The packages to install can be found in requirements.txt
.
from pathlib import Path
from load_data import load_data
filepath = Path(...) # path to the .tiff or .tif file
color_sequence = 'RG' # means :[Red frame, Green frame, Red frame, ...]
# red_film and green_film are of shape (n_frames, n_rows, n_columns).
movie_dict = load_data(filepath, color_sequence)
movie_dict["R"] # array of shape (n_red_frames, n_rows, n_cols)
movie_dict["G"] # array of shape (n_green_frames, n_rows, n_cols)
# display a frame
frame = movie_dict["G"][10]
plt.imshow(frame, cmap="gray")
plt.show()
Example of a frame (from our data : EMCV1_0524_1RRL, red, 11th frame)
First, it is needed to calculate the background
from background_estimation import all
#N'importer que la fonction utile et pas all
#Pas besoin d'importer numpy et pandas aussi
averaged_movie_img = calculate_local_average(movie_dict["R"], 10)
averaged_movie_img #One specific frame of movie_dict['color'], with averaged pixel value (here it is the frame 10 from the red movie)
Example on an averaged image
Then, it is possible to eliminate the background The first method is a substraction
background_substracted_img = background_elimination_substract(movie_dict["R"][10], averaged_movie_img)
background_substracted_img # Specific frame with the background elimination method applied to it(here : frame 10, red movie)
Example of an background eliminated image