def purge_previous_im(self, tar_name):
if self.save_as_tar:
if os.path.isfile(tar_name):
os.remove(tar_name)
else:
filter = ['*.bmp', '*.png', '*.tif']
filelist = utils.listFiles(self._saveDir, filter)
for fileinfo in filelist:
im_path = fileinfo.absoluteFilePath()
os.remove(im_path)
def load_bagging_model():
filter = ['model*.json']
info_list = listFiles('./saved_models/', filter)
if len(info_list) == 0:
raise FileNotFoundError('There is no model saved')
estimators = []
for file_info in info_list:
model = load_model(file_info.baseName())
estimators.append(model)
bag = Bagging()
bag.set_estimator(estimators)
return bag
from common import utils
from mitos_extract_anotations import candidateSelection as cs
from common.Params import Params as P
if __name__ == '__main__':
filter = ['*.bmp', '*.png', '*.jpg']
file_list = utils.listFiles(P().basedir + 'normalizado/testHeStain', filter)
params = cs.Candidates_extractor_params(file_list)
params.candidates_json_save_path = P().basedir + 'anotations/test_cand.json'
params.save_candidates_dir_path = P().basedir + 'test/no-mitosis/'
params.save_mitosis_dir_path = P().basedir + 'test/mitosis/'
params.bsave_img_keypoints = True
params.bappend_mitosis_to_json = True
cutter = cs.Candidates_extractor(params)
cutter.extract()
#import sys
#sys.path.append('C:/Users/PelaoT/Desktop/Practica/codigo')
from mitos_extract_anotations import candidateSelection as cs
from common.utils import listFiles
import sys
filters = ['*.bmp', '*.png', '*.jpg']
if sys.platform == 'win32':
file_list = listFiles(
'C:/Users/PelaoT/Desktop/Practica/dataset/eval/heStain/', filters)
params = cs.Candidates_extractor_params(file_list)
params.save_candidates_dir_path = 'C:/Users/PelaoT/Desktop/Practica/dataset/eval/no-mitosis/'
params.save_mitosis_dir_path = 'C:/Users/PelaoT/Desktop/Practica/dataset/eval/mitosis/'
params.candidates_json_save_path = 'C:/Users/PelaoT/Desktop/Practica/dataset/eval/test.json'
else:
file_list = listFiles('/home/facosta/dataset/normalizado/testHeStain/',
filters)
params = cs.Candidates_extractor_params(file_list)
params.save_candidates_dir_path = '/home/facosta/dataset/test/no-mitosis/'
params.save_mitosis_dir_path = '/home/facosta/dataset/test/mitosis/'
params.candidates_json_save_path = '/home/facosta/dataset/test//test.json'
extractor = cs.Candidates_extractor(params)
extractor.extract()
#
# import cv2
# import numpy as np
#
# def get_center(rectangle):
im[y,x] = 255
#cv2.imwrite('holap.png', im)
return im
def findCentroidsByBounRect(point):
x,y,w,h = cv2.boundingRect(point)
cx = int(x + (w/2))
cy = int(y + (h/2))
return cy, cx
baseDir = 'D:/Descargas/mitosis_evaluation_set_A/test/'
filter = ['*.csv']
fileList = utils.listFiles(baseDir, filter)
jsonDict = {}
i= 1
total = len(fileList)
for fileInfo in fileList:
mitosRegion = []
csvPath = fileInfo.absoluteFilePath()
csvFile = open(csvPath)
for line in csvFile:
splitted = str.split(line, ',')
point = generatePoint(splitted)
center = findCentroidsByBounRect(point)
pointDict = {"row" : center[0], "col" : center[1]}