def prepare_patient_images(patient_id, intermediate_crop=0):
file_lst = []
prefix = str(patient_id).rjust(4, '0')
src_files = helpers.get_files(settings.BASE_PREPROCESSEDIMAGES_DIR, prefix + "*.png")
patient_dir = helpers.get_pred_patient_dir(patient_id)
helpers.create_dir_if_not_exists(patient_dir)
patient_img_dir = helpers.get_pred_patient_img_dir(patient_id)
helpers.create_dir_if_not_exists(patient_img_dir)
helpers.delete_files(patient_img_dir, "*.png")
dummy = numpy.zeros((settings.TARGET_SIZE, settings.TARGET_SIZE))
cv2.imwrite(patient_img_dir + "dummy_overlay.png", dummy)
for src_path in src_files:
file_name = ntpath.basename(src_path)
org_img = cv2.imread(src_path, cv2.IMREAD_GRAYSCALE)
cropped_img = helpers.prepare_cropped_sax_image(org_img, clahe=True, intermediate_crop=intermediate_crop, rotate=0)
if SCALE_SIZE is not None:
cropped_img = cv2.resize(cropped_img, (SCALE_SIZE, SCALE_SIZE), interpolation=cv2.INTER_AREA)
cv2.imwrite(patient_img_dir + file_name, cropped_img)
file_lst.append([file_name, "dummy_overlay.png"])
with open(patient_img_dir + "pred.lst", "wb") as f:
writer = csv.writer(f, delimiter='\t')
writer.writerows(file_lst)
def convert_sax_images(rescale=True, base_size=256, crop_size=256):
target_dir = settings.BASE_PREPROCESSEDIMAGES_DIR
print("Deleting old files..")
helpers.delete_files(target_dir, "*.png")
print("patient\trows\tcols\tspacing\tfile_name")
file_count = 0
for dicom_data in helpers.enumerate_sax_files():
file_count += 1
if dicom_data.in_plane_encoding_direction not in ["ROW", "COL"]:
raise Exception("ROW,COL")
if dicom_data.spacing[0] != dicom_data.spacing[1]:
raise Exception("Data spacings not equal")
print(
str(dicom_data.patient_id) + "\t" + str(dicom_data.rows) + "\t" +
str(dicom_data.columns) + "\t" + str(dicom_data.series_number) +
"\t" + str(dicom_data.spacing) + "\t" +
str(dicom_data.slice_thickness) + "\t" +
str(dicom_data.sequence_name) + "\t" +
str(dicom_data.image_position) + "\t" +
str(dicom_data.slice_location) + "\t" +
dicom_data.in_plane_encoding_direction)
location_id = int(dicom_data.slice_location) + 10000
time_id = dicom_data.create_time
location_id_str = str(location_id).rjust(5, '0')
img_path = target_dir + str(dicom_data.patient_id).rjust(
4, '0') + "_" + dicom_data.series_description.rjust(
8, '0') + "_" + str(dicom_data.instance_number).rjust(
2, '0'
) + "_" + location_id_str + "_" + dicom_data.file_name.replace(
".dcm", ".png")
scipy.misc.imsave(img_path, dicom_data.pixel_array)
img = cv2.imread(img_path, 0)
if dicom_data.in_plane_encoding_direction == "COL":
# rotate counter clockwise when image is column oriented..
img = cv2.transpose(img)
img = cv2.flip(img, 0)
print("")
if rescale:
scale = dicom_data.spacing[0]
img = cv2.resize(img, (0, 0), fx=scale, fy=scale)
sq_img = get_square_crop(img, base_size=base_size, crop_size=crop_size)
clahe = cv2.createCLAHE(tileGridSize=(1, 1))
cl_img = clahe.apply(sq_img)
cv2.imwrite(img_path, cl_img)
def convert_sax_images(rescale=True, base_size=256, crop_size=256):
target_dir = settings.BASE_PREPROCESSEDIMAGES_DIR
print "Deleting old files.."
helpers.delete_files(target_dir, "*.png")
print "patient\trows\tcols\tspacing\tfile_name"
file_count = 0
for dicom_data in helpers.enumerate_sax_files():
file_count += 1
if dicom_data.in_plane_encoding_direction not in ["ROW", "COL"]:
raise Exception("ROW,COL")
if dicom_data.spacing[0] != dicom_data.spacing[1]:
raise Exception("Data spacings not equal")
print str(dicom_data.patient_id) + "\t" + str(dicom_data.rows) + "\t" + str(dicom_data.columns) + "\t" + str(dicom_data.series_number) + "\t" + str(dicom_data.spacing) + "\t" + str(dicom_data.slice_thickness) + "\t" + str(dicom_data.sequence_name) + "\t" + str(dicom_data.image_position) + "\t" + str(dicom_data.slice_location) + "\t" + dicom_data.in_plane_encoding_direction
location_id = int(dicom_data.slice_location) + 10000
time_id = dicom_data.create_time
location_id_str = str(location_id).rjust(5, '0')
img_path = target_dir + str(dicom_data.patient_id).rjust(4, '0') + "_" + dicom_data.series_description.rjust(8, '0') + "_" + str(dicom_data.instance_number).rjust(2, '0') + "_" + location_id_str + "_" + dicom_data.file_name.replace(".dcm", ".png")
scipy.misc.imsave(img_path, dicom_data.pixel_array)
img = cv2.imread(img_path, 0)
if dicom_data.in_plane_encoding_direction == "COL":
# rotate counter clockwise when image is column oriented..
img = cv2.transpose(img)
img = cv2.flip(img, 0)
print ""
if rescale:
scale = dicom_data.spacing[0]
img = cv2.resize(img, (0, 0), fx=scale, fy=scale)
sq_img = get_square_crop(img, base_size=base_size, crop_size=crop_size)
clahe = cv2.createCLAHE(tileGridSize=(1, 1))
cl_img = clahe.apply(sq_img)
cv2.imwrite(img_path, cl_img)
def predict_overlays_patient(patient_id, pred_model_name, pred_model_iter, save_transparents=False, threshold_value=-1):
src_image_dir = helpers.get_pred_patient_img_dir(patient_id)
overlay_dir = helpers.get_pred_patient_overlay_dir(patient_id)
helpers.delete_files(overlay_dir, "*.png")
transparent_overlay_dir = helpers.get_pred_patient_transparent_overlay_dir(patient_id)
helpers.delete_files(transparent_overlay_dir, "*.png")
num_lines = sum(1 for l in open(src_image_dir + "pred.lst"))
batch_size = 1
for try_size in [2, 3, 4, 5]:
if num_lines % try_size == 0:
batch_size = try_size
pred_model = mx.model.FeedForward.load(pred_model_name, pred_model_iter, ctx=mx.gpu(), numpy_batch_size=batch_size)
if not settings.QUICK_MODE:
# 5 crops
predictions_list = []
predictions = []
for crop_indents in [[1, 1], [1, CROP_SIZE - 1], [CROP_SIZE - 1, 1], [CROP_SIZE - 1, CROP_SIZE - 1], [CROP_SIZE / 2, CROP_SIZE / 2]]:
# for crop_indents in [[CROP_SIZE / 2, CROP_SIZE / 2], [CROP_SIZE / 2, 1], [CROP_SIZE / 2, CROP_SIZE - 1]]:
pred_iter = FileIter(root_dir=src_image_dir, flist_name="pred.lst", batch_size=batch_size, augment=False, mean_image=None, crop_size=INPUT_SIZE, crop_indent_x=crop_indents[0], crop_indent_y=crop_indents[1])
tmp_predictions = pred_model.predict(pred_iter)
predictions_list.append(tmp_predictions)
averaged_overlays = []
for image_index in range(0, predictions_list[0].shape[0]):
min_pixels = 99999999.
min_index = - 1
max_pixels = -99999999.
max_index = - 1
for crop_index in range(0, len(predictions_list)):
pred_overlay = predictions_list[crop_index][image_index]
pixel_sum = pred_overlay.sum()
if pixel_sum < min_pixels:
min_pixels = pixel_sum
min_index = crop_index
if pixel_sum > max_pixels:
max_pixels = pixel_sum
max_index = crop_index
sum_overlay = None
sum_item_count = 0
min_index = -1
for crop_index in range(0, len(predictions_list)):
if crop_index != max_index:
continue
pred_overlay = predictions_list[crop_index][image_index]
if sum_overlay is None:
sum_overlay = pred_overlay
sum_item_count += 1
else:
sum_overlay += pred_overlay
sum_item_count += 1
sum_overlay /= sum_item_count
averaged_overlays.append(sum_overlay)
predictions = numpy.vstack(averaged_overlays)
else:
pred_iter = FileIter(root_dir=src_image_dir, flist_name="pred.lst", batch_size=batch_size, augment=False, mean_image=None, crop_size=INPUT_SIZE)
predictions = pred_model.predict(pred_iter)
for i in range(len(predictions)):
y = predictions[i]
y = y.reshape(INPUT_SIZE, INPUT_SIZE)
border_size = CROP_SIZE / 2
y = cv2.copyMakeBorder(y, border_size, border_size, border_size, border_size, cv2.BORDER_CONSTANT, value=0)
y *= 255
if threshold_value >= 0:
y[y <= threshold_value] = 0
y[y > threshold_value] = 255
file_name = ntpath.basename(pred_iter.image_files[i])
cv2.imwrite(overlay_dir + file_name, y)
if save_transparents:
channels = cv2.split(y)
# make argb
empty = numpy.zeros(channels[0].shape, dtype=numpy.float32)
alpha = channels[0].copy()
alpha[alpha == 255] = 75
channels = (channels[0], channels[0], empty, alpha)
transparent_overlay = cv2.merge(channels)
cv2.imwrite(transparent_overlay_dir + file_name, transparent_overlay)
