from lmdbWriter import open_csv
import numpy as np
labels_dict = open_csv('../data/trainLabels.csv')
keys = []
for key in labels_dict.keys():
s = key.split('_')
keys.append(s[0])
keys = list(set(keys))
for key in keys:
if labels_dict[ key + '_right'] > labels_dict[ key + '_left']:
labels_dict[key + '_left'] = labels_dict[key + '_right']
elif labels_dict[key + '_left'] > labels_dict[ key + '_right']:
labels_dict[key + '_right'] = labels_dict[key + '_left']
trainY = np.zeros((len(labels_dict.keys()), 1))
for i, key in enumerate(labels_dict.keys()):
trainY[i, 0] = labels_dict[key]
np.save('trainY_corrected.npy', trainY)
import sys
from caffe.io import load_image
from skimage.util import view_as_windows
import os
import numpy as np
from kMeansFeatureExtractor import *
from lmdbWriter import open_csv
imageDim = (512, 512, 3)
rfSize = 16
numPatches = 50
images = os.listdir('../data/resized/trainOriginal')
labels_dict = open_csv('../data/trainLabels.csv')
values = labels_dict.values()
total_numPatches = values.count(0) * 40 + (values.count(1) + values.count(2) +
values.count(3) +
values.count(4)) * 140
patches = np.zeros((total_numPatches, rfSize * rfSize * 3))
whitening = True
maxIter = 50
batchSize = 1000
j = 0
values = labels_dict.values()
for each in images:
if labels_dict[each.split('.')[0]] > 0:
numPatches = 140
else:
import numpy as np
from lmdbWriter import open_csv
import matplotlib.image as mpimg
import os
labels = open_csv('../data/trainLabels.csv')
values = np.array(labels.values())
data = []
for i in range(0,5):
data.append(np.zeros((values[values == i].shape[0], 512*512)))
print len(data)
class_counters = [0]*5
i = 0
for each in os.listdir('../data/resized/trainOriginal/'):
if each.split('.jpeg')[0] in labels.keys():
if values[i] > 0:
class_counters[values[i]] += 1
i += 1
print class_counters
continue
data[values[i]][class_counters[values[i]], :] = mpimg.imread('../data/resized/train512/' + each)[:,:,2].flatten()
class_counters[values[i]] += 1
i += 1
print class_counters
np.save('separated_data/class_0.npy', data[0])