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+3
-2
@@ -1,7 +1,7 @@
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FROM debian:13.3
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# Install system dependencies
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RUN apt-get update && apt-get install -y wget libgl1 libglib2.0-0 patchelf
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RUN apt-get update && apt-get install -y wget libgl1 libglib2.0-0 patchelf git
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# Copy project files
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COPY ./src /src
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@@ -19,7 +19,8 @@ ENV PATH="/home/student/.local/bin:$PATH"
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WORKDIR /src
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RUN uv venv --python 3.11
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ENV PATH="/src/.venv/bin:$PATH"
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RUN uv pip install notebook open-iris
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ENV IRIS_ENV="DEV"
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RUN uv pip install notebook git+https://github.com/worldcoin/open-iris.git@v1.11.0
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RUN patchelf --clear-execstack /src/.venv/lib/python3.11/site-packages/onnxruntime/capi/*.so
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# Launch Jupyter Notebook
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+97
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pipeline_conf = {
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"metadata": {"pipeline_name": "iris_pipeline", "iris_version": "1.11.0"},
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"pipeline": [
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{
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"name": "segmentation",
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"algorithm": {"class_name": "iris.MultilabelSegmentation.create_from_hugging_face", "params": {}},
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"inputs": [{"name": "image", "source_node": "input"}],
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"callbacks": None,
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},
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{
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"name": "segmentation_binarization",
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"algorithm": {"class_name": "iris.MultilabelSegmentationBinarization", "params": {
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"eyeball_threshold": 0.1,
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"iris_threshold": 0.1,
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"pupil_threshold": 0.1,
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"eyelashes_threshold": 0.1
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}},
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"inputs": [{"name": "segmentation_map", "source_node": "segmentation"}],
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"callbacks": None,
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},
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{
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"name": "vectorization",
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"algorithm": {"class_name": "iris.ContouringAlgorithm", "params": {}},
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"inputs": [{"name": "geometry_mask", "source_node": "segmentation_binarization", "index": 0}],
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"callbacks": None,
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},
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{
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"name": "specular_reflection_detection",
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"algorithm": {"class_name": "iris.SpecularReflectionDetection", "params": {}},
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"inputs": [{"name": "ir_image", "source_node": "input"}],
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"callbacks": None,
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},
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{
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"name": "interpolation",
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"algorithm": {"class_name": "iris.ContourInterpolation", "params": {}},
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"inputs": [{"name": "polygons", "source_node": "vectorization"}],
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"callbacks": None,
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},
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{
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"name": "distance_filter",
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"algorithm": {"class_name": "iris.ContourPointNoiseEyeballDistanceFilter", "params": {}},
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"inputs": [
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{"name": "polygons", "source_node": "interpolation"},
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{"name": "geometry_mask", "source_node": "segmentation_binarization", "index": 1},
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],
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"callbacks": None,
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},
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{
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"name": "eye_orientation",
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"algorithm": {"class_name": "iris.MomentOfArea", "params": {
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"eccentricity_threshold": 0.05
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}},
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"inputs": [{"name": "geometries", "source_node": "distance_filter"}],
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"callbacks": None,
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},
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{
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"name": "eye_center_estimation",
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"algorithm": {"class_name": "iris.BisectorsMethod", "params": {}},
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"inputs": [{"name": "geometries", "source_node": "distance_filter"}],
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"callbacks": None,
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},
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{
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"name": "smoothing",
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"algorithm": {"class_name": "iris.Smoothing", "params": {}},
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"inputs": [
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{"name": "polygons", "source_node": "distance_filter"},
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{"name": "eye_centers", "source_node": "eye_center_estimation"},
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],
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"callbacks": None,
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},
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{
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"name": "geometry_estimation",
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"algorithm": {
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"class_name": "iris.FusionExtrapolation",
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"params": {
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"circle_extrapolation": {"class_name": "iris.LinearExtrapolation", "params": {"dphi": 0.703125}},
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"ellipse_fit": {"class_name": "iris.LSQEllipseFitWithRefinement", "params": {"dphi": 0.703125}},
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"algorithm_switch_std_threshold": 3.5,
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},
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},
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"inputs": [
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{"name": "input_polygons", "source_node": "smoothing"},
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{"name": "eye_center", "source_node": "eye_center_estimation"},
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],
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"callbacks": None,
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},
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{
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"name": "pupil_to_iris_property_estimation",
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"algorithm": {"class_name": "iris.PupilIrisPropertyCalculator", "params": {}},
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"inputs": [
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{"name": "geometries", "source_node": "geometry_estimation"},
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{"name": "eye_centers", "source_node": "eye_center_estimation"},
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],
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"callbacks": None,
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},
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{
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"name": "offgaze_estimation",
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"algorithm": {"class_name": "iris.EccentricityOffgazeEstimation", "params": {}},
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"inputs": [{"name": "geometries", "source_node": "geometry_estimation"}],
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"callbacks": None,
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},
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{
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"name": "occlusion90_calculator",
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"algorithm": {"class_name": "iris.OcclusionCalculator", "params": {"quantile_angle": 90.0}},
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"inputs": [
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{"name": "noise_mask", "source_node": "segmentation_binarization", "index": 1},
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{"name": "extrapolated_polygons", "source_node": "geometry_estimation"},
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{"name": "eye_orientation", "source_node": "eye_orientation"},
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{"name": "eye_centers", "source_node": "eye_center_estimation"},
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],
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"callbacks": None,
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},
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{
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"name": "occlusion30_calculator",
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"algorithm": {"class_name": "iris.OcclusionCalculator", "params": {"quantile_angle": 30.0}},
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"inputs": [
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{"name": "noise_mask", "source_node": "segmentation_binarization", "index": 1},
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{"name": "extrapolated_polygons", "source_node": "geometry_estimation"},
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{"name": "eye_orientation", "source_node": "eye_orientation"},
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{"name": "eye_centers", "source_node": "eye_center_estimation"},
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],
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"callbacks": None,
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},
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{
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"name": "noise_masks_aggregation",
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"algorithm": {"class_name": "iris.NoiseMaskUnion", "params": {}},
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"inputs": [
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{
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"name": "elements",
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"source_node": [
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{"name": "segmentation_binarization", "index": 1},
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{"name": "specular_reflection_detection"},
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],
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}
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],
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"callbacks": None,
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},
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{
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"name": "normalization",
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"algorithm": {"class_name": "iris.PerspectiveNormalization", "params": {}},
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"inputs": [
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{"name": "image", "source_node": "input"},
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{"name": "noise_mask", "source_node": "noise_masks_aggregation"},
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{"name": "extrapolated_contours", "source_node": "geometry_estimation"},
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{"name": "eye_orientation", "source_node": "eye_orientation"},
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],
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"callbacks": None,
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},
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{
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"name": "filter_bank",
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"algorithm": {
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"class_name": "iris.ConvFilterBank",
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"params": {
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"filters": [
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{
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"class_name": "iris.GaborFilter",
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"params": {
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"kernel_size": [41, 21],
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"sigma_phi": 7,
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"sigma_rho": 6.13,
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"theta_degrees": 90.0,
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"lambda_phi": 28.0,
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"dc_correction": True,
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"to_fixpoints": True,
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},
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},
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{
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"class_name": "iris.GaborFilter",
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"params": {
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"kernel_size": [17, 21],
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"sigma_phi": 2,
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"sigma_rho": 5.86,
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"theta_degrees": 90.0,
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"lambda_phi": 8,
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"dc_correction": True,
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"to_fixpoints": True,
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},
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},
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],
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"probe_schemas": [
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{"class_name": "iris.RegularProbeSchema", "params": {"n_rows": 16, "n_cols": 256}},
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{"class_name": "iris.RegularProbeSchema", "params": {"n_rows": 16, "n_cols": 256}},
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],
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},
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},
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"inputs": [{"name": "normalization_output", "source_node": "normalization"}],
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"callbacks": None,
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},
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{
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"name": "encoder",
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"algorithm": {"class_name": "iris.IrisEncoder", "params": {}},
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"inputs": [{"name": "response", "source_node": "filter_bank"}],
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"callbacks": None,
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},
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{
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"name": "bounding_box_estimation",
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"algorithm": {"class_name": "iris.IrisBBoxCalculator", "params": {}},
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"inputs": [
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{"name": "ir_image", "source_node": "input"},
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{"name": "geometry_polygons", "source_node": "geometry_estimation"},
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],
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"callbacks": None,
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},
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],
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}
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@@ -5,16 +5,18 @@ import matplotlib.pyplot as plt
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import iris
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from pipeline import pipeline_conf
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def process(path: str) -> None:
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path = pathlib.Path(path)
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iris_pipeline = iris.IRISPipeline(env=iris.IRISPipeline.DEBUGGING_ENVIRONMENT)
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iris_pipeline = iris.IRISPipeline(env=iris.IRISPipeline.DEBUGGING_ENVIRONMENT, config=pipeline_conf)
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iris_visualizer = iris.visualisation.IRISVisualizer()
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outputs = []
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for image_path in (path / "input").iterdir():
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if image_path.suffix != ".png":
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if image_path.suffix != ".jpg":
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continue
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print(f"Processing {image_path}...")
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@@ -26,8 +28,7 @@ def process(path: str) -> None:
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img_data=image_pixels,
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eye_side=eye_side,
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)
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output = iris_pipeline(image_pixels, eye_side=eye_side)
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output = iris_pipeline(iris.IRImage(img_data=image_pixels, image_id="image_id", eye_side="right"))
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iris_visualizer.plot_segmentation_map(
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ir_image=img,
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@@ -35,7 +36,7 @@ def process(path: str) -> None:
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)
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(path / "segmentation").mkdir(parents=True, exist_ok=True)
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plt.savefig(path / "segmentation" / f"{image_path.stem}.png")
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iris_visualizer.plot_all_geometry(
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ir_image=img,
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geometry_polygons=iris_pipeline.call_trace["geometry_estimation"],
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@@ -45,16 +46,12 @@ def process(path: str) -> None:
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(path / "geometry").mkdir(parents=True, exist_ok=True)
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plt.savefig(path / "geometry" / f"{image_path.stem}.png")
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iris_visualizer.plot_normalized_iris(
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normalized_iris=iris_pipeline.call_trace["normalization"],
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)
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(path / "normalized").mkdir(parents=True, exist_ok=True)
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plt.savefig(path / "normalized" / f"{image_path.stem}.png")
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iris_visualizer.plot_iris_template(
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iris_template=iris_pipeline.call_trace["encoder"],
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)
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data = iris_pipeline.call_trace["normalization"].normalized_image
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plt.imsave(path / "normalized" / f"{image_path.stem}.png", data, cmap='Greys_r')
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(path / "template").mkdir(parents=True, exist_ok=True)
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plt.savefig(path / "template" / f"{image_path.stem}.png")
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data = output["iris_template"].iris_codes[0][:, :, 0]
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plt.imsave(path / "template" / f"{image_path.stem}.png", data, cmap='binary')
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outputs.append((image_path, img, output))
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