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import configparser
import copy
import cv2
import numpy as np
import torch
from jde.utils.datasets import letterbox
from torchvision import transforms
__all__ = ["YOLOXCustomMapper", "JDECustomMapper", "LinearMapper"]
def yolox_style_scaling(img, input_size, padding=False):
r = min(input_size[0] / img.shape[0], input_size[1] / img.shape[1])
resized_img = cv2.resize(
img,
(int(img.shape[1] * r), int(img.shape[0] * r)),
interpolation=cv2.INTER_LINEAR,
).astype(np.uint8)
if padding:
padded_img = np.ones((input_size[0], input_size[1], 3), dtype=np.uint8) * 114
padded_img[: int(img.shape[0] * r), : int(img.shape[1] * r)] = resized_img
return padded_img
return resized_img
class YOLOXCustomMapper:
"""
A callable which takes a dataset dict in CompressAI-Vision generic dataset format, but for YOLOX evaluation,
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
preproc function at
<https://github.com/Megvii-BaseDetection/YOLOX/yolox/data/data_augment.py>
Full license statement can be found at
<https://github.com/Megvii-BaseDetection/YOLOX?tab=Apache-2.0-1-ov-file#readme>
"""
def __init__(self, img_size=[640, 640], aug_transforms=None):
"""
Args:
img_size: expected input size (Height, Width)
"""
self.input_img_size = img_size
if aug_transforms != None:
self.aug_transforms = aug_transforms
else:
self.aug_transforms = transforms.Compose([transforms.ToTensor()])
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)
# replicate the implemetation of the original codes
# Read image
org_img = cv2.imread(dataset_dict["file_name"]) # return img in BGR by default
assert (
len(org_img.shape) == 3
), f"detect an input image with 2 chs, {dataset_dict['file_name']}"
dataset_dict["height"], dataset_dict["width"], _ = org_img.shape
# yolox style input scaling
# 1st scaling
resized_img = yolox_style_scaling(org_img, self.input_img_size)
# 2nd scaling & padding
resized_img = yolox_style_scaling(
resized_img, self.input_img_size, padding=True
)
tensor_image = self.aug_transforms(
np.ascontiguousarray(resized_img, dtype=np.float32)
)
# old way
# kept BGR & swap axis
# image = resized_img.transpose(2, 0, 1)
# normalize contiguous array of image
# image = np.ascontiguousarray(image, dtype=np.float32)
# to tensor
# tensor_image = torch.as_tensor(image)
dataset_dict["image"] = tensor_image
return dataset_dict
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)
