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ownutilities.py
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ownutilities.py
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import json
import warnings
import torch
import torch.nn.functional as F
import numpy as np
import matplotlib.pyplot as plt
from argparse import Namespace
import os
import sys
#required to prevent ModuleNotFoundError for 'flow_plot'. The flow_library is a submodule, which imports its own functions and can therefore not be imported with flow_library.flow_plot
sys.path.append("flow_library")
from PIL import Image
from torch.utils.data import DataLoader, Subset
from helper_functions import datasets
from helper_functions.config_specs import Paths, Conf
class InputPadder:
"""Pads images such that dimensions are divisible by divisor
This method is taken from https://github.com/princeton-vl/RAFT/blob/master/core/utils/utils.py
"""
def __init__(self, dims, divisor=8, mode='sintel'):
self.ht, self.wd = dims[-2:]
pad_ht = (((self.ht // divisor) + 1) * divisor - self.ht) % divisor
pad_wd = (((self.wd // divisor) + 1) * divisor - self.wd) % divisor
if mode == 'sintel':
self._pad = [pad_wd//2, pad_wd - pad_wd//2, pad_ht//2, pad_ht - pad_ht//2]
else:
self._pad = [pad_wd//2, pad_wd - pad_wd//2, 0, pad_ht]
def pad(self, *inputs):
"""Pad a batch of input images such that the image size is divisible by the factor specified as divisor
Returns:
list: padded input images
"""
return [F.pad(x, self._pad, mode='replicate') for x in inputs]
def get_dimensions(self):
"""get the original spatial dimension of the image
Returns:
int: original image height and width
"""
return self.ht, self.wd
def unpad(self,x):
"""undo the padding and restore original spatial dimension
Args:
x (tensor): a tensor with padded dimensions
Returns:
tesnor: tensor with removed padding (i.e. original spatial dimension)
"""
ht, wd = x.shape[-2:]
c = [self._pad[2], ht-self._pad[3], self._pad[0], wd-self._pad[1]]
return x[..., c[0]:c[1], c[2]:c[3]]
def import_and_load(net='RAFT', make_unit_input=False, variable_change=False, device=torch.device("cpu"), make_scaled_input_model=False, **kwargs):
"""import a model and load pretrained weights for it
Args:
net (str, optional):
the desired network to load. Defaults to 'RAFT'.
make_unit_input (bool, optional):
model will assume input images in range [0,1] and transform to [0,255]. Defaults to False.
variable_change (bool, optional):
apply change of variables (COV). Defaults to False.
device (torch.device, optional):
changes the selected device. Defaults to torch.device("cpu").
make_scaled_input_model (bool, optional):
load a scaled input model which uses make_unit_input and variable_change as specified. Defaults to False.
Raises:
RuntimeWarning: Unknown model type
Returns:
torch.nn.Module: PyTorch optical flow model with loaded weights
"""
if make_unit_input==True or variable_change==True or make_scaled_input_model:
from helper_functions.own_models import ScaledInputModel
model = ScaledInputModel(net, make_unit_input=make_unit_input, variable_change=variable_change, device=device, **kwargs)
print("--> transforming model to 'make_unit_input'=%s, 'variable_change'=%s\n" % (str(make_unit_input), str(variable_change)))
path_weights = model.return_path_weights()
else:
model = None
path_weights = ""
custom_weight_path = kwargs["custom_weight_path"] if "custom_weight_path" in kwargs else ""
try:
if net == 'RAFT':
from models.raft.raft import RAFT
# set the path to the corresponding weights for initializing the model
path_weights = custom_weight_path or 'models/_pretrained_weights/raft-sintel.pth'
# possible adjustements to the config can be made in the file
# found in the same directory as _pretrained_weights
path_config = os.path.join(*path_weights.split(os.path.sep)[:-2], "_config", "raft_config.json")
with open(path_config) as file:
config = json.load(file)
model = torch.nn.DataParallel(RAFT(config))
# load pretrained weights
model.load_state_dict(torch.load(path_weights, map_location=device))
elif net == 'GMA':
from models.gma.network import RAFTGMA
# set the path to the corresponding weights for initializing the model
path_weights = custom_weight_path or 'models/_pretrained_weights/gma-sintel.pth'
# possible adjustements to the config file can be made
# under models/_config/gma_config.json
with open("models/_config/gma_config.json") as file:
config = json.load(file)
# GMA accepts only a Namespace object when initializing
config = Namespace(**config)
model = torch.nn.DataParallel(RAFTGMA(config))
model.load_state_dict(torch.load(path_weights, map_location=device))
elif net == "FlowFormer":
from models.FlowFormer.core.FlowFormer import build_flowformer
from models.FlowFormer.configs.things_eval import get_cfg as get_things_cfg
path_weights = custom_weight_path or 'models/_pretrained_weights/flowformer_weights/sintel.pth'
cfg = get_things_cfg()
model_args = Namespace(model=path_weights, mixed_precision=False, alternate_corr=False)
cfg.update(vars(model_args))
model = torch.nn.DataParallel(build_flowformer(cfg))
model.load_state_dict(torch.load(cfg.model, map_location=torch.device('cpu')))
elif net =='PWCNet':
from models.PWCNet.PWCNet import PWCDCNet
# set path to pretrained weights:
path_weights = custom_weight_path or 'models/_pretrained_weights/pwc_net_chairs.pth.tar'
with warnings.catch_warnings():
# this will catch the deprecated warning for spynet and pwcnet to avoid messy console
warnings.simplefilter("ignore", UserWarning)
model = PWCDCNet()
weights = torch.load(path_weights, map_location=device)
if 'state_dict' in weights.keys():
model.load_state_dict(weights['state_dict'])
else:
model.load_state_dict(weights)
model.to(device)
elif net =='SpyNet':
from models.SpyNet.SpyNet import Network as SpyNet
# weights for SpyNet are loaded during initialization
model = SpyNet(nlevels=6, pretrained=True)
model.to(device)
elif net[:8] == "FlowNet2":
# hard coding configuration for FlowNet2
args_fn = Namespace(fp16=False, rgb_max=255.0)
if net == "FlowNet2":
from models.FlowNet.FlowNet2 import FlowNet2
# set path to pretrained weights
path_weights = custom_weight_path or 'models/_pretrained_weights/FlowNet2_checkpoint.pth.tar'
model = FlowNet2(args_fn, div_flow=20, batchNorm=False)
elif net == "FlowNet2S":
from models.FlowNet.FlowNet2S import FlowNet2S
# set path to pretrained weights
path_weights = custom_weight_path or 'models/_pretrained_weights/FlowNet2-S_checkpoint.pth.tar'
model = FlowNet2S(args_fn, div_flow=20, batchNorm=False)
elif net == "FlowNet2C":
from models.FlowNet.FlowNet2C import FlowNet2C
# set path to pretrained weights
path_weights = custom_weight_path or 'models/_pretrained_weights/FlowNet2-C_checkpoint.pth.tar'
model = FlowNet2C(args_fn, div_flow=20, batchNorm=False)
else:
raise ValueError("Unknown FlowNet2 type: %s" % (net))
weights = torch.load(path_weights, map_location=device)
model.load_state_dict(weights['state_dict'])
model.to(device)
elif net == "FlowNetCRobust":
from models.FlowNetCRobust.FlowNetC_flexible_larger_field import FlowNetC_flexible_larger_field
# initialize model and load pretrained weights
path_weights = custom_weight_path or 'models/_pretrained_weights/RobustFlowNetC.pth'
model = FlowNetC_flexible_larger_field(kernel_size=3, number_of_reps=3)
weights = torch.load(path_weights, map_location=device)
model.load_state_dict(weights)
model.to(device)
elif net[:7] == "FlowNet":
# hard coding configuration for FlowNet
args_fn = Namespace(fp16=False, rgb_max=255.0)
if net == "FlowNetC":
# set path to pretrained weights
path_weights = custom_weight_path or 'models/_pretrained_weights/FlowNet2-C_checkpoint.pth.tar'
print("Loading FlowNetC weights from: %s" % (path_weights) )
from models.FlowNetC.FlowNetC import FlowNetC
model = FlowNetC(div_flow=20, batchNorm=False)
# from models.FlowNet.FlowNetC import FlowNetC
# model = FlowNetC(args_fn,div_flow=20, batchNorm=False)
else:
raise ValueError("Unknown FlowNet type: %s" % (net))
weights = torch.load(path_weights, map_location=device)
model.load_state_dict(weights['state_dict'])
model.to(device)
if model is None:
raise RuntimeWarning('The network %s is not a valid model option for import_and_load(network). No model was loaded. Use "RAFT", "GMA", "FlowNetC", "PWCNet" or "SpyNet" instead.' % (net))
except FileNotFoundError as e:
print("\nLoading the model failed, because the checkpoint path was invalid. Are the checkpoints placed in models/_pretrained_weights/? If this folder is empty, consider to execute the checkpoint loading script from scripts/load_all_weights.sh. The full error that caused the loading failure is below:\n\n%s" % e)
exit()
print("--> flow network is set to %s" % net)
return model, path_weights
def prepare_dataloader(mode='training', dataset='Sintel', shuffle=False, batch_size=1, small_run=False, sintel_subsplit=False, dstype='clean', num_repeats=1):
"""Get a PyTorch dataloader for the specified dataset
Args:
mode (str, optional):
Specify the split of the dataset [training | evaluation]. Defaults to 'training'.
dataset (str, optional):
Specify the dataset used [Sintel | Kitti15]. Defaults to 'Sintel'.
shuffle (bool, optional):
Use random sampling. Defaults to False.
batch_size (int, optional):
Defaults to 1.
small_run (bool, optional):
For debugging: Will load only 32 images. Defaults to False.
dstype (str, optional):
Specific for Sintel dataset. Dataset type [clean | final] . Defaults to 'clean'.
Raises:
ValueError: Unknown mode.
ValueError: Unkown dataset.
Returns:
torch.utils.data.DataLoader: Dataloader which can be used for FGSM.
"""
if dataset == 'Sintel':
if not sintel_subsplit:
if mode == 'training':
dataset = datasets.MpiSintel(split=Paths.splits("sintel_train"),
root=Paths.config("sintel_mpi"), dstype=dstype, has_gt=True)
elif mode == 'evaluation':
dataset = datasets.MpiSintel(split=Paths.splits("sintel_eval"),
root=Paths.config("sintel_mpi"), dstype=dstype, has_gt=True) # gt from validation split
else:
raise ValueError(f'The specified mode: {mode} is unknown.')
else:
raise ValueError(f'The specified mode: {mode} is unknown.')
elif dataset == 'SintelSplitZhao':
# Sintel-train and validation split from S. Zhao et al. "MaskFlownet: Asymmetric feature matching with learnable occlusion mask" (CVPR 2020)
if mode == 'training':
dataset = datasets.MpiSintel(split=Paths.splits("sintel_train"), root=Paths.config("sintel_mpi"), dstype=dstype, has_gt=True,
scenes=["alley_1","alley_2","ambush_4","ambush_5","ambush_7","bamboo_1","bandage_1","bandage_2","cave_2","market_2","market_5","mountain_1","shaman_2","shaman_3","sleeping_1","sleeping_2","temple_3"])
elif mode == 'evaluation':
dataset = datasets.MpiSintel(split=Paths.splits("sintel_train"), root=Paths.config("sintel_mpi"), dstype=dstype, has_gt=True,
scenes=["ambush_2","ambush_6","bamboo_2","cave_4","market_6","temple_2"])
else:
raise ValueError(f'The specified mode: {mode} is unknown.')
elif dataset == 'SintelSplitYang':
# Sintel-train and validation split (in-distribution) from G. Yang et al. "High-resolution optical flow from 1D attention and correlation" (ICCV 2021)
if mode == 'training':
dataset = datasets.MpiSintel(split=Paths.splits("sintel_train"), root=Paths.config("sintel_mpi"), dstype=dstype, has_gt=True,
scenes=["alley_1","alley_2","ambush_4","ambush_5","ambush_6","ambush_7","bamboo_1","bandage_1","bandage_2","cave_4","market_5","market_6","mountain_1","shaman_3","sleeping_1","sleeping_2","temple_3"])
elif mode == 'evaluation':
dataset = datasets.MpiSintel(split=Paths.splits("sintel_train"), root=Paths.config("sintel_mpi"), dstype=dstype, has_gt=True,
scenes=["ambush_2", "bamboo_2", "cave_2", "market_2", "shaman_2", "temple_2"], every_nth_img=3)
else:
raise ValueError(f'The specified mode: {mode} is unknown.')
elif dataset == 'Kitti15':
# The KITTI15 dataset from M. Menze et al. "Object scene flow for autonomous vehicles" (CVPR 2015)
if mode == 'training':
dataset = datasets.KITTI(split=Paths.splits("kitti_train"), aug_params=None, root=Paths.config("kitti15"), has_gt=True)
elif mode == 'evaluation':
dataset = datasets.KITTI(split=Paths.splits("kitti_eval"), aug_params=None, root=Paths.config("kitti15"), has_gt=False)
else:
raise ValueError(f'The specified mode: {mode} is unknown.')
elif dataset == "KittiRaw":
if mode == 'training':
dataset = datasets.KITTIRaw(split='training',root=Paths.config("kitti_raw"), has_gt=False)
else:
raise ValueError("'testing' not yet implementet for KittiRaw")
elif dataset == "Spring":
# The Spring dataset from L. Mehl et al. "Spring: A high-resolution high-detail dataset and benchmark for scene flow, optical flow and stereo" (CVPR 2023)
if mode == 'training':
dataset = datasets.Spring(split=Paths.splits("spring_train"), root=Paths.config("spring"), has_gt=True, fwd_only=False)
elif mode == 'evaluation':
dataset = datasets.Spring(split=Paths.splits("spring_eval"), root=Paths.config("spring"), has_gt=False, fwd_only=False)
else:
raise ValueError(f'The specified mode: {mode} is unknown.')
elif dataset == "SpringSplitScheurer":
# Spring-train and validation split from E. Scheurer et al. "Detection defenses: An empty promise against adversarial patch attacks on optical flow " (arXiv 2023)
if mode == 'training':
dataset = datasets.Spring(split=Paths.splits("spring_train"), root=Paths.config("spring"), has_gt=True,
scenes=["0001", "0004", "0005", "0006", "0007", "0008", "0009", "0011", "0012", "0013", "0014", "0015", "0016", "0017", "0020", "0021", "0022", "0023", "0024", "0025", "0027", "0030", "0033", "0036", "0037", "0038", "0039", "0041", "0043", "0044", "0047"],
fwd_only=True, camera=["left"], half_dimensions=True)
elif mode == 'evaluation':
dataset = datasets.Spring(split=Paths.splits("spring_train"), root=Paths.config("spring"), has_gt=True,
scenes=["0002","0010","0018","0026","0032","0045"],
fwd_only=True, camera=["left"], half_dimensions=True)
else:
raise ValueError(f'The specified mode: {mode} is unknown.')
elif dataset == "HD1KSplitScheurer":
# HD1K-train and validation split from E. Scheurer et al. "Detection defenses: An empty promise against adversarial patch attacks on optical flow " (arXiv 2023)
if mode == 'training':
scenes_tr = ['000000','000001','000002','000003','000004','000005','000006','000007','000008','000010','000011','000012','000014','000015','000016','000017','000020','000021','000022','000023','000024','000025','000026','000027','000028','000029','000030','000031','000033','000034','000035']
dataset = datasets.HD1K(root=Paths.config("hd1k"), has_gt=True, scenes=scenes_tr, half_dimensions=True)
elif mode == 'evaluation':
scenes_val = ["000009", "000013", "000018", "000019", "000032"]
dataset = datasets.HD1K(root=Paths.config("hd1k"), has_gt=True, scenes=scenes_val, half_dimensions=True)
else:
raise ValueError(f'The specified mode: {mode} is unknown.')
elif dataset == "DrivingSample":
# Driving sample from E. Scheurer et al. "Detection defenses: An empty promise against adversarial patch attacks on optical flow " (arXiv 2023)
if mode == 'training':
dataset = datasets.Driving(root=Paths.config("driving"), has_gt=True,
dstype=[dstype], focallength=["15mm"], drivingcamview=["forward"], direction=["forward"], speed=["fast"], camera=["left"])
elif mode == 'evaluation':
dataset = datasets.Driving(root=Paths.config("driving"), has_gt=True,
dstype=[dstype], focallength=["15mm"], drivingcamview=["forward"], direction=["forward"], speed=["fast"], camera=["left"])
else:
raise ValueError(f'The specified mode: {mode} is unknown.')
else:
raise ValueError("Unknown dataset %s, use either 'Sintel', 'Kitti15', 'Spring', 'SintelSplitZhao', 'SpringSplitScheurer', 'HD1KSplitScheurer' or 'DrivingSample'." %(dataset))
# if e.g. the evaluation dataset does not provide a ground truth this is specified
ds_has_gt = dataset.has_groundtruth()
if small_run:
reduced_num_samples = 32
rand_indices = np.random.randint(0, len(dataset), reduced_num_samples)
indices = np.arange(0, reduced_num_samples)
dataset = Subset(dataset, indices)
if num_repeats > 1:
dataset = datasets.RepetitiveDataset(dataset, num_repeats=num_repeats)
return DataLoader(dataset, batch_size=batch_size, shuffle=shuffle), ds_has_gt
def preprocess_img(network, *images):
"""Manipulate input images, such that the specified network is able to handle them
Args:
network (str):
Specify the network to which the input images are adapted
Returns:
InputPadder, *tensor:
returns the Padder object used to adapt the image dimensions as well as the transformed images
"""
if network in ['RAFT', 'GMA', 'FlowFormer']:
padder = InputPadder(images[0].shape)
output = padder.pad(*images)
elif network == 'PWCNet':
images = [(img / 255.) for img in images]
padder = InputPadder(images[0].shape, divisor=64)
output = padder.pad(*images)
elif network == 'SpyNet':
# normalize images to [0, 1]
images = [ img / 255. for img in images ]
# make image divisibile by 64
padder = InputPadder(images[0].shape, divisor=64)
output = padder.pad(*images)
elif network[:7] == 'FlowNet':
# normalization only for FlowNet, not FlowNet2
if not network[:8] == 'FlowNet2':
images = [ img / 255. for img in images ]
# make image divisibile by 64
padder = InputPadder(images[0].shape, divisor=64)
output = padder.pad(*images)
else:
padder = None
output = images
return padder, output
def postprocess_flow(network, padder, *flows):
"""Manipulate the output flow by removing the padding
Args:
network (str): name of the network used to create the flow
padder (InputPadder): instance of InputPadder class used during preprocessing
flows (*tensor): (batch) of flow fields
Returns:
*tensor: output with removed padding
"""
if padder != None:
# remove padding
return [padder.unpad(flow) for flow in flows]
else:
return flows
def compute_flow(model, network, x1, x2, test_mode=True, **kwargs):
"""subroutine to call the forward pass of the network
Args:
model (torch.nn.module):
instance of optical flow model
network (str):
name of the network. [scaled_input_model | RAFT | GMA | FlowNet2 | SpyNet | PWCNet]
x1 (tensor):
first image of a frame sequence
x2 (tensor):
second image of a frame sequence
test_mode (bool, optional):
applies only to RAFT and GMA such that the forward call yields only the final flow field. Defaults to True.
Returns:
tensor: optical flow field
"""
if network == "scaled_input_model":
flow = model(x1,x2, test_mode=True, **kwargs)
elif network == 'RAFT':
_, flow = model(x1, x2, test_mode=test_mode, **kwargs)
elif network == 'GMA':
_, flow = model(x1, x2, iters=6, test_mode=test_mode, **kwargs)
elif network == 'FlowFormer':
flow = model(x1, x2)[0]
elif network == 'FlowNetCRobust':
flow = model(x1, x2)
elif network[:7] == 'FlowNet':
# all flow net types need image tensor of dimensions [batch, colors, image12, x, y] = [b,3,2,x,y]
# FlowNet2-variants: all fine now, input [0,255] is taken.
flow = model(x1,x2) # for FlowNetC/FlowNetC.py
# x = torch.stack((x1, x2), dim=-3)
# flow = model(x) # for FlowNet/FlowNetC.py
elif network in ['PWCNet', 'SpyNet']:
with warnings.catch_warnings():
# this will catch the deprecated warning for spynet and pwcnet to avoid messy console
warnings.filterwarnings("ignore", message="nn.functional.upsample is deprecated. Use nn.functional.interpolate instead.")
warnings.filterwarnings("ignore", message="Default upsampling behavior when mode={} is changed")
warnings.simplefilter("ignore", UserWarning)
flow = model(x1,x2, **kwargs)
else:
flow = model(x1,x2, **kwargs)
return flow
def model_takes_unit_input(model):
"""Boolean check if a network needs input in range [0,1] or [0,255]
Args:
model (str):
name of the model
Returns:
bool: True -> [0,1], False -> [0,255]
"""
if model in ["PWCNet", "SpyNet", "FlowNetCRobust"] or (model[:7] == 'FlowNet' and not model[:8]=='FlowNet2'):
return True
return False
def flow_length(flow):
"""Calculates the length of the flow vectors of a flow field
Args:
flow (tensor):
flow field tensor of dimensions (b,2,H,W) or (2,H,W)
Returns:
torch.float: length of the flow vectors f_ij, computed as sqrt(u_ij^2 + v_ij^2) in a tensor of (b,1,H,W) or (1,H,W)
"""
flow_pow = torch.pow(flow,2)
flow_norm_pow = torch.sum(flow_pow, -3, keepdim=True)
return torch.sqrt(flow_norm_pow)
def maximum_flow(flow):
"""Calculates the length of the longest flow vector of a flow field
Args:
flow (tensor):
a flow field tensor of dimensions (b,2,H,W) or (2,H,W)
Returns:
float: length of the longest flow vector f_ij, computed as sqrt(u_ij^2 + v_ij^2)
"""
return torch.max(flow_length(flow)).cpu().detach().numpy()
def quickvis_tensor(t, filename):
"""Saves a tensor with three dimensions as image to a specified file location.
Args:
t (tensor):
3-dimensional tensor, following the dimension order (c,H,W)
filename (str):
name for the image to save, including path and file extension
"""
# check if filename already contains .png extension
if not filename.endswith('.png'):
filename += '.png'
valid = False
if len(t.size())==3:
img = t.detach().cpu().numpy()
valid = True
elif len(t.size())==4 and t.size()[0] == 1:
img = t[0,:,:,:].detach().cpu().numpy()
valid = True
else:
print("Encountered invalid tensor dimensions %s, abort printing." %str(t.size()))
if valid:
img = np.rollaxis(img, 0, 3)
data = img.astype(np.uint8)
data = Image.fromarray(data)
data.save(filename)
def quickvisualization_tensor(t, filename, min=0., max=255.):
"""Saves a batch (>= 1) of image tensors with three dimensions as images to a specified file location.
Also rescales the color values according to the specified range of the color scale.
Args:
t (tensor):
batch of 3-dimensional tensor, following the dimension order (b,c,H,W)
filename (str):
name for the image to save, including path and file extension. Batches will append a number at the end of the filename.
min (float, optional):
minimum value of the color scale used by tensor. Defaults to 0.
max (float, optional):
maximum value of the color scale used by tensor Defaults to 255.
"""
# rescale to [0,255]
t = (t.detach().clone() - min) / (max - min) * 255.
if len(t.size())==3 or (len(t.size())==4 and t.size()[0] == 1):
quickvis_tensor(t, filename)
elif len(t.size())==4:
for i in range(t.size()[0]):
if i == 0:
quickvis_tensor(t[i,:,:,:], filename)
else:
quickvis_tensor(t[i,:,:,:], filename+"_"+str(i))
else:
print("Encountered unprocessable tensor dimensions %s, abort printing." %str(t.size()))
def quickvis_flow(flow, filename, auto_scale=True, max_scale=-1):
"""Saves a flow field tensor with two dimensions as image to a specified file location.
Args:
flow (tensor):
2-dimensional tensor (c=2), following the dimension order (c,H,W) or (1,c,H,W)
filename (str):
name for the image to save, including path and file extension.
auto_scale (bool, optional):
automatically scale color values. Defaults to True.
max_scale (int, optional):
if auto_scale is false, scale flow by this value. Defaults to -1.
"""
from flow_plot import colorplot_light
# check if filename already contains .png extension
if not filename.endswith('.png'):
filename += '.png'
valid = False
if len(flow.size())==3:
flow_img = flow.clone().detach().cpu().numpy()
valid = True
elif len(flow.size())==4 and flow.size()[0] == 1:
flow_img = flow[0,:,:,:].clone().detach().cpu().numpy()
valid = True
else:
print("Encountered invalid tensor dimensions %s, abort printing." %str(flow.size()))
if valid:
# make directory and ignore if it exists
if not os.path.dirname(filename) == "":
os.makedirs(os.path.dirname(filename), exist_ok=True)
# write flow
flow_img = np.rollaxis(flow_img, 0, 3)
data = colorplot_light(flow_img, auto_scale=auto_scale, max_scale=max_scale, return_max=False)
data = data.astype(np.uint8)
data = Image.fromarray(data)
data.save(filename)
def quickvisualization_flow(flow, filename, auto_scale=True, max_scale=-1):
"""Saves a batch (>= 1) of 2-dimensional flow field tensors as images to a specified file location.
Args:
flow (tensor):
single or batch of 2-dimensional flow tensors, following the dimension order (c,H,W) or (b,c,H,W)
filename (str):
name for the image to save, including path and file extension.
auto_scale (bool, optional):
automatically scale color values. Defaults to True.
max_scale (int, optional):
if auto_scale is false, scale flow by this value. Defaults to -1.
"""
if len(flow.size())==3 or (len(flow.size())==4 and flow.size()[0] == 1):
quickvis_flow(flow, filename, auto_scale=auto_scale, max_scale=max_scale)
elif len(flow.size())==4:
for i in range(flow.size()[0]):
if i == 0:
quickvis_flow(flow[i,:,:,:], filename, auto_scale=auto_scale, max_scale=max_scale)
else:
quickvis_flow(flow[i,:,:,:], filename+"_"+str(i), auto_scale=auto_scale, max_scale=max_scale)
else:
print("Encountered unprocessable tensor dimensions %s, abort printing." %str(flow.size()))
def show_images(*imgs, names=None, colorbars=False, wait=False, show=False, save=False, path='./plot.png', dpi=300):
"""plots images in a row in a single window. This function is only used for debugging purposes.
Args:
*imgs (tuple): Will be called with ax.imshow(imgs[i])
colorbars (bool, optional): If True, all images will have a colorbar. Defaults to False.
wait (bool, optional): If plot is not blocking will wait for buttonpress if True. Defaults to False.
show (bool, optional): If True, the plot will block until window closed. Defaults to False.
"""
imgs=list(imgs)
# convert torch to numpy and squeeze batch dimension
for i in range(len(imgs)):
if isinstance(imgs[i], torch.Tensor):
imgs[i] = imgs[i].detach().cpu().numpy().squeeze()
if isinstance(imgs[i], np.ndarray):
imgs[i] = imgs[i].squeeze()
# if first dim is 2, assume it is a flow field
for i in range(len(imgs)):
if imgs[i].shape[0]==2:
from flow_plot import colorplot_light
imgs[i] = colorplot_light(imgs[i].transpose(1,2,0), return_max=False)
elif imgs[i].shape[-1]==2:
from flow_plot import colorplot_light
imgs[i] = colorplot_light(imgs[i], return_max=False)
if imgs[i].shape[0]==3:
imgs[i] = imgs[i].transpose(1,2,0)
n = len(imgs)
fig, axes = plt.subplots(1, n, constrained_layout=True)
if n == 1:
axes = [axes]
for i, ax in enumerate(axes):
if imgs[i].shape[-1] == 3:
p = ax.imshow(imgs[i])
else:
p = ax.imshow(imgs[i], cmap='gray')
ax.axis('off')
if names is not None:
ax.set_title(names[i], fontsize='small', fontname='serif')
if colorbars:
plt.colorbar(p, ax=ax)
if save:
# if one image save with plt.imsave
if n == 1:
if imgs[0].shape[-1] == 3:
plt.imsave(path, imgs[0])
else:
plt.imsave(path, imgs[0], cmap='gray')
return
plt.savefig(path, bbox_inches='tight', dpi=dpi)
if show:
plt.show()
elif wait:
plt.show(block=False)
plt.pause(.1)
plt.waitforbuttonpress()
def valid(X,i,j):
""" check if the pixel is in the image """
if i<0 or j<0 or i>=X.shape[-2] or j>=X.shape[-1]:
return False
return True
def B_d(I,i,j,e=5):
""" return the list of valid pixels in the ball of radius e centered in (i,j) """
res = []
for di in range(-e,e+1):
for dj in range(-e,e+1):
if valid(I,i+di,j+dj) and di**2 + dj**2 <= e**2 and (di!=0 or dj!=0):
res.append((i+di,j+dj))
return res