from chirality_data_analysis.example_data import * from chirality_image_analysis import image_analysis as chi from chirality_image_analysis.utility import * from chirality_data_analysis import data_analysis as chd currentData = desiredColonies[desiredColonies.name == 'ReplicateB'] latestDate = currentData.irow(currentData['date'].argmax()) latestPath = latestDate['path'] img = ski.io.imread(latestPath) fluor1 = img[:, :, 0] fluor2 = img[:, :, 1] brightfield = img[:, :, 2] filteredLabels, center, finalRadius = chi.findSectors(fluor1, brightfield, showPictures=False, exportBinaryEdges=True) showImage(ski.color.label2rgb(filteredLabels, bg_label=0)) # Now that we have the labels, we need to get the position data of each label # and filter them all down to one pixel chiralityData = chd.getChiralityData(filteredLabels, center) # Look at the problem data, label #5 #problemData = chiralityData[chiralityData['label'] == 5] #print problemData.head(30) chd.makeChiralityPlot(chiralityData) chd.visualizeSectors(chiralityData) # Overlay the sectors on the image now chd.visualizeSectors(chiralityData, overImage=True)
from chirality_image_analysis import image_analysis as chi from chirality_image_analysis.utility import * from chirality_data_analysis import data_analysis as chd currentData = desiredColonies[desiredColonies.name == 'ReplicateB'] latestDate = currentData.irow(currentData['date'].argmax()) latestPath = latestDate['path'] img = ski.io.imread(latestPath) fluor1 = img[:, :, 0] fluor2 = img[:, :, 1] brightfield = img[:, :, 2] binaryEdges, center, finalRadius = chi.getBinaryData(fluor1, brightfield, select_center_manually=True, originalImage=img) filteredLabels = chi.findSectors(binaryEdges, path_to_export_binary='/home/bryan') showImage(ski.color.label2rgb(filteredLabels, bg_label=0)) # Now that we have the labels, we need to get the position data of each label # and filter them all down to one pixel chiralityData = chd.getChiralityData(filteredLabels, center) # Look at the problem data, label #5 #problemData = chiralityData[chiralityData['label'] == 5] #print problemData.head(30) chd.makeChiralityPlot(chiralityData) chd.visualizeSectors(chiralityData) # Overlay the sectors on the image now chd.visualizeSectors(chiralityData, overImage=True)
__author__ = 'bryan'
import matplotlib
matplotlib.use('Qt4Agg')
import matplotlib.pyplot as plt
from chirality_data_analysis.example_data import *
from chirality_image_analysis import image_analysis as chi
from chirality_image_analysis.utility import *
import skimage as ski
import skimage.color
currentData = desiredColonies[desiredColonies.name == 'ReplicateB']
latestDate = currentData.irow(currentData['date'].argmax())
latestPath = latestDate['path']
filteredLabels = chi.findSectors(latestPath, showPictures=False)
showImage(ski.color.label2rgb(filteredLabels, bg_label=0))
for l in np.unique(filteredLabels):
showImage(filteredLabels == l)
plt.show()
