def gen_ds_list(listfile=dslist):
dataset = list()
tags_to_labels, labels_to_tags = gen_ydata.load_mappings()
with open(listfile, 'r') as fd:
for line in fd:
[sample_dir_path, s_n_imgs] = line.strip().split('\t')
n_imgs = int(s_n_imgs)
m = matcher.match(sample_dir_path)
if (m):
g = m.groups()
matching_classes = decode_classes(g)
y = classes_to_y(matching_classes)
fullfpath = "%s/%s" % (dsdir, sample_dir_path)
csvpath = "%s.csv" % fullfpath
if (os.path.exists(csvpath)):
#Fully load CSV data
csvdata = gen_ydata.parse_csv(csvpath,
n_imgs,
mapping=tags_to_labels)
else:
csvdata = None
dataset.append(
(fullfpath, n_imgs, y, matching_classes, csvdata))
else:
print("WARNING: dir %s does not match pattern" % fpath)
return dataset
import cv2
import classif_mehdi_fcn
import analytics
import gen_ydata
from threading import Thread, Lock
#from Queue import Queue
from skimage import io #
from socketIO_client_nexus import SocketIO, LoggingNamespace #
#socketIO = SocketIO('93.24.79.14', 3333) #
#socketIO = SocketIO('localhost', 3333, LoggingNamespace) #
tags_to_labels, labels_to_tags = gen_ydata.load_mappings()
def entropy_from_hmaps(hmaps):
entropy = np.zeros(hmaps.shape[:2])
for i in range(len(classif_mehdi_fcn.classes)):
entropy += - hmaps[:,:,i]*np.log(hmaps[:,:,i])
return entropy
def compute_histograms(hmaps,entropy_th=None):
histos = list()
if(entropy_th):
entropy = entropy_from_hmaps(hmaps)
good_pixels = np.array(entropy<entropy_th,dtype=np.float)