def run_inference(model,
sess,
batch_size,
input_dir_path,
output_dir_path,
num_parallel_calls=1,
prob_thres=0.5,
eps=64,
min_samples=1,
isWeightedAvg=False):
input_file_paths = [str(f) for f in Path(input_dir_path).glob('*.png')]
input_files = np.asarray(input_file_paths, dtype=np.str)
input_file_dataset = tf.data.Dataset.from_tensor_slices(input_files)
img_dataset = input_file_dataset.map(lambda file: get_image_tf(file),
num_parallel_calls=1)
img_dataset = img_dataset\
.map(lambda img: normalize(img, "resnet_custom"))\
.batch(batch_size=batch_size)
img_iterator = img_dataset.make_one_shot_iterator()
next_batch = img_iterator.get_next()
prob_result = np.empty((0, 1))
while True:
try:
img_batch = sess.run(next_batch)
pred_np = model.predict(img_batch, batch_size)
prob_result = np.concatenate((prob_result, pred_np), axis=0)
except tf.errors.OutOfRangeError:
print("prediction result size: {}".format(prob_result.shape))
break
assert prob_result.shape[0] == input_files.shape[0]
mitosis_probs = prob_result[prob_result > prob_thres]
input_files = input_files.reshape(-1, 1)
mitosis_patch_files = input_files[prob_result > prob_thres]
inference_result = []
for i in range(mitosis_patch_files.size):
row, col = get_location_from_file_name(mitosis_patch_files[i])
prob = mitosis_probs[i]
inference_result.append((row, col, prob))
if len(inference_result) > 0:
clustered_pred_locations = dbscan_clustering(
inference_result,
eps=eps,
min_samples=min_samples,
isWeightedAvg=isWeightedAvg)
tuple_2_csv(inference_result,
os.path.join(output_dir_path, 'mitosis_locations.csv'))
tuple_2_csv(
clustered_pred_locations,
os.path.join(output_dir_path, 'clustered_mitosis_locations.csv'))
else:
print("Do not have mitosis in {}".format(input_dir_path))
def run_mitosis_classification(model,
sess,
batch_size,
input_dir_path,
output_dir_path,
augmentation_number,
mitosis_tile_size=64,
num_parallel_calls=1,
prefetch=32,
prob_thres=0.5,
eps=64, min_samples=1,
isWeightedAvg=False):
input_file_paths = [str(f) for f in Path(input_dir_path).glob('*.png')]
input_files = np.asarray(input_file_paths, dtype=np.str)
input_file_dataset = tf.data.Dataset.from_tensor_slices(input_files)
img_dataset = input_file_dataset.map(lambda file: get_image_tf(file),
num_parallel_calls=1)
if augmentation_number == 1:
img_dataset = img_dataset\
.map(lambda img: normalize(img, "resnet_custom"),
num_parallel_calls=num_parallel_calls)\
.batch(batch_size)\
.prefetch(prefetch)
# Make sure all the files in the dataset are feeded into inference
float_steps = len(input_file_paths) / batch_size
int_steps = len(input_file_paths) // batch_size
steps = math.ceil(float_steps) if float_steps > int_steps else int_steps
else:
img_dataset = img_dataset \
.map(lambda img: create_augmented_batch(img, augmentation_number,
mitosis_tile_size),
num_parallel_calls=num_parallel_calls) \
.map(lambda img: normalize(img, "resnet_custom"),
num_parallel_calls=num_parallel_calls) \
.prefetch(prefetch)
steps = len(input_file_paths)
img_iterator = img_dataset.make_one_shot_iterator()
next_batch = img_iterator.get_next()
while True:
try:
pred_np = model.predict(next_batch, steps=steps)
print("Prediction result shape: ", pred_np.shape)
except tf.errors.OutOfRangeError:
print("Please check the steps parameter. steps = {}, "
"batch_size = {}, input_tile_size = {}, "
"augmentation_number = {}"
.format(steps, batch_size, input_files.shape,
augmentation_number))
break
prob_result = \
np.average(pred_np.reshape(-1, augmentation_number), axis=1)
print("Finish the inference on {} with {} input tiles"
.format(input_dir_path, prob_result.shape))
assert prob_result.shape[0] == input_files.shape[0]
mitosis_probs = prob_result[prob_result > prob_thres]
input_files = input_files.reshape(-1, 1)
mitosis_patch_files = input_files[prob_result > prob_thres]
inference_result = []
for i in range(mitosis_patch_files.size):
row, col = get_location_from_file_name(mitosis_patch_files[i])
prob = mitosis_probs[i]
inference_result.append((row, col, prob))
if len(inference_result) > 0:
clustered_pred_locations = dbscan_clustering(
inference_result, eps=eps, min_samples=min_samples,
isWeightedAvg=isWeightedAvg)
tuple_2_csv(
inference_result,
os.path.join(output_dir_path, 'mitosis_locations.csv'))
tuple_2_csv(
clustered_pred_locations,
os.path.join(output_dir_path, 'clustered_mitosis_locations.csv'))
else:
print("Do not have mitosis in {}".format(input_dir_path))