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Added picking_methods, more filters, and a showcase file
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‎examples/Showcase_point_picker.ipynb‎

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Tutorial on how to automatically select good features to track"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"import sys\n",
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"repo_root = os.path.abspath(\"..\")\n",
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"sys.path.insert(0, repo_root)\n",
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"\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"import pyidi"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Load the video"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"filename = '../data/data_synthetic.cih'\n",
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"video = pyidi.VideoReader(filename)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### `Lucas Kanade` method as a reference"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"tol = 1e-8\n",
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"roi_size = (5,5)\n",
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"idi_lk = pyidi.LucasKanade(video)\n",
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"idi_lk.configure(reference_image = (0,100), resume_analysis = False, tol=tol, roi_size=roi_size)\n",
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"reference_image = idi_lk._set_reference_image(video, idi_lk.reference_image)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Generate a score image\n",
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"- Initialize the Feature Selector\n",
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"- Set a filter type\n",
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"- Set roi_size. Either directly in set_filter or trough set_roi\n",
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"- Apply filter to generate the score_image (saved in feature_selector.score_image)\n",
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"\n",
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"All available methods are listed when initializing the FeatureSelector, unless verbose = False"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from pyidi.Automatic_selection.feature_selector import FeatureSelector\n",
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"feature_selector = FeatureSelector(reference_image)\n",
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"\n",
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"feature_selector.set_filter('ST', roi_size = roi_size)\n",
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"# feature_selector.set_filter('DF', dij=(1, 0), c = 0.1)\n",
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"# feature_selector.set_filter('HARRIS', alpha = 0.04)\n",
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"# feature_selector.set_filter('TRIGGS', alpha = 0.05)\n",
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"# feature_selector.set_filter('HM')\n",
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"\n",
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"feature_selector.set_roi(roi_size)\n",
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"\n",
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"feature_selector.apply_filter()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"- Set a picker and parse parameters\n",
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"- Pick features. If no score_image is generated yet, pick_points() will call apply_filter() to generate one. pick_points() will also take a score_image directly, like this: pick_points(score_image)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"%matplotlib inline\n",
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"feature_selector.set_picker('LM', min_distance = 10, n_points = 50)\n",
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"# feature_selector.set_picker('ANMS', n_points=50)\n",
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"# feature_selector.set_picker('DS', n_points=50, min_distance = 7, min_score = 50)\n",
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"points = feature_selector.pick_points()\n",
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"\n",
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"fig, ax = plt.subplots(2)\n",
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"ax[0].imshow(reference_image, cmap = 'grey')\n",
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"ax[0].plot(points[:,1], points[:,0], 'x')\n",
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"ax[1].imshow(feature_selector.score_image, cmap = 'grey')\n",
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"ax[1].plot(points[:,1], points[:,0], 'x')\n",
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"plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Masking\n",
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"I had been using ROIselect (now SubsetSelection) to generate a ROI polygon. I only select features inside of the ROI\n",
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"\n",
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"I think will think about how to include this more elegantly in the pipeline, but here is an example below"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# from pyidi.GUIs.selection import SubsetSelection\n",
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"from matplotlib.path import Path\n",
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"\n",
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"# roi_select = SubsetSelection(video)\n",
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"# roi_select.polygon\n",
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"# polygon_new = np.array(roi_select.polygon)\n",
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"\n",
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"polygon = np.array([[4, 4, 97, 98, 4], [25, 218, 219, 25, 25]])\n",
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"path = Path(polygon.T)\n",
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"\n",
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"\n",
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"mask_image = path.contains_points(np.array([(i,j) for i in range(reference_image.shape[0]) for j in range(reference_image.shape[1])])).reshape(reference_image.shape)\n",
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"mask_image = mask_image.reshape(reference_image.shape)\n",
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"\n",
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"score_image = feature_selector.score_image\n",
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"score_image[~mask_image] = 0\n",
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"\n",
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"points = feature_selector.pick_points()\n",
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"\n",
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"fig, ax = plt.subplots(2)\n",
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"ax[0].imshow(reference_image, cmap = 'grey')\n",
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"ax[0].plot(points[:,1], points[:,0], 'x')\n",
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"ax[1].imshow(score_image, cmap = 'grey')\n",
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"ax[1].plot(points[:,1], points[:,0], 'x')\n",
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"plt.show()"
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]
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}
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],
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"metadata": {
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"hide_input": false,
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"kernelspec": {
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"display_name": "pyidi-env",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.18"
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},
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"toc": {
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"base_numbering": 1,
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"nav_menu": {},
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"number_sections": true,
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"sideBar": true,
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"skip_h1_title": false,
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"title_cell": "Table of Contents",
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"title_sidebar": "Contents",
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"toc_cell": false,
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"toc_position": {},
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"toc_section_display": true,
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"toc_window_display": false
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

‎pyidi/Automatic_selection/feature_selector.py‎

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import numpy as np
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from scipy.ndimage import generic_filter
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from .filters import ShiTomasi
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available_filter_shortcuts = {'ST': ShiTomasi}
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from .filters import ShiTomasi, DirectionalFilter, Harris, Triggs, HarmonicMean
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available_filter_shortcuts = {
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'ST': ShiTomasi,
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'DF': DirectionalFilter,
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'HARRIS': Harris,
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'TRIGGS': Triggs,
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'HM': HarmonicMean,
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}
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from .picking_methods import LocalMaxima, ANMS, DescendingScore
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available_pickers_shortcuts = {
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'LM': LocalMaxima,
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'ANMS': ANMS,
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'DS': DescendingScore,
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}
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class FeatureSelector():
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""" Selects features from an image using different filters.
@@ -14,34 +28,96 @@ class FeatureSelector():
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Args:
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image (ndarray): The image to select features from.
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"""
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def __init__(self, image) -> None:
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def __init__(self, image, verbose=True) -> None:
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self.image = image
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self.roi_size = 9
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self.available_filter_shortcuts = available_filter_shortcuts
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return
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self.roi_size = (9, 9)
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self.score_image = None
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if verbose:
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print("\nAvailable Filters and the parameters they take:")
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for name, cls in available_filter_shortcuts.items():
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parameters = getattr(cls, 'parameters', [])
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filter_parameters = getattr(cls, 'filter_parameters', [])
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param_str = f"({', '.join(parameters)})" if parameters else ""
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filter_param_str = f"({', '.join(filter_parameters)})" if filter_parameters else ""
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print(f" '{name}': {cls.__name__} {param_str} {filter_param_str}")
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print("\nAvailable Pickers and the parameters they take:")
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for name, cls in available_pickers_shortcuts.items():
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parameters = getattr(cls, 'parameters', [])
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param_str = f"({', '.join(parameters)})" if parameters else ""
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print(f" '{name}': {cls.__name__} {param_str}")
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def set_filter(self, filter_shortcut):
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"""
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Sets filter
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"""
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if isinstance(filter_shortcut, str) and filter_shortcut in self.available_filter_shortcuts.keys():
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self.filter_shortcut = filter_shortcut
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self.filter = self.available_filter_shortcuts[filter_shortcut](self.image)
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else:
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"Filter shortcut not recognized"
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def set_filter(self, name, **kwargs):
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name = name.upper()
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if name not in available_filter_shortcuts:
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raise ValueError(f"Filter '{name}' not recognized. Available: {list(available_filter_shortcuts.keys())}")
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filter_class = available_filter_shortcuts[name]
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# Extract and remove 'roi_size' if present
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roi_size = kwargs.pop('roi_size', None)
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if roi_size is not None:
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self.set_roi(roi_size)
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# Validate kwargs
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supported1 = getattr(filter_class, 'parameters', [])
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supported2 = getattr(filter_class, 'filter_parameters', [])
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supported = supported1 + supported2
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for k in kwargs:
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if k not in supported:
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raise TypeError(f"Parameter '{k}' not supported by filter '{name}'. Supported: {supported}")
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self.filter = filter_class(self.image, **kwargs)
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def set_picker(self, name, **kwargs):
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name = name.upper()
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if name not in available_pickers_shortcuts:
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raise ValueError(f"Picker '{name}' not recognized. Available: {list(available_pickers_shortcuts.keys())}")
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picker_class = available_pickers_shortcuts[name]
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supported = getattr(picker_class, 'parameters', [])
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for k in kwargs:
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if k not in supported:
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raise TypeError(f"Parameter '{k}' not supported by picker '{name}'. Supported: {supported}")
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self.picker = picker_class(**kwargs)
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def set_roi(self, roi_size):
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self.roi_size = roi_size
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if isinstance(roi_size, (int, float)):
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self.roi_size = (roi_size, roi_size)
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else:
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self.roi_size = roi_size
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#### Apply filter
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def apply_filter(self):
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row_of_interest = self.filter.n_layers//2
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if self.filter.parameter is None:
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score_image = generic_filter(self.filter.to_filter, self.filter.filter, size=(self.roi_size, self.roi_size, self.filter.n_layers))[..., row_of_interest]
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if self.filter.n_layers == 0:
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# 2D filter window
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size = (self.roi_size[0], self.roi_size[1])
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row_of_interest = 0
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else:
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# 3D filter window
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size = (self.roi_size[0], self.roi_size[1], self.filter.n_layers)
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if self.filter.filter_parameters is None:
101+
score_image = generic_filter(self.filter.to_filter, self.filter.filter, size=size)
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else:
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score_image = generic_filter(self.filter.to_filter, self.filter.filter, size=(self.roi_size, self.roi_size, self.filter.n_layers), extra_arguments = (self.filter.parameter, ))[..., row_of_interest]
103+
score_image = generic_filter(self.filter.to_filter, self.filter.filter, size=size, extra_arguments = (self.filter.filter_parameters, ))
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# For 3D filters, slice the middle layer:
106+
if self.filter.n_layers > 0:
107+
row_of_interest = self.filter.n_layers // 2
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score_image = score_image[..., row_of_interest]
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43110
score_image[np.isnan(score_image)] = 0
44111
self.score_image = score_image
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return
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#### Pick points
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#### Pick points
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def pick_points(self, score_image = None):
116+
if self.picker is None:
117+
raise RuntimeError("Picker not set. Use set_picker() first.")
118+
if score_image is not None:
119+
self.score_image = score_image
120+
elif self.score_image is None:
121+
self.apply_filter()
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points = self.picker.pick(self.score_image)
123+
return points

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