def preprocess_data(self, batch) :
#in batch[0] are all the vidis. They are the same for each fpv elements
#in batch[1] are all the frames. A group of fpv is one video
fpv=self.nb_frames
data_array = batch[0]
num_videos = int(len(data_array)/fpv)
x = np.zeros((num_videos, fpv,
self.crop_size[0], self.crop_size[1], self.nchannels),
dtype='float32')
y = np.empty(num_videos, dtype='int64')
for i in xrange(num_videos) :
if self.translate_labels:
y[i] = translate[batch[1][i*fpv]]
else:
y[i] = batch[1][i*fpv]
do_flip = self.rng.rand(1)[0]
bbox = self.crop()
for j in xrange(fpv):
data = data_array[i*fpv+j]
#this data was stored in uint8
data = StringIO(data.tostring())
data.seek(0)
img = Image.open(data)
if (img.size[0] != self.input_size[1] and
img.size[1] != self.input_size[0]):
img = img.resize((int(self.input_size[1]),
int(self.input_size[0])),
Image.ANTIALIAS)
img = img.crop(bbox)
img = img.resize((int(self.crop_size[1]),
int(self.crop_size[0])),
Image.ANTIALIAS)
# cv2.imshow('img', np.array(img))
# cv2.waitKey(0)
# cv2.destroyAllWindows()
img = (np.array(img).astype(np.float32) / 255.0) * self.scale
if self.nchannels == 1:
img = img[:, :, None]
if self.swap_rgb and self.nchannels == 3:
img = img[:, :, [2, 1, 0]]
x[i, j, :, :, :] = img[:, :, :]
### Flip
if self.flip == 'flip' or (self.flip == 'random'
and do_flip > 0.5):
new_image = np.empty_like(x[i, j, :, :, :])
for c in xrange(self.nchannels):
new_image[:,:,c] = np.fliplr(x[i, j, :, :, c])
x[i, j, :, :, :] = new_image
#import cv2
#cv2.imshow('img', x[i, 0, :, :, :])
#cv2.waitKey(0)
#cv2.destroyAllWindows()
return (x, y)
def preprocess_data(self, batch):
#in batch[0] are all the vidis. They are the same for each fpv elements
#in batch[1] are all the frames. A group of fpv is one video
fpv = self.nb_frames
data_array = batch[0]
num_videos = int(len(data_array) / fpv)
x = np.zeros((num_videos, fpv, self.crop_size[0], self.crop_size[1],
self.nchannels),
dtype='float32')
y = np.empty(num_videos, dtype='int64')
for i in xrange(num_videos):
if self.translate_labels:
y[i] = translate[batch[1][i * fpv]]
else:
y[i] = batch[1][i * fpv]
do_flip = self.rng.rand(1)[0]
bbox = self.crop()
for j in xrange(fpv):
data = data_array[i * fpv + j]
#this data was stored in uint8
data = StringIO(data.tostring())
data.seek(0)
img = Image.open(data)
if (img.size[0] != self.input_size[1]
and img.size[1] != self.input_size[0]):
img = img.resize(
(int(self.input_size[1]), int(self.input_size[0])),
Image.ANTIALIAS)
img = img.crop(bbox)
img = img.resize(
(int(self.crop_size[1]), int(self.crop_size[0])),
Image.ANTIALIAS)
# cv2.imshow('img', np.array(img))
# cv2.waitKey(0)
# cv2.destroyAllWindows()
img = (np.array(img).astype(np.float32) / 255.0) * self.scale
if self.nchannels == 1:
img = img[:, :, None]
if self.swap_rgb and self.nchannels == 3:
img = img[:, :, [2, 1, 0]]
x[i, j, :, :, :] = img[:, :, :]
### Flip
if self.flip == 'flip' or (self.flip == 'random'
and do_flip > 0.5):
new_image = np.empty_like(x[i, j, :, :, :])
for c in xrange(self.nchannels):
new_image[:, :, c] = np.fliplr(x[i, j, :, :, c])
x[i, j, :, :, :] = new_image
#import cv2
#cv2.imshow('img', x[i, 0, :, :, :])
#cv2.waitKey(0)
#cv2.destroyAllWindows()
return (x, y)
