Use cases

Use-case with the graphical user interface (GUI)¶

GUI case 1: Automated Physarum polycephalum tracking¶

Problem: Track the surface area over time of one individual.

Steps¶

1. Launch the GUI:

Cellects

2. Load images via Data Localisation:

• Prefix: im
• Extension: .tif
• Arena number per folder: 1

3. Image analysis → apply config, validate specimen/arena, etc.

4. Video tracking → Done → Post processing → Read

5. Save one result to export CSV time series.

Use-cases with an application programming interface (API)¶

Complete analysis examples¶

API case 1: Automated Physarum polycephalum tracking¶

This section runs the script-based pipeline (if example data is present).

In [1]:

from pathlib import Path

DATA_DIR = Path("..") / "data"
DATA_DIR.resolve()

if not DATA_DIR.exists():
    raise FileNotFoundError(
        f"Example data folder not found: {DATA_DIR}. "
        "Run this notebook from the project root or provide the example dataset."
    )

from pathlib import Path DATA_DIR = Path("..") / "data" DATA_DIR.resolve() if not DATA_DIR.exists(): raise FileNotFoundError( f"Example data folder not found: {DATA_DIR}. " "Run this notebook from the project root or provide the example dataset." )

In [2]:

from matplotlib import pyplot as plt
from cellects.core.script_based_run import load_data, run_image_analysis, write_videos, run_all_arenas
from cellects.utils.load_display_save import show

po = load_data(pathway=str(DATA_DIR / "single_experiment"), sample_number=1, extension='tif')
po = run_image_analysis(po)
po = write_videos(po)
po.vars['frame_by_frame_segmentation'] = True
po = run_all_arenas(po)

show(po.last_image.bgr[:, :, ::-1], show=False)

# 6) Plot growth curve
plt.figure()
plt.plot(po.one_row_per_frame['time'], po.one_row_per_frame['area'], color='#73C6AC')
plt.xlabel('Time (hours)')
plt.ylabel('Surface area (pixels)')
plt.title('Plasmodium growth: area over time')
plt.show()

from matplotlib import pyplot as plt from cellects.core.script_based_run import load_data, run_image_analysis, write_videos, run_all_arenas from cellects.utils.load_display_save import show po = load_data(pathway=str(DATA_DIR / "single_experiment"), sample_number=1, extension='tif') po = run_image_analysis(po) po = write_videos(po) po.vars['frame_by_frame_segmentation'] = True po = run_all_arenas(po) show(po.last_image.bgr[:, :, ::-1], show=False) # 6) Plot growth curve plt.figure() plt.plot(po.one_row_per_frame['time'], po.one_row_per_frame['area'], color='#73C6AC') plt.xlabel('Time (hours)') plt.ylabel('Surface area (pixels)') plt.title('Plasmodium growth: area over time') plt.show()

Saving the video bunch n°1 (tot=1)... 

API case 2: Colony growth tracking (synthetic example)¶

Problem: Get the surface area over time of several appearing colonies.

This example is self-contained to produce graphical representation.

In [3]:

import numpy as np
from matplotlib import pyplot as plt
from pathlib import Path
from cellects.utils.load_display_save import show
from cellects.core.script_based_run import (
    generate_colony_like_video,
    load_data,
    run_image_analysis,
    run_one_video_analysis,
)

# 1) Generate synthetic data
rgb_video = generate_colony_like_video()

# 2) Display (optional)
# movie(rgb_video)

# 3) Segment
po = load_data(rgb_video, pathway='')
po.vars['several_blob_per_arena'] = True
po = run_image_analysis(po, last_im=rgb_video[-1, ...])


# 4) Video tracking
po.vars['maximal_growth_factor'] = 0.5
MA = run_one_video_analysis(po, with_video_in_ram=True, remove_files=True)

# 5) Show video segmentation mask (color represent time)
show(np.cumsum(MA.binary, 0)[-1], show=False)

# 6) Plot growth curve
plt.figure()
plt.plot(MA.one_row_per_frame['time'], MA.one_row_per_frame['area_total'], color='#73C6AC')
plt.xlabel('Time (frame)')
plt.ylabel('Summed surface area (pixels)')
plt.title('Total growth of simulated Colonies ')
plt.show()

import numpy as np from matplotlib import pyplot as plt from pathlib import Path from cellects.utils.load_display_save import show from cellects.core.script_based_run import ( generate_colony_like_video, load_data, run_image_analysis, run_one_video_analysis, ) # 1) Generate synthetic data rgb_video = generate_colony_like_video() # 2) Display (optional) # movie(rgb_video) # 3) Segment po = load_data(rgb_video, pathway='') po.vars['several_blob_per_arena'] = True po = run_image_analysis(po, last_im=rgb_video[-1, ...]) # 4) Video tracking po.vars['maximal_growth_factor'] = 0.5 MA = run_one_video_analysis(po, with_video_in_ram=True, remove_files=True) # 5) Show video segmentation mask (color represent time) show(np.cumsum(MA.binary, 0)[-1], show=False) # 6) Plot growth curve plt.figure() plt.plot(MA.one_row_per_frame['time'], MA.one_row_per_frame['area_total'], color='#73C6AC') plt.xlabel('Time (frame)') plt.ylabel('Summed surface area (pixels)') plt.title('Total growth of simulated Colonies ') plt.show()

Small task examples¶

Task 1: Write a timelapse video from images¶

Problem: Convert a set of images into a timelapse video.

To use this functionality, install Cellects and run the following python code:

from cellects.utils.load_display_save import write_video_from_images
write_video_from_images("path/to/images")

Task 2: Extracting image capture timings¶

Problem: Get the capture timing of every image using their exif data

To use this functionality, install Cellects and run the following python code:

from cellects.utils.load_display_save import extract_time
extract_time("path/to/images")