def _generateRandomBatches(self):
'Generates random batches of samples'
while 1:
# Generate batches
X = []
y = np.zeros([self.batch_size, self.nb_classes])
imageIdxs = r.sample(range(0, self.nb_images),
self.images_per_batch)
for idx, imageIdx in enumerate(imageIdxs):
imageX, imageY = self._generateBatchFromIDs([imageIdx])
# Variables
s_idx = 0
f_idx = min(2, len(imageY))
nb_bgs = self.nb_samples_per_image - f_idx
idx = idx * self.nb_samples_per_image
# Data
imageMeta = self.imagesMeta[self.dataID[imageIdx]]
imageXCut = utils.spliceXData(imageX, s_idx, f_idx)
imageYCut = imageY[s_idx:f_idx, :]
if nb_bgs > 0:
bbs = utils.createBackgroundBBs(imageMeta, nb_bgs,
self.images_path)
imageXBG, imageYBG = self._generateBatchFromBGs(
imageMeta, bbs)
imageXCmb = utils.concatXData(imageXCut, imageXBG)
# print(y.shape, idx, self.nb_samples_per_image, len(imageIdxs))
imageYCmb = np.append(imageYCut, imageYBG, 0)
else:
imageXCmb = imageXCut
imageYCmb = imageYCut
X = utils.concatXData(X, imageXCmb)
y[idx:idx + self.nb_samples_per_image, :] = imageYCmb
yield X, y
def _generateIterativeBatches(self):
'Generates iterative batches of samples'
while 1:
imageIdx = 1
hoiinimageidx = 0
# Generate batches
for i in range(self.nb_batches):
X = []
y = []
imgs = []
imageIdxs = []
for imageIdx in range(imageIdx - 1, self.nb_images):
imageIdxs.append(imageIdx)
if self.do_meta:
imageX, imageY, imageMeta, img = self._generateBatchFromIDs(
[imageIdx])
else:
imageX, imageY = self._generateBatchFromIDs([imageIdx])
img = None
s_idx = 0
f_idx = len(imageY)
if hoiinimageidx > 0:
s_idx = hoiinimageidx
if hoiinimageidx == len(imageY):
hoiinimageidx = 0
continue
# hoiinimageidx = 0
if (len(imageY) -
hoiinimageidx) + len(y) >= self.batch_size:
hoiinimageidx = hoiinimageidx + len(imageY) - (
(len(imageY) + len(y)) - self.batch_size)
f_idx = hoiinimageidx
else:
hoiinimageidx = 0
imageXCut = utils.spliceXData(imageX, s_idx, f_idx)
X = utils.concatXData(X, imageXCut)
y.extend(imageY[s_idx:f_idx, :])
imgs.append(img)
# y.extend([self.dataID[imageIdx] for i in range(s_idx, f_idx)])
if len(y) == self.batch_size:
break
imageIdx += 1
y = np.array(y)
if self.do_meta:
yield X, y, imageMeta, imgs[0]
else:
yield X, y
def _generateIterativeImageCentricBatches(self):
'Generates iterative batches of samples'
while 1:
imageIdx = 1
# Generate batches
for i in range(self.nb_batches):
X = []
y = []
batchIdx = 0
for imageIdx in range(imageIdx - 1, self.nb_images):
imageX, imageY = self._generateBatchFromIDs([imageIdx],
batchIdx)
X = utils.concatXData(X, imageX)
y.extend(imageY)
batchIdx += 1
if len(y) == self.images_per_batch or True:
break
y = np.array(y)
yield X, y