def predict():
# initialize the data dictionary that will be returned from the
# view
data = {"success": False}
global graph
global sess
# ensure an image was properly uploaded to our endpoint
if flask.request.method == "POST":
if flask.request.files.get("image"):
file = flask.request.files["image"]
print(file.filename)
path = os.path.join(app.config['UPLOAD_FOLDER'], file.filename)
file.save(path)
sitk_image, _ = load_image(path)
image = preprocess_sitk_image_for_slice_detection(sitk_image)
spacing = sitk_image.GetSpacing()
print(image.shape)
with graph.as_default():
set_session(sess)
preds = model.predict(image)
pred_z, prob = decode_slice_detection_prediction(preds)
slice_z = adjust_detected_position_spacing(pred_z, spacing)
print('Slice detected at position {} with confidence {}'.format(slice_z, prob))
data["predictions"] = [{'filename': file.filename, 'slice_z': slice_z, 'probability': prob}]
# indicate that the request was a success
data["success"] = True
# return the data dictionary as a JSON response
return flask.jsonify(data)
def process_file(image_path, filename, prob_threshold=0.1):
global segmentation_model
global slice_detection_model
print(image_path)
pred_id = uuid.uuid4().hex
results = {"success": False, "prediction": {'id': pred_id}}
try:
sitk_image, _ = load_image(image_path)
except:
return 1
image2d, image2d_preview = preprocess_sitk_image_for_slice_detection(
sitk_image)
image3d = sitk.GetArrayFromImage(sitk_image)
spacing = sitk_image.GetSpacing()
size = list(sitk_image.GetSize())
with graph.as_default():
set_session(sess)
preds = slice_detection_model.predict(image2d)
pred_z, prob = decode_slice_detection_prediction(preds)
slice_z = adjust_detected_position_spacing(pred_z, spacing)
slice_detected = prob > prob_threshold
if slice_detected:
results["prediction"]["slice_z"] = slice_z
slice_image = image3d[slice_z, :, :]
with graph.as_default():
set_session(sess)
seg_image = segmentation_model.predict(
preprocess_test_image(slice_image[np.newaxis, :, :,
np.newaxis]))
seg_image = seg_image[0]
out_seg_image = np.flipud(
blend2d(np.squeeze(preprocess_test_image(slice_image)),
np.squeeze(seg_image > 0.5), 0.5))
image2d_preview = place_line_on_img(image2d_preview,
pred_z,
pred_z,
r=1)
cv2.imwrite(f'{settings.UPLOAD_FOLDER}/{filename}_slice-{pred_id}.jpg',
to256(np.squeeze(preprocess_test_image(slice_image))))
cv2.imwrite(
f'{settings.UPLOAD_FOLDER}/{filename}_frontal-{pred_id}.jpg',
to256(np.squeeze(image2d_preview)))
cv2.imwrite(f'{settings.UPLOAD_FOLDER}/{filename}_seg-{pred_id}.jpg',
to256(np.squeeze(out_seg_image)))
results["prediction"]["muscle_attenuation"] = '{0:.2f} HU'.format(
compute_muscle_attenuation(slice_image,
np.squeeze(seg_image > 0.5)))
results["prediction"]["muscle_area"] = '{0:.2f}'.format(
compute_muscle_area(np.squeeze(seg_image > 0.5),
spacing,
units='cm'))
results["prediction"]["slice_prob"] = '{0:.2f}%'.format(100 * prob)
results["success"] = True
if results["success"]:
result_str = 'Slice detected at position {0} of {1} with {2:.2f}% confidence '.format(
slice_z, size[2], 100 * prob)
else:
result_str = 'Slice not detected'
results["prediction"]["str"] = result_str
return results
}
sitk_image, _ = load_image(image_path)
image2d, image2d_preview = preprocess_sitk_image_for_slice_detection(
sitk_image)
image3d = sitk.GetArrayFromImage(sitk_image)
spacing = sitk_image.GetSpacing()
size = list(sitk_image.GetSize())
with graph.as_default():
set_session(sess)
preds = slice_detection_model.predict(image2d)
pred_z, prob = decode_slice_detection_prediction(preds)
slice_z = adjust_detected_position_spacing(
pred_z, spacing)
slice_detected = prob > prob_threshold
#print(slice_z)
if slice_detected:
results["prediction"]["slice_z"] = slice_z
results["prediction"][
"slice_prob"] = '{0:.2f}%'.format(100 * prob)
results["success"] = True
#creating the (2) jpeg imageS of slice position and L3 slice
#######################################
image = image3d
slice_image = image[slice_z, :, :]