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step1_preprocess_luna16.py
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step1_preprocess_luna16.py
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import settings
import helpers
import SimpleITK # conda install -c https://conda.anaconda.org/simpleitk SimpleITK
import numpy
import pandas
import ntpath
import cv2 # conda install -c https://conda.anaconda.org/menpo opencv3
import shutil
import random
import math
import multiprocessing
from bs4 import BeautifulSoup # conda install beautifulsoup4, coda install lxml
import os
import glob
random.seed(1321)
numpy.random.seed(1321)
#This function is used to get the scan of a certain patient id from the project's directory
def find_mhd_file(patient_id):
for subject_no in range(settings.LUNA_SUBSET_START_INDEX, 10):
src_dir = settings.LUNA16_RAW_SRC_DIR + "subset" + str(subject_no) + "/"
for src_path in glob.glob(src_dir + "*.mhd"):
if patient_id in src_path:
return src_path
return None
#This function loads the lidc annotations xml files in the resources folder, extract the annotations, characterstics.
def load_lidc_xml(xml_path, agreement_threshold=0, only_patient=None, save_nodules=False):
pos_lines = []
neg_lines = []
extended_lines = []
with open(xml_path, 'r') as xml_file:
markup = xml_file.read()
xml = BeautifulSoup(markup, features="xml")
if xml.LidcReadMessage is None:
return None, None, None
patient_id = xml.LidcReadMessage.ResponseHeader.SeriesInstanceUid.text
if only_patient is not None:
if only_patient != patient_id:
return None, None, None
src_path = find_mhd_file(patient_id)
if src_path is None:
return None, None, None
print(patient_id)
itk_img = SimpleITK.ReadImage(src_path)
img_array = SimpleITK.GetArrayFromImage(itk_img)
num_z, height, width = img_array.shape #heightXwidth constitute the transverse plane
origin = numpy.array(itk_img.GetOrigin()) # x,y,z Origin in world coordinates (mm)
spacing = numpy.array(itk_img.GetSpacing()) # spacing of voxels in world coor. (mm)
rescale = spacing / settings.TARGET_VOXEL_MM #The rescaling factor needed the setup our scan on the same scale as others
reading_sessions = xml.LidcReadMessage.find_all("readingSession")
for reading_session in reading_sessions:
# print("Sesion")
nodules = reading_session.find_all("unblindedReadNodule")
#Extracting the coordinates of nodule for each nodule
for nodule in nodules:
nodule_id = nodule.noduleID.text
# print(" ", nodule.noduleID)
rois = nodule.find_all("roi")
x_min = y_min = z_min = 999999
x_max = y_max = z_max = -999999
if len(rois) < 2:
continue
for roi in rois:
z_pos = float(roi.imageZposition.text)
z_min = min(z_min, z_pos)
z_max = max(z_max, z_pos)
edge_maps = roi.find_all("edgeMap")
for edge_map in edge_maps:
x = int(edge_map.xCoord.text)
y = int(edge_map.yCoord.text)
x_min = min(x_min, x)
y_min = min(y_min, y)
x_max = max(x_max, x)
y_max = max(y_max, y)
if x_max == x_min:
continue
if y_max == y_min:
continue
x_diameter = x_max - x_min
x_center = x_min + x_diameter / 2
y_diameter = y_max - y_min
y_center = y_min + y_diameter / 2
z_diameter = z_max - z_min
z_center = z_min + z_diameter / 2
z_center -= origin[2]
z_center /= spacing[2]
x_center_perc = round(x_center / img_array.shape[2], 4)
y_center_perc = round(y_center / img_array.shape[1], 4)
z_center_perc = round(z_center / img_array.shape[0], 4)
diameter = max(x_diameter , y_diameter)
diameter_perc = round(max(x_diameter / img_array.shape[2], y_diameter / img_array.shape[1]), 4)
if nodule.characteristics is None:
print("!!!!Nodule:", nodule_id, " has no charecteristics")
continue
if nodule.characteristics.malignancy is None:
print("!!!!Nodule:", nodule_id, " has no malignacy")
continue
#Extracting the chars
malignacy = nodule.characteristics.malignancy.text
sphericiy = nodule.characteristics.sphericity.text
margin = nodule.characteristics.margin.text
spiculation = nodule.characteristics.spiculation.text
texture = nodule.characteristics.texture.text
calcification = nodule.characteristics.calcification.text
internal_structure = nodule.characteristics.internalStructure.text
lobulation = nodule.characteristics.lobulation.text
subtlety = nodule.characteristics.subtlety.text
line = [nodule_id, x_center_perc, y_center_perc, z_center_perc, diameter_perc, malignacy]
extended_line = [patient_id, nodule_id, x_center_perc, y_center_perc, z_center_perc, diameter_perc, malignacy, sphericiy, margin, spiculation, texture, calcification, internal_structure, lobulation, subtlety ]
pos_lines.append(line)
extended_lines.append(extended_line)
nonNodules = reading_session.find_all("nonNodule")
#Extract the coordinates and chars for the non nodule masses in the data
for nonNodule in nonNodules:
z_center = float(nonNodule.imageZposition.text)
z_center -= origin[2]
z_center /= spacing[2]
x_center = int(nonNodule.locus.xCoord.text)
y_center = int(nonNodule.locus.yCoord.text)
nodule_id = nonNodule.nonNoduleID.text
x_center_perc = round(x_center / img_array.shape[2], 4)
y_center_perc = round(y_center / img_array.shape[1], 4)
z_center_perc = round(z_center / img_array.shape[0], 4)
diameter_perc = round(max(6 / img_array.shape[2], 6 / img_array.shape[1]), 4)
# print("Non nodule!", z_center)
line = [nodule_id, x_center_perc, y_center_perc, z_center_perc, diameter_perc, 0]
neg_lines.append(line)
if agreement_threshold > 1:
filtered_lines = []
for pos_line1 in pos_lines:
id1 = pos_line1[0]
x1 = pos_line1[1]
y1 = pos_line1[2]
z1 = pos_line1[3]
d1 = pos_line1[4]
overlaps = 0
for pos_line2 in pos_lines:
id2 = pos_line2[0]
if id1 == id2:
continue
x2 = pos_line2[1]
y2 = pos_line2[2]
z2 = pos_line2[3]
d2 = pos_line1[4]
dist = math.sqrt(math.pow(x1 - x2, 2) + math.pow(y1 - y2, 2) + math.pow(z1 - z2, 2))
if dist < d1 or dist < d2:
overlaps += 1
if overlaps >= agreement_threshold:
filtered_lines.append(pos_line1)
# else:
# print("Too few overlaps")
pos_lines = filtered_lines
#Saving everything
df_annos = pandas.DataFrame(pos_lines, columns=["anno_index", "coord_x", "coord_y", "coord_z", "diameter", "malscore"])
df_annos.to_csv(settings.LUNA16_EXTRACTED_IMAGE_DIR + "_labels/" + patient_id + "_annos_pos_lidc.csv", index=False)
df_neg_annos = pandas.DataFrame(neg_lines, columns=["anno_index", "coord_x", "coord_y", "coord_z", "diameter", "malscore"])
df_neg_annos.to_csv(settings.LUNA16_EXTRACTED_IMAGE_DIR + "_labels/" + patient_id + "_annos_neg_lidc.csv", index=False)
# return [patient_id, spacing[0], spacing[1], spacing[2]]
return pos_lines, neg_lines, extended_lines
#A function to normalize an image using Min Max method
def normalize(image):
MIN_BOUND = -1000.0
MAX_BOUND = 400.0
image = (image - MIN_BOUND) / (MAX_BOUND - MIN_BOUND)
image[image > 1] = 1.
image[image < 0] = 0.
return image
#The logic of image processing performed
def process_image(src_path):
patient_id = ntpath.basename(src_path).replace(".mhd", "")
print("Patient: ", patient_id)
dst_dir = settings.LUNA16_EXTRACTED_IMAGE_DIR + patient_id + "/"
if not os.path.exists(dst_dir):
os.mkdir(dst_dir)
itk_img = SimpleITK.ReadImage(src_path)
img_array = SimpleITK.GetArrayFromImage(itk_img)
print("Img array: ", img_array.shape)
origin = numpy.array(itk_img.GetOrigin()) # x,y,z Origin in world coordinates (mm)
print("Origin (x,y,z): ", origin)
direction = numpy.array(itk_img.GetDirection()) # x,y,z Origin in world coordinates (mm)
print("Direction: ", direction)
spacing = numpy.array(itk_img.GetSpacing()) # spacing of voxels in world coor. (mm)
print("Spacing (x,y,z): ", spacing)
rescale = spacing / settings.TARGET_VOXEL_MM
print("Rescale: ", rescale)
#Rescale our scan
img_array = helpers.rescale_patient_images(img_array, spacing, settings.TARGET_VOXEL_MM)
img_list = []
#Iterate on each frame
for i in range(img_array.shape[0]):
img = img_array[i]
#Get the segmented lungs, so we know the regions of interest, using Segmentation
seg_img, mask = helpers.get_segmented_lungs(img.copy())
img_list.append(seg_img)
img = normalize(img)
cv2.imwrite(dst_dir + "img_" + str(i).rjust(4, '0') + "_i.png", img * 255)
cv2.imwrite(dst_dir + "img_" + str(i).rjust(4, '0') + "_m.png", mask * 255)
#Not used
def process_pos_annotations_patient(src_path, patient_id):
df_node = pandas.read_csv("resources/luna16_annotations/annotations.csv")
dst_dir = settings.LUNA16_EXTRACTED_IMAGE_DIR + "_labels/"
if not os.path.exists(dst_dir):
os.mkdir(dst_dir)
dst_dir = dst_dir + patient_id + "/"
if not os.path.exists(dst_dir):
os.mkdir(dst_dir)
itk_img = SimpleITK.ReadImage(src_path)
img_array = SimpleITK.GetArrayFromImage(itk_img)
print("Img array: ", img_array.shape)
df_patient = df_node[df_node["seriesuid"] == patient_id]
print("Annos: ", len(df_patient))
num_z, height, width = img_array.shape #heightXwidth constitute the transverse plane
origin = numpy.array(itk_img.GetOrigin()) # x,y,z Origin in world coordinates (mm)
print("Origin (x,y,z): ", origin)
spacing = numpy.array(itk_img.GetSpacing()) # spacing of voxels in world coor. (mm)
print("Spacing (x,y,z): ", spacing)
rescale = spacing /settings.TARGET_VOXEL_MM
print("Rescale: ", rescale)
direction = numpy.array(itk_img.GetDirection()) # x,y,z Origin in world coordinates (mm)
print("Direction: ", direction)
flip_direction_x = False
flip_direction_y = False
if round(direction[0]) == -1:
origin[0] *= -1
direction[0] = 1
flip_direction_x = True
print("Swappint x origin")
if round(direction[4]) == -1:
origin[1] *= -1
direction[4] = 1
flip_direction_y = True
print("Swappint y origin")
print("Direction: ", direction)
assert abs(sum(direction) - 3) < 0.01
patient_imgs = helpers.load_patient_images(patient_id, settings.LUNA16_EXTRACTED_IMAGE_DIR, "*_i.png")
pos_annos = []
df_patient = df_node[df_node["seriesuid"] == patient_id]
anno_index = 0
for index, annotation in df_patient.iterrows():
node_x = annotation["coordX"]
if flip_direction_x:
node_x *= -1
node_y = annotation["coordY"]
if flip_direction_y:
node_y *= -1
node_z = annotation["coordZ"]
diam_mm = annotation["diameter_mm"]
print("Node org (x,y,z,diam): ", (round(node_x, 2), round(node_y, 2), round(node_z, 2), round(diam_mm, 2)))
center_float = numpy.array([node_x, node_y, node_z])
center_int = numpy.rint((center_float-origin) / spacing)
# center_int = numpy.rint((center_float - origin) )
print("Node tra (x,y,z,diam): ", (center_int[0], center_int[1], center_int[2]))
# center_int_rescaled = numpy.rint(((center_float-origin) / spacing) * rescale)
center_float_rescaled = (center_float - origin) / settings.TARGET_VOXEL_MM
center_float_percent = center_float_rescaled / patient_imgs.swapaxes(0, 2).shape
# center_int = numpy.rint((center_float - origin) )
print("Node sca (x,y,z,diam): ", (center_float_rescaled[0], center_float_rescaled[1], center_float_rescaled[2]))
diameter_pixels = diam_mm / settings.TARGET_VOXEL_MM
diameter_percent = diameter_pixels / float(patient_imgs.shape[1])
pos_annos.append([anno_index, round(center_float_percent[0], 4), round(center_float_percent[1], 4), round(center_float_percent[2], 4), round(diameter_percent, 4), 1])
anno_index += 1
df_annos = pandas.DataFrame(pos_annos, columns=["anno_index", "coord_x", "coord_y", "coord_z", "diameter", "malscore"])
df_annos.to_csv(settings.LUNA16_EXTRACTED_IMAGE_DIR + "_labels/" + patient_id + "_annos_pos.csv", index=False)
return [patient_id, spacing[0], spacing[1], spacing[2]]
#Not used
def process_excluded_annotations_patient(src_path, patient_id):
df_node = pandas.read_csv("resources/luna16_annotations/annotations_excluded.csv")
dst_dir = settings.LUNA16_EXTRACTED_IMAGE_DIR + "_labels/"
if not os.path.exists(dst_dir):
os.mkdir(dst_dir)
dst_dir = dst_dir + patient_id + "/"
if not os.path.exists(dst_dir):
os.mkdir(dst_dir)
# pos_annos_df = pandas.read_csv(TRAIN_DIR + "metadata/" + patient_id + "_annos_pos_lidc.csv")
pos_annos_df = pandas.read_csv(settings.LUNA16_EXTRACTED_IMAGE_DIR + "_labels/" + patient_id + "_annos_pos.csv")
pos_annos_manual = None
manual_path = settings.EXTRA_DATA_DIR + "luna16_manual_labels/" + patient_id + ".csv"
if os.path.exists(manual_path):
pos_annos_manual = pandas.read_csv(manual_path)
dmm = pos_annos_manual["dmm"] # check
itk_img = SimpleITK.ReadImage(src_path)
img_array = SimpleITK.GetArrayFromImage(itk_img)
print("Img array: ", img_array.shape)
df_patient = df_node[df_node["seriesuid"] == patient_id]
print("Annos: ", len(df_patient))
num_z, height, width = img_array.shape #heightXwidth constitute the transverse plane
origin = numpy.array(itk_img.GetOrigin()) # x,y,z Origin in world coordinates (mm)
print("Origin (x,y,z): ", origin)
spacing = numpy.array(itk_img.GetSpacing()) # spacing of voxels in world coor. (mm)
print("Spacing (x,y,z): ", spacing)
rescale = spacing / settings.TARGET_VOXEL_MM
print("Rescale: ", rescale)
direction = numpy.array(itk_img.GetDirection()) # x,y,z Origin in world coordinates (mm)
print("Direction: ", direction)
flip_direction_x = False
flip_direction_y = False
if round(direction[0]) == -1:
origin[0] *= -1
direction[0] = 1
flip_direction_x = True
print("Swappint x origin")
if round(direction[4]) == -1:
origin[1] *= -1
direction[4] = 1
flip_direction_y = True
print("Swappint y origin")
print("Direction: ", direction)
assert abs(sum(direction) - 3) < 0.01
patient_imgs = helpers.load_patient_images(patient_id, settings.LUNA16_EXTRACTED_IMAGE_DIR, "*_i.png")
neg_annos = []
df_patient = df_node[df_node["seriesuid"] == patient_id]
anno_index = 0
for index, annotation in df_patient.iterrows():
node_x = annotation["coordX"]
if flip_direction_x:
node_x *= -1
node_y = annotation["coordY"]
if flip_direction_y:
node_y *= -1
node_z = annotation["coordZ"]
center_float = numpy.array([node_x, node_y, node_z])
center_int = numpy.rint((center_float-origin) / spacing)
center_float_rescaled = (center_float - origin) / settings.TARGET_VOXEL_MM
center_float_percent = center_float_rescaled / patient_imgs.swapaxes(0, 2).shape
# center_int = numpy.rint((center_float - origin) )
# print("Node sca (x,y,z,diam): ", (center_float_rescaled[0], center_float_rescaled[1], center_float_rescaled[2]))
diameter_pixels = 6 / settings.TARGET_VOXEL_MM
diameter_percent = diameter_pixels / float(patient_imgs.shape[1])
ok = True
for index, row in pos_annos_df.iterrows():
pos_coord_x = row["coord_x"] * patient_imgs.shape[2]
pos_coord_y = row["coord_y"] * patient_imgs.shape[1]
pos_coord_z = row["coord_z"] * patient_imgs.shape[0]
diameter = row["diameter"] * patient_imgs.shape[2]
print((pos_coord_x, pos_coord_y, pos_coord_z))
print(center_float_rescaled)
dist = math.sqrt(math.pow(pos_coord_x - center_float_rescaled[0], 2) + math.pow(pos_coord_y - center_float_rescaled[1], 2) + math.pow(pos_coord_z - center_float_rescaled[2], 2))
if dist < (diameter + 64): # make sure we have a big margin
ok = False
print("################### Too close", center_float_rescaled)
break
if pos_annos_manual is not None and ok:
for index, row in pos_annos_manual.iterrows():
pos_coord_x = row["x"] * patient_imgs.shape[2]
pos_coord_y = row["y"] * patient_imgs.shape[1]
pos_coord_z = row["z"] * patient_imgs.shape[0]
diameter = row["d"] * patient_imgs.shape[2]
print((pos_coord_x, pos_coord_y, pos_coord_z))
print(center_float_rescaled)
dist = math.sqrt(math.pow(pos_coord_x - center_float_rescaled[0], 2) + math.pow(pos_coord_y - center_float_rescaled[1], 2) + math.pow(pos_coord_z - center_float_rescaled[2], 2))
if dist < (diameter + 72): # make sure we have a big margin
ok = False
print("################### Too close", center_float_rescaled)
break
if not ok:
continue
neg_annos.append([anno_index, round(center_float_percent[0], 4), round(center_float_percent[1], 4), round(center_float_percent[2], 4), round(diameter_percent, 4), 1])
anno_index += 1
df_annos = pandas.DataFrame(neg_annos, columns=["anno_index", "coord_x", "coord_y", "coord_z", "diameter", "malscore"])
df_annos.to_csv(settings.LUNA16_EXTRACTED_IMAGE_DIR + "_labels/" + patient_id + "_annos_excluded.csv", index=False)
return [patient_id, spacing[0], spacing[1], spacing[2]]
#Not used
def process_luna_candidates_patient(src_path, patient_id):
dst_dir = settings.LUNA16_EXTRACTED_IMAGE_DIR + "/_labels/"
img_dir = dst_dir + patient_id + "/"
df_pos_annos = pandas.read_csv(dst_dir + patient_id + "_annos_pos_lidc.csv")
if not os.path.exists(dst_dir):
os.mkdir(dst_dir)
pos_annos_manual = None
manual_path = settings.EXTRA_DATA_DIR + "luna16_manual_labels/" + patient_id + ".csv"
if os.path.exists(manual_path):
pos_annos_manual = pandas.read_csv(manual_path)
itk_img = SimpleITK.ReadImage(src_path)
img_array = SimpleITK.GetArrayFromImage(itk_img)
print("Img array: ", img_array.shape)
print("Pos annos: ", len(df_pos_annos))
num_z, height, width = img_array.shape #heightXwidth constitute the transverse plane
origin = numpy.array(itk_img.GetOrigin()) # x,y,z Origin in world coordinates (mm)
print("Origin (x,y,z): ", origin)
spacing = numpy.array(itk_img.GetSpacing()) # spacing of voxels in world coor. (mm)
print("Spacing (x,y,z): ", spacing)
rescale = spacing / settings.TARGET_VOXEL_MM
print("Rescale: ", rescale)
direction = numpy.array(itk_img.GetDirection()) # x,y,z Origin in world coordinates (mm)
print("Direction: ", direction)
flip_direction_x = False
flip_direction_y = False
if round(direction[0]) == -1:
origin[0] *= -1
direction[0] = 1
flip_direction_x = True
print("Swappint x origin")
if round(direction[4]) == -1:
origin[1] *= -1
direction[4] = 1
flip_direction_y = True
print("Swappint y origin")
print("Direction: ", direction)
assert abs(sum(direction) - 3) < 0.01
src_df = pandas.read_csv("resources/luna16_annotations/" + "candidates_V2.csv")
src_df = src_df[src_df["seriesuid"] == patient_id]
src_df = src_df[src_df["class"] == 0]
patient_imgs = helpers.load_patient_images(patient_id, settings.LUNA16_EXTRACTED_IMAGE_DIR, "*_i.png")
candidate_list = []
for df_index, candiate_row in src_df.iterrows():
node_x = candiate_row["coordX"]
if flip_direction_x:
node_x *= -1
node_y = candiate_row["coordY"]
if flip_direction_y:
node_y *= -1
node_z = candiate_row["coordZ"]
candidate_diameter = 6
# print("Node org (x,y,z,diam): ", (round(node_x, 2), round(node_y, 2), round(node_z, 2), round(candidate_diameter, 2)))
center_float = numpy.array([node_x, node_y, node_z])
center_int = numpy.rint((center_float-origin) / spacing)
# center_int = numpy.rint((center_float - origin) )
# print("Node tra (x,y,z,diam): ", (center_int[0], center_int[1], center_int[2]))
# center_int_rescaled = numpy.rint(((center_float-origin) / spacing) * rescale)
center_float_rescaled = (center_float - origin) / settings.TARGET_VOXEL_MM
center_float_percent = center_float_rescaled / patient_imgs.swapaxes(0, 2).shape
# center_int = numpy.rint((center_float - origin) )
# print("Node sca (x,y,z,diam): ", (center_float_rescaled[0], center_float_rescaled[1], center_float_rescaled[2]))
coord_x = center_float_rescaled[0]
coord_y = center_float_rescaled[1]
coord_z = center_float_rescaled[2]
ok = True
for index, row in df_pos_annos.iterrows():
pos_coord_x = row["coord_x"] * patient_imgs.shape[2]
pos_coord_y = row["coord_y"] * patient_imgs.shape[1]
pos_coord_z = row["coord_z"] * patient_imgs.shape[0]
diameter = row["diameter"] * patient_imgs.shape[2]
dist = math.sqrt(math.pow(pos_coord_x - coord_x, 2) + math.pow(pos_coord_y - coord_y, 2) + math.pow(pos_coord_z - coord_z, 2))
if dist < (diameter + 64): # make sure we have a big margin
ok = False
print("################### Too close", (coord_x, coord_y, coord_z))
break
if pos_annos_manual is not None and ok:
for index, row in pos_annos_manual.iterrows():
pos_coord_x = row["x"] * patient_imgs.shape[2]
pos_coord_y = row["y"] * patient_imgs.shape[1]
pos_coord_z = row["z"] * patient_imgs.shape[0]
diameter = row["d"] * patient_imgs.shape[2]
print((pos_coord_x, pos_coord_y, pos_coord_z))
print(center_float_rescaled)
dist = math.sqrt(math.pow(pos_coord_x - center_float_rescaled[0], 2) + math.pow(pos_coord_y - center_float_rescaled[1], 2) + math.pow(pos_coord_z - center_float_rescaled[2], 2))
if dist < (diameter + 72): # make sure we have a big margin
ok = False
print("################### Too close", center_float_rescaled)
break
if not ok:
continue
candidate_list.append([len(candidate_list), round(center_float_percent[0], 4), round(center_float_percent[1], 4), round(center_float_percent[2], 4), round(candidate_diameter / patient_imgs.shape[0], 4), 0])
df_candidates = pandas.DataFrame(candidate_list, columns=["anno_index", "coord_x", "coord_y", "coord_z", "diameter", "malscore"])
df_candidates.to_csv(dst_dir + patient_id + "_candidates_luna.csv", index=False)
#Not used
def process_auto_candidates_patient(src_path, patient_id, sample_count=1000, candidate_type="white"):
dst_dir = settings.LUNA16_EXTRACTED_IMAGE_DIR + "/_labels/"
img_dir = settings.LUNA16_EXTRACTED_IMAGE_DIR + patient_id + "/"
df_pos_annos = pandas.read_csv(dst_dir + patient_id + "_annos_pos_lidc.csv")
pos_annos_manual = None
manual_path = settings.EXTRA_DATA_DIR + "luna16_manual_labels/" + patient_id + ".csv"
if os.path.exists(manual_path):
pos_annos_manual = pandas.read_csv(manual_path)
if not os.path.exists(dst_dir):
os.mkdir(dst_dir)
itk_img = SimpleITK.ReadImage(src_path)
img_array = SimpleITK.GetArrayFromImage(itk_img)
print("Img array: ", img_array.shape)
print("Pos annos: ", len(df_pos_annos))
num_z, height, width = img_array.shape #heightXwidth constitute the transverse plane
origin = numpy.array(itk_img.GetOrigin()) # x,y,z Origin in world coordinates (mm)
print("Origin (x,y,z): ", origin)
spacing = numpy.array(itk_img.GetSpacing()) # spacing of voxels in world coor. (mm)
print("Spacing (x,y,z): ", spacing)
rescale = spacing / settings.TARGET_VOXEL_MM
print("Rescale: ", rescale)
if candidate_type == "white":
wildcard = "*_c.png"
else:
wildcard = "*_m.png"
src_files = glob.glob(img_dir + wildcard)
src_files.sort()
src_candidate_maps = [cv2.imread(src_file, cv2.IMREAD_GRAYSCALE) for src_file in src_files]
candidate_list = []
tries = 0
while len(candidate_list) < sample_count and tries < 10000:
tries += 1
coord_z = int(numpy.random.normal(len(src_files) / 2, len(src_files) / 6))
coord_z = max(coord_z, 0)
coord_z = min(coord_z, len(src_files) - 1)
candidate_map = src_candidate_maps[coord_z]
if candidate_type == "edge":
candidate_map = cv2.Canny(candidate_map.copy(), 100, 200)
non_zero_indices = numpy.nonzero(candidate_map)
if len(non_zero_indices[0]) == 0:
continue
nonzero_index = random.randint(0, len(non_zero_indices[0]) - 1)
coord_y = non_zero_indices[0][nonzero_index]
coord_x = non_zero_indices[1][nonzero_index]
ok = True
candidate_diameter = 6
for index, row in df_pos_annos.iterrows():
pos_coord_x = row["coord_x"] * src_candidate_maps[0].shape[1]
pos_coord_y = row["coord_y"] * src_candidate_maps[0].shape[0]
pos_coord_z = row["coord_z"] * len(src_files)
diameter = row["diameter"] * src_candidate_maps[0].shape[1]
dist = math.sqrt(math.pow(pos_coord_x - coord_x, 2) + math.pow(pos_coord_y - coord_y, 2) + math.pow(pos_coord_z - coord_z, 2))
if dist < (diameter + 48): # make sure we have a big margin
ok = False
print("# Too close", (coord_x, coord_y, coord_z))
break
if pos_annos_manual is not None:
for index, row in pos_annos_manual.iterrows():
pos_coord_x = row["x"] * src_candidate_maps[0].shape[1]
pos_coord_y = row["y"] * src_candidate_maps[0].shape[0]
pos_coord_z = row["z"] * len(src_files)
diameter = row["d"] * src_candidate_maps[0].shape[1]
# print((pos_coord_x, pos_coord_y, pos_coord_z))
# print(center_float_rescaled)
dist = math.sqrt(math.pow(pos_coord_x - coord_x, 2) + math.pow(pos_coord_y - coord_y, 2) + math.pow(pos_coord_z - coord_z, 2))
if dist < (diameter + 72): # make sure we have a big margin
ok = False
print("#Too close", (coord_x, coord_y, coord_z))
break
if not ok:
continue
perc_x = round(coord_x / src_candidate_maps[coord_z].shape[1], 4)
perc_y = round(coord_y / src_candidate_maps[coord_z].shape[0], 4)
perc_z = round(coord_z / len(src_files), 4)
candidate_list.append([len(candidate_list), perc_x, perc_y, perc_z, round(candidate_diameter / src_candidate_maps[coord_z].shape[1], 4), 0])
if tries > 9999:
print("****** WARING!! TOO MANY TRIES ************************************")
df_candidates = pandas.DataFrame(candidate_list, columns=["anno_index", "coord_x", "coord_y", "coord_z", "diameter", "malscore"])
df_candidates.to_csv(dst_dir + patient_id + "_candidates_" + candidate_type + ".csv", index=False)
#a way to use multiprocessing to process many images at the same time.
def process_images(path):
src_path = path
if only_process_patient is None and True:
pool = multiprocessing.Pool(settings.WORKER_POOL_SIZE)
pool.map(process_image, src_paths)
else:
print(src_path)
process_image(src_path)
#not used.
def process_pos_annotations_patient2():
candidate_index = 0
only_patient = "197063290812663596858124411210"
only_patient = None
for subject_no in range(settings.LUNA_SUBSET_START_INDEX, 10):
src_dir = settings.LUNA16_RAW_SRC_DIR + "subset" + str(subject_no) + "/"
for src_path in glob.glob(src_dir + "*.mhd"):
if only_patient is not None and only_patient not in src_path:
continue
patient_id = ntpath.basename(src_path).replace(".mhd", "")
print(candidate_index, " patient: ", patient_id)
process_pos_annotations_patient(src_path, patient_id)
candidate_index += 1
#not used
def process_excluded_annotations_patients(only_patient=None):
candidate_index = 0
for subject_no in range(settings.LUNA_SUBSET_START_INDEX, 10):
src_dir = settings.LUNA16_RAW_SRC_DIR + "subset" + str(subject_no) + "/"
for src_path in glob.glob(src_dir + "*.mhd"):
if only_patient is not None and only_patient not in src_path:
continue
patient_id = ntpath.basename(src_path).replace(".mhd", "")
print(candidate_index, " patient: ", patient_id)
process_excluded_annotations_patient(src_path, patient_id)
candidate_index += 1
def process_auto_candidates_patients():
for subject_no in range(settings.LUNA_SUBSET_START_INDEX, 10):
src_dir = settings.LUNA16_RAW_SRC_DIR + "subset" + str(subject_no) + "/"
for patient_index, src_path in enumerate(glob.glob(src_dir + "*.mhd")):
# if not "100621383016233746780170740405" in src_path:
# continue
patient_id = ntpath.basename(src_path).replace(".mhd", "")
print("Patient: ", patient_index, " ", patient_id)
# process_auto_candidates_patient(src_path, patient_id, sample_count=500, candidate_type="white")
process_auto_candidates_patient(src_path, patient_id, sample_count=200, candidate_type="edge")
def process_luna_candidates_patients(only_patient_id=None):
for subject_no in range(settings.LUNA_SUBSET_START_INDEX, 10):
src_dir = settings.LUNA16_RAW_SRC_DIR + "subset" + str(subject_no) + "/"
for patient_index, src_path in enumerate(glob.glob(src_dir + "*.mhd")):
# if not "100621383016233746780170740405" in src_path:
# continue
patient_id = ntpath.basename(src_path).replace(".mhd", "")
if only_patient_id is not None and patient_id != only_patient_id:
continue
print("Patient: ", patient_index, " ", patient_id)
process_luna_candidates_patient(src_path, patient_id)
def process_lidc_annotations(only_patient=None, agreement_threshold=0):
# lines.append(",".join())
file_no = 0
pos_count = 0
neg_count = 0
all_lines = []
for anno_dir in [d for d in glob.glob("resources/luna16_annotations/*") if os.path.isdir(d)]:
xml_paths = glob.glob(anno_dir + "/*.xml")
for xml_path in xml_paths:
print(file_no, ": ", xml_path)
pos, neg, extended = load_lidc_xml(xml_path=xml_path, only_patient=only_patient, agreement_threshold=agreement_threshold)
if pos is not None:
pos_count += len(pos)
neg_count += len(neg)
print("Pos: ", pos_count, " Neg: ", neg_count)
file_no += 1
all_lines += extended
# if file_no > 10:
# break
# extended_line = [nodule_id, x_center_perc, y_center_perc, z_center_perc, diameter_perc, malignacy, sphericiy, margin, spiculation, texture, calcification, internal_structure, lobulation, subtlety ]
df_annos = pandas.DataFrame(all_lines, columns=["patient_id", "anno_index", "coord_x", "coord_y", "coord_z", "diameter", "malscore", "sphericiy", "margin", "spiculation", "texture", "calcification", "internal_structure", "lobulation", "subtlety"])
df_annos.to_csv(settings.BASE_DIR + "lidc_annotations.csv", index=False)
if __name__ == "__main__":
if True:
only_process_patient = None
process_images(delete_existing=False, only_process_patient=only_process_patient)
if True:
process_lidc_annotations(only_patient=None, agreement_threshold=0)
if True:
process_pos_annotations_patient2()
process_excluded_annotations_patients(only_patient=None)
if True:
process_luna_candidates_patients(only_patient_id=None)
if True:
process_auto_candidates_patients()