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# TRI-VIDAR - Copyright 2022 Toyota Research Institute. All rights reserved.
import json
import os
import random
from collections import OrderedDict
import numpy as np
import torch
from vidar.utils.decorators import iterate1
from vidar.utils.types import is_seq, is_tensor, is_dict, is_int
def stack_sample(sample, lidar_sample=None, radar_sample=None):
"""
Stack samples from multiple cameras
Parameters
----------
sample : list[Dict]
List of camera samples
lidar_sample : list[Dict]
List of lidar samples
radar_sample : list[Dict]
List of radar samples
Returns
-------
stacked_sample: Dict
Stacked sample
"""
# If there are no tensors, return empty list
if len(sample) == 0:
return None
# If there is only one sensor don't do anything
if len(sample) == 1:
sample = sample[0]
return sample
# Otherwise, stack sample
first_sample = sample[0]
stacked_sample = {}
for key, val in first_sample.items():
# Global keys (do not stack)
if key in ['idx', 'dataset_idx']:
stacked_sample[key] = first_sample[key]
# Meta keys
elif key in ['meta']:
stacked_sample[key] = {}
for key2 in first_sample[key].keys():
stacked_sample[key][key2] = {}
for key3 in first_sample[key][key2].keys():
stacked_sample[key][key2][key3] = torch.stack(
[torch.tensor(s[key][key2][key3]) for s in sample], 0)
# Stack tensors
elif is_tensor(val):
stacked_sample[key] = torch.stack([s[key] for s in sample], 0)
# Stack list
elif is_seq(first_sample[key]):
stacked_sample[key] = []
# Stack list of torch tensors
if is_tensor(first_sample[key][0]):
for i in range(len(first_sample[key])):
stacked_sample[key].append(
torch.stack([s[key][i] for s in sample], 0))
else:
stacked_sample[key] = [s[key] for s in sample]
# Repeat for dictionaries
elif is_dict(first_sample[key]):
stacked_sample[key] = stack_sample([s[key] for s in sample])
# Append lists
else:
stacked_sample[key] = [s[key] for s in sample]
# Return stacked sample
return stacked_sample
def merge_sample(samples):
"""Merge information from multiple samples"""
merged_sample = {}
for sample in samples:
for key, val in sample.items():
if key not in merged_sample:
merged_sample[key] = val
else:
merged_sample[key] = merge_sample([merged_sample[key], val])
return merged_sample
def parse_crop(cfg, shape):
"""Parse crop information to generate borders"""
borders = None
if cfg.has('crop_borders'):
borders = parse_crop_borders(cfg.crop_borders, shape)
if cfg.has('crop_random'):
if borders is None:
borders = [0, 0, shape[1], shape[0]]
borders = parse_crop_random(borders, cfg.crop_random)
return borders
def parse_crop_borders(borders, shape):
"""
Calculate borders for cropping.
Parameters
----------
borders : Tuple
Border input for parsing. Can be one of the following forms:
(int, int, int, int): y, height, x, width
(int, int): y, x --> y, height = image_height - y, x, width = image_width - x
Negative numbers are taken from image borders, according to the shape argument
Float numbers for y and x are treated as percentage, according to the shape argument,
and in this case height and width are centered at that point.
shape : Tuple
Image shape (image_height, image_width), used to determine negative crop boundaries
Returns
-------
borders : Tuple
Parsed borders for cropping (left, top, right, bottom)
"""
# Return full image if there are no borders to crop
if len(borders) == 0:
return 0, 0, shape[1], shape[0]
# Copy borders for modification
borders = list(borders).copy()
# If borders are 4-dimensional
if len(borders) == 4:
borders = [borders[2], borders[0], borders[3], borders[1]]
if is_int(borders[0]):
# If horizontal cropping is integer (regular cropping)
borders[0] += shape[1] if borders[0] < 0 else 0
borders[2] += shape[1] if borders[2] <= 0 else borders[0]
else:
# If horizontal cropping is float (center cropping)
center_w, half_w = borders[0] * shape[1], borders[2] / 2
borders[0] = int(center_w - half_w)
borders[2] = int(center_w + half_w)
if is_int(borders[1]):
# If vertical cropping is integer (regular cropping)
borders[1] += shape[0] if borders[1] < 0 else 0
borders[3] += shape[0] if borders[3] <= 0 else borders[1]
else:
# If vertical cropping is float (center cropping)
center_h, half_h = borders[1] * shape[0], borders[3] / 2
borders[1] = int(center_h - half_h)
borders[3] = int(center_h + half_h)
# If borders are 2-dimensional
elif len(borders) == 2:
borders = [borders[1], borders[0]]
if is_int(borders[0]):
# If cropping is integer (regular cropping)
borders = (max(0, borders[0]),
max(0, borders[1]),
shape[1] + min(0, borders[0]),
shape[0] + min(0, borders[1]))
else:
# If cropping is float (center cropping)
center_w, half_w = borders[0] * shape[1], borders[1] / 2
center_h, half_h = borders[0] * shape[0], borders[1] / 2
borders = (int(center_w - half_w), int(center_h - half_h),
int(center_w + half_w), int(center_h + half_h))
# Otherwise, invalid
else:
raise NotImplementedError('Crop tuple must have 2 or 4 values.')
# Assert that borders are valid
assert 0 <= borders[0] < borders[2] <= shape[1] and \
0 <= borders[1] < borders[3] <= shape[0], 'Crop borders {} are invalid'.format(borders)
# Return updated borders
return borders
def parse_crop_random(borders, shape):
"""
Create borders for random cropping.
Crops are generated anywhere in the image inside the borders
Parameters
----------
borders : Tuple
Area of the image where random cropping can happen (left, top, right, bottom)
shape : Tuple
Cropped output shape (height, width)
Returns
-------
borders : tuple
Parsed borders for cropping (left, top, right, bottom)
"""
# Return full borders if there is no random crop
if len(shape) == 0:
return borders
# Check if random crop is valid
assert 0 < shape[1] <= borders[2] - borders[0] and \
0 < shape[0] <= borders[3] - borders[1], 'Random crop must be smaller than the image'
# Sample a crop
x = random.randint(borders[0], borders[2] - shape[1])
y = random.randint(borders[1], borders[3] - shape[0])
# Return new borders
return x, y, x + shape[1], y + shape[0]
@iterate1
def invert_pose(pose):
"""
Inverts a transformation matrix (pose)
Parameters
----------
pose : np.Array
Input pose [4, 4]
Returns
-------
inv_pose : np.Array
Inverted pose [4, 4]
"""
inv_pose = np.eye(4)
inv_pose[:3, :3] = np.transpose(pose[:3, :3])
inv_pose[:3, -1] = - inv_pose[:3, :3] @ pose[:3, -1]
# return np.linalg.inv(pose)
return inv_pose
def make_relative_pose(samples):
"""
Convert sample poses to relative frane of reference (based on the first target frame)
Parameters
----------
samples : list[Dict]
Input samples
Returns
-------
samples : list[Dict]
Relative samples
"""
# Do nothing if there is no pose
if 'pose' not in samples[0]:
return samples
# Get inverse current poses
inv_pose = [invert_pose(samples[i]['pose'][0]) for i in range(len(samples))]
# For each camera
for i in range(len(samples)):
# For each context
for j in samples[0]['pose'].keys():
if j == 0:
if i > 0:
samples[i]['pose'][j] = \
samples[i]['pose'][j] @ inv_pose[0]
else:
samples[i]['pose'][j] = \
samples[i]['pose'][j] @ inv_pose[i]
return samples
def dummy_intrinsics(image):
"""
Return dummy intrinsics calculated based on image resolution
Parameters
----------
image : PIL Image
Image from which intrinsics will be calculated
Returns
-------
intrinsics : np.Array
Image intrinsics (fx = cx = w/2, fy = cy = h/2) [3,3]
"""
w, h = [float(d) for d in image.size]
return np.array([[w/2, 0., w/2. - 0.5],
[0., h/2, h/2. - 0.5],
[0., 0., 1.]])
def load_ontology(name, filter_list=None):
"""Loads ontology from file and optionally filters it"""
filename = 'vidar/ontologies/{}.json'.format(name)
if os.path.exists(filename):
ontology = json.load(open(filename, 'r'))
if filter_list is not None and len(filter_list) > 0:
ontology = filter_ontology(ontology, filter_list)
return ontology
else:
return None
def save_ontology(ontology, name):
"""Save ontology to a JSON file"""
if is_seq(ontology):
ontology = ontology[0]
for key in ontology.keys():
ontology[key]['color'] = ontology[key]['color'].tolist()
json.dump(ontology, open('ontologies/{}.json'.format(name), 'w'))
def filter_ontology(ontology, values):
"""Filter ontology to remove certain classes"""
new_ontology = OrderedDict()
for i, val in enumerate(values[1:]):
new_ontology[i] = ontology[str(val)]
return new_ontology
def convert_ontology(semantic_id, ontology_convert):
"""Convert from one ontology to another"""
if ontology_convert is None:
return semantic_id
else:
semantic_id_convert = semantic_id.copy()
for key, val in ontology_convert.items():
semantic_id_convert[semantic_id == key] = val
return semantic_id_convert
def initialize_ontology(base, ontology):
"""Initialize ontology and conversion table if necessary"""
return load_ontology(base), None
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