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decode_image.py
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import numpy as np
import PIL.Image as pil
try:
import skimage.transform
except ImportError as e:
print(
f"{e}, [scikit-image] package and it's dependencies is required for ADDS."
)
from PIL import Image
from ..registry import PIPELINES
@PIPELINES.register()
class ImageDecoder(object):
"""Decode Image
"""
def __init__(self,
dataset,
frame_idxs,
num_scales,
side_map,
full_res_shape,
img_ext,
backend='cv2'):
self.backend = backend
self.dataset = dataset
self.frame_idxs = frame_idxs
self.num_scales = num_scales
self.side_map = side_map
self.full_res_shape = full_res_shape
self.img_ext = img_ext
def _pil_loader(self, path):
with open(path, 'rb') as f:
with Image.open(f) as img:
return img.convert('RGB')
def get_color(self, folder, frame_index, side):
color = self._pil_loader(
self.get_image_path(self.dataset, folder, frame_index, side))
return color
def get_image_path(self, dataset, folder, frame_index, side):
if dataset == "kitti":
f_str = "{:010d}{}".format(frame_index, self.img_ext)
image_path = os.path.join(self.data_path, folder, f_str)
elif dataset == "kitti_odom":
f_str = "{:06d}{}".format(frame_index, self.img_ext)
image_path = os.path.join(self.data_path,
"sequences/{:02d}".format(int(folder)),
"image_{}".format(self.side_map[side]),
f_str)
elif dataset == "kitti_depth":
f_str = "{:010d}{}".format(frame_index, self.img_ext)
image_path = os.path.join(
self.data_path, folder,
"image_0{}/data".format(self.side_map[side]), f_str)
return image_path
def get_depth(self, dataset, folder, frame_index, side):
if dataset == "kitii_depth":
f_str = "{:010d}.png".format(frame_index)
depth_path = os.path.join(
self.data_path, folder,
"proj_depth/groundtruth/image_0{}".format(self.side_map[side]),
f_str)
depth_gt = pil.open(depth_path)
depth_gt = depth_gt.resize(self.full_res_shape, pil.NEAREST)
depth_gt = np.array(depth_gt).astype(np.float32) / 256
else:
f_str = "{:010d}{}".format(frame_index, self.img_ext)
depth_path = os.path.join(self.data_path, folder + '_gt', f_str)
img_file = Image.open(depth_path)
depth_png = np.array(img_file, dtype=int)
img_file.close()
# make sure we have a proper 16bit depth map here.. not 8bit!
assert np.max(depth_png) > 255, \
"np.max(depth_png)={}, path={}".format(np.max(depth_png), depth_path)
depth_gt = depth_png.astype(np.float) / 256.
depth_gt = depth_gt[160:960 - 160, :]
depth_gt = skimage.transform.resize(depth_gt,
self.full_res_shape[::-1],
order=0,
preserve_range=True,
mode='constant')
return depth_gt
def __call__(self, results):
"""
Perform mp4 decode operations.
return:
List where each item is a numpy array after decoder.
"""
if results.get('mode', None) == 'infer':
imgs = {}
imgs[("color", 0,
-1)] = Image.open(results["filename"]).convert("RGB")
results['imgs'] = imgs
return results
self.data_path = results['data_path']
results['backend'] = self.backend
imgs = {}
results['frame_idxs'] = self.frame_idxs
results['num_scales'] = self.num_scales
file_name = results['filename']
folder = results['folder']
frame_index = results['frame_index']
line = file_name.split('/')
istrain = folder.split('_')[1]
if 'mode' not in results:
results['mode'] = istrain
results['day_or_night'] = folder.split('_')[0]
if istrain == "train":
if folder[0] == 'd':
folder2 = folder + '_fake_night'
flag = 0
else:
folder2 = folder + '_fake_day'
tmp = folder
folder = folder2
folder2 = tmp
flag = 1
if len(line) == 3:
side = line[2]
else:
side = None
results['side'] = side
for i in self.frame_idxs:
if i == "s":
other_side = {"r": "l", "l": "r"}[side]
imgs[("color", i,
-1)] = self.get_color(folder, frame_index, other_side)
imgs[("color_n", i,
-1)] = self.get_color(folder2, frame_index,
other_side)
else:
imgs[("color", i,
-1)] = self.get_color(folder, frame_index + i, side)
imgs[("color_n", i,
-1)] = self.get_color(folder2, frame_index + i, side)
istrain = folder.split('_')[1]
if istrain != 'train':
if flag:
depth_gt = self.get_depth(folder2, frame_index, side)
else:
depth_gt = self.get_depth(folder, frame_index, side)
imgs["depth_gt"] = np.expand_dims(depth_gt, 0)
elif istrain == 'val':
if len(line) == 3:
side = line[2]
else:
side = None
for i in self.frame_idxs:
if i == "s":
other_side = {"r": "l", "l": "r"}[side]
imgs[("color", i,
-1)] = self.get_color(folder, frame_index, other_side)
else:
imgs[("color", i,
-1)] = self.get_color(folder, frame_index + i, side)
# adjusting intrinsics to match each scale in the pyramid
depth_gt = self.get_depth(self.dataset, folder, frame_index, side)
imgs["depth_gt"] = np.expand_dims(depth_gt, 0)
results['imgs'] = imgs
return results