Merge pull request #87 from worldcoin/dev

1.5.0 release

Author: ycbiometrics

src/iris/_version.py

@@ -1 +1 @@
-__version__ = "1.4.0"
+__version__ = "1.5.0"

src/iris/nodes/encoder/iris_encoder.py

@@ -26,11 +26,11 @@ class Parameters(Algorithm.Parameters):
 
     __parameters_type__ = Parameters
 
-    def __init__(self, mask_threshold: float = 0.9, callbacks: List[Callback] = []) -> None:
+    def __init__(self, mask_threshold: float = 0.93, callbacks: List[Callback] = []) -> None:
         """Assign parameters.
 
         Args:
-            mask_threshold (float): threshold to binarize mask_responses, in the range of [0,1]. Defaults to 0.9.
+            mask_threshold (float): threshold to binarize mask_responses, in the range of [0,1]. Defaults to 0.93.
             callbacks (List[Callback]): callbacks list. Defaults to [].
         """
         super().__init__(mask_threshold=mask_threshold, callbacks=callbacks)
@@ -48,7 +48,6 @@ def run(self, response: IrisFilterResponse) -> IrisTemplate:
         mask_codes: List[np.ndarray] = []
 
         for iris_response, mask_response in zip(response.iris_responses, response.mask_responses):
-
             iris_code = np.stack([iris_response.real > 0, iris_response.imag > 0], axis=-1)
             mask_code = np.stack(
                 [mask_response.real >= self.params.mask_threshold, mask_response.imag >= self.params.mask_threshold],

src/iris/nodes/iris_response/conv_filter_bank.py

@@ -152,17 +152,19 @@ def _convolve(
                 rbot = min(r_probe + p_rows + 1, i_rows - 1)
                 iris_patch = padded_iris[rtop:rbot, c_probe : c_probe + k_cols]
                 mask_patch = padded_mask[rtop:rbot, c_probe : c_probe + k_cols]
+                ktop = p_rows - iris_patch.shape[0] // 2
+                filter_patch = img_filter.kernel_values[ktop : ktop + iris_patch.shape[0], :]
 
                 # Perform convolution at [i,j] probed pixel position.
-                ktop = p_rows - iris_patch.shape[0] // 2
-                iris_response[i][j] = (
-                    (iris_patch * img_filter.kernel_values[ktop : ktop + iris_patch.shape[0], :]).sum()
-                    / iris_patch.shape[0]
-                    / k_cols
-                )
-                mask_response[i][j] = (
-                    0 if iris_response[i][j] == 0 else (mask_patch.sum() / iris_patch.shape[0] / k_cols)
-                )
+                iris_response[i][j] = (iris_patch * filter_patch).sum() / iris_patch.shape[0] / k_cols
+                if iris_response[i][j] == 0:
+                    mask_response[i][j] = 0
+                else:
+                    mask_response[i][j] = (
+                        1
+                        if mask_patch.all()
+                        else np.abs(filter_patch[mask_patch.astype(bool)].imag).sum() / np.abs(filter_patch.imag).sum()
+                    )
 
         iris_response.real = iris_response.real / img_filter.kernel_norm.real
         iris_response.imag = iris_response.imag / img_filter.kernel_norm.imag

src/iris/pipelines/confs/pipeline.yaml

@@ -1,6 +1,6 @@
 metadata:
   pipeline_name: iris_pipeline
-  iris_version: 1.4.0
+  iris_version: 1.5.0
 
 pipeline:
   - name: segmentation

src/iris/utils/visualisation.py

@@ -389,7 +389,7 @@ def plot_iris_filter_response(
         iris_filter_response: Union[iris_dc.IrisFilterResponse, Dict[str, List[np.ndarray]]],
         space: Literal["cartesian", "polar"] = "cartesian",
         plot_mask: bool = True,
-        mask_threshold: float = 0.9,
+        mask_threshold: float = 0.93,
         vlim: float = 1e-3,
         ax: Optional[matplotlib.axes._axes.Axes] = None,
     ) -> Canvas:
@@ -399,7 +399,7 @@ def plot_iris_filter_response(
             iris_filter_response (Union[iris_dc.IrisFilterResponse, Dict[str, List[np.ndarray]]]): iris filter response to visualise.
             space (Literal["cartesian", "polar"], optional): Wether to plot the response in cartesian or polar coordinates. Defaults to cartesian.
             plot_mask (bool, optional): Wether to overlay the mask response in transparency. Defaults to True.
-            mask_threshold (float, optional): Wether to overlay the mask in transparency. Defaults to 0.9.
+            mask_threshold (float, optional): Wether to overlay the mask in transparency. Defaults to 0.93.
             vlim (float, optional): The maximal value displayed in real, imaginary and amplitude graphs. Defaults to 1e-3
             ax (Optional[matplotlib.axes._axes.Axes]): ax to plot the figure at. Defaults to None.
 
@@ -525,7 +525,7 @@ def plot_iris_template_and_normalized_iris(
                         array=mask_code[:, :, 1],
                         target_shape=normalized_iris.normalized_image.shape[::-1],
                     )
-                    mask_resized[mask_resized > 0.9] = np.nan
+                    mask_resized[mask_resized > 0.93] = np.nan
                     axs[2 * i + j].imshow(mask_resized, alpha=0.8, cmap="gray", vmin=0, vmax=1)
 
         return fig, axs