# Copyright (c) 2022-2024, InterDigital Communications, Inc
# All rights reserved.
# Redistribution and use in source and binary forms, with or without
# modification, are permitted (subject to the limitations in the disclaimer
# below) provided that the following conditions are met:
# * Redistributions of source code must retain the above copyright notice,
# this list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
# * Neither the name of InterDigital Communications, Inc nor the names of its
# contributors may be used to endorse or promote products derived from this
# software without specific prior written permission.
# NO EXPRESS OR IMPLIED LICENSES TO ANY PARTY'S PATENT RIGHTS ARE GRANTED BY
# THIS LICENSE. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND
# CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT
# NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A
# PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR
# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
# OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
# WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR
# OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF
# ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import configparser
import copy
import cv2
import numpy as np
import torch
from jde.utils.datasets import letterbox
__all__ = ["JDECustomMapper", "LinearMapper"]
class JDECustomMapper:
"""
A callable which takes a dataset dict in CompressAI-Vision generic dataset format, but for JDE Tracking,
and map it into a format used by the model.
This is the default callable to be used to map your dataset dict into inference data.
This callable function refers to
LoadImages function in Towards-Realtime-MOT/utils/datasets.py at
<https://github.com/Zhongdao/Towards-Realtime-MOT/blob/master/utils/datasets.py>
Full license statement can be found at
<https://github.com/Zhongdao/Towards-Realtime-MOT/blob/master/LICENSE>
"""
def __init__(self, img_size=[608, 1088]):
"""
Args:
img_size: expected input size (Height, Width)
"""
self.height, self.width = img_size
def __call__(self, dataset_dict):
"""
Args:
dataset_dict (dict): Metadata of one image.
Returns:
dict: a format that compressai-vision pipelines accept
"""
dataset_dict = copy.deepcopy(dataset_dict)
# the copied dictionary will be modified by code below
dataset_dict.pop("annotations", None)
# Read image
org_img = cv2.imread(dataset_dict["file_name"]) # return img in BGR by default
dataset_dict["height"], dataset_dict["width"], _ = org_img.shape
# Padded resize
image, _, _, _ = letterbox(org_img, height=self.height, width=self.width)
# convert to RGB & swap axis
image = image[:, :, ::-1].transpose(2, 0, 1)
# normalize contiguous array of image
image = np.ascontiguousarray(image, dtype=np.float32) / 255.0
# to tensor
dataset_dict["image"] = torch.as_tensor(image)
return dataset_dict
class LinearMapper:
"""
A callable which takes a dataset dict in CompressAI-Vision generic dataset format, but for JDE Tracking,
and map it into a format used by the model.
This is the default callable to be used to map your dataset dict into inference data.
This callable function refers to
LoadImages function in Towards-Realtime-MOT/utils/datasets.py at
<https://github.com/Zhongdao/Towards-Realtime-MOT/blob/master/utils/datasets.py>
Full license statement can be found at
<https://github.com/Zhongdao/Towards-Realtime-MOT/blob/master/LICENSE>
"""
def __init__(self, bgr=False):
"""
Args:
img_size: expected input size (Height, Width)
"""
self.bgr_output = bgr
def __call__(self, dataset_dict):
"""
Args:
dataset_dict (dict): Metadata of one image.
Returns:
dict: a format that compressai-vision pipelines accept
"""
dataset_dict = copy.deepcopy(dataset_dict)
# the copied dictionary will be modified by code below
dataset_dict.pop("annotations", None)
# Read image
org_img = cv2.imread(dataset_dict["file_name"]) # return img in BGR by default
dataset_dict["height"], dataset_dict["width"], _ = org_img.shape
# convert to RGB & swap axis
if self.bgr_output:
image = org_img.transpose(2, 0, 1)
else:
image = org_img[:, :, ::-1].transpose(2, 0, 1)
# normalize contiguous array of image
image = np.ascontiguousarray(image, dtype=np.float32) / 255.0
# to tensor
dataset_dict["image"] = torch.as_tensor(image)
return dataset_dict
[docs]def get_seq_info(seq_info_path):
config = configparser.ConfigParser()
config.read(seq_info_path)
fps = config["Sequence"]["frameRate"]
total_frame = config["Sequence"]["seqLength"]
name = f'{config["Sequence"]["name"]}_{config["Sequence"]["imWidth"]}x{config["Sequence"]["imHeight"]}_{fps}'
return name, int(fps), int(total_frame)
