def upload_study_me(file_path, is_segmentation_model, host, port):
file_dict = []
headers = {'Content-Type': 'multipart/related; '}
request_json = {
'request':
'post',
'route':
'/',
'inference_command':
'get-probability-mask'
if is_segmentation_model else 'get-bounding-box-2d'
}
images = load_image_data(file_path)
# images = sort_images(images)
width = 0
height = 0
count = 0
for image in images:
try:
dcm_file = pydicom.dcmread(image.path)
if width == 0 or height == 0:
width = dcm_file.Columns
height = dcm_file.Rows
count += 1
field = str(count)
fo = open(image.path, 'rb').read()
filename = os.path.basename(os.path.normpath(image.path))
file_dict.append((field, (filename, fo, 'application/dicom')))
except:
print('File {} is not a DICOM file'.format(image.path))
continue
print('Sending {} files...'.format(count))
request_json['depth'] = count
request_json['height'] = height
request_json['width'] = width
file_dict.insert(
0,
('request_json',
('request', json.dumps(request_json).encode('utf-8'), 'text/json')))
me = MultipartEncoder(fields=file_dict)
boundary = me.content_type.split('boundary=')[1]
headers['Content-Type'] = headers['Content-Type'] + 'boundary="{}"'.format(
boundary)
r = requests.post('http://' + host + ':' + port + '/',
data=me,
headers=headers)
if r.status_code != 200:
print("Got error status code ", r.status_code)
exit(1)
multipart_data = decoder.MultipartDecoder.from_response(r)
json_response = json.loads(multipart_data.parts[0].text)
print("JSON response:", json_response)
mask_count = len(json_response["parts"])
masks = [
np.frombuffer(p.content, dtype=np.uint8)
for p in multipart_data.parts[1:mask_count + 1]
]
if is_segmentation_model:
output_folder = 'output'
if images[0].position is None:
# We must sort the images by their instance UID based on the order of the response:
identifiers = [
part['dicom_image']['SOPInstanceUID']
for part in json_response["parts"]
]
filtered_images = []
for id in identifiers:
image = next((img for img in images if img.instanceUID == id))
filtered_images.append(image)
test_inference_mask.generate_images_for_single_image_masks(
filtered_images, masks, output_folder)
else:
test_inference_mask.generate_images_with_masks(
images, masks, output_folder)
print("Segmentation mask images generated in folder: {}".format(
output_folder))
print("Saving output masks to files 'output/output_masks_*.npy")
for index, mask in enumerate(masks):
mask.tofile('output/output_masks_{}.npy'.format(index + 1))
def upload_study_me(file_path,
model_type,
host,
port,
output_folder,
attachments,
override_inference_command=None,
send_study_size=False):
file_dict = []
headers = {'Content-Type': 'multipart/related; '}
images = load_image_data(file_path)
images = sort_images(images)
if model_type == BOUNDING_BOX:
print("Performing bounding box prediction")
inference_command = 'get-bounding-box-2d'
elif model_type == SEGMENTATION_MODEL:
if images[0].position is None:
# No spatial information available. Perform 2D segmentation
print("Performing 2D mask segmentation")
inference_command = 'get-probability-mask-2D'
else:
print("Performing 3D mask segmentation")
inference_command = 'get-probability-mask-3D'
else:
inference_command = 'other'
if override_inference_command:
inference_command = override_inference_command
request_json = {
'request': 'post',
'route': '/',
'inference_command': inference_command
}
count = 0
width = 0
height = 0
for att in attachments:
count += 1
field = str(count)
fo = open(att, 'rb').read()
filename = os.path.basename(os.path.normpath(att))
file_dict.append((field, (filename, fo, 'application/octet-stream')))
for image in images:
try:
dcm_file = pydicom.dcmread(image.path)
if width == 0 or height == 0:
width = dcm_file.Columns
height = dcm_file.Rows
count += 1
field = str(count)
fo = open(image.path, 'rb').read()
filename = os.path.basename(os.path.normpath(image.path))
file_dict.append((field, (filename, fo, 'application/dicom')))
except:
print('File {} is not a DICOM file'.format(image.path))
continue
print('Sending {} files...'.format(len(images)))
if send_study_size:
request_json['depth'] = count
request_json['height'] = height
request_json['width'] = width
file_dict.insert(
0,
('request_json',
('request', json.dumps(request_json).encode('utf-8'), 'text/json')))
me = MultipartEncoder(fields=file_dict)
boundary = me.content_type.split('boundary=')[1]
headers['Content-Type'] = headers['Content-Type'] + 'boundary="{}"'.format(
boundary)
r = requests.post('http://' + host + ':' + port + '/',
data=me,
headers=headers)
if r.status_code != 200:
print("Got error status code ", r.status_code)
exit(1)
multipart_data = decoder.MultipartDecoder.from_response(r)
json_response = json.loads(multipart_data.parts[0].text)
print("JSON response:", json_response)
if model_type == SEGMENTATION_MODEL:
mask_count = len(json_response["parts"])
# Assert that we get one binary part for each object in 'parts'
# The additional two multipart object are: JSON response and request:response digests
assert mask_count == len(multipart_data.parts) - 2, \
"The server must return one binary buffer for each object in `parts`. Got {} buffers and {} 'parts' objects" \
.format(len(multipart_data.parts) - 2, mask_count)
masks = [
np.frombuffer(p.content, dtype=np.uint8)
for p in multipart_data.parts[1:mask_count + 1]
]
if images[0].position is None:
# We must sort the images by their instance UID based on the order of the response:
identifiers = [
part['dicom_image']['SOPInstanceUID']
for part in json_response["parts"]
]
filtered_images = []
for id in identifiers:
image = next((img for img in images if img.instanceUID == id))
filtered_images.append(image)
test_inference_mask.generate_images_for_single_image_masks(
filtered_images, masks, json_response, output_folder)
else:
test_inference_mask.generate_images_with_masks(
images, masks, json_response, output_folder)
print("Segmentation mask images generated in folder: {}".format(
output_folder))
print("Saving output masks to files '{}/output_masks_*.npy".format(
output_folder))
for index, mask in enumerate(masks):
mask.tofile('{}/output_masks_{}.npy'.format(
output_folder, index + 1))
elif model_type == BOUNDING_BOX:
boxes = json_response['bounding_boxes_2d']
test_inference_boxes.generate_images_with_boxes(
images, boxes, output_folder)
with open(os.path.join(output_folder, 'response.json'), 'w') as outfile:
json.dump(json_response, outfile)