def make_workspace(self, scheme, images, adjustments):
module = G.GrayToColor()
module.scheme_choice.value = scheme
image_names = [
"image%d" % i if images[i] is not None else G.LEAVE_THIS_BLACK
for i in range(7)
]
for image_name_setting, image_name, adjustment_setting, adjustment\
in zip((module.red_image_name, module.green_image_name,
module.blue_image_name,
module.cyan_image_name, module.magenta_image_name,
module.yellow_image_name, module.gray_image_name),
image_names,
(module.red_adjustment_factor, module.green_adjustment_factor,
module.blue_adjustment_factor, module.cyan_adjustment_factor,
module.magenta_adjustment_factor, module.yellow_adjustment_factor,
module.gray_adjustment_factor),
adjustments):
image_name_setting.value = image_name
adjustment_setting.value = adjustment
module.rgb_image_name.value = OUTPUT_IMAGE_NAME
module.module_num = 1
pipeline = cpp.Pipeline()
def callback(caller, event):
self.assertFalse(isinstance(event, cpp.RunExceptionEvent))
pipeline.add_listener(callback)
pipeline.add_module(module)
image_set_list = cpi.ImageSetList()
image_set = image_set_list.get_image_set(0)
for image, image_name in zip(images, image_names):
if image is not None:
image_set.add(image_name, cpi.Image(image))
workspace = cpw.Workspace(pipeline, module, image_set, cpo.ObjectSet(),
cpmeas.Measurements(), image_set_list)
return workspace, module
def make_workspace(self, ground_truth, test):
'''Make a workspace with a ground-truth image and a test image
ground_truth and test are dictionaries with the following keys:
image - the pixel data
mask - (optional) the mask data
crop_mask - (optional) a cropping mask
returns a workspace and module
'''
module = C.CalculateImageOverlap()
module.module_num = 1
module.obj_or_img.value = O_IMG
module.ground_truth.value = GROUND_TRUTH_IMAGE_NAME
module.test_img.value = TEST_IMAGE_NAME
pipeline = cpp.Pipeline()
def callback(caller, event):
self.assertFalse(isinstance(event, cpp.RunExceptionEvent))
pipeline.add_listener(callback)
pipeline.add_module(module)
image_set_list = cpi.ImageSetList()
image_set = image_set_list.get_image_set(0)
for name, d in ((GROUND_TRUTH_IMAGE_NAME, ground_truth),
(TEST_IMAGE_NAME, test)):
image = cpi.Image(d["image"],
mask=d.get("mask"),
crop_mask=d.get("crop_mask"))
image_set.add(name, image)
workspace = cpw.Workspace(pipeline, module, image_set, cpo.ObjectSet(),
cpmeas.Measurements(), image_set_list)
return workspace, module
def run_imagemath(self, images, modify_module_fn=None, measurement = None):
'''Run the ImageMath module, returning the image created
images - a list of dictionaries. The dictionary has keys:
pixel_data - image pixel data
mask - mask for image
cropping - cropping mask for image
modify_module_fn - a function of the signature, fn(module)
that allows the test to modify the module.
measurement - an image measurement value
'''
image_set_list = cpi.ImageSetList()
image_set = image_set_list.get_image_set(0)
module = I.ImageMath()
module.module_num = 1
for i,image in enumerate(images):
pixel_data = image['pixel_data']
mask = image.get('mask', None)
cropping = image.get('cropping', None)
if i >= 2:
module.add_image()
name = 'inputimage%s'%i
module.images[i].image_name.value = name
img = cpi.Image(pixel_data, mask=mask, crop_mask=cropping)
image_set.add(name, img)
module.output_image_name.value = 'outputimage'
if modify_module_fn is not None:
modify_module_fn(module)
pipeline = cpp.Pipeline()
pipeline.add_module(module)
measurements = cpmeas.Measurements()
if measurement is not None:
measurements.add_image_measurement(MEASUREMENT_NAME, str(measurement))
workspace = cpw.Workspace(pipeline, module, image_set, cpo.ObjectSet(),
measurements, image_set_list)
module.run(workspace)
return image_set.get_image('outputimage')
def run_module(self, image, mask=None, fn=None):
'''Run the FlipAndRotate module
image - pixel data to be transformed
mask - optional mask on the pixel data
fn - function with signature, "fn(module)" that will be
called with the FlipAndRotate module
returns an Image object containing the flipped/rotated/masked/cropped
image and the angle measurement.
'''
img = cpi.Image(image, mask)
image_set_list = cpi.ImageSetList()
image_set = image_set_list.get_image_set(0)
image_set.add(IMAGE_NAME, img)
module = F.FlipAndRotate()
module.image_name.value = IMAGE_NAME
module.output_name.value = OUTPUT_IMAGE
module.module_num = 1
if fn is not None:
fn(module)
pipeline = cpp.Pipeline()
pipeline.add_module(module)
def error_callback(caller, event):
self.assertFalse(isinstance(event, cpp.RunExceptionEvent))
pipeline.add_listener(error_callback)
measurements = cpmeas.Measurements()
workspace = cpw.Workspace(pipeline, module, image_set, cpo.ObjectSet(),
measurements, image_set_list)
module.run(workspace)
feature = F.M_ROTATION_F % OUTPUT_IMAGE
self.assertTrue(
feature in measurements.get_feature_names(cpmeas.IMAGE))
angle = measurements.get_current_image_measurement(feature)
output_image = image_set.get_image(OUTPUT_IMAGE)
return (output_image, angle)
def make_workspace(self, image_measurements, object_measurements):
'''Make a workspace with a FlagImage module and the given measurements
image_measurements - a sequence of single image measurements. Use
image_measurement_name(i) to get the name of
the i th measurement
object_measurements - a seequence of sequences of object measurements.
These are stored under object, OBJECT_NAME with
measurement name object_measurement_name(i) for
the i th measurement.
returns module, workspace
'''
module = F.FlagImage()
measurements = cpmeas.Measurements()
for i in range(len(image_measurements)):
measurements.add_image_measurement(image_measurement_name(i),
image_measurements[i])
for i in range(len(object_measurements)):
measurements.add_measurement(OBJECT_NAME,
object_measurement_name(i),
np.array(object_measurements[i]))
flag = module.flags[0]
self.assertTrue(isinstance(flag, cps.SettingsGroup))
flag.category.value = MEASUREMENT_CATEGORY
flag.feature_name.value = MEASUREMENT_FEATURE
module.module_num = 1
pipeline = cpp.Pipeline()
def callback(caller,event):
self.assertFalse(isinstance(event, cpp.RunExceptionEvent))
pipeline.add_listener(callback)
pipeline.add_module(module)
image_set_list = cpi.ImageSetList()
image_set = image_set_list.get_image_set(0)
workspace = cpw.Workspace(pipeline, module, image_set, cpo.ObjectSet(),
measurements, image_set_list)
return module, workspace
def test_01_04_split_channels(self):
np.random.seed(13)
image = np.random.uniform(size=(20, 10, 5))
image_set_list = cpi.ImageSetList()
image_set = image_set_list.get_image_set(0)
image_set.add(IMAGE_NAME, cpi.Image(image))
module = cpm_ctg.ColorToGray()
module.module_num = 1
module.image_name.value = IMAGE_NAME
module.combine_or_split.value = cpm_ctg.SPLIT
module.rgb_or_channels.value = cpm_ctg.CH_CHANNELS
module.add_channel()
module.add_channel()
channel_indexes = np.array([1, 4, 2])
for i, channel_index in enumerate(channel_indexes):
module.channels[i].channel_choice.value = module.channel_names[
channel_index]
module.channels[i].image_name.value = OUTPUT_IMAGE_F % i
pipeline = cpp.Pipeline()
def callback(caller, event):
self.assertFalse(isinstance(event, cpp.RunExceptionEvent))
pipeline.add_listener(callback)
pipeline.add_module(module)
workspace = Workspace(pipeline, module, image_set, cpo.ObjectSet(),
cpm.Measurements(), image_set_list)
module.run(workspace)
for i, channel_index in enumerate(channel_indexes):
pixels = image_set.get_image(
module.channels[i].image_name.value).pixel_data
self.assertEqual(pixels.ndim, 2)
self.assertEqual(tuple(pixels.shape), (20, 10))
np.testing.assert_almost_equal(image[:, :, channel_index], pixels)
def test_02_03_double_mask(self):
labels = np.zeros((10, 15), int)
labels[2:5, 3:8] = 1
labels[5:8, 10:14] = 2
object_set = cpo.ObjectSet()
objects = cpo.Objects()
objects.segmented = labels
object_set.add_objects(objects, OBJECTS_NAME)
image_set_list = cpi.ImageSetList()
image_set = image_set_list.get_image_set(0)
np.random.seed(0)
pixel_data = np.random.uniform(size=(10, 15)).astype(np.float32)
mask = np.random.uniform(size=(10, 15)) > .5
image_set.add(IMAGE_NAME, cpi.Image(pixel_data, mask))
expected_mask = (mask & (labels > 0))
pipeline = cpp.Pipeline()
module = M.MaskImage()
module.source_choice.value = M.IO_OBJECTS
module.object_name.value = OBJECTS_NAME
module.image_name.value = IMAGE_NAME
module.masked_image_name.value = MASKED_IMAGE_NAME
module.invert_mask.value = False
module.module_num = 1
workspace = cpw.Workspace(pipeline, module, image_set, object_set,
cpmeas.Measurements(), image_set_list)
module.run(workspace)
masked_image = workspace.image_set.get_image(MASKED_IMAGE_NAME)
self.assertTrue(isinstance(masked_image, cpi.Image))
self.assertTrue(np.all(masked_image.pixel_data[expected_mask] ==
pixel_data[expected_mask]))
self.assertTrue(np.all(masked_image.pixel_data[~ expected_mask] == 0))
self.assertTrue(np.all(masked_image.mask == expected_mask))
self.assertTrue(np.all(masked_image.masking_objects.segmented == labels))
def make_workspace(self, gridding, labels=None):
module = I.IdentifyObjectsInGrid()
module.module_num = 1
module.grid_name.value = GRID_NAME
module.output_objects_name.value = OUTPUT_OBJECTS_NAME
module.guiding_object_name.value = GUIDING_OBJECTS_NAME
module.outlines_name.value = OUTLINES_NAME
image_set_list = cpi.ImageSetList()
object_set = cpo.ObjectSet()
if labels is not None:
my_objects = cpo.Objects()
my_objects.segmented = labels
object_set.add_objects(my_objects, GUIDING_OBJECTS_NAME)
pipeline = cpp.Pipeline()
def callback(caller,event):
self.assertFalse(isinstance(event, cpp.RunExceptionEvent))
pipeline.add_listener(callback)
pipeline.add_module(module)
workspace = cpw.Workspace(pipeline, module,
image_set_list.get_image_set(0),
object_set, cpmeas.Measurements(),
image_set_list)
workspace.set_grid(GRID_NAME, gridding)
return (workspace, module)
def make_tile_workspace(self, images):
module = T.Tile()
module.module_num = 1
module.tile_method.value = T.T_ACROSS_CYCLES
module.input_image.value = INPUT_IMAGE_NAME
module.output_image.value = OUTPUT_IMAGE_NAME
pipeline = cpp.Pipeline()
def callback(caller, event):
self.assertFalse(isinstance(event, cpp.RunExceptionEvent))
pipeline.add_listener(callback)
pipeline.add_module(module)
image_set_list = cpi.ImageSetList()
for i, image in enumerate(images):
image_set = image_set_list.get_image_set(i)
image_set.add(INPUT_IMAGE_NAME, cpi.Image(image))
workspace = cpw.Workspace(pipeline, module,
image_set_list.get_image_set(0),
cpo.ObjectSet(), cpmeas.Measurements(),
image_set_list)
return workspace, module
def make_workspace(self, pixel_data, mask=None, objects=None):
image_set_list = cpi.ImageSetList()
image_set = image_set_list.get_image_set(0)
object_set = cpo.ObjectSet()
image = cpi.Image(pixel_data)
if not mask is None:
image.mask = mask
image_set.add(MY_IMAGE, image)
if not objects is None:
o = cpo.Objects()
o.segmented = objects
object_set.add_objects(o, MY_OBJECTS)
module = miq.MeasureImageQuality()
module.images_choice.value = miq.O_SELECT
module.image_groups[0].include_image_scalings.value = False
module.image_groups[0].image_names.value = MY_IMAGE
module.image_groups[0].use_all_threshold_methods.value = False
module.module_num = 1
pipeline = cpp.Pipeline()
pipeline.add_module(module)
workspace = cpw.Workspace(pipeline, module, image_set,
object_set,
cpmeas.Measurements(),image_set_list)
return workspace
def run_tteesstt(self, objects, level):
module = instantiate_module(MODULE_NAME)
module.objects_name.value = OBJECTS_NAME
module.module_num = 1
pipeline = cpp.Pipeline()
pipeline.add_module(module)
object_set = cpo.ObjectSet()
object_set.add_objects(objects, OBJECTS_NAME)
#
# Make the workspace
#
measurements = cpmeas.Measurements()
workspace = cpw.Workspace(pipeline, module, None, object_set,
measurements, None)
module.run(workspace)
values = measurements.get_measurement(OBJECTS_NAME, "Example5_MeanDistance")
self.assertEqual(len(values), objects.count)
for labels, indices in objects.get_labels():
for l in np.unique(labels):
#
# Test very slowly = 1 object per pass
#
if l == 0:
continue
d = scipy.ndimage.distance_transform_edt(labels == l)
value = np.sum(d) / np.sum(labels == l)
if abs(value - values[l-1]) > .0001:
if level == 1:
self.fail("You got the wrong answer (label = %d, mine = %f, yours = %f" %
l, value, values[l-1])
else:
sys.stderr.write("Oh too bad, did not pass level %d\n" % level)
return
if level > 1:
print "+%d for you!" % level
def make_workspace(self, pixel_data, mask=None):
image = cpi.Image(pixel_data, mask)
image_set_list = cpi.ImageSetList()
image_set = image_set_list.get_image_set(0)
image_set.add(IMAGE_NAME, image)
module = ID.IdentifyDeadWorms()
module.module_num = 1
module.image_name.value = IMAGE_NAME
module.object_name.value = OBJECTS_NAME
pipeline = cpp.Pipeline()
def callback(caller, event):
self.assertFalse(isinstance(event, cpp.LoadExceptionEvent))
self.assertFalse(isinstance(event, cpp.RunExceptionEvent))
pipeline.add_listener(callback)
pipeline.add_module(module)
workspace = cpw.Workspace(pipeline, module, image_set, cpo.ObjectSet(),
cpmeas.Measurements(), image_set_list)
return workspace, module
def test_01_02_run(self):
module = instantiate_module(MODULE_NAME)
module.input_objects_name.value = INPUT_OBJECTS_NAME
module.output_objects_name.value = OUTPUT_OBJECTS_NAME
module.module_num = 1
pipeline = cpp.Pipeline()
pipeline.add_module(module)
object_set = cpo.ObjectSet()
#
# Pick a bunch of random points, dilate them using the distance
# transform and then label the result.
#
r = np.random.RandomState()
r.seed(12)
bimg = np.ones((100, 100), bool)
bimg[r.randint(0,100, 50), r.randint(0, 100, 50)] = False
labels, count = label(distance_transform_edt(bimg) <= 5)
#
# Make the input objects
#
input_objects = cpo.Objects()
input_objects.segmented = labels
expected = skeletonize_labels(labels)
object_set.add_objects(input_objects, INPUT_OBJECTS_NAME)
#
# Make the workspace
#
workspace = cpw.Workspace(pipeline, module, None, object_set,
cpmeas.Measurements(), None)
module.run(workspace)
self.assertTrue(OUTPUT_OBJECTS_NAME in object_set.object_names,
"Could not find the output objects in the object set")
output_objects = object_set.get_objects(OUTPUT_OBJECTS_NAME)
np.testing.assert_array_equal(expected, output_objects.segmented)
def make_workspace(self,
scheme,
images,
adjustments=None,
colors=None,
weights=None):
module = G.GrayToColor()
module.scheme_choice.value = scheme
if scheme not in (G.SCHEME_COMPOSITE, G.SCHEME_STACK):
image_names = [
"image%d" % i if images[i] is not None else G.LEAVE_THIS_BLACK
for i in range(7)
]
for image_name_setting, image_name, adjustment_setting, adjustment\
in zip((module.red_image_name, module.green_image_name,
module.blue_image_name,
module.cyan_image_name, module.magenta_image_name,
module.yellow_image_name, module.gray_image_name),
image_names,
(module.red_adjustment_factor, module.green_adjustment_factor,
module.blue_adjustment_factor, module.cyan_adjustment_factor,
module.magenta_adjustment_factor, module.yellow_adjustment_factor,
module.gray_adjustment_factor),
adjustments):
image_name_setting.value = image_name
adjustment_setting.value = adjustment
else:
while len(module.stack_channels) < len(images):
module.add_stack_channel_cb()
image_names = []
if weights is None:
weights = [1.0] * len(images)
if colors is None:
colors = [
G.DEFAULT_COLORS[i % len(G.DEFAULT_COLORS)]
for i in range(len(images))
]
for i, (image, color,
weight) in enumerate(zip(images, colors, weights)):
image_name = 'image%d' % (i + 1)
image_names.append(image_name)
module.stack_channels[i].image_name.value = image_name
module.stack_channels[i].color.value = color
module.stack_channels[i].weight.value = weight
module.rgb_image_name.value = OUTPUT_IMAGE_NAME
module.module_num = 1
pipeline = cpp.Pipeline()
def callback(caller, event):
self.assertFalse(isinstance(event, cpp.RunExceptionEvent))
pipeline.add_listener(callback)
pipeline.add_module(module)
image_set_list = cpi.ImageSetList()
image_set = image_set_list.get_image_set(0)
for image, image_name in zip(images, image_names):
if image is not None:
image_set.add(image_name, cpi.Image(image))
workspace = cpw.Workspace(pipeline, module, image_set, cpo.ObjectSet(),
cpmeas.Measurements(), image_set_list)
return workspace, module
def test_02_01_compare_to_matlab(self):
expected = {
'EC50_DistCytoplasm_Correlation_Correlation_CorrGreenCorrBlue':
3.982812,
'EC50_DistCytoplasm_Intensity_LowerQuartileIntensity_CorrGreen':
4.139827,
'EC50_DistCytoplasm_Intensity_MedianIntensity_CorrGreen':
4.178600,
'EC50_DistCytoplasm_Intensity_MinIntensityEdge_CorrGreen':
4.059770,
'EC50_DistCytoplasm_Intensity_MinIntensity_CorrGreen':
4.066357,
'EC50_DistCytoplasm_Math_Ratio1':
4.491367,
'EC50_DistCytoplasm_Math_Ratio2':
3.848722,
'EC50_DistCytoplasm_Texture_AngularSecondMoment_CorrGreen_1':
4.948056,
'EC50_DistCytoplasm_Texture_Entropy_CorrGreen_1':
4.687104,
'EC50_DistCytoplasm_Texture_InfoMeas2_CorrGreen_1':
5.0285,
'EC50_DistCytoplasm_Texture_InverseDifferenceMoment_CorrGreen_1':
4.319017,
'EC50_DistCytoplasm_Texture_SumAverage_CorrGreen_1':
4.548876,
'EC50_DistCytoplasm_Texture_SumEntropy_CorrGreen_1':
4.779139,
'EC50_DistCytoplasm_Texture_Variance_CorrGreen_1':
4.218379,
'EC50_DistanceCells_Correlation_Correlation_CorrGreenCorrBlue':
3.708711,
'EC50_DistanceCells_Intensity_IntegratedIntensityEdge_CorrGreen':
4.135146,
'EC50_DistanceCells_Intensity_LowerQuartileIntensity_CorrGreen':
4.5372,
'EC50_DistanceCells_Intensity_MeanIntensityEdge_CorrGreen':
4.1371,
'EC50_DistanceCells_Intensity_MinIntensityEdge_CorrGreen':
4.033999,
'EC50_DistanceCells_Intensity_MinIntensity_CorrGreen':
4.079470,
'EC50_DistanceCells_Texture_AngularSecondMoment_CorrGreen_1':
5.118689,
'EC50_DistanceCells_Texture_Correlation_CorrGreen_1':
4.002074,
'EC50_DistanceCells_Texture_Entropy_CorrGreen_1':
5.008000,
'EC50_DistanceCells_Texture_InfoMeas1_CorrGreen_1':
3.883586,
'EC50_DistanceCells_Texture_InverseDifferenceMoment_CorrGreen_1':
3.977216,
'EC50_DistanceCells_Texture_SumAverage_CorrGreen_1':
4.9741,
'EC50_DistanceCells_Texture_SumEntropy_CorrGreen_1':
5.1455,
'EC50_DistanceCells_Texture_SumVariance_CorrGreen_1':
4.593041,
'EC50_DistanceCells_Texture_Variance_CorrGreen_1':
4.619517,
'EC50_Nuclei_Correlation_Correlation_CorrGreenCorrBlue':
3.751133,
'EC50_Nuclei_Math_Ratio1':
4.491367,
'EC50_Nuclei_Math_Ratio2':
3.848722,
'EC50_Nuclei_Texture_SumAverage_CorrGreen_1':
3.765297,
'EC50_PropCells_AreaShape_Area':
4.740853,
'EC50_PropCells_AreaShape_MajorAxisLength':
5.064460,
'EC50_PropCells_AreaShape_MinorAxisLength':
4.751471,
'EC50_PropCells_AreaShape_Perimeter':
4.949292,
'EC50_PropCells_Correlation_Correlation_CorrGreenCorrBlue':
3.772565,
'EC50_PropCells_Texture_GaborX_CorrGreen_1':
5.007167,
'EC50_PropCells_Texture_InfoMeas2_CorrBlue_1':
4.341353,
'EC50_PropCells_Texture_SumVariance_CorrBlue_1':
4.298359,
'EC50_PropCells_Texture_SumVariance_CorrGreen_1':
4.610826,
'EC50_PropCells_Texture_Variance_CorrBlue_1':
4.396352,
'EC50_PropCells_Texture_Variance_CorrGreen_1':
4.632468,
'EC50_PropCytoplasm_AreaShape_Area':
4.669679,
'EC50_PropCytoplasm_AreaShape_MinorAxisLength':
4.754476,
'EC50_PropCytoplasm_AreaShape_Perimeter':
4.949292,
'EC50_PropCytoplasm_Correlation_Correlation_CorrGreenCorrBlue':
4.072830,
'EC50_PropCytoplasm_Intensity_IntegratedIntensity_CorrGreen':
4.0934,
'EC50_PropCytoplasm_Intensity_LowerQuartileIntensity_CorrGreen':
3.925800,
'EC50_PropCytoplasm_Intensity_MedianIntensity_CorrGreen':
3.9252,
'EC50_PropCytoplasm_Texture_AngularSecondMoment_CorrGreen_1':
4.777481,
'EC50_PropCytoplasm_Texture_Entropy_CorrGreen_1':
4.4432,
'EC50_PropCytoplasm_Texture_GaborX_CorrGreen_1':
5.163371,
'EC50_PropCytoplasm_Texture_InfoMeas2_CorrGreen_1':
4.701046,
'EC50_PropCytoplasm_Texture_SumEntropy_CorrGreen_1':
4.510543,
'EC50_ThresholdedCells_Texture_AngularSecondMoment_CorrBlue_1':
4.560315,
'EC50_ThresholdedCells_Texture_AngularSecondMoment_CorrGreen_1':
4.966674,
'EC50_ThresholdedCells_Texture_Entropy_CorrBlue_1':
4.457866,
'EC50_ThresholdedCells_Texture_InfoMeas2_CorrBlue_1':
4.624049,
'EC50_ThresholdedCells_Texture_SumAverage_CorrBlue_1':
4.686706,
'EC50_ThresholdedCells_Texture_SumEntropy_CorrBlue_1':
4.537378,
'EC50_ThresholdedCells_Texture_SumVariance_CorrBlue_1':
4.322820,
'EC50_ThresholdedCells_Texture_SumVariance_CorrGreen_1':
4.742158,
'EC50_ThresholdedCells_Texture_Variance_CorrBlue_1':
4.265549,
'EC50_ThresholdedCells_Texture_Variance_CorrGreen_1':
4.860020,
'OneTailedZfactor_DistCytoplasm_Intensity_MedianIntensity_CorrGreen':
-4.322503,
'OneTailedZfactor_DistCytoplasm_Intensity_MinIntensityEdge_CorrGreen':
-4.322503,
'OneTailedZfactor_DistCytoplasm_Intensity_MinIntensity_CorrGreen':
-4.322503,
'OneTailedZfactor_DistCytoplasm_Math_Ratio1':
0.622059,
'OneTailedZfactor_DistCytoplasm_Math_Ratio2':
-4.508284,
'OneTailedZfactor_DistCytoplasm_Texture_Entropy_CorrGreen_1':
-4.645887,
'OneTailedZfactor_DistCytoplasm_Texture_InfoMeas2_CorrGreen_1':
-4.279118,
'OneTailedZfactor_DistCytoplasm_Texture_SumAverage_CorrGreen_1':
-4.765570,
'OneTailedZfactor_DistCytoplasm_Texture_SumEntropy_CorrGreen_1':
-4.682335,
'OneTailedZfactor_DistCytoplasm_Texture_Variance_CorrGreen_1':
-4.415607,
'OneTailedZfactor_DistanceCells_Intensity_MeanIntensityEdge_CorrGreen':
-4.200105,
'OneTailedZfactor_DistanceCells_Intensity_MinIntensityEdge_CorrGreen':
-4.316452,
'OneTailedZfactor_DistanceCells_Intensity_MinIntensity_CorrGreen':
-4.316452,
'OneTailedZfactor_DistanceCells_Texture_Correlation_CorrGreen_1':
0.202500,
'OneTailedZfactor_DistanceCells_Texture_Entropy_CorrGreen_1':
-4.404815,
'OneTailedZfactor_DistanceCells_Texture_InfoMeas1_CorrGreen_1':
-4.508513,
'OneTailedZfactor_DistanceCells_Texture_SumAverage_CorrGreen_1':
-4.225356,
'OneTailedZfactor_DistanceCells_Texture_SumEntropy_CorrGreen_1':
-4.382768,
'OneTailedZfactor_DistanceCells_Texture_SumVariance_CorrGreen_1':
0.492125,
'OneTailedZfactor_DistanceCells_Texture_Variance_CorrGreen_1':
0.477360,
'OneTailedZfactor_Nuclei_Correlation_Correlation_CorrGreenCorrBlue':
0.563780,
'OneTailedZfactor_Nuclei_Math_Ratio1':
0.622059,
'OneTailedZfactor_Nuclei_Math_Ratio2':
-4.508284,
'OneTailedZfactor_Nuclei_Texture_SumAverage_CorrGreen_1':
0.426178,
'OneTailedZfactor_PropCells_AreaShape_Area':
-4.216674,
'OneTailedZfactor_PropCells_AreaShape_MajorAxisLength':
-4.119131,
'OneTailedZfactor_PropCells_AreaShape_MinorAxisLength':
-4.109793,
'OneTailedZfactor_PropCells_AreaShape_Perimeter':
-4.068050,
'OneTailedZfactor_PropCells_Correlation_Correlation_CorrGreenCorrBlue':
0.765440,
'OneTailedZfactor_PropCells_Texture_GaborX_CorrGreen_1':
0.114982,
'OneTailedZfactor_PropCells_Texture_InfoMeas2_CorrBlue_1':
0.108409,
'OneTailedZfactor_PropCells_Texture_SumVariance_CorrBlue_1':
0.191251,
'OneTailedZfactor_PropCells_Texture_SumVariance_CorrGreen_1':
0.559865,
'OneTailedZfactor_PropCells_Texture_Variance_CorrBlue_1':
0.254078,
'OneTailedZfactor_PropCells_Texture_Variance_CorrGreen_1':
0.556108,
'OneTailedZfactor_PropCytoplasm_AreaShape_Area':
-4.223021,
'OneTailedZfactor_PropCytoplasm_AreaShape_MinorAxisLength':
-4.095632,
'OneTailedZfactor_PropCytoplasm_AreaShape_Perimeter':
-4.068050,
'OneTailedZfactor_PropCytoplasm_Intensity_MedianIntensity_CorrGreen':
-4.194663,
'OneTailedZfactor_PropCytoplasm_Texture_Entropy_CorrGreen_1':
-4.443338,
'OneTailedZfactor_PropCytoplasm_Texture_GaborX_CorrGreen_1':
0.207265,
'OneTailedZfactor_PropCytoplasm_Texture_InfoMeas2_CorrGreen_1':
-4.297250,
'OneTailedZfactor_PropCytoplasm_Texture_SumEntropy_CorrGreen_1':
-4.525324,
'OneTailedZfactor_ThresholdedCells_Texture_Entropy_CorrBlue_1':
0.167795,
'OneTailedZfactor_ThresholdedCells_Texture_InfoMeas2_CorrBlue_1':
0.067560,
'OneTailedZfactor_ThresholdedCells_Texture_SumAverage_CorrBlue_1':
0.478527,
'OneTailedZfactor_ThresholdedCells_Texture_SumEntropy_CorrBlue_1':
0.155119,
'OneTailedZfactor_ThresholdedCells_Texture_SumVariance_CorrBlue_1':
0.535907,
'OneTailedZfactor_ThresholdedCells_Texture_SumVariance_CorrGreen_1':
0.572801,
'OneTailedZfactor_ThresholdedCells_Texture_Variance_CorrBlue_1':
0.423454,
'OneTailedZfactor_ThresholdedCells_Texture_Variance_CorrGreen_1':
0.440500,
'Vfactor_DistCytoplasm_Correlation_Correlation_CorrGreenCorrBlue':
0.500429,
'Vfactor_DistCytoplasm_Intensity_LowerQuartileIntensity_CorrGreen':
0.325675,
'Vfactor_DistCytoplasm_Intensity_MedianIntensity_CorrGreen':
0.323524,
'Vfactor_DistCytoplasm_Intensity_MinIntensityEdge_CorrGreen':
0.138487,
'Vfactor_DistCytoplasm_Intensity_MinIntensity_CorrGreen':
0.128157,
'Vfactor_DistCytoplasm_Math_Ratio1':
0.503610,
'Vfactor_DistCytoplasm_Math_Ratio2':
0.319610,
'Vfactor_DistCytoplasm_Texture_AngularSecondMoment_CorrGreen_1':
0.522880,
'Vfactor_DistCytoplasm_Texture_Entropy_CorrGreen_1':
0.504303,
'Vfactor_DistCytoplasm_Texture_InfoMeas2_CorrGreen_1':
0.289432,
'Vfactor_DistCytoplasm_Texture_InverseDifferenceMoment_CorrGreen_1':
0.234123,
'Vfactor_DistCytoplasm_Texture_SumAverage_CorrGreen_1':
0.591687,
'Vfactor_DistCytoplasm_Texture_SumEntropy_CorrGreen_1':
0.520356,
'Vfactor_DistCytoplasm_Texture_Variance_CorrGreen_1':
-0.007649,
'Vfactor_DistanceCells_Correlation_Correlation_CorrGreenCorrBlue':
0.761198,
'Vfactor_DistanceCells_Intensity_IntegratedIntensityEdge_CorrGreen':
0.234655,
'Vfactor_DistanceCells_Intensity_LowerQuartileIntensity_CorrGreen':
0.252240,
'Vfactor_DistanceCells_Intensity_MeanIntensityEdge_CorrGreen':
0.195125,
'Vfactor_DistanceCells_Intensity_MinIntensityEdge_CorrGreen':
0.138299,
'Vfactor_DistanceCells_Intensity_MinIntensity_CorrGreen':
0.126784,
'Vfactor_DistanceCells_Texture_AngularSecondMoment_CorrGreen_1':
0.342691,
'Vfactor_DistanceCells_Texture_Correlation_CorrGreen_1':
0.314396,
'Vfactor_DistanceCells_Texture_Entropy_CorrGreen_1':
0.311771,
'Vfactor_DistanceCells_Texture_InfoMeas1_CorrGreen_1':
0.410631,
'Vfactor_DistanceCells_Texture_InverseDifferenceMoment_CorrGreen_1':
0.170576,
'Vfactor_DistanceCells_Texture_SumAverage_CorrGreen_1':
0.223147,
'Vfactor_DistanceCells_Texture_SumEntropy_CorrGreen_1':
0.269519,
'Vfactor_DistanceCells_Texture_SumVariance_CorrGreen_1':
0.571528,
'Vfactor_DistanceCells_Texture_Variance_CorrGreen_1':
0.566272,
'Vfactor_Nuclei_Correlation_Correlation_CorrGreenCorrBlue':
0.705051,
'Vfactor_Nuclei_Math_Ratio1':
0.503610,
'Vfactor_Nuclei_Math_Ratio2':
0.319610,
'Vfactor_Nuclei_Texture_SumAverage_CorrGreen_1':
0.553708,
'Vfactor_PropCells_AreaShape_Area':
0.340093,
'Vfactor_PropCells_AreaShape_MajorAxisLength':
0.243838,
'Vfactor_PropCells_AreaShape_MinorAxisLength':
0.320691,
'Vfactor_PropCells_AreaShape_Perimeter':
0.238915,
'Vfactor_PropCells_Correlation_Correlation_CorrGreenCorrBlue':
0.723520,
'Vfactor_PropCells_Texture_GaborX_CorrGreen_1':
0.213161,
'Vfactor_PropCells_Texture_InfoMeas2_CorrBlue_1':
0.199791,
'Vfactor_PropCells_Texture_SumVariance_CorrBlue_1':
0.078959,
'Vfactor_PropCells_Texture_SumVariance_CorrGreen_1':
0.642844,
'Vfactor_PropCells_Texture_Variance_CorrBlue_1':
0.199105,
'Vfactor_PropCells_Texture_Variance_CorrGreen_1':
0.640818,
'Vfactor_PropCytoplasm_AreaShape_Area':
0.325845,
'Vfactor_PropCytoplasm_AreaShape_MinorAxisLength':
0.312258,
'Vfactor_PropCytoplasm_AreaShape_Perimeter':
0.238915,
'Vfactor_PropCytoplasm_Correlation_Correlation_CorrGreenCorrBlue':
0.337565,
'Vfactor_PropCytoplasm_Intensity_IntegratedIntensity_CorrGreen':
0.292900,
'Vfactor_PropCytoplasm_Intensity_LowerQuartileIntensity_CorrGreen':
0.175528,
'Vfactor_PropCytoplasm_Intensity_MedianIntensity_CorrGreen':
0.193308,
'Vfactor_PropCytoplasm_Texture_AngularSecondMoment_CorrGreen_1':
0.276152,
'Vfactor_PropCytoplasm_Texture_Entropy_CorrGreen_1':
0.239567,
'Vfactor_PropCytoplasm_Texture_GaborX_CorrGreen_1':
0.332380,
'Vfactor_PropCytoplasm_Texture_InfoMeas2_CorrGreen_1':
0.379141,
'Vfactor_PropCytoplasm_Texture_SumEntropy_CorrGreen_1':
0.337740,
'Vfactor_ThresholdedCells_Texture_AngularSecondMoment_CorrBlue_1':
0.334520,
'Vfactor_ThresholdedCells_Texture_AngularSecondMoment_CorrGreen_1':
0.192882,
'Vfactor_ThresholdedCells_Texture_Entropy_CorrBlue_1':
0.276245,
'Vfactor_ThresholdedCells_Texture_InfoMeas2_CorrBlue_1':
0.139166,
'Vfactor_ThresholdedCells_Texture_SumAverage_CorrBlue_1':
0.465237,
'Vfactor_ThresholdedCells_Texture_SumEntropy_CorrBlue_1':
0.355399,
'Vfactor_ThresholdedCells_Texture_SumVariance_CorrBlue_1':
0.453937,
'Vfactor_ThresholdedCells_Texture_SumVariance_CorrGreen_1':
0.564371,
'Vfactor_ThresholdedCells_Texture_Variance_CorrBlue_1':
0.360566,
'Vfactor_ThresholdedCells_Texture_Variance_CorrGreen_1':
0.548770,
'Zfactor_DistCytoplasm_Correlation_Correlation_CorrGreenCorrBlue':
0.531914,
'Zfactor_DistCytoplasm_Intensity_LowerQuartileIntensity_CorrGreen':
0.265558,
'Zfactor_DistCytoplasm_Intensity_MedianIntensity_CorrGreen':
0.178586,
'Zfactor_DistCytoplasm_Intensity_MinIntensityEdge_CorrGreen':
0.084566,
'Zfactor_DistCytoplasm_Intensity_MinIntensity_CorrGreen':
0.086476,
'Zfactor_DistCytoplasm_Math_Ratio1':
0.623284,
'Zfactor_DistCytoplasm_Math_Ratio2':
0.358916,
'Zfactor_DistCytoplasm_Texture_AngularSecondMoment_CorrGreen_1':
0.429510,
'Zfactor_DistCytoplasm_Texture_Entropy_CorrGreen_1':
0.508275,
'Zfactor_DistCytoplasm_Texture_InfoMeas2_CorrGreen_1':
0.068695,
'Zfactor_DistCytoplasm_Texture_InverseDifferenceMoment_CorrGreen_1':
0.347949,
'Zfactor_DistCytoplasm_Texture_SumAverage_CorrGreen_1':
0.646576,
'Zfactor_DistCytoplasm_Texture_SumEntropy_CorrGreen_1':
0.494276,
'Zfactor_DistCytoplasm_Texture_Variance_CorrGreen_1':
0.179011,
'Zfactor_DistanceCells_Correlation_Correlation_CorrGreenCorrBlue':
0.824686,
'Zfactor_DistanceCells_Intensity_IntegratedIntensityEdge_CorrGreen':
0.027644,
'Zfactor_DistanceCells_Intensity_LowerQuartileIntensity_CorrGreen':
0.088491,
'Zfactor_DistanceCells_Intensity_MeanIntensityEdge_CorrGreen':
0.065056,
'Zfactor_DistanceCells_Intensity_MinIntensityEdge_CorrGreen':
0.089658,
'Zfactor_DistanceCells_Intensity_MinIntensity_CorrGreen':
0.078017,
'Zfactor_DistanceCells_Texture_AngularSecondMoment_CorrGreen_1':
0.238131,
'Zfactor_DistanceCells_Texture_Correlation_CorrGreen_1':
0.301107,
'Zfactor_DistanceCells_Texture_Entropy_CorrGreen_1':
0.251143,
'Zfactor_DistanceCells_Texture_InfoMeas1_CorrGreen_1':
0.564957,
'Zfactor_DistanceCells_Texture_InverseDifferenceMoment_CorrGreen_1':
0.302767,
'Zfactor_DistanceCells_Texture_SumAverage_CorrGreen_1':
0.036459,
'Zfactor_DistanceCells_Texture_SumEntropy_CorrGreen_1':
0.159798,
'Zfactor_DistanceCells_Texture_SumVariance_CorrGreen_1':
0.516938,
'Zfactor_DistanceCells_Texture_Variance_CorrGreen_1':
0.501186,
'Zfactor_Nuclei_Correlation_Correlation_CorrGreenCorrBlue':
0.691408,
'Zfactor_Nuclei_Math_Ratio1':
0.623284,
'Zfactor_Nuclei_Math_Ratio2':
0.358916,
'Zfactor_Nuclei_Texture_SumAverage_CorrGreen_1':
0.587347,
'Zfactor_PropCells_AreaShape_Area':
0.132425,
'Zfactor_PropCells_AreaShape_MajorAxisLength':
0.034809,
'Zfactor_PropCells_AreaShape_MinorAxisLength':
0.113864,
'Zfactor_PropCells_AreaShape_Perimeter':
0.005984,
'Zfactor_PropCells_Correlation_Correlation_CorrGreenCorrBlue':
0.717632,
'Zfactor_PropCells_Texture_GaborX_CorrGreen_1':
0.251023,
'Zfactor_PropCells_Texture_InfoMeas2_CorrBlue_1':
0.149719,
'Zfactor_PropCells_Texture_SumVariance_CorrBlue_1':
0.102050,
'Zfactor_PropCells_Texture_SumVariance_CorrGreen_1':
0.611960,
'Zfactor_PropCells_Texture_Variance_CorrBlue_1':
0.197090,
'Zfactor_PropCells_Texture_Variance_CorrGreen_1':
0.614879,
'Zfactor_PropCytoplasm_AreaShape_Area':
0.205042,
'Zfactor_PropCytoplasm_AreaShape_MinorAxisLength':
0.072682,
'Zfactor_PropCytoplasm_AreaShape_Perimeter':
0.005984,
'Zfactor_PropCytoplasm_Correlation_Correlation_CorrGreenCorrBlue':
0.272017,
'Zfactor_PropCytoplasm_Intensity_IntegratedIntensity_CorrGreen':
0.115327,
'Zfactor_PropCytoplasm_Intensity_LowerQuartileIntensity_CorrGreen':
0.141850,
'Zfactor_PropCytoplasm_Intensity_MedianIntensity_CorrGreen':
0.105803,
'Zfactor_PropCytoplasm_Texture_AngularSecondMoment_CorrGreen_1':
0.107640,
'Zfactor_PropCytoplasm_Texture_Entropy_CorrGreen_1':
0.067896,
'Zfactor_PropCytoplasm_Texture_GaborX_CorrGreen_1':
0.136688,
'Zfactor_PropCytoplasm_Texture_InfoMeas2_CorrGreen_1':
0.334749,
'Zfactor_PropCytoplasm_Texture_SumEntropy_CorrGreen_1':
0.208829,
'Zfactor_ThresholdedCells_Texture_AngularSecondMoment_CorrBlue_1':
0.263467,
'Zfactor_ThresholdedCells_Texture_AngularSecondMoment_CorrGreen_1':
0.124355,
'Zfactor_ThresholdedCells_Texture_Entropy_CorrBlue_1':
0.236433,
'Zfactor_ThresholdedCells_Texture_InfoMeas2_CorrBlue_1':
0.125845,
'Zfactor_ThresholdedCells_Texture_SumAverage_CorrBlue_1':
0.449333,
'Zfactor_ThresholdedCells_Texture_SumEntropy_CorrBlue_1':
0.323243,
'Zfactor_ThresholdedCells_Texture_SumVariance_CorrBlue_1':
0.507477,
'Zfactor_ThresholdedCells_Texture_SumVariance_CorrGreen_1':
0.599000,
'Zfactor_ThresholdedCells_Texture_Variance_CorrBlue_1':
0.361424,
'Zfactor_ThresholdedCells_Texture_Variance_CorrGreen_1':
0.481393
}
temp_dir = tempfile.mkdtemp()
try:
cpprefs.set_headless()
cpprefs.set_default_output_directory(temp_dir)
print "Writing output to %s" % temp_dir
path = os.path.split(__file__)[0]
measurements = loadmat(os.path.join(path,
'calculatestatistics.mat'),
struct_as_record=True)
measurements = measurements['m']
image_set_list = cpi.ImageSetList()
image_set = image_set_list.get_image_set(0)
m = cpmeas.Measurements()
doses = [
0, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 0, 0, 1, 2, 3, 4, 5, 6, 7,
8, 9, 10, 0, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 0, 0, 1, 2, 3,
4, 5, 6, 7, 8, 9, 10, 10, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 10,
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 10, 0, 1, 2, 3, 4, 5, 6, 7, 8,
9, 0, 10, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0
]
for i, dose in enumerate(doses):
m.add_image_measurement("Dose", dose)
for object_name in measurements.dtype.fields:
omeasurements = measurements[object_name][0, 0]
for feature_name in omeasurements.dtype.fields:
data = omeasurements[feature_name][0, 0][0, i]
m.add_measurement(object_name, feature_name, data)
if i < len(doses) - 1:
m.next_image_set()
pipeline = cpp.Pipeline()
module = C.CalculateStatistics()
module.grouping_values.value = "Dose"
module.dose_values[0].log_transform.value = False
module.dose_values[0].measurement.value = "Dose"
module.dose_values[0].wants_save_figure.value = True
module.dose_values[0].figure_name.value = "EC49_"
module.module_num = 1
pipeline.add_module(module)
def callback(caller, event):
self.assertFalse(isinstance(event, cpp.RunExceptionEvent))
workspace = cpw.Workspace(pipeline, module, image_set,
cpo.ObjectSet(), m, image_set_list)
module.post_run(workspace)
for feature_name in m.get_feature_names(cpmeas.EXPERIMENT):
if not expected.has_key(feature_name):
print "Missing measurement: %s" % feature_name
continue
value = m.get_experiment_measurement(feature_name)
e_value = expected[feature_name]
diff = abs(value - e_value) * 2 / abs(value + e_value)
self.assertTrue(
diff < .05, "%s: Matlab: %f, Python: %f diff: %f" %
(feature_name, e_value, value, diff))
if diff > .01:
print(
"Warning: > 1%% difference for %s: Matlab: %f, Python: %f diff: %f"
% (feature_name, e_value, value, diff))
if feature_name.startswith("EC50"):
filename = "EC49_" + feature_name[5:] + ".pdf"
self.assertTrue(
os.path.isfile(os.path.join(temp_dir, filename)))
finally:
try:
workspace.measurements.hdf5_dict.hdf5_file.close()
except:
pass
for filename in os.listdir(temp_dir):
path = os.path.join(temp_dir, filename)
os.remove(path)
os.rmdir(temp_dir)
return
image_numbers = np.arange(1, n_image_sets + 1)
for image_number in image_numbers:
m[cpmeas.IMAGE, cpmeas.GROUP_NUMBER, image_number] = 1
m[cpmeas.IMAGE, cpmeas.GROUP_INDEX, image_number] = image_number
input_modules, other_modules = self.split_pipeline(pipeline)
workspace = cpw.Workspace(pipeline, None, m, None, m, None)
logger.info("Preparing group")
for module in other_modules:
module.prepare_group(
workspace, dict([("image_number", i) for i in image_numbers]),
image_numbers)
for image_index in range(n_image_sets):
object_set = cpo.ObjectSet()
m.next_image_set(image_index + 1)
for channel_name in channel_names:
dataset = image_group[channel_name]
pixel_data = dataset[image_index]
m.add(channel_name, cpi.Image(pixel_data))
for module in other_modules:
workspace = cpw.Workspace(pipeline, module, m, object_set, m,
None)
try:
logger.info("Running module # %d: %s" %
(module.module_num, module.module_name))
pipeline.run_module(module, workspace)
if workspace.disposition in \
(cpw.DISPOSITION_SKIP, cpw.DISPOSITION_CANCEL):
def run_create_webpage(self,
image_paths,
thumb_paths=None,
metadata=None,
alter_fn=None):
'''Run the create_webpage module, returning the resulting HTML document
image_paths - list of path / filename tuples. The function will
write an image to each of these and put images and
measurements into the workspace for each.
thumb_paths - if present a list of path / filename tuples. Same as above
metadata - a dictionary of feature / string values
alter_fn - function taking a CreateWebPage module, for you to
alter the module's settings
'''
np.random.seed(0)
module = C.CreateWebPage()
module.module_num = 1
module.orig_image_name.value = IMAGE_NAME
module.web_page_file_name.value = DEFAULT_HTML_FILE
if alter_fn is not None:
alter_fn(module)
pipeline = cpp.Pipeline()
def callback(caller, event):
self.assertFalse(isinstance(event, cpp.RunExceptionEvent))
pipeline.add_listener(callback)
pipeline.add_module(module)
images = [(IMAGE_NAME, image_paths)]
if thumb_paths:
images += [(THUMB_NAME, thumb_paths)]
self.assertEqual(len(image_paths), len(thumb_paths))
module.wants_thumbnails.value = True
module.thumbnail_image_name.value = THUMB_NAME
else:
module.wants_thumbnails.value = False
measurements = cpmeas.Measurements()
image_set_list = cpi.ImageSetList()
workspace = cpw.Workspace(pipeline, module, None, None, measurements,
image_set_list, None)
self.assertTrue(module.prepare_run(workspace))
module.prepare_group(workspace, {}, np.arange(1, len(image_paths) + 1))
for i in range(len(image_paths)):
image_set = image_set_list.get_image_set(i)
if metadata is not None:
for key in metadata.keys():
values = metadata[key]
feature = cpmeas.C_METADATA + "_" + key
measurements.add_image_measurement(feature, values[i])
for image_name, paths in images:
pixel_data = np.random.uniform(size=(10, 13))
img = cpi.Image(pixel_data)
image_set.add(image_name, img)
path_name, file_name = paths[i]
if path_name is None:
path_name = cpprefs.get_default_image_directory()
full_path = os.path.abspath(
os.path.join(self.directory, path_name, file_name))
imsave(full_path, pixel_data)
path_feature = '_'.join((C_PATH_NAME, image_name))
file_feature = '_'.join((C_FILE_NAME, image_name))
measurements.add_image_measurement(path_feature,
os.path.split(full_path)[0])
measurements.add_image_measurement(file_feature, file_name)
workspace = cpw.Workspace(pipeline, module, image_set,
cpo.ObjectSet(), measurements,
image_set_list)
module.run(workspace)
if i < len(image_paths) - 1:
measurements.next_image_set()
module.post_group(workspace, {})
module.post_run(workspace)
def test_12_01_load_unicode(self):
base_directory = tempfile.mkdtemp()
directory = u"\u2211\u03B1"
filename = u"\u03B2.jpg"
base_path = os.path.join(base_directory, directory)
os.mkdir(base_path)
path = os.path.join(base_path, filename)
csv_filename = u"\u03b3.csv"
csv_path = os.path.join(base_path, csv_filename)
unicode_value = u"\u03b4.csv"
try:
r = np.random.RandomState()
r.seed(1101)
labels = r.randint(0, 10, size=(30, 20)).astype(np.uint8)
img = PIL.Image.fromarray(labels, "L")
img.save(path, "PNG")
csv_text = (
"Image_FileName_MyFile,Image_PathName_MyFile,Metadata_Unicode\n"
"%s,%s,%s\n" %
(filename.encode('utf8'), base_path.encode('utf8'),
unicode_value.encode('utf8')))
pipeline, module, _ = self.make_pipeline(csv_text, csv_path)
image_set_list = cpi.ImageSetList()
m = cpmeas.Measurements()
workspace = cpw.Workspace(pipeline, module, None, None, m,
image_set_list)
self.assertTrue(module.prepare_run(workspace))
self.assertEqual(len(m.get_image_numbers()), 1)
key_names, group_list = pipeline.get_groupings(workspace)
self.assertEqual(len(group_list), 1)
group_keys, image_numbers = group_list[0]
self.assertEqual(len(image_numbers), 1)
module.prepare_group(workspace, group_keys, image_numbers)
image_set = image_set_list.get_image_set(image_numbers[0] - 1)
workspace = cpw.Workspace(pipeline, module, image_set,
cpo.ObjectSet(), m, image_set_list)
module.run(workspace)
pixel_data = image_set.get_image("MyFile").pixel_data
self.assertEqual(pixel_data.shape[0], 30)
self.assertEqual(pixel_data.shape[1], 20)
value = m.get_current_image_measurement("Metadata_Unicode")
self.assertEqual(value, unicode_value)
finally:
if os.path.exists(path):
try:
os.unlink(path)
except:
pass
if os.path.exists(csv_path):
try:
os.unlink(csv_path)
except:
pass
if os.path.exists(base_path):
try:
os.rmdir(base_path)
except:
pass
if os.path.exists(base_directory):
try:
os.rmdir(base_directory)
except:
pass
def make_workspace(self,
control_points,
lengths,
radii,
image,
mask=None,
auximage=None):
'''Create a workspace containing the control point measurements
control_points - an n x 2 x m array where n is the # of control points,
and m is the number of objects.
lengths - the length of each object
radii - the radii_from_training defining the radius at each control pt
image - the image to be straightened
mask - the mask associated with the image (default = no mask)
auximage - a second image to be straightnened (default = no second image)
'''
module = S.StraightenWorms()
module.objects_name.value = OBJECTS_NAME
module.straightened_objects_name.value = STRAIGHTENED_OBJECTS_NAME
module.images[0].image_name.value = IMAGE_NAME
module.images[
0].straightened_image_name.value = STRAIGHTENED_IMAGE_NAME
module.flip_image.value = IMAGE_NAME
module.module_num = 1
# Trick the module into thinking it's read the data file
class P:
def __init__(self):
self.radii_from_training = radii
module.training_set_directory.dir_choice = cps.URL_FOLDER_NAME
module.training_set_directory.custom_path = "http://www.cellprofiler.org"
module.training_set_file_name.value = "TrainingSet.xml"
module.training_params = {"TrainingSet.xml": (P(), "URL")}
pipeline = cpp.Pipeline()
pipeline.add_module(module)
def callback(caller, event):
self.assertFalse(isinstance(event, cpp.RunExceptionEvent))
pipeline.add_listener(callback)
m = cpmeas.Measurements()
for i, (y, x) in enumerate(control_points):
for v, f in ((x, S.F_CONTROL_POINT_X), (y, S.F_CONTROL_POINT_Y)):
feature = "_".join((S.C_WORM, f, str(i + 1)))
m.add_measurement(OBJECTS_NAME, feature, v)
feature = "_".join((S.C_WORM, S.F_LENGTH))
m.add_measurement(OBJECTS_NAME, feature, lengths)
image_set_list = cpi.ImageSetList()
image_set = image_set_list.get_image_set(0)
image_set.add(IMAGE_NAME, cpi.Image(image, mask))
if auximage is not None:
image_set.add(AUX_IMAGE_NAME, cpi.Image(auximage))
module.add_image()
module.images[1].image_name.value = AUX_IMAGE_NAME
module.images[
1].straightened_image_name.value = AUX_STRAIGHTENED_IMAGE_NAME
object_set = cpo.ObjectSet()
objects = cpo.Objects()
labels = np.zeros(image.shape, int)
for i in range(control_points.shape[2]):
if lengths[i] == 0:
continue
self.rebuild_worm_from_control_points_approx(
control_points[:, :, i], radii, labels, i + 1)
objects.segmented = labels
object_set.add_objects(objects, OBJECTS_NAME)
workspace = cpw.Workspace(pipeline, module, image_set, object_set, m,
image_set_list)
return workspace, module
def test_01_02_run(self):
"""Run with a rectangle, cross and circle"""
object_set = cpo.ObjectSet()
labels = np.zeros((10,20),int)
labels[1:9,1:5] = 1
labels[1:9,11] = 2
labels[4,6:19] = 2
objects = cpo.Objects()
objects.segmented = labels
object_set.add_objects(objects, "SomeObjects")
labels = np.zeros((115,115),int)
x,y = np.mgrid[-50:51,-50:51]
labels[:101, :101][x**2+y**2<=2500] = 1
objects = cpo.Objects()
objects.segmented = labels
object_set.add_objects(objects, "OtherObjects")
module = cpmoas.MeasureObjectAreaShape()
settings = ["SomeObjects","OtherObjects","Yes"]
module.set_settings_from_values(settings, 1, module.module_class())
module.module_num = 1
image_set_list = cpi.ImageSetList()
measurements = cpmeas.Measurements()
pipeline = cpp.Pipeline()
pipeline.add_module(module)
workspace = cpw.Workspace(pipeline, module,
image_set_list.get_image_set(0),
object_set, measurements, image_set_list)
module.run(workspace)
self.features_and_columns_match(measurements, module)
a = measurements.get_current_measurement('SomeObjects','AreaShape_Area')
self.assertEqual(len(a),2)
self.assertEqual(a[0],32)
self.assertEqual(a[1],20)
#
# Mini-test of the form factor of a circle
#
ff = measurements.get_current_measurement('OtherObjects',
'AreaShape_FormFactor')
self.assertEqual(len(ff),1)
perim = measurements.get_current_measurement('OtherObjects',
'AreaShape_Perimeter')
area = measurements.get_current_measurement('OtherObjects',
'AreaShape_Area')
# The perimeter is obtained geometrically and is overestimated.
expected = 100 * np.pi
diff = abs((perim[0] - expected)/(perim[0] + expected))
self.assertTrue(diff < .05, "perimeter off by %f" % diff)
wrongness = (perim[0] / expected)**2
# It's an approximate circle...
expected = np.pi * 50.0 **2
diff = abs((area[0] - expected) / (area[0] + expected))
self.assertTrue(diff < .05, "area off by %f" %diff)
wrongness *= expected / area[0]
self.assertAlmostEqual(ff[0] * wrongness, 1.0)
for object_name, object_count in (('SomeObjects',2),
('OtherObjects',1)):
for measurement in module.get_measurements(pipeline,object_name,
'AreaShape'):
feature_name = 'AreaShape_%s'%(measurement)
m = measurements.get_current_measurement(object_name,
feature_name)
self.assertEqual(len(m),object_count)
def run_module(self,
image,
labels,
center_labels=None,
center_choice=M.C_CENTERS_OF_OTHER,
bin_count=4,
maximum_radius=100,
wants_scaled=True,
wants_workspace=False):
'''Run the module, returning the measurements
image - matrix representing the image to be analyzed
labels - labels matrix of objects to be analyzed
center_labels - labels matrix of alternate centers or None for self
centers
bin_count - # of radial bins
'''
module = M.MeasureObjectRadialDistribution()
module.images[0].image_name.value = IMAGE_NAME
module.objects[0].object_name.value = OBJECT_NAME
object_set = cpo.ObjectSet()
main_objects = cpo.Objects()
main_objects.segmented = labels
object_set.add_objects(main_objects, OBJECT_NAME)
if center_labels is None:
module.objects[0].center_choice.value = M.C_SELF
else:
module.objects[0].center_choice.value = center_choice
module.objects[0].center_object_name.value = CENTER_NAME
center_objects = cpo.Objects()
center_objects.segmented = center_labels
object_set.add_objects(center_objects, CENTER_NAME)
module.bin_counts[0].bin_count.value = bin_count
module.bin_counts[0].wants_scaled.value = wants_scaled
module.bin_counts[0].maximum_radius.value = maximum_radius
module.add_heatmap()
module.add_heatmap()
module.add_heatmap()
for i, (a, f) in enumerate(
((M.A_FRAC_AT_D, M.F_FRAC_AT_D), (M.A_MEAN_FRAC, M.F_MEAN_FRAC),
(M.A_RADIAL_CV, M.F_RADIAL_CV))):
module.heatmaps[i].image_name.value = IMAGE_NAME
module.heatmaps[i].object_name.value = OBJECT_NAME
module.heatmaps[i].bin_count.value = str(bin_count)
module.heatmaps[i].wants_to_save_display.value = True
display_name = HEAT_MAP_NAME + f
module.heatmaps[i].display_name.value = display_name
module.heatmaps[i].colormap.value = "gray"
module.heatmaps[i].measurement.value = a
pipeline = cpp.Pipeline()
measurements = cpmeas.Measurements()
image_set_list = cpi.ImageSetList()
image_set = measurements
img = cpi.Image(image)
image_set.add(IMAGE_NAME, img)
workspace = cpw.Workspace(pipeline, module, image_set, object_set,
measurements, image_set_list)
module.run(workspace)
if wants_workspace:
return measurements, workspace
return measurements
def rruunn(self,
input_labels,
relabel_option,
unify_option=R.UNIFY_DISTANCE,
unify_method=R.UM_DISCONNECTED,
distance_threshold=5,
minimum_intensity_fraction=.9,
where_algorithm=R.CA_CLOSEST_POINT,
image=None,
wants_outlines=False,
outline_name="None",
parent_object="Parent_object",
parent_labels=np.zeros((10, 20), int)):
'''Run the RelabelObjects module
returns the labels matrix and the workspace.
'''
module = R.RelabelObjects()
module.module_num = 1
module.objects_name.value = INPUT_OBJECTS_NAME
module.output_objects_name.value = OUTPUT_OBJECTS_NAME
module.relabel_option.value = relabel_option
module.unify_option.value = unify_option
module.unification_method.value = unify_method
module.parent_object.value = parent_object
module.distance_threshold.value = distance_threshold
module.minimum_intensity_fraction.value = minimum_intensity_fraction
module.wants_image.value = (image is not None)
module.where_algorithm.value = where_algorithm
module.wants_outlines.value = wants_outlines
module.outlines_name.value = outline_name
pipeline = cpp.Pipeline()
def callback(caller, event):
self.assertFalse(isinstance(event, cpp.RunExceptionEvent))
pipeline.add_listener(callback)
pipeline.add_module(module)
image_set_list = cpi.ImageSetList()
image_set = image_set_list.get_image_set(0)
if image is not None:
img = cpi.Image(image)
image_set.add(IMAGE_NAME, img)
module.image_name.value = IMAGE_NAME
object_set = cpo.ObjectSet()
o = cpo.Objects()
o.segmented = input_labels
object_set.add_objects(o, INPUT_OBJECTS_NAME)
parent_object_set = cpo.ObjectSet()
p = cpo.Objects()
p.segmented = parent_labels
object_set.add_objects(p, parent_object)
workspace = cpw.Workspace(pipeline, module, image_set, object_set,
cpmeas.Measurements(), image_set_list)
module.run(workspace)
output_objects = workspace.object_set.get_objects(OUTPUT_OBJECTS_NAME)
return output_objects.segmented, workspace
def test_01_02_run(self):
module = instantiate_module(MODULE_NAME)
module.input_objects_name.value = INPUT_OBJECTS_NAME
module.output_objects_name.value = OUTPUT_OBJECTS_NAME
module.module_num = 1
pipeline = cpp.Pipeline()
pipeline.add_module(module)
object_set = cpo.ObjectSet()
#
# Pick a bunch of random points, dilate them using the distance
# transform and then label the result.
#
r = np.random.RandomState()
r.seed(12)
bimg = np.ones((100, 100), bool)
bimg[r.randint(0, 100, 50), r.randint(0, 100, 50)] = False
labels, count = label(distance_transform_edt(bimg) <= 5)
#
# Make the input objects
#
input_objects = cpo.Objects()
input_objects.segmented = labels
expected = skeletonize_labels(labels)
object_set.add_objects(input_objects, INPUT_OBJECTS_NAME)
#
# Make the workspace
#
measurements = cpmeas.Measurements()
workspace = cpw.Workspace(pipeline, module, None, object_set,
measurements, None)
module.run(workspace)
#
# Calculate the centers using Numpy. Scipy can do this too.
# But maybe it's instructive to show you how to go at the labels
# matrix using Numpy.
#
# We're going to get the centroids by taking the average value
# of x and y per object.
#
y, x = np.mgrid[0:labels.shape[0], 0:labels.shape[1]].astype(float)
#
# np.bincount counts the number of occurrences of each integer value.
# You need to operate on a 1d array - if you flatten the labels
# and weights, their pixels still align.
#
# We do [1:] to discard the background which is labeled 0
#
# The optional second argument to np.bincount is the "weight". For
# each label value, maintain a running sum of the weights.
#
areas = np.bincount(expected.flatten())[1:]
total_x = np.bincount(expected.flatten(), weights=x.flatten())[1:]
total_y = np.bincount(expected.flatten(), weights=y.flatten())[1:]
expected_location_x = total_x / areas
expected_location_y = total_y / areas
#
# Now check against the measurements.
#
count_feature = I.C_COUNT + "_" + OUTPUT_OBJECTS_NAME
self.assertTrue(
measurements.has_feature(cpmeas.IMAGE, count_feature),
"Your module did not produce a %s measurement" % count_feature)
count = measurements.get_measurement(cpmeas.IMAGE, count_feature)
self.assertEqual(count, len(areas))
for ftr, expected in ((I.M_LOCATION_CENTER_X, expected_location_x),
(I.M_LOCATION_CENTER_Y, expected_location_y)):
self.assertTrue(measurements.has_feature(OUTPUT_OBJECTS_NAME, ftr))
location = measurements.get_measurement(OUTPUT_OBJECTS_NAME, ftr)
np.testing.assert_almost_equal(location, expected)
