Update docs

_downloads/0b576ad9020dc38b1ea8157e97debdda/5_map_gaze.ipynb

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+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "%matplotlib inline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n# Run gaze mapping\n\nThis example shows how to run gaze mapping with a connected Pupil Core\nheadset.\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>This example requires the dependencies for\n    `streaming<streaming_dependencies>` and\n    `pupil detection<pupil_detection_dependencies>`.</p></div>\n\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import sys\nimport logging\n\nimport pupil_recording_interface as pri"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Set Pupil Core generation\n\nSet the generation of your Pupil Core device (1, 2 or 3)\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "pupil_gen = 2"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Set folder containing calibration\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "folder = \"~/pupil_capture_settings\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Set up stream configurations\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "configs = [\n    pri.VideoStream.Config(\n        device_type=\"uvc\",\n        device_uid=f\"Pupil Cam{pupil_gen} ID2\",\n        name=\"world\",\n        resolution=(1280, 720),\n        fps=30,\n        pipeline=[pri.GazeMapper.Config(), pri.VideoDisplay.Config()],\n    ),\n    pri.VideoStream.Config(\n        device_type=\"uvc\",\n        device_uid=f\"Pupil Cam{pupil_gen} ID0\",\n        name=\"eye0\",\n        resolution=(320, 240) if pupil_gen == 1 else (192, 192),\n        fps=120,\n        color_format=\"gray\",\n        pipeline=[\n            pri.PupilDetector.Config(),\n            pri.VideoDisplay.Config(flip=True),\n        ],\n    ),\n    pri.VideoStream.Config(\n        device_type=\"uvc\",\n        device_uid=f\"Pupil Cam{pupil_gen} ID1\",\n        name=\"eye1\",\n        resolution=(320, 240) if pupil_gen == 1 else (192, 192),\n        fps=120,\n        color_format=\"gray\",\n        pipeline=[pri.PupilDetector.Config(), pri.VideoDisplay.Config()],\n    ),\n]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Set up logger\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "logging.basicConfig(\n    stream=sys.stdout, level=logging.INFO, format=\"%(message)s\"\n)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Run manager\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>When running the script from the command line, press 'Ctrl+C' to stop the\n    manager. When running from a Jupyter notebook, interrupt the kernel\n    (*Kernel > Interrupt Kernel* or press 'Esc' and then twice 'i').</p></div>\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "with pri.StreamManager(configs, folder=folder) as manager:\n    while not manager.stopped:\n        if manager.all_streams_running:\n            status = manager.format_status(\n                \"pupil.confidence\", max_cols=72, sleep=0.1, nan_format=None,\n            )\n            print(\"\\r\" + status, end=\"\")\n\nprint(\"\\nStopped\")"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.15"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

_downloads/553e6445460593299cbe43e4bbf31c44/1_plot_gaze_pipeline.py

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+"""
+.. _gaze_pipeline_example:
+
+Gaze mapping pipeline
+=====================
+
+This example shows how to perform post-hoc pupil detection, calibration marker
+detection, calibration and gaze mapping.
+
+.. note::
+
+    This example requires the dependencies for
+    :ref:`pupil detection<pupil_detection_dependencies>` as well as
+    ``matplotlib``.
+"""
+import pupil_recording_interface as pri
+
+
+# %%
+# Set recording folder
+# --------------------
+folder = pri.get_test_recording()
+
+# %%
+# Create readers for video streams
+# --------------------------------
+world_reader = pri.VideoReader(folder)
+eye0_reader = pri.VideoReader(folder, stream="eye0", color_format="gray")
+eye1_reader = pri.VideoReader(folder, stream="eye1", color_format="gray")
+
+# %%
+# Detect pupils
+# -------------
+pupil_detector_eye0 = pri.PupilDetector(camera_id=0)
+pupil_list_eye0 = pupil_detector_eye0.batch_run(eye0_reader)
+
+pupil_detector_eye1 = pri.PupilDetector(camera_id=1)
+pupil_list_eye1 = pupil_detector_eye1.batch_run(eye1_reader)
+
+# %%
+# Detect calibration markers
+# --------------------------
+marker_detector = pri.CircleDetector()
+marker_list = marker_detector.batch_run(world_reader)
+
+# %%
+# Merge pupil data and run calibration
+# ------------------------------------
+calibration = pri.Calibration(resolution=world_reader.resolution)
+pupil_list = pri.merge_pupils(pupil_list_eye0, pupil_list_eye1)
+calibration_result = calibration.batch_run(pupil_list, marker_list)
+
+# %%
+# Map gaze as dataset
+# -------------------
+gaze_mapper = pri.GazeMapper(calibration=calibration_result)
+gaze = gaze_mapper.batch_run(
+    pupil_list, return_type="dataset", info=world_reader.info
+)
+
+# %%
+# Plot gaze
+# ---------
+norm_pos = gaze.gaze_norm_pos.where(gaze.gaze_confidence_2d > 0.7)
+norm_pos.plot.line(x="time")

_downloads/6de29d18e706bf0cd32d01aca6a0b99d/1_plot_gaze_pipeline.ipynb

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+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "%matplotlib inline"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "\n\n# Gaze mapping pipeline\n\nThis example shows how to perform post-hoc pupil detection, calibration marker\ndetection, calibration and gaze mapping.\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>This example requires the dependencies for\n    `pupil detection<pupil_detection_dependencies>` as well as\n    ``matplotlib``.</p></div>\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "import pupil_recording_interface as pri"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Set recording folder\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "folder = pri.get_test_recording()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Create readers for video streams\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "world_reader = pri.VideoReader(folder)\neye0_reader = pri.VideoReader(folder, stream=\"eye0\", color_format=\"gray\")\neye1_reader = pri.VideoReader(folder, stream=\"eye1\", color_format=\"gray\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Detect pupils\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "pupil_detector_eye0 = pri.PupilDetector(camera_id=0)\npupil_list_eye0 = pupil_detector_eye0.batch_run(eye0_reader)\n\npupil_detector_eye1 = pri.PupilDetector(camera_id=1)\npupil_list_eye1 = pupil_detector_eye1.batch_run(eye1_reader)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Detect calibration markers\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "marker_detector = pri.CircleDetector()\nmarker_list = marker_detector.batch_run(world_reader)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Merge pupil data and run calibration\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "calibration = pri.Calibration(resolution=world_reader.resolution)\npupil_list = pri.merge_pupils(pupil_list_eye0, pupil_list_eye1)\ncalibration_result = calibration.batch_run(pupil_list, marker_list)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Map gaze as dataset\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "gaze_mapper = pri.GazeMapper(calibration=calibration_result)\ngaze = gaze_mapper.batch_run(\n    pupil_list, return_type=\"dataset\", info=world_reader.info\n)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "## Plot gaze\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "norm_pos = gaze.gaze_norm_pos.where(gaze.gaze_confidence_2d > 0.7)\nnorm_pos.plot.line(x=\"time\")"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.15"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}