ssd_mobilenet_v2_coco_2018_03_29

.gitignore

@@ -1,3 +0,0 @@
-ssd_mobilenet_v2_coco_2018_03_29/demo
-
-**/demo

east_text_detection/LICENSE

@@ -1,674 +0,0 @@
-                    GNU GENERAL PUBLIC LICENSE
-                       Version 3, 29 June 2007
-
- Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
- Everyone is permitted to copy and distribute verbatim copies
- of this license document, but changing it is not allowed.
-
-                            Preamble
-
-  The GNU General Public License is a free, copyleft license for
-software and other kinds of works.
-
-  The licenses for most software and other practical works are designed
-to take away your freedom to share and change the works.  By contrast,
-the GNU General Public License is intended to guarantee your freedom to
-share and change all versions of a program--to make sure it remains free
-software for all its users.  We, the Free Software Foundation, use the
-GNU General Public License for most of our software; it applies also to
-any other work released this way by its authors.  You can apply it to
-your programs, too.
-
-  When we speak of free software, we are referring to freedom, not
-price.  Our General Public Licenses are designed to make sure that you
-have the freedom to distribute copies of free software (and charge for
-them if you wish), that you receive source code or can get it if you
-want it, that you can change the software or use pieces of it in new
-free programs, and that you know you can do these things.
-
-  To protect your rights, we need to prevent others from denying you
-these rights or asking you to surrender the rights.  Therefore, you have
-certain responsibilities if you distribute copies of the software, or if
-you modify it: responsibilities to respect the freedom of others.
-
-  For example, if you distribute copies of such a program, whether
-gratis or for a fee, you must pass on to the recipients the same
-freedoms that you received.  You must make sure that they, too, receive
-or can get the source code.  And you must show them these terms so they
-know their rights.
-
-  Developers that use the GNU GPL protect your rights with two steps:
-(1) assert copyright on the software, and (2) offer you this License
-giving you legal permission to copy, distribute and/or modify it.
-
-  For the developers' and authors' protection, the GPL clearly explains
-that there is no warranty for this free software.  For both users' and
-authors' sake, the GPL requires that modified versions be marked as
-changed, so that their problems will not be attributed erroneously to
-authors of previous versions.
-
-  Some devices are designed to deny users access to install or run
-modified versions of the software inside them, although the manufacturer
-can do so.  This is fundamentally incompatible with the aim of
-protecting users' freedom to change the software.  The systematic
-pattern of such abuse occurs in the area of products for individuals to
-use, which is precisely where it is most unacceptable.  Therefore, we
-have designed this version of the GPL to prohibit the practice for those
-products.  If such problems arise substantially in other domains, we
-stand ready to extend this provision to those domains in future versions
-of the GPL, as needed to protect the freedom of users.
-
-  Finally, every program is threatened constantly by software patents.
-States should not allow patents to restrict development and use of
-software on general-purpose computers, but in those that do, we wish to
-avoid the special danger that patents applied to a free program could
-make it effectively proprietary.  To prevent this, the GPL assures that
-patents cannot be used to render the program non-free.
-
-  The precise terms and conditions for copying, distribution and
-modification follow.
-
-                       TERMS AND CONDITIONS
-
-  0. Definitions.
-
-  "This License" refers to version 3 of the GNU General Public License.
-
-  "Copyright" also means copyright-like laws that apply to other kinds of
-works, such as semiconductor masks.
-
-  "The Program" refers to any copyrightable work licensed under this
-License.  Each licensee is addressed as "you".  "Licensees" and
-"recipients" may be individuals or organizations.
-
-  To "modify" a work means to copy from or adapt all or part of the work
-in a fashion requiring copyright permission, other than the making of an
-exact copy.  The resulting work is called a "modified version" of the
-earlier work or a work "based on" the earlier work.
-
-  A "covered work" means either the unmodified Program or a work based
-on the Program.
-
-  To "propagate" a work means to do anything with it that, without
-permission, would make you directly or secondarily liable for
-infringement under applicable copyright law, except executing it on a
-computer or modifying a private copy.  Propagation includes copying,
-distribution (with or without modification), making available to the
-public, and in some countries other activities as well.
-
-  To "convey" a work means any kind of propagation that enables other
-parties to make or receive copies.  Mere interaction with a user through
-a computer network, with no transfer of a copy, is not conveying.
-
-  An interactive user interface displays "Appropriate Legal Notices"
-to the extent that it includes a convenient and prominently visible
-feature that (1) displays an appropriate copyright notice, and (2)
-tells the user that there is no warranty for the work (except to the
-extent that warranties are provided), that licensees may convey the
-work under this License, and how to view a copy of this License.  If
-the interface presents a list of user commands or options, such as a
-menu, a prominent item in the list meets this criterion.
-
-  1. Source Code.
-
-  The "source code" for a work means the preferred form of the work
-for making modifications to it.  "Object code" means any non-source
-form of a work.
-
-  A "Standard Interface" means an interface that either is an official
-standard defined by a recognized standards body, or, in the case of
-interfaces specified for a particular programming language, one that
-is widely used among developers working in that language.
-
-  The "System Libraries" of an executable work include anything, other
-than the work as a whole, that (a) is included in the normal form of
-packaging a Major Component, but which is not part of that Major
-Component, and (b) serves only to enable use of the work with that
-Major Component, or to implement a Standard Interface for which an
-implementation is available to the public in source code form.  A
-"Major Component", in this context, means a major essential component
-(kernel, window system, and so on) of the specific operating system
-(if any) on which the executable work runs, or a compiler used to
-produce the work, or an object code interpreter used to run it.
-
-  The "Corresponding Source" for a work in object code form means all
-the source code needed to generate, install, and (for an executable
-work) run the object code and to modify the work, including scripts to
-control those activities.  However, it does not include the work's
-System Libraries, or general-purpose tools or generally available free
-programs which are used unmodified in performing those activities but
-which are not part of the work.  For example, Corresponding Source
-includes interface definition files associated with source files for
-the work, and the source code for shared libraries and dynamically
-linked subprograms that the work is specifically designed to require,
-such as by intimate data communication or control flow between those
-subprograms and other parts of the work.
-
-  The Corresponding Source need not include anything that users
-can regenerate automatically from other parts of the Corresponding
-Source.
-
-  The Corresponding Source for a work in source code form is that
-same work.
-
-  2. Basic Permissions.
-
-  All rights granted under this License are granted for the term of
-copyright on the Program, and are irrevocable provided the stated
-conditions are met.  This License explicitly affirms your unlimited
-permission to run the unmodified Program.  The output from running a
-covered work is covered by this License only if the output, given its
-content, constitutes a covered work.  This License acknowledges your
-rights of fair use or other equivalent, as provided by copyright law.
-
-  You may make, run and propagate covered works that you do not
-convey, without conditions so long as your license otherwise remains
-in force.  You may convey covered works to others for the sole purpose
-of having them make modifications exclusively for you, or provide you
-with facilities for running those works, provided that you comply with
-the terms of this License in conveying all material for which you do
-not control copyright.  Those thus making or running the covered works
-for you must do so exclusively on your behalf, under your direction
-and control, on terms that prohibit them from making any copies of
-your copyrighted material outside their relationship with you.
-
-  Conveying under any other circumstances is permitted solely under
-the conditions stated below.  Sublicensing is not allowed; section 10
-makes it unnecessary.
-
-  3. Protecting Users' Legal Rights From Anti-Circumvention Law.
-
-  No covered work shall be deemed part of an effective technological
-measure under any applicable law fulfilling obligations under article
-11 of the WIPO copyright treaty adopted on 20 December 1996, or
-similar laws prohibiting or restricting circumvention of such
-measures.
-
-  When you convey a covered work, you waive any legal power to forbid
-circumvention of technological measures to the extent such circumvention
-is effected by exercising rights under this License with respect to
-the covered work, and you disclaim any intention to limit operation or
-modification of the work as a means of enforcing, against the work's
-users, your or third parties' legal rights to forbid circumvention of
-technological measures.
-
-  4. Conveying Verbatim Copies.
-
-  You may convey verbatim copies of the Program's source code as you
-receive it, in any medium, provided that you conspicuously and
-appropriately publish on each copy an appropriate copyright notice;
-keep intact all notices stating that this License and any
-non-permissive terms added in accord with section 7 apply to the code;
-keep intact all notices of the absence of any warranty; and give all
-recipients a copy of this License along with the Program.
-
-  You may charge any price or no price for each copy that you convey,
-and you may offer support or warranty protection for a fee.
-
-  5. Conveying Modified Source Versions.
-
-  You may convey a work based on the Program, or the modifications to
-produce it from the Program, in the form of source code under the
-terms of section 4, provided that you also meet all of these conditions:
-
-    a) The work must carry prominent notices stating that you modified
-    it, and giving a relevant date.
-
-    b) The work must carry prominent notices stating that it is
-    released under this License and any conditions added under section
-    7.  This requirement modifies the requirement in section 4 to
-    "keep intact all notices".
-
-    c) You must license the entire work, as a whole, under this
-    License to anyone who comes into possession of a copy.  This
-    License will therefore apply, along with any applicable section 7
-    additional terms, to the whole of the work, and all its parts,
-    regardless of how they are packaged.  This License gives no
-    permission to license the work in any other way, but it does not
-    invalidate such permission if you have separately received it.
-
-    d) If the work has interactive user interfaces, each must display
-    Appropriate Legal Notices; however, if the Program has interactive
-    interfaces that do not display Appropriate Legal Notices, your
-    work need not make them do so.
-
-  A compilation of a covered work with other separate and independent
-works, which are not by their nature extensions of the covered work,
-and which are not combined with it such as to form a larger program,
-in or on a volume of a storage or distribution medium, is called an
-"aggregate" if the compilation and its resulting copyright are not
-used to limit the access or legal rights of the compilation's users
-beyond what the individual works permit.  Inclusion of a covered work
-in an aggregate does not cause this License to apply to the other
-parts of the aggregate.
-
-  6. Conveying Non-Source Forms.
-
-  You may convey a covered work in object code form under the terms
-of sections 4 and 5, provided that you also convey the
-machine-readable Corresponding Source under the terms of this License,
-in one of these ways:
-
-    a) Convey the object code in, or embodied in, a physical product
-    (including a physical distribution medium), accompanied by the
-    Corresponding Source fixed on a durable physical medium
-    customarily used for software interchange.
-
-    b) Convey the object code in, or embodied in, a physical product
-    (including a physical distribution medium), accompanied by a
-    written offer, valid for at least three years and valid for as
-    long as you offer spare parts or customer support for that product
-    model, to give anyone who possesses the object code either (1) a
-    copy of the Corresponding Source for all the software in the
-    product that is covered by this License, on a durable physical
-    medium customarily used for software interchange, for a price no
-    more than your reasonable cost of physically performing this
-    conveying of source, or (2) access to copy the
-    Corresponding Source from a network server at no charge.
-
-    c) Convey individual copies of the object code with a copy of the
-    written offer to provide the Corresponding Source.  This
-    alternative is allowed only occasionally and noncommercially, and
-    only if you received the object code with such an offer, in accord
-    with subsection 6b.
-
-    d) Convey the object code by offering access from a designated
-    place (gratis or for a charge), and offer equivalent access to the
-    Corresponding Source in the same way through the same place at no
-    further charge.  You need not require recipients to copy the
-    Corresponding Source along with the object code.  If the place to
-    copy the object code is a network server, the Corresponding Source
-    may be on a different server (operated by you or a third party)
-    that supports equivalent copying facilities, provided you maintain
-    clear directions next to the object code saying where to find the
-    Corresponding Source.  Regardless of what server hosts the
-    Corresponding Source, you remain obligated to ensure that it is
-    available for as long as needed to satisfy these requirements.
-
-    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 <https://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
-<https://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
-<https://www.gnu.org/licenses/why-not-lgpl.html>.

east_text_detection/README.md

@@ -1,67 +0,0 @@
-# EAST Text Detection
-
-Scene-text detection with the EAST (Efficient and Accurate Scene Text) detector.
-The model was originally distributed as a frozen TensorFlow graph
-(`frozen_east_text_detection.pb`) and converted to ONNX for use with OpenCV's DNN module.
-
-## Model Details
-- **Architecture**: EAST with a ResNet-50 backbone and a feature-fusion head
-- **Input**: RGB image, 320×320, raw 0–255 float, mean `(123.68, 116.78, 103.94)`, swapRB,
-  NCHW layout (`input_images:0`, shape `[1, 3, 320, 320]`)
-- **Outputs**:
-  - `feature_fusion/Conv_7/Sigmoid:0` — score map, shape `[1, 1, 80, 80]`
-  - `feature_fusion/concat_3:0` — RBOX geometry, shape `[1, 5, 80, 80]`
-- **Post-processing**: OpenCV's `TextDetectionModel_EAST` decodes the score/geometry maps
-  into rotated boxes (confidence threshold + rotated-NMS)
-- **Framework**: ONNX (converted from the TensorFlow frozen graph via tf2onnx, opset 15)
-- **Original weights**: https://github.com/argman/EAST
-
-Both input and outputs are emitted in NCHW so OpenCV consumes them directly.
-
-## Usage
-
-### Python
-```bash
-python demo.py --model east_text_detection_2026jul.onnx --image example_outputs/input_image.png --output example_outputs/output_image.png
-```
-
-Or import directly:
-```python
-import cv2
-
-model = cv2.dnn.TextDetectionModel_EAST("east_text_detection_2026jul.onnx")
-# see demo.py for the full inference pipeline
-```
-
-### C++
-The C++ demo runs inference with OpenCV's DNN module (default engine — no ONNX Runtime
-needed). Adjust the OpenCV paths to your setup:
-```bash
-OCV=/path/to/opencv                          # OpenCV source tree
-OCVBUILD=/path/to/opencv/build               # OpenCV build directory (generated headers + libs)
-g++ -std=c++17 demo.cpp -o demo \
-  -I$OCV/include \
-  -I$OCV/modules/core/include \
-  -I$OCV/modules/dnn/include \
-  -I$OCV/modules/imgproc/include \
-  -I$OCV/modules/imgcodecs/include \
-  -I$OCVBUILD \
-  -L$OCVBUILD/lib -Wl,-rpath,$OCVBUILD/lib -lopencv_dnn -lopencv_imgcodecs -lopencv_imgproc -lopencv_core
-./demo --model east_text_detection_2026jul.onnx --image example_outputs/input_image.png
-```
-
-## Conversion
-The ONNX model was exported from the frozen TensorFlow graph with tf2onnx (opset 15)
-via [convert_to_onnx.py](./convert_to_onnx.py) — input `input_images:0`, outputs
-`feature_fusion/Conv_7/Sigmoid:0` and `feature_fusion/concat_3:0`. Both the input and the
-outputs are forced to NCHW (`inputs_as_nchw` / `outputs_as_nchw`) so the tensors match
-OpenCV's layout; without `outputs_as_nchw` the score/geometry maps come out as NHWC and the
-EAST decoder rejects them. Requires `tensorflow`, `tf2onnx`, and `onnx`.
-
-```bash
-python convert_to_onnx.py --pb ../pb/frozen_east_text_detection.pb
-```
-
-## License
-See [LICENSE](./LICENSE) — the model originates from [argman/EAST](https://github.com/argman/EAST),
-released under the GNU General Public License v3.0.

east_text_detection/convert_to_onnx.py

@@ -1,44 +0,0 @@
-import argparse
-import datetime
-
-import onnx
-import tensorflow as tf
-import tf2onnx
-
-
-def load_graph_def(pb_path):
-    with tf.io.gfile.GFile(pb_path, "rb") as f:
-        graph_def = tf.compat.v1.GraphDef()
-        graph_def.ParseFromString(f.read())
-    return graph_def
-
-
-def main():
-    parser = argparse.ArgumentParser(description="Export frozen_east_text_detection.pb to ONNX")
-    parser.add_argument("--pb", default="../pb/frozen_east_text_detection.pb")
-    parser.add_argument("--opset", type=int, default=15)
-    args = parser.parse_args()
-
-    graph_def = load_graph_def(args.pb)
-
-    outputs = ["feature_fusion/Conv_7/Sigmoid:0", "feature_fusion/concat_3:0"]
-    model_proto, _ = tf2onnx.convert.from_graph_def(
-        graph_def,
-        input_names=["input_images:0"],
-        output_names=outputs,
-        inputs_as_nchw=["input_images:0"],
-        outputs_as_nchw=outputs,
-        opset=args.opset,
-        shape_override={"input_images:0": [1, 320, 320, 3]},
-    )
-    onnx.checker.check_model(model_proto)
-
-    stamp = datetime.datetime.now().strftime("%Y%b").lower()
-    onnx_path = "east_text_detection_%s.onnx" % stamp
-    with open(onnx_path, "wb") as f:
-        f.write(model_proto.SerializeToString())
-    print("wrote", onnx_path)
-
-
-if __name__ == "__main__":
-    main()

east_text_detection/demo.cpp

@@ -1,49 +0,0 @@
-#include <opencv2/dnn.hpp>
-#include <opencv2/imgproc.hpp>
-#include <opencv2/imgcodecs.hpp>
-#include <iostream>
-#include <string>
-#include <vector>
-
-static std::string argVal(int argc, char** argv, const std::string& key, const std::string& def)
-{
-    for (int i = 1; i + 1 < argc; ++i)
-        if (key == argv[i]) return argv[i + 1];
-    return def;
-}
-
-int main(int argc, char** argv)
-{
-    std::string model  = argVal(argc, argv, "--model",  "east_text_detection_2026jul.onnx");
-    std::string image  = argVal(argc, argv, "--image",  "example_outputs/input_image.png");
-    std::string output = argVal(argc, argv, "--output", "example_outputs/output_image.png");
-
-    cv::Mat img = cv::imread(image);
-    if (img.empty())
-    {
-        std::cerr << "could not read image: " << image << std::endl;
-        return 1;
-    }
-
-    cv::dnn::TextDetectionModel_EAST east(model);
-    east.setConfidenceThreshold(0.5f).setNMSThreshold(0.4f);
-    east.setInputParams(1.0, cv::Size(320, 320), cv::Scalar(123.68, 116.78, 103.94), true, false);
-
-    std::vector<cv::RotatedRect> boxes;
-    east.detectTextRectangles(img, boxes);
-    std::cout << "detections " << boxes.size() << std::endl;
-
-    cv::Mat out = img.clone();
-    for (const cv::RotatedRect& box : boxes)
-    {
-        cv::Mat pts;
-        cv::boxPoints(box, pts);
-        std::vector<cv::Point> poly(4);
-        for (int i = 0; i < 4; ++i)
-            poly[i] = cv::Point(cvRound(pts.at<float>(i, 0)), cvRound(pts.at<float>(i, 1)));
-        cv::polylines(out, poly, true, cv::Scalar(0, 255, 0), 2);
-    }
-    cv::imwrite(output, out);
-    std::cout << "wrote " << output << std::endl;
-    return 0;
-}

east_text_detection/demo.py

@@ -1,39 +0,0 @@
-import argparse
-import os
-
-import cv2 as cv
-import numpy as np
-
-here = os.path.dirname(os.path.abspath(__file__))
-
-
-def main():
-    parser = argparse.ArgumentParser(description="EAST scene-text detection (ONNX) demo")
-    parser.add_argument("--model", default=os.path.join(here, "east_text_detection_2026jul.onnx"))
-    parser.add_argument("--image", default=os.path.join(here, "example_outputs", "input_image.png"))
-    parser.add_argument("--output", default=os.path.join(here, "example_outputs", "output_image.png"))
-    parser.add_argument("--conf", type=float, default=0.5, help="confidence threshold")
-    parser.add_argument("--nms", type=float, default=0.4, help="NMS threshold")
-    args = parser.parse_args()
-
-    img = cv.imread(args.image)
-    if img is None:
-        raise SystemExit("could not read image: %s" % args.image)
-
-    model = cv.dnn.TextDetectionModel_EAST(args.model)
-    model.setConfidenceThreshold(args.conf).setNMSThreshold(args.nms)
-    model.setInputParams(1.0, (320, 320), (123.68, 116.78, 103.94), True, False)
-
-    boxes, confidences = model.detectTextRectangles(img)
-    print("detections", len(boxes))
-
-    out = img.copy()
-    for box in boxes:
-        pts = cv.boxPoints(box).astype(np.int32)
-        cv.polylines(out, [pts], True, (0, 255, 0), 2)
-    cv.imwrite(args.output, out)
-    print("wrote", args.output)
-
-
-if __name__ == "__main__":
-    main()

east_text_detection/east_text_detection_2026jul.onnx

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:63f96881e90b81f3f0e7fd79dc705ead31276da70d3c7dca008ca28d3554883a
-size 96217443

efficientdet-d0/LICENSE

@@ -1,203 +0,0 @@
-Copyright 2020 Google Research.  All rights reserved.
-
-                                 Apache License
-                           Version 2.0, January 2004
-                        http://www.apache.org/licenses/
-
-   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
-
-   1. Definitions.
-
-      "License" shall mean the terms and conditions for use, reproduction,
-      and distribution as defined by Sections 1 through 9 of this document.
-
-      "Licensor" shall mean the copyright owner or entity authorized by
-      the copyright owner that is granting the License.
-
-      "Legal Entity" shall mean the union of the acting entity and all
-      other entities that control, are controlled by, or are under common
-      control with that entity. For the purposes of this definition,
-      "control" means (i) the power, direct or indirect, to cause the
-      direction or management of such entity, whether by contract or
-      otherwise, or (ii) ownership of fifty percent (50%) or more of the
-      outstanding shares, or (iii) beneficial ownership of such entity.
-
-      "You" (or "Your") shall mean an individual or Legal Entity
-      exercising permissions granted by this License.
-
-      "Source" form shall mean the preferred form for making modifications,
-      including but not limited to software source code, documentation
-      source, and configuration files.
-
-      "Object" form shall mean any form resulting from mechanical
-      transformation or translation of a Source form, including but
-      not limited to compiled object code, generated documentation,
-      and conversions to other media types.
-
-      "Work" shall mean the work of authorship, whether in Source or
-      Object form, made available under the License, as indicated by a
-      copyright notice that is included in or attached to the work
-      (an example is provided in the Appendix below).
-
-      "Derivative Works" shall mean any work, whether in Source or Object
-      form, that is based on (or derived from) the Work and for which the
-      editorial revisions, annotations, elaborations, or other modifications
-      represent, as a whole, an original work of authorship. For the purposes
-      of this License, Derivative Works shall not include works that remain
-      separable from, or merely link (or bind by name) to the interfaces of,
-      the Work and Derivative Works thereof.
-
-      "Contribution" shall mean any work of authorship, including
-      the original version of the Work and any modifications or additions
-      to that Work or Derivative Works thereof, that is intentionally
-      submitted to Licensor for inclusion in the Work by the copyright owner
-      or by an individual or Legal Entity authorized to submit on behalf of
-      the copyright owner. For the purposes of this definition, "submitted"
-      means any form of electronic, verbal, or written communication sent
-      to the Licensor or its representatives, including but not limited to
-      communication on electronic mailing lists, source code control systems,
-      and issue tracking systems that are managed by, or on behalf of, the
-      Licensor for the purpose of discussing and improving the Work, but
-      excluding communication that is conspicuously marked or otherwise
-      designated in writing by the copyright owner as "Not a Contribution."
-
-      "Contributor" shall mean Licensor and any individual or Legal Entity
-      on behalf of whom a Contribution has been received by Licensor and
-      subsequently incorporated within the Work.
-
-   2. Grant of Copyright License. Subject to the terms and conditions of
-      this License, each Contributor hereby grants to You a perpetual,
-      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
-      copyright license to reproduce, prepare Derivative Works of,
-      publicly display, publicly perform, sublicense, and distribute the
-      Work and such Derivative Works in Source or Object form.
-
-   3. Grant of Patent License. Subject to the terms and conditions of
-      this License, each Contributor hereby grants to You a perpetual,
-      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
-      (except as stated in this section) patent license to make, have made,
-      use, offer to sell, sell, import, and otherwise transfer the Work,
-      where such license applies only to those patent claims licensable
-      by such Contributor that are necessarily infringed by their
-      Contribution(s) alone or by combination of their Contribution(s)
-      with the Work to which such Contribution(s) was submitted. If You
-      institute patent litigation against any entity (including a
-      cross-claim or counterclaim in a lawsuit) alleging that the Work
-      or a Contribution incorporated within the Work constitutes direct
-      or contributory patent infringement, then any patent licenses
-      granted to You under this License for that Work shall terminate
-      as of the date such litigation is filed.
-
-   4. Redistribution. You may reproduce and distribute copies of the
-      Work or Derivative Works thereof in any medium, with or without
-      modifications, and in Source or Object form, provided that You
-      meet the following conditions:
-
-      (a) You must give any other recipients of the Work or
-          Derivative Works a copy of this License; and
-
-      (b) You must cause any modified files to carry prominent notices
-          stating that You changed the files; and
-
-      (c) You must retain, in the Source form of any Derivative Works
-          that You distribute, all copyright, patent, trademark, and
-          attribution notices from the Source form of the Work,
-          excluding those notices that do not pertain to any part of
-          the Derivative Works; and
-
-      (d) If the Work includes a "NOTICE" text file as part of its
-          distribution, then any Derivative Works that You distribute must
-          include a readable copy of the attribution notices contained
-          within such NOTICE file, excluding those notices that do not
-          pertain to any part of the Derivative Works, in at least one
-          of the following places: within a NOTICE text file distributed
-          as part of the Derivative Works; within the Source form or
-          documentation, if provided along with the Derivative Works; or,
-          within a display generated by the Derivative Works, if and
-          wherever such third-party notices normally appear. The contents
-          of the NOTICE file are for informational purposes only and
-          do not modify the License. You may add Your own attribution
-          notices within Derivative Works that You distribute, alongside
-          or as an addendum to the NOTICE text from the Work, provided
-          that such additional attribution notices cannot be construed
-          as modifying the License.
-
-      You may add Your own copyright statement to Your modifications and
-      may provide additional or different license terms and conditions
-      for use, reproduction, or distribution of Your modifications, or
-      for any such Derivative Works as a whole, provided Your use,
-      reproduction, and distribution of the Work otherwise complies with
-      the conditions stated in this License.
-
-   5. Submission of Contributions. Unless You explicitly state otherwise,
-      any Contribution intentionally submitted for inclusion in the Work
-      by You to the Licensor shall be under the terms and conditions of
-      this License, without any additional terms or conditions.
-      Notwithstanding the above, nothing herein shall supersede or modify
-      the terms of any separate license agreement you may have executed
-      with Licensor regarding such Contributions.
-
-   6. Trademarks. This License does not grant permission to use the trade
-      names, trademarks, service marks, or product names of the Licensor,
-      except as required for reasonable and customary use in describing the
-      origin of the Work and reproducing the content of the NOTICE file.
-
-   7. Disclaimer of Warranty. Unless required by applicable law or
-      agreed to in writing, Licensor provides the Work (and each
-      Contributor provides its Contributions) on an "AS IS" BASIS,
-      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
-      implied, including, without limitation, any warranties or conditions
-      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
-      PARTICULAR PURPOSE. You are solely responsible for determining the
-      appropriateness of using or redistributing the Work and assume any
-      risks associated with Your exercise of permissions under this License.
-
-   8. Limitation of Liability. In no event and under no legal theory,
-      whether in tort (including negligence), contract, or otherwise,
-      unless required by applicable law (such as deliberate and grossly
-      negligent acts) or agreed to in writing, shall any Contributor be
-      liable to You for damages, including any direct, indirect, special,
-      incidental, or consequential damages of any character arising as a
-      result of this License or out of the use or inability to use the
-      Work (including but not limited to damages for loss of goodwill,
-      work stoppage, computer failure or malfunction, or any and all
-      other commercial damages or losses), even if such Contributor
-      has been advised of the possibility of such damages.
-
-   9. Accepting Warranty or Additional Liability. While redistributing
-      the Work or Derivative Works thereof, You may choose to offer,
-      and charge a fee for, acceptance of support, warranty, indemnity,
-      or other liability obligations and/or rights consistent with this
-      License. However, in accepting such obligations, You may act only
-      on Your own behalf and on Your sole responsibility, not on behalf
-      of any other Contributor, and only if You agree to indemnify,
-      defend, and hold each Contributor harmless for any liability
-      incurred by, or claims asserted against, such Contributor by reason
-      of your accepting any such warranty or additional liability.
-
-   END OF TERMS AND CONDITIONS
-
-   APPENDIX: How to apply the Apache License to your work.
-
-      To apply the Apache License to your work, attach the following
-      boilerplate notice, with the fields enclosed by brackets "[]"
-      replaced with your own identifying information. (Don't include
-      the brackets!)  The text should be enclosed in the appropriate
-      comment syntax for the file format. We also recommend that a
-      file or class name and description of purpose be included on the
-      same "printed page" as the copyright notice for easier
-      identification within third-party archives.
-
-   Copyright 2020, Google Research.
-
-   Licensed under the Apache License, Version 2.0 (the "License");
-   you may not use this file except in compliance with the License.
-   You may obtain a copy of the License at
-
-       http://www.apache.org/licenses/LICENSE-2.0
-
-   Unless required by applicable law or agreed to in writing, software
-   distributed under the License is distributed on an "AS IS" BASIS,
-   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   See the License for the specific language governing permissions and
-   limitations under the License.	

efficientdet-d0/README.md

@@ -1,62 +0,0 @@
-# EfficientDet-D0
-
-Object detection with EfficientDet-D0 trained on COCO. The model was originally
-distributed as a frozen TensorFlow graph (`efficientdet-d0.pb`) and converted to
-ONNX for use with OpenCV's DNN module. This is a **backbone-only** export: the
-graph emits raw class logits and box regressions, while anchor generation, sigmoid,
-box decoding and non-maximum suppression are performed in host code (see the demos).
-
-## Model Details
-- **Architecture**: EfficientDet-D0
-- **Input**: RGB image, 512×512, raw uint8, NHWC layout (`image_arrays:0`, shape `[1, 512, 512, 3]`)
-- **Output**: raw class logits (`concat:0`, shape `[1, 49104, 90]`) and box regression (`concat_1:0`, shape `[1, 49104, 4]`); anchor decode + NMS are done in host code, not in the graph
-- **Framework**: ONNX (converted from the TensorFlow frozen graph via tf2onnx, opset 18)
-- **Original weights**: https://www.dropbox.com/s/9mqp99fd2tpuqn6/efficientdet-d0.pb?dl=1
-
-The graph outputs are per-anchor predictions only. The demos build the 49104 anchors
-(5 pyramid levels × 9 anchors/cell), apply sigmoid to the logits, decode the box
-regressions relative to the anchors, threshold on confidence and run NMS (IoU 0.6).
-
-## Usage
-
-### Python
-```bash
-python demo.py --model efficientdet-d0_2026jul.onnx --image example_outputs/input_image.png --output example_outputs/output_image.png --conf 0.4
-```
-
-Or import directly:
-```python
-import cv2
-
-net = cv2.dnn.readNet("efficientdet-d0_2026jul.onnx")
-# see demo.py for the full anchor decode + NMS pipeline
-```
-
-### C++
-The C++ demo runs inference with OpenCV's DNN module. Adjust the OpenCV paths to your setup:
-```bash
-OCV=/path/to/opencv                          # OpenCV source tree
-OCVBUILD=/path/to/opencv/build               # OpenCV build directory (generated headers + libs)
-g++ -std=c++17 demo.cpp -o demo \
-  -I$OCV/include \
-  -I$OCV/modules/core/include \
-  -I$OCV/modules/dnn/include \
-  -I$OCV/modules/imgproc/include \
-  -I$OCV/modules/imgcodecs/include \
-  -I$OCVBUILD \
-  -L$OCVBUILD/lib -Wl,-rpath,$OCVBUILD/lib -lopencv_dnn -lopencv_imgcodecs -lopencv_imgproc -lopencv_core
-./demo --model efficientdet-d0_2026jul.onnx --image example_outputs/input_image.png --output example_outputs/output_image.png
-```
-
-## Conversion
-The ONNX model was exported from the frozen TensorFlow graph with tf2onnx (opset 18)
-via [convert_to_onnx.py](./convert_to_onnx.py) — input `image_arrays:0`, outputs
-`concat:0` and `concat_1:0`, input shape overridden to `[1, 512, 512, 3]`. Requires
-`tensorflow`, `tf2onnx`, and `onnx`.
-
-```bash
-python convert_to_onnx.py --pb ../pb/efficientdet-d0.pb
-```
-
-## License
-See [LICENSE](./LICENSE) — released under the Apache License 2.0.

efficientdet-d0/convert_to_onnx.py

@@ -1,41 +0,0 @@
-import argparse
-import datetime
-
-import onnx
-import tensorflow as tf
-import tf2onnx
-
-
-def load_graph_def(pb_path):
-    with tf.io.gfile.GFile(pb_path, "rb") as f:
-        graph_def = tf.compat.v1.GraphDef()
-        graph_def.ParseFromString(f.read())
-    return graph_def
-
-
-def main():
-    parser = argparse.ArgumentParser(description="Export efficientdet-d0.pb to ONNX")
-    parser.add_argument("--pb", default="../pb/efficientdet-d0.pb")
-    parser.add_argument("--opset", type=int, default=18)
-    args = parser.parse_args()
-
-    graph_def = load_graph_def(args.pb)
-
-    model_proto, _ = tf2onnx.convert.from_graph_def(
-        graph_def,
-        input_names=["image_arrays:0"],
-        output_names=["concat:0", "concat_1:0"],
-        opset=args.opset,
-        shape_override={"image_arrays:0": [1, 512, 512, 3]},
-    )
-    onnx.checker.check_model(model_proto)
-
-    stamp = datetime.datetime.now().strftime("%Y%b").lower()
-    onnx_path = "efficientdet-d0_%s.onnx" % stamp
-    with open(onnx_path, "wb") as f:
-        f.write(model_proto.SerializeToString())
-    print("wrote", onnx_path)
-
-
-if __name__ == "__main__":
-    main()

efficientdet-d0/demo.cpp

@@ -1,127 +0,0 @@
-#include <opencv2/dnn.hpp>
-#include <opencv2/imgproc.hpp>
-#include <opencv2/imgcodecs.hpp>
-#include <algorithm>
-#include <array>
-#include <cmath>
-#include <iostream>
-#include <string>
-#include <vector>
-
-using namespace cv;
-
-static std::string argVal(int argc, char** argv, const std::string& key, const std::string& def)
-{
-    for (int i = 1; i + 1 < argc; ++i)
-        if (key == argv[i]) return argv[i + 1];
-    return def;
-}
-
-struct Det { float x1, y1, x2, y2, score; int cid; };
-
-int main(int argc, char** argv)
-{
-    std::string model  = argVal(argc, argv, "--model",  "efficientdet-d0_2026jul.onnx");
-    std::string image  = argVal(argc, argv, "--image",  "example_outputs/input_image.png");
-    std::string output = argVal(argc, argv, "--output", "example_outputs/output_image.png");
-    float conf = std::stof(argVal(argc, argv, "--conf", "0.4"));
-
-    const int sz = 512;
-
-    Mat img = imread(image);
-    if (img.empty()) { std::cerr << "could not read image: " << image << std::endl; return 1; }
-
-    Mat rgb;
-    cvtColor(img, rgb, COLOR_BGR2RGB);
-    resize(rgb, rgb, Size(sz, sz));
-    if (!rgb.isContinuous()) rgb = rgb.clone();
-
-    int blobShape[] = {1, sz, sz, 3};
-    Mat blob(4, blobShape, CV_8U, rgb.data);
-    dnn::Net net = dnn::readNetFromONNX(model);
-    net.setInput(blob);
-    std::vector<Mat> outs;
-    net.forward(outs, net.getUnconnectedOutLayersNames());
-
-    const float* boxp = nullptr;
-    const float* clsp = nullptr;
-    int n = 0, nc = 0;
-    for (size_t i = 0; i < outs.size(); ++i)
-    {
-        const Mat& o = outs[i];
-        const float* p = (const float*)o.data;
-        int last = o.size[o.dims - 1];
-        if (last == 4) { boxp = p; n = o.size[o.dims - 2]; }
-        else { clsp = p; nc = last; }
-    }
-
-    std::vector<std::array<float, 2>> baseWH;
-    double asp[3][2] = {{1.0, 1.0}, {1.4, 0.7}, {0.7, 1.4}};
-    for (int i = 0; i < 3; ++i) {
-        double s = std::pow(2.0, i / 3.0);
-        for (int a = 0; a < 3; ++a)
-            baseWH.push_back({(float)(32.0 * s * asp[a][0]), (float)(32.0 * s * asp[a][1])});
-    }
-    std::vector<float> acx, acy, aw, ah;
-    for (int lvl = 0; lvl < 5; ++lvl) {
-        int f = sz / (8 << lvl);
-        int step = 8 << lvl;
-        int m = 1 << lvl;
-        for (int y = 0; y < f; ++y)
-            for (int x = 0; x < f; ++x) {
-                float cx = (x + 0.5f) * step;
-                float cy = (y + 0.5f) * step;
-                for (auto& b : baseWH) {
-                    acx.push_back(cx); acy.push_back(cy);
-                    aw.push_back(b[0] * m); ah.push_back(b[1] * m);
-                }
-            }
-    }
-
-    std::vector<Det> dets;
-    for (int a = 0; a < n; ++a) {
-        const float* bp = boxp + (size_t)a * 4;
-        float ycenter = bp[0] * ah[a] + acy[a];
-        float xcenter = bp[1] * aw[a] + acx[a];
-        float bhv = std::exp(bp[2]) * ah[a];
-        float bwv = std::exp(bp[3]) * aw[a];
-        const float* cp = clsp + (size_t)a * nc;
-        int best = 0; float bestLogit = cp[0];
-        for (int c = 1; c < nc; ++c) if (cp[c] > bestLogit) { bestLogit = cp[c]; best = c; }
-        float score = 1.0f / (1.0f + std::exp(-bestLogit));
-        if (score > conf)
-            dets.push_back({(xcenter - bwv / 2) / sz, (ycenter - bhv / 2) / sz,
-                            (xcenter + bwv / 2) / sz, (ycenter + bhv / 2) / sz, score, best});
-    }
-
-    std::sort(dets.begin(), dets.end(), [](const Det& a, const Det& b) { return a.score > b.score; });
-    std::vector<char> removed(dets.size(), 0);
-    std::vector<int> pick;
-    for (size_t i = 0; i < dets.size(); ++i) {
-        if (removed[i]) continue;
-        pick.push_back((int)i);
-        for (size_t j = i + 1; j < dets.size(); ++j) {
-            if (removed[j]) continue;
-            float xx1 = std::max(dets[i].x1, dets[j].x1);
-            float yy1 = std::max(dets[i].y1, dets[j].y1);
-            float xx2 = std::min(dets[i].x2, dets[j].x2);
-            float yy2 = std::min(dets[i].y2, dets[j].y2);
-            float inter = std::max(0.0f, xx2 - xx1) * std::max(0.0f, yy2 - yy1);
-            float ai = (dets[i].x2 - dets[i].x1) * (dets[i].y2 - dets[i].y1);
-            float aj = (dets[j].x2 - dets[j].x1) * (dets[j].y2 - dets[j].y1);
-            if (inter / (ai + aj - inter + 1e-9f) > 0.6f) removed[j] = 1;
-        }
-    }
-
-    std::cout << "efficientdet-d0 " << pick.size() << " detections" << std::endl;
-    int w = img.cols, h = img.rows;
-    for (int idx : pick) {
-        const Det& d = dets[idx];
-        std::cout << format("%d %.3f %.3f %.3f %.3f %.3f", d.cid, d.score, d.x1, d.y1, d.x2, d.y2) << std::endl;
-        rectangle(img, Point((int)(d.x1 * w), (int)(d.y1 * h)), Point((int)(d.x2 * w), (int)(d.y2 * h)), Scalar(0, 255, 0), 2);
-        putText(img, format("%d:%.2f", d.cid, d.score), Point((int)(d.x1 * w), (int)(d.y1 * h) - 5), FONT_HERSHEY_SIMPLEX, 0.5, Scalar(0, 255, 0), 1);
-    }
-    imwrite(output, img);
-    std::cout << "wrote " << output << std::endl;
-    return 0;
-}

efficientdet-d0/demo.py

@@ -1,105 +0,0 @@
-import argparse
-import glob
-import os
-
-import cv2 as cv
-import numpy as np
-
-here = os.path.dirname(os.path.abspath(__file__))
-sz = 512
-
-
-def build_anchors():
-    scales = [2.0 ** (i / 3.0) for i in range(3)]
-    aspects = [(1.0, 1.0), (1.4, 0.7), (0.7, 1.4)]
-    base = []
-    for s in scales:
-        for aw, ah in aspects:
-            base.append((32.0 * s * aw, 32.0 * s * ah))
-    anchors = []
-    for lvl in range(5):
-        f = sz // (8 * 2 ** lvl)
-        step = 8 * 2 ** lvl
-        m = 2 ** lvl
-        for y in range(f):
-            for x in range(f):
-                cx = (x + 0.5) * step
-                cy = (y + 0.5) * step
-                for bw, bh in base:
-                    anchors.append((cx, cy, bw * m, bh * m))
-    return np.array(anchors, np.float32)
-
-
-def main():
-    parser = argparse.ArgumentParser(description="EfficientDet-D0 (ONNX) object detection demo")
-    parser.add_argument("--model", default=None)
-    parser.add_argument("--image", default=os.path.join(here, "example_outputs", "input_image.png"))
-    parser.add_argument("--output", default=os.path.join(here, "example_outputs", "output_image.png"))
-    parser.add_argument("--conf", type=float, default=0.4)
-    args = parser.parse_args()
-
-    model = args.model
-    if model is None:
-        found = glob.glob(os.path.join(here, "*.onnx"))
-        if not found:
-            raise SystemExit("no onnx, run convert_to_onnx.py")
-        model = found[0]
-
-    img = cv.imread(args.image)
-    if img is None:
-        raise SystemExit("could not read image: %s" % args.image)
-
-    anchors = build_anchors()
-    acx, acy, aw, ah = anchors[:, 0], anchors[:, 1], anchors[:, 2], anchors[:, 3]
-
-    net = cv.dnn.readNetFromONNX(model)
-    inp = cv.resize(cv.cvtColor(img, cv.COLOR_BGR2RGB), (sz, sz))
-    net.setInput(inp[None].astype(np.uint8))
-    res = net.forward(net.getUnconnectedOutLayersNames())
-    box = next(a for a in res if a.shape[-1] == 4).reshape(-1, 4)
-    cls = next(a for a in res if a.shape[-1] != 4).reshape(box.shape[0], -1)
-
-    ycenter = box[:, 0] * ah + acy
-    xcenter = box[:, 1] * aw + acx
-    bh = np.exp(box[:, 2]) * ah
-    bw = np.exp(box[:, 3]) * aw
-    boxes = np.stack([xcenter - bw / 2, ycenter - bh / 2, xcenter + bw / 2, ycenter + bh / 2], 1) / sz
-
-    prob = 1.0 / (1.0 + np.exp(-cls))
-    cid = prob.argmax(1)
-    scores = prob.max(1)
-
-    keep = scores > args.conf
-    boxes = boxes[keep]
-    scores = scores[keep]
-    cid = cid[keep]
-    order = scores.argsort()[::-1]
-    pick = []
-    while order.size:
-        i = order[0]
-        pick.append(i)
-        xx1 = np.maximum(boxes[i, 0], boxes[order[1:], 0])
-        yy1 = np.maximum(boxes[i, 1], boxes[order[1:], 1])
-        xx2 = np.minimum(boxes[i, 2], boxes[order[1:], 2])
-        yy2 = np.minimum(boxes[i, 3], boxes[order[1:], 3])
-        iw = np.maximum(0, xx2 - xx1)
-        ih = np.maximum(0, yy2 - yy1)
-        inter = iw * ih
-        ai = (boxes[i, 2] - boxes[i, 0]) * (boxes[i, 3] - boxes[i, 1])
-        aj = (boxes[order[1:], 2] - boxes[order[1:], 0]) * (boxes[order[1:], 3] - boxes[order[1:], 1])
-        iou = inter / (ai + aj - inter + 1e-9)
-        order = order[1:][iou <= 0.6]
-
-    print("efficientdet-d0", len(pick), "detections")
-    h, w = img.shape[:2]
-    for i in pick:
-        x1, y1, x2, y2 = boxes[i]
-        print(int(cid[i]), round(float(scores[i]), 3), round(float(x1), 3), round(float(y1), 3), round(float(x2), 3), round(float(y2), 3))
-        cv.rectangle(img, (int(x1 * w), int(y1 * h)), (int(x2 * w), int(y2 * h)), (0, 255, 0), 2)
-        cv.putText(img, "%d:%.2f" % (int(cid[i]), scores[i]), (int(x1 * w), int(y1 * h) - 5), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1)
-    cv.imwrite(args.output, img)
-    print("wrote", args.output)
-
-
-if __name__ == "__main__":
-    main()

efficientdet-d0/efficientdet-d0_2026jul.onnx

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:db344f69adf1c529e08a36bbaa1779d98b4b81621a2f046656fb966bdfdc6298
-size 15671001

faster_rcnn_inception_v2_coco_2018_01_28/LICENSE

@@ -1,203 +0,0 @@
-Copyright 2015 The TensorFlow Authors.  All rights reserved.
-
-                                 Apache License
-                           Version 2.0, January 2004
-                        http://www.apache.org/licenses/
-
-   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
-
-   1. Definitions.
-
-      "License" shall mean the terms and conditions for use, reproduction,
-      and distribution as defined by Sections 1 through 9 of this document.
-
-      "Licensor" shall mean the copyright owner or entity authorized by
-      the copyright owner that is granting the License.
-
-      "Legal Entity" shall mean the union of the acting entity and all
-      other entities that control, are controlled by, or are under common
-      control with that entity. For the purposes of this definition,
-      "control" means (i) the power, direct or indirect, to cause the
-      direction or management of such entity, whether by contract or
-      otherwise, or (ii) ownership of fifty percent (50%) or more of the
-      outstanding shares, or (iii) beneficial ownership of such entity.
-
-      "You" (or "Your") shall mean an individual or Legal Entity
-      exercising permissions granted by this License.
-
-      "Source" form shall mean the preferred form for making modifications,
-      including but not limited to software source code, documentation
-      source, and configuration files.
-
-      "Object" form shall mean any form resulting from mechanical
-      transformation or translation of a Source form, including but
-      not limited to compiled object code, generated documentation,
-      and conversions to other media types.
-
-      "Work" shall mean the work of authorship, whether in Source or
-      Object form, made available under the License, as indicated by a
-      copyright notice that is included in or attached to the work
-      (an example is provided in the Appendix below).
-
-      "Derivative Works" shall mean any work, whether in Source or Object
-      form, that is based on (or derived from) the Work and for which the
-      editorial revisions, annotations, elaborations, or other modifications
-      represent, as a whole, an original work of authorship. For the purposes
-      of this License, Derivative Works shall not include works that remain
-      separable from, or merely link (or bind by name) to the interfaces of,
-      the Work and Derivative Works thereof.
-
-      "Contribution" shall mean any work of authorship, including
-      the original version of the Work and any modifications or additions
-      to that Work or Derivative Works thereof, that is intentionally
-      submitted to Licensor for inclusion in the Work by the copyright owner
-      or by an individual or Legal Entity authorized to submit on behalf of
-      the copyright owner. For the purposes of this definition, "submitted"
-      means any form of electronic, verbal, or written communication sent
-      to the Licensor or its representatives, including but not limited to
-      communication on electronic mailing lists, source code control systems,
-      and issue tracking systems that are managed by, or on behalf of, the
-      Licensor for the purpose of discussing and improving the Work, but
-      excluding communication that is conspicuously marked or otherwise
-      designated in writing by the copyright owner as "Not a Contribution."
-
-      "Contributor" shall mean Licensor and any individual or Legal Entity
-      on behalf of whom a Contribution has been received by Licensor and
-      subsequently incorporated within the Work.
-
-   2. Grant of Copyright License. Subject to the terms and conditions of
-      this License, each Contributor hereby grants to You a perpetual,
-      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
-      copyright license to reproduce, prepare Derivative Works of,
-      publicly display, publicly perform, sublicense, and distribute the
-      Work and such Derivative Works in Source or Object form.
-
-   3. Grant of Patent License. Subject to the terms and conditions of
-      this License, each Contributor hereby grants to You a perpetual,
-      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
-      (except as stated in this section) patent license to make, have made,
-      use, offer to sell, sell, import, and otherwise transfer the Work,
-      where such license applies only to those patent claims licensable
-      by such Contributor that are necessarily infringed by their
-      Contribution(s) alone or by combination of their Contribution(s)
-      with the Work to which such Contribution(s) was submitted. If You
-      institute patent litigation against any entity (including a
-      cross-claim or counterclaim in a lawsuit) alleging that the Work
-      or a Contribution incorporated within the Work constitutes direct
-      or contributory patent infringement, then any patent licenses
-      granted to You under this License for that Work shall terminate
-      as of the date such litigation is filed.
-
-   4. Redistribution. You may reproduce and distribute copies of the
-      Work or Derivative Works thereof in any medium, with or without
-      modifications, and in Source or Object form, provided that You
-      meet the following conditions:
-
-      (a) You must give any other recipients of the Work or
-          Derivative Works a copy of this License; and
-
-      (b) You must cause any modified files to carry prominent notices
-          stating that You changed the files; and
-
-      (c) You must retain, in the Source form of any Derivative Works
-          that You distribute, all copyright, patent, trademark, and
-          attribution notices from the Source form of the Work,
-          excluding those notices that do not pertain to any part of
-          the Derivative Works; and
-
-      (d) If the Work includes a "NOTICE" text file as part of its
-          distribution, then any Derivative Works that You distribute must
-          include a readable copy of the attribution notices contained
-          within such NOTICE file, excluding those notices that do not
-          pertain to any part of the Derivative Works, in at least one
-          of the following places: within a NOTICE text file distributed
-          as part of the Derivative Works; within the Source form or
-          documentation, if provided along with the Derivative Works; or,
-          within a display generated by the Derivative Works, if and
-          wherever such third-party notices normally appear. The contents
-          of the NOTICE file are for informational purposes only and
-          do not modify the License. You may add Your own attribution
-          notices within Derivative Works that You distribute, alongside
-          or as an addendum to the NOTICE text from the Work, provided
-          that such additional attribution notices cannot be construed
-          as modifying the License.
-
-      You may add Your own copyright statement to Your modifications and
-      may provide additional or different license terms and conditions
-      for use, reproduction, or distribution of Your modifications, or
-      for any such Derivative Works as a whole, provided Your use,
-      reproduction, and distribution of the Work otherwise complies with
-      the conditions stated in this License.
-
-   5. Submission of Contributions. Unless You explicitly state otherwise,
-      any Contribution intentionally submitted for inclusion in the Work
-      by You to the Licensor shall be under the terms and conditions of
-      this License, without any additional terms or conditions.
-      Notwithstanding the above, nothing herein shall supersede or modify
-      the terms of any separate license agreement you may have executed
-      with Licensor regarding such Contributions.
-
-   6. Trademarks. This License does not grant permission to use the trade
-      names, trademarks, service marks, or product names of the Licensor,
-      except as required for reasonable and customary use in describing the
-      origin of the Work and reproducing the content of the NOTICE file.
-
-   7. Disclaimer of Warranty. Unless required by applicable law or
-      agreed to in writing, Licensor provides the Work (and each
-      Contributor provides its Contributions) on an "AS IS" BASIS,
-      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
-      implied, including, without limitation, any warranties or conditions
-      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
-      PARTICULAR PURPOSE. You are solely responsible for determining the
-      appropriateness of using or redistributing the Work and assume any
-      risks associated with Your exercise of permissions under this License.
-
-   8. Limitation of Liability. In no event and under no legal theory,
-      whether in tort (including negligence), contract, or otherwise,
-      unless required by applicable law (such as deliberate and grossly
-      negligent acts) or agreed to in writing, shall any Contributor be
-      liable to You for damages, including any direct, indirect, special,
-      incidental, or consequential damages of any character arising as a
-      result of this License or out of the use or inability to use the
-      Work (including but not limited to damages for loss of goodwill,
-      work stoppage, computer failure or malfunction, or any and all
-      other commercial damages or losses), even if such Contributor
-      has been advised of the possibility of such damages.
-
-   9. Accepting Warranty or Additional Liability. While redistributing
-      the Work or Derivative Works thereof, You may choose to offer,
-      and charge a fee for, acceptance of support, warranty, indemnity,
-      or other liability obligations and/or rights consistent with this
-      License. However, in accepting such obligations, You may act only
-      on Your own behalf and on Your sole responsibility, not on behalf
-      of any other Contributor, and only if You agree to indemnify,
-      defend, and hold each Contributor harmless for any liability
-      incurred by, or claims asserted against, such Contributor by reason
-      of your accepting any such warranty or additional liability.
-
-   END OF TERMS AND CONDITIONS
-
-   APPENDIX: How to apply the Apache License to your work.
-
-      To apply the Apache License to your work, attach the following
-      boilerplate notice, with the fields enclosed by brackets "[]"
-      replaced with your own identifying information. (Don't include
-      the brackets!)  The text should be enclosed in the appropriate
-      comment syntax for the file format. We also recommend that a
-      file or class name and description of purpose be included on the
-      same "printed page" as the copyright notice for easier
-      identification within third-party archives.
-
-   Copyright 2015, The TensorFlow Authors.
-
-   Licensed under the Apache License, Version 2.0 (the "License");
-   you may not use this file except in compliance with the License.
-   You may obtain a copy of the License at
-
-       http://www.apache.org/licenses/LICENSE-2.0
-
-   Unless required by applicable law or agreed to in writing, software
-   distributed under the License is distributed on an "AS IS" BASIS,
-   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   See the License for the specific language governing permissions and
-limitations under the License.

faster_rcnn_inception_v2_coco_2018_01_28/README.md

@@ -1,49 +0,0 @@
-# Faster-RCNN InceptionV2 (COCO)
-
-Object detection with the Faster-RCNN meta-architecture and an Inception v2 backbone,
-trained on the COCO dataset. The model was originally distributed as a frozen TensorFlow
-graph (`faster_rcnn_inception_v2_coco_2018_01_28.pb`) from the TensorFlow Object Detection
-API and converted to ONNX for inference with OpenCV's DNN module.
-
-## Model Details
-- **Architecture**: Faster-RCNN with an Inception v2 backbone
-- **Input**: RGB image, uint8, NHWC layout (`image_tensor:0`, shape `[1, H, W, 3]`)
-- **Output**: `detection_boxes:0` (normalized `[ymin, xmin, ymax, xmax]`), `detection_scores:0`, `detection_classes:0` (1-based COCO ids), `num_detections:0`
-- **Framework**: ONNX (converted from the TensorFlow frozen graph via tf2onnx, opset 18)
-- **Original weights**: http://download.tensorflow.org/models/object_detection/faster_rcnn_inception_v2_coco_2018_01_28.tar.gz
-
-## Usage
-
-### Python
-```bash
-python demo.py --model faster_rcnn_inception_v2_coco_2018_01_28_2026jul.onnx --image example_outputs/input_image.png --output example_outputs/output_image.png --conf 0.3
-```
-
-### C++
-The C++ demo runs inference with OpenCV's DNN module. Adjust the OpenCV paths to your setup:
-```bash
-OCV=/path/to/opencv                          # OpenCV source tree
-OCVBUILD=/path/to/opencv/build               # OpenCV build directory (generated headers + libs)
-g++ -std=c++17 demo.cpp -o demo \
-  -I$OCV/include \
-  -I$OCV/modules/core/include \
-  -I$OCV/modules/dnn/include \
-  -I$OCV/modules/imgproc/include \
-  -I$OCV/modules/imgcodecs/include \
-  -I$OCVBUILD \
-  -L$OCVBUILD/lib -Wl,-rpath,$OCVBUILD/lib -lopencv_dnn -lopencv_imgcodecs -lopencv_imgproc -lopencv_core
-./demo --model faster_rcnn_inception_v2_coco_2018_01_28_2026jul.onnx --image example_outputs/input_image.png --output example_outputs/output_image.png
-```
-
-## Conversion
-The ONNX model was exported from the frozen TensorFlow graph with tf2onnx (opset 18)
-via [convert_to_onnx.py](./convert_to_onnx.py) — input `image_tensor:0`, outputs
-`detection_boxes:0`, `detection_scores:0`, `detection_classes:0`, `num_detections:0`.
-Requires `tensorflow`, `tf2onnx`, and `onnx`.
-
-```bash
-python convert_to_onnx.py --pb ../pb/faster_rcnn_inception_v2_coco_2018_01_28.pb
-```
-
-## License
-See [LICENSE](./LICENSE) — the model is released by the TensorFlow Authors under the Apache License 2.0.

faster_rcnn_inception_v2_coco_2018_01_28/convert_to_onnx.py

@@ -1,40 +0,0 @@
-import argparse
-import datetime
-
-import onnx
-import tensorflow as tf
-import tf2onnx
-
-
-def load_graph_def(pb_path):
-    with tf.io.gfile.GFile(pb_path, "rb") as f:
-        graph_def = tf.compat.v1.GraphDef()
-        graph_def.ParseFromString(f.read())
-    return graph_def
-
-
-def main():
-    parser = argparse.ArgumentParser(description="Export faster_rcnn_inception_v2_coco_2018_01_28.pb to ONNX")
-    parser.add_argument("--pb", default="../pb/faster_rcnn_inception_v2_coco_2018_01_28.pb")
-    parser.add_argument("--opset", type=int, default=18)
-    args = parser.parse_args()
-
-    graph_def = load_graph_def(args.pb)
-
-    model_proto, _ = tf2onnx.convert.from_graph_def(
-        graph_def,
-        input_names=["image_tensor:0"],
-        output_names=["detection_boxes:0", "detection_scores:0", "detection_classes:0", "num_detections:0"],
-        opset=args.opset,
-    )
-    onnx.checker.check_model(model_proto)
-
-    stamp = datetime.datetime.now().strftime("%Y%b").lower()
-    onnx_path = "faster_rcnn_inception_v2_coco_2018_01_28_%s.onnx" % stamp
-    with open(onnx_path, "wb") as f:
-        f.write(model_proto.SerializeToString())
-    print("wrote", onnx_path)
-
-
-if __name__ == "__main__":
-    main()

faster_rcnn_inception_v2_coco_2018_01_28/demo.cpp

@@ -1,83 +0,0 @@
-#include <opencv2/dnn.hpp>
-#include <opencv2/imgproc.hpp>
-#include <opencv2/imgcodecs.hpp>
-#include <array>
-#include <cstdint>
-#include <iostream>
-#include <string>
-#include <vector>
-
-static std::string argVal(int argc, char** argv, const std::string& key, const std::string& def)
-{
-    for (int i = 1; i + 1 < argc; ++i)
-        if (key == argv[i]) return argv[i + 1];
-    return def;
-}
-
-int main(int argc, char** argv)
-{
-    std::string model  = argVal(argc, argv, "--model",  "faster_rcnn_inception_v2_coco_2018_01_28_2026jul.onnx");
-    std::string image  = argVal(argc, argv, "--image",  "example_outputs/input_image.png");
-    std::string output = argVal(argc, argv, "--output", "example_outputs/output_image.png");
-    float conf = std::stof(argVal(argc, argv, "--conf", "0.3"));
-
-    cv::Mat img = cv::imread(image);
-    if (img.empty())
-    {
-        std::cerr << "could not read image: " << image << std::endl;
-        return 1;
-    }
-
-    const int W = 800, H = 600;
-    cv::Mat rgb;
-    cv::cvtColor(img, rgb, cv::COLOR_BGR2RGB);
-    cv::resize(rgb, rgb, cv::Size(W, H));
-    if (!rgb.isContinuous()) rgb = rgb.clone();
-
-    int blobShape[] = {1, H, W, 3};
-    cv::Mat blob(4, blobShape, CV_8U, rgb.data);
-    cv::dnn::Net net = cv::dnn::readNetFromONNX(model, cv::dnn::ENGINE_ORT);
-    net.setInput(blob);
-    std::vector<cv::String> out_strs = {"detection_boxes:0", "detection_scores:0", "detection_classes:0", "num_detections:0"};
-    std::vector<cv::Mat> outs;
-    net.forward(outs, out_strs);
-
-    float* boxes = nullptr;
-    float* scores = nullptr;
-    float* classes = nullptr;
-    float* numd = nullptr;
-    for (size_t i = 0; i < out_strs.size(); ++i)
-    {
-        float* p = (float*)outs[i].data;
-        const std::string& n = out_strs[i];
-        if (n.find("detection_boxes") != std::string::npos) boxes = p;
-        else if (n.find("detection_scores") != std::string::npos) scores = p;
-        else if (n.find("detection_classes") != std::string::npos) classes = p;
-        else if (n.find("num_detections") != std::string::npos) numd = p;
-    }
-    int nd = (int)numd[0];
-
-    int w = img.cols, h = img.rows;
-    std::vector<int> kept;
-    for (int i = 0; i < nd; ++i)
-        if (scores[i] >= conf) kept.push_back(i);
-
-    std::cout << "faster_rcnn_inception_v2_coco_2018_01_28 " << kept.size() << " detections" << std::endl;
-    for (int i : kept)
-    {
-        int cls = (int)classes[i] - 1;
-        float score = scores[i];
-        float ymin = boxes[i * 4 + 0], xmin = boxes[i * 4 + 1];
-        float ymax = boxes[i * 4 + 2], xmax = boxes[i * 4 + 3];
-        cv::Point p1((int)(xmin * w), (int)(ymin * h));
-        cv::Point p2((int)(xmax * w), (int)(ymax * h));
-        cv::rectangle(img, p1, p2, cv::Scalar(0, 255, 0), 2);
-        cv::putText(img, cv::format("%d:%.2f", cls, score), cv::Point(p1.x, p1.y - 5),
-                    cv::FONT_HERSHEY_SIMPLEX, 0.5, cv::Scalar(0, 255, 0), 1);
-        std::cout << cls << " " << cv::format("%.3f %.3f %.3f %.3f %.3f", score, xmin, ymin, xmax, ymax) << std::endl;
-    }
-
-    cv::imwrite(output, img);
-    std::cout << "wrote " << output << std::endl;
-    return 0;
-}

faster_rcnn_inception_v2_coco_2018_01_28/demo.py

@@ -1,51 +0,0 @@
-import argparse
-import glob
-import os
-
-import cv2 as cv
-import numpy as np
-
-here = os.path.dirname(os.path.abspath(__file__))
-
-
-def main():
-    parser = argparse.ArgumentParser(description="Faster-RCNN InceptionV2 (COCO) ONNX detection demo")
-    parser.add_argument("--model", default=None)
-    parser.add_argument("--image", default=os.path.join(here, "example_outputs", "input_image.png"))
-    parser.add_argument("--output", default=os.path.join(here, "example_outputs", "output_image.png"))
-    parser.add_argument("--conf", type=float, default=0.3)
-    args = parser.parse_args()
-
-    model = args.model or glob.glob(os.path.join(here, "*.onnx"))[0]
-    img = cv.imread(args.image)
-    if img is None:
-        raise SystemExit("could not read image: %s" % args.image)
-
-    rgb = cv.resize(cv.cvtColor(img, cv.COLOR_BGR2RGB), (800, 600))
-    net = cv.dnn.readNetFromONNX(model, cv.dnn.ENGINE_ORT)
-    onames = ["detection_boxes:0", "detection_scores:0", "detection_classes:0", "num_detections:0"]
-    net.setInput(rgb[None].astype(np.uint8))
-    res = net.forward(onames)
-    boxes = res[[i for i, n in enumerate(onames) if "detection_boxes" in n][0]].reshape(-1, 4)
-    scores = res[[i for i, n in enumerate(onames) if "detection_scores" in n][0]].reshape(-1)
-    classes = res[[i for i, n in enumerate(onames) if "detection_classes" in n][0]].reshape(-1)
-    nd = int(res[[i for i, n in enumerate(onames) if "num_detections" in n][0]].reshape(-1)[0])
-
-    out = np.zeros((nd, 7), np.float32)
-    out[:, 1] = classes[:nd] - 1
-    out[:, 2] = scores[:nd]
-    out[:, 3:7] = boxes[:nd][:, [1, 0, 3, 2]]
-
-    h, w = img.shape[:2]
-    kept = [row for row in out if row[2] >= args.conf]
-    print(os.path.basename(here), len(kept), "detections")
-    for row in kept:
-        cv.rectangle(img, (int(row[3] * w), int(row[4] * h)), (int(row[5] * w), int(row[6] * h)), (0, 255, 0), 2)
-        cv.putText(img, "%d:%.2f" % (int(row[1]), row[2]), (int(row[3] * w), int(row[4] * h) - 5), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1)
-        print(int(row[1]), round(float(row[2]), 3), round(float(row[3]), 3), round(float(row[4]), 3), round(float(row[5]), 3), round(float(row[6]), 3))
-    cv.imwrite(args.output, img)
-    print("wrote", args.output)
-
-
-if __name__ == "__main__":
-    main()

faster_rcnn_inception_v2_coco_2018_01_28/faster_rcnn_inception_v2_coco_2018_01_28_2026jul.onnx

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:bcf541da5a58e9d8ab6c16e4c803ccc4e0da7dd62ad89311ae2a75c36b39835b
-size 57016094

faster_rcnn_resnet50_coco_2018_01_28/LICENSE

@@ -1,203 +0,0 @@
-Copyright 2015 The TensorFlow Authors.  All rights reserved.
-
-                                 Apache License
-                           Version 2.0, January 2004
-                        http://www.apache.org/licenses/
-
-   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
-
-   1. Definitions.
-
-      "License" shall mean the terms and conditions for use, reproduction,
-      and distribution as defined by Sections 1 through 9 of this document.
-
-      "Licensor" shall mean the copyright owner or entity authorized by
-      the copyright owner that is granting the License.
-
-      "Legal Entity" shall mean the union of the acting entity and all
-      other entities that control, are controlled by, or are under common
-      control with that entity. For the purposes of this definition,
-      "control" means (i) the power, direct or indirect, to cause the
-      direction or management of such entity, whether by contract or
-      otherwise, or (ii) ownership of fifty percent (50%) or more of the
-      outstanding shares, or (iii) beneficial ownership of such entity.
-
-      "You" (or "Your") shall mean an individual or Legal Entity
-      exercising permissions granted by this License.
-
-      "Source" form shall mean the preferred form for making modifications,
-      including but not limited to software source code, documentation
-      source, and configuration files.
-
-      "Object" form shall mean any form resulting from mechanical
-      transformation or translation of a Source form, including but
-      not limited to compiled object code, generated documentation,
-      and conversions to other media types.
-
-      "Work" shall mean the work of authorship, whether in Source or
-      Object form, made available under the License, as indicated by a
-      copyright notice that is included in or attached to the work
-      (an example is provided in the Appendix below).
-
-      "Derivative Works" shall mean any work, whether in Source or Object
-      form, that is based on (or derived from) the Work and for which the
-      editorial revisions, annotations, elaborations, or other modifications
-      represent, as a whole, an original work of authorship. For the purposes
-      of this License, Derivative Works shall not include works that remain
-      separable from, or merely link (or bind by name) to the interfaces of,
-      the Work and Derivative Works thereof.
-
-      "Contribution" shall mean any work of authorship, including
-      the original version of the Work and any modifications or additions
-      to that Work or Derivative Works thereof, that is intentionally
-      submitted to Licensor for inclusion in the Work by the copyright owner
-      or by an individual or Legal Entity authorized to submit on behalf of
-      the copyright owner. For the purposes of this definition, "submitted"
-      means any form of electronic, verbal, or written communication sent
-      to the Licensor or its representatives, including but not limited to
-      communication on electronic mailing lists, source code control systems,
-      and issue tracking systems that are managed by, or on behalf of, the
-      Licensor for the purpose of discussing and improving the Work, but
-      excluding communication that is conspicuously marked or otherwise
-      designated in writing by the copyright owner as "Not a Contribution."
-
-      "Contributor" shall mean Licensor and any individual or Legal Entity
-      on behalf of whom a Contribution has been received by Licensor and
-      subsequently incorporated within the Work.
-
-   2. Grant of Copyright License. Subject to the terms and conditions of
-      this License, each Contributor hereby grants to You a perpetual,
-      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
-      copyright license to reproduce, prepare Derivative Works of,
-      publicly display, publicly perform, sublicense, and distribute the
-      Work and such Derivative Works in Source or Object form.
-
-   3. Grant of Patent License. Subject to the terms and conditions of
-      this License, each Contributor hereby grants to You a perpetual,
-      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
-      (except as stated in this section) patent license to make, have made,
-      use, offer to sell, sell, import, and otherwise transfer the Work,
-      where such license applies only to those patent claims licensable
-      by such Contributor that are necessarily infringed by their
-      Contribution(s) alone or by combination of their Contribution(s)
-      with the Work to which such Contribution(s) was submitted. If You
-      institute patent litigation against any entity (including a
-      cross-claim or counterclaim in a lawsuit) alleging that the Work
-      or a Contribution incorporated within the Work constitutes direct
-      or contributory patent infringement, then any patent licenses
-      granted to You under this License for that Work shall terminate
-      as of the date such litigation is filed.
-
-   4. Redistribution. You may reproduce and distribute copies of the
-      Work or Derivative Works thereof in any medium, with or without
-      modifications, and in Source or Object form, provided that You
-      meet the following conditions:
-
-      (a) You must give any other recipients of the Work or
-          Derivative Works a copy of this License; and
-
-      (b) You must cause any modified files to carry prominent notices
-          stating that You changed the files; and
-
-      (c) You must retain, in the Source form of any Derivative Works
-          that You distribute, all copyright, patent, trademark, and
-          attribution notices from the Source form of the Work,
-          excluding those notices that do not pertain to any part of
-          the Derivative Works; and
-
-      (d) If the Work includes a "NOTICE" text file as part of its
-          distribution, then any Derivative Works that You distribute must
-          include a readable copy of the attribution notices contained
-          within such NOTICE file, excluding those notices that do not
-          pertain to any part of the Derivative Works, in at least one
-          of the following places: within a NOTICE text file distributed
-          as part of the Derivative Works; within the Source form or
-          documentation, if provided along with the Derivative Works; or,
-          within a display generated by the Derivative Works, if and
-          wherever such third-party notices normally appear. The contents
-          of the NOTICE file are for informational purposes only and
-          do not modify the License. You may add Your own attribution
-          notices within Derivative Works that You distribute, alongside
-          or as an addendum to the NOTICE text from the Work, provided
-          that such additional attribution notices cannot be construed
-          as modifying the License.
-
-      You may add Your own copyright statement to Your modifications and
-      may provide additional or different license terms and conditions
-      for use, reproduction, or distribution of Your modifications, or
-      for any such Derivative Works as a whole, provided Your use,
-      reproduction, and distribution of the Work otherwise complies with
-      the conditions stated in this License.
-
-   5. Submission of Contributions. Unless You explicitly state otherwise,
-      any Contribution intentionally submitted for inclusion in the Work
-      by You to the Licensor shall be under the terms and conditions of
-      this License, without any additional terms or conditions.
-      Notwithstanding the above, nothing herein shall supersede or modify
-      the terms of any separate license agreement you may have executed
-      with Licensor regarding such Contributions.
-
-   6. Trademarks. This License does not grant permission to use the trade
-      names, trademarks, service marks, or product names of the Licensor,
-      except as required for reasonable and customary use in describing the
-      origin of the Work and reproducing the content of the NOTICE file.
-
-   7. Disclaimer of Warranty. Unless required by applicable law or
-      agreed to in writing, Licensor provides the Work (and each
-      Contributor provides its Contributions) on an "AS IS" BASIS,
-      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
-      implied, including, without limitation, any warranties or conditions
-      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
-      PARTICULAR PURPOSE. You are solely responsible for determining the
-      appropriateness of using or redistributing the Work and assume any
-      risks associated with Your exercise of permissions under this License.
-
-   8. Limitation of Liability. In no event and under no legal theory,
-      whether in tort (including negligence), contract, or otherwise,
-      unless required by applicable law (such as deliberate and grossly
-      negligent acts) or agreed to in writing, shall any Contributor be
-      liable to You for damages, including any direct, indirect, special,
-      incidental, or consequential damages of any character arising as a
-      result of this License or out of the use or inability to use the
-      Work (including but not limited to damages for loss of goodwill,
-      work stoppage, computer failure or malfunction, or any and all
-      other commercial damages or losses), even if such Contributor
-      has been advised of the possibility of such damages.
-
-   9. Accepting Warranty or Additional Liability. While redistributing
-      the Work or Derivative Works thereof, You may choose to offer,
-      and charge a fee for, acceptance of support, warranty, indemnity,
-      or other liability obligations and/or rights consistent with this
-      License. However, in accepting such obligations, You may act only
-      on Your own behalf and on Your sole responsibility, not on behalf
-      of any other Contributor, and only if You agree to indemnify,
-      defend, and hold each Contributor harmless for any liability
-      incurred by, or claims asserted against, such Contributor by reason
-      of your accepting any such warranty or additional liability.
-
-   END OF TERMS AND CONDITIONS
-
-   APPENDIX: How to apply the Apache License to your work.
-
-      To apply the Apache License to your work, attach the following
-      boilerplate notice, with the fields enclosed by brackets "[]"
-      replaced with your own identifying information. (Don't include
-      the brackets!)  The text should be enclosed in the appropriate
-      comment syntax for the file format. We also recommend that a
-      file or class name and description of purpose be included on the
-      same "printed page" as the copyright notice for easier
-      identification within third-party archives.
-
-   Copyright 2015, The TensorFlow Authors.
-
-   Licensed under the Apache License, Version 2.0 (the "License");
-   you may not use this file except in compliance with the License.
-   You may obtain a copy of the License at
-
-       http://www.apache.org/licenses/LICENSE-2.0
-
-   Unless required by applicable law or agreed to in writing, software
-   distributed under the License is distributed on an "AS IS" BASIS,
-   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   See the License for the specific language governing permissions and
-limitations under the License.

faster_rcnn_resnet50_coco_2018_01_28/README.md

@@ -1,49 +0,0 @@
-# Faster-RCNN ResNet-50 (COCO)
-
-Object detection with the Faster-RCNN meta-architecture and a ResNet-50 backbone,
-trained on the COCO dataset. The model was originally distributed as a frozen TensorFlow
-graph (`faster_rcnn_resnet50_coco_2018_01_28.pb`) from the TensorFlow Object Detection
-API and converted to ONNX for inference with OpenCV's DNN module.
-
-## Model Details
-- **Architecture**: Faster-RCNN with a ResNet-50 backbone
-- **Input**: RGB image, uint8, NHWC layout (`image_tensor:0`, shape `[1, H, W, 3]`)
-- **Output**: `detection_boxes:0` (normalized `[ymin, xmin, ymax, xmax]`), `detection_scores:0`, `detection_classes:0` (1-based COCO ids), `num_detections:0`
-- **Framework**: ONNX (converted from the TensorFlow frozen graph via tf2onnx, opset 18)
-- **Original weights**: http://download.tensorflow.org/models/object_detection/faster_rcnn_resnet50_coco_2018_01_28.tar.gz
-
-## Usage
-
-### Python
-```bash
-python demo.py --model faster_rcnn_resnet50_coco_2018_01_28_2026jul.onnx --image example_outputs/input_image.png --output example_outputs/output_image.png --conf 0.3
-```
-
-### C++
-The C++ demo runs inference with OpenCV's DNN module. Adjust the OpenCV paths to your setup:
-```bash
-OCV=/path/to/opencv                          # OpenCV source tree
-OCVBUILD=/path/to/opencv/build               # OpenCV build directory (generated headers + libs)
-g++ -std=c++17 demo.cpp -o demo \
-  -I$OCV/include \
-  -I$OCV/modules/core/include \
-  -I$OCV/modules/dnn/include \
-  -I$OCV/modules/imgproc/include \
-  -I$OCV/modules/imgcodecs/include \
-  -I$OCVBUILD \
-  -L$OCVBUILD/lib -Wl,-rpath,$OCVBUILD/lib -lopencv_dnn -lopencv_imgcodecs -lopencv_imgproc -lopencv_core
-./demo --model faster_rcnn_resnet50_coco_2018_01_28_2026jul.onnx --image example_outputs/input_image.png --output example_outputs/output_image.png
-```
-
-## Conversion
-The ONNX model was exported from the frozen TensorFlow graph with tf2onnx (opset 18)
-via [convert_to_onnx.py](./convert_to_onnx.py) — input `image_tensor:0`, outputs
-`detection_boxes:0`, `detection_scores:0`, `detection_classes:0`, `num_detections:0`.
-Requires `tensorflow`, `tf2onnx`, and `onnx`.
-
-```bash
-python convert_to_onnx.py --pb ../pb/faster_rcnn_resnet50_coco_2018_01_28.pb
-```
-
-## License
-See [LICENSE](./LICENSE) — the model is released by the TensorFlow Authors under the Apache License 2.0.

faster_rcnn_resnet50_coco_2018_01_28/convert_to_onnx.py

@@ -1,40 +0,0 @@
-import argparse
-import datetime
-
-import onnx
-import tensorflow as tf
-import tf2onnx
-
-
-def load_graph_def(pb_path):
-    with tf.io.gfile.GFile(pb_path, "rb") as f:
-        graph_def = tf.compat.v1.GraphDef()
-        graph_def.ParseFromString(f.read())
-    return graph_def
-
-
-def main():
-    parser = argparse.ArgumentParser(description="Export faster_rcnn_resnet50_coco_2018_01_28.pb to ONNX")
-    parser.add_argument("--pb", default="../pb/faster_rcnn_resnet50_coco_2018_01_28.pb")
-    parser.add_argument("--opset", type=int, default=18)
-    args = parser.parse_args()
-
-    graph_def = load_graph_def(args.pb)
-
-    model_proto, _ = tf2onnx.convert.from_graph_def(
-        graph_def,
-        input_names=["image_tensor:0"],
-        output_names=["detection_boxes:0", "detection_scores:0", "detection_classes:0", "num_detections:0"],
-        opset=args.opset,
-    )
-    onnx.checker.check_model(model_proto)
-
-    stamp = datetime.datetime.now().strftime("%Y%b").lower()
-    onnx_path = "faster_rcnn_resnet50_coco_2018_01_28_%s.onnx" % stamp
-    with open(onnx_path, "wb") as f:
-        f.write(model_proto.SerializeToString())
-    print("wrote", onnx_path)
-
-
-if __name__ == "__main__":
-    main()

faster_rcnn_resnet50_coco_2018_01_28/demo.cpp

@@ -1,83 +0,0 @@
-#include <opencv2/dnn.hpp>
-#include <opencv2/imgproc.hpp>
-#include <opencv2/imgcodecs.hpp>
-#include <array>
-#include <cstdint>
-#include <iostream>
-#include <string>
-#include <vector>
-
-static std::string argVal(int argc, char** argv, const std::string& key, const std::string& def)
-{
-    for (int i = 1; i + 1 < argc; ++i)
-        if (key == argv[i]) return argv[i + 1];
-    return def;
-}
-
-int main(int argc, char** argv)
-{
-    std::string model  = argVal(argc, argv, "--model",  "faster_rcnn_resnet50_coco_2018_01_28_2026jul.onnx");
-    std::string image  = argVal(argc, argv, "--image",  "example_outputs/input_image.png");
-    std::string output = argVal(argc, argv, "--output", "example_outputs/output_image.png");
-    float conf = std::stof(argVal(argc, argv, "--conf", "0.3"));
-
-    cv::Mat img = cv::imread(image);
-    if (img.empty())
-    {
-        std::cerr << "could not read image: " << image << std::endl;
-        return 1;
-    }
-
-    const int W = 800, H = 600;
-    cv::Mat rgb;
-    cv::cvtColor(img, rgb, cv::COLOR_BGR2RGB);
-    cv::resize(rgb, rgb, cv::Size(W, H));
-    if (!rgb.isContinuous()) rgb = rgb.clone();
-
-    int blobShape[] = {1, H, W, 3};
-    cv::Mat blob(4, blobShape, CV_8U, rgb.data);
-    cv::dnn::Net net = cv::dnn::readNetFromONNX(model, cv::dnn::ENGINE_ORT);
-    net.setInput(blob);
-    std::vector<cv::String> out_strs = {"detection_boxes:0", "detection_scores:0", "detection_classes:0", "num_detections:0"};
-    std::vector<cv::Mat> outs;
-    net.forward(outs, out_strs);
-
-    float* boxes = nullptr;
-    float* scores = nullptr;
-    float* classes = nullptr;
-    float* numd = nullptr;
-    for (size_t i = 0; i < out_strs.size(); ++i)
-    {
-        float* p = (float*)outs[i].data;
-        const std::string& n = out_strs[i];
-        if (n.find("detection_boxes") != std::string::npos) boxes = p;
-        else if (n.find("detection_scores") != std::string::npos) scores = p;
-        else if (n.find("detection_classes") != std::string::npos) classes = p;
-        else if (n.find("num_detections") != std::string::npos) numd = p;
-    }
-    int nd = (int)numd[0];
-
-    int w = img.cols, h = img.rows;
-    std::vector<int> kept;
-    for (int i = 0; i < nd; ++i)
-        if (scores[i] >= conf) kept.push_back(i);
-
-    std::cout << "faster_rcnn_resnet50_coco_2018_01_28 " << kept.size() << " detections" << std::endl;
-    for (int i : kept)
-    {
-        int cls = (int)classes[i] - 1;
-        float score = scores[i];
-        float ymin = boxes[i * 4 + 0], xmin = boxes[i * 4 + 1];
-        float ymax = boxes[i * 4 + 2], xmax = boxes[i * 4 + 3];
-        cv::Point p1((int)(xmin * w), (int)(ymin * h));
-        cv::Point p2((int)(xmax * w), (int)(ymax * h));
-        cv::rectangle(img, p1, p2, cv::Scalar(0, 255, 0), 2);
-        cv::putText(img, cv::format("%d:%.2f", cls, score), cv::Point(p1.x, p1.y - 5),
-                    cv::FONT_HERSHEY_SIMPLEX, 0.5, cv::Scalar(0, 255, 0), 1);
-        std::cout << cls << " " << cv::format("%.3f %.3f %.3f %.3f %.3f", score, xmin, ymin, xmax, ymax) << std::endl;
-    }
-
-    cv::imwrite(output, img);
-    std::cout << "wrote " << output << std::endl;
-    return 0;
-}

faster_rcnn_resnet50_coco_2018_01_28/demo.py

@@ -1,51 +0,0 @@
-import argparse
-import glob
-import os
-
-import cv2 as cv
-import numpy as np
-
-here = os.path.dirname(os.path.abspath(__file__))
-
-
-def main():
-    parser = argparse.ArgumentParser(description="Faster-RCNN ResNet-50 (COCO) ONNX detection demo")
-    parser.add_argument("--model", default=None)
-    parser.add_argument("--image", default=os.path.join(here, "example_outputs", "input_image.png"))
-    parser.add_argument("--output", default=os.path.join(here, "example_outputs", "output_image.png"))
-    parser.add_argument("--conf", type=float, default=0.3)
-    args = parser.parse_args()
-
-    model = args.model or glob.glob(os.path.join(here, "*.onnx"))[0]
-    img = cv.imread(args.image)
-    if img is None:
-        raise SystemExit("could not read image: %s" % args.image)
-
-    rgb = cv.resize(cv.cvtColor(img, cv.COLOR_BGR2RGB), (800, 600))
-    net = cv.dnn.readNetFromONNX(model, cv.dnn.ENGINE_ORT)
-    onames = ["detection_boxes:0", "detection_scores:0", "detection_classes:0", "num_detections:0"]
-    net.setInput(rgb[None].astype(np.uint8))
-    res = net.forward(onames)
-    boxes = res[[i for i, n in enumerate(onames) if "detection_boxes" in n][0]].reshape(-1, 4)
-    scores = res[[i for i, n in enumerate(onames) if "detection_scores" in n][0]].reshape(-1)
-    classes = res[[i for i, n in enumerate(onames) if "detection_classes" in n][0]].reshape(-1)
-    nd = int(res[[i for i, n in enumerate(onames) if "num_detections" in n][0]].reshape(-1)[0])
-
-    out = np.zeros((nd, 7), np.float32)
-    out[:, 1] = classes[:nd] - 1
-    out[:, 2] = scores[:nd]
-    out[:, 3:7] = boxes[:nd][:, [1, 0, 3, 2]]
-
-    h, w = img.shape[:2]
-    kept = [row for row in out if row[2] >= args.conf]
-    print(os.path.basename(here), len(kept), "detections")
-    for row in kept:
-        cv.rectangle(img, (int(row[3] * w), int(row[4] * h)), (int(row[5] * w), int(row[6] * h)), (0, 255, 0), 2)
-        cv.putText(img, "%d:%.2f" % (int(row[1]), row[2]), (int(row[3] * w), int(row[4] * h) - 5), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1)
-        print(int(row[1]), round(float(row[2]), 3), round(float(row[3]), 3), round(float(row[4]), 3), round(float(row[5]), 3), round(float(row[6]), 3))
-    cv.imwrite(args.output, img)
-    print("wrote", args.output)
-
-
-if __name__ == "__main__":
-    main()

faster_rcnn_resnet50_coco_2018_01_28/faster_rcnn_resnet50_coco_2018_01_28_2026jul.onnx

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:217613103b36eba4771087bdae63a7c04ed545e7850087f205990db7e5f18b88
-size 120414387

mask_rcnn_inception_v2_coco_2018_01_28/LICENSE

@@ -1,203 +0,0 @@
-Copyright 2015 The TensorFlow Authors.  All rights reserved.
-
-                                 Apache License
-                           Version 2.0, January 2004
-                        http://www.apache.org/licenses/
-
-   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
-
-   1. Definitions.
-
-      "License" shall mean the terms and conditions for use, reproduction,
-      and distribution as defined by Sections 1 through 9 of this document.
-
-      "Licensor" shall mean the copyright owner or entity authorized by
-      the copyright owner that is granting the License.
-
-      "Legal Entity" shall mean the union of the acting entity and all
-      other entities that control, are controlled by, or are under common
-      control with that entity. For the purposes of this definition,
-      "control" means (i) the power, direct or indirect, to cause the
-      direction or management of such entity, whether by contract or
-      otherwise, or (ii) ownership of fifty percent (50%) or more of the
-      outstanding shares, or (iii) beneficial ownership of such entity.
-
-      "You" (or "Your") shall mean an individual or Legal Entity
-      exercising permissions granted by this License.
-
-      "Source" form shall mean the preferred form for making modifications,
-      including but not limited to software source code, documentation
-      source, and configuration files.
-
-      "Object" form shall mean any form resulting from mechanical
-      transformation or translation of a Source form, including but
-      not limited to compiled object code, generated documentation,
-      and conversions to other media types.
-
-      "Work" shall mean the work of authorship, whether in Source or
-      Object form, made available under the License, as indicated by a
-      copyright notice that is included in or attached to the work
-      (an example is provided in the Appendix below).
-
-      "Derivative Works" shall mean any work, whether in Source or Object
-      form, that is based on (or derived from) the Work and for which the
-      editorial revisions, annotations, elaborations, or other modifications
-      represent, as a whole, an original work of authorship. For the purposes
-      of this License, Derivative Works shall not include works that remain
-      separable from, or merely link (or bind by name) to the interfaces of,
-      the Work and Derivative Works thereof.
-
-      "Contribution" shall mean any work of authorship, including
-      the original version of the Work and any modifications or additions
-      to that Work or Derivative Works thereof, that is intentionally
-      submitted to Licensor for inclusion in the Work by the copyright owner
-      or by an individual or Legal Entity authorized to submit on behalf of
-      the copyright owner. For the purposes of this definition, "submitted"
-      means any form of electronic, verbal, or written communication sent
-      to the Licensor or its representatives, including but not limited to
-      communication on electronic mailing lists, source code control systems,
-      and issue tracking systems that are managed by, or on behalf of, the
-      Licensor for the purpose of discussing and improving the Work, but
-      excluding communication that is conspicuously marked or otherwise
-      designated in writing by the copyright owner as "Not a Contribution."
-
-      "Contributor" shall mean Licensor and any individual or Legal Entity
-      on behalf of whom a Contribution has been received by Licensor and
-      subsequently incorporated within the Work.
-
-   2. Grant of Copyright License. Subject to the terms and conditions of
-      this License, each Contributor hereby grants to You a perpetual,
-      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
-      copyright license to reproduce, prepare Derivative Works of,
-      publicly display, publicly perform, sublicense, and distribute the
-      Work and such Derivative Works in Source or Object form.
-
-   3. Grant of Patent License. Subject to the terms and conditions of
-      this License, each Contributor hereby grants to You a perpetual,
-      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
-      (except as stated in this section) patent license to make, have made,
-      use, offer to sell, sell, import, and otherwise transfer the Work,
-      where such license applies only to those patent claims licensable
-      by such Contributor that are necessarily infringed by their
-      Contribution(s) alone or by combination of their Contribution(s)
-      with the Work to which such Contribution(s) was submitted. If You
-      institute patent litigation against any entity (including a
-      cross-claim or counterclaim in a lawsuit) alleging that the Work
-      or a Contribution incorporated within the Work constitutes direct
-      or contributory patent infringement, then any patent licenses
-      granted to You under this License for that Work shall terminate
-      as of the date such litigation is filed.
-
-   4. Redistribution. You may reproduce and distribute copies of the
-      Work or Derivative Works thereof in any medium, with or without
-      modifications, and in Source or Object form, provided that You
-      meet the following conditions:
-
-      (a) You must give any other recipients of the Work or
-          Derivative Works a copy of this License; and
-
-      (b) You must cause any modified files to carry prominent notices
-          stating that You changed the files; and
-
-      (c) You must retain, in the Source form of any Derivative Works
-          that You distribute, all copyright, patent, trademark, and
-          attribution notices from the Source form of the Work,
-          excluding those notices that do not pertain to any part of
-          the Derivative Works; and
-
-      (d) If the Work includes a "NOTICE" text file as part of its
-          distribution, then any Derivative Works that You distribute must
-          include a readable copy of the attribution notices contained
-          within such NOTICE file, excluding those notices that do not
-          pertain to any part of the Derivative Works, in at least one
-          of the following places: within a NOTICE text file distributed
-          as part of the Derivative Works; within the Source form or
-          documentation, if provided along with the Derivative Works; or,
-          within a display generated by the Derivative Works, if and
-          wherever such third-party notices normally appear. The contents
-          of the NOTICE file are for informational purposes only and
-          do not modify the License. You may add Your own attribution
-          notices within Derivative Works that You distribute, alongside
-          or as an addendum to the NOTICE text from the Work, provided
-          that such additional attribution notices cannot be construed
-          as modifying the License.
-
-      You may add Your own copyright statement to Your modifications and
-      may provide additional or different license terms and conditions
-      for use, reproduction, or distribution of Your modifications, or
-      for any such Derivative Works as a whole, provided Your use,
-      reproduction, and distribution of the Work otherwise complies with
-      the conditions stated in this License.
-
-   5. Submission of Contributions. Unless You explicitly state otherwise,
-      any Contribution intentionally submitted for inclusion in the Work
-      by You to the Licensor shall be under the terms and conditions of
-      this License, without any additional terms or conditions.
-      Notwithstanding the above, nothing herein shall supersede or modify
-      the terms of any separate license agreement you may have executed
-      with Licensor regarding such Contributions.
-
-   6. Trademarks. This License does not grant permission to use the trade
-      names, trademarks, service marks, or product names of the Licensor,
-      except as required for reasonable and customary use in describing the
-      origin of the Work and reproducing the content of the NOTICE file.
-
-   7. Disclaimer of Warranty. Unless required by applicable law or
-      agreed to in writing, Licensor provides the Work (and each
-      Contributor provides its Contributions) on an "AS IS" BASIS,
-      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
-      implied, including, without limitation, any warranties or conditions
-      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
-      PARTICULAR PURPOSE. You are solely responsible for determining the
-      appropriateness of using or redistributing the Work and assume any
-      risks associated with Your exercise of permissions under this License.
-
-   8. Limitation of Liability. In no event and under no legal theory,
-      whether in tort (including negligence), contract, or otherwise,
-      unless required by applicable law (such as deliberate and grossly
-      negligent acts) or agreed to in writing, shall any Contributor be
-      liable to You for damages, including any direct, indirect, special,
-      incidental, or consequential damages of any character arising as a
-      result of this License or out of the use or inability to use the
-      Work (including but not limited to damages for loss of goodwill,
-      work stoppage, computer failure or malfunction, or any and all
-      other commercial damages or losses), even if such Contributor
-      has been advised of the possibility of such damages.
-
-   9. Accepting Warranty or Additional Liability. While redistributing
-      the Work or Derivative Works thereof, You may choose to offer,
-      and charge a fee for, acceptance of support, warranty, indemnity,
-      or other liability obligations and/or rights consistent with this
-      License. However, in accepting such obligations, You may act only
-      on Your own behalf and on Your sole responsibility, not on behalf
-      of any other Contributor, and only if You agree to indemnify,
-      defend, and hold each Contributor harmless for any liability
-      incurred by, or claims asserted against, such Contributor by reason
-      of your accepting any such warranty or additional liability.
-
-   END OF TERMS AND CONDITIONS
-
-   APPENDIX: How to apply the Apache License to your work.
-
-      To apply the Apache License to your work, attach the following
-      boilerplate notice, with the fields enclosed by brackets "[]"
-      replaced with your own identifying information. (Don't include
-      the brackets!)  The text should be enclosed in the appropriate
-      comment syntax for the file format. We also recommend that a
-      file or class name and description of purpose be included on the
-      same "printed page" as the copyright notice for easier
-      identification within third-party archives.
-
-   Copyright 2015, The TensorFlow Authors.
-
-   Licensed under the Apache License, Version 2.0 (the "License");
-   you may not use this file except in compliance with the License.
-   You may obtain a copy of the License at
-
-       http://www.apache.org/licenses/LICENSE-2.0
-
-   Unless required by applicable law or agreed to in writing, software
-   distributed under the License is distributed on an "AS IS" BASIS,
-   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   See the License for the specific language governing permissions and
-limitations under the License.

mask_rcnn_inception_v2_coco_2018_01_28/README.md

@@ -1,51 +0,0 @@
-# Mask-RCNN Inception v2 COCO
-
-Instance segmentation with the Mask-RCNN Inception v2 network trained on the COCO dataset.
-The model was originally distributed as a frozen TensorFlow graph
-(`mask_rcnn_inception_v2_coco_2018_01_28.pb`) from the TensorFlow Object Detection API
-and converted to ONNX for use with OpenCV's DNN module.
-
-## Model Details
-- **Architecture**: Mask-RCNN with an Inception v2 backbone
-- **Input**: RGB image, uint8, NHWC layout (`image_tensor:0`); the demo resizes to 800×800
-- **Output**: `num_detections:0`, `detection_boxes:0` (normalized `[ymin, xmin, ymax, xmax]`),
-  `detection_scores:0`, `detection_classes:0` (COCO ids, subtract 1 for a 0-based label),
-  and `detection_masks:0` (a 15×15 mask per detection, resized to its box)
-- **Framework**: ONNX (converted from the TensorFlow frozen graph via tf2onnx, opset 18)
-- **Original weights**: http://download.tensorflow.org/models/object_detection/mask_rcnn_inception_v2_coco_2018_01_28.tar.gz
-
-## Usage
-
-### Python
-```bash
-python demo.py --model mask_rcnn_inception_v2_coco_2018_01_28_2026jul.onnx --image example_outputs/input_image.png --output example_outputs/output_image.png --conf 0.3
-```
-
-### C++
-The C++ demo runs inference with OpenCV's DNN module. Adjust the OpenCV paths to your setup:
-```bash
-OCV=/path/to/opencv                          # OpenCV source tree
-OCVBUILD=/path/to/opencv/build               # OpenCV build directory (generated headers + libs)
-g++ -std=c++17 demo.cpp -o demo \
-  -I$OCV/include \
-  -I$OCV/modules/core/include \
-  -I$OCV/modules/dnn/include \
-  -I$OCV/modules/imgproc/include \
-  -I$OCV/modules/imgcodecs/include \
-  -I$OCVBUILD \
-  -L$OCVBUILD/lib -Wl,-rpath,$OCVBUILD/lib -lopencv_dnn -lopencv_imgcodecs -lopencv_imgproc -lopencv_core
-./demo --model mask_rcnn_inception_v2_coco_2018_01_28_2026jul.onnx --image example_outputs/input_image.png --output example_outputs/output_image.png
-```
-
-## Conversion
-The ONNX model was exported from the frozen TensorFlow graph with tf2onnx (opset 18)
-via [convert_to_onnx.py](./convert_to_onnx.py) — input `image_tensor:0`, outputs
-`num_detections:0`, `detection_boxes:0`, `detection_scores:0`, `detection_classes:0`,
-and `detection_masks:0`. Requires `tensorflow`, `tf2onnx`, and `onnx`.
-
-```bash
-python convert_to_onnx.py --pb ../pb/mask_rcnn_inception_v2_coco_2018_01_28.pb
-```
-
-## License
-See [LICENSE](./LICENSE) — the model is released by the TensorFlow Authors under the Apache License 2.0.

mask_rcnn_inception_v2_coco_2018_01_28/convert_to_onnx.py

@@ -1,46 +0,0 @@
-import argparse
-import datetime
-
-import onnx
-import tensorflow as tf
-import tf2onnx
-
-
-def load_graph_def(pb_path):
-    with tf.io.gfile.GFile(pb_path, "rb") as f:
-        graph_def = tf.compat.v1.GraphDef()
-        graph_def.ParseFromString(f.read())
-    return graph_def
-
-
-def main():
-    parser = argparse.ArgumentParser(description="Export mask_rcnn_inception_v2_coco_2018_01_28.pb to ONNX")
-    parser.add_argument("--pb", default="../pb/mask_rcnn_inception_v2_coco_2018_01_28.pb")
-    parser.add_argument("--opset", type=int, default=18)
-    args = parser.parse_args()
-
-    graph_def = load_graph_def(args.pb)
-
-    model_proto, _ = tf2onnx.convert.from_graph_def(
-        graph_def,
-        input_names=["image_tensor:0"],
-        output_names=[
-            "num_detections:0",
-            "detection_boxes:0",
-            "detection_scores:0",
-            "detection_classes:0",
-            "detection_masks:0",
-        ],
-        opset=args.opset,
-    )
-    onnx.checker.check_model(model_proto)
-
-    stamp = datetime.datetime.now().strftime("%Y%b").lower()
-    onnx_path = "mask_rcnn_inception_v2_coco_2018_01_28_%s.onnx" % stamp
-    with open(onnx_path, "wb") as f:
-        f.write(model_proto.SerializeToString())
-    print("wrote", onnx_path)
-
-
-if __name__ == "__main__":
-    main()

mask_rcnn_inception_v2_coco_2018_01_28/demo.cpp

@@ -1,109 +0,0 @@
-#include <opencv2/dnn.hpp>
-#include <opencv2/imgproc.hpp>
-#include <opencv2/imgcodecs.hpp>
-#include <algorithm>
-#include <array>
-#include <cstdint>
-#include <iostream>
-#include <string>
-#include <vector>
-
-static std::string argVal(int argc, char** argv, const std::string& key, const std::string& def)
-{
-    for (int i = 1; i + 1 < argc; ++i)
-        if (key == argv[i]) return argv[i + 1];
-    return def;
-}
-
-int main(int argc, char** argv)
-{
-    std::string model  = argVal(argc, argv, "--model",  "mask_rcnn_inception_v2_coco_2018_01_28_2026jul.onnx");
-    std::string image  = argVal(argc, argv, "--image",  "example_outputs/input_image.png");
-    std::string output = argVal(argc, argv, "--output", "example_outputs/output_image.png");
-    float conf = std::stof(argVal(argc, argv, "--conf", "0.3"));
-
-    cv::Mat img = cv::imread(image);
-    if (img.empty())
-    {
-        std::cerr << "could not read image: " << image << std::endl;
-        return 1;
-    }
-
-    const int W = 800, H = 800;
-    cv::Mat rgb;
-    cv::cvtColor(img, rgb, cv::COLOR_BGR2RGB);
-    cv::resize(rgb, rgb, cv::Size(W, H));
-    if (!rgb.isContinuous()) rgb = rgb.clone();
-
-    int blobShape[] = {1, H, W, 3};
-    cv::Mat blob(4, blobShape, CV_8U, rgb.data);
-    cv::dnn::Net net = cv::dnn::readNetFromONNX(model, cv::dnn::ENGINE_ORT);
-    net.setInput(blob);
-    std::vector<cv::String> out_strs = {"num_detections:0", "detection_boxes:0", "detection_scores:0", "detection_classes:0", "detection_masks:0"};
-    std::vector<cv::Mat> outs;
-    net.forward(outs, out_strs);
-
-    float* boxes = nullptr;
-    float* scores = nullptr;
-    float* classes = nullptr;
-    float* numd = nullptr;
-    float* masks = nullptr;
-    for (size_t i = 0; i < out_strs.size(); ++i)
-    {
-        float* p = (float*)outs[i].data;
-        const std::string& n = out_strs[i];
-        if (n.find("detection_boxes") != std::string::npos) boxes = p;
-        else if (n.find("detection_scores") != std::string::npos) scores = p;
-        else if (n.find("detection_classes") != std::string::npos) classes = p;
-        else if (n.find("num_detections") != std::string::npos) numd = p;
-        else if (n.find("detection_masks") != std::string::npos) masks = p;
-    }
-    int nd = (int)numd[0];
-
-    int w = img.cols, h = img.rows;
-    cv::Mat out = img.clone();
-    std::vector<int> kept;
-    for (int i = 0; i < nd; ++i)
-        if (scores[i] >= conf) kept.push_back(i);
-
-    std::cout << "mask_rcnn_inception_v2_coco_2018_01_28 " << kept.size() << " detections" << std::endl;
-    for (int i : kept)
-    {
-        int cls = (int)classes[i] - 1;
-        float score = scores[i];
-        float y1 = boxes[i * 4 + 0], x1 = boxes[i * 4 + 1];
-        float y2 = boxes[i * 4 + 2], x2 = boxes[i * 4 + 3];
-        int px1 = std::max(0, (int)(x1 * w)), py1 = std::max(0, (int)(y1 * h));
-        int px2 = std::min(w, (int)(x2 * w)), py2 = std::min(h, (int)(y2 * h));
-
-        unsigned s = (unsigned)i * 2654435761u + 1u;
-        int col[3];
-        for (int c = 0; c < 3; ++c) { s = s * 1664525u + 1013904223u; col[c] = 80 + (int)((s >> 8) % 176u); }
-        cv::Scalar color(col[0], col[1], col[2]);
-
-        if (px2 > px1 && py2 > py1)
-        {
-            cv::Mat m15(15, 15, CV_32F, masks + (size_t)i * 225);
-            cv::Mat mr;
-            cv::resize(m15, mr, cv::Size(px2 - px1, py2 - py1));
-            cv::Mat roi = out(cv::Rect(px1, py1, px2 - px1, py2 - py1));
-            for (int y = 0; y < roi.rows; ++y)
-            {
-                cv::Vec3b* rp = roi.ptr<cv::Vec3b>(y);
-                const float* mp = mr.ptr<float>(y);
-                for (int x = 0; x < roi.cols; ++x)
-                    if (mp[x] > 0.5f)
-                        for (int c = 0; c < 3; ++c)
-                            rp[x][c] = (uchar)(0.5 * rp[x][c] + 0.5 * col[c]);
-            }
-        }
-        cv::rectangle(out, cv::Point(px1, py1), cv::Point(px2, py2), color, 2);
-        cv::putText(out, cv::format("%d:%.2f", cls, score), cv::Point(px1, py1 - 5),
-                    cv::FONT_HERSHEY_SIMPLEX, 0.5, color, 1);
-        std::cout << cls << " " << cv::format("%.3f %.3f %.3f %.3f %.3f", score, x1, y1, x2, y2) << std::endl;
-    }
-
-    cv::imwrite(output, out);
-    std::cout << "wrote " << output << std::endl;
-    return 0;
-}

mask_rcnn_inception_v2_coco_2018_01_28/demo.py

@@ -1,59 +0,0 @@
-import argparse
-import glob
-import os
-
-import cv2 as cv
-import numpy as np
-
-here = os.path.dirname(os.path.abspath(__file__))
-
-
-def main():
-    parser = argparse.ArgumentParser(description="Mask-RCNN Inception v2 COCO (OpenCV DNN) detection + mask demo")
-    found = glob.glob(os.path.join(here, "*.onnx"))
-    parser.add_argument("--model", default=found[0] if found else None)
-    parser.add_argument("--image", default=os.path.join(here, "example_outputs", "input_image.png"))
-    parser.add_argument("--output", default=os.path.join(here, "example_outputs", "output_image.png"))
-    parser.add_argument("--conf", type=float, default=0.3)
-    args = parser.parse_args()
-
-    img = cv.imread(args.image)
-    if img is None:
-        raise SystemExit("could not read image: %s" % args.image)
-
-    net = cv.dnn.readNetFromONNX(args.model, cv.dnn.ENGINE_ORT)
-    rgb = cv.resize(cv.cvtColor(img, cv.COLOR_BGR2RGB), (800, 800))
-    onames = ["num_detections:0", "detection_boxes:0", "detection_scores:0", "detection_classes:0", "detection_masks:0"]
-    net.setInput(rgb[None].astype(np.uint8))
-    res = net.forward(onames)
-    boxes = res[[i for i, n in enumerate(onames) if "detection_boxes" in n][0]].reshape(-1, 4)
-    scores = res[[i for i, n in enumerate(onames) if "detection_scores" in n][0]].reshape(-1)
-    classes = res[[i for i, n in enumerate(onames) if "detection_classes" in n][0]].reshape(-1)
-    nd = int(res[[i for i, n in enumerate(onames) if "num_detections" in n][0]].reshape(-1)[0])
-    masks = res[[i for i, n in enumerate(onames) if "detection_masks" in n][0]].reshape(-1, 15, 15)
-
-    h, w = img.shape[:2]
-    out = img.copy()
-    kept = [i for i in range(nd) if scores[i] >= args.conf]
-    print("mask_rcnn_inception_v2_coco_2018_01_28", len(kept), "detections")
-    for i in kept:
-        cls = int(classes[i]) - 1
-        score = float(scores[i])
-        y1, x1, y2, x2 = boxes[i]
-        px1, py1 = max(0, int(x1 * w)), max(0, int(y1 * h))
-        px2, py2 = min(w, int(x2 * w)), min(h, int(y2 * h))
-        color = tuple(int(c) for c in np.random.default_rng(i).integers(80, 256, 3))
-        if px2 > px1 and py2 > py1:
-            m = cv.resize(masks[i], (px2 - px1, py2 - py1)) > 0.5
-            roi = out[py1:py2, px1:px2]
-            roi[m] = (0.5 * roi[m] + 0.5 * np.array(color)).astype(np.uint8)
-        cv.rectangle(out, (px1, py1), (px2, py2), color, 2)
-        cv.putText(out, "%d:%.2f" % (cls, score), (px1, py1 - 5), cv.FONT_HERSHEY_SIMPLEX, 0.5, color, 1)
-        print(cls, round(score, 3), round(float(x1), 3), round(float(y1), 3), round(float(x2), 3), round(float(y2), 3))
-
-    cv.imwrite(args.output, out)
-    print("wrote", args.output)
-
-
-if __name__ == "__main__":
-    main()

mask_rcnn_inception_v2_coco_2018_01_28/mask_rcnn_inception_v2_coco_2018_01_28_2026jul.onnx

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:27cdd89df33ab94f61fd278350f66996efec3437937b5679a311ba55e386690f
-size 66728941

opencv_face_detector_uint8/LICENSE

@@ -1,203 +0,0 @@
-Copyright (c) OpenCV team and the opencv_3rdparty contributors.  All rights reserved.
-
-                                 Apache License
-                           Version 2.0, January 2004
-                        http://www.apache.org/licenses/
-
-   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
-
-   1. Definitions.
-
-      "License" shall mean the terms and conditions for use, reproduction,
-      and distribution as defined by Sections 1 through 9 of this document.
-
-      "Licensor" shall mean the copyright owner or entity authorized by
-      the copyright owner that is granting the License.
-
-      "Legal Entity" shall mean the union of the acting entity and all
-      other entities that control, are controlled by, or are under common
-      control with that entity. For the purposes of this definition,
-      "control" means (i) the power, direct or indirect, to cause the
-      direction or management of such entity, whether by contract or
-      otherwise, or (ii) ownership of fifty percent (50%) or more of the
-      outstanding shares, or (iii) beneficial ownership of such entity.
-
-      "You" (or "Your") shall mean an individual or Legal Entity
-      exercising permissions granted by this License.
-
-      "Source" form shall mean the preferred form for making modifications,
-      including but not limited to software source code, documentation
-      source, and configuration files.
-
-      "Object" form shall mean any form resulting from mechanical
-      transformation or translation of a Source form, including but
-      not limited to compiled object code, generated documentation,
-      and conversions to other media types.
-
-      "Work" shall mean the work of authorship, whether in Source or
-      Object form, made available under the License, as indicated by a
-      copyright notice that is included in or attached to the work
-      (an example is provided in the Appendix below).
-
-      "Derivative Works" shall mean any work, whether in Source or Object
-      form, that is based on (or derived from) the Work and for which the
-      editorial revisions, annotations, elaborations, or other modifications
-      represent, as a whole, an original work of authorship. For the purposes
-      of this License, Derivative Works shall not include works that remain
-      separable from, or merely link (or bind by name) to the interfaces of,
-      the Work and Derivative Works thereof.
-
-      "Contribution" shall mean any work of authorship, including
-      the original version of the Work and any modifications or additions
-      to that Work or Derivative Works thereof, that is intentionally
-      submitted to Licensor for inclusion in the Work by the copyright owner
-      or by an individual or Legal Entity authorized to submit on behalf of
-      the copyright owner. For the purposes of this definition, "submitted"
-      means any form of electronic, verbal, or written communication sent
-      to the Licensor or its representatives, including but not limited to
-      communication on electronic mailing lists, source code control systems,
-      and issue tracking systems that are managed by, or on behalf of, the
-      Licensor for the purpose of discussing and improving the Work, but
-      excluding communication that is conspicuously marked or otherwise
-      designated in writing by the copyright owner as "Not a Contribution."
-
-      "Contributor" shall mean Licensor and any individual or Legal Entity
-      on behalf of whom a Contribution has been received by Licensor and
-      subsequently incorporated within the Work.
-
-   2. Grant of Copyright License. Subject to the terms and conditions of
-      this License, each Contributor hereby grants to You a perpetual,
-      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
-      copyright license to reproduce, prepare Derivative Works of,
-      publicly display, publicly perform, sublicense, and distribute the
-      Work and such Derivative Works in Source or Object form.
-
-   3. Grant of Patent License. Subject to the terms and conditions of
-      this License, each Contributor hereby grants to You a perpetual,
-      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
-      (except as stated in this section) patent license to make, have made,
-      use, offer to sell, sell, import, and otherwise transfer the Work,
-      where such license applies only to those patent claims licensable
-      by such Contributor that are necessarily infringed by their
-      Contribution(s) alone or by combination of their Contribution(s)
-      with the Work to which such Contribution(s) was submitted. If You
-      institute patent litigation against any entity (including a
-      cross-claim or counterclaim in a lawsuit) alleging that the Work
-      or a Contribution incorporated within the Work constitutes direct
-      or contributory patent infringement, then any patent licenses
-      granted to You under this License for that Work shall terminate
-      as of the date such litigation is filed.
-
-   4. Redistribution. You may reproduce and distribute copies of the
-      Work or Derivative Works thereof in any medium, with or without
-      modifications, and in Source or Object form, provided that You
-      meet the following conditions:
-
-      (a) You must give any other recipients of the Work or
-          Derivative Works a copy of this License; and
-
-      (b) You must cause any modified files to carry prominent notices
-          stating that You changed the files; and
-
-      (c) You must retain, in the Source form of any Derivative Works
-          that You distribute, all copyright, patent, trademark, and
-          attribution notices from the Source form of the Work,
-          excluding those notices that do not pertain to any part of
-          the Derivative Works; and
-
-      (d) If the Work includes a "NOTICE" text file as part of its
-          distribution, then any Derivative Works that You distribute must
-          include a readable copy of the attribution notices contained
-          within such NOTICE file, excluding those notices that do not
-          pertain to any part of the Derivative Works, in at least one
-          of the following places: within a NOTICE text file distributed
-          as part of the Derivative Works; within the Source form or
-          documentation, if provided along with the Derivative Works; or,
-          within a display generated by the Derivative Works, if and
-          wherever such third-party notices normally appear. The contents
-          of the NOTICE file are for informational purposes only and
-          do not modify the License. You may add Your own attribution
-          notices within Derivative Works that You distribute, alongside
-          or as an addendum to the NOTICE text from the Work, provided
-          that such additional attribution notices cannot be construed
-          as modifying the License.
-
-      You may add Your own copyright statement to Your modifications and
-      may provide additional or different license terms and conditions
-      for use, reproduction, or distribution of Your modifications, or
-      for any such Derivative Works as a whole, provided Your use,
-      reproduction, and distribution of the Work otherwise complies with
-      the conditions stated in this License.
-
-   5. Submission of Contributions. Unless You explicitly state otherwise,
-      any Contribution intentionally submitted for inclusion in the Work
-      by You to the Licensor shall be under the terms and conditions of
-      this License, without any additional terms or conditions.
-      Notwithstanding the above, nothing herein shall supersede or modify
-      the terms of any separate license agreement you may have executed
-      with Licensor regarding such Contributions.
-
-   6. Trademarks. This License does not grant permission to use the trade
-      names, trademarks, service marks, or product names of the Licensor,
-      except as required for reasonable and customary use in describing the
-      origin of the Work and reproducing the content of the NOTICE file.
-
-   7. Disclaimer of Warranty. Unless required by applicable law or
-      agreed to in writing, Licensor provides the Work (and each
-      Contributor provides its Contributions) on an "AS IS" BASIS,
-      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
-      implied, including, without limitation, any warranties or conditions
-      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
-      PARTICULAR PURPOSE. You are solely responsible for determining the
-      appropriateness of using or redistributing the Work and assume any
-      risks associated with Your exercise of permissions under this License.
-
-   8. Limitation of Liability. In no event and under no legal theory,
-      whether in tort (including negligence), contract, or otherwise,
-      unless required by applicable law (such as deliberate and grossly
-      negligent acts) or agreed to in writing, shall any Contributor be
-      liable to You for damages, including any direct, indirect, special,
-      incidental, or consequential damages of any character arising as a
-      result of this License or out of the use or inability to use the
-      Work (including but not limited to damages for loss of goodwill,
-      work stoppage, computer failure or malfunction, or any and all
-      other commercial damages or losses), even if such Contributor
-      has been advised of the possibility of such damages.
-
-   9. Accepting Warranty or Additional Liability. While redistributing
-      the Work or Derivative Works thereof, You may choose to offer,
-      and charge a fee for, acceptance of support, warranty, indemnity,
-      or other liability obligations and/or rights consistent with this
-      License. However, in accepting such obligations, You may act only
-      on Your own behalf and on Your sole responsibility, not on behalf
-      of any other Contributor, and only if You agree to indemnify,
-      defend, and hold each Contributor harmless for any liability
-      incurred by, or claims asserted against, such Contributor by reason
-      of your accepting any such warranty or additional liability.
-
-   END OF TERMS AND CONDITIONS
-
-   APPENDIX: How to apply the Apache License to your work.
-
-      To apply the Apache License to your work, attach the following
-      boilerplate notice, with the fields enclosed by brackets "[]"
-      replaced with your own identifying information. (Don't include
-      the brackets!)  The text should be enclosed in the appropriate
-      comment syntax for the file format. We also recommend that a
-      file or class name and description of purpose be included on the
-      same "printed page" as the copyright notice for easier
-      identification within third-party archives.
-
-   Copyright (c) OpenCV team and the opencv_3rdparty contributors.
-
-   Licensed under the Apache License, Version 2.0 (the "License");
-   you may not use this file except in compliance with the License.
-   You may obtain a copy of the License at
-
-       http://www.apache.org/licenses/LICENSE-2.0
-
-   Unless required by applicable law or agreed to in writing, software
-   distributed under the License is distributed on an "AS IS" BASIS,
-   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   See the License for the specific language governing permissions and
-limitations under the License.

opencv_face_detector_uint8/README.md

@@ -1,67 +0,0 @@
-# OpenCV SSD Face Detector (UINT8)
-
-Single-shot face detection with the OpenCV SSD ResNet-10 network. The model ships in the
-OpenCV project as a quantized frozen TensorFlow graph (`opencv_face_detector_uint8.pb`) and is
-converted here to ONNX for use with OpenCV's DNN module. Only the backbone is
-exported — PriorBox generation, the confidence softmax, variance decode, score threshold and NMS
-are run in host code (see `demo.py` / `demo.cpp`).
-
-## Model Details
-- **Architecture**: SSD with a ResNet-10 backbone (face detector)
-- **Input**: BGR image, 300×300, mean-subtracted by `[104, 177, 123]` (no scaling, no RGB swap),
-  NHWC layout (`data:0`, shape `[1, 300, 300, 3]`)
-- **Output**: `mbox_loc` (`[1, 35568]`, box regressions) and `mbox_conf_flatten`
-  (`[1, 17784]`, 2-class face/background logits); PriorBox decode + softmax + NMS are done in the demo
-- **Framework**: ONNX (converted from the TensorFlow frozen graph — uint8 weights with the
-  `Dequantize` nodes folded to float `Const` — via tf2onnx, opset 18)
-- **Original weights**: https://github.com/opencv/opencv_3rdparty/raw/8033c2bc31b3256f0d461c919ecc01c2428ca03b/opencv_face_detector_uint8.pb
-
-The 6 SSD prior layers (min/max size, aspect ratios, step, feature-map size), the variances
-`[0.1, 0.1, 0.2, 0.2]`, the default confidence threshold `0.4` and the NMS IoU `0.3` are all
-defined in the demo scripts.
-
-## Usage
-
-### Python
-```bash
-python demo.py --model opencv_face_detector_uint8_2026jul.onnx --image example_outputs/input_image.png --output example_outputs/output_image.png --conf 0.4
-```
-
-Or import directly:
-```python
-import cv2
-
-net = cv2.dnn.readNet("opencv_face_detector_uint8_2026jul.onnx")
-# see demo.py for the full PriorBox decode + softmax + NMS pipeline
-```
-
-### C++
-The C++ demo runs inference with OpenCV's DNN module. Adjust the OpenCV paths to your setup:
-```bash
-OCV=/path/to/opencv                          # OpenCV source tree
-OCVBUILD=/path/to/opencv/build               # OpenCV build directory (generated headers + libs)
-g++ -std=c++17 demo.cpp -o demo \
-  -I$OCV/include \
-  -I$OCV/modules/core/include \
-  -I$OCV/modules/dnn/include \
-  -I$OCV/modules/imgproc/include \
-  -I$OCV/modules/imgcodecs/include \
-  -I$OCVBUILD \
-  -L$OCVBUILD/lib -Wl,-rpath,$OCVBUILD/lib -lopencv_dnn -lopencv_imgcodecs -lopencv_imgproc -lopencv_core
-./demo --model opencv_face_detector_uint8_2026jul.onnx --image example_outputs/input_image.png --output example_outputs/output_image.png
-```
-
-## Conversion
-The ONNX model was exported from the frozen TensorFlow graph with tf2onnx (opset 18) via
-[convert_to_onnx.py](./convert_to_onnx.py). The `.pb` stores its weights behind `Dequantize`
-nodes, so the script first folds every `Dequantize` node to a float `Const` before conversion.
-Inputs `data:0`, outputs `mbox_loc:0` and `mbox_conf_flatten:0`, input shape overridden to
-`[1, 300, 300, 3]`. Requires `tensorflow`, `tf2onnx`, and `onnx`.
-
-```bash
-python convert_to_onnx.py --pb ../pb/opencv_face_detector_uint8.pb
-```
-
-## License
-See [LICENSE](./LICENSE) — this is the OpenCV face detector distributed via `opencv_3rdparty`
-under the Apache License 2.0.

opencv_face_detector_uint8/convert_to_onnx.py

@@ -1,73 +0,0 @@
-import argparse
-import datetime
-
-import numpy as np
-import tensorflow as tf
-import tf2onnx
-import onnx
-from tensorflow.python.framework import graph_util, tensor_util
-
-
-def dequantize(graph_def, outputs):
-    dmin = {n.name: n.input[1] for n in graph_def.node if n.op == "Dequantize"}
-    folded = {}
-    with tf.Graph().as_default() as g:
-        tf.import_graph_def(graph_def, name="")
-        deq = list(dmin)
-        with tf.compat.v1.Session(graph=g) as sess:
-            for name in deq:
-                v = np.asarray(sess.run(g.get_tensor_by_name(name + ":0")), np.float32)
-                if not np.isfinite(v).all():
-                    mn = np.float32(sess.run(g.get_tensor_by_name(dmin[name] + ":0")))
-                    v = np.full(v.shape, mn, np.float32)
-                folded[name] = v
-    new = tf.compat.v1.GraphDef()
-    for n in graph_def.node:
-        if n.op == "Dequantize":
-            v = folded[n.name]
-            c = new.node.add()
-            c.op = "Const"
-            c.name = n.name
-            c.attr["dtype"].type = tf.float32.as_datatype_enum
-            c.attr["value"].tensor.CopyFrom(tensor_util.make_tensor_proto(v, tf.float32, v.shape))
-        else:
-            new.node.add().CopyFrom(n)
-    return graph_util.extract_sub_graph(new, [o.split(":")[0] for o in outputs])
-
-
-def load_graph_def(pb_path):
-    with tf.io.gfile.GFile(pb_path, "rb") as f:
-        graph_def = tf.compat.v1.GraphDef()
-        graph_def.ParseFromString(f.read())
-    return graph_def
-
-
-def main():
-    parser = argparse.ArgumentParser(description="Export opencv_face_detector_uint8.pb (backbone) to ONNX")
-    parser.add_argument("--pb", default="../pb/opencv_face_detector_uint8.pb")
-    parser.add_argument("--opset", type=int, default=18)
-    args = parser.parse_args()
-
-    output_names = ["mbox_loc:0", "mbox_conf_flatten:0"]
-
-    graph_def = load_graph_def(args.pb)
-    graph_def = dequantize(graph_def, output_names)
-
-    model_proto, _ = tf2onnx.convert.from_graph_def(
-        graph_def,
-        input_names=["data:0"],
-        output_names=output_names,
-        opset=args.opset,
-        shape_override={"data:0": [1, 300, 300, 3]},
-    )
-    onnx.checker.check_model(model_proto)
-
-    stamp = datetime.datetime.now().strftime("%Y%b").lower()
-    onnx_path = "opencv_face_detector_uint8_%s.onnx" % stamp
-    with open(onnx_path, "wb") as f:
-        f.write(model_proto.SerializeToString())
-    print("wrote", onnx_path)
-
-
-if __name__ == "__main__":
-    main()

opencv_face_detector_uint8/demo.cpp

@@ -1,146 +0,0 @@
-#include <opencv2/dnn.hpp>
-#include <opencv2/imgproc.hpp>
-#include <opencv2/imgcodecs.hpp>
-#include <algorithm>
-#include <array>
-#include <cmath>
-#include <iostream>
-#include <string>
-#include <vector>
-
-using namespace cv;
-
-static std::string argVal(int argc, char** argv, const std::string& key, const std::string& def)
-{
-    for (int i = 1; i + 1 < argc; ++i)
-        if (key == argv[i]) return argv[i + 1];
-    return def;
-}
-
-struct Layer { float mn, mx; std::vector<int> ars; int step, fm; };
-
-int main(int argc, char** argv)
-{
-    std::string model  = argVal(argc, argv, "--model",  "opencv_face_detector_uint8_2026jul.onnx");
-    std::string image  = argVal(argc, argv, "--image",  "example_outputs/input_image.png");
-    std::string output = argVal(argc, argv, "--output", "example_outputs/output_image.png");
-    float thr = std::stof(argVal(argc, argv, "--conf", "0.4"));
-
-    const int sz = 300;
-    Mat img = imread(image);
-    if (img.empty())
-    {
-        std::cerr << "could not read image: " << image << std::endl;
-        return 1;
-    }
-
-    Mat inp;
-    resize(img, inp, Size(sz, sz));
-    inp.convertTo(inp, CV_32F);
-    subtract(inp, Scalar(104, 177, 123), inp);
-    if (!inp.isContinuous()) inp = inp.clone();
-
-    int blobShape[] = {1, sz, sz, 3};
-    Mat blob(4, blobShape, CV_32F, inp.data);
-    dnn::Net net = dnn::readNetFromONNX(model);
-    net.setInput(blob);
-    std::vector<Mat> outs;
-    net.forward(outs, net.getUnconnectedOutLayersNames());
-
-    const float* loc = nullptr;
-    const float* conf = nullptr;
-    for (size_t i = 0; i < outs.size(); ++i)
-    {
-        const Mat& o = outs[i];
-        size_t tot = o.total();
-        const float* p = (const float*)o.data;
-        if (tot == 35568) loc = p;
-        else if (tot == 17784) conf = p;
-    }
-
-    std::vector<Layer> layers = {
-        {30, 60, {2}, 8, 38},
-        {60, 111, {2, 3}, 16, 19},
-        {111, 162, {2, 3}, 32, 10},
-        {162, 213, {2, 3}, 64, 5},
-        {213, 264, {2}, 100, 5},
-        {264, 315, {2}, 300, 5},
-    };
-    std::vector<Vec4f> priors;
-    for (const Layer& L : layers)
-    {
-        std::vector<float> ratios = {1.0f};
-        for (int a : L.ars) { ratios.push_back((float)a); ratios.push_back(1.0f / a); }
-        for (int y = 0; y < L.fm; ++y)
-            for (int x = 0; x < L.fm; ++x)
-            {
-                float cx = (x + 0.5f) * L.step;
-                float cy = (y + 0.5f) * L.step;
-                std::vector<Vec2f> boxes = {{L.mn, L.mn}, {std::sqrt(L.mn * L.mx), std::sqrt(L.mn * L.mx)}};
-                for (size_t k = 1; k < ratios.size(); ++k)
-                {
-                    float a = ratios[k];
-                    boxes.push_back({L.mn * std::sqrt(a), L.mn / std::sqrt(a)});
-                }
-                for (const Vec2f& b : boxes)
-                    priors.push_back({cx, cy, b[0], b[1]});
-            }
-    }
-
-    const float var[4] = {0.1f, 0.1f, 0.2f, 0.2f};
-    int n = (int)priors.size();
-    std::vector<Rect2f> boxes;
-    std::vector<float> scores;
-    for (int i = 0; i < n; ++i)
-    {
-        float c0 = conf[i * 2], c1 = conf[i * 2 + 1];
-        float m = std::max(c0, c1);
-        float e0 = std::exp(c0 - m), e1 = std::exp(c1 - m);
-        float s = e1 / (e0 + e1);
-        if (s <= thr) continue;
-        float pcx = priors[i][0] / sz, pcy = priors[i][1] / sz;
-        float pw = priors[i][2] / sz, ph = priors[i][3] / sz;
-        float cx = pcx + loc[i * 4] * var[0] * pw;
-        float cy = pcy + loc[i * 4 + 1] * var[1] * ph;
-        float bw = pw * std::exp(loc[i * 4 + 2] * var[2]);
-        float bh = ph * std::exp(loc[i * 4 + 3] * var[3]);
-        boxes.push_back(Rect2f(cx - bw / 2, cy - bh / 2, bw, bh));
-        scores.push_back(s);
-    }
-
-    std::vector<int> order(scores.size());
-    for (size_t i = 0; i < order.size(); ++i) order[i] = (int)i;
-    std::sort(order.begin(), order.end(), [&](int a, int b){ return scores[a] > scores[b]; });
-    std::vector<char> removed(order.size(), 0);
-    std::vector<int> pick;
-    for (size_t oi = 0; oi < order.size(); ++oi)
-    {
-        if (removed[oi]) continue;
-        int i = order[oi];
-        pick.push_back(i);
-        for (size_t oj = oi + 1; oj < order.size(); ++oj)
-        {
-            if (removed[oj]) continue;
-            int j = order[oj];
-            const Rect2f& a = boxes[i];
-            const Rect2f& b = boxes[j];
-            float xx1 = std::max(a.x, b.x), yy1 = std::max(a.y, b.y);
-            float xx2 = std::min(a.x + a.width, b.x + b.width);
-            float yy2 = std::min(a.y + a.height, b.y + b.height);
-            float inter = std::max(0.f, xx2 - xx1) * std::max(0.f, yy2 - yy1);
-            float iou = inter / (a.area() + b.area() - inter + 1e-9f);
-            if (iou > 0.3f) removed[oj] = 1;
-        }
-    }
-
-    int W = img.cols, H = img.rows;
-    for (int i : pick)
-    {
-        const Rect2f& b = boxes[i];
-        rectangle(img, Point(int(b.x * W), int(b.y * H)),
-                  Point(int((b.x + b.width) * W), int((b.y + b.height) * H)), Scalar(0, 255, 0), 2);
-    }
-    imwrite(output, img);
-    std::cout << "opencv_face_detector_uint8 " << pick.size() << " faces" << std::endl;
-    return 0;
-}

opencv_face_detector_uint8/demo.py

@@ -1,109 +0,0 @@
-import argparse
-import glob
-import os
-
-import cv2 as cv
-import numpy as np
-
-here = os.path.dirname(os.path.abspath(__file__))
-
-sz = 300
-layers = [
-    (30, 60, [2], 8, 38),
-    (60, 111, [2, 3], 16, 19),
-    (111, 162, [2, 3], 32, 10),
-    (162, 213, [2, 3], 64, 5),
-    (213, 264, [2], 100, 5),
-    (264, 315, [2], 300, 5),
-]
-var = [0.1, 0.1, 0.2, 0.2]
-
-
-def build_priors():
-    p = []
-    for mn, mx, ars, step, fm in layers:
-        ratios = [1.0]
-        for a in ars:
-            ratios += [a, 1.0 / a]
-        for y in range(fm):
-            for x in range(fm):
-                cx = (x + 0.5) * step
-                cy = (y + 0.5) * step
-                boxes = [(mn, mn), ((mn * mx) ** 0.5, (mn * mx) ** 0.5)]
-                for a in ratios[1:]:
-                    boxes.append((mn * a ** 0.5, mn / a ** 0.5))
-                for bw, bh in boxes:
-                    p.append([cx, cy, bw, bh])
-    return np.array(p, np.float32)
-
-
-def default_model():
-    files = [f for f in glob.glob(os.path.join(here, "*.onnx")) if "known_good" not in os.path.basename(f)]
-    return files[0] if files else os.path.join(here, "opencv_face_detector_uint8.onnx")
-
-
-def main():
-    parser = argparse.ArgumentParser(description="OpenCV SSD face detector (ONNX) demo")
-    parser.add_argument("--model", default=default_model())
-    parser.add_argument("--image", default=os.path.join(here, "example_outputs", "input_image.png"))
-    parser.add_argument("--output", default=os.path.join(here, "example_outputs", "output_image.png"))
-    parser.add_argument("--conf", type=float, default=0.4)
-    args = parser.parse_args()
-
-    img = cv.imread(args.image)
-    if img is None:
-        raise SystemExit("could not read image: %s" % args.image)
-
-    inp = cv.resize(img, (sz, sz)).astype(np.float32) - np.array([104.0, 177.0, 123.0], np.float32)
-
-    net = cv.dnn.readNetFromONNX(args.model)
-    onames = net.getUnconnectedOutLayersNames()
-    net.setInput(inp[None])
-    res = net.forward(onames)
-    loc = res[[i for i, n in enumerate(onames) if "mbox_loc" in n][0]].reshape(-1, 4)
-    conf = res[[i for i, n in enumerate(onames) if "mbox_conf" in n][0]].reshape(-1, 2)
-
-    priors = build_priors()
-    pcx = priors[:, 0] / sz
-    pcy = priors[:, 1] / sz
-    pw = priors[:, 2] / sz
-    ph = priors[:, 3] / sz
-
-    e = np.exp(conf - conf.max(1, keepdims=True))
-    sm = e / e.sum(1, keepdims=True)
-    scores = sm[:, 1]
-
-    cx = pcx + loc[:, 0] * var[0] * pw
-    cy = pcy + loc[:, 1] * var[1] * ph
-    bw = pw * np.exp(loc[:, 2] * var[2])
-    bh = ph * np.exp(loc[:, 3] * var[3])
-    boxes = np.stack([cx - bw / 2, cy - bh / 2, cx + bw / 2, cy + bh / 2], 1)
-
-    keep = scores > args.conf
-    boxes = boxes[keep]
-    scores = scores[keep]
-    order = scores.argsort()[::-1]
-    pick = []
-    while order.size:
-        i = order[0]
-        pick.append(i)
-        xx1 = np.maximum(boxes[i, 0], boxes[order[1:], 0])
-        yy1 = np.maximum(boxes[i, 1], boxes[order[1:], 1])
-        xx2 = np.minimum(boxes[i, 2], boxes[order[1:], 2])
-        yy2 = np.minimum(boxes[i, 3], boxes[order[1:], 3])
-        inter = np.maximum(0, xx2 - xx1) * np.maximum(0, yy2 - yy1)
-        ai = (boxes[i, 2] - boxes[i, 0]) * (boxes[i, 3] - boxes[i, 1])
-        aj = (boxes[order[1:], 2] - boxes[order[1:], 0]) * (boxes[order[1:], 3] - boxes[order[1:], 1])
-        iou = inter / (ai + aj - inter + 1e-9)
-        order = order[1:][iou <= 0.3]
-
-    h, w = img.shape[:2]
-    for i in pick:
-        x1, y1, x2, y2 = boxes[i]
-        cv.rectangle(img, (int(x1 * w), int(y1 * h)), (int(x2 * w), int(y2 * h)), (0, 255, 0), 2)
-    cv.imwrite(args.output, img)
-    print("opencv_face_detector_uint8", len(pick), "faces")
-
-
-if __name__ == "__main__":
-    main()

opencv_face_detector_uint8/opencv_face_detector_uint8_2026jul.onnx

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:f5b1efe9c4e792a010ac36248d92b64c3d94f9bdec764951d1e8684d919b1e40
-size 10671719

ssd_inception_v2_coco_2017_11_17/LICENSE

@@ -1,212 +0,0 @@
-Copyright 2022 Google LLC. All rights reserved.
-
-All files in the following folders:
-/community
-/official
-/orbit
-/research
-/tensorflow_models
-
-Are licensed as follows:
-
-                                 Apache License
-                           Version 2.0, January 2004
-                        http://www.apache.org/licenses/
-
-   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
-
-   1. Definitions.
-
-      "License" shall mean the terms and conditions for use, reproduction,
-      and distribution as defined by Sections 1 through 9 of this document.
-
-      "Licensor" shall mean the copyright owner or entity authorized by
-      the copyright owner that is granting the License.
-
-      "Legal Entity" shall mean the union of the acting entity and all
-      other entities that control, are controlled by, or are under common
-      control with that entity. For the purposes of this definition,
-      "control" means (i) the power, direct or indirect, to cause the
-      direction or management of such entity, whether by contract or
-      otherwise, or (ii) ownership of fifty percent (50%) or more of the
-      outstanding shares, or (iii) beneficial ownership of such entity.
-
-      "You" (or "Your") shall mean an individual or Legal Entity
-      exercising permissions granted by this License.
-
-      "Source" form shall mean the preferred form for making modifications,
-      including but not limited to software source code, documentation
-      source, and configuration files.
-
-      "Object" form shall mean any form resulting from mechanical
-      transformation or translation of a Source form, including but
-      not limited to compiled object code, generated documentation,
-      and conversions to other media types.
-
-      "Work" shall mean the work of authorship, whether in Source or
-      Object form, made available under the License, as indicated by a
-      copyright notice that is included in or attached to the work
-      (an example is provided in the Appendix below).
-
-      "Derivative Works" shall mean any work, whether in Source or Object
-      form, that is based on (or derived from) the Work and for which the
-      editorial revisions, annotations, elaborations, or other modifications
-      represent, as a whole, an original work of authorship. For the purposes
-      of this License, Derivative Works shall not include works that remain
-      separable from, or merely link (or bind by name) to the interfaces of,
-      the Work and Derivative Works thereof.
-
-      "Contribution" shall mean any work of authorship, including
-      the original version of the Work and any modifications or additions
-      to that Work or Derivative Works thereof, that is intentionally
-      submitted to Licensor for inclusion in the Work by the copyright owner
-      or by an individual or Legal Entity authorized to submit on behalf of
-      the copyright owner. For the purposes of this definition, "submitted"
-      means any form of electronic, verbal, or written communication sent
-      to the Licensor or its representatives, including but not limited to
-      communication on electronic mailing lists, source code control systems,
-      and issue tracking systems that are managed by, or on behalf of, the
-      Licensor for the purpose of discussing and improving the Work, but
-      excluding communication that is conspicuously marked or otherwise
-      designated in writing by the copyright owner as "Not a Contribution."
-
-      "Contributor" shall mean Licensor and any individual or Legal Entity
-      on behalf of whom a Contribution has been received by Licensor and
-      subsequently incorporated within the Work.
-
-   2. Grant of Copyright License. Subject to the terms and conditions of
-      this License, each Contributor hereby grants to You a perpetual,
-      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
-      copyright license to reproduce, prepare Derivative Works of,
-      publicly display, publicly perform, sublicense, and distribute the
-      Work and such Derivative Works in Source or Object form.
-
-   3. Grant of Patent License. Subject to the terms and conditions of
-      this License, each Contributor hereby grants to You a perpetual,
-      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
-      (except as stated in this section) patent license to make, have made,
-      use, offer to sell, sell, import, and otherwise transfer the Work,
-      where such license applies only to those patent claims licensable
-      by such Contributor that are necessarily infringed by their
-      Contribution(s) alone or by combination of their Contribution(s)
-      with the Work to which such Contribution(s) was submitted. If You
-      institute patent litigation against any entity (including a
-      cross-claim or counterclaim in a lawsuit) alleging that the Work
-      or a Contribution incorporated within the Work constitutes direct
-      or contributory patent infringement, then any patent licenses
-      granted to You under this License for that Work shall terminate
-      as of the date such litigation is filed.
-
-   4. Redistribution. You may reproduce and distribute copies of the
-      Work or Derivative Works thereof in any medium, with or without
-      modifications, and in Source or Object form, provided that You
-      meet the following conditions:
-
-      (a) You must give any other recipients of the Work or
-          Derivative Works a copy of this License; and
-
-      (b) You must cause any modified files to carry prominent notices
-          stating that You changed the files; and
-
-      (c) You must retain, in the Source form of any Derivative Works
-          that You distribute, all copyright, patent, trademark, and
-          attribution notices from the Source form of the Work,
-          excluding those notices that do not pertain to any part of
-          the Derivative Works; and
-
-      (d) If the Work includes a "NOTICE" text file as part of its
-          distribution, then any Derivative Works that You distribute must
-          include a readable copy of the attribution notices contained
-          within such NOTICE file, excluding those notices that do not
-          pertain to any part of the Derivative Works, in at least one
-          of the following places: within a NOTICE text file distributed
-          as part of the Derivative Works; within the Source form or
-          documentation, if provided along with the Derivative Works; or,
-          within a display generated by the Derivative Works, if and
-          wherever such third-party notices normally appear. The contents
-          of the NOTICE file are for informational purposes only and
-          do not modify the License. You may add Your own attribution
-          notices within Derivative Works that You distribute, alongside
-          or as an addendum to the NOTICE text from the Work, provided
-          that such additional attribution notices cannot be construed
-          as modifying the License.
-
-      You may add Your own copyright statement to Your modifications and
-      may provide additional or different license terms and conditions
-      for use, reproduction, or distribution of Your modifications, or
-      for any such Derivative Works as a whole, provided Your use,
-      reproduction, and distribution of the Work otherwise complies with
-      the conditions stated in this License.
-
-   5. Submission of Contributions. Unless You explicitly state otherwise,
-      any Contribution intentionally submitted for inclusion in the Work
-      by You to the Licensor shall be under the terms and conditions of
-      this License, without any additional terms or conditions.
-      Notwithstanding the above, nothing herein shall supersede or modify
-      the terms of any separate license agreement you may have executed
-      with Licensor regarding such Contributions.
-
-   6. Trademarks. This License does not grant permission to use the trade
-      names, trademarks, service marks, or product names of the Licensor,
-      except as required for reasonable and customary use in describing the
-      origin of the Work and reproducing the content of the NOTICE file.
-
-   7. Disclaimer of Warranty. Unless required by applicable law or
-      agreed to in writing, Licensor provides the Work (and each
-      Contributor provides its Contributions) on an "AS IS" BASIS,
-      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
-      implied, including, without limitation, any warranties or conditions
-      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
-      PARTICULAR PURPOSE. You are solely responsible for determining the
-      appropriateness of using or redistributing the Work and assume any
-      risks associated with Your exercise of permissions under this License.
-
-   8. Limitation of Liability. In no event and under no legal theory,
-      whether in tort (including negligence), contract, or otherwise,
-      unless required by applicable law (such as deliberate and grossly
-      negligent acts) or agreed to in writing, shall any Contributor be
-      liable to You for damages, including any direct, indirect, special,
-      incidental, or consequential damages of any character arising as a
-      result of this License or out of the use or inability to use the
-      Work (including but not limited to damages for loss of goodwill,
-      work stoppage, computer failure or malfunction, or any and all
-      other commercial damages or losses), even if such Contributor
-      has been advised of the possibility of such damages.
-
-   9. Accepting Warranty or Additional Liability. While redistributing
-      the Work or Derivative Works thereof, You may choose to offer,
-      and charge a fee for, acceptance of support, warranty, indemnity,
-      or other liability obligations and/or rights consistent with this
-      License. However, in accepting such obligations, You may act only
-      on Your own behalf and on Your sole responsibility, not on behalf
-      of any other Contributor, and only if You agree to indemnify,
-      defend, and hold each Contributor harmless for any liability
-      incurred by, or claims asserted against, such Contributor by reason
-      of your accepting any such warranty or additional liability.
-
-   END OF TERMS AND CONDITIONS
-
-   APPENDIX: How to apply the Apache License to your work.
-
-      To apply the Apache License to your work, attach the following
-      boilerplate notice, with the fields enclosed by brackets "[]"
-      replaced with your own identifying information. (Don't include
-      the brackets!)  The text should be enclosed in the appropriate
-      comment syntax for the file format. We also recommend that a
-      file or class name and description of purpose be included on the
-      same "printed page" as the copyright notice for easier
-      identification within third-party archives.
-
-   Copyright 2016, The Authors.
-
-   Licensed under the Apache License, Version 2.0 (the "License");
-   you may not use this file except in compliance with the License.
-   You may obtain a copy of the License at
-
-       http://www.apache.org/licenses/LICENSE-2.0
-
-   Unless required by applicable law or agreed to in writing, software
-   distributed under the License is distributed on an "AS IS" BASIS,
-   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   See the License for the specific language governing permissions and
-   limitations under the License.