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# TRI-VIDAR - Copyright 2022 Toyota Research Institute. All rights reserved.
from copy import deepcopy
import cv2
import numpy as np
import torch
import torchvision.transforms as transforms
from torchvision.transforms import InterpolationMode
from vidar.utils.data import keys_with
from vidar.utils.decorators import iterate1
from vidar.utils.types import is_seq
@iterate1
def resize_pil(image, shape, interpolation=InterpolationMode.LANCZOS):
"""
Resizes input image
Parameters
----------
image : Image PIL
Input image
shape : Tuple
Output shape [H,W]
interpolation : Int
Interpolation mode
Returns
-------
image : Image PIL
Resized image
"""
transform = transforms.Resize(shape, interpolation=interpolation)
return transform(image)
@iterate1
def resize_npy(depth, shape, expand=True):
"""
Resizes depth map
Parameters
----------
depth : np.Array
Depth map [h,w]
shape : Tuple
Output shape (H,W)
expand : Bool
Expand output to [H,W,1]
Returns
-------
depth : np.Array
Resized depth map [H,W]
"""
# If a single number is provided, use resize ratio
if not is_seq(shape):
shape = tuple(int(s * shape) for s in depth.shape)
# Resize depth map
depth = cv2.resize(depth, dsize=tuple(shape[::-1]), interpolation=cv2.INTER_NEAREST)
# Return resized depth map
return np.expand_dims(depth, axis=2) if expand else depth
@iterate1
def resize_npy_preserve(depth, shape):
"""
Resizes depth map preserving all valid depth pixels
Multiple downsampled points can be assigned to the same pixel.
Parameters
----------
depth : np.Array
Depth map [h,w]
shape : Tuple
Output shape (H,W)
Returns
-------
depth : np.Array
Resized depth map [H,W,1]
"""
# If a single number is provided, use resize ratio
if not is_seq(shape):
shape = tuple(int(s * shape) for s in depth.shape)
# Store dimensions and reshapes to single column
depth = np.squeeze(depth)
h, w = depth.shape
x = depth.reshape(-1)
# Create coordinate grid
uv = np.mgrid[:h, :w].transpose(1, 2, 0).reshape(-1, 2)
# Filters valid points
idx = x > 0
crd, val = uv[idx], x[idx]
# Downsamples coordinates
crd[:, 0] = (crd[:, 0] * (shape[0] / h)).astype(np.int32)
crd[:, 1] = (crd[:, 1] * (shape[1] / w)).astype(np.int32)
# Filters points inside image
idx = (crd[:, 0] < shape[0]) & (crd[:, 1] < shape[1])
crd, val = crd[idx], val[idx]
# Creates downsampled depth image and assigns points
depth = np.zeros(shape)
depth[crd[:, 0], crd[:, 1]] = val
# Return resized depth map
return np.expand_dims(depth, axis=2)
@iterate1
def resize_torch_preserve(depth, shape):
"""
Resizes depth map preserving all valid depth pixels
Multiple downsampled points can be assigned to the same pixel.
Parameters
----------
depth : torch.Tensor
Depth map [B,1,h,w]
shape : Tuple
Output shape (H,W)
Returns
-------
depth : torch.Tensor
Resized depth map [B,1,H,W]
"""
if depth.dim() == 4:
return torch.stack([resize_torch_preserve(depth[i], shape)
for i in range(depth.shape[0])], 0)
# If a single number is provided, use resize ratio
if not is_seq(shape):
shape = tuple(int(s * shape) for s in depth.shape)
# Store dimensions and reshapes to single column
c, h, w = depth.shape
# depth = np.squeeze(depth)
# h, w = depth.shape
x = depth.reshape(-1)
# Create coordinate grid
uv = np.mgrid[:h, :w].transpose(1, 2, 0).reshape(-1, 2)
# Filters valid points
idx = x > 0
crd, val = uv[idx], x[idx]
# Downsamples coordinates
crd[:, 0] = (crd[:, 0] * (shape[0] / h)).astype(np.int32)
crd[:, 1] = (crd[:, 1] * (shape[1] / w)).astype(np.int32)
# Filters points inside image
idx = (crd[:, 0] < shape[0]) & (crd[:, 1] < shape[1])
crd, val = crd[idx], val[idx]
# Creates downsampled depth image and assigns points
depth = torch.zeros(shape, device=depth.device, dtype=depth.dtype)
depth[crd[:, 0], crd[:, 1]] = val
# Return resized depth map
return depth.unsqueeze(0)
@iterate1
def resize_npy_multiply(data, shape):
"""Resize a numpy array and scale its content accordingly"""
ratio_w = shape[0] / data.shape[0]
ratio_h = shape[1] / data.shape[1]
out = resize_npy(data, shape, expand=False)
out[..., 0] *= ratio_h
out[..., 1] *= ratio_w
return out
@iterate1
def resize_intrinsics(intrinsics, original, resized):
"""
Resize camera intrinsics matrix to match a target resolution
Parameters
----------
intrinsics : np.Array
Original intrinsics matrix [3,3]
original : Tuple
Original image resolution [W,H]
resized : Tuple
Target image resolution [w,h]
Returns
-------
intrinsics : np.Array
Resized intrinsics matrix [3,3]
"""
intrinsics = np.copy(intrinsics)
intrinsics[0] *= resized[0] / original[0]
intrinsics[1] *= resized[1] / original[1]
return intrinsics
@iterate1
def resize_sample_input(sample, shape, shape_supervision=None,
depth_downsample=1.0, preserve_depth=False,
pil_interpolation=InterpolationMode.LANCZOS):
"""
Resizes the input information of a sample
Parameters
----------
sample : Dict
Dictionary with sample values
shape : tuple (H,W)
Output shape
shape_supervision : Tuple
Output supervision shape (H,W)
depth_downsample: Float
Resize ratio for depth maps
preserve_depth : Bool
Preserve depth maps when resizing
pil_interpolation : Int
Interpolation mode
Returns
-------
sample : Dict
Resized sample
"""
# Intrinsics
for key in keys_with(sample, 'intrinsics', without='raw'):
if f'raw_{key}' not in sample.keys():
sample[f'raw_{key}'] = deepcopy(sample[key])
sample[key] = resize_intrinsics(sample[key], list(sample['rgb'].values())[0].size, shape[::-1])
# RGB
for key in keys_with(sample, 'rgb', without='raw'):
sample[key] = resize_pil(sample[key], shape, interpolation=pil_interpolation)
# Mask
for key in keys_with(sample, 'mask', without='raw'):
sample[key] = resize_pil(sample[key], shape, interpolation=InterpolationMode.NEAREST)
# Input depth
for key in keys_with(sample, 'input_depth'):
shape_depth = [int(s * depth_downsample) for s in shape]
resize_npy_depth = resize_npy_preserve if preserve_depth else resize_npy
sample[key] = resize_npy_depth(sample[key], shape_depth)
return sample
@iterate1
def resize_sample_supervision(sample, shape, depth_downsample=1.0, preserve_depth=False):
"""
Resizes the output information of a sample
Parameters
----------
sample : Dict
Dictionary with sample values
shape : Tuple
Output shape (H,W)
depth_downsample: Float
Resize ratio for depth maps
preserve_depth : Bool
Preserve depth maps when resizing
Returns
-------
sample : Dict
Resized sample
"""
# Depth
for key in keys_with(sample, 'depth', without='input_depth'):
shape_depth = [int(s * depth_downsample) for s in shape]
resize_npy_depth = resize_npy_preserve if preserve_depth else resize_npy
sample[key] = resize_npy_depth(sample[key], shape_depth)
# Semantic
for key in keys_with(sample, 'semantic'):
sample[key] = resize_npy(sample[key], shape, expand=False)
# Optical flow
for key in keys_with(sample, 'optical_flow'):
sample[key] = resize_npy_multiply(sample[key], shape)
# Scene flow
for key in keys_with(sample, 'scene_flow'):
sample[key] = resize_npy(sample[key], shape, expand=False)
# Return resized sample
return sample
def resize_sample(sample, shape, shape_supervision=None, depth_downsample=1.0, preserve_depth=False,
pil_interpolation=InterpolationMode.LANCZOS):
"""
Resizes a sample, including image, intrinsics and depth maps.
Parameters
----------
sample : Dict
Dictionary with sample values
shape : Tuple
Output shape (H,W)
shape_supervision : Tuple
Output shape (H,W)
depth_downsample: Float
Resize ratio for depth maps
preserve_depth : Bool
Preserve depth maps when resizing
pil_interpolation : Int
Interpolation mode
Returns
-------
sample : Dict
Resized sample
"""
# Resize input information
sample = resize_sample_input(sample, shape,
depth_downsample=depth_downsample,
preserve_depth=preserve_depth,
pil_interpolation=pil_interpolation)
# Resize output information
sample = resize_sample_supervision(sample, shape_supervision,
depth_downsample=depth_downsample,
preserve_depth=preserve_depth)
# Return resized sample
return sample
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