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import numpy as np
import torch
import torch.utils.data as data
import torch.nn.functional as F
import csv
import os
import cv2
import math
import random
import json
import pickle
import os.path as osp
from .rgbd_utils import *
class RGBDStream(data.Dataset):
def __init__(self, datapath, frame_rate=-1, image_size=[384,512], crop_size=[0,0]):
self.datapath = datapath
self.frame_rate = frame_rate
self.image_size = image_size
self.crop_size = crop_size
self._build_dataset_index()
@staticmethod
def image_read(image_file):
return cv2.imread(image_file)
@staticmethod
def depth_read(depth_file):
return np.load(depth_file)
def __len__(self):
return len(self.images)
def __getitem__(self, index):
""" return training video """
image = self.__class__.image_read(self.images[index])
image = torch.from_numpy(image).float()
image = image.permute(2, 0, 1)
try:
tstamp = self.tstamps[index]
except:
tstamp = index
pose = torch.from_numpy(self.poses[index]).float()
intrinsic = torch.from_numpy(self.intrinsics[index]).float()
# resize image
sx = self.image_size[1] / image.shape[2]
sy = self.image_size[0] / image.shape[1]
image = F.interpolate(image[None], self.image_size, mode='bilinear', align_corners=False)[0]
fx, fy, cx, cy = intrinsic.unbind(dim=0)
fx, cx = sx * fx, sx * cx
fy, cy = sy * fy, sy * cy
# crop image
if self.crop_size[0] > 0:
cy = cy - self.crop_size[0]
image = image[:,self.crop_size[0]:-self.crop_size[0],:]
if self.crop_size[1] > 0:
cx = cx - self.crop_size[1]
image = image[:,:,self.crop_size[1]:-self.crop_size[1]]
intrinsic = torch.stack([fx, fy, cx, cy])
return tstamp, image, pose, intrinsic
class ImageStream(data.Dataset):
def __init__(self, datapath, intrinsics, rate=1, image_size=[384,512]):
rgb_list = osp.join(datapath, 'rgb.txt')
if os.path.isfile(rgb_list):
rgb_list = np.loadtxt(rgb_list, delimiter=' ', dtype=np.unicode_)
self.timestamps = rgb_list[:,0].astype(np.float)
self.images = [os.path.join(datapath, x) for x in rgb_list[:,1]]
self.images = self.images[::rate]
self.timestamps = self.timestamps[::rate]
else:
import glob
self.images = sorted(glob.glob(osp.join(datapath, '*.jpg'))) + sorted(glob.glob(osp.join(datapath, '*.png')))
self.images = self.images[::rate]
self.intrinsics = intrinsics
self.image_size = image_size
def __len__(self):
return len(self.images)
@staticmethod
def image_read(imfile):
return cv2.imread(imfile)
def __getitem__(self, index):
""" return training video """
image = self.__class__.image_read(self.images[index])
try:
tstamp = self.timestamps[index]
except:
tstamp = index
ht0, wd0 = image.shape[:2]
ht1, wd1 = self.image_size
intrinsics = torch.as_tensor(self.intrinsics)
intrinsics[0] *= wd1 / wd0
intrinsics[1] *= ht1 / ht0
intrinsics[2] *= wd1 / wd0
intrinsics[3] *= ht1 / ht0
# resize image
ikwargs = {'mode': 'bilinear', 'align_corners': True}
image = torch.from_numpy(image).float().permute(2, 0, 1)
image = F.interpolate(image[None], self.image_size, **ikwargs)[0]
return tstamp, image, intrinsics
class StereoStream(data.Dataset):
def __init__(self, datapath, intrinsics, rate=1, image_size=[384,512],
map_left=None, map_right=None, left_root='image_left', right_root='image_right'):
import glob
self.intrinsics = intrinsics
self.image_size = image_size
imgs = sorted(glob.glob(osp.join(datapath, left_root, '*.png')))[::rate]
self.images_l = []
self.images_r = []
self.tstamps = []
for img_l in imgs:
img_r = img_l.replace(left_root, right_root)
if os.path.isfile(img_r):
t = np.float(img_l.split('/')[-1].replace('.png', ''))
self.tstamps.append(t)
self.images_l += [ img_l ]
self.images_r += [ img_r ]
self.map_left = map_left
self.map_right = map_right
def __len__(self):
return len(self.images_l)
@staticmethod
def image_read(imfile, imap=None):
image = cv2.imread(imfile)
if imap is not None:
image = cv2.remap(image, imap[0], imap[1], interpolation=cv2.INTER_LINEAR)
return image
def __getitem__(self, index):
""" return training video """
tstamp = self.tstamps[index]
image_l = self.__class__.image_read(self.images_l[index], self.map_left)
image_r = self.__class__.image_read(self.images_r[index], self.map_right)
ht0, wd0 = image_l.shape[:2]
ht1, wd1 = self.image_size
intrinsics = torch.as_tensor(self.intrinsics)
intrinsics[0] *= wd1 / wd0
intrinsics[1] *= ht1 / ht0
intrinsics[2] *= wd1 / wd0
intrinsics[3] *= ht1 / ht0
image_l = torch.from_numpy(image_l).float().permute(2, 0, 1)
image_r = torch.from_numpy(image_r).float().permute(2, 0, 1)
# resize image
ikwargs = {'mode': 'bilinear', 'align_corners': True}
image_l = F.interpolate(image_l[None], self.image_size, **ikwargs)[0]
image_r = F.interpolate(image_r[None], self.image_size, **ikwargs)[0]
return tstamp, image_l, image_r, intrinsics
# class RGBDStream(data.Dataset):
# def __init__(self, datapath, intrinsics=None, rate=1, image_size=[384,512]):
# assoc_file = osp.join(datapath, 'associated.txt')
# assoc_list = np.loadtxt(assoc_file, delimiter=' ', dtype=np.unicode_)
# self.intrinsics = intrinsics
# self.image_size = image_size
# self.timestamps = assoc_list[:,0].astype(np.float)[::rate]
# self.images = [os.path.join(datapath, x) for x in assoc_list[:,1]][::rate]
# self.depths = [os.path.join(datapath, x) for x in assoc_list[:,3]][::rate]
# def __len__(self):
# return len(self.images)
# @staticmethod
# def image_read(imfile):
# return cv2.imread(imfile)
# @staticmethod
# def depth_read(depth_file):
# depth = cv2.imread(depth_file, cv2.IMREAD_ANYDEPTH)
# return depth.astype(np.float32) / 5000.0
# def __getitem__(self, index):
# """ return training video """
# tstamp = self.timestamps[index]
# image = self.__class__.image_read(self.images[index])
# depth = self.__class__.depth_read(self.depths[index])
# ht0, wd0 = image.shape[:2]
# ht1, wd1 = self.image_size
# intrinsics = torch.as_tensor(self.intrinsics)
# intrinsics[0] *= wd1 / wd0
# intrinsics[1] *= ht1 / ht0
# intrinsics[2] *= wd1 / wd0
# intrinsics[3] *= ht1 / ht0
# # resize image
# ikwargs = {'mode': 'bilinear', 'align_corners': True}
# image = torch.from_numpy(image).float().permute(2, 0, 1)
# image = F.interpolate(image[None], self.image_size, **ikwargs)[0]
# depth = torch.from_numpy(depth).float()[None,None]
# depth = F.interpolate(depth, self.image_size, mode='nearest').squeeze()
# return tstamp, image, depth, intrinsics
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