def label_detected_nucleus(nucleus_dir, ground_truth_dir):
ground_truth_csvs = [str(f) for f in Path(ground_truth_dir).glob('*/*.csv')]
label_output = []
for ground_truth_csv in ground_truth_csvs:
ground_truth_dir, base = os.path.split(ground_truth_csv)
sub_dir = os.path.split(ground_truth_dir)[1]
inference_csv = os.path.join(nucleus_dir, "{}-{}".format(sub_dir, base))
label_csv = os.path.join(nucleus_dir,
"label-{}-{}".format(sub_dir, base))
ground_truth_locations = get_locations_from_csv(
ground_truth_csv, hasHeader=False, hasProb=False)
inference_locations = get_locations_from_csv(
inference_csv, hasHeader=True, hasProb=True)
label_output.clear()
for (y1, x1, prob) in inference_locations:
inside = False
for (y2, x2) in ground_truth_locations:
if is_inside(x1, y1, x2, y2, 32):
inside = True
label_output.append((y1, x1, prob, True))
break
if not inside:
label_output.append((y1, x1, prob, False))
print(len(label_output), len(inference_locations))
assert len(label_output) == len(inference_locations)
tuple_2_csv(label_output, label_csv, ['Y', 'X', 'prob', 'is_mitosis'])
def check_nucleus_inference(inference_dir, ground_truth_dir):
ground_truth_csvs = [
str(f) for f in Path(ground_truth_dir).glob('*/*.csv')
]
matched_count = 0
total_count = 0
for ground_truth_csv in ground_truth_csvs:
ground_truth_dir, base = os.path.split(ground_truth_csv)
sub_dir = os.path.split(ground_truth_dir)[1]
inference_csv = os.path.join(inference_dir,
"{}-{}".format(sub_dir, base))
ground_truth_locations = get_locations_from_csv(ground_truth_csv,
hasHeader=False,
hasProb=False)
inference_locations = get_locations_from_csv(inference_csv,
hasHeader=True,
hasProb=False)
for (x1, y1) in ground_truth_locations:
total_count = total_count + 1
for (x2, y2) in inference_locations:
if is_inside(x2, y2, x1, y1, 32):
matched_count = matched_count + 1
break
print("There are {} ground truth points, found {} of them.".format(
total_count, matched_count))
def combine_csvs(input_dir, output_dir, hasHeader=True, hasProb=True,
clean_output_dir=False):
if clean_output_dir:
shutil.rmtree(output_dir)
input_files = [str(f) for f in Path(input_dir).glob('**/**/*.csv')]
combine_csvs = {}
for input_file in input_files:
points = get_locations_from_csv(input_file, hasHeader=hasHeader,
hasProb=hasProb)
basename, y_offset, x_offset = \
os.path.basename(input_file).split('.')[0].split("_")
if not basename in combine_csvs:
combine_csvs[basename] = []
y_offset = int(y_offset)
x_offset = int(x_offset)
for i in range(len(points)):
points[i] = \
(points[i][0] + y_offset, points[i][1] + x_offset, points[i][2])
combine_csvs[basename].extend(points)
os.makedirs(output_dir, exist_ok=True)
for combined_csv in combine_csvs:
tuple_2_csv(combine_csvs[combined_csv],
os.path.join(output_dir, combined_csv) + '.csv',
columns=['Y', 'X', 'prob'])
def cluster_prediction_result(pred_dir,
eps,
min_samples,
hasHeader,
isWeightedAvg=False,
prob_threshold=0):
""" cluster the prediction results to avoid the duplicated
predictions introduced by the small stride.
Args:
pred_dir: directory for the prediction result
eps: maximum distance between two samples for them to be considered
as in the same neighborhood.
min_samples: number of samples (or total weight) in a neighborhood
for a point to be considered as a core point.
hasHeader: boolean value to indicate if the csv file has the header
isWeightedAvg: boolean value to indicate if add the prediction.
probabilities as the weight to compute the averaged coordinates
of each cluster.
prob_threshold: probability threshold over which the location is
considered a positive prediction for the purposes of clustering.
"""
pred_files = list_files(pred_dir, "*.csv")
pred_files = get_file_id(pred_files, GROUND_TRUTH_FILE_ID_RE)
clustered_dir = os.path.dirname(pred_dir + "/") + "_clustered/"
if os.path.exists(clustered_dir):
print(clustered_dir)
shutil.rmtree(clustered_dir)
for k, pred_file in pred_files.items():
print(pred_file)
pred_locations = get_locations_from_csv(pred_file,
hasHeader=hasHeader,
hasProb=True)
pred_locations = [
p for p in pred_locations if float(p[2]) > prob_threshold
]
# apply dbscan clustering on each prediction file
if len(pred_locations) > 0:
clustered_pred_locations = dbscan_clustering(
pred_locations,
eps=eps,
min_samples=min_samples,
isWeightedAvg=isWeightedAvg)
# save the prediction results
clustered_file_name = pred_file.replace(pred_dir, clustered_dir)
tuple_2_csv(clustered_pred_locations,
clustered_file_name,
columns={'row', 'col', 'avg_prob'})
def csv_2_arr(csv_file, h, w, hasHeader):
"""Convert a csv file with the columns (row,col,val) to a 2D array in
which [row,col] = val.
Args:
csv_file: csv file path.
h: The number of rows in the resulting array.
w: The number of columns in the resulting array.
hasHeader: boolean value to indicate if the input csv file have a
header.
Returns:
A 2D NumPy array of shape (h, w) in which [row,col] = val.
"""
points = get_locations_from_csv(csv_file,
hasHeader=hasHeader,
hasProb=True)
points = np.asarray(points)
return ijv_2_arr(points, h, w)
def extract_patches(img_dir,
location_csv_dir,
output_patch_basedir,
patch_size=64):
location_csv_files = [str(f) for f in Path(location_csv_dir).glob('*.csv')]
if len(location_csv_files) == 0:
raise ValueError(
"Please check the input dir for the location csv files.")
for location_csv_file in location_csv_files:
print("Processing {} ......".format(location_csv_file))
points = get_locations_from_csv(location_csv_file,
hasHeader=True,
hasProb=False)
# Get the image file name.
subfolder = os.path.basename(location_csv_file) \
.replace('-', '/') \
.replace('.csv', '')
img_file = os.path.join(img_dir, "{}.tif".format(subfolder))
print("Processing {} ......".format(img_file))
img = cv2.imread(img_file)
img = np.asarray(img)[:, :, ::-1]
output_patch_dir = os.path.join(output_patch_basedir, subfolder)
if not os.path.exists(output_patch_dir):
os.makedirs(output_patch_dir, exist_ok=True)
for (row, col) in points:
patch = extract_patch(img, row, col, patch_size)
save_patch(patch,
path=output_patch_dir,
lab=0,
case=0,
region=0,
row=row,
col=col,
rotation=0,
row_shift=0,
col_shift=0,
suffix=0,
ext="png")
