{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n# Circular motion\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "import pandas as pd\nimport torch\n\nimport matplotlib.pyplot as plt\nfrom matplotlib import animation\nfrom IPython.display import HTML\n\nfrom deadleaves import (\n    LeafGeometryGenerator,\n    LeafAppearanceSampler,\n    ImageRenderer,\n    LeafTopology,\n)\n\nimage_shape = (512, 731)\narea = image_shape[0] * image_shape[1]\n\ngeometry = LeafGeometryGenerator(\n    leaf_shape=\"circular\",\n    shape_param_distributions={\n        \"area\": {\"uniform\": {\"low\": area * 0.005, \"high\": area * 0.01}},\n    },\n    image_shape=image_shape,\n    n_sample=250,\n    position_mask={\n        \"shape\": \"circular\",\n        \"params\": {\n            \"area\": area * 0.35,\n        },\n    },\n)\n\nleaf_table, segmentation_map = geometry.generate_segmentation()\n\ncolor_params = {\n    \"H\": {\"uniform\": {\"low\": 0.0, \"high\": 0.2}},\n    \"S\": {\"uniform\": {\"low\": 0.0, \"high\": 1.0}},\n    \"V\": {\"uniform\": {\"low\": 0.0, \"high\": 1.0}},\n}\n\nappearance = LeafAppearanceSampler(\n    leaf_table=leaf_table,\n)\n\ntable = appearance.sample_color(color_param_distributions=color_params)\n\ntopology = LeafTopology(image_shape=image_shape)\n\n\ndef apply_circular_motion(\n    leaf_table: pd.DataFrame,\n    image_size: tuple[int, int] | torch.Size,\n    angle_step: float,\n    angular_jitter: float = 0.0,\n    radial_jitter: float = 0.0,\n) -> pd.DataFrame:\n\n    table = leaf_table.copy()\n\n    cy = image_size[0] / 2\n    cx = image_size[1] / 2\n\n    x = torch.tensor(table[\"x_pos\"]) - cx\n    y = torch.tensor(table[\"y_pos\"]) - cy\n\n    # Polar coordinates\n    r = torch.sqrt(x**2 + y**2)\n    theta = torch.arctan2(y, x)\n\n    # update angle\n    theta += angle_step\n    if angular_jitter > 0:\n        theta += torch.distributions.normal.Normal(0.0, angular_jitter).sample(\n            (len(theta),)\n        )\n\n    # update radius\n    if radial_jitter > 0:\n        r += torch.distributions.normal.Normal(0.0, radial_jitter).sample((len(r),))\n        r = torch.clip(r, 0.0, None)\n\n    # back to Cartesian\n    table[\"x_pos\"] = cx + r * torch.cos(theta)\n    table[\"y_pos\"] = cy + r * torch.sin(theta)\n\n    return table\n\n\nframes = []\nfor t in range(10):\n    table = apply_circular_motion(\n        leaf_table=table,\n        image_size=segmentation_map.shape,\n        angle_step=torch.pi / 10,\n        angular_jitter=0.1,\n    )\n\n    renderer = ImageRenderer(\n        leaf_table=table,\n        image_shape=image_shape,\n        background_color=torch.tensor(1.0),\n    )\n    image = renderer.render_image()\n    frames.append(image.cpu())\n\n\n# Generate gif\nfig, ax = plt.subplots(figsize=(7.31, 5.12))\nim = ax.imshow(frames[0])\nax.axis(\"off\")\nax.set_position((0.0, 0.0, 1.0, 1.0))\n\n\ndef update(i):\n    im.set_data(frames[i])\n    return [im]\n\n\nani = animation.FuncAnimation(\n    fig,\n    update,\n    frames=len(frames),\n    interval=100,\n    blit=True,\n)\n\nplt.close(fig)\nHTML(ani.to_jshtml())"
      ]
    }
  ],
  "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": "3.12.3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}