def __getitem__(self, index):
index = index % self._size
im1_filename = self._image_list[index][0]
im2_filename = self._image_list[index][1]
# read float32 images and flow
im1_np0 = common.read_image_as_byte(im1_filename)
im2_np0 = common.read_image_as_byte(im2_filename)
# possibly apply photometric transformations
im1, im2 = self._photometric_transform(im1_np0, im2_np0)
# e.g. "clean/alley_1/"
basedir = os.path.splitext(
os.path.dirname(im1_filename).replace(self._substract_base,
"")[1:])[0]
# example filename
basename = os.path.splitext(os.path.basename(im1_filename))[0]
example_dict = {
"input1": im1,
"input2": im2,
"index": index,
"basedir": basedir,
"basename": basename
}
return example_dict
def __getitem__(self, index):
index = index % self._size
im1_filename = self._image_list[index][0]
im2_filename = self._image_list[index][1]
flo_filename = self._flow_list[index]
# read float32 images and flow
im1_np0 = common.read_image_as_byte(im1_filename)
im2_np0 = common.read_image_as_byte(im2_filename)
flo_np0 = common.read_flo_as_float32(flo_filename)
# possibly apply photometric transformations
im1, im2 = self._photometric_transform(im1_np0, im2_np0)
# convert flow to FloatTensor
flo = common.numpy2torch(flo_np0)
# target_occ: initialized by zero (not used)
target_occ = common.numpy2torch(
common.read_occ_image_as_float32(im1_filename)) * 0
# example filename
basename = os.path.basename(im1_filename)[:5]
example_dict = {
"input1": im1,
"input2": im2,
"target1": flo,
"target_occ1": target_occ,
"index": index,
"basename": basename
}
return example_dict
def __getitem__(self, index):
index = index % self._size
im1_filename = self._image_list[index][0]
im2_filename = self._image_list[index][1]
flo_f_filename = self._flow_list[index][0]
# read float32 images and flow
im1_np0 = common.read_image_as_byte(im1_filename)
im2_np0 = common.read_image_as_byte(im2_filename)
flo_f_np0, valid_mask = read_png_flow(flo_f_filename)
if self.preprocessing_crop:
im1_np0, im2_np0, flo_f_np0, valid_mask = \
kitti_random_crop(im1_np0, im2_np0, flo_f_np0, valid_mask)
# possibly apply photometric transformations
im1, im2 = self._photometric_transform(im1_np0, im2_np0)
# convert flow to FloatTensor
flo_f = common.numpy2torch(flo_f_np0)
valid_mask_f = common.numpy2torch(valid_mask)
# example filename
full_basename = os.path.basename(im1_filename).split('.')[0]
basename = full_basename[:6]
if 'data_scene_flow' in im1_filename:
version = '2015'
else:
version = '2012'
example_dict = {
"input1": im1,
"input2": im2,
"target1": flo_f,
"target2": flo_f,
"index": index,
"basename": basename,
"input_valid": valid_mask_f,
"full_basename": full_basename,
"kitti_version": version
}
if self._args.get_tmp_results:
fname = os.path.join(version, full_basename + '.png')
file_path = os.path.join(self._args.get_tmp_results, fname)
flo_tmp, valid_mask_tmp = read_png_flow(file_path)
flo_tmp = common.numpy2torch(flo_tmp)
valid_mask_tmp = common.numpy2torch(valid_mask_tmp)
example_dict['flo_res'] = flo_tmp
example_dict['valid_mask_res'] = valid_mask_tmp
return example_dict
def __getitem__(self, index):
index = index % self._size
im1_filename = self._image_list[index][0]
im2_filename = self._image_list[index][1]
flo_f_filename = self._flow_list[index][0]
flo_b_filename = self._flow_list[index][1]
occ1_filename = self._occ_list[index][0]
occ2_filename = self._occ_list[index][1]
# read float32 images and flow
im1_np0 = common.read_image_as_byte(im1_filename)
im2_np0 = common.read_image_as_byte(im2_filename)
flo_f_np0 = common.read_flo_as_float32(flo_f_filename)
flo_b_np0 = common.read_flo_as_float32(flo_b_filename)
occ1_np0 = common.read_occ_image_as_float32(occ1_filename)
occ2_np0 = common.read_occ_image_as_float32(occ2_filename)
# temp - check isnan
if np.any(np.isnan(flo_f_np0)):
flo_f_np0 = fillingInNaN(flo_f_np0)
if np.any(np.isnan(flo_b_np0)):
flo_b_np0 = fillingInNaN(flo_b_np0)
# possibly apply photometric transformations
im1, im2 = self._photometric_transform(im1_np0, im2_np0)
# convert flow to FloatTensor
flo_f = common.numpy2torch(flo_f_np0)
flo_b = common.numpy2torch(flo_b_np0)
# convert occ to FloatTensor
occ1 = common.numpy2torch(occ1_np0)
occ2 = common.numpy2torch(occ2_np0)
# example filename
basename = os.path.basename(im1_filename)[:5]
example_dict = {
"input1": im1,
"input2": im2,
"target1": flo_f,
"target2": flo_b,
"target_occ1": occ1,
"target_occ2": occ2,
"index": index,
"basename": basename
}
return example_dict
def __getitem__(self, index):
index = index % self._size
im1_filename = self._image_list[index][0]
im2_filename = self._image_list[index][1]
flo_filename = self._flow_list[index]
occ_filename = self._occ_list[index]
# read float32 images and flow
im1_np0 = common.read_image_as_byte(im1_filename)
im2_np0 = common.read_image_as_byte(im2_filename)
flo_np0 = common.read_flo_as_float32(flo_filename)
occ_np0 = common.read_occ_image_as_float32(occ_filename)
# possibly apply photometric transformations
im1, im2 = self._photometric_transform(im1_np0, im2_np0)
flo = common.numpy2torch(flo_np0)
occ = common.numpy2torch(occ_np0)
# show if transform if requested
if self.show_aug:
plt.subplot(221)
plt.title('1 (Input)')
show_image(im1_np0, subplote=True)
plt.subplot(222)
plt.title('2 (Photometric)')
show_image(im1, subplote=True)
# e.g. "clean/alley_1/"
basedir = os.path.splitext(
os.path.dirname(im1_filename).replace(self._substract_base,
"")[1:])[0]
# example filename
basename = os.path.splitext(os.path.basename(im1_filename))[0]
example_dict = {
"input1": im1,
"input2": im2,
"index": index,
"basedir": basedir,
"basename": basename,
"target1": flo,
"target_occ1": occ
}
return example_dict
def __getitem__(self, index):
index = index % self._size
im1_filename = self._image_list[index][0]
im2_filename = self._image_list[index][1]
# read float32 images and flow
im1_np0 = common.read_image_as_byte(im1_filename)
im2_np0 = common.read_image_as_byte(im2_filename)
# possibly apply photometric transformations
im1, im2 = self._photometric_transform(im1_np0, im2_np0)
# example filename
# basename = os.path.basename(im1_filename)[:9]
full_basename = os.path.basename(im1_filename).split('.')[0]
basename = full_basename[:9]
if 'data_scene_flow' in im1_filename:
version = '2015'
else:
version = '2012'
example_dict = {
"input1": im1,
"input2": im2,
"index": index,
"basename": basename,
"full_basename": full_basename
}
if self._args.get_tmp_results:
fname = os.path.join(full_basename + '.png')
file_path = os.path.join(self._args.get_tmp_results, fname)
flo_tmp, valid_mask_tmp = read_png_flow(file_path)
flo_tmp = common.numpy2torch(flo_tmp)
valid_mask_tmp = common.numpy2torch(valid_mask_tmp)
example_dict['flo_res'] = flo_tmp
example_dict['valid_mask_res'] = valid_mask_tmp
return example_dict