Welcome to impro’s documentation!¶
Impro. is a package for data processing in super-resolution microscopy. It contains high perfomant GPU based visualization and algorithms for 2D and 3D data. Main features:
- Cuda accelerated 2D Alpha Shapes
- Automated image alignment via General Hough Transform
- Huge list of filters for localization datasets
- Customizable Opengl widget based on modelview perspective standard
- Pearson correlation
References¶
A detailed explantation of the algorithm and it’s functionality can be found at:
Example¶
Basic usage example. Preprocessing is adapted to the dataset provided in Super-resolution correlator.
from impro.data.image_factory import ImageFactory
from impro.analysis.filter import Filter
from impro.analysis.analysis_facade import *
# Read and preprocess SIM image
image = ImageFactory.create_image_file(r"path_to_file.czi")
# Example for image preprocessing
image_array = image.data[:, 3] / 6
image_array = np.clip(image_array[0], 0, 255)
image_array = np.flipud(image_array)
image_array = (image_array).astype("uint8")[0:1400, 0:1400]
image_array = np.fliplr(image_array)
# Read dSTORM data
storm = ImageFactory.create_storm_file(r"path_to_file.txt")
# Preprocess dSTORM point data
indices = Filter.local_density_filter(storm.stormData, 100.0, 18)
storm_data = storm.stormData[indices]
# Render dSTORM data to image
im = create_alpha_shape(storm_data, 130)
col = int(im.shape[1]/200)
row = int(im.shape[0]/200)
source_points, target_points, overlay, results = find_mapping(sim, storm, n_row=row, n_col=col)
source_points, target_points = error_management(results, source_points, target_points, n_row=row)
M = transform.estimate_transform("affine",source_points,target_points)
correlation_index = pearson_correlation(sim, cv2.cvtColor(storm, cv2.COLOR_RGBA2GRAY), M)