def load_image(self, file_path, im_type=None):
"""Load the image directly by the file path.
Parameters
----------
file_path : str, file-like object
The path to the DICOM image or I/O stream.
im_type : {'open', 'mlcs', None}
Specifies what image type is being loaded in. If None, will try to determine the type from the name.
The name must have 'open' or 'dmlc' in the name.
"""
img = Image.load(file_path)
if im_type is not None:
if _is_open_type(im_type):
self.image_open = img
elif _is_dmlc_type(im_type):
self.image_dmlc = img
else:
# try to guess type by the name
imtype = self._try_to_guess_image_type(osp.basename(file_path))
if imtype is not None:
self.load_image(file_path, imtype)
else:
raise ValueError(
"Image type was not given nor could it be determined from the path name. Please enter and image type."
)
def test_all(self):
futures = []
start = time.time()
with concurrent.futures.ProcessPoolExecutor() as exec:
for pdir, sdir, files in os.walk(self.image_bank_dir):
for file in files:
filepath = osp.join(pdir, file)
try:
Image.load(filepath)
except:
pass
else:
future = exec.submit(run_star, filepath)
futures.append(future)
for future in concurrent.futures.as_completed(futures):
print(future.result())
end = time.time() - start
print('Processing of {} files took {}s'.format(len(futures), end))
def test_all(self):
futures = []
start = time.time()
with concurrent.futures.ProcessPoolExecutor() as exec:
for pdir, sdir, files in os.walk(self.image_bank_dir):
for file in files:
filepath = osp.join(pdir, file)
try:
Image.load(filepath)
except:
pass
else:
future = exec.submit(run_star, filepath)
futures.append(future)
for future in concurrent.futures.as_completed(futures):
print(future.result())
end = time.time() - start
print('Processing of {} files took {}s'.format(len(futures), end))
def load_image(self, filepath):
"""Load the image via the file path.
Parameters
----------
filepath : str
Path to the file to be loaded.
"""
self.image = Image.load(filepath)
def load_image(self, filepath):
"""Load the image via the file path.
Parameters
----------
filepath : str
Path to the file to be loaded.
"""
self.image = Image.load(filepath)
def _find_bb(self):
"""Find the BB within the radiation field. Iteratively searches for a circle-like object
by lowering a low-pass threshold value until found.
Returns
-------
Point
The weighted-pixel value location of the BB.
"""
def is_boxlike(array):
"""Whether the binary object's dimensions are symmetric, i.e. box-like"""
ymin, ymax, xmin, xmax = get_bounding_box(array)
y = abs(ymax - ymin)
x = abs(xmax - xmin)
if x > max(y * 1.05, y+3) or x < min(y * 0.95, y-3):
return False
return True
# get initial starting conditions
hmin = np.percentile(self.array, 5)
hmax = self.array.max()
spread = hmax - hmin
max_thresh = hmax
# search for the BB by iteratively lowering the low-pass threshold value until the BB is found.
found = False
while not found:
try:
lower_thresh = hmax - spread / 2
t = np.where((max_thresh > self) & (self >= lower_thresh), 1, 0)
labeled_arr, num_roi = ndimage.measurements.label(t)
roi_sizes, bin_edges = np.histogram(labeled_arr, bins=num_roi + 1)
bw_node_cleaned = np.where(labeled_arr == np.argsort(roi_sizes)[-3], 1, 0)
expected_fill_ratio = np.pi / 4
actual_fill_ratio = get_filled_area_ratio(bw_node_cleaned)
if (expected_fill_ratio * 1.1 < actual_fill_ratio) or (actual_fill_ratio < expected_fill_ratio * 0.9):
raise ValueError
if not is_boxlike(bw_node_cleaned):
raise ValueError
except (IndexError, ValueError):
max_thresh -= 0.05 * spread
if max_thresh < hmin:
raise ValueError("Unable to locate the BB")
else:
found = True
# determine the center of mass of the BB
inv_img = Image.load(self.array)
inv_img.invert()
x_arr = np.abs(np.average(bw_node_cleaned, weights=inv_img, axis=0))
x_com = SingleProfile(x_arr).fwxm_center(interpolate=True)
y_arr = np.abs(np.average(bw_node_cleaned, weights=inv_img, axis=1))
y_com = SingleProfile(y_arr).fwxm_center(interpolate=True)
return Point(x_com, y_com)
def __init__(self, filepath=None):
"""
Parameters
----------
filepath : None, str
If None, image must be loaded later.
If a str, path to the image file.
"""
if filepath is not None and is_valid_file(filepath):
self.image = Image.load(filepath)
else:
self.image = np.zeros((1,1))
def load_image(self, file_path, filter=None):
"""Load the image
Parameters
----------
file_path : str
Path to the image file.
filter : int, None
If None (default), no filtering will be done to the image.
If an int, will perform median filtering over image of size *filter*.
"""
self.image = Image.load(file_path)
if isinstance(filter, int):
self.image.median_filter(size=filter)
self._check_for_noise()
self.image.check_inversion()
def load_image(self, file_path, filter=None):
"""Load the image
Parameters
----------
file_path : str
Path to the image file.
filter : int, None
If None (default), no filtering will be done to the image.
If an int, will perform median filtering over image of size *filter*.
"""
self.image = Image.load(file_path)
if isinstance(filter, int):
self.image.median_filter(size=filter)
self._check_for_noise()
self.image.check_inversion()
def load_image(self, file_path, im_type=None):
"""Load the image directly by the file path.
Parameters
----------
file_path : str, file-like object
The path to the DICOM image or I/O stream.
im_type : {'open', 'mlcs', None}
Specifies what image type is being loaded in. If None, will try to determine the type from the name.
The name must have 'open' or 'dmlc' in the name.
"""
img = Image.load(file_path)
if im_type is not None:
if _is_open_type(im_type):
self.image_open = img
elif _is_dmlc_type(im_type):
self.image_dmlc = img
else:
# try to guess type by the name
imtype = self._try_to_guess_image_type(osp.basename(file_path))
if imtype is not None:
self.load_image(file_path, imtype)
else:
raise ValueError("Image type was not given nor could it be determined from the path name. Please enter and image type.")
def test_array(self):
arr = np.arange(36).reshape(6, 6)
img = Image.load(arr)
self.assertIsInstance(img, ArrayImage)
def setUpClass(cls):
image = Image.load(cls.image_file_location)
cls.profile = cls.klass(cls.center_point, cls.radius, image.array)
def test_file(self):
img = Image.load(tif_path)
self.assertIsInstance(img, FileImage)
def test_array(self):
arr = np.arange(36).reshape(6, 6)
img = Image.load(arr)
self.assertIsInstance(img, ArrayImage)
def test_nonsense(self):
with self.assertRaises(TypeError):
Image.load('blahblah')
def setUp(self):
self.img = Image.load(tif_path)
self.dcm = Image.load(dcm_path)
array = np.arange(42).reshape(6, 7)
self.arr = Image.load(array)
def test_dicom(self):
img = Image.load(dcm_path)
self.assertIsInstance(img, DicomImage)
def load_demo_image(self):
"""Load the demo image."""
demo_file = osp.join(osp.dirname(__file__), 'demo_files', 'flatsym', 'flatsym_demo.dcm')
self.image = Image.load(demo_file)
def test_file(self):
img = Image.load(tif_path)
self.assertIsInstance(img, FileImage)
def test_dicom(self):
img = Image.load(dcm_path)
self.assertIsInstance(img, DicomImage)
def setUpClass(cls):
cls.dcm = Image.load(dcm_path)
def test_nonsense(self):
with self.assertRaises(TypeError):
Image.load('blahblah')
def setUp(self):
self.img = Image.load(tif_path)
self.dcm = Image.load(dcm_path)
array = np.arange(42).reshape(6, 7)
self.arr = Image.load(array)
def setUpClass(cls):
image = Image.load(cls.image_file_location)
cls.profile = cls.klass(cls.center_point, cls.radius, image.array)
def setUpClass(cls):
cls.dcm = Image.load(dcm_path)
