{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n# Leaves in natural images\n\nReplication of Wallis and Bex, 2012\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "import torch\nimport pandas as pd\nfrom deadleaves import LeafGeometryGenerator, LeafAppearanceSampler, ImageRenderer\nfrom deadleaves.utils import choose_compute_backend\nfrom PIL import Image\nfrom torchvision.transforms.functional import pil_to_tensor\n\ndevice = choose_compute_backend()\nreference_image = Image.open(\"../../examples/images/ulb_5136-2516.jpg\").resize(\n    (731, 512), box=(0, 0, 3810, 2667)\n)\nimage_tensor = pil_to_tensor(pic=reference_image).to(device=device) / 255\n\nY, X = torch.meshgrid(\n    torch.arange(731),\n    torch.arange(512),\n    indexing=\"xy\",\n)\n\ncombined_leaf_table = pd.DataFrame()\nfor radius in [50, 120, 200]:\n    for angle in [0, 0.5 * torch.pi, torch.pi, 1.5 * torch.pi]:\n        x_pos = int(radius * torch.cos(torch.Tensor([angle]))) + 256\n        y_pos = int(radius * torch.sin(torch.Tensor([angle]))) + 365.5\n        area = torch.Tensor([radius * 10])\n        dist_from_center = torch.sqrt((X - x_pos) ** 2 + (Y - y_pos) ** 2)\n        mask = (dist_from_center <= torch.sqrt(area / torch.pi)).to(device)\n\n        model = LeafGeometryGenerator(\n            leaf_shape=\"ellipsoid\",\n            shape_param_distributions={\n                \"area\": {\"uniform\": {\"low\": 100.0, \"high\": 200.0}},\n                \"aspect_ratio\": {\"uniform\": {\"low\": 0.5, \"high\": 2}},\n                \"orientation\": {\"uniform\": {\"low\": 0.0, \"high\": 2 * torch.pi}},\n            },\n            image_shape=(512, 731),\n            position_mask=mask,\n        )\n        leaf_table, segmentation_map = model.generate_segmentation()\n        combined_leaf_table = pd.concat(\n            [combined_leaf_table, leaf_table], ignore_index=True\n        )\n\ncombined_leaf_table.leaf_idx = combined_leaf_table.index + 1\n\ncolor_params = {\n    \"R\": {\n        \"constant\": {\n            \"value\": {\n                \"from\": [\"x_pos\", \"y_pos\"],\n                \"fn\": lambda x: image_tensor[\n                    0, x[\"y_pos\"].astype(int), x[\"x_pos\"].astype(int)\n                ],\n            }\n        }\n    },\n    \"G\": {\n        \"constant\": {\n            \"value\": {\n                \"from\": [\"x_pos\", \"y_pos\"],\n                \"fn\": lambda x: image_tensor[\n                    1, x[\"y_pos\"].astype(int), x[\"x_pos\"].astype(int)\n                ],\n            }\n        }\n    },\n    \"B\": {\n        \"constant\": {\n            \"value\": {\n                \"from\": [\"x_pos\", \"y_pos\"],\n                \"fn\": lambda x: image_tensor[\n                    2, x[\"y_pos\"].astype(int), x[\"x_pos\"].astype(int)\n                ],\n            }\n        }\n    },\n}\n\ncolormodel = LeafAppearanceSampler(leaf_table=combined_leaf_table)\ncolormodel.sample_color(color_param_distributions=color_params)\n\nrenderer = ImageRenderer(leaf_table=colormodel.leaf_table, image_shape=(512, 731))\nimage = renderer.render_image()\nmask = renderer.segmentation_map == 0\nimage[mask] = image_tensor.permute(1, 2, 0)[mask]\nrenderer.show(image)"
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