Maintenance to clip and threshold application arguments (#44)

* Refactor spots_clip and spots_threshold to clip and threshold

* Change default clip value to True

deepcell_spots/applications/polaris.py

@@ -23,7 +23,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Singleplex FISH analysis application"""
+
+"""Singleplex and multiplex FISH analysis application"""
 
 from __future__ import absolute_import, division, print_function
 
@@ -180,16 +181,16 @@ def __init__(self,
             self.segmentation_app = None
             warnings.warn('No segmentation application instantiated.')
 
-    def _predict_spots_image(self, spots_image, spots_threshold, spots_clip):
+    def _predict_spots_image(self, spots_image, threshold, clip):
         """Iterate through all channels and generate model output (probability maps)
 
         Args:
             spots_image (numpy.array): Input image for spot detection with shape
                 ``[batch, x, y, channel]``.
-            spots_threshold (float): Probability threshold for a pixel to be
+            threshold (float): Probability threshold for a pixel to be
                 considered as a spot.
-            spots_clip (bool): Determines if pixel values will be clipped by percentile.
-                Defaults to false.
+            clip (bool): Determines if pixel values will be clipped by percentile.
+                Defaults to True.
 
         Returns:
             numpy.array: Output probability map with shape ``[batch, x, y, channel]``.
@@ -199,18 +200,16 @@ def _predict_spots_image(self, spots_image, spots_threshold, spots_clip):
         for idx_channel in range(spots_image.shape[-1]):
             output_image[..., idx_channel] = self.spots_app.predict(
                 image=spots_image[..., idx_channel:idx_channel+1],
-                # TODO: threshold is disabled, but must feed a float [0,1] number
-                threshold=spots_threshold,
-                clip=spots_clip
+                clip=clip
             )['classification'][..., 1]
         return output_image
 
     def predict(self,
                 spots_image,
                 segmentation_image=None,
                 image_mpp=None,
-                spots_threshold=0.95,
-                spots_clip=False,
+                threshold=0.95,
+                clip=True,
                 maxpool_extra_pixel_num=0,
                 decoding_training_kwargs=None):
         """Generates prediction output consisting of a labeled cell segmentation image,
@@ -228,10 +227,10 @@ def predict(self,
             segmentation_image (numpy.array): Input image for cell segmentation with shape
                 ``[batch, x, y, channel]``. Defaults to None.
             image_mpp (float): Microns per pixel for ``image``.
-            spots_threshold (float): Probability threshold for a pixel to be
+            threshold (float): Probability threshold for a pixel to be
                 considered as a spot.
-            spots_clip (bool): Determines if pixel values will be clipped by percentile.
-                Defaults to false.
+            clip (bool): Determines if pixel values will be clipped by percentile.
+                Defaults to True.
             maxpool_extra_pixel_num (int): Number of extra pixel for max pooling. Defaults
                 to 0, means no max pooling. For any number t, there will be a pool with
                 shape ``[-t, t] x [-t, t]``.
@@ -247,16 +246,16 @@ def predict(self,
             df_intensities (pandas.DataFrame): Columns are channels and rows are spots.
             segmentation_result (numpy.array): Segmentation mask with shape ``[batch, x, y, 1]``.
         """
-        if spots_threshold < 0 or spots_threshold > 1:
+        if threshold < 0 or threshold > 1:
             raise ValueError('Threshold of %s was input. Threshold value must be '
                              'between 0 and 1.'.format())
 
-        output_image = self._predict_spots_image(spots_image, spots_threshold, spots_clip)
+        output_image = self._predict_spots_image(spots_image, threshold, clip)
 
         clipped_output_image = np.clip(output_image, 0, 1)
         max_proj_images = np.max(clipped_output_image, axis=-1)
         spots_locations = max_cp_array_to_point_list_max(max_proj_images,
-                                                         threshold=spots_threshold, min_distance=1)
+                                                         threshold=threshold, min_distance=1)
 
         spots_intensities = extract_spots_prob_from_coords_maxpool(
             clipped_output_image, spots_locations, extra_pixel_num=maxpool_extra_pixel_num)

deepcell_spots/applications/polaris_test.py

@@ -81,9 +81,9 @@ def test_polaris_app(self):
             app = Polaris()
             spots_image = np.random.rand(1, 128, 128, 1)
             with self.assertRaises(ValueError):
-                _ = app.predict(spots_image=spots_image, spots_threshold=1.1)
+                _ = app.predict(spots_image=spots_image, threshold=1.1)
             with self.assertRaises(ValueError):
-                _ = app.predict(spots_image=spots_image, spots_threshold=-1.1)
+                _ = app.predict(spots_image=spots_image, threshold=-1.1)
 
             # test segmentation app error
             app = Polaris(segmentation_type='no segmentation')

deepcell_spots/applications/spot_detection.py

@@ -240,7 +240,7 @@ def predict(self,
                 preprocess_kwargs=None,
                 postprocess_kwargs=None,
                 threshold=0.95,
-                clip=False):
+                clip=True):
         """Generates a list of coordinate spot locations of the input
         running prediction with appropriate pre and post processing
         functions.
@@ -260,8 +260,9 @@ def predict(self,
             postprocess_kwargs (dict): Keyword arguments to pass to the
                 post-processing function.
             threshold (float): Probability threshold for a pixel to be
-                                considered as a spot.
+                considered as a spot.
             clip (bool): Determines if pixel values will be clipped by percentile.
+                Defaults to True.
 
         Raises:
             ValueError: Input data must match required rank of the application,