V1.0
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import torch
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import numpy as np
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from models.experimental import attempt_load
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from utils.general import non_max_suppression, scale_coords
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from utils.BaseDetector import baseDet
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from utils.torch_utils import select_device
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from utils.datasets import letterbox
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class Detector(baseDet):
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def __init__(self):
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super(Detector, self).__init__()
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self.init_model()
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self.build_config()
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def init_model(self):
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self.weights = 'weights/highway_sign20231016.pt'
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# self.weights = 'weights/smogfire_20221225.pt'
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self.device = '0' if torch.cuda.is_available() else 'cpu'
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self.device = select_device(self.device)
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model = attempt_load(self.weights, map_location=self.device)
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model.to(self.device).eval()
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model.half()
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# torch.save(model, 'test.pt')
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self.m = model
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self.names = model.module.names if hasattr(
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model, 'module') else model.names
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def preprocess(self, img):
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img0 = img.copy()
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img = letterbox(img, new_shape=self.img_size)[0]
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img = img[:, :, ::-1].transpose(2, 0, 1)
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img = np.ascontiguousarray(img)
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img = torch.from_numpy(img).to(self.device)
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img = img.half() # 半精度
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img /= 255.0 # 图像归一化
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if img.ndimension() == 3:
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img = img.unsqueeze(0)
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return img0, img
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def detect(self, im):
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im0, img = self.preprocess(im)
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pred = self.m(img, augment=False)[0]
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pred = pred.float()
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pred = non_max_suppression(pred, self.threshold, 0.4)
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pred_boxes = []
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for det in pred:
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if det is not None and len(det):
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det[:, :4] = scale_coords(
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img.shape[2:], det[:, :4], im0.shape).round()
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for *x, conf, cls_id in det:
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lbl = self.names[int(cls_id)]
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#if not lbl in ['highway_sign','other_sign']:
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if not lbl in ['other_sign']:
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#if not lbl in ['highway_sign']:
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#if not lbl in [ 'guardrail','highway_sign','other_sign','zhuitong','shuima']:
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#if not lbl in ['person', 'car', 'truck']:
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# if not lbl in ['smog', 'fire']:
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continue
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x1, y1 = int(x[0]), int(x[1])
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x2, y2 = int(x[2]), int(x[3])
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pred_boxes.append(
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(x1, y1, x2, y2, lbl, conf))
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return im, pred_boxes
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@ -0,0 +1,674 @@
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GNU GENERAL PUBLIC LICENSE
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Version 3, 29 June 2007
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Copyright (C) 2007 Free Software Foundation, Inc. <http://fsf.org/>
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Everyone is permitted to copy and distribute verbatim copies
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of this license document, but changing it is not allowed.
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Preamble
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|
The GNU General Public License is a free, copyleft license for
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software and other kinds of works.
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The licenses for most software and other practical works are designed
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to take away your freedom to share and change the works. By contrast,
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the GNU General Public License is intended to guarantee your freedom to
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share and change all versions of a program--to make sure it remains free
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software for all its users. We, the Free Software Foundation, use the
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GNU General Public License for most of our software; it applies also to
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any other work released this way by its authors. You can apply it to
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your programs, too.
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When we speak of free software, we are referring to freedom, not
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have the freedom to distribute copies of free software (and charge for
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want it, that you can change the software or use pieces of it in new
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To protect your rights, we need to prevent others from denying you
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certain responsibilities if you distribute copies of the software, or if
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you modify it: responsibilities to respect the freedom of others.
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For example, if you distribute copies of such a program, whether
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Developers that use the GNU GPL protect your rights with two steps:
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For the developers' and authors' protection, the GPL clearly explains
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Some devices are designed to deny users access to install or run
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stand ready to extend this provision to those domains in future versions
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Finally, every program is threatened constantly by software patents.
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States should not allow patents to restrict development and use of
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The precise terms and conditions for copying, distribution and
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modification follow.
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TERMS AND CONDITIONS
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0. Definitions.
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"This License" refers to version 3 of the GNU General Public License.
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"Copyright" also means copyright-like laws that apply to other kinds of
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works, such as semiconductor masks.
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"The Program" refers to any copyrightable work licensed under this
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License. Each licensee is addressed as "you". "Licensees" and
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To "modify" a work means to copy from or adapt all or part of the work
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earlier work or a work "based on" the earlier work.
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A "covered work" means either the unmodified Program or a work based
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To "propagate" a work means to do anything with it that, without
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permission, would make you directly or secondarily liable for
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infringement under applicable copyright law, except executing it on a
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To "convey" a work means any kind of propagation that enables other
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An interactive user interface displays "Appropriate Legal Notices"
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to the extent that it includes a convenient and prominently visible
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menu, a prominent item in the list meets this criterion.
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1. Source Code.
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The "source code" for a work means the preferred form of the work
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A "Standard Interface" means an interface that either is an official
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is widely used among developers working in that language.
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The "System Libraries" of an executable work include anything, other
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than the work as a whole, that (a) is included in the normal form of
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"Major Component", in this context, means a major essential component
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The Corresponding Source need not include anything that users
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The Corresponding Source for a work in source code form is that
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All rights granted under this License are granted for the term of
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content, constitutes a covered work. This License acknowledges your
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You may make, run and propagate covered works that you do not
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Conveying under any other circumstances is permitted solely under
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No covered work shall be deemed part of an effective technological
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You may convey a covered work in object code form under the terms
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e) Convey the object code using peer-to-peer transmission, provided
|
||||||
|
you inform other peers where the object code and Corresponding
|
||||||
|
Source of the work are being offered to the general public at no
|
||||||
|
charge under subsection 6d.
|
||||||
|
|
||||||
|
A separable portion of the object code, whose source code is excluded
|
||||||
|
from the Corresponding Source as a System Library, need not be
|
||||||
|
included in conveying the object code work.
|
||||||
|
|
||||||
|
A "User Product" is either (1) a "consumer product", which means any
|
||||||
|
tangible personal property which is normally used for personal, family,
|
||||||
|
or household purposes, or (2) anything designed or sold for incorporation
|
||||||
|
into a dwelling. In determining whether a product is a consumer product,
|
||||||
|
doubtful cases shall be resolved in favor of coverage. For a particular
|
||||||
|
product received by a particular user, "normally used" refers to a
|
||||||
|
typical or common use of that class of product, regardless of the status
|
||||||
|
of the particular user or of the way in which the particular user
|
||||||
|
actually uses, or expects or is expected to use, the product. A product
|
||||||
|
is a consumer product regardless of whether the product has substantial
|
||||||
|
commercial, industrial or non-consumer uses, unless such uses represent
|
||||||
|
the only significant mode of use of the product.
|
||||||
|
|
||||||
|
"Installation Information" for a User Product means any methods,
|
||||||
|
procedures, authorization keys, or other information required to install
|
||||||
|
and execute modified versions of a covered work in that User Product from
|
||||||
|
a modified version of its Corresponding Source. The information must
|
||||||
|
suffice to ensure that the continued functioning of the modified object
|
||||||
|
code is in no case prevented or interfered with solely because
|
||||||
|
modification has been made.
|
||||||
|
|
||||||
|
If you convey an object code work under this section in, or with, or
|
||||||
|
specifically for use in, a User Product, and the conveying occurs as
|
||||||
|
part of a transaction in which the right of possession and use of the
|
||||||
|
User Product is transferred to the recipient in perpetuity or for a
|
||||||
|
fixed term (regardless of how the transaction is characterized), the
|
||||||
|
Corresponding Source conveyed under this section must be accompanied
|
||||||
|
by the Installation Information. But this requirement does not apply
|
||||||
|
if neither you nor any third party retains the ability to install
|
||||||
|
modified object code on the User Product (for example, the work has
|
||||||
|
been installed in ROM).
|
||||||
|
|
||||||
|
The requirement to provide Installation Information does not include a
|
||||||
|
requirement to continue to provide support service, warranty, or updates
|
||||||
|
for a work that has been modified or installed by the recipient, or for
|
||||||
|
the User Product in which it has been modified or installed. Access to a
|
||||||
|
network may be denied when the modification itself materially and
|
||||||
|
adversely affects the operation of the network or violates the rules and
|
||||||
|
protocols for communication across the network.
|
||||||
|
|
||||||
|
Corresponding Source conveyed, and Installation Information provided,
|
||||||
|
in accord with this section must be in a format that is publicly
|
||||||
|
documented (and with an implementation available to the public in
|
||||||
|
source code form), and must require no special password or key for
|
||||||
|
unpacking, reading or copying.
|
||||||
|
|
||||||
|
7. Additional Terms.
|
||||||
|
|
||||||
|
"Additional permissions" are terms that supplement the terms of this
|
||||||
|
License by making exceptions from one or more of its conditions.
|
||||||
|
Additional permissions that are applicable to the entire Program shall
|
||||||
|
be treated as though they were included in this License, to the extent
|
||||||
|
that they are valid under applicable law. If additional permissions
|
||||||
|
apply only to part of the Program, that part may be used separately
|
||||||
|
under those permissions, but the entire Program remains governed by
|
||||||
|
this License without regard to the additional permissions.
|
||||||
|
|
||||||
|
When you convey a copy of a covered work, you may at your option
|
||||||
|
remove any additional permissions from that copy, or from any part of
|
||||||
|
it. (Additional permissions may be written to require their own
|
||||||
|
removal in certain cases when you modify the work.) You may place
|
||||||
|
additional permissions on material, added by you to a covered work,
|
||||||
|
for which you have or can give appropriate copyright permission.
|
||||||
|
|
||||||
|
Notwithstanding any other provision of this License, for material you
|
||||||
|
add to a covered work, you may (if authorized by the copyright holders of
|
||||||
|
that material) supplement the terms of this License with terms:
|
||||||
|
|
||||||
|
a) Disclaiming warranty or limiting liability differently from the
|
||||||
|
terms of sections 15 and 16 of this License; or
|
||||||
|
|
||||||
|
b) Requiring preservation of specified reasonable legal notices or
|
||||||
|
author attributions in that material or in the Appropriate Legal
|
||||||
|
Notices displayed by works containing it; or
|
||||||
|
|
||||||
|
c) Prohibiting misrepresentation of the origin of that material, or
|
||||||
|
requiring that modified versions of such material be marked in
|
||||||
|
reasonable ways as different from the original version; or
|
||||||
|
|
||||||
|
d) Limiting the use for publicity purposes of names of licensors or
|
||||||
|
authors of the material; or
|
||||||
|
|
||||||
|
e) Declining to grant rights under trademark law for use of some
|
||||||
|
trade names, trademarks, or service marks; or
|
||||||
|
|
||||||
|
f) Requiring indemnification of licensors and authors of that
|
||||||
|
material by anyone who conveys the material (or modified versions of
|
||||||
|
it) with contractual assumptions of liability to the recipient, for
|
||||||
|
any liability that these contractual assumptions directly impose on
|
||||||
|
those licensors and authors.
|
||||||
|
|
||||||
|
All other non-permissive additional terms are considered "further
|
||||||
|
restrictions" within the meaning of section 10. If the Program as you
|
||||||
|
received it, or any part of it, contains a notice stating that it is
|
||||||
|
governed by this License along with a term that is a further
|
||||||
|
restriction, you may remove that term. If a license document contains
|
||||||
|
a further restriction but permits relicensing or conveying under this
|
||||||
|
License, you may add to a covered work material governed by the terms
|
||||||
|
of that license document, provided that the further restriction does
|
||||||
|
not survive such relicensing or conveying.
|
||||||
|
|
||||||
|
If you add terms to a covered work in accord with this section, you
|
||||||
|
must place, in the relevant source files, a statement of the
|
||||||
|
additional terms that apply to those files, or a notice indicating
|
||||||
|
where to find the applicable terms.
|
||||||
|
|
||||||
|
Additional terms, permissive or non-permissive, may be stated in the
|
||||||
|
form of a separately written license, or stated as exceptions;
|
||||||
|
the above requirements apply either way.
|
||||||
|
|
||||||
|
8. Termination.
|
||||||
|
|
||||||
|
You may not propagate or modify a covered work except as expressly
|
||||||
|
provided under this License. Any attempt otherwise to propagate or
|
||||||
|
modify it is void, and will automatically terminate your rights under
|
||||||
|
this License (including any patent licenses granted under the third
|
||||||
|
paragraph of section 11).
|
||||||
|
|
||||||
|
However, if you cease all violation of this License, then your
|
||||||
|
license from a particular copyright holder is reinstated (a)
|
||||||
|
provisionally, unless and until the copyright holder explicitly and
|
||||||
|
finally terminates your license, and (b) permanently, if the copyright
|
||||||
|
holder fails to notify you of the violation by some reasonable means
|
||||||
|
prior to 60 days after the cessation.
|
||||||
|
|
||||||
|
Moreover, your license from a particular copyright holder is
|
||||||
|
reinstated permanently if the copyright holder notifies you of the
|
||||||
|
violation by some reasonable means, this is the first time you have
|
||||||
|
received notice of violation of this License (for any work) from that
|
||||||
|
copyright holder, and you cure the violation prior to 30 days after
|
||||||
|
your receipt of the notice.
|
||||||
|
|
||||||
|
Termination of your rights under this section does not terminate the
|
||||||
|
licenses of parties who have received copies or rights from you under
|
||||||
|
this License. If your rights have been terminated and not permanently
|
||||||
|
reinstated, you do not qualify to receive new licenses for the same
|
||||||
|
material under section 10.
|
||||||
|
|
||||||
|
9. Acceptance Not Required for Having Copies.
|
||||||
|
|
||||||
|
You are not required to accept this License in order to receive or
|
||||||
|
run a copy of the Program. Ancillary propagation of a covered work
|
||||||
|
occurring solely as a consequence of using peer-to-peer transmission
|
||||||
|
to receive a copy likewise does not require acceptance. However,
|
||||||
|
nothing other than this License grants you permission to propagate or
|
||||||
|
modify any covered work. These actions infringe copyright if you do
|
||||||
|
not accept this License. Therefore, by modifying or propagating a
|
||||||
|
covered work, you indicate your acceptance of this License to do so.
|
||||||
|
|
||||||
|
10. Automatic Licensing of Downstream Recipients.
|
||||||
|
|
||||||
|
Each time you convey a covered work, the recipient automatically
|
||||||
|
receives a license from the original licensors, to run, modify and
|
||||||
|
propagate that work, subject to this License. You are not responsible
|
||||||
|
for enforcing compliance by third parties with this License.
|
||||||
|
|
||||||
|
An "entity transaction" is a transaction transferring control of an
|
||||||
|
organization, or substantially all assets of one, or subdividing an
|
||||||
|
organization, or merging organizations. If propagation of a covered
|
||||||
|
work results from an entity transaction, each party to that
|
||||||
|
transaction who receives a copy of the work also receives whatever
|
||||||
|
licenses to the work the party's predecessor in interest had or could
|
||||||
|
give under the previous paragraph, plus a right to possession of the
|
||||||
|
Corresponding Source of the work from the predecessor in interest, if
|
||||||
|
the predecessor has it or can get it with reasonable efforts.
|
||||||
|
|
||||||
|
You may not impose any further restrictions on the exercise of the
|
||||||
|
rights granted or affirmed under this License. For example, you may
|
||||||
|
not impose a license fee, royalty, or other charge for exercise of
|
||||||
|
rights granted under this License, and you may not initiate litigation
|
||||||
|
(including a cross-claim or counterclaim in a lawsuit) alleging that
|
||||||
|
any patent claim is infringed by making, using, selling, offering for
|
||||||
|
sale, or importing the Program or any portion of it.
|
||||||
|
|
||||||
|
11. Patents.
|
||||||
|
|
||||||
|
A "contributor" is a copyright holder who authorizes use under this
|
||||||
|
License of the Program or a work on which the Program is based. The
|
||||||
|
work thus licensed is called the contributor's "contributor version".
|
||||||
|
|
||||||
|
A contributor's "essential patent claims" are all patent claims
|
||||||
|
owned or controlled by the contributor, whether already acquired or
|
||||||
|
hereafter acquired, that would be infringed by some manner, permitted
|
||||||
|
by this License, of making, using, or selling its contributor version,
|
||||||
|
but do not include claims that would be infringed only as a
|
||||||
|
consequence of further modification of the contributor version. For
|
||||||
|
purposes of this definition, "control" includes the right to grant
|
||||||
|
patent sublicenses in a manner consistent with the requirements of
|
||||||
|
this License.
|
||||||
|
|
||||||
|
Each contributor grants you a non-exclusive, worldwide, royalty-free
|
||||||
|
patent license under the contributor's essential patent claims, to
|
||||||
|
make, use, sell, offer for sale, import and otherwise run, modify and
|
||||||
|
propagate the contents of its contributor version.
|
||||||
|
|
||||||
|
In the following three paragraphs, a "patent license" is any express
|
||||||
|
agreement or commitment, however denominated, not to enforce a patent
|
||||||
|
(such as an express permission to practice a patent or covenant not to
|
||||||
|
sue for patent infringement). To "grant" such a patent license to a
|
||||||
|
party means to make such an agreement or commitment not to enforce a
|
||||||
|
patent against the party.
|
||||||
|
|
||||||
|
If you convey a covered work, knowingly relying on a patent license,
|
||||||
|
and the Corresponding Source of the work is not available for anyone
|
||||||
|
to copy, free of charge and under the terms of this License, through a
|
||||||
|
publicly available network server or other readily accessible means,
|
||||||
|
then you must either (1) cause the Corresponding Source to be so
|
||||||
|
available, or (2) arrange to deprive yourself of the benefit of the
|
||||||
|
patent license for this particular work, or (3) arrange, in a manner
|
||||||
|
consistent with the requirements of this License, to extend the patent
|
||||||
|
license to downstream recipients. "Knowingly relying" means you have
|
||||||
|
actual knowledge that, but for the patent license, your conveying the
|
||||||
|
covered work in a country, or your recipient's use of the covered work
|
||||||
|
in a country, would infringe one or more identifiable patents in that
|
||||||
|
country that you have reason to believe are valid.
|
||||||
|
|
||||||
|
If, pursuant to or in connection with a single transaction or
|
||||||
|
arrangement, you convey, or propagate by procuring conveyance of, a
|
||||||
|
covered work, and grant a patent license to some of the parties
|
||||||
|
receiving the covered work authorizing them to use, propagate, modify
|
||||||
|
or convey a specific copy of the covered work, then the patent license
|
||||||
|
you grant is automatically extended to all recipients of the covered
|
||||||
|
work and works based on it.
|
||||||
|
|
||||||
|
A patent license is "discriminatory" if it does not include within
|
||||||
|
the scope of its coverage, prohibits the exercise of, or is
|
||||||
|
conditioned on the non-exercise of one or more of the rights that are
|
||||||
|
specifically granted under this License. You may not convey a covered
|
||||||
|
work if you are a party to an arrangement with a third party that is
|
||||||
|
in the business of distributing software, under which you make payment
|
||||||
|
to the third party based on the extent of your activity of conveying
|
||||||
|
the work, and under which the third party grants, to any of the
|
||||||
|
parties who would receive the covered work from you, a discriminatory
|
||||||
|
patent license (a) in connection with copies of the covered work
|
||||||
|
conveyed by you (or copies made from those copies), or (b) primarily
|
||||||
|
for and in connection with specific products or compilations that
|
||||||
|
contain the covered work, unless you entered into that arrangement,
|
||||||
|
or that patent license was granted, prior to 28 March 2007.
|
||||||
|
|
||||||
|
Nothing in this License shall be construed as excluding or limiting
|
||||||
|
any implied license or other defenses to infringement that may
|
||||||
|
otherwise be available to you under applicable patent law.
|
||||||
|
|
||||||
|
12. No Surrender of Others' Freedom.
|
||||||
|
|
||||||
|
If conditions are imposed on you (whether by court order, agreement or
|
||||||
|
otherwise) that contradict the conditions of this License, they do not
|
||||||
|
excuse you from the conditions of this License. If you cannot convey a
|
||||||
|
covered work so as to satisfy simultaneously your obligations under this
|
||||||
|
License and any other pertinent obligations, then as a consequence you may
|
||||||
|
not convey it at all. For example, if you agree to terms that obligate you
|
||||||
|
to collect a royalty for further conveying from those to whom you convey
|
||||||
|
the Program, the only way you could satisfy both those terms and this
|
||||||
|
License would be to refrain entirely from conveying the Program.
|
||||||
|
|
||||||
|
13. Use with the GNU Affero General Public License.
|
||||||
|
|
||||||
|
Notwithstanding any other provision of this License, you have
|
||||||
|
permission to link or combine any covered work with a work licensed
|
||||||
|
under version 3 of the GNU Affero General Public License into a single
|
||||||
|
combined work, and to convey the resulting work. The terms of this
|
||||||
|
License will continue to apply to the part which is the covered work,
|
||||||
|
but the special requirements of the GNU Affero General Public License,
|
||||||
|
section 13, concerning interaction through a network will apply to the
|
||||||
|
combination as such.
|
||||||
|
|
||||||
|
14. Revised Versions of this License.
|
||||||
|
|
||||||
|
The Free Software Foundation may publish revised and/or new versions of
|
||||||
|
the GNU General Public License from time to time. Such new versions will
|
||||||
|
be similar in spirit to the present version, but may differ in detail to
|
||||||
|
address new problems or concerns.
|
||||||
|
|
||||||
|
Each version is given a distinguishing version number. If the
|
||||||
|
Program specifies that a certain numbered version of the GNU General
|
||||||
|
Public License "or any later version" applies to it, you have the
|
||||||
|
option of following the terms and conditions either of that numbered
|
||||||
|
version or of any later version published by the Free Software
|
||||||
|
Foundation. If the Program does not specify a version number of the
|
||||||
|
GNU General Public License, you may choose any version ever published
|
||||||
|
by the Free Software Foundation.
|
||||||
|
|
||||||
|
If the Program specifies that a proxy can decide which future
|
||||||
|
versions of the GNU General Public License can be used, that proxy's
|
||||||
|
public statement of acceptance of a version permanently authorizes you
|
||||||
|
to choose that version for the Program.
|
||||||
|
|
||||||
|
Later license versions may give you additional or different
|
||||||
|
permissions. However, no additional obligations are imposed on any
|
||||||
|
author or copyright holder as a result of your choosing to follow a
|
||||||
|
later version.
|
||||||
|
|
||||||
|
15. Disclaimer of Warranty.
|
||||||
|
|
||||||
|
THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
|
||||||
|
APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
|
||||||
|
HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
|
||||||
|
OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
|
||||||
|
THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
|
||||||
|
PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
|
||||||
|
IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
|
||||||
|
ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
|
||||||
|
|
||||||
|
16. Limitation of Liability.
|
||||||
|
|
||||||
|
IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
|
||||||
|
WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
|
||||||
|
THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
|
||||||
|
GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
|
||||||
|
USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
|
||||||
|
DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
|
||||||
|
PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
|
||||||
|
EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
|
||||||
|
SUCH DAMAGES.
|
||||||
|
|
||||||
|
17. Interpretation of Sections 15 and 16.
|
||||||
|
|
||||||
|
If the disclaimer of warranty and limitation of liability provided
|
||||||
|
above cannot be given local legal effect according to their terms,
|
||||||
|
reviewing courts shall apply local law that most closely approximates
|
||||||
|
an absolute waiver of all civil liability in connection with the
|
||||||
|
Program, unless a warranty or assumption of liability accompanies a
|
||||||
|
copy of the Program in return for a fee.
|
||||||
|
|
||||||
|
END OF TERMS AND CONDITIONS
|
||||||
|
|
||||||
|
How to Apply These Terms to Your New Programs
|
||||||
|
|
||||||
|
If you develop a new program, and you want it to be of the greatest
|
||||||
|
possible use to the public, the best way to achieve this is to make it
|
||||||
|
free software which everyone can redistribute and change under these terms.
|
||||||
|
|
||||||
|
To do so, attach the following notices to the program. It is safest
|
||||||
|
to attach them to the start of each source file to most effectively
|
||||||
|
state the exclusion of warranty; and each file should have at least
|
||||||
|
the "copyright" line and a pointer to where the full notice is found.
|
||||||
|
|
||||||
|
<one line to give the program's name and a brief idea of what it does.>
|
||||||
|
Copyright (C) <year> <name of author>
|
||||||
|
|
||||||
|
This program is free software: you can redistribute it and/or modify
|
||||||
|
it under the terms of the GNU General Public License as published by
|
||||||
|
the Free Software Foundation, either version 3 of the License, or
|
||||||
|
(at your option) any later version.
|
||||||
|
|
||||||
|
This program is distributed in the hope that it will be useful,
|
||||||
|
but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||||
|
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||||
|
GNU General Public License for more details.
|
||||||
|
|
||||||
|
You should have received a copy of the GNU General Public License
|
||||||
|
along with this program. If not, see <http://www.gnu.org/licenses/>.
|
||||||
|
|
||||||
|
Also add information on how to contact you by electronic and paper mail.
|
||||||
|
|
||||||
|
If the program does terminal interaction, make it output a short
|
||||||
|
notice like this when it starts in an interactive mode:
|
||||||
|
|
||||||
|
<program> Copyright (C) <year> <name of author>
|
||||||
|
This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
|
||||||
|
This is free software, and you are welcome to redistribute it
|
||||||
|
under certain conditions; type `show c' for details.
|
||||||
|
|
||||||
|
The hypothetical commands `show w' and `show c' should show the appropriate
|
||||||
|
parts of the General Public License. Of course, your program's commands
|
||||||
|
might be different; for a GUI interface, you would use an "about box".
|
||||||
|
|
||||||
|
You should also get your employer (if you work as a programmer) or school,
|
||||||
|
if any, to sign a "copyright disclaimer" for the program, if necessary.
|
||||||
|
For more information on this, and how to apply and follow the GNU GPL, see
|
||||||
|
<http://www.gnu.org/licenses/>.
|
||||||
|
|
||||||
|
The GNU General Public License does not permit incorporating your program
|
||||||
|
into proprietary programs. If your program is a subroutine library, you
|
||||||
|
may consider it more useful to permit linking proprietary applications with
|
||||||
|
the library. If this is what you want to do, use the GNU Lesser General
|
||||||
|
Public License instead of this License. But first, please read
|
||||||
|
<http://www.gnu.org/philosophy/why-not-lgpl.html>.
|
||||||
140
README.md
140
README.md
|
|
@ -1,3 +1,139 @@
|
||||||
# Yolov5_Deepsort
|
# 本文禁止转载!
|
||||||
|
|
||||||
|
|
||||||
|
本文地址：[https://blog.csdn.net/weixin_44936889/article/details/112002152](https://blog.csdn.net/weixin_44936889/article/details/112002152)
|
||||||
|
|
||||||
|
# 项目简介：
|
||||||
|
使用YOLOv5+Deepsort实现车辆行人追踪和计数，代码封装成一个Detector类，更容易嵌入到自己的项目中。
|
||||||
|
|
||||||
|
代码地址（欢迎star）：
|
||||||
|
|
||||||
|
[https://github.com/Sharpiless/yolov5-deepsort/](https://github.com/Sharpiless/yolov5-deepsort/)
|
||||||
|
|
||||||
|
最终效果：
|
||||||
|
|
||||||
|
# YOLOv5检测器：
|
||||||
|
|
||||||
|
```python
|
||||||
|
class Detector(baseDet):
|
||||||
|
|
||||||
|
def __init__(self):
|
||||||
|
super(Detector, self).__init__()
|
||||||
|
self.init_model()
|
||||||
|
self.build_config()
|
||||||
|
|
||||||
|
def init_model(self):
|
||||||
|
|
||||||
|
self.weights = 'weights/yolov5m.pt'
|
||||||
|
self.device = '0' if torch.cuda.is_available() else 'cpu'
|
||||||
|
self.device = select_device(self.device)
|
||||||
|
model = attempt_load(self.weights, map_location=self.device)
|
||||||
|
model.to(self.device).eval()
|
||||||
|
model.half()
|
||||||
|
# torch.save(model, 'test.pt')
|
||||||
|
self.m = model
|
||||||
|
self.names = model.module.names if hasattr(
|
||||||
|
model, 'module') else model.names
|
||||||
|
|
||||||
|
def preprocess(self, img):
|
||||||
|
|
||||||
|
img0 = img.copy()
|
||||||
|
img = letterbox(img, new_shape=self.img_size)[0]
|
||||||
|
img = img[:, :, ::-1].transpose(2, 0, 1)
|
||||||
|
img = np.ascontiguousarray(img)
|
||||||
|
img = torch.from_numpy(img).to(self.device)
|
||||||
|
img = img.half() # 半精度
|
||||||
|
img /= 255.0 # 图像归一化
|
||||||
|
if img.ndimension() == 3:
|
||||||
|
img = img.unsqueeze(0)
|
||||||
|
|
||||||
|
return img0, img
|
||||||
|
|
||||||
|
def detect(self, im):
|
||||||
|
|
||||||
|
im0, img = self.preprocess(im)
|
||||||
|
|
||||||
|
pred = self.m(img, augment=False)[0]
|
||||||
|
pred = pred.float()
|
||||||
|
pred = non_max_suppression(pred, self.threshold, 0.4)
|
||||||
|
|
||||||
|
pred_boxes = []
|
||||||
|
for det in pred:
|
||||||
|
|
||||||
|
if det is not None and len(det):
|
||||||
|
det[:, :4] = scale_coords(
|
||||||
|
img.shape[2:], det[:, :4], im0.shape).round()
|
||||||
|
|
||||||
|
for *x, conf, cls_id in det:
|
||||||
|
lbl = self.names[int(cls_id)]
|
||||||
|
if not lbl in ['person', 'car', 'truck']:
|
||||||
|
continue
|
||||||
|
x1, y1 = int(x[0]), int(x[1])
|
||||||
|
x2, y2 = int(x[2]), int(x[3])
|
||||||
|
pred_boxes.append(
|
||||||
|
(x1, y1, x2, y2, lbl, conf))
|
||||||
|
|
||||||
|
return im, pred_boxes
|
||||||
|
|
||||||
|
```
|
||||||
|
|
||||||
|
调用 self.detect 方法返回图像和预测结果
|
||||||
|
|
||||||
|
# DeepSort追踪器：
|
||||||
|
|
||||||
|
```python
|
||||||
|
deepsort = DeepSort(cfg.DEEPSORT.REID_CKPT,
|
||||||
|
max_dist=cfg.DEEPSORT.MAX_DIST, min_confidence=cfg.DEEPSORT.MIN_CONFIDENCE,
|
||||||
|
nms_max_overlap=cfg.DEEPSORT.NMS_MAX_OVERLAP, max_iou_distance=cfg.DEEPSORT.MAX_IOU_DISTANCE,
|
||||||
|
max_age=cfg.DEEPSORT.MAX_AGE, n_init=cfg.DEEPSORT.N_INIT, nn_budget=cfg.DEEPSORT.NN_BUDGET,
|
||||||
|
use_cuda=True)
|
||||||
|
```
|
||||||
|
|
||||||
|
调用 self.update 方法更新追踪结果
|
||||||
|
|
||||||
|
# 运行demo：
|
||||||
|
|
||||||
|
```bash
|
||||||
|
python demo.py
|
||||||
|
```
|
||||||
|
|
||||||
|
# 训练自己的模型：
|
||||||
|
参考我的另一篇博客：
|
||||||
|
|
||||||
|
[【小白CV】手把手教你用YOLOv5训练自己的数据集（从Windows环境配置到模型部署）](https://blog.csdn.net/weixin_44936889/article/details/110661862)
|
||||||
|
|
||||||
|
训练好后放到 weights 文件夹下
|
||||||
|
|
||||||
|
# 调用接口：
|
||||||
|
|
||||||
|
## 创建检测器：
|
||||||
|
|
||||||
|
```python
|
||||||
|
from AIDetector_pytorch import Detector
|
||||||
|
|
||||||
|
det = Detector()
|
||||||
|
```
|
||||||
|
|
||||||
|
## 调用检测接口：
|
||||||
|
|
||||||
|
```python
|
||||||
|
result = det.feedCap(im)
|
||||||
|
```
|
||||||
|
|
||||||
|
其中 im 为 BGR 图像
|
||||||
|
|
||||||
|
返回的 result 是字典，result['frame'] 返回可视化后的图像
|
||||||
|
|
||||||
|
# 联系作者：
|
||||||
|
|
||||||
|
> B站：[https://space.bilibili.com/470550823](https://space.bilibili.com/470550823)
|
||||||
|
|
||||||
|
> CSDN：[https://blog.csdn.net/weixin_44936889](https://blog.csdn.net/weixin_44936889)
|
||||||
|
|
||||||
|
> AI Studio：[https://aistudio.baidu.com/aistudio/personalcenter/thirdview/67156](https://aistudio.baidu.com/aistudio/personalcenter/thirdview/67156)
|
||||||
|
|
||||||
|
> Github：[https://github.com/Sharpiless](https://github.com/Sharpiless)
|
||||||
|
|
||||||
|
遵循 GNU General Public License v3.0 协议，标明目标检测部分来源：https://github.com/ultralytics/yolov5/
|
||||||
|
|
||||||
|
|
||||||
船舶、车辆等目标追踪
|
|
||||||
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
|
|
@ -0,0 +1,10 @@
|
||||||
|
DEEPSORT:
|
||||||
|
REID_CKPT: "deep_sort/deep_sort/deep/checkpoint/ckpt.t7"
|
||||||
|
MAX_DIST: 0.2
|
||||||
|
MIN_CONFIDENCE: 0.3
|
||||||
|
NMS_MAX_OVERLAP: 0.5
|
||||||
|
MAX_IOU_DISTANCE: 0.7
|
||||||
|
MAX_AGE: 70
|
||||||
|
N_INIT: 3
|
||||||
|
NN_BUDGET: 100
|
||||||
|
|
||||||
|
|
@ -0,0 +1,3 @@
|
||||||
|
# Deep Sort
|
||||||
|
|
||||||
|
This is the implemention of deep sort with pytorch.
|
||||||
|
|
@ -0,0 +1,21 @@
|
||||||
|
from .deep_sort import DeepSort
|
||||||
|
|
||||||
|
|
||||||
|
__all__ = ['DeepSort', 'build_tracker']
|
||||||
|
|
||||||
|
|
||||||
|
def build_tracker(cfg, use_cuda):
|
||||||
|
return DeepSort(cfg.DEEPSORT.REID_CKPT,
|
||||||
|
max_dist=cfg.DEEPSORT.MAX_DIST, min_confidence=cfg.DEEPSORT.MIN_CONFIDENCE,
|
||||||
|
nms_max_overlap=cfg.DEEPSORT.NMS_MAX_OVERLAP, max_iou_distance=cfg.DEEPSORT.MAX_IOU_DISTANCE,
|
||||||
|
max_age=cfg.DEEPSORT.MAX_AGE, n_init=cfg.DEEPSORT.N_INIT, nn_budget=cfg.DEEPSORT.NN_BUDGET, use_cuda=use_cuda)
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
Binary file not shown.
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Binary file not shown.
Binary file not shown.
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|
|
@ -0,0 +1,15 @@
|
||||||
|
import torch
|
||||||
|
|
||||||
|
features = torch.load("features.pth")
|
||||||
|
qf = features["qf"]
|
||||||
|
ql = features["ql"]
|
||||||
|
gf = features["gf"]
|
||||||
|
gl = features["gl"]
|
||||||
|
|
||||||
|
scores = qf.mm(gf.t())
|
||||||
|
res = scores.topk(5, dim=1)[1][:,0]
|
||||||
|
top1correct = gl[res].eq(ql).sum().item()
|
||||||
|
|
||||||
|
print("Acc top1:{:.3f}".format(top1correct/ql.size(0)))
|
||||||
|
|
||||||
|
|
||||||
|
|
@ -0,0 +1,55 @@
|
||||||
|
import torch
|
||||||
|
import torchvision.transforms as transforms
|
||||||
|
import numpy as np
|
||||||
|
import cv2
|
||||||
|
import logging
|
||||||
|
|
||||||
|
from .model import Net
|
||||||
|
|
||||||
|
class Extractor(object):
|
||||||
|
def __init__(self, model_path, use_cuda=True):
|
||||||
|
self.net = Net(reid=True)
|
||||||
|
self.device = "cuda" if torch.cuda.is_available() and use_cuda else "cpu"
|
||||||
|
state_dict = torch.load(model_path, map_location=lambda storage, loc: storage)['net_dict']
|
||||||
|
self.net.load_state_dict(state_dict)
|
||||||
|
logger = logging.getLogger("root.tracker")
|
||||||
|
logger.info("Loading weights from {}... Done!".format(model_path))
|
||||||
|
self.net.to(self.device)
|
||||||
|
self.size = (64, 128)
|
||||||
|
self.norm = transforms.Compose([
|
||||||
|
transforms.ToTensor(),
|
||||||
|
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
|
||||||
|
])
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
def _preprocess(self, im_crops):
|
||||||
|
"""
|
||||||
|
TODO:
|
||||||
|
1. to float with scale from 0 to 1
|
||||||
|
2. resize to (64, 128) as Market1501 dataset did
|
||||||
|
3. concatenate to a numpy array
|
||||||
|
3. to torch Tensor
|
||||||
|
4. normalize
|
||||||
|
"""
|
||||||
|
def _resize(im, size):
|
||||||
|
return cv2.resize(im.astype(np.float32)/255., size)
|
||||||
|
|
||||||
|
im_batch = torch.cat([self.norm(_resize(im, self.size)).unsqueeze(0) for im in im_crops], dim=0).float()
|
||||||
|
return im_batch
|
||||||
|
|
||||||
|
|
||||||
|
def __call__(self, im_crops):
|
||||||
|
im_batch = self._preprocess(im_crops)
|
||||||
|
with torch.no_grad():
|
||||||
|
im_batch = im_batch.to(self.device)
|
||||||
|
features = self.net(im_batch)
|
||||||
|
return features.cpu().numpy()
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == '__main__':
|
||||||
|
img = cv2.imread("demo.jpg")[:,:,(2,1,0)]
|
||||||
|
extr = Extractor("checkpoint/ckpt.t7")
|
||||||
|
feature = extr(img)
|
||||||
|
print(feature.shape)
|
||||||
|
|
||||||
|
|
@ -0,0 +1,104 @@
|
||||||
|
import torch
|
||||||
|
import torch.nn as nn
|
||||||
|
import torch.nn.functional as F
|
||||||
|
|
||||||
|
class BasicBlock(nn.Module):
|
||||||
|
def __init__(self, c_in, c_out,is_downsample=False):
|
||||||
|
super(BasicBlock,self).__init__()
|
||||||
|
self.is_downsample = is_downsample
|
||||||
|
if is_downsample:
|
||||||
|
self.conv1 = nn.Conv2d(c_in, c_out, 3, stride=2, padding=1, bias=False)
|
||||||
|
else:
|
||||||
|
self.conv1 = nn.Conv2d(c_in, c_out, 3, stride=1, padding=1, bias=False)
|
||||||
|
self.bn1 = nn.BatchNorm2d(c_out)
|
||||||
|
self.relu = nn.ReLU(True)
|
||||||
|
self.conv2 = nn.Conv2d(c_out,c_out,3,stride=1,padding=1, bias=False)
|
||||||
|
self.bn2 = nn.BatchNorm2d(c_out)
|
||||||
|
if is_downsample:
|
||||||
|
self.downsample = nn.Sequential(
|
||||||
|
nn.Conv2d(c_in, c_out, 1, stride=2, bias=False),
|
||||||
|
nn.BatchNorm2d(c_out)
|
||||||
|
)
|
||||||
|
elif c_in != c_out:
|
||||||
|
self.downsample = nn.Sequential(
|
||||||
|
nn.Conv2d(c_in, c_out, 1, stride=1, bias=False),
|
||||||
|
nn.BatchNorm2d(c_out)
|
||||||
|
)
|
||||||
|
self.is_downsample = True
|
||||||
|
|
||||||
|
def forward(self,x):
|
||||||
|
y = self.conv1(x)
|
||||||
|
y = self.bn1(y)
|
||||||
|
y = self.relu(y)
|
||||||
|
y = self.conv2(y)
|
||||||
|
y = self.bn2(y)
|
||||||
|
if self.is_downsample:
|
||||||
|
x = self.downsample(x)
|
||||||
|
return F.relu(x.add(y),True)
|
||||||
|
|
||||||
|
def make_layers(c_in,c_out,repeat_times, is_downsample=False):
|
||||||
|
blocks = []
|
||||||
|
for i in range(repeat_times):
|
||||||
|
if i ==0:
|
||||||
|
blocks += [BasicBlock(c_in,c_out, is_downsample=is_downsample),]
|
||||||
|
else:
|
||||||
|
blocks += [BasicBlock(c_out,c_out),]
|
||||||
|
return nn.Sequential(*blocks)
|
||||||
|
|
||||||
|
class Net(nn.Module):
|
||||||
|
def __init__(self, num_classes=751 ,reid=False):
|
||||||
|
super(Net,self).__init__()
|
||||||
|
# 3 128 64
|
||||||
|
self.conv = nn.Sequential(
|
||||||
|
nn.Conv2d(3,64,3,stride=1,padding=1),
|
||||||
|
nn.BatchNorm2d(64),
|
||||||
|
nn.ReLU(inplace=True),
|
||||||
|
# nn.Conv2d(32,32,3,stride=1,padding=1),
|
||||||
|
# nn.BatchNorm2d(32),
|
||||||
|
# nn.ReLU(inplace=True),
|
||||||
|
nn.MaxPool2d(3,2,padding=1),
|
||||||
|
)
|
||||||
|
# 32 64 32
|
||||||
|
self.layer1 = make_layers(64,64,2,False)
|
||||||
|
# 32 64 32
|
||||||
|
self.layer2 = make_layers(64,128,2,True)
|
||||||
|
# 64 32 16
|
||||||
|
self.layer3 = make_layers(128,256,2,True)
|
||||||
|
# 128 16 8
|
||||||
|
self.layer4 = make_layers(256,512,2,True)
|
||||||
|
# 256 8 4
|
||||||
|
self.avgpool = nn.AvgPool2d((8,4),1)
|
||||||
|
# 256 1 1
|
||||||
|
self.reid = reid
|
||||||
|
self.classifier = nn.Sequential(
|
||||||
|
nn.Linear(512, 256),
|
||||||
|
nn.BatchNorm1d(256),
|
||||||
|
nn.ReLU(inplace=True),
|
||||||
|
nn.Dropout(),
|
||||||
|
nn.Linear(256, num_classes),
|
||||||
|
)
|
||||||
|
|
||||||
|
def forward(self, x):
|
||||||
|
x = self.conv(x)
|
||||||
|
x = self.layer1(x)
|
||||||
|
x = self.layer2(x)
|
||||||
|
x = self.layer3(x)
|
||||||
|
x = self.layer4(x)
|
||||||
|
x = self.avgpool(x)
|
||||||
|
x = x.view(x.size(0),-1)
|
||||||
|
# B x 128
|
||||||
|
if self.reid:
|
||||||
|
x = x.div(x.norm(p=2,dim=1,keepdim=True))
|
||||||
|
return x
|
||||||
|
# classifier
|
||||||
|
x = self.classifier(x)
|
||||||
|
return x
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == '__main__':
|
||||||
|
net = Net()
|
||||||
|
x = torch.randn(4,3,128,64)
|
||||||
|
y = net(x)
|
||||||
|
import ipdb; ipdb.set_trace()
|
||||||
|
|
||||||
|
|
||||||
|
|
@ -0,0 +1,106 @@
|
||||||
|
import torch
|
||||||
|
import torch.nn as nn
|
||||||
|
import torch.nn.functional as F
|
||||||
|
|
||||||
|
class BasicBlock(nn.Module):
|
||||||
|
def __init__(self, c_in, c_out,is_downsample=False):
|
||||||
|
super(BasicBlock,self).__init__()
|
||||||
|
self.is_downsample = is_downsample
|
||||||
|
if is_downsample:
|
||||||
|
self.conv1 = nn.Conv2d(c_in, c_out, 3, stride=2, padding=1, bias=False)
|
||||||
|
else:
|
||||||
|
self.conv1 = nn.Conv2d(c_in, c_out, 3, stride=1, padding=1, bias=False)
|
||||||
|
self.bn1 = nn.BatchNorm2d(c_out)
|
||||||
|
self.relu = nn.ReLU(True)
|
||||||
|
self.conv2 = nn.Conv2d(c_out,c_out,3,stride=1,padding=1, bias=False)
|
||||||
|
self.bn2 = nn.BatchNorm2d(c_out)
|
||||||
|
if is_downsample:
|
||||||
|
self.downsample = nn.Sequential(
|
||||||
|
nn.Conv2d(c_in, c_out, 1, stride=2, bias=False),
|
||||||
|
nn.BatchNorm2d(c_out)
|
||||||
|
)
|
||||||
|
elif c_in != c_out:
|
||||||
|
self.downsample = nn.Sequential(
|
||||||
|
nn.Conv2d(c_in, c_out, 1, stride=1, bias=False),
|
||||||
|
nn.BatchNorm2d(c_out)
|
||||||
|
)
|
||||||
|
self.is_downsample = True
|
||||||
|
|
||||||
|
def forward(self,x):
|
||||||
|
y = self.conv1(x)
|
||||||
|
y = self.bn1(y)
|
||||||
|
y = self.relu(y)
|
||||||
|
y = self.conv2(y)
|
||||||
|
y = self.bn2(y)
|
||||||
|
if self.is_downsample:
|
||||||
|
x = self.downsample(x)
|
||||||
|
return F.relu(x.add(y),True)
|
||||||
|
|
||||||
|
def make_layers(c_in,c_out,repeat_times, is_downsample=False):
|
||||||
|
blocks = []
|
||||||
|
for i in range(repeat_times):
|
||||||
|
if i ==0:
|
||||||
|
blocks += [BasicBlock(c_in,c_out, is_downsample=is_downsample),]
|
||||||
|
else:
|
||||||
|
blocks += [BasicBlock(c_out,c_out),]
|
||||||
|
return nn.Sequential(*blocks)
|
||||||
|
|
||||||
|
class Net(nn.Module):
|
||||||
|
def __init__(self, num_classes=625 ,reid=False):
|
||||||
|
super(Net,self).__init__()
|
||||||
|
# 3 128 64
|
||||||
|
self.conv = nn.Sequential(
|
||||||
|
nn.Conv2d(3,32,3,stride=1,padding=1),
|
||||||
|
nn.BatchNorm2d(32),
|
||||||
|
nn.ELU(inplace=True),
|
||||||
|
nn.Conv2d(32,32,3,stride=1,padding=1),
|
||||||
|
nn.BatchNorm2d(32),
|
||||||
|
nn.ELU(inplace=True),
|
||||||
|
nn.MaxPool2d(3,2,padding=1),
|
||||||
|
)
|
||||||
|
# 32 64 32
|
||||||
|
self.layer1 = make_layers(32,32,2,False)
|
||||||
|
# 32 64 32
|
||||||
|
self.layer2 = make_layers(32,64,2,True)
|
||||||
|
# 64 32 16
|
||||||
|
self.layer3 = make_layers(64,128,2,True)
|
||||||
|
# 128 16 8
|
||||||
|
self.dense = nn.Sequential(
|
||||||
|
nn.Dropout(p=0.6),
|
||||||
|
nn.Linear(128*16*8, 128),
|
||||||
|
nn.BatchNorm1d(128),
|
||||||
|
nn.ELU(inplace=True)
|
||||||
|
)
|
||||||
|
# 256 1 1
|
||||||
|
self.reid = reid
|
||||||
|
self.batch_norm = nn.BatchNorm1d(128)
|
||||||
|
self.classifier = nn.Sequential(
|
||||||
|
nn.Linear(128, num_classes),
|
||||||
|
)
|
||||||
|
|
||||||
|
def forward(self, x):
|
||||||
|
x = self.conv(x)
|
||||||
|
x = self.layer1(x)
|
||||||
|
x = self.layer2(x)
|
||||||
|
x = self.layer3(x)
|
||||||
|
|
||||||
|
x = x.view(x.size(0),-1)
|
||||||
|
if self.reid:
|
||||||
|
x = self.dense[0](x)
|
||||||
|
x = self.dense[1](x)
|
||||||
|
x = x.div(x.norm(p=2,dim=1,keepdim=True))
|
||||||
|
return x
|
||||||
|
x = self.dense(x)
|
||||||
|
# B x 128
|
||||||
|
# classifier
|
||||||
|
x = self.classifier(x)
|
||||||
|
return x
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == '__main__':
|
||||||
|
net = Net(reid=True)
|
||||||
|
x = torch.randn(4,3,128,64)
|
||||||
|
y = net(x)
|
||||||
|
import ipdb; ipdb.set_trace()
|
||||||
|
|
||||||
|
|
||||||
|
|
@ -0,0 +1,77 @@
|
||||||
|
import torch
|
||||||
|
import torch.backends.cudnn as cudnn
|
||||||
|
import torchvision
|
||||||
|
|
||||||
|
import argparse
|
||||||
|
import os
|
||||||
|
|
||||||
|
from model import Net
|
||||||
|
|
||||||
|
parser = argparse.ArgumentParser(description="Train on market1501")
|
||||||
|
parser.add_argument("--data-dir",default='data',type=str)
|
||||||
|
parser.add_argument("--no-cuda",action="store_true")
|
||||||
|
parser.add_argument("--gpu-id",default=0,type=int)
|
||||||
|
args = parser.parse_args()
|
||||||
|
|
||||||
|
# device
|
||||||
|
device = "cuda:{}".format(args.gpu_id) if torch.cuda.is_available() and not args.no_cuda else "cpu"
|
||||||
|
if torch.cuda.is_available() and not args.no_cuda:
|
||||||
|
cudnn.benchmark = True
|
||||||
|
|
||||||
|
# data loader
|
||||||
|
root = args.data_dir
|
||||||
|
query_dir = os.path.join(root,"query")
|
||||||
|
gallery_dir = os.path.join(root,"gallery")
|
||||||
|
transform = torchvision.transforms.Compose([
|
||||||
|
torchvision.transforms.Resize((128,64)),
|
||||||
|
torchvision.transforms.ToTensor(),
|
||||||
|
torchvision.transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
|
||||||
|
])
|
||||||
|
queryloader = torch.utils.data.DataLoader(
|
||||||
|
torchvision.datasets.ImageFolder(query_dir, transform=transform),
|
||||||
|
batch_size=64, shuffle=False
|
||||||
|
)
|
||||||
|
galleryloader = torch.utils.data.DataLoader(
|
||||||
|
torchvision.datasets.ImageFolder(gallery_dir, transform=transform),
|
||||||
|
batch_size=64, shuffle=False
|
||||||
|
)
|
||||||
|
|
||||||
|
# net definition
|
||||||
|
net = Net(reid=True)
|
||||||
|
assert os.path.isfile("./checkpoint/ckpt.t7"), "Error: no checkpoint file found!"
|
||||||
|
print('Loading from checkpoint/ckpt.t7')
|
||||||
|
checkpoint = torch.load("./checkpoint/ckpt.t7")
|
||||||
|
net_dict = checkpoint['net_dict']
|
||||||
|
net.load_state_dict(net_dict, strict=False)
|
||||||
|
net.eval()
|
||||||
|
net.to(device)
|
||||||
|
|
||||||
|
# compute features
|
||||||
|
query_features = torch.tensor([]).float()
|
||||||
|
query_labels = torch.tensor([]).long()
|
||||||
|
gallery_features = torch.tensor([]).float()
|
||||||
|
gallery_labels = torch.tensor([]).long()
|
||||||
|
|
||||||
|
with torch.no_grad():
|
||||||
|
for idx,(inputs,labels) in enumerate(queryloader):
|
||||||
|
inputs = inputs.to(device)
|
||||||
|
features = net(inputs).cpu()
|
||||||
|
query_features = torch.cat((query_features, features), dim=0)
|
||||||
|
query_labels = torch.cat((query_labels, labels))
|
||||||
|
|
||||||
|
for idx,(inputs,labels) in enumerate(galleryloader):
|
||||||
|
inputs = inputs.to(device)
|
||||||
|
features = net(inputs).cpu()
|
||||||
|
gallery_features = torch.cat((gallery_features, features), dim=0)
|
||||||
|
gallery_labels = torch.cat((gallery_labels, labels))
|
||||||
|
|
||||||
|
gallery_labels -= 2
|
||||||
|
|
||||||
|
# save features
|
||||||
|
features = {
|
||||||
|
"qf": query_features,
|
||||||
|
"ql": query_labels,
|
||||||
|
"gf": gallery_features,
|
||||||
|
"gl": gallery_labels
|
||||||
|
}
|
||||||
|
torch.save(features,"features.pth")
|
||||||
Binary file not shown.
|
After Width: | Height: | Size: 59 KiB |
|
|
@ -0,0 +1,189 @@
|
||||||
|
import argparse
|
||||||
|
import os
|
||||||
|
import time
|
||||||
|
|
||||||
|
import numpy as np
|
||||||
|
import matplotlib.pyplot as plt
|
||||||
|
import torch
|
||||||
|
import torch.backends.cudnn as cudnn
|
||||||
|
import torchvision
|
||||||
|
|
||||||
|
from model import Net
|
||||||
|
|
||||||
|
parser = argparse.ArgumentParser(description="Train on market1501")
|
||||||
|
parser.add_argument("--data-dir",default='data',type=str)
|
||||||
|
parser.add_argument("--no-cuda",action="store_true")
|
||||||
|
parser.add_argument("--gpu-id",default=0,type=int)
|
||||||
|
parser.add_argument("--lr",default=0.1, type=float)
|
||||||
|
parser.add_argument("--interval",'-i',default=20,type=int)
|
||||||
|
parser.add_argument('--resume', '-r',action='store_true')
|
||||||
|
args = parser.parse_args()
|
||||||
|
|
||||||
|
# device
|
||||||
|
device = "cuda:{}".format(args.gpu_id) if torch.cuda.is_available() and not args.no_cuda else "cpu"
|
||||||
|
if torch.cuda.is_available() and not args.no_cuda:
|
||||||
|
cudnn.benchmark = True
|
||||||
|
|
||||||
|
# data loading
|
||||||
|
root = args.data_dir
|
||||||
|
train_dir = os.path.join(root,"train")
|
||||||
|
test_dir = os.path.join(root,"test")
|
||||||
|
transform_train = torchvision.transforms.Compose([
|
||||||
|
torchvision.transforms.RandomCrop((128,64),padding=4),
|
||||||
|
torchvision.transforms.RandomHorizontalFlip(),
|
||||||
|
torchvision.transforms.ToTensor(),
|
||||||
|
torchvision.transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
|
||||||
|
])
|
||||||
|
transform_test = torchvision.transforms.Compose([
|
||||||
|
torchvision.transforms.Resize((128,64)),
|
||||||
|
torchvision.transforms.ToTensor(),
|
||||||
|
torchvision.transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
|
||||||
|
])
|
||||||
|
trainloader = torch.utils.data.DataLoader(
|
||||||
|
torchvision.datasets.ImageFolder(train_dir, transform=transform_train),
|
||||||
|
batch_size=64,shuffle=True
|
||||||
|
)
|
||||||
|
testloader = torch.utils.data.DataLoader(
|
||||||
|
torchvision.datasets.ImageFolder(test_dir, transform=transform_test),
|
||||||
|
batch_size=64,shuffle=True
|
||||||
|
)
|
||||||
|
num_classes = max(len(trainloader.dataset.classes), len(testloader.dataset.classes))
|
||||||
|
|
||||||
|
# net definition
|
||||||
|
start_epoch = 0
|
||||||
|
net = Net(num_classes=num_classes)
|
||||||
|
if args.resume:
|
||||||
|
assert os.path.isfile("./checkpoint/ckpt.t7"), "Error: no checkpoint file found!"
|
||||||
|
print('Loading from checkpoint/ckpt.t7')
|
||||||
|
checkpoint = torch.load("./checkpoint/ckpt.t7")
|
||||||
|
# import ipdb; ipdb.set_trace()
|
||||||
|
net_dict = checkpoint['net_dict']
|
||||||
|
net.load_state_dict(net_dict)
|
||||||
|
best_acc = checkpoint['acc']
|
||||||
|
start_epoch = checkpoint['epoch']
|
||||||
|
net.to(device)
|
||||||
|
|
||||||
|
# loss and optimizer
|
||||||
|
criterion = torch.nn.CrossEntropyLoss()
|
||||||
|
optimizer = torch.optim.SGD(net.parameters(), args.lr, momentum=0.9, weight_decay=5e-4)
|
||||||
|
best_acc = 0.
|
||||||
|
|
||||||
|
# train function for each epoch
|
||||||
|
def train(epoch):
|
||||||
|
print("\nEpoch : %d"%(epoch+1))
|
||||||
|
net.train()
|
||||||
|
training_loss = 0.
|
||||||
|
train_loss = 0.
|
||||||
|
correct = 0
|
||||||
|
total = 0
|
||||||
|
interval = args.interval
|
||||||
|
start = time.time()
|
||||||
|
for idx, (inputs, labels) in enumerate(trainloader):
|
||||||
|
# forward
|
||||||
|
inputs,labels = inputs.to(device),labels.to(device)
|
||||||
|
outputs = net(inputs)
|
||||||
|
loss = criterion(outputs, labels)
|
||||||
|
|
||||||
|
# backward
|
||||||
|
optimizer.zero_grad()
|
||||||
|
loss.backward()
|
||||||
|
optimizer.step()
|
||||||
|
|
||||||
|
# accumurating
|
||||||
|
training_loss += loss.item()
|
||||||
|
train_loss += loss.item()
|
||||||
|
correct += outputs.max(dim=1)[1].eq(labels).sum().item()
|
||||||
|
total += labels.size(0)
|
||||||
|
|
||||||
|
# print
|
||||||
|
if (idx+1)%interval == 0:
|
||||||
|
end = time.time()
|
||||||
|
print("[progress:{:.1f}%]time:{:.2f}s Loss:{:.5f} Correct:{}/{} Acc:{:.3f}%".format(
|
||||||
|
100.*(idx+1)/len(trainloader), end-start, training_loss/interval, correct, total, 100.*correct/total
|
||||||
|
))
|
||||||
|
training_loss = 0.
|
||||||
|
start = time.time()
|
||||||
|
|
||||||
|
return train_loss/len(trainloader), 1.- correct/total
|
||||||
|
|
||||||
|
def test(epoch):
|
||||||
|
global best_acc
|
||||||
|
net.eval()
|
||||||
|
test_loss = 0.
|
||||||
|
correct = 0
|
||||||
|
total = 0
|
||||||
|
start = time.time()
|
||||||
|
with torch.no_grad():
|
||||||
|
for idx, (inputs, labels) in enumerate(testloader):
|
||||||
|
inputs, labels = inputs.to(device), labels.to(device)
|
||||||
|
outputs = net(inputs)
|
||||||
|
loss = criterion(outputs, labels)
|
||||||
|
|
||||||
|
test_loss += loss.item()
|
||||||
|
correct += outputs.max(dim=1)[1].eq(labels).sum().item()
|
||||||
|
total += labels.size(0)
|
||||||
|
|
||||||
|
print("Testing ...")
|
||||||
|
end = time.time()
|
||||||
|
print("[progress:{:.1f}%]time:{:.2f}s Loss:{:.5f} Correct:{}/{} Acc:{:.3f}%".format(
|
||||||
|
100.*(idx+1)/len(testloader), end-start, test_loss/len(testloader), correct, total, 100.*correct/total
|
||||||
|
))
|
||||||
|
|
||||||
|
# saving checkpoint
|
||||||
|
acc = 100.*correct/total
|
||||||
|
if acc > best_acc:
|
||||||
|
best_acc = acc
|
||||||
|
print("Saving parameters to checkpoint/ckpt.t7")
|
||||||
|
checkpoint = {
|
||||||
|
'net_dict':net.state_dict(),
|
||||||
|
'acc':acc,
|
||||||
|
'epoch':epoch,
|
||||||
|
}
|
||||||
|
if not os.path.isdir('checkpoint'):
|
||||||
|
os.mkdir('checkpoint')
|
||||||
|
torch.save(checkpoint, './checkpoint/ckpt.t7')
|
||||||
|
|
||||||
|
return test_loss/len(testloader), 1.- correct/total
|
||||||
|
|
||||||
|
# plot figure
|
||||||
|
x_epoch = []
|
||||||
|
record = {'train_loss':[], 'train_err':[], 'test_loss':[], 'test_err':[]}
|
||||||
|
fig = plt.figure()
|
||||||
|
ax0 = fig.add_subplot(121, title="loss")
|
||||||
|
ax1 = fig.add_subplot(122, title="top1err")
|
||||||
|
def draw_curve(epoch, train_loss, train_err, test_loss, test_err):
|
||||||
|
global record
|
||||||
|
record['train_loss'].append(train_loss)
|
||||||
|
record['train_err'].append(train_err)
|
||||||
|
record['test_loss'].append(test_loss)
|
||||||
|
record['test_err'].append(test_err)
|
||||||
|
|
||||||
|
x_epoch.append(epoch)
|
||||||
|
ax0.plot(x_epoch, record['train_loss'], 'bo-', label='train')
|
||||||
|
ax0.plot(x_epoch, record['test_loss'], 'ro-', label='val')
|
||||||
|
ax1.plot(x_epoch, record['train_err'], 'bo-', label='train')
|
||||||
|
ax1.plot(x_epoch, record['test_err'], 'ro-', label='val')
|
||||||
|
if epoch == 0:
|
||||||
|
ax0.legend()
|
||||||
|
ax1.legend()
|
||||||
|
fig.savefig("train.jpg")
|
||||||
|
|
||||||
|
# lr decay
|
||||||
|
def lr_decay():
|
||||||
|
global optimizer
|
||||||
|
for params in optimizer.param_groups:
|
||||||
|
params['lr'] *= 0.1
|
||||||
|
lr = params['lr']
|
||||||
|
print("Learning rate adjusted to {}".format(lr))
|
||||||
|
|
||||||
|
def main():
|
||||||
|
for epoch in range(start_epoch, start_epoch+40):
|
||||||
|
train_loss, train_err = train(epoch)
|
||||||
|
test_loss, test_err = test(epoch)
|
||||||
|
draw_curve(epoch, train_loss, train_err, test_loss, test_err)
|
||||||
|
if (epoch+1)%20==0:
|
||||||
|
lr_decay()
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == '__main__':
|
||||||
|
main()
|
||||||
|
|
@ -0,0 +1,100 @@
|
||||||
|
import numpy as np
|
||||||
|
import torch
|
||||||
|
|
||||||
|
from .deep.feature_extractor import Extractor
|
||||||
|
from .sort.nn_matching import NearestNeighborDistanceMetric
|
||||||
|
from .sort.preprocessing import non_max_suppression
|
||||||
|
from .sort.detection import Detection
|
||||||
|
from .sort.tracker import Tracker
|
||||||
|
|
||||||
|
|
||||||
|
__all__ = ['DeepSort']
|
||||||
|
|
||||||
|
|
||||||
|
class DeepSort(object):
|
||||||
|
def __init__(self, model_path, max_dist=0.2, min_confidence=0.3, nms_max_overlap=1.0, max_iou_distance=0.7, max_age=70, n_init=3, nn_budget=100, use_cuda=True):
|
||||||
|
self.min_confidence = min_confidence
|
||||||
|
self.nms_max_overlap = nms_max_overlap
|
||||||
|
|
||||||
|
self.extractor = Extractor(model_path, use_cuda=use_cuda)
|
||||||
|
|
||||||
|
max_cosine_distance = max_dist
|
||||||
|
nn_budget = 100
|
||||||
|
metric = NearestNeighborDistanceMetric(
|
||||||
|
"cosine", max_cosine_distance, nn_budget)
|
||||||
|
self.tracker = Tracker(
|
||||||
|
metric, max_iou_distance=max_iou_distance, max_age=max_age, n_init=n_init)
|
||||||
|
|
||||||
|
def update(self, bbox_xywh, confidences, clss, ori_img):
|
||||||
|
self.height, self.width = ori_img.shape[:2]
|
||||||
|
# generate detections
|
||||||
|
features = self._get_features(bbox_xywh, ori_img)
|
||||||
|
bbox_tlwh = self._xywh_to_tlwh(bbox_xywh)
|
||||||
|
detections = [Detection(bbox_tlwh[i], clss[i], conf, features[i]) for i, conf in enumerate(
|
||||||
|
confidences) if conf > self.min_confidence]
|
||||||
|
# update tracker
|
||||||
|
self.tracker.predict()
|
||||||
|
self.tracker.update(detections)
|
||||||
|
|
||||||
|
# output bbox identities
|
||||||
|
outputs = []
|
||||||
|
for track in self.tracker.tracks:
|
||||||
|
if not track.is_confirmed() or track.time_since_update > 1:
|
||||||
|
continue
|
||||||
|
box = track.to_tlwh()
|
||||||
|
x1, y1, x2, y2 = self._tlwh_to_xyxy(box)
|
||||||
|
outputs.append((x1, y1, x2, y2, track.cls_, track.track_id))
|
||||||
|
return outputs
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def _xywh_to_tlwh(bbox_xywh):
|
||||||
|
if isinstance(bbox_xywh, np.ndarray):
|
||||||
|
bbox_tlwh = bbox_xywh.copy()
|
||||||
|
elif isinstance(bbox_xywh, torch.Tensor):
|
||||||
|
bbox_tlwh = bbox_xywh.clone()
|
||||||
|
if bbox_tlwh.size(0):
|
||||||
|
bbox_tlwh[:, 0] = bbox_xywh[:, 0] - bbox_xywh[:, 2]/2.
|
||||||
|
bbox_tlwh[:, 1] = bbox_xywh[:, 1] - bbox_xywh[:, 3]/2.
|
||||||
|
return bbox_tlwh
|
||||||
|
|
||||||
|
def _xywh_to_xyxy(self, bbox_xywh):
|
||||||
|
x, y, w, h = bbox_xywh
|
||||||
|
x1 = max(int(x-w/2), 0)
|
||||||
|
x2 = min(int(x+w/2), self.width-1)
|
||||||
|
y1 = max(int(y-h/2), 0)
|
||||||
|
y2 = min(int(y+h/2), self.height-1)
|
||||||
|
return x1, y1, x2, y2
|
||||||
|
|
||||||
|
def _tlwh_to_xyxy(self, bbox_tlwh):
|
||||||
|
"""
|
||||||
|
TODO:
|
||||||
|
Convert bbox from xtl_ytl_w_h to xc_yc_w_h
|
||||||
|
Thanks JieChen91@github.com for reporting this bug!
|
||||||
|
"""
|
||||||
|
x, y, w, h = bbox_tlwh
|
||||||
|
x1 = max(int(x), 0)
|
||||||
|
x2 = min(int(x+w), self.width-1)
|
||||||
|
y1 = max(int(y), 0)
|
||||||
|
y2 = min(int(y+h), self.height-1)
|
||||||
|
return x1, y1, x2, y2
|
||||||
|
|
||||||
|
def _xyxy_to_tlwh(self, bbox_xyxy):
|
||||||
|
x1, y1, x2, y2 = bbox_xyxy
|
||||||
|
|
||||||
|
t = x1
|
||||||
|
l = y1
|
||||||
|
w = int(x2-x1)
|
||||||
|
h = int(y2-y1)
|
||||||
|
return t, l, w, h
|
||||||
|
|
||||||
|
def _get_features(self, bbox_xywh, ori_img):
|
||||||
|
im_crops = []
|
||||||
|
for box in bbox_xywh:
|
||||||
|
x1, y1, x2, y2 = self._xywh_to_xyxy(box)
|
||||||
|
im = ori_img[y1:y2, x1:x2]
|
||||||
|
im_crops.append(im)
|
||||||
|
if im_crops:
|
||||||
|
features = self.extractor(im_crops)
|
||||||
|
else:
|
||||||
|
features = np.array([])
|
||||||
|
return features
|
||||||
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|
|
@ -0,0 +1,28 @@
|
||||||
|
# vim: expandtab:ts=4:sw=4
|
||||||
|
import numpy as np
|
||||||
|
|
||||||
|
|
||||||
|
class Detection(object):
|
||||||
|
|
||||||
|
def __init__(self, tlwh, cls_, confidence, feature):
|
||||||
|
self.tlwh = np.asarray(tlwh, dtype=np.float)
|
||||||
|
self.cls_ = cls_
|
||||||
|
self.confidence = float(confidence)
|
||||||
|
self.feature = np.asarray(feature, dtype=np.float32)
|
||||||
|
|
||||||
|
def to_tlbr(self):
|
||||||
|
"""Convert bounding box to format `(min x, min y, max x, max y)`, i.e.,
|
||||||
|
`(top left, bottom right)`.
|
||||||
|
"""
|
||||||
|
ret = self.tlwh.copy()
|
||||||
|
ret[2:] += ret[:2]
|
||||||
|
return ret
|
||||||
|
|
||||||
|
def to_xyah(self):
|
||||||
|
"""Convert bounding box to format `(center x, center y, aspect ratio,
|
||||||
|
height)`, where the aspect ratio is `width / height`.
|
||||||
|
"""
|
||||||
|
ret = self.tlwh.copy()
|
||||||
|
ret[:2] += ret[2:] / 2
|
||||||
|
ret[2] /= ret[3]
|
||||||
|
return ret
|
||||||
|
|
@ -0,0 +1,81 @@
|
||||||
|
# vim: expandtab:ts=4:sw=4
|
||||||
|
from __future__ import absolute_import
|
||||||
|
import numpy as np
|
||||||
|
from . import linear_assignment
|
||||||
|
|
||||||
|
|
||||||
|
def iou(bbox, candidates):
|
||||||
|
"""Computer intersection over union.
|
||||||
|
|
||||||
|
Parameters
|
||||||
|
----------
|
||||||
|
bbox : ndarray
|
||||||
|
A bounding box in format `(top left x, top left y, width, height)`.
|
||||||
|
candidates : ndarray
|
||||||
|
A matrix of candidate bounding boxes (one per row) in the same format
|
||||||
|
as `bbox`.
|
||||||
|
|
||||||
|
Returns
|
||||||
|
-------
|
||||||
|
ndarray
|
||||||
|
The intersection over union in [0, 1] between the `bbox` and each
|
||||||
|
candidate. A higher score means a larger fraction of the `bbox` is
|
||||||
|
occluded by the candidate.
|
||||||
|
|
||||||
|
"""
|
||||||
|
bbox_tl, bbox_br = bbox[:2], bbox[:2] + bbox[2:]
|
||||||
|
candidates_tl = candidates[:, :2]
|
||||||
|
candidates_br = candidates[:, :2] + candidates[:, 2:]
|
||||||
|
|
||||||
|
tl = np.c_[np.maximum(bbox_tl[0], candidates_tl[:, 0])[:, np.newaxis],
|
||||||
|
np.maximum(bbox_tl[1], candidates_tl[:, 1])[:, np.newaxis]]
|
||||||
|
br = np.c_[np.minimum(bbox_br[0], candidates_br[:, 0])[:, np.newaxis],
|
||||||
|
np.minimum(bbox_br[1], candidates_br[:, 1])[:, np.newaxis]]
|
||||||
|
wh = np.maximum(0., br - tl)
|
||||||
|
|
||||||
|
area_intersection = wh.prod(axis=1)
|
||||||
|
area_bbox = bbox[2:].prod()
|
||||||
|
area_candidates = candidates[:, 2:].prod(axis=1)
|
||||||
|
return area_intersection / (area_bbox + area_candidates - area_intersection)
|
||||||
|
|
||||||
|
|
||||||
|
def iou_cost(tracks, detections, track_indices=None,
|
||||||
|
detection_indices=None):
|
||||||
|
"""An intersection over union distance metric.
|
||||||
|
|
||||||
|
Parameters
|
||||||
|
----------
|
||||||
|
tracks : List[deep_sort.track.Track]
|
||||||
|
A list of tracks.
|
||||||
|
detections : List[deep_sort.detection.Detection]
|
||||||
|
A list of detections.
|
||||||
|
track_indices : Optional[List[int]]
|
||||||
|
A list of indices to tracks that should be matched. Defaults to
|
||||||
|
all `tracks`.
|
||||||
|
detection_indices : Optional[List[int]]
|
||||||
|
A list of indices to detections that should be matched. Defaults
|
||||||
|
to all `detections`.
|
||||||
|
|
||||||
|
Returns
|
||||||
|
-------
|
||||||
|
ndarray
|
||||||
|
Returns a cost matrix of shape
|
||||||
|
len(track_indices), len(detection_indices) where entry (i, j) is
|
||||||
|
`1 - iou(tracks[track_indices[i]], detections[detection_indices[j]])`.
|
||||||
|
|
||||||
|
"""
|
||||||
|
if track_indices is None:
|
||||||
|
track_indices = np.arange(len(tracks))
|
||||||
|
if detection_indices is None:
|
||||||
|
detection_indices = np.arange(len(detections))
|
||||||
|
|
||||||
|
cost_matrix = np.zeros((len(track_indices), len(detection_indices)))
|
||||||
|
for row, track_idx in enumerate(track_indices):
|
||||||
|
if tracks[track_idx].time_since_update > 1:
|
||||||
|
cost_matrix[row, :] = linear_assignment.INFTY_COST
|
||||||
|
continue
|
||||||
|
|
||||||
|
bbox = tracks[track_idx].to_tlwh()
|
||||||
|
candidates = np.asarray([detections[i].tlwh for i in detection_indices])
|
||||||
|
cost_matrix[row, :] = 1. - iou(bbox, candidates)
|
||||||
|
return cost_matrix
|
||||||
|
|
@ -0,0 +1,229 @@
|
||||||
|
# vim: expandtab:ts=4:sw=4
|
||||||
|
import numpy as np
|
||||||
|
import scipy.linalg
|
||||||
|
|
||||||
|
|
||||||
|
"""
|
||||||
|
Table for the 0.95 quantile of the chi-square distribution with N degrees of
|
||||||
|
freedom (contains values for N=1, ..., 9). Taken from MATLAB/Octave's chi2inv
|
||||||
|
function and used as Mahalanobis gating threshold.
|
||||||
|
"""
|
||||||
|
chi2inv95 = {
|
||||||
|
1: 3.8415,
|
||||||
|
2: 5.9915,
|
||||||
|
3: 7.8147,
|
||||||
|
4: 9.4877,
|
||||||
|
5: 11.070,
|
||||||
|
6: 12.592,
|
||||||
|
7: 14.067,
|
||||||
|
8: 15.507,
|
||||||
|
9: 16.919}
|
||||||
|
|
||||||
|
|
||||||
|
class KalmanFilter(object):
|
||||||
|
"""
|
||||||
|
A simple Kalman filter for tracking bounding boxes in image space.
|
||||||
|
|
||||||
|
The 8-dimensional state space
|
||||||
|
|
||||||
|
x, y, a, h, vx, vy, va, vh
|
||||||
|
|
||||||
|
contains the bounding box center position (x, y), aspect ratio a, height h,
|
||||||
|
and their respective velocities.
|
||||||
|
|
||||||
|
Object motion follows a constant velocity model. The bounding box location
|
||||||
|
(x, y, a, h) is taken as direct observation of the state space (linear
|
||||||
|
observation model).
|
||||||
|
|
||||||
|
"""
|
||||||
|
|
||||||
|
def __init__(self):
|
||||||
|
ndim, dt = 4, 1.
|
||||||
|
|
||||||
|
# Create Kalman filter model matrices.
|
||||||
|
self._motion_mat = np.eye(2 * ndim, 2 * ndim)
|
||||||
|
for i in range(ndim):
|
||||||
|
self._motion_mat[i, ndim + i] = dt
|
||||||
|
self._update_mat = np.eye(ndim, 2 * ndim)
|
||||||
|
|
||||||
|
# Motion and observation uncertainty are chosen relative to the current
|
||||||
|
# state estimate. These weights control the amount of uncertainty in
|
||||||
|
# the model. This is a bit hacky.
|
||||||
|
self._std_weight_position = 1. / 20
|
||||||
|
self._std_weight_velocity = 1. / 160
|
||||||
|
|
||||||
|
def initiate(self, measurement):
|
||||||
|
"""Create track from unassociated measurement.
|
||||||
|
|
||||||
|
Parameters
|
||||||
|
----------
|
||||||
|
measurement : ndarray
|
||||||
|
Bounding box coordinates (x, y, a, h) with center position (x, y),
|
||||||
|
aspect ratio a, and height h.
|
||||||
|
|
||||||
|
Returns
|
||||||
|
-------
|
||||||
|
(ndarray, ndarray)
|
||||||
|
Returns the mean vector (8 dimensional) and covariance matrix (8x8
|
||||||
|
dimensional) of the new track. Unobserved velocities are initialized
|
||||||
|
to 0 mean.
|
||||||
|
|
||||||
|
"""
|
||||||
|
mean_pos = measurement
|
||||||
|
mean_vel = np.zeros_like(mean_pos)
|
||||||
|
mean = np.r_[mean_pos, mean_vel]
|
||||||
|
|
||||||
|
std = [
|
||||||
|
2 * self._std_weight_position * measurement[3],
|
||||||
|
2 * self._std_weight_position * measurement[3],
|
||||||
|
1e-2,
|
||||||
|
2 * self._std_weight_position * measurement[3],
|
||||||
|
10 * self._std_weight_velocity * measurement[3],
|
||||||
|
10 * self._std_weight_velocity * measurement[3],
|
||||||
|
1e-5,
|
||||||
|
10 * self._std_weight_velocity * measurement[3]]
|
||||||
|
covariance = np.diag(np.square(std))
|
||||||
|
return mean, covariance
|
||||||
|
|
||||||
|
def predict(self, mean, covariance):
|
||||||
|
"""Run Kalman filter prediction step.
|
||||||
|
|
||||||
|
Parameters
|
||||||
|
----------
|
||||||
|
mean : ndarray
|
||||||
|
The 8 dimensional mean vector of the object state at the previous
|
||||||
|
time step.
|
||||||
|
covariance : ndarray
|
||||||
|
The 8x8 dimensional covariance matrix of the object state at the
|
||||||
|
previous time step.
|
||||||
|
|
||||||
|
Returns
|
||||||
|
-------
|
||||||
|
(ndarray, ndarray)
|
||||||
|
Returns the mean vector and covariance matrix of the predicted
|
||||||
|
state. Unobserved velocities are initialized to 0 mean.
|
||||||
|
|
||||||
|
"""
|
||||||
|
std_pos = [
|
||||||
|
self._std_weight_position * mean[3],
|
||||||
|
self._std_weight_position * mean[3],
|
||||||
|
1e-2,
|
||||||
|
self._std_weight_position * mean[3]]
|
||||||
|
std_vel = [
|
||||||
|
self._std_weight_velocity * mean[3],
|
||||||
|
self._std_weight_velocity * mean[3],
|
||||||
|
1e-5,
|
||||||
|
self._std_weight_velocity * mean[3]]
|
||||||
|
motion_cov = np.diag(np.square(np.r_[std_pos, std_vel]))
|
||||||
|
|
||||||
|
mean = np.dot(self._motion_mat, mean)
|
||||||
|
covariance = np.linalg.multi_dot((
|
||||||
|
self._motion_mat, covariance, self._motion_mat.T)) + motion_cov
|
||||||
|
|
||||||
|
return mean, covariance
|
||||||
|
|
||||||
|
def project(self, mean, covariance):
|
||||||
|
"""Project state distribution to measurement space.
|
||||||
|
|
||||||
|
Parameters
|
||||||
|
----------
|
||||||
|
mean : ndarray
|
||||||
|
The state's mean vector (8 dimensional array).
|
||||||
|
covariance : ndarray
|
||||||
|
The state's covariance matrix (8x8 dimensional).
|
||||||
|
|
||||||
|
Returns
|
||||||
|
-------
|
||||||
|
(ndarray, ndarray)
|
||||||
|
Returns the projected mean and covariance matrix of the given state
|
||||||
|
estimate.
|
||||||
|
|
||||||
|
"""
|
||||||
|
std = [
|
||||||
|
self._std_weight_position * mean[3],
|
||||||
|
self._std_weight_position * mean[3],
|
||||||
|
1e-1,
|
||||||
|
self._std_weight_position * mean[3]]
|
||||||
|
innovation_cov = np.diag(np.square(std))
|
||||||
|
|
||||||
|
mean = np.dot(self._update_mat, mean)
|
||||||
|
covariance = np.linalg.multi_dot((
|
||||||
|
self._update_mat, covariance, self._update_mat.T))
|
||||||
|
return mean, covariance + innovation_cov
|
||||||
|
|
||||||
|
def update(self, mean, covariance, measurement):
|
||||||
|
"""Run Kalman filter correction step.
|
||||||
|
|
||||||
|
Parameters
|
||||||
|
----------
|
||||||
|
mean : ndarray
|
||||||
|
The predicted state's mean vector (8 dimensional).
|
||||||
|
covariance : ndarray
|
||||||
|
The state's covariance matrix (8x8 dimensional).
|
||||||
|
measurement : ndarray
|
||||||
|
The 4 dimensional measurement vector (x, y, a, h), where (x, y)
|
||||||
|
is the center position, a the aspect ratio, and h the height of the
|
||||||
|
bounding box.
|
||||||
|
|
||||||
|
Returns
|
||||||
|
-------
|
||||||
|
(ndarray, ndarray)
|
||||||
|
Returns the measurement-corrected state distribution.
|
||||||
|
|
||||||
|
"""
|
||||||
|
projected_mean, projected_cov = self.project(mean, covariance)
|
||||||
|
|
||||||
|
chol_factor, lower = scipy.linalg.cho_factor(
|
||||||
|
projected_cov, lower=True, check_finite=False)
|
||||||
|
kalman_gain = scipy.linalg.cho_solve(
|
||||||
|
(chol_factor, lower), np.dot(covariance, self._update_mat.T).T,
|
||||||
|
check_finite=False).T
|
||||||
|
innovation = measurement - projected_mean
|
||||||
|
|
||||||
|
new_mean = mean + np.dot(innovation, kalman_gain.T)
|
||||||
|
new_covariance = covariance - np.linalg.multi_dot((
|
||||||
|
kalman_gain, projected_cov, kalman_gain.T))
|
||||||
|
return new_mean, new_covariance
|
||||||
|
|
||||||
|
def gating_distance(self, mean, covariance, measurements,
|
||||||
|
only_position=False):
|
||||||
|
"""Compute gating distance between state distribution and measurements.
|
||||||
|
|
||||||
|
A suitable distance threshold can be obtained from `chi2inv95`. If
|
||||||
|
`only_position` is False, the chi-square distribution has 4 degrees of
|
||||||
|
freedom, otherwise 2.
|
||||||
|
|
||||||
|
Parameters
|
||||||
|
----------
|
||||||
|
mean : ndarray
|
||||||
|
Mean vector over the state distribution (8 dimensional).
|
||||||
|
covariance : ndarray
|
||||||
|
Covariance of the state distribution (8x8 dimensional).
|
||||||
|
measurements : ndarray
|
||||||
|
An Nx4 dimensional matrix of N measurements, each in
|
||||||
|
format (x, y, a, h) where (x, y) is the bounding box center
|
||||||
|
position, a the aspect ratio, and h the height.
|
||||||
|
only_position : Optional[bool]
|
||||||
|
If True, distance computation is done with respect to the bounding
|
||||||
|
box center position only.
|
||||||
|
|
||||||
|
Returns
|
||||||
|
-------
|
||||||
|
ndarray
|
||||||
|
Returns an array of length N, where the i-th element contains the
|
||||||
|
squared Mahalanobis distance between (mean, covariance) and
|
||||||
|
`measurements[i]`.
|
||||||
|
|
||||||
|
"""
|
||||||
|
mean, covariance = self.project(mean, covariance)
|
||||||
|
if only_position:
|
||||||
|
mean, covariance = mean[:2], covariance[:2, :2]
|
||||||
|
measurements = measurements[:, :2]
|
||||||
|
|
||||||
|
cholesky_factor = np.linalg.cholesky(covariance)
|
||||||
|
d = measurements - mean
|
||||||
|
z = scipy.linalg.solve_triangular(
|
||||||
|
cholesky_factor, d.T, lower=True, check_finite=False,
|
||||||
|
overwrite_b=True)
|
||||||
|
squared_maha = np.sum(z * z, axis=0)
|
||||||
|
return squared_maha
|
||||||
|
|
@ -0,0 +1,159 @@
|
||||||
|
# vim: expandtab:ts=4:sw=4
|
||||||
|
from __future__ import absolute_import
|
||||||
|
import numpy as np
|
||||||
|
# from sklearn.utils.linear_assignment_ import linear_assignment
|
||||||
|
from scipy.optimize import linear_sum_assignment as linear_assignment
|
||||||
|
from . import kalman_filter
|
||||||
|
|
||||||
|
|
||||||
|
INFTY_COST = 1e+5
|
||||||
|
|
||||||
|
|
||||||
|
def min_cost_matching(
|
||||||
|
distance_metric, max_distance, tracks, detections, track_indices=None,
|
||||||
|
detection_indices=None):
|
||||||
|
if track_indices is None:
|
||||||
|
track_indices = np.arange(len(tracks))
|
||||||
|
if detection_indices is None:
|
||||||
|
detection_indices = np.arange(len(detections))
|
||||||
|
|
||||||
|
if len(detection_indices) == 0 or len(track_indices) == 0:
|
||||||
|
return [], track_indices, detection_indices # Nothing to match.
|
||||||
|
|
||||||
|
cost_matrix = distance_metric(
|
||||||
|
tracks, detections, track_indices, detection_indices)
|
||||||
|
cost_matrix[cost_matrix > max_distance] = max_distance + 1e-5
|
||||||
|
|
||||||
|
row_indices, col_indices = linear_assignment(cost_matrix)
|
||||||
|
|
||||||
|
matches, unmatched_tracks, unmatched_detections = [], [], []
|
||||||
|
for col, detection_idx in enumerate(detection_indices):
|
||||||
|
if col not in col_indices:
|
||||||
|
unmatched_detections.append(detection_idx)
|
||||||
|
for row, track_idx in enumerate(track_indices):
|
||||||
|
if row not in row_indices:
|
||||||
|
unmatched_tracks.append(track_idx)
|
||||||
|
for row, col in zip(row_indices, col_indices):
|
||||||
|
track_idx = track_indices[row]
|
||||||
|
detection_idx = detection_indices[col]
|
||||||
|
if cost_matrix[row, col] > max_distance:
|
||||||
|
unmatched_tracks.append(track_idx)
|
||||||
|
unmatched_detections.append(detection_idx)
|
||||||
|
else:
|
||||||
|
matches.append((track_idx, detection_idx))
|
||||||
|
return matches, unmatched_tracks, unmatched_detections
|
||||||
|
|
||||||
|
|
||||||
|
def matching_cascade(
|
||||||
|
distance_metric, max_distance, cascade_depth, tracks, detections,
|
||||||
|
track_indices=None, detection_indices=None):
|
||||||
|
"""Run matching cascade.
|
||||||
|
|
||||||
|
Parameters
|
||||||
|
----------
|
||||||
|
distance_metric : Callable[List[Track], List[Detection], List[int], List[int]) -> ndarray
|
||||||
|
The distance metric is given a list of tracks and detections as well as
|
||||||
|
a list of N track indices and M detection indices. The metric should
|
||||||
|
return the NxM dimensional cost matrix, where element (i, j) is the
|
||||||
|
association cost between the i-th track in the given track indices and
|
||||||
|
the j-th detection in the given detection indices.
|
||||||
|
max_distance : float
|
||||||
|
Gating threshold. Associations with cost larger than this value are
|
||||||
|
disregarded.
|
||||||
|
cascade_depth: int
|
||||||
|
The cascade depth, should be se to the maximum track age.
|
||||||
|
tracks : List[track.Track]
|
||||||
|
A list of predicted tracks at the current time step.
|
||||||
|
detections : List[detection.Detection]
|
||||||
|
A list of detections at the current time step.
|
||||||
|
track_indices : Optional[List[int]]
|
||||||
|
List of track indices that maps rows in `cost_matrix` to tracks in
|
||||||
|
`tracks` (see description above). Defaults to all tracks.
|
||||||
|
detection_indices : Optional[List[int]]
|
||||||
|
List of detection indices that maps columns in `cost_matrix` to
|
||||||
|
detections in `detections` (see description above). Defaults to all
|
||||||
|
detections.
|
||||||
|
|
||||||
|
Returns
|
||||||
|
-------
|
||||||
|
(List[(int, int)], List[int], List[int])
|
||||||
|
Returns a tuple with the following three entries:
|
||||||
|
* A list of matched track and detection indices.
|
||||||
|
* A list of unmatched track indices.
|
||||||
|
* A list of unmatched detection indices.
|
||||||
|
|
||||||
|
"""
|
||||||
|
if track_indices is None:
|
||||||
|
track_indices = list(range(len(tracks)))
|
||||||
|
if detection_indices is None:
|
||||||
|
detection_indices = list(range(len(detections)))
|
||||||
|
|
||||||
|
unmatched_detections = detection_indices
|
||||||
|
matches = []
|
||||||
|
for level in range(cascade_depth):
|
||||||
|
if len(unmatched_detections) == 0: # No detections left
|
||||||
|
break
|
||||||
|
|
||||||
|
track_indices_l = [
|
||||||
|
k for k in track_indices
|
||||||
|
if tracks[k].time_since_update == 1 + level
|
||||||
|
]
|
||||||
|
if len(track_indices_l) == 0: # Nothing to match at this level
|
||||||
|
continue
|
||||||
|
|
||||||
|
matches_l, _, unmatched_detections = \
|
||||||
|
min_cost_matching(
|
||||||
|
distance_metric, max_distance, tracks, detections,
|
||||||
|
track_indices_l, unmatched_detections)
|
||||||
|
matches += matches_l
|
||||||
|
unmatched_tracks = list(set(track_indices) - set(k for k, _ in matches))
|
||||||
|
return matches, unmatched_tracks, unmatched_detections
|
||||||
|
|
||||||
|
|
||||||
|
def gate_cost_matrix(
|
||||||
|
kf, cost_matrix, tracks, detections, track_indices, detection_indices,
|
||||||
|
gated_cost=INFTY_COST, only_position=False):
|
||||||
|
"""Invalidate infeasible entries in cost matrix based on the state
|
||||||
|
distributions obtained by Kalman filtering.
|
||||||
|
|
||||||
|
Parameters
|
||||||
|
----------
|
||||||
|
kf : The Kalman filter.
|
||||||
|
cost_matrix : ndarray
|
||||||
|
The NxM dimensional cost matrix, where N is the number of track indices
|
||||||
|
and M is the number of detection indices, such that entry (i, j) is the
|
||||||
|
association cost between `tracks[track_indices[i]]` and
|
||||||
|
`detections[detection_indices[j]]`.
|
||||||
|
tracks : List[track.Track]
|
||||||
|
A list of predicted tracks at the current time step.
|
||||||
|
detections : List[detection.Detection]
|
||||||
|
A list of detections at the current time step.
|
||||||
|
track_indices : List[int]
|
||||||
|
List of track indices that maps rows in `cost_matrix` to tracks in
|
||||||
|
`tracks` (see description above).
|
||||||
|
detection_indices : List[int]
|
||||||
|
List of detection indices that maps columns in `cost_matrix` to
|
||||||
|
detections in `detections` (see description above).
|
||||||
|
gated_cost : Optional[float]
|
||||||
|
Entries in the cost matrix corresponding to infeasible associations are
|
||||||
|
set this value. Defaults to a very large value.
|
||||||
|
only_position : Optional[bool]
|
||||||
|
If True, only the x, y position of the state distribution is considered
|
||||||
|
during gating. Defaults to False.
|
||||||
|
|
||||||
|
Returns
|
||||||
|
-------
|
||||||
|
ndarray
|
||||||
|
Returns the modified cost matrix.
|
||||||
|
|
||||||
|
"""
|
||||||
|
gating_dim = 2 if only_position else 4
|
||||||
|
gating_threshold = kalman_filter.chi2inv95[gating_dim]
|
||||||
|
measurements = np.asarray(
|
||||||
|
[detections[i].to_xyah() for i in detection_indices])
|
||||||
|
for row, track_idx in enumerate(track_indices):
|
||||||
|
track = tracks[track_idx]
|
||||||
|
gating_distance = kf.gating_distance(
|
||||||
|
track.mean, track.covariance, measurements, only_position)
|
||||||
|
cost_matrix[row, gating_distance > gating_threshold] = gated_cost
|
||||||
|
return cost_matrix
|
||||||
|
|
@ -0,0 +1,177 @@
|
||||||
|
# vim: expandtab:ts=4:sw=4
|
||||||
|
import numpy as np
|
||||||
|
|
||||||
|
|
||||||
|
def _pdist(a, b):
|
||||||
|
"""Compute pair-wise squared distance between points in `a` and `b`.
|
||||||
|
|
||||||
|
Parameters
|
||||||
|
----------
|
||||||
|
a : array_like
|
||||||
|
An NxM matrix of N samples of dimensionality M.
|
||||||
|
b : array_like
|
||||||
|
An LxM matrix of L samples of dimensionality M.
|
||||||
|
|
||||||
|
Returns
|
||||||
|
-------
|
||||||
|
ndarray
|
||||||
|
Returns a matrix of size len(a), len(b) such that eleement (i, j)
|
||||||
|
contains the squared distance between `a[i]` and `b[j]`.
|
||||||
|
|
||||||
|
"""
|
||||||
|
a, b = np.asarray(a), np.asarray(b)
|
||||||
|
if len(a) == 0 or len(b) == 0:
|
||||||
|
return np.zeros((len(a), len(b)))
|
||||||
|
a2, b2 = np.square(a).sum(axis=1), np.square(b).sum(axis=1)
|
||||||
|
r2 = -2. * np.dot(a, b.T) + a2[:, None] + b2[None, :]
|
||||||
|
r2 = np.clip(r2, 0., float(np.inf))
|
||||||
|
return r2
|
||||||
|
|
||||||
|
|
||||||
|
def _cosine_distance(a, b, data_is_normalized=False):
|
||||||
|
"""Compute pair-wise cosine distance between points in `a` and `b`.
|
||||||
|
|
||||||
|
Parameters
|
||||||
|
----------
|
||||||
|
a : array_like
|
||||||
|
An NxM matrix of N samples of dimensionality M.
|
||||||
|
b : array_like
|
||||||
|
An LxM matrix of L samples of dimensionality M.
|
||||||
|
data_is_normalized : Optional[bool]
|
||||||
|
If True, assumes rows in a and b are unit length vectors.
|
||||||
|
Otherwise, a and b are explicitly normalized to lenght 1.
|
||||||
|
|
||||||
|
Returns
|
||||||
|
-------
|
||||||
|
ndarray
|
||||||
|
Returns a matrix of size len(a), len(b) such that eleement (i, j)
|
||||||
|
contains the squared distance between `a[i]` and `b[j]`.
|
||||||
|
|
||||||
|
"""
|
||||||
|
if not data_is_normalized:
|
||||||
|
a = np.asarray(a) / np.linalg.norm(a, axis=1, keepdims=True)
|
||||||
|
b = np.asarray(b) / np.linalg.norm(b, axis=1, keepdims=True)
|
||||||
|
return 1. - np.dot(a, b.T)
|
||||||
|
|
||||||
|
|
||||||
|
def _nn_euclidean_distance(x, y):
|
||||||
|
""" Helper function for nearest neighbor distance metric (Euclidean).
|
||||||
|
|
||||||
|
Parameters
|
||||||
|
----------
|
||||||
|
x : ndarray
|
||||||
|
A matrix of N row-vectors (sample points).
|
||||||
|
y : ndarray
|
||||||
|
A matrix of M row-vectors (query points).
|
||||||
|
|
||||||
|
Returns
|
||||||
|
-------
|
||||||
|
ndarray
|
||||||
|
A vector of length M that contains for each entry in `y` the
|
||||||
|
smallest Euclidean distance to a sample in `x`.
|
||||||
|
|
||||||
|
"""
|
||||||
|
distances = _pdist(x, y)
|
||||||
|
return np.maximum(0.0, distances.min(axis=0))
|
||||||
|
|
||||||
|
|
||||||
|
def _nn_cosine_distance(x, y):
|
||||||
|
""" Helper function for nearest neighbor distance metric (cosine).
|
||||||
|
|
||||||
|
Parameters
|
||||||
|
----------
|
||||||
|
x : ndarray
|
||||||
|
A matrix of N row-vectors (sample points).
|
||||||
|
y : ndarray
|
||||||
|
A matrix of M row-vectors (query points).
|
||||||
|
|
||||||
|
Returns
|
||||||
|
-------
|
||||||
|
ndarray
|
||||||
|
A vector of length M that contains for each entry in `y` the
|
||||||
|
smallest cosine distance to a sample in `x`.
|
||||||
|
|
||||||
|
"""
|
||||||
|
distances = _cosine_distance(x, y)
|
||||||
|
return distances.min(axis=0)
|
||||||
|
|
||||||
|
|
||||||
|
class NearestNeighborDistanceMetric(object):
|
||||||
|
"""
|
||||||
|
A nearest neighbor distance metric that, for each target, returns
|
||||||
|
the closest distance to any sample that has been observed so far.
|
||||||
|
|
||||||
|
Parameters
|
||||||
|
----------
|
||||||
|
metric : str
|
||||||
|
Either "euclidean" or "cosine".
|
||||||
|
matching_threshold: float
|
||||||
|
The matching threshold. Samples with larger distance are considered an
|
||||||
|
invalid match.
|
||||||
|
budget : Optional[int]
|
||||||
|
If not None, fix samples per class to at most this number. Removes
|
||||||
|
the oldest samples when the budget is reached.
|
||||||
|
|
||||||
|
Attributes
|
||||||
|
----------
|
||||||
|
samples : Dict[int -> List[ndarray]]
|
||||||
|
A dictionary that maps from target identities to the list of samples
|
||||||
|
that have been observed so far.
|
||||||
|
|
||||||
|
"""
|
||||||
|
|
||||||
|
def __init__(self, metric, matching_threshold, budget=None):
|
||||||
|
|
||||||
|
|
||||||
|
if metric == "euclidean":
|
||||||
|
self._metric = _nn_euclidean_distance
|
||||||
|
elif metric == "cosine":
|
||||||
|
self._metric = _nn_cosine_distance
|
||||||
|
else:
|
||||||
|
raise ValueError(
|
||||||
|
"Invalid metric; must be either 'euclidean' or 'cosine'")
|
||||||
|
self.matching_threshold = matching_threshold
|
||||||
|
self.budget = budget
|
||||||
|
self.samples = {}
|
||||||
|
|
||||||
|
def partial_fit(self, features, targets, active_targets):
|
||||||
|
"""Update the distance metric with new data.
|
||||||
|
|
||||||
|
Parameters
|
||||||
|
----------
|
||||||
|
features : ndarray
|
||||||
|
An NxM matrix of N features of dimensionality M.
|
||||||
|
targets : ndarray
|
||||||
|
An integer array of associated target identities.
|
||||||
|
active_targets : List[int]
|
||||||
|
A list of targets that are currently present in the scene.
|
||||||
|
|
||||||
|
"""
|
||||||
|
for feature, target in zip(features, targets):
|
||||||
|
self.samples.setdefault(target, []).append(feature)
|
||||||
|
if self.budget is not None:
|
||||||
|
self.samples[target] = self.samples[target][-self.budget:]
|
||||||
|
self.samples = {k: self.samples[k] for k in active_targets}
|
||||||
|
|
||||||
|
def distance(self, features, targets):
|
||||||
|
"""Compute distance between features and targets.
|
||||||
|
|
||||||
|
Parameters
|
||||||
|
----------
|
||||||
|
features : ndarray
|
||||||
|
An NxM matrix of N features of dimensionality M.
|
||||||
|
targets : List[int]
|
||||||
|
A list of targets to match the given `features` against.
|
||||||
|
|
||||||
|
Returns
|
||||||
|
-------
|
||||||
|
ndarray
|
||||||
|
Returns a cost matrix of shape len(targets), len(features), where
|
||||||
|
element (i, j) contains the closest squared distance between
|
||||||
|
`targets[i]` and `features[j]`.
|
||||||
|
|
||||||
|
"""
|
||||||
|
cost_matrix = np.zeros((len(targets), len(features)))
|
||||||
|
for i, target in enumerate(targets):
|
||||||
|
cost_matrix[i, :] = self._metric(self.samples[target], features)
|
||||||
|
return cost_matrix
|
||||||
|
|
@ -0,0 +1,73 @@
|
||||||
|
# vim: expandtab:ts=4:sw=4
|
||||||
|
import numpy as np
|
||||||
|
import cv2
|
||||||
|
|
||||||
|
|
||||||
|
def non_max_suppression(boxes, max_bbox_overlap, scores=None):
|
||||||
|
"""Suppress overlapping detections.
|
||||||
|
|
||||||
|
Original code from [1]_ has been adapted to include confidence score.
|
||||||
|
|
||||||
|
.. [1] http://www.pyimagesearch.com/2015/02/16/
|
||||||
|
faster-non-maximum-suppression-python/
|
||||||
|
|
||||||
|
Examples
|
||||||
|
--------
|
||||||
|
|
||||||
|
>>> boxes = [d.roi for d in detections]
|
||||||
|
>>> scores = [d.confidence for d in detections]
|
||||||
|
>>> indices = non_max_suppression(boxes, max_bbox_overlap, scores)
|
||||||
|
>>> detections = [detections[i] for i in indices]
|
||||||
|
|
||||||
|
Parameters
|
||||||
|
----------
|
||||||
|
boxes : ndarray
|
||||||
|
Array of ROIs (x, y, width, height).
|
||||||
|
max_bbox_overlap : float
|
||||||
|
ROIs that overlap more than this values are suppressed.
|
||||||
|
scores : Optional[array_like]
|
||||||
|
Detector confidence score.
|
||||||
|
|
||||||
|
Returns
|
||||||
|
-------
|
||||||
|
List[int]
|
||||||
|
Returns indices of detections that have survived non-maxima suppression.
|
||||||
|
|
||||||
|
"""
|
||||||
|
if len(boxes) == 0:
|
||||||
|
return []
|
||||||
|
|
||||||
|
boxes = boxes.astype(np.float)
|
||||||
|
pick = []
|
||||||
|
|
||||||
|
x1 = boxes[:, 0]
|
||||||
|
y1 = boxes[:, 1]
|
||||||
|
x2 = boxes[:, 2] + boxes[:, 0]
|
||||||
|
y2 = boxes[:, 3] + boxes[:, 1]
|
||||||
|
|
||||||
|
area = (x2 - x1 + 1) * (y2 - y1 + 1)
|
||||||
|
if scores is not None:
|
||||||
|
idxs = np.argsort(scores)
|
||||||
|
else:
|
||||||
|
idxs = np.argsort(y2)
|
||||||
|
|
||||||
|
while len(idxs) > 0:
|
||||||
|
last = len(idxs) - 1
|
||||||
|
i = idxs[last]
|
||||||
|
pick.append(i)
|
||||||
|
|
||||||
|
xx1 = np.maximum(x1[i], x1[idxs[:last]])
|
||||||
|
yy1 = np.maximum(y1[i], y1[idxs[:last]])
|
||||||
|
xx2 = np.minimum(x2[i], x2[idxs[:last]])
|
||||||
|
yy2 = np.minimum(y2[i], y2[idxs[:last]])
|
||||||
|
|
||||||
|
w = np.maximum(0, xx2 - xx1 + 1)
|
||||||
|
h = np.maximum(0, yy2 - yy1 + 1)
|
||||||
|
|
||||||
|
overlap = (w * h) / area[idxs[:last]]
|
||||||
|
|
||||||
|
idxs = np.delete(
|
||||||
|
idxs, np.concatenate(
|
||||||
|
([last], np.where(overlap > max_bbox_overlap)[0])))
|
||||||
|
|
||||||
|
return pick
|
||||||
|
|
@ -0,0 +1,168 @@
|
||||||
|
# vim: expandtab:ts=4:sw=4
|
||||||
|
|
||||||
|
|
||||||
|
class TrackState:
|
||||||
|
"""
|
||||||
|
Enumeration type for the single target track state. Newly created tracks are
|
||||||
|
classified as `tentative` until enough evidence has been collected. Then,
|
||||||
|
the track state is changed to `confirmed`. Tracks that are no longer alive
|
||||||
|
are classified as `deleted` to mark them for removal from the set of active
|
||||||
|
tracks.
|
||||||
|
|
||||||
|
"""
|
||||||
|
|
||||||
|
Tentative = 1
|
||||||
|
Confirmed = 2
|
||||||
|
Deleted = 3
|
||||||
|
|
||||||
|
|
||||||
|
class Track:
|
||||||
|
"""
|
||||||
|
A single target track with state space `(x, y, a, h)` and associated
|
||||||
|
velocities, where `(x, y)` is the center of the bounding box, `a` is the
|
||||||
|
aspect ratio and `h` is the height.
|
||||||
|
|
||||||
|
Parameters
|
||||||
|
----------
|
||||||
|
mean : ndarray
|
||||||
|
Mean vector of the initial state distribution.
|
||||||
|
covariance : ndarray
|
||||||
|
Covariance matrix of the initial state distribution.
|
||||||
|
track_id : int
|
||||||
|
A unique track identifier.
|
||||||
|
n_init : int
|
||||||
|
Number of consecutive detections before the track is confirmed. The
|
||||||
|
track state is set to `Deleted` if a miss occurs within the first
|
||||||
|
`n_init` frames.
|
||||||
|
max_age : int
|
||||||
|
The maximum number of consecutive misses before the track state is
|
||||||
|
set to `Deleted`.
|
||||||
|
feature : Optional[ndarray]
|
||||||
|
Feature vector of the detection this track originates from. If not None,
|
||||||
|
this feature is added to the `features` cache.
|
||||||
|
|
||||||
|
Attributes
|
||||||
|
----------
|
||||||
|
mean : ndarray
|
||||||
|
Mean vector of the initial state distribution.
|
||||||
|
covariance : ndarray
|
||||||
|
Covariance matrix of the initial state distribution.
|
||||||
|
track_id : int
|
||||||
|
A unique track identifier.
|
||||||
|
hits : int
|
||||||
|
Total number of measurement updates.
|
||||||
|
age : int
|
||||||
|
Total number of frames since first occurance.
|
||||||
|
time_since_update : int
|
||||||
|
Total number of frames since last measurement update.
|
||||||
|
state : TrackState
|
||||||
|
The current track state.
|
||||||
|
features : List[ndarray]
|
||||||
|
A cache of features. On each measurement update, the associated feature
|
||||||
|
vector is added to this list.
|
||||||
|
|
||||||
|
"""
|
||||||
|
|
||||||
|
def __init__(self, mean, cls_, covariance, track_id, n_init, max_age,
|
||||||
|
feature=None):
|
||||||
|
self.mean = mean
|
||||||
|
self.cls_ = cls_
|
||||||
|
self.covariance = covariance
|
||||||
|
self.track_id = track_id
|
||||||
|
self.hits = 1
|
||||||
|
self.age = 1
|
||||||
|
self.time_since_update = 0
|
||||||
|
|
||||||
|
self.state = TrackState.Tentative
|
||||||
|
self.features = []
|
||||||
|
if feature is not None:
|
||||||
|
self.features.append(feature)
|
||||||
|
|
||||||
|
self._n_init = n_init
|
||||||
|
self._max_age = max_age
|
||||||
|
|
||||||
|
def to_tlwh(self):
|
||||||
|
"""Get current position in bounding box format `(top left x, top left y,
|
||||||
|
width, height)`.
|
||||||
|
|
||||||
|
Returns
|
||||||
|
-------
|
||||||
|
ndarray
|
||||||
|
The bounding box.
|
||||||
|
|
||||||
|
"""
|
||||||
|
ret = self.mean[:4].copy()
|
||||||
|
ret[2] *= ret[3]
|
||||||
|
ret[:2] -= ret[2:] / 2
|
||||||
|
return ret
|
||||||
|
|
||||||
|
def to_tlbr(self):
|
||||||
|
"""Get current position in bounding box format `(min x, miny, max x,
|
||||||
|
max y)`.
|
||||||
|
|
||||||
|
Returns
|
||||||
|
-------
|
||||||
|
ndarray
|
||||||
|
The bounding box.
|
||||||
|
|
||||||
|
"""
|
||||||
|
ret = self.to_tlwh()
|
||||||
|
ret[2:] = ret[:2] + ret[2:]
|
||||||
|
return ret
|
||||||
|
|
||||||
|
def predict(self, kf):
|
||||||
|
"""Propagate the state distribution to the current time step using a
|
||||||
|
Kalman filter prediction step.
|
||||||
|
|
||||||
|
Parameters
|
||||||
|
----------
|
||||||
|
kf : kalman_filter.KalmanFilter
|
||||||
|
The Kalman filter.
|
||||||
|
|
||||||
|
"""
|
||||||
|
self.mean, self.covariance = kf.predict(self.mean, self.covariance)
|
||||||
|
self.age += 1
|
||||||
|
self.time_since_update += 1
|
||||||
|
|
||||||
|
def update(self, kf, detection):
|
||||||
|
"""Perform Kalman filter measurement update step and update the feature
|
||||||
|
cache.
|
||||||
|
|
||||||
|
Parameters
|
||||||
|
----------
|
||||||
|
kf : kalman_filter.KalmanFilter
|
||||||
|
The Kalman filter.
|
||||||
|
detection : Detection
|
||||||
|
The associated detection.
|
||||||
|
|
||||||
|
"""
|
||||||
|
self.mean, self.covariance = kf.update(
|
||||||
|
self.mean, self.covariance, detection.to_xyah())
|
||||||
|
self.features.append(detection.feature)
|
||||||
|
self.cls_ = detection.cls_
|
||||||
|
|
||||||
|
self.hits += 1
|
||||||
|
self.time_since_update = 0
|
||||||
|
if self.state == TrackState.Tentative and self.hits >= self._n_init:
|
||||||
|
self.state = TrackState.Confirmed
|
||||||
|
|
||||||
|
def mark_missed(self):
|
||||||
|
"""Mark this track as missed (no association at the current time step).
|
||||||
|
"""
|
||||||
|
if self.state == TrackState.Tentative:
|
||||||
|
self.state = TrackState.Deleted
|
||||||
|
elif self.time_since_update > self._max_age:
|
||||||
|
self.state = TrackState.Deleted
|
||||||
|
|
||||||
|
def is_tentative(self):
|
||||||
|
"""Returns True if this track is tentative (unconfirmed).
|
||||||
|
"""
|
||||||
|
return self.state == TrackState.Tentative
|
||||||
|
|
||||||
|
def is_confirmed(self):
|
||||||
|
"""Returns True if this track is confirmed."""
|
||||||
|
return self.state == TrackState.Confirmed
|
||||||
|
|
||||||
|
def is_deleted(self):
|
||||||
|
"""Returns True if this track is dead and should be deleted."""
|
||||||
|
return self.state == TrackState.Deleted
|
||||||
|
|
@ -0,0 +1,109 @@
|
||||||
|
# vim: expandtab:ts=4:sw=4
|
||||||
|
from __future__ import absolute_import
|
||||||
|
import numpy as np
|
||||||
|
from . import kalman_filter
|
||||||
|
from . import linear_assignment
|
||||||
|
from . import iou_matching
|
||||||
|
from .track import Track
|
||||||
|
|
||||||
|
|
||||||
|
class Tracker:
|
||||||
|
|
||||||
|
def __init__(self, metric, max_iou_distance=0.7, max_age=70, n_init=3):
|
||||||
|
self.metric = metric
|
||||||
|
self.max_iou_distance = max_iou_distance
|
||||||
|
self.max_age = max_age
|
||||||
|
self.n_init = n_init
|
||||||
|
|
||||||
|
self.kf = kalman_filter.KalmanFilter()
|
||||||
|
self.tracks = []
|
||||||
|
self._next_id = 1
|
||||||
|
|
||||||
|
def predict(self):
|
||||||
|
"""Propagate track state distributions one time step forward.
|
||||||
|
|
||||||
|
This function should be called once every time step, before `update`.
|
||||||
|
"""
|
||||||
|
for track in self.tracks:
|
||||||
|
track.predict(self.kf)
|
||||||
|
|
||||||
|
def update(self, detections):
|
||||||
|
"""Perform measurement update and track management.
|
||||||
|
|
||||||
|
Parameters
|
||||||
|
----------
|
||||||
|
detections : List[deep_sort.detection.Detection]
|
||||||
|
A list of detections at the current time step.
|
||||||
|
|
||||||
|
"""
|
||||||
|
# Run matching cascade.
|
||||||
|
matches, unmatched_tracks, unmatched_detections = \
|
||||||
|
self._match(detections)
|
||||||
|
|
||||||
|
# Update track set.
|
||||||
|
for track_idx, detection_idx in matches:
|
||||||
|
self.tracks[track_idx].update(
|
||||||
|
self.kf, detections[detection_idx])
|
||||||
|
for track_idx in unmatched_tracks:
|
||||||
|
self.tracks[track_idx].mark_missed()
|
||||||
|
for detection_idx in unmatched_detections:
|
||||||
|
self._initiate_track(detections[detection_idx])
|
||||||
|
self.tracks = [t for t in self.tracks if not t.is_deleted()]
|
||||||
|
|
||||||
|
# Update distance metric.
|
||||||
|
active_targets = [t.track_id for t in self.tracks if t.is_confirmed()]
|
||||||
|
features, targets = [], []
|
||||||
|
for track in self.tracks:
|
||||||
|
if not track.is_confirmed():
|
||||||
|
continue
|
||||||
|
features += track.features
|
||||||
|
targets += [track.track_id for _ in track.features]
|
||||||
|
track.features = []
|
||||||
|
self.metric.partial_fit(
|
||||||
|
np.asarray(features), np.asarray(targets), active_targets)
|
||||||
|
|
||||||
|
def _match(self, detections):
|
||||||
|
|
||||||
|
def gated_metric(tracks, dets, track_indices, detection_indices):
|
||||||
|
features = np.array([dets[i].feature for i in detection_indices])
|
||||||
|
targets = np.array([tracks[i].track_id for i in track_indices])
|
||||||
|
cost_matrix = self.metric.distance(features, targets)
|
||||||
|
cost_matrix = linear_assignment.gate_cost_matrix(
|
||||||
|
self.kf, cost_matrix, tracks, dets, track_indices,
|
||||||
|
detection_indices)
|
||||||
|
|
||||||
|
return cost_matrix
|
||||||
|
|
||||||
|
# Split track set into confirmed and unconfirmed tracks.
|
||||||
|
confirmed_tracks = [
|
||||||
|
i for i, t in enumerate(self.tracks) if t.is_confirmed()]
|
||||||
|
unconfirmed_tracks = [
|
||||||
|
i for i, t in enumerate(self.tracks) if not t.is_confirmed()]
|
||||||
|
|
||||||
|
# Associate confirmed tracks using appearance features.
|
||||||
|
matches_a, unmatched_tracks_a, unmatched_detections = \
|
||||||
|
linear_assignment.matching_cascade(
|
||||||
|
gated_metric, self.metric.matching_threshold, self.max_age,
|
||||||
|
self.tracks, detections, confirmed_tracks)
|
||||||
|
|
||||||
|
# Associate remaining tracks together with unconfirmed tracks using IOU.
|
||||||
|
iou_track_candidates = unconfirmed_tracks + [
|
||||||
|
k for k in unmatched_tracks_a if
|
||||||
|
self.tracks[k].time_since_update == 1]
|
||||||
|
unmatched_tracks_a = [
|
||||||
|
k for k in unmatched_tracks_a if
|
||||||
|
self.tracks[k].time_since_update != 1]
|
||||||
|
matches_b, unmatched_tracks_b, unmatched_detections = \
|
||||||
|
linear_assignment.min_cost_matching(
|
||||||
|
iou_matching.iou_cost, self.max_iou_distance, self.tracks,
|
||||||
|
detections, iou_track_candidates, unmatched_detections)
|
||||||
|
matches = matches_a + matches_b
|
||||||
|
unmatched_tracks = list(set(unmatched_tracks_a + unmatched_tracks_b))
|
||||||
|
return matches, unmatched_tracks, unmatched_detections
|
||||||
|
|
||||||
|
def _initiate_track(self, detection):
|
||||||
|
mean, covariance = self.kf.initiate(detection.to_xyah())
|
||||||
|
self.tracks.append(Track(
|
||||||
|
mean, detection.cls_, covariance, self._next_id, self.n_init, self.max_age,
|
||||||
|
detection.feature))
|
||||||
|
self._next_id += 1
|
||||||
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|
|
@ -0,0 +1,13 @@
|
||||||
|
from os import environ
|
||||||
|
|
||||||
|
|
||||||
|
def assert_in(file, files_to_check):
|
||||||
|
if file not in files_to_check:
|
||||||
|
raise AssertionError("{} does not exist in the list".format(str(file)))
|
||||||
|
return True
|
||||||
|
|
||||||
|
|
||||||
|
def assert_in_env(check_list: list):
|
||||||
|
for item in check_list:
|
||||||
|
assert_in(item, environ.keys())
|
||||||
|
return True
|
||||||
|
|
@ -0,0 +1,36 @@
|
||||||
|
import numpy as np
|
||||||
|
import cv2
|
||||||
|
|
||||||
|
palette = (2 ** 11 - 1, 2 ** 15 - 1, 2 ** 20 - 1)
|
||||||
|
|
||||||
|
|
||||||
|
def compute_color_for_labels(label):
|
||||||
|
"""
|
||||||
|
Simple function that adds fixed color depending on the class
|
||||||
|
"""
|
||||||
|
color = [int((p * (label ** 2 - label + 1)) % 255) for p in palette]
|
||||||
|
return tuple(color)
|
||||||
|
|
||||||
|
|
||||||
|
def draw_boxes(img, bbox, identities=None, offset=(0,0)):
|
||||||
|
for i,box in enumerate(bbox):
|
||||||
|
x1,y1,x2,y2 = [int(i) for i in box]
|
||||||
|
x1 += offset[0]
|
||||||
|
x2 += offset[0]
|
||||||
|
y1 += offset[1]
|
||||||
|
y2 += offset[1]
|
||||||
|
# box text and bar
|
||||||
|
id = int(identities[i]) if identities is not None else 0
|
||||||
|
color = compute_color_for_labels(id)
|
||||||
|
label = '{}{:d}'.format("", id)
|
||||||
|
t_size = cv2.getTextSize(label, cv2.FONT_HERSHEY_PLAIN, 2 , 2)[0]
|
||||||
|
cv2.rectangle(img,(x1, y1),(x2,y2),color,3)
|
||||||
|
cv2.rectangle(img,(x1, y1),(x1+t_size[0]+3,y1+t_size[1]+4), color,-1)
|
||||||
|
cv2.putText(img,label,(x1,y1+t_size[1]+4), cv2.FONT_HERSHEY_PLAIN, 2, [255,255,255], 2)
|
||||||
|
return img
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == '__main__':
|
||||||
|
for i in range(82):
|
||||||
|
print(compute_color_for_labels(i))
|
||||||
|
|
@ -0,0 +1,103 @@
|
||||||
|
import os
|
||||||
|
import numpy as np
|
||||||
|
import copy
|
||||||
|
import motmetrics as mm
|
||||||
|
mm.lap.default_solver = 'lap'
|
||||||
|
from utils.io import read_results, unzip_objs
|
||||||
|
|
||||||
|
|
||||||
|
class Evaluator(object):
|
||||||
|
|
||||||
|
def __init__(self, data_root, seq_name, data_type):
|
||||||
|
self.data_root = data_root
|
||||||
|
self.seq_name = seq_name
|
||||||
|
self.data_type = data_type
|
||||||
|
|
||||||
|
self.load_annotations()
|
||||||
|
self.reset_accumulator()
|
||||||
|
|
||||||
|
def load_annotations(self):
|
||||||
|
assert self.data_type == 'mot'
|
||||||
|
|
||||||
|
gt_filename = os.path.join(self.data_root, self.seq_name, 'gt', 'gt.txt')
|
||||||
|
self.gt_frame_dict = read_results(gt_filename, self.data_type, is_gt=True)
|
||||||
|
self.gt_ignore_frame_dict = read_results(gt_filename, self.data_type, is_ignore=True)
|
||||||
|
|
||||||
|
def reset_accumulator(self):
|
||||||
|
self.acc = mm.MOTAccumulator(auto_id=True)
|
||||||
|
|
||||||
|
def eval_frame(self, frame_id, trk_tlwhs, trk_ids, rtn_events=False):
|
||||||
|
# results
|
||||||
|
trk_tlwhs = np.copy(trk_tlwhs)
|
||||||
|
trk_ids = np.copy(trk_ids)
|
||||||
|
|
||||||
|
# gts
|
||||||
|
gt_objs = self.gt_frame_dict.get(frame_id, [])
|
||||||
|
gt_tlwhs, gt_ids = unzip_objs(gt_objs)[:2]
|
||||||
|
|
||||||
|
# ignore boxes
|
||||||
|
ignore_objs = self.gt_ignore_frame_dict.get(frame_id, [])
|
||||||
|
ignore_tlwhs = unzip_objs(ignore_objs)[0]
|
||||||
|
|
||||||
|
|
||||||
|
# remove ignored results
|
||||||
|
keep = np.ones(len(trk_tlwhs), dtype=bool)
|
||||||
|
iou_distance = mm.distances.iou_matrix(ignore_tlwhs, trk_tlwhs, max_iou=0.5)
|
||||||
|
if len(iou_distance) > 0:
|
||||||
|
match_is, match_js = mm.lap.linear_sum_assignment(iou_distance)
|
||||||
|
match_is, match_js = map(lambda a: np.asarray(a, dtype=int), [match_is, match_js])
|
||||||
|
match_ious = iou_distance[match_is, match_js]
|
||||||
|
|
||||||
|
match_js = np.asarray(match_js, dtype=int)
|
||||||
|
match_js = match_js[np.logical_not(np.isnan(match_ious))]
|
||||||
|
keep[match_js] = False
|
||||||
|
trk_tlwhs = trk_tlwhs[keep]
|
||||||
|
trk_ids = trk_ids[keep]
|
||||||
|
|
||||||
|
# get distance matrix
|
||||||
|
iou_distance = mm.distances.iou_matrix(gt_tlwhs, trk_tlwhs, max_iou=0.5)
|
||||||
|
|
||||||
|
# acc
|
||||||
|
self.acc.update(gt_ids, trk_ids, iou_distance)
|
||||||
|
|
||||||
|
if rtn_events and iou_distance.size > 0 and hasattr(self.acc, 'last_mot_events'):
|
||||||
|
events = self.acc.last_mot_events # only supported by https://github.com/longcw/py-motmetrics
|
||||||
|
else:
|
||||||
|
events = None
|
||||||
|
return events
|
||||||
|
|
||||||
|
def eval_file(self, filename):
|
||||||
|
self.reset_accumulator()
|
||||||
|
|
||||||
|
result_frame_dict = read_results(filename, self.data_type, is_gt=False)
|
||||||
|
frames = sorted(list(set(self.gt_frame_dict.keys()) | set(result_frame_dict.keys())))
|
||||||
|
for frame_id in frames:
|
||||||
|
trk_objs = result_frame_dict.get(frame_id, [])
|
||||||
|
trk_tlwhs, trk_ids = unzip_objs(trk_objs)[:2]
|
||||||
|
self.eval_frame(frame_id, trk_tlwhs, trk_ids, rtn_events=False)
|
||||||
|
|
||||||
|
return self.acc
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def get_summary(accs, names, metrics=('mota', 'num_switches', 'idp', 'idr', 'idf1', 'precision', 'recall')):
|
||||||
|
names = copy.deepcopy(names)
|
||||||
|
if metrics is None:
|
||||||
|
metrics = mm.metrics.motchallenge_metrics
|
||||||
|
metrics = copy.deepcopy(metrics)
|
||||||
|
|
||||||
|
mh = mm.metrics.create()
|
||||||
|
summary = mh.compute_many(
|
||||||
|
accs,
|
||||||
|
metrics=metrics,
|
||||||
|
names=names,
|
||||||
|
generate_overall=True
|
||||||
|
)
|
||||||
|
|
||||||
|
return summary
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def save_summary(summary, filename):
|
||||||
|
import pandas as pd
|
||||||
|
writer = pd.ExcelWriter(filename)
|
||||||
|
summary.to_excel(writer)
|
||||||
|
writer.save()
|
||||||
|
|
@ -0,0 +1,133 @@
|
||||||
|
import os
|
||||||
|
from typing import Dict
|
||||||
|
import numpy as np
|
||||||
|
|
||||||
|
# from utils.log import get_logger
|
||||||
|
|
||||||
|
|
||||||
|
def write_results(filename, results, data_type):
|
||||||
|
if data_type == 'mot':
|
||||||
|
save_format = '{frame},{id},{x1},{y1},{w},{h},-1,-1,-1,-1\n'
|
||||||
|
elif data_type == 'kitti':
|
||||||
|
save_format = '{frame} {id} pedestrian 0 0 -10 {x1} {y1} {x2} {y2} -10 -10 -10 -1000 -1000 -1000 -10\n'
|
||||||
|
else:
|
||||||
|
raise ValueError(data_type)
|
||||||
|
|
||||||
|
with open(filename, 'w') as f:
|
||||||
|
for frame_id, tlwhs, track_ids in results:
|
||||||
|
if data_type == 'kitti':
|
||||||
|
frame_id -= 1
|
||||||
|
for tlwh, track_id in zip(tlwhs, track_ids):
|
||||||
|
if track_id < 0:
|
||||||
|
continue
|
||||||
|
x1, y1, w, h = tlwh
|
||||||
|
x2, y2 = x1 + w, y1 + h
|
||||||
|
line = save_format.format(frame=frame_id, id=track_id, x1=x1, y1=y1, x2=x2, y2=y2, w=w, h=h)
|
||||||
|
f.write(line)
|
||||||
|
|
||||||
|
|
||||||
|
# def write_results(filename, results_dict: Dict, data_type: str):
|
||||||
|
# if not filename:
|
||||||
|
# return
|
||||||
|
# path = os.path.dirname(filename)
|
||||||
|
# if not os.path.exists(path):
|
||||||
|
# os.makedirs(path)
|
||||||
|
|
||||||
|
# if data_type in ('mot', 'mcmot', 'lab'):
|
||||||
|
# save_format = '{frame},{id},{x1},{y1},{w},{h},1,-1,-1,-1\n'
|
||||||
|
# elif data_type == 'kitti':
|
||||||
|
# save_format = '{frame} {id} pedestrian -1 -1 -10 {x1} {y1} {x2} {y2} -1 -1 -1 -1000 -1000 -1000 -10 {score}\n'
|
||||||
|
# else:
|
||||||
|
# raise ValueError(data_type)
|
||||||
|
|
||||||
|
# with open(filename, 'w') as f:
|
||||||
|
# for frame_id, frame_data in results_dict.items():
|
||||||
|
# if data_type == 'kitti':
|
||||||
|
# frame_id -= 1
|
||||||
|
# for tlwh, track_id in frame_data:
|
||||||
|
# if track_id < 0:
|
||||||
|
# continue
|
||||||
|
# x1, y1, w, h = tlwh
|
||||||
|
# x2, y2 = x1 + w, y1 + h
|
||||||
|
# line = save_format.format(frame=frame_id, id=track_id, x1=x1, y1=y1, x2=x2, y2=y2, w=w, h=h, score=1.0)
|
||||||
|
# f.write(line)
|
||||||
|
# logger.info('Save results to {}'.format(filename))
|
||||||
|
|
||||||
|
|
||||||
|
def read_results(filename, data_type: str, is_gt=False, is_ignore=False):
|
||||||
|
if data_type in ('mot', 'lab'):
|
||||||
|
read_fun = read_mot_results
|
||||||
|
else:
|
||||||
|
raise ValueError('Unknown data type: {}'.format(data_type))
|
||||||
|
|
||||||
|
return read_fun(filename, is_gt, is_ignore)
|
||||||
|
|
||||||
|
|
||||||
|
"""
|
||||||
|
labels={'ped', ... % 1
|
||||||
|
'person_on_vhcl', ... % 2
|
||||||
|
'car', ... % 3
|
||||||
|
'bicycle', ... % 4
|
||||||
|
'mbike', ... % 5
|
||||||
|
'non_mot_vhcl', ... % 6
|
||||||
|
'static_person', ... % 7
|
||||||
|
'distractor', ... % 8
|
||||||
|
'occluder', ... % 9
|
||||||
|
'occluder_on_grnd', ... %10
|
||||||
|
'occluder_full', ... % 11
|
||||||
|
'reflection', ... % 12
|
||||||
|
'crowd' ... % 13
|
||||||
|
};
|
||||||
|
"""
|
||||||
|
|
||||||
|
|
||||||
|
def read_mot_results(filename, is_gt, is_ignore):
|
||||||
|
valid_labels = {1}
|
||||||
|
ignore_labels = {2, 7, 8, 12}
|
||||||
|
results_dict = dict()
|
||||||
|
if os.path.isfile(filename):
|
||||||
|
with open(filename, 'r') as f:
|
||||||
|
for line in f.readlines():
|
||||||
|
linelist = line.split(',')
|
||||||
|
if len(linelist) < 7:
|
||||||
|
continue
|
||||||
|
fid = int(linelist[0])
|
||||||
|
if fid < 1:
|
||||||
|
continue
|
||||||
|
results_dict.setdefault(fid, list())
|
||||||
|
|
||||||
|
if is_gt:
|
||||||
|
if 'MOT16-' in filename or 'MOT17-' in filename:
|
||||||
|
label = int(float(linelist[7]))
|
||||||
|
mark = int(float(linelist[6]))
|
||||||
|
if mark == 0 or label not in valid_labels:
|
||||||
|
continue
|
||||||
|
score = 1
|
||||||
|
elif is_ignore:
|
||||||
|
if 'MOT16-' in filename or 'MOT17-' in filename:
|
||||||
|
label = int(float(linelist[7]))
|
||||||
|
vis_ratio = float(linelist[8])
|
||||||
|
if label not in ignore_labels and vis_ratio >= 0:
|
||||||
|
continue
|
||||||
|
else:
|
||||||
|
continue
|
||||||
|
score = 1
|
||||||
|
else:
|
||||||
|
score = float(linelist[6])
|
||||||
|
|
||||||
|
tlwh = tuple(map(float, linelist[2:6]))
|
||||||
|
target_id = int(linelist[1])
|
||||||
|
|
||||||
|
results_dict[fid].append((tlwh, target_id, score))
|
||||||
|
|
||||||
|
return results_dict
|
||||||
|
|
||||||
|
|
||||||
|
def unzip_objs(objs):
|
||||||
|
if len(objs) > 0:
|
||||||
|
tlwhs, ids, scores = zip(*objs)
|
||||||
|
else:
|
||||||
|
tlwhs, ids, scores = [], [], []
|
||||||
|
tlwhs = np.asarray(tlwhs, dtype=float).reshape(-1, 4)
|
||||||
|
|
||||||
|
return tlwhs, ids, scores
|
||||||
|
|
@ -0,0 +1,383 @@
|
||||||
|
"""
|
||||||
|
References:
|
||||||
|
https://medium.com/analytics-vidhya/creating-a-custom-logging-mechanism-for-real-time-object-detection-using-tdd-4ca2cfcd0a2f
|
||||||
|
"""
|
||||||
|
import json
|
||||||
|
from os import makedirs
|
||||||
|
from os.path import exists, join
|
||||||
|
from datetime import datetime
|
||||||
|
|
||||||
|
|
||||||
|
class JsonMeta(object):
|
||||||
|
HOURS = 3
|
||||||
|
MINUTES = 59
|
||||||
|
SECONDS = 59
|
||||||
|
PATH_TO_SAVE = 'LOGS'
|
||||||
|
DEFAULT_FILE_NAME = 'remaining'
|
||||||
|
|
||||||
|
|
||||||
|
class BaseJsonLogger(object):
|
||||||
|
"""
|
||||||
|
This is the base class that returns __dict__ of its own
|
||||||
|
it also returns the dicts of objects in the attributes that are list instances
|
||||||
|
|
||||||
|
"""
|
||||||
|
|
||||||
|
def dic(self):
|
||||||
|
# returns dicts of objects
|
||||||
|
out = {}
|
||||||
|
for k, v in self.__dict__.items():
|
||||||
|
if hasattr(v, 'dic'):
|
||||||
|
out[k] = v.dic()
|
||||||
|
elif isinstance(v, list):
|
||||||
|
out[k] = self.list(v)
|
||||||
|
else:
|
||||||
|
out[k] = v
|
||||||
|
return out
|
||||||
|
|
||||||
|
@staticmethod
|
||||||
|
def list(values):
|
||||||
|
# applies the dic method on items in the list
|
||||||
|
return [v.dic() if hasattr(v, 'dic') else v for v in values]
|
||||||
|
|
||||||
|
|
||||||
|
class Label(BaseJsonLogger):
|
||||||
|
"""
|
||||||
|
For each bounding box there are various categories with confidences. Label class keeps track of that information.
|
||||||
|
"""
|
||||||
|
|
||||||
|
def __init__(self, category: str, confidence: float):
|
||||||
|
self.category = category
|
||||||
|
self.confidence = confidence
|
||||||
|
|
||||||
|
|
||||||
|
class Bbox(BaseJsonLogger):
|
||||||
|
"""
|
||||||
|
This module stores the information for each frame and use them in JsonParser
|
||||||
|
Attributes:
|
||||||
|
labels (list): List of label module.
|
||||||
|
top (int):
|
||||||
|
left (int):
|
||||||
|
width (int):
|
||||||
|
height (int):
|
||||||
|
|
||||||
|
Args:
|
||||||
|
bbox_id (float):
|
||||||
|
top (int):
|
||||||
|
left (int):
|
||||||
|
width (int):
|
||||||
|
height (int):
|
||||||
|
|
||||||
|
References:
|
||||||
|
Check Label module for better understanding.
|
||||||
|
|
||||||
|
|
||||||
|
"""
|
||||||
|
|
||||||
|
def __init__(self, bbox_id, top, left, width, height):
|
||||||
|
self.labels = []
|
||||||
|
self.bbox_id = bbox_id
|
||||||
|
self.top = top
|
||||||
|
self.left = left
|
||||||
|
self.width = width
|
||||||
|
self.height = height
|
||||||
|
|
||||||
|
def add_label(self, category, confidence):
|
||||||
|
# adds category and confidence only if top_k is not exceeded.
|
||||||
|
self.labels.append(Label(category, confidence))
|
||||||
|
|
||||||
|
def labels_full(self, value):
|
||||||
|
return len(self.labels) == value
|
||||||
|
|
||||||
|
|
||||||
|
class Frame(BaseJsonLogger):
|
||||||
|
"""
|
||||||
|
This module stores the information for each frame and use them in JsonParser
|
||||||
|
Attributes:
|
||||||
|
timestamp (float): The elapsed time of captured frame
|
||||||
|
frame_id (int): The frame number of the captured video
|
||||||
|
bboxes (list of Bbox objects): Stores the list of bbox objects.
|
||||||
|
|
||||||
|
References:
|
||||||
|
Check Bbox class for better information
|
||||||
|
|
||||||
|
Args:
|
||||||
|
timestamp (float):
|
||||||
|
frame_id (int):
|
||||||
|
|
||||||
|
"""
|
||||||
|
|
||||||
|
def __init__(self, frame_id: int, timestamp: float = None):
|
||||||
|
self.frame_id = frame_id
|
||||||
|
self.timestamp = timestamp
|
||||||
|
self.bboxes = []
|
||||||
|
|
||||||
|
def add_bbox(self, bbox_id: int, top: int, left: int, width: int, height: int):
|
||||||
|
bboxes_ids = [bbox.bbox_id for bbox in self.bboxes]
|
||||||
|
if bbox_id not in bboxes_ids:
|
||||||
|
self.bboxes.append(Bbox(bbox_id, top, left, width, height))
|
||||||
|
else:
|
||||||
|
raise ValueError("Frame with id: {} already has a Bbox with id: {}".format(self.frame_id, bbox_id))
|
||||||
|
|
||||||
|
def add_label_to_bbox(self, bbox_id: int, category: str, confidence: float):
|
||||||
|
bboxes = {bbox.id: bbox for bbox in self.bboxes}
|
||||||
|
if bbox_id in bboxes.keys():
|
||||||
|
res = bboxes.get(bbox_id)
|
||||||
|
res.add_label(category, confidence)
|
||||||
|
else:
|
||||||
|
raise ValueError('the bbox with id: {} does not exists!'.format(bbox_id))
|
||||||
|
|
||||||
|
|
||||||
|
class BboxToJsonLogger(BaseJsonLogger):
|
||||||
|
"""
|
||||||
|
ُ This module is designed to automate the task of logging jsons. An example json is used
|
||||||
|
to show the contents of json file shortly
|
||||||
|
Example:
|
||||||
|
{
|
||||||
|
"video_details": {
|
||||||
|
"frame_width": 1920,
|
||||||
|
"frame_height": 1080,
|
||||||
|
"frame_rate": 20,
|
||||||
|
"video_name": "/home/gpu/codes/MSD/pedestrian_2/project/public/camera1.avi"
|
||||||
|
},
|
||||||
|
"frames": [
|
||||||
|
{
|
||||||
|
"frame_id": 329,
|
||||||
|
"timestamp": 3365.1254
|
||||||
|
"bboxes": [
|
||||||
|
{
|
||||||
|
"labels": [
|
||||||
|
{
|
||||||
|
"category": "pedestrian",
|
||||||
|
"confidence": 0.9
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"bbox_id": 0,
|
||||||
|
"top": 1257,
|
||||||
|
"left": 138,
|
||||||
|
"width": 68,
|
||||||
|
"height": 109
|
||||||
|
}
|
||||||
|
]
|
||||||
|
}],
|
||||||
|
|
||||||
|
Attributes:
|
||||||
|
frames (dict): It's a dictionary that maps each frame_id to json attributes.
|
||||||
|
video_details (dict): information about video file.
|
||||||
|
top_k_labels (int): shows the allowed number of labels
|
||||||
|
start_time (datetime object): we use it to automate the json output by time.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
top_k_labels (int): shows the allowed number of labels
|
||||||
|
|
||||||
|
"""
|
||||||
|
|
||||||
|
def __init__(self, top_k_labels: int = 1):
|
||||||
|
self.frames = {}
|
||||||
|
self.video_details = self.video_details = dict(frame_width=None, frame_height=None, frame_rate=None,
|
||||||
|
video_name=None)
|
||||||
|
self.top_k_labels = top_k_labels
|
||||||
|
self.start_time = datetime.now()
|
||||||
|
|
||||||
|
def set_top_k(self, value):
|
||||||
|
self.top_k_labels = value
|
||||||
|
|
||||||
|
def frame_exists(self, frame_id: int) -> bool:
|
||||||
|
"""
|
||||||
|
Args:
|
||||||
|
frame_id (int):
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
bool: true if frame_id is recognized
|
||||||
|
"""
|
||||||
|
return frame_id in self.frames.keys()
|
||||||
|
|
||||||
|
def add_frame(self, frame_id: int, timestamp: float = None) -> None:
|
||||||
|
"""
|
||||||
|
Args:
|
||||||
|
frame_id (int):
|
||||||
|
timestamp (float): opencv captured frame time property
|
||||||
|
|
||||||
|
Raises:
|
||||||
|
ValueError: if frame_id would not exist in class frames attribute
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
None
|
||||||
|
|
||||||
|
"""
|
||||||
|
if not self.frame_exists(frame_id):
|
||||||
|
self.frames[frame_id] = Frame(frame_id, timestamp)
|
||||||
|
else:
|
||||||
|
raise ValueError("Frame id: {} already exists".format(frame_id))
|
||||||
|
|
||||||
|
def bbox_exists(self, frame_id: int, bbox_id: int) -> bool:
|
||||||
|
"""
|
||||||
|
Args:
|
||||||
|
frame_id:
|
||||||
|
bbox_id:
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
bool: if bbox exists in frame bboxes list
|
||||||
|
"""
|
||||||
|
bboxes = []
|
||||||
|
if self.frame_exists(frame_id=frame_id):
|
||||||
|
bboxes = [bbox.bbox_id for bbox in self.frames[frame_id].bboxes]
|
||||||
|
return bbox_id in bboxes
|
||||||
|
|
||||||
|
def find_bbox(self, frame_id: int, bbox_id: int):
|
||||||
|
"""
|
||||||
|
|
||||||
|
Args:
|
||||||
|
frame_id:
|
||||||
|
bbox_id:
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
bbox_id (int):
|
||||||
|
|
||||||
|
Raises:
|
||||||
|
ValueError: if bbox_id does not exist in the bbox list of specific frame.
|
||||||
|
"""
|
||||||
|
if not self.bbox_exists(frame_id, bbox_id):
|
||||||
|
raise ValueError("frame with id: {} does not contain bbox with id: {}".format(frame_id, bbox_id))
|
||||||
|
bboxes = {bbox.bbox_id: bbox for bbox in self.frames[frame_id].bboxes}
|
||||||
|
return bboxes.get(bbox_id)
|
||||||
|
|
||||||
|
def add_bbox_to_frame(self, frame_id: int, bbox_id: int, top: int, left: int, width: int, height: int) -> None:
|
||||||
|
"""
|
||||||
|
|
||||||
|
Args:
|
||||||
|
frame_id (int):
|
||||||
|
bbox_id (int):
|
||||||
|
top (int):
|
||||||
|
left (int):
|
||||||
|
width (int):
|
||||||
|
height (int):
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
None
|
||||||
|
|
||||||
|
Raises:
|
||||||
|
ValueError: if bbox_id already exist in frame information with frame_id
|
||||||
|
ValueError: if frame_id does not exist in frames attribute
|
||||||
|
"""
|
||||||
|
if self.frame_exists(frame_id):
|
||||||
|
frame = self.frames[frame_id]
|
||||||
|
if not self.bbox_exists(frame_id, bbox_id):
|
||||||
|
frame.add_bbox(bbox_id, top, left, width, height)
|
||||||
|
else:
|
||||||
|
raise ValueError(
|
||||||
|
"frame with frame_id: {} already contains the bbox with id: {} ".format(frame_id, bbox_id))
|
||||||
|
else:
|
||||||
|
raise ValueError("frame with frame_id: {} does not exist".format(frame_id))
|
||||||
|
|
||||||
|
def add_label_to_bbox(self, frame_id: int, bbox_id: int, category: str, confidence: float):
|
||||||
|
"""
|
||||||
|
Args:
|
||||||
|
frame_id:
|
||||||
|
bbox_id:
|
||||||
|
category:
|
||||||
|
confidence: the confidence value returned from yolo detection
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
None
|
||||||
|
|
||||||
|
Raises:
|
||||||
|
ValueError: if labels quota (top_k_labels) exceeds.
|
||||||
|
"""
|
||||||
|
bbox = self.find_bbox(frame_id, bbox_id)
|
||||||
|
if not bbox.labels_full(self.top_k_labels):
|
||||||
|
bbox.add_label(category, confidence)
|
||||||
|
else:
|
||||||
|
raise ValueError("labels in frame_id: {}, bbox_id: {} is fulled".format(frame_id, bbox_id))
|
||||||
|
|
||||||
|
def add_video_details(self, frame_width: int = None, frame_height: int = None, frame_rate: int = None,
|
||||||
|
video_name: str = None):
|
||||||
|
self.video_details['frame_width'] = frame_width
|
||||||
|
self.video_details['frame_height'] = frame_height
|
||||||
|
self.video_details['frame_rate'] = frame_rate
|
||||||
|
self.video_details['video_name'] = video_name
|
||||||
|
|
||||||
|
def output(self):
|
||||||
|
output = {'video_details': self.video_details}
|
||||||
|
result = list(self.frames.values())
|
||||||
|
output['frames'] = [item.dic() for item in result]
|
||||||
|
return output
|
||||||
|
|
||||||
|
def json_output(self, output_name):
|
||||||
|
"""
|
||||||
|
Args:
|
||||||
|
output_name:
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
None
|
||||||
|
|
||||||
|
Notes:
|
||||||
|
It creates the json output with `output_name` name.
|
||||||
|
"""
|
||||||
|
if not output_name.endswith('.json'):
|
||||||
|
output_name += '.json'
|
||||||
|
with open(output_name, 'w') as file:
|
||||||
|
json.dump(self.output(), file)
|
||||||
|
file.close()
|
||||||
|
|
||||||
|
def set_start(self):
|
||||||
|
self.start_time = datetime.now()
|
||||||
|
|
||||||
|
def schedule_output_by_time(self, output_dir=JsonMeta.PATH_TO_SAVE, hours: int = 0, minutes: int = 0,
|
||||||
|
seconds: int = 60) -> None:
|
||||||
|
"""
|
||||||
|
Notes:
|
||||||
|
Creates folder and then periodically stores the jsons on that address.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
output_dir (str): the directory where output files will be stored
|
||||||
|
hours (int):
|
||||||
|
minutes (int):
|
||||||
|
seconds (int):
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
None
|
||||||
|
|
||||||
|
"""
|
||||||
|
end = datetime.now()
|
||||||
|
interval = 0
|
||||||
|
interval += abs(min([hours, JsonMeta.HOURS]) * 3600)
|
||||||
|
interval += abs(min([minutes, JsonMeta.MINUTES]) * 60)
|
||||||
|
interval += abs(min([seconds, JsonMeta.SECONDS]))
|
||||||
|
diff = (end - self.start_time).seconds
|
||||||
|
|
||||||
|
if diff > interval:
|
||||||
|
output_name = self.start_time.strftime('%Y-%m-%d %H-%M-%S') + '.json'
|
||||||
|
if not exists(output_dir):
|
||||||
|
makedirs(output_dir)
|
||||||
|
output = join(output_dir, output_name)
|
||||||
|
self.json_output(output_name=output)
|
||||||
|
self.frames = {}
|
||||||
|
self.start_time = datetime.now()
|
||||||
|
|
||||||
|
def schedule_output_by_frames(self, frames_quota, frame_counter, output_dir=JsonMeta.PATH_TO_SAVE):
|
||||||
|
"""
|
||||||
|
saves as the number of frames quota increases higher.
|
||||||
|
:param frames_quota:
|
||||||
|
:param frame_counter:
|
||||||
|
:param output_dir:
|
||||||
|
:return:
|
||||||
|
"""
|
||||||
|
pass
|
||||||
|
|
||||||
|
def flush(self, output_dir):
|
||||||
|
"""
|
||||||
|
Notes:
|
||||||
|
We use this function to output jsons whenever possible.
|
||||||
|
like the time that we exit the while loop of opencv.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
output_dir:
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
None
|
||||||
|
|
||||||
|
"""
|
||||||
|
filename = self.start_time.strftime('%Y-%m-%d %H-%M-%S') + '-remaining.json'
|
||||||
|
output = join(output_dir, filename)
|
||||||
|
self.json_output(output_name=output)
|
||||||
|
|
@ -0,0 +1,17 @@
|
||||||
|
import logging
|
||||||
|
|
||||||
|
|
||||||
|
def get_logger(name='root'):
|
||||||
|
formatter = logging.Formatter(
|
||||||
|
# fmt='%(asctime)s [%(levelname)s]: %(filename)s(%(funcName)s:%(lineno)s) >> %(message)s')
|
||||||
|
fmt='%(asctime)s [%(levelname)s]: %(message)s', datefmt='%Y-%m-%d %H:%M:%S')
|
||||||
|
|
||||||
|
handler = logging.StreamHandler()
|
||||||
|
handler.setFormatter(formatter)
|
||||||
|
|
||||||
|
logger = logging.getLogger(name)
|
||||||
|
logger.setLevel(logging.INFO)
|
||||||
|
logger.addHandler(handler)
|
||||||
|
return logger
|
||||||
|
|
||||||
|
|
||||||
|
|
@ -0,0 +1,39 @@
|
||||||
|
import os
|
||||||
|
import yaml
|
||||||
|
from easydict import EasyDict as edict
|
||||||
|
|
||||||
|
class YamlParser(edict):
|
||||||
|
"""
|
||||||
|
This is yaml parser based on EasyDict.
|
||||||
|
"""
|
||||||
|
def __init__(self, cfg_dict=None, config_file=None):
|
||||||
|
if cfg_dict is None:
|
||||||
|
cfg_dict = {}
|
||||||
|
|
||||||
|
if config_file is not None:
|
||||||
|
assert(os.path.isfile(config_file))
|
||||||
|
with open(config_file, 'r') as fo:
|
||||||
|
cfg_dict.update(yaml.load(fo.read()))
|
||||||
|
|
||||||
|
super(YamlParser, self).__init__(cfg_dict)
|
||||||
|
|
||||||
|
|
||||||
|
def merge_from_file(self, config_file):
|
||||||
|
with open(config_file, 'r') as fo:
|
||||||
|
# self.update(yaml.load(fo.read()))
|
||||||
|
self.update(yaml.safe_load(fo.read()))
|
||||||
|
|
||||||
|
|
||||||
|
def merge_from_dict(self, config_dict):
|
||||||
|
self.update(config_dict)
|
||||||
|
|
||||||
|
|
||||||
|
def get_config(config_file=None):
|
||||||
|
return YamlParser(config_file=config_file)
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
cfg = YamlParser(config_file="../configs/yolov3.yaml")
|
||||||
|
cfg.merge_from_file("../configs/deep_sort.yaml")
|
||||||
|
|
||||||
|
import ipdb; ipdb.set_trace()
|
||||||
|
|
@ -0,0 +1,39 @@
|
||||||
|
from functools import wraps
|
||||||
|
from time import time
|
||||||
|
|
||||||
|
|
||||||
|
def is_video(ext: str):
|
||||||
|
"""
|
||||||
|
Returns true if ext exists in
|
||||||
|
allowed_exts for video files.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
ext:
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
|
||||||
|
"""
|
||||||
|
|
||||||
|
allowed_exts = ('.mp4', '.webm', '.ogg', '.avi', '.wmv', '.mkv', '.3gp')
|
||||||
|
return any((ext.endswith(x) for x in allowed_exts))
|
||||||
|
|
||||||
|
|
||||||
|
def tik_tok(func):
|
||||||
|
"""
|
||||||
|
keep track of time for each process.
|
||||||
|
Args:
|
||||||
|
func:
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
|
||||||
|
"""
|
||||||
|
@wraps(func)
|
||||||
|
def _time_it(*args, **kwargs):
|
||||||
|
start = time()
|
||||||
|
try:
|
||||||
|
return func(*args, **kwargs)
|
||||||
|
finally:
|
||||||
|
end_ = time()
|
||||||
|
print("time: {:.03f}s, fps: {:.03f}".format(end_ - start, 1 / (end_ - start)))
|
||||||
|
|
||||||
|
return _time_it
|
||||||
|
|
@ -0,0 +1,56 @@
|
||||||
|
from AIDetector_pytorch import Detector
|
||||||
|
import imutils
|
||||||
|
import cv2
|
||||||
|
import os
|
||||||
|
os.environ['KMP_DUPLICATE_LIB_OK']='TRUE'
|
||||||
|
file = open('track_result.txt', 'w')
|
||||||
|
|
||||||
|
def main():
|
||||||
|
|
||||||
|
name = 'demo'
|
||||||
|
|
||||||
|
det = Detector()
|
||||||
|
# cap = cv2.VideoCapture('D:/TH/5_smoke/Yolov5-Deepsort-main/smogfire_video14.mp4')
|
||||||
|
cap = cv2.VideoCapture('/home/thsw/WJ/nyh/CODE/Yolov5-Deepsort-main/video/roadsign_orin.mp4')
|
||||||
|
fps = int(cap.get(5))
|
||||||
|
print('fps:', fps)
|
||||||
|
t = int(1000/fps)
|
||||||
|
|
||||||
|
videoWriter = None
|
||||||
|
frame_count = 0
|
||||||
|
|
||||||
|
while True:
|
||||||
|
|
||||||
|
# try:
|
||||||
|
_, im = cap.read()
|
||||||
|
if im is None:
|
||||||
|
break
|
||||||
|
frame_count += 1
|
||||||
|
file.write(str('第'+str(frame_count)+'帧\n*********************************************'))
|
||||||
|
result = det.feedCap(im)
|
||||||
|
result = result['frame']
|
||||||
|
result = imutils.resize(result, height=500)
|
||||||
|
if videoWriter is None:
|
||||||
|
fourcc = cv2.VideoWriter_fourcc(
|
||||||
|
'm', 'p', '4', 'v') # opencv3.0
|
||||||
|
videoWriter = cv2.VideoWriter(
|
||||||
|
'./detect_result/result_roadsign.mp4', fourcc, fps, (result.shape[1], result.shape[0]))
|
||||||
|
|
||||||
|
videoWriter.write(result)
|
||||||
|
# cv2.imshow(name, result)
|
||||||
|
# cv2.waitKey(t)
|
||||||
|
|
||||||
|
# if cv2.getWindowProperty(name, cv2.WND_PROP_AUTOSIZE) < 1:
|
||||||
|
# # 点x退出
|
||||||
|
# break
|
||||||
|
# except Exception as e:
|
||||||
|
# print(e)
|
||||||
|
# break
|
||||||
|
|
||||||
|
cap.release()
|
||||||
|
videoWriter.release()
|
||||||
|
# cv2.destroyAllWindows()
|
||||||
|
|
||||||
|
if __name__ == '__main__':
|
||||||
|
|
||||||
|
main()
|
||||||
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Reference in New Issue