2. Create videos

This tutorial demonstrates how to generate fluorescence microscopy videos of the AnDi trajectories using the function transform_to_video.

1. Setup

Importing the dependencies needed to run this tutorial.

import numpy as np
import random
import imageio
import matplotlib.pyplot as plt
import deeptrack as dt
from andi_datasets.models_phenom import models_phenom

/opt/miniconda3/envs/handi/lib/python3.10/site-packages/deeptrack/backend/_config.py:11: UserWarning: cupy not installed. GPU-accelerated simulations will not be possible
  warnings.warn(
/opt/miniconda3/envs/handi/lib/python3.10/site-packages/deeptrack/backend/_config.py:25: UserWarning: cupy not installed, CPU acceleration not enabled
  warnings.warn("cupy not installed, CPU acceleration not enabled")
2023-06-22 13:47:17.661504: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.

2. Defining example diffusion model

As an example, We generate the trajectories of dimerization model from models_phenom.

2.1. Dimerization

Defining simulation parameters.

T = 100 # number of time steps (frames)
N = 50 # number of particles (trajectories)
L = 1.5 * 128 # length of the box (pixels) -> extending fov by 1.5 times
D = 0.1 # diffusion coefficient (pixels^2/frame)

trajs, labels = models_phenom().dimerization(
    N=N,
    L=L,
    T=T,
    alphas=[1.2, 0.7],
    Ds=[10 * D, 0.1 * D],
    r=1,  # radius of the particles
    Pb=1,  # binding probability
    Pu=0,  # unbinding probability
)

Plotting trajectories.

for traj in np.moveaxis(trajs, 0, 1):
    plt.plot(traj[:,0], traj[:,1])
plt.show()

3. Generating videos

3.1. Import functions

For generating videos we import transform_to_video function from andi_datasets package. Additionally we import play_video function to display the videos within the jupyter notebook.

from andi_datasets.utils_videos import transform_to_video, play_video

3.2. Usage

The trajectory data generated can be directly passed through transform_to_video to generate fluorescence videos of the particles.

3.2.1. Generating a sample video

video = transform_to_video(
    trajs,
)

play_video(video)

Once Loop Reflect