Merge pull request #151 from worldcoin/dev

v1.9.3 release

Author: wiktorlazarski

.github/workflows/deploy-docs.yml

@@ -67,6 +67,7 @@ jobs:
       - name: Checkout gh-pages branch
         run: |
           git fetch origin gh-pages:gh-pages || git checkout --orphan gh-pages
+          git stash
           git checkout gh-pages
 
       - name: Preserve existing .nojekyll from root

scripts/common/run_iris_pipeline.py

@@ -3,6 +3,7 @@
 import os
 
 import cv2
+from iris.io.dataclasses import IRImage
 from iris.pipelines.iris_pipeline import IRISPipeline
 
 if __name__ == "__main__":
@@ -22,6 +23,6 @@
 
     img_data = cv2.imread(args.in_img, cv2.IMREAD_GRAYSCALE)
 
-    out = iris_pipeline(img_data, "right")
+    out = iris_pipeline.estimate(IRImage(img_data=img_data, image_id="image_id1", eye_side="right"))
 
     logging.info("Run iris pipeline inference script FINISHED")

src/iris/_version.py

@@ -1 +1 @@
-__version__ = "1.9.2"
+__version__ = "1.9.3"

src/iris/io/dataclasses.py

@@ -1,7 +1,7 @@
 from __future__ import annotations
 
 from functools import cached_property
-from typing import Any, Callable, Dict, List, Literal, Tuple, Union
+from typing import Any, Callable, Dict, List, Literal, Optional, Tuple, Union
 
 import numpy as np
 from pydantic import BaseModel, Field, NonNegativeInt, root_validator, validator
@@ -22,6 +22,7 @@ class IRImage(ImmutableModel):
     """Data holder for input IR image."""
 
     img_data: np.ndarray
+    image_id: Optional[str]
     eye_side: Literal["left", "right"]
 
     @property

src/iris/nodes/iris_response/conv_filter_bank.py

@@ -94,7 +94,12 @@ def __init__(
             filters (List[ImageFilter]): List of image filters.
             probe_schemas (List[ProbeSchema]): List of corresponding probe schemas.
         """
-        super().__init__(iris_code_version=iris_code_version, maskisduplicated=maskisduplicated, filters=filters, probe_schemas=probe_schemas)
+        super().__init__(
+            iris_code_version=iris_code_version,
+            maskisduplicated=maskisduplicated,
+            filters=filters,
+            probe_schemas=probe_schemas,
+        )
 
     def run(self, normalization_output: NormalizedIris) -> IrisFilterResponse:
         """Apply filters to a normalized iris image.
@@ -160,15 +165,30 @@ def _convolve(
 
                 # Perform convolution at [i,j] probed pixel position.
                 iris_response[i][j] = (iris_patch * filter_patch).sum() / iris_patch.shape[0] / k_cols
-                if iris_response[i][j] == 0:   
+                if iris_response[i][j] == 0:
                     mask_response[i][j] = 0
                 else:
                     if self.params.maskisduplicated:
-                        val = 1 if mask_patch.all() else np.abs(filter_patch[mask_patch.astype(bool)].imag).sum() / np.abs(filter_patch.imag).sum()
+                        val = (
+                            1
+                            if mask_patch.all()
+                            else np.abs(filter_patch[mask_patch.astype(bool)].imag).sum()
+                            / np.abs(filter_patch.imag).sum()
+                        )
                         mask_response[i][j] = np.complex64(val + 1j * val)
                     else:
-                        val_real = 1 if mask_patch.all() else np.abs(filter_patch[mask_patch.astype(bool)].real).sum() / np.abs(filter_patch.real).sum()
-                        val_imag = 1 if mask_patch.all() else np.abs(filter_patch[mask_patch.astype(bool)].imag).sum() / np.abs(filter_patch.imag).sum()
+                        val_real = (
+                            1
+                            if mask_patch.all()
+                            else np.abs(filter_patch[mask_patch.astype(bool)].real).sum()
+                            / np.abs(filter_patch.real).sum()
+                        )
+                        val_imag = (
+                            1
+                            if mask_patch.all()
+                            else np.abs(filter_patch[mask_patch.astype(bool)].imag).sum()
+                            / np.abs(filter_patch.imag).sum()
+                        )
                         mask_response[i][j] = np.complex64(val_real + 1j * val_imag)
 
         iris_response.real = iris_response.real / img_filter.kernel_norm.real

src/iris/nodes/iris_response_refinement/fragile_bits_refinement.py

@@ -51,8 +51,13 @@ def __init__(
             maskisduplicated (bool, optional): If True, the mask is duplicated for both real and imaginary parts.
             callbacks(List[Callback]): List of callbacks. Defaults to [].
         """
-        super().__init__(value_threshold=value_threshold, fragile_type=fragile_type, maskisduplicated=maskisduplicated, callbacks=callbacks)
-        
+        super().__init__(
+            value_threshold=value_threshold,
+            fragile_type=fragile_type,
+            maskisduplicated=maskisduplicated,
+            callbacks=callbacks,
+        )
+
     def run(self, response: IrisFilterResponse) -> IrisFilterResponse:
         """Generate refined IrisFilterResponse.
 
@@ -97,7 +102,7 @@ def run(self, response: IrisFilterResponse) -> IrisFilterResponse:
                     iris_response_r >= self.params.value_threshold[0], iris_response_r <= self.params.value_threshold[1]
                 )
                 # min angle away from the coordinate lines
-                
+
                 if self.params.maskisduplicated:
                     mask_value = mask_value_r * iris_mask.imag
                     fragile_masks.append(mask_value + 1j * mask_value)

src/iris/nodes/matcher/hamming_distance_matcher_interface.py

@@ -1,7 +1,7 @@
 import abc
 from typing import Any, List
-import numpy as np
 
+import numpy as np
 from pydantic import conint
 
 from iris.io.class_configs import ImmutableModel
@@ -25,7 +25,7 @@ def __init__(self, **kwargs) -> None:
             rotation_shift (int = 15): rotation allowed in matching, converted to columns. Defaults to 15.
         """
         self.params = self.__parameters_type__(**kwargs)
-    
+
     def load_weights(self, weights_path: str) -> List[np.array]:
         """Load weights from a file.
 
@@ -35,7 +35,7 @@ def load_weights(self, weights_path: str) -> List[np.array]:
         Returns:
             List[Any]: Loaded weights.
         """
-        with open(weights_path, 'rb') as f:
+        with open(weights_path, "rb") as f:
             try:
                 weights = np.load(f, allow_pickle=True)
                 if isinstance(weights, np.ndarray):

src/iris/nodes/matcher/utils.py

@@ -72,10 +72,10 @@ def count_nonmatchbits(
 
     if half_width:
         totalirisbitcount = np.sum(
-            [[np.sum(x[hw:, ...])*2, np.sum(x[:hw, ...])*2] for x, hw in zip(irisbitcount, half_width)], axis=0
+            [[np.sum(x[hw:, ...]) * 2, np.sum(x[:hw, ...]) * 2] for x, hw in zip(irisbitcount, half_width)], axis=0
         )
         totalmaskbitcount = np.sum(
-            [[np.sum(y[hw:, ...])*2, np.sum(y[:hw, ...])*2] for y, hw in zip(maskbitcount, half_width)], axis=0
+            [[np.sum(y[hw:, ...]) * 2, np.sum(y[:hw, ...]) * 2] for y, hw in zip(maskbitcount, half_width)], axis=0
         )
     else:
         totalirisbitcount = np.sum(irisbitcount)

src/iris/orchestration/output_builders.py

@@ -89,6 +89,7 @@ def __get_iris_pipeline_metadata(call_trace: PipelineCallTraceStorage) -> Dict[s
 
     return {
         "iris_version": __version__,
+        "image_id": ir_image.image_id,
         "image_size": (ir_image.width, ir_image.height),
         "eye_side": ir_image.eye_side,
         "eye_centers": __safe_serialize(call_trace.get("eye_center_estimation")),
@@ -144,9 +145,9 @@ def __get_templates_aggregation_metadata(call_trace: PipelineCallTraceStorage) -
             "reference_template_id": aligned_templates.reference_template_id if aligned_templates is not None else None,
             "distances": __safe_serialize(aligned_templates.distances) if aligned_templates is not None else None,
         },
-        "post_identity_filter_templates_count": len(identity_filtered_templates)
-        if identity_filtered_templates is not None
-        else None,
+        "post_identity_filter_templates_count": (
+            len(identity_filtered_templates) if identity_filtered_templates is not None else None
+        ),
     }
 
 
@@ -216,9 +217,9 @@ def __get_multiframe_iris_pipeline_metadata(call_trace: PipelineCallTraceStorage
     OutputFieldSpec(key="error", extractor=__get_error, safe_serialize=False),
     OutputFieldSpec(
         key="iris_template",
-        extractor=lambda ct: ct.get("templates_aggregation").as_iris_template()
-        if ct.get("templates_aggregation") is not None
-        else None,
+        extractor=lambda ct: (
+            ct.get("templates_aggregation").as_iris_template() if ct.get("templates_aggregation") is not None else None
+        ),
         safe_serialize=True,
     ),
     OutputFieldSpec(key="metadata", extractor=__get_templates_aggregation_metadata, safe_serialize=False),
@@ -228,9 +229,9 @@ def __get_multiframe_iris_pipeline_metadata(call_trace: PipelineCallTraceStorage
     OutputFieldSpec(key="error", extractor=__get_error, safe_serialize=False),
     OutputFieldSpec(
         key="iris_template",
-        extractor=lambda ct: ct.get("templates_aggregation").as_iris_template()
-        if ct.get("templates_aggregation") is not None
-        else None,
+        extractor=lambda ct: (
+            ct.get("templates_aggregation").as_iris_template() if ct.get("templates_aggregation") is not None else None
+        ),
         safe_serialize=False,
     ),
     OutputFieldSpec(key="metadata", extractor=__get_templates_aggregation_metadata, safe_serialize=False),
@@ -242,9 +243,9 @@ def __get_multiframe_iris_pipeline_metadata(call_trace: PipelineCallTraceStorage
     OutputFieldSpec(key="error", extractor=__get_error, safe_serialize=False),
     OutputFieldSpec(
         key="iris_template",
-        extractor=lambda ct: ct.get("aggregation_result", {}).get("iris_template")
-        if ct.get("aggregation_result")
-        else None,
+        extractor=lambda ct: (
+            ct.get("aggregation_result", {}).get("iris_template") if ct.get("aggregation_result") else None
+        ),
         safe_serialize=True,
     ),
     OutputFieldSpec(key="metadata", extractor=__get_multiframe_iris_pipeline_metadata, safe_serialize=False),
@@ -267,9 +268,9 @@ def __get_multiframe_iris_pipeline_metadata(call_trace: PipelineCallTraceStorage
     OutputFieldSpec(key="error", extractor=__get_error, safe_serialize=False),
     OutputFieldSpec(
         key="iris_template",
-        extractor=lambda ct: ct.get("aggregation_result", {}).get("iris_template")
-        if ct.get("aggregation_result")
-        else None,
+        extractor=lambda ct: (
+            ct.get("aggregation_result", {}).get("iris_template") if ct.get("aggregation_result") else None
+        ),
         safe_serialize=False,
     ),
     OutputFieldSpec(key="metadata", extractor=__get_multiframe_iris_pipeline_metadata, safe_serialize=False),

src/iris/pipelines/confs/multiframe_iris_pipeline.yaml

@@ -1,12 +1,12 @@
 metadata:
   pipeline_name: multiframe_iris_pipeline
-  iris_version: 1.9.2
+  iris_version: 1.9.3
 
 # Configuration for individual image processing (IRISPipeline)
 iris_pipeline:
   metadata:
     pipeline_name: iris_pipeline
-    iris_version: 1.9.2
+    iris_version: 1.9.3
 
   pipeline:
     - name: segmentation
@@ -314,7 +314,7 @@ iris_pipeline:
 # Configuration for template aggregation (TemplatesAggregationPipeline)
 templates_aggregation_pipeline:
   metadata:
-    iris_version: 1.9.2
+    iris_version: 1.9.3
     pipeline_name: templates_aggregation
 
   pipeline: