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Simple OpenGL visualization starter for Kaggle TrackML 2018
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| #!/usr/bin/env python3 | |
| """ | |
| Install glumpy dependencies:: | |
| pip install Cython pyopengl | |
| brew install glfw freetype # on OS X | |
| Install glumpy:: | |
| pip install glumpy | |
| """ | |
| import argparse | |
| from pathlib import Path | |
| from glumpy import app, gl, gloo | |
| from glumpy.transforms import Trackball, Position | |
| import numpy as np | |
| import pandas as pd | |
| point_vertex = """ | |
| attribute vec3 position; | |
| attribute vec4 color; | |
| attribute float point_size; | |
| varying vec4 v_color; | |
| void main() | |
| { | |
| v_color = color; | |
| gl_PointSize = point_size; | |
| gl_Position = <transform>; | |
| } | |
| """ | |
| point_fragment = """ | |
| varying vec4 v_color; | |
| void main() | |
| { | |
| gl_FragColor = v_color; | |
| } | |
| """ | |
| def main(): | |
| parser = argparse.ArgumentParser() | |
| arg = parser.add_argument | |
| arg('event_prefix', help='path to any event file or it\'s prefix') | |
| arg('--n-tracks', type=int, default=20) | |
| args = parser.parse_args() | |
| print('Loading event data...') | |
| event_prefix = Path(args.event_prefix) | |
| event_id = event_prefix.stem.split('-')[0] | |
| event_prefix = str(event_prefix.parent / event_id) | |
| hits_df = pd.read_csv(event_prefix + '-hits.csv') | |
| # cells_df = pd.read_csv(event_prefix + '-cells.csv') | |
| particles_df = pd.read_csv(event_prefix + '-particles.csv') | |
| truth_df = pd.read_csv(event_prefix + '-truth.csv') | |
| print(f'{event_id}: {len(hits_df):,} hits, {len(particles_df):,} particles') | |
| print('done.') | |
| point_prog = gloo.Program(point_vertex, point_fragment) | |
| window = app.Window(width=1024, height=1024, color=(1, 1, 1, 1)) | |
| trackball = Trackball(Position('position')) | |
| point_prog['transform'] = trackball | |
| window.attach(point_prog['transform']) | |
| vtype = [('position', np.float32, 3), | |
| ('color', np.float32, 4), | |
| ('point_size', np.float32, 1)] | |
| axes = np.zeros(6, vtype) | |
| axes['position'] = np.array( | |
| [[-1, 0, 0], [1, 0, 0], | |
| [0, -1, 0], [0, 1, 0], | |
| [0, 0, -1], [0, 0, 1], | |
| ]) | |
| axes['color'] = np.array([[0, 1, 0, 1]] * len(axes)) | |
| axes['point_size'] = np.array([8] * len(axes)) | |
| points = np.zeros(len(hits_df), vtype) | |
| for i, axis in enumerate('xyz'): | |
| points['position'][:, i] = hits_df[axis].values / 1000 | |
| points['color'] = np.array([[0, 0, 0, 1]] * len(points)) | |
| points['point_size'] = np.array([2] * len(points)) | |
| # points first so that points indices have zero offset | |
| vertices = np.concatenate([points, axes]).view(gloo.VertexBuffer) | |
| point_prog.bind(vertices) | |
| point_indices = (np.arange(len(points), dtype=np.uint32) | |
| .view(gloo.IndexBuffer)) | |
| axes_indices = ((len(points) + np.arange(len(axes), dtype=np.uint32)) | |
| .view(gloo.IndexBuffer)) | |
| # draw some random particles | |
| particle_indices = [] | |
| for particle_id in (particles_df[particles_df['nhits'] > 3] | |
| .sample(args.n_tracks, random_state=42) | |
| ['particle_id'].values): | |
| p_truth = truth_df[truth_df['particle_id'] == particle_id] | |
| p_hits = hits_df[hits_df['hit_id'].isin(p_truth['hit_id'])] | |
| p_hits = p_hits.sort_values('z') | |
| particle_indices.extend(zip(p_hits.index, p_hits.index[1:])) | |
| for idx in p_hits.index: | |
| vertices['color'][idx] = [1, 0, 0, 1] | |
| vertices['point_size'][idx] = 10 | |
| particle_indices = (np.array(particle_indices, dtype=np.uint32) | |
| .view(gloo.IndexBuffer)) | |
| gl.glLineWidth(2) | |
| @window.event | |
| def on_draw(dt): | |
| window.clear() | |
| gl.glDisable(gl.GL_DEPTH_TEST) | |
| point_prog.draw(gl.GL_POINTS, point_indices) | |
| gl.glEnable(gl.GL_DEPTH_TEST) | |
| point_prog.draw(gl.GL_LINES, particle_indices) | |
| point_prog.draw(gl.GL_LINES, axes_indices) | |
| point_prog.draw(gl.GL_POINTS, axes_indices) | |
| app.run() | |
| if __name__ == '__main__': | |
| main() |
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