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# MIT License
# Copyright (c) 2022 Intelligent Systems Lab Org
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
# File author: Shariq Farooq Bhat
import os
import cv2
import numpy as np
import torch
from PIL import Image
from torch.utils.data import DataLoader, Dataset
from torchvision import transforms
class ToTensor(object):
def __init__(self):
# self.normalize = transforms.Normalize(
# mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
self.normalize = lambda x: x
# self.resize = transforms.Resize((375, 1242))
def __call__(self, sample):
image, depth = sample['image'], sample['depth']
image = self.to_tensor(image)
image = self.normalize(image)
depth = self.to_tensor(depth)
# image = self.resize(image)
return {'image': image, 'depth': depth, 'dataset': "vkitti"}
def to_tensor(self, pic):
if isinstance(pic, np.ndarray):
img = torch.from_numpy(pic.transpose((2, 0, 1)))
return img
# # handle PIL Image
if pic.mode == 'I':
img = torch.from_numpy(np.array(pic, np.int32, copy=False))
elif pic.mode == 'I;16':
img = torch.from_numpy(np.array(pic, np.int16, copy=False))
else:
img = torch.ByteTensor(
torch.ByteStorage.from_buffer(pic.tobytes()))
# PIL image mode: 1, L, P, I, F, RGB, YCbCr, RGBA, CMYK
if pic.mode == 'YCbCr':
nchannel = 3
elif pic.mode == 'I;16':
nchannel = 1
else:
nchannel = len(pic.mode)
img = img.view(pic.size[1], pic.size[0], nchannel)
img = img.transpose(0, 1).transpose(0, 2).contiguous()
if isinstance(img, torch.ByteTensor):
return img.float()
else:
return img
class VKITTI2(Dataset):
def __init__(self, data_dir_root, do_kb_crop=True, split="test"):
import glob
# image paths are of the form <data_dir_root>/rgb/<scene>/<variant>/frames/<rgb,depth>/Camera<0,1>/rgb_{}.jpg
self.image_files = glob.glob(os.path.join(
data_dir_root, "rgb", "**", "frames", "rgb", "Camera_0", '*.jpg'), recursive=True)
self.depth_files = [r.replace("/rgb/", "/depth/").replace(
"rgb_", "depth_").replace(".jpg", ".png") for r in self.image_files]
self.do_kb_crop = True
self.transform = ToTensor()
# If train test split is not created, then create one.
# Split is such that 8% of the frames from each scene are used for testing.
if not os.path.exists(os.path.join(data_dir_root, "train.txt")):
import random
scenes = set([os.path.basename(os.path.dirname(
os.path.dirname(os.path.dirname(f)))) for f in self.image_files])
train_files = []
test_files = []
for scene in scenes:
scene_files = [f for f in self.image_files if os.path.basename(
os.path.dirname(os.path.dirname(os.path.dirname(f)))) == scene]
random.shuffle(scene_files)
train_files.extend(scene_files[:int(len(scene_files) * 0.92)])
test_files.extend(scene_files[int(len(scene_files) * 0.92):])
with open(os.path.join(data_dir_root, "train.txt"), "w") as f:
f.write("\n".join(train_files))
with open(os.path.join(data_dir_root, "test.txt"), "w") as f:
f.write("\n".join(test_files))
if split == "train":
with open(os.path.join(data_dir_root, "train.txt"), "r") as f:
self.image_files = f.read().splitlines()
self.depth_files = [r.replace("/rgb/", "/depth/").replace(
"rgb_", "depth_").replace(".jpg", ".png") for r in self.image_files]
elif split == "test":
with open(os.path.join(data_dir_root, "test.txt"), "r") as f:
self.image_files = f.read().splitlines()
self.depth_files = [r.replace("/rgb/", "/depth/").replace(
"rgb_", "depth_").replace(".jpg", ".png") for r in self.image_files]
def __getitem__(self, idx):
image_path = self.image_files[idx]
depth_path = self.depth_files[idx]
image = Image.open(image_path)
# depth = Image.open(depth_path)
depth = cv2.imread(depth_path, cv2.IMREAD_ANYCOLOR |
cv2.IMREAD_ANYDEPTH) / 100.0 # cm to m
depth = Image.fromarray(depth)
# print("dpeth min max", depth.min(), depth.max())
# print(np.shape(image))
# print(np.shape(depth))
if self.do_kb_crop:
if idx == 0:
print("Using KB input crop")
height = image.height
width = image.width
top_margin = int(height - 352)
left_margin = int((width - 1216) / 2)
depth = depth.crop(
(left_margin, top_margin, left_margin + 1216, top_margin + 352))
image = image.crop(
(left_margin, top_margin, left_margin + 1216, top_margin + 352))
# uv = uv[:, top_margin:top_margin + 352, left_margin:left_margin + 1216]
image = np.asarray(image, dtype=np.float32) / 255.0
# depth = np.asarray(depth, dtype=np.uint16) /1.
depth = np.asarray(depth, dtype=np.float32) / 1.
depth[depth > 80] = -1
depth = depth[..., None]
sample = dict(image=image, depth=depth)
# return sample
sample = self.transform(sample)
if idx == 0:
print(sample["image"].shape)
return sample
def __len__(self):
return len(self.image_files)
def get_vkitti2_loader(data_dir_root, batch_size=1, **kwargs):
dataset = VKITTI2(data_dir_root)
return DataLoader(dataset, batch_size, **kwargs)
if __name__ == "__main__":
loader = get_vkitti2_loader(
data_dir_root="/home/bhatsf/shortcuts/datasets/vkitti2")
print("Total files", len(loader.dataset))
for i, sample in enumerate(loader):
print(sample["image"].shape)
print(sample["depth"].shape)
print(sample["dataset"])
print(sample['depth'].min(), sample['depth'].max())
if i > 5:
break
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