dir.glob("prediction-*.nii.gz"):
ref_path = misc.find_file(
LiTS_ROOTS, res_path.stem.replace("prediction",
"segmentation"))
_, res = nii_kits.nii_reader(res_path)
res = np.clip(res - 1, 0, 1)
_, ref = nii_kits.nii_reader(ref_path)
ref = np.clip(ref - 1, 0, 1)
met = metric_kits.tumor_detection_metrics(res,
ref,
verbose=True,
name=res_path.stem)
metrics.append(met)
f.write(
"{:s} tp: {:d}, fp: {:d}, pos: {:d}, precision: {:.3f}, recall: {:.3f}\n"
.format(res_path.stem, met["tp"], met["fp"], met["pos"],
met["precision"], met["recall"]))
tps = np.sum([met["tp"] for met in metrics])
fps = np.sum([met["fp"] for met in metrics])
pos = np.sum([met["pos"] for met in metrics])
print("#" * 80)
f.write("#" * 80 + "\n")
info = (
"TPs: {:3d} FPs: {:3d} Pos: {:3d} Precision: {:.3f} Recall: {:.3f}"
.format(tps, fps, pos, tps / (tps + fps), tps / pos))
print(info)
f.write(info)
def check_np_hist(num=10, xrng=(-200, 250), bins=100, yrng=(0, 0.02)):
name = "volume-{}.nii".format(num)
image_path = misc.find_file(LiTS_ROOTS, name)
print(image_path)
mask_path = image_path.parent / image_path.name.replace(
"volume", "segmentation")
_, image = nii_kits.nii_reader(image_path)
_, mask = nii_kits.nii_reader(mask_path)
a, b = image[mask == 1].flat, image[mask == 2].flat
plt.hist(a, bins=bins, range=xrng, alpha=0.8, density=True)
plt.hist(b, bins=bins, range=xrng, alpha=0.8, density=True)
val3, bin3 = np.histogram(a, bins=bins, range=xrng, density=True)
val4, bin4 = np.histogram(b, bins=bins, range=xrng, density=True)
def foo(x):
x = np.asarray(x)
return (x[1:] + x[:-1]) / 2
plt.plot(foo(bin3), val3)
plt.plot(foo(bin4), val4)
plt.xlim(xrng)
plt.ylim(yrng)
plt.show()
def get_file_list(self):
if self.liver_range is None and self.bbox_file.exists():
with self.bbox_file.open("rb") as f:
self.liver_range = pickle.load(f)
if not self.table:
for path in self.pred_dir.glob("*.nii.gz"):
hdr, _ = nii_kits.nii_reader(str(path), only_meta=True)
names = path.stem.split(".")[0].split("-")
name = "Pred-{:03d}".format(int(names[1]))
if self.liver_range is not None:
rng = self.liver_range[path.name.replace("prediction", "volume").split(".")[0]][0]
self.table.append((name, "{}/{}".format(rng[5] - rng[2] + 1,
hdr.get_data_shape()[-1])))
else:
self.table.append((name, "{}".format(hdr.get_data_shape()[-1])))
return self.table