├── .gitignore
├── LICENSE
├── README.md
├── conf.yaml
├── dataset_processor
├── __init__.py
├── data.py
├── filter.py
├── processor.py
├── tools
│ ├── __init__.py
│ ├── tagger.py
│ └── upscale.py
└── uitl.py
├── doc
└── doc_cn.md
├── main.py
├── requirements.txt
└── setup.py
/.gitignore:
--------------------------------------------------------------------------------
1 | test.py
2 | \__pycache__
3 | test.yaml
4 | tag_images_by_wd14_tagger.py
5 | \models
6 | \.idea
7 | \venv
--------------------------------------------------------------------------------
/LICENSE:
--------------------------------------------------------------------------------
1 | GNU GENERAL PUBLIC LICENSE
2 | Version 3, 29 June 2007
3 |
4 | Copyright (C) 2007 Free Software Foundation, Inc.
5 | Everyone is permitted to copy and distribute verbatim copies
6 | of this license document, but changing it is not allowed.
7 |
8 | Preamble
9 |
10 | The GNU General Public License is a free, copyleft license for
11 | software and other kinds of works.
12 |
13 | The licenses for most software and other practical works are designed
14 | to take away your freedom to share and change the works. By contrast,
15 | the GNU General Public License is intended to guarantee your freedom to
16 | share and change all versions of a program--to make sure it remains free
17 | software for all its users. We, the Free Software Foundation, use the
18 | GNU General Public License for most of our software; it applies also to
19 | any other work released this way by its authors. You can apply it to
20 | your programs, too.
21 |
22 | When we speak of free software, we are referring to freedom, not
23 | price. Our General Public Licenses are designed to make sure that you
24 | have the freedom to distribute copies of free software (and charge for
25 | them if you wish), that you receive source code or can get it if you
26 | want it, that you can change the software or use pieces of it in new
27 | free programs, and that you know you can do these things.
28 |
29 | To protect your rights, we need to prevent others from denying you
30 | these rights or asking you to surrender the rights. Therefore, you have
31 | certain responsibilities if you distribute copies of the software, or if
32 | you modify it: responsibilities to respect the freedom of others.
33 |
34 | For example, if you distribute copies of such a program, whether
35 | gratis or for a fee, you must pass on to the recipients the same
36 | freedoms that you received. You must make sure that they, too, receive
37 | or can get the source code. And you must show them these terms so they
38 | know their rights.
39 |
40 | Developers that use the GNU GPL protect your rights with two steps:
41 | (1) assert copyright on the software, and (2) offer you this License
42 | giving you legal permission to copy, distribute and/or modify it.
43 |
44 | For the developers' and authors' protection, the GPL clearly explains
45 | that there is no warranty for this free software. For both users' and
46 | authors' sake, the GPL requires that modified versions be marked as
47 | changed, so that their problems will not be attributed erroneously to
48 | authors of previous versions.
49 |
50 | Some devices are designed to deny users access to install or run
51 | modified versions of the software inside them, although the manufacturer
52 | can do so. This is fundamentally incompatible with the aim of
53 | protecting users' freedom to change the software. The systematic
54 | pattern of such abuse occurs in the area of products for individuals to
55 | use, which is precisely where it is most unacceptable. Therefore, we
56 | have designed this version of the GPL to prohibit the practice for those
57 | products. If such problems arise substantially in other domains, we
58 | stand ready to extend this provision to those domains in future versions
59 | of the GPL, as needed to protect the freedom of users.
60 |
61 | Finally, every program is threatened constantly by software patents.
62 | States should not allow patents to restrict development and use of
63 | software on general-purpose computers, but in those that do, we wish to
64 | avoid the special danger that patents applied to a free program could
65 | make it effectively proprietary. To prevent this, the GPL assures that
66 | patents cannot be used to render the program non-free.
67 |
68 | The precise terms and conditions for copying, distribution and
69 | modification follow.
70 |
71 | TERMS AND CONDITIONS
72 |
73 | 0. Definitions.
74 |
75 | "This License" refers to version 3 of the GNU General Public License.
76 |
77 | "Copyright" also means copyright-like laws that apply to other kinds of
78 | works, such as semiconductor masks.
79 |
80 | "The Program" refers to any copyrightable work licensed under this
81 | License. Each licensee is addressed as "you". "Licensees" and
82 | "recipients" may be individuals or organizations.
83 |
84 | To "modify" a work means to copy from or adapt all or part of the work
85 | in a fashion requiring copyright permission, other than the making of an
86 | exact copy. The resulting work is called a "modified version" of the
87 | earlier work or a work "based on" the earlier work.
88 |
89 | A "covered work" means either the unmodified Program or a work based
90 | on the Program.
91 |
92 | To "propagate" a work means to do anything with it that, without
93 | permission, would make you directly or secondarily liable for
94 | infringement under applicable copyright law, except executing it on a
95 | computer or modifying a private copy. Propagation includes copying,
96 | distribution (with or without modification), making available to the
97 | public, and in some countries other activities as well.
98 |
99 | To "convey" a work means any kind of propagation that enables other
100 | parties to make or receive copies. Mere interaction with a user through
101 | a computer network, with no transfer of a copy, is not conveying.
102 |
103 | An interactive user interface displays "Appropriate Legal Notices"
104 | to the extent that it includes a convenient and prominently visible
105 | feature that (1) displays an appropriate copyright notice, and (2)
106 | tells the user that there is no warranty for the work (except to the
107 | extent that warranties are provided), that licensees may convey the
108 | work under this License, and how to view a copy of this License. If
109 | the interface presents a list of user commands or options, such as a
110 | menu, a prominent item in the list meets this criterion.
111 |
112 | 1. Source Code.
113 |
114 | The "source code" for a work means the preferred form of the work
115 | for making modifications to it. "Object code" means any non-source
116 | form of a work.
117 |
118 | A "Standard Interface" means an interface that either is an official
119 | standard defined by a recognized standards body, or, in the case of
120 | interfaces specified for a particular programming language, one that
121 | is widely used among developers working in that language.
122 |
123 | The "System Libraries" of an executable work include anything, other
124 | than the work as a whole, that (a) is included in the normal form of
125 | packaging a Major Component, but which is not part of that Major
126 | Component, and (b) serves only to enable use of the work with that
127 | Major Component, or to implement a Standard Interface for which an
128 | implementation is available to the public in source code form. A
129 | "Major Component", in this context, means a major essential component
130 | (kernel, window system, and so on) of the specific operating system
131 | (if any) on which the executable work runs, or a compiler used to
132 | produce the work, or an object code interpreter used to run it.
133 |
134 | The "Corresponding Source" for a work in object code form means all
135 | the source code needed to generate, install, and (for an executable
136 | work) run the object code and to modify the work, including scripts to
137 | control those activities. However, it does not include the work's
138 | System Libraries, or general-purpose tools or generally available free
139 | programs which are used unmodified in performing those activities but
140 | which are not part of the work. For example, Corresponding Source
141 | includes interface definition files associated with source files for
142 | the work, and the source code for shared libraries and dynamically
143 | linked subprograms that the work is specifically designed to require,
144 | such as by intimate data communication or control flow between those
145 | subprograms and other parts of the work.
146 |
147 | The Corresponding Source need not include anything that users
148 | can regenerate automatically from other parts of the Corresponding
149 | Source.
150 |
151 | The Corresponding Source for a work in source code form is that
152 | same work.
153 |
154 | 2. Basic Permissions.
155 |
156 | All rights granted under this License are granted for the term of
157 | copyright on the Program, and are irrevocable provided the stated
158 | conditions are met. This License explicitly affirms your unlimited
159 | permission to run the unmodified Program. The output from running a
160 | covered work is covered by this License only if the output, given its
161 | content, constitutes a covered work. This License acknowledges your
162 | rights of fair use or other equivalent, as provided by copyright law.
163 |
164 | You may make, run and propagate covered works that you do not
165 | convey, without conditions so long as your license otherwise remains
166 | in force. You may convey covered works to others for the sole purpose
167 | of having them make modifications exclusively for you, or provide you
168 | with facilities for running those works, provided that you comply with
169 | the terms of this License in conveying all material for which you do
170 | not control copyright. Those thus making or running the covered works
171 | for you must do so exclusively on your behalf, under your direction
172 | and control, on terms that prohibit them from making any copies of
173 | your copyrighted material outside their relationship with you.
174 |
175 | Conveying under any other circumstances is permitted solely under
176 | the conditions stated below. Sublicensing is not allowed; section 10
177 | makes it unnecessary.
178 |
179 | 3. Protecting Users' Legal Rights From Anti-Circumvention Law.
180 |
181 | No covered work shall be deemed part of an effective technological
182 | measure under any applicable law fulfilling obligations under article
183 | 11 of the WIPO copyright treaty adopted on 20 December 1996, or
184 | similar laws prohibiting or restricting circumvention of such
185 | measures.
186 |
187 | When you convey a covered work, you waive any legal power to forbid
188 | circumvention of technological measures to the extent such circumvention
189 | is effected by exercising rights under this License with respect to
190 | the covered work, and you disclaim any intention to limit operation or
191 | modification of the work as a means of enforcing, against the work's
192 | users, your or third parties' legal rights to forbid circumvention of
193 | technological measures.
194 |
195 | 4. Conveying Verbatim Copies.
196 |
197 | You may convey verbatim copies of the Program's source code as you
198 | receive it, in any medium, provided that you conspicuously and
199 | appropriately publish on each copy an appropriate copyright notice;
200 | keep intact all notices stating that this License and any
201 | non-permissive terms added in accord with section 7 apply to the code;
202 | keep intact all notices of the absence of any warranty; and give all
203 | recipients a copy of this License along with the Program.
204 |
205 | You may charge any price or no price for each copy that you convey,
206 | and you may offer support or warranty protection for a fee.
207 |
208 | 5. Conveying Modified Source Versions.
209 |
210 | You may convey a work based on the Program, or the modifications to
211 | produce it from the Program, in the form of source code under the
212 | terms of section 4, provided that you also meet all of these conditions:
213 |
214 | a) The work must carry prominent notices stating that you modified
215 | it, and giving a relevant date.
216 |
217 | b) The work must carry prominent notices stating that it is
218 | released under this License and any conditions added under section
219 | 7. This requirement modifies the requirement in section 4 to
220 | "keep intact all notices".
221 |
222 | c) You must license the entire work, as a whole, under this
223 | License to anyone who comes into possession of a copy. This
224 | License will therefore apply, along with any applicable section 7
225 | additional terms, to the whole of the work, and all its parts,
226 | regardless of how they are packaged. This License gives no
227 | permission to license the work in any other way, but it does not
228 | invalidate such permission if you have separately received it.
229 |
230 | d) If the work has interactive user interfaces, each must display
231 | Appropriate Legal Notices; however, if the Program has interactive
232 | interfaces that do not display Appropriate Legal Notices, your
233 | work need not make them do so.
234 |
235 | A compilation of a covered work with other separate and independent
236 | works, which are not by their nature extensions of the covered work,
237 | and which are not combined with it such as to form a larger program,
238 | in or on a volume of a storage or distribution medium, is called an
239 | "aggregate" if the compilation and its resulting copyright are not
240 | used to limit the access or legal rights of the compilation's users
241 | beyond what the individual works permit. Inclusion of a covered work
242 | in an aggregate does not cause this License to apply to the other
243 | parts of the aggregate.
244 |
245 | 6. Conveying Non-Source Forms.
246 |
247 | You may convey a covered work in object code form under the terms
248 | of sections 4 and 5, provided that you also convey the
249 | machine-readable Corresponding Source under the terms of this License,
250 | in one of these ways:
251 |
252 | a) Convey the object code in, or embodied in, a physical product
253 | (including a physical distribution medium), accompanied by the
254 | Corresponding Source fixed on a durable physical medium
255 | customarily used for software interchange.
256 |
257 | b) Convey the object code in, or embodied in, a physical product
258 | (including a physical distribution medium), accompanied by a
259 | written offer, valid for at least three years and valid for as
260 | long as you offer spare parts or customer support for that product
261 | model, to give anyone who possesses the object code either (1) a
262 | copy of the Corresponding Source for all the software in the
263 | product that is covered by this License, on a durable physical
264 | medium customarily used for software interchange, for a price no
265 | more than your reasonable cost of physically performing this
266 | conveying of source, or (2) access to copy the
267 | Corresponding Source from a network server at no charge.
268 |
269 | c) Convey individual copies of the object code with a copy of the
270 | written offer to provide the Corresponding Source. This
271 | alternative is allowed only occasionally and noncommercially, and
272 | only if you received the object code with such an offer, in accord
273 | with subsection 6b.
274 |
275 | d) Convey the object code by offering access from a designated
276 | place (gratis or for a charge), and offer equivalent access to the
277 | Corresponding Source in the same way through the same place at no
278 | further charge. You need not require recipients to copy the
279 | Corresponding Source along with the object code. If the place to
280 | copy the object code is a network server, the Corresponding Source
281 | may be on a different server (operated by you or a third party)
282 | that supports equivalent copying facilities, provided you maintain
283 | clear directions next to the object code saying where to find the
284 | Corresponding Source. Regardless of what server hosts the
285 | Corresponding Source, you remain obligated to ensure that it is
286 | available for as long as needed to satisfy these requirements.
287 |
288 | e) Convey the object code using peer-to-peer transmission, provided
289 | you inform other peers where the object code and Corresponding
290 | Source of the work are being offered to the general public at no
291 | charge under subsection 6d.
292 |
293 | A separable portion of the object code, whose source code is excluded
294 | from the Corresponding Source as a System Library, need not be
295 | included in conveying the object code work.
296 |
297 | A "User Product" is either (1) a "consumer product", which means any
298 | tangible personal property which is normally used for personal, family,
299 | or household purposes, or (2) anything designed or sold for incorporation
300 | into a dwelling. In determining whether a product is a consumer product,
301 | doubtful cases shall be resolved in favor of coverage. For a particular
302 | product received by a particular user, "normally used" refers to a
303 | typical or common use of that class of product, regardless of the status
304 | of the particular user or of the way in which the particular user
305 | actually uses, or expects or is expected to use, the product. A product
306 | is a consumer product regardless of whether the product has substantial
307 | commercial, industrial or non-consumer uses, unless such uses represent
308 | the only significant mode of use of the product.
309 |
310 | "Installation Information" for a User Product means any methods,
311 | procedures, authorization keys, or other information required to install
312 | and execute modified versions of a covered work in that User Product from
313 | a modified version of its Corresponding Source. The information must
314 | suffice to ensure that the continued functioning of the modified object
315 | code is in no case prevented or interfered with solely because
316 | modification has been made.
317 |
318 | If you convey an object code work under this section in, or with, or
319 | specifically for use in, a User Product, and the conveying occurs as
320 | part of a transaction in which the right of possession and use of the
321 | User Product is transferred to the recipient in perpetuity or for a
322 | fixed term (regardless of how the transaction is characterized), the
323 | Corresponding Source conveyed under this section must be accompanied
324 | by the Installation Information. But this requirement does not apply
325 | if neither you nor any third party retains the ability to install
326 | modified object code on the User Product (for example, the work has
327 | been installed in ROM).
328 |
329 | The requirement to provide Installation Information does not include a
330 | requirement to continue to provide support service, warranty, or updates
331 | for a work that has been modified or installed by the recipient, or for
332 | the User Product in which it has been modified or installed. Access to a
333 | network may be denied when the modification itself materially and
334 | adversely affects the operation of the network or violates the rules and
335 | protocols for communication across the network.
336 |
337 | Corresponding Source conveyed, and Installation Information provided,
338 | in accord with this section must be in a format that is publicly
339 | documented (and with an implementation available to the public in
340 | source code form), and must require no special password or key for
341 | unpacking, reading or copying.
342 |
343 | 7. Additional Terms.
344 |
345 | "Additional permissions" are terms that supplement the terms of this
346 | License by making exceptions from one or more of its conditions.
347 | Additional permissions that are applicable to the entire Program shall
348 | be treated as though they were included in this License, to the extent
349 | that they are valid under applicable law. If additional permissions
350 | apply only to part of the Program, that part may be used separately
351 | under those permissions, but the entire Program remains governed by
352 | this License without regard to the additional permissions.
353 |
354 | When you convey a copy of a covered work, you may at your option
355 | remove any additional permissions from that copy, or from any part of
356 | it. (Additional permissions may be written to require their own
357 | removal in certain cases when you modify the work.) You may place
358 | additional permissions on material, added by you to a covered work,
359 | for which you have or can give appropriate copyright permission.
360 |
361 | Notwithstanding any other provision of this License, for material you
362 | add to a covered work, you may (if authorized by the copyright holders of
363 | that material) supplement the terms of this License with terms:
364 |
365 | a) Disclaiming warranty or limiting liability differently from the
366 | terms of sections 15 and 16 of this License; or
367 |
368 | b) Requiring preservation of specified reasonable legal notices or
369 | author attributions in that material or in the Appropriate Legal
370 | Notices displayed by works containing it; or
371 |
372 | c) Prohibiting misrepresentation of the origin of that material, or
373 | requiring that modified versions of such material be marked in
374 | reasonable ways as different from the original version; or
375 |
376 | d) Limiting the use for publicity purposes of names of licensors or
377 | authors of the material; or
378 |
379 | e) Declining to grant rights under trademark law for use of some
380 | trade names, trademarks, or service marks; or
381 |
382 | f) Requiring indemnification of licensors and authors of that
383 | material by anyone who conveys the material (or modified versions of
384 | it) with contractual assumptions of liability to the recipient, for
385 | any liability that these contractual assumptions directly impose on
386 | those licensors and authors.
387 |
388 | All other non-permissive additional terms are considered "further
389 | restrictions" within the meaning of section 10. If the Program as you
390 | received it, or any part of it, contains a notice stating that it is
391 | governed by this License along with a term that is a further
392 | restriction, you may remove that term. If a license document contains
393 | a further restriction but permits relicensing or conveying under this
394 | License, you may add to a covered work material governed by the terms
395 | of that license document, provided that the further restriction does
396 | not survive such relicensing or conveying.
397 |
398 | If you add terms to a covered work in accord with this section, you
399 | must place, in the relevant source files, a statement of the
400 | additional terms that apply to those files, or a notice indicating
401 | where to find the applicable terms.
402 |
403 | Additional terms, permissive or non-permissive, may be stated in the
404 | form of a separately written license, or stated as exceptions;
405 | the above requirements apply either way.
406 |
407 | 8. Termination.
408 |
409 | You may not propagate or modify a covered work except as expressly
410 | provided under this License. Any attempt otherwise to propagate or
411 | modify it is void, and will automatically terminate your rights under
412 | this License (including any patent licenses granted under the third
413 | paragraph of section 11).
414 |
415 | However, if you cease all violation of this License, then your
416 | license from a particular copyright holder is reinstated (a)
417 | provisionally, unless and until the copyright holder explicitly and
418 | finally terminates your license, and (b) permanently, if the copyright
419 | holder fails to notify you of the violation by some reasonable means
420 | prior to 60 days after the cessation.
421 |
422 | Moreover, your license from a particular copyright holder is
423 | reinstated permanently if the copyright holder notifies you of the
424 | violation by some reasonable means, this is the first time you have
425 | received notice of violation of this License (for any work) from that
426 | copyright holder, and you cure the violation prior to 30 days after
427 | your receipt of the notice.
428 |
429 | Termination of your rights under this section does not terminate the
430 | licenses of parties who have received copies or rights from you under
431 | this License. If your rights have been terminated and not permanently
432 | reinstated, you do not qualify to receive new licenses for the same
433 | material under section 10.
434 |
435 | 9. Acceptance Not Required for Having Copies.
436 |
437 | You are not required to accept this License in order to receive or
438 | run a copy of the Program. Ancillary propagation of a covered work
439 | occurring solely as a consequence of using peer-to-peer transmission
440 | to receive a copy likewise does not require acceptance. However,
441 | nothing other than this License grants you permission to propagate or
442 | modify any covered work. These actions infringe copyright if you do
443 | not accept this License. Therefore, by modifying or propagating a
444 | covered work, you indicate your acceptance of this License to do so.
445 |
446 | 10. Automatic Licensing of Downstream Recipients.
447 |
448 | Each time you convey a covered work, the recipient automatically
449 | receives a license from the original licensors, to run, modify and
450 | propagate that work, subject to this License. You are not responsible
451 | for enforcing compliance by third parties with this License.
452 |
453 | An "entity transaction" is a transaction transferring control of an
454 | organization, or substantially all assets of one, or subdividing an
455 | organization, or merging organizations. If propagation of a covered
456 | work results from an entity transaction, each party to that
457 | transaction who receives a copy of the work also receives whatever
458 | licenses to the work the party's predecessor in interest had or could
459 | give under the previous paragraph, plus a right to possession of the
460 | Corresponding Source of the work from the predecessor in interest, if
461 | the predecessor has it or can get it with reasonable efforts.
462 |
463 | You may not impose any further restrictions on the exercise of the
464 | rights granted or affirmed under this License. For example, you may
465 | not impose a license fee, royalty, or other charge for exercise of
466 | rights granted under this License, and you may not initiate litigation
467 | (including a cross-claim or counterclaim in a lawsuit) alleging that
468 | any patent claim is infringed by making, using, selling, offering for
469 | sale, or importing the Program or any portion of it.
470 |
471 | 11. Patents.
472 |
473 | A "contributor" is a copyright holder who authorizes use under this
474 | License of the Program or a work on which the Program is based. The
475 | work thus licensed is called the contributor's "contributor version".
476 |
477 | A contributor's "essential patent claims" are all patent claims
478 | owned or controlled by the contributor, whether already acquired or
479 | hereafter acquired, that would be infringed by some manner, permitted
480 | by this License, of making, using, or selling its contributor version,
481 | but do not include claims that would be infringed only as a
482 | consequence of further modification of the contributor version. For
483 | purposes of this definition, "control" includes the right to grant
484 | patent sublicenses in a manner consistent with the requirements of
485 | this License.
486 |
487 | Each contributor grants you a non-exclusive, worldwide, royalty-free
488 | patent license under the contributor's essential patent claims, to
489 | make, use, sell, offer for sale, import and otherwise run, modify and
490 | propagate the contents of its contributor version.
491 |
492 | In the following three paragraphs, a "patent license" is any express
493 | agreement or commitment, however denominated, not to enforce a patent
494 | (such as an express permission to practice a patent or covenant not to
495 | sue for patent infringement). To "grant" such a patent license to a
496 | party means to make such an agreement or commitment not to enforce a
497 | patent against the party.
498 |
499 | If you convey a covered work, knowingly relying on a patent license,
500 | and the Corresponding Source of the work is not available for anyone
501 | to copy, free of charge and under the terms of this License, through a
502 | publicly available network server or other readily accessible means,
503 | then you must either (1) cause the Corresponding Source to be so
504 | available, or (2) arrange to deprive yourself of the benefit of the
505 | patent license for this particular work, or (3) arrange, in a manner
506 | consistent with the requirements of this License, to extend the patent
507 | license to downstream recipients. "Knowingly relying" means you have
508 | actual knowledge that, but for the patent license, your conveying the
509 | covered work in a country, or your recipient's use of the covered work
510 | in a country, would infringe one or more identifiable patents in that
511 | country that you have reason to believe are valid.
512 |
513 | If, pursuant to or in connection with a single transaction or
514 | arrangement, you convey, or propagate by procuring conveyance of, a
515 | covered work, and grant a patent license to some of the parties
516 | receiving the covered work authorizing them to use, propagate, modify
517 | or convey a specific copy of the covered work, then the patent license
518 | you grant is automatically extended to all recipients of the covered
519 | work and works based on it.
520 |
521 | A patent license is "discriminatory" if it does not include within
522 | the scope of its coverage, prohibits the exercise of, or is
523 | conditioned on the non-exercise of one or more of the rights that are
524 | specifically granted under this License. You may not convey a covered
525 | work if you are a party to an arrangement with a third party that is
526 | in the business of distributing software, under which you make payment
527 | to the third party based on the extent of your activity of conveying
528 | the work, and under which the third party grants, to any of the
529 | parties who would receive the covered work from you, a discriminatory
530 | patent license (a) in connection with copies of the covered work
531 | conveyed by you (or copies made from those copies), or (b) primarily
532 | for and in connection with specific products or compilations that
533 | contain the covered work, unless you entered into that arrangement,
534 | or that patent license was granted, prior to 28 March 2007.
535 |
536 | Nothing in this License shall be construed as excluding or limiting
537 | any implied license or other defenses to infringement that may
538 | otherwise be available to you under applicable patent law.
539 |
540 | 12. No Surrender of Others' Freedom.
541 |
542 | If conditions are imposed on you (whether by court order, agreement or
543 | otherwise) that contradict the conditions of this License, they do not
544 | excuse you from the conditions of this License. If you cannot convey a
545 | covered work so as to satisfy simultaneously your obligations under this
546 | License and any other pertinent obligations, then as a consequence you may
547 | not convey it at all. For example, if you agree to terms that obligate you
548 | to collect a royalty for further conveying from those to whom you convey
549 | the Program, the only way you could satisfy both those terms and this
550 | License would be to refrain entirely from conveying the Program.
551 |
552 | 13. Use with the GNU Affero General Public License.
553 |
554 | Notwithstanding any other provision of this License, you have
555 | permission to link or combine any covered work with a work licensed
556 | under version 3 of the GNU Affero General Public License into a single
557 | combined work, and to convey the resulting work. The terms of this
558 | License will continue to apply to the part which is the covered work,
559 | but the special requirements of the GNU Affero General Public License,
560 | section 13, concerning interaction through a network will apply to the
561 | combination as such.
562 |
563 | 14. Revised Versions of this License.
564 |
565 | The Free Software Foundation may publish revised and/or new versions of
566 | the GNU General Public License from time to time. Such new versions will
567 | be similar in spirit to the present version, but may differ in detail to
568 | address new problems or concerns.
569 |
570 | Each version is given a distinguishing version number. If the
571 | Program specifies that a certain numbered version of the GNU General
572 | Public License "or any later version" applies to it, you have the
573 | option of following the terms and conditions either of that numbered
574 | version or of any later version published by the Free Software
575 | Foundation. If the Program does not specify a version number of the
576 | GNU General Public License, you may choose any version ever published
577 | by the Free Software Foundation.
578 |
579 | If the Program specifies that a proxy can decide which future
580 | versions of the GNU General Public License can be used, that proxy's
581 | public statement of acceptance of a version permanently authorizes you
582 | to choose that version for the Program.
583 |
584 | Later license versions may give you additional or different
585 | permissions. However, no additional obligations are imposed on any
586 | author or copyright holder as a result of your choosing to follow a
587 | later version.
588 |
589 | 15. Disclaimer of Warranty.
590 |
591 | THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
592 | APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
593 | HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
594 | OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
595 | THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
596 | PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
597 | IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
598 | ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
599 |
600 | 16. Limitation of Liability.
601 |
602 | IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
603 | WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
604 | THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
605 | GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
606 | USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
607 | DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
608 | PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
609 | EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
610 | SUCH DAMAGES.
611 |
612 | 17. Interpretation of Sections 15 and 16.
613 |
614 | If the disclaimer of warranty and limitation of liability provided
615 | above cannot be given local legal effect according to their terms,
616 | reviewing courts shall apply local law that most closely approximates
617 | an absolute waiver of all civil liability in connection with the
618 | Program, unless a warranty or assumption of liability accompanies a
619 | copy of the Program in return for a fee.
620 |
621 | END OF TERMS AND CONDITIONS
622 |
623 | How to Apply These Terms to Your New Programs
624 |
625 | If you develop a new program, and you want it to be of the greatest
626 | possible use to the public, the best way to achieve this is to make it
627 | free software which everyone can redistribute and change under these terms.
628 |
629 | To do so, attach the following notices to the program. It is safest
630 | to attach them to the start of each source file to most effectively
631 | state the exclusion of warranty; and each file should have at least
632 | the "copyright" line and a pointer to where the full notice is found.
633 |
634 |
635 | Copyright (C)
636 |
637 | This program is free software: you can redistribute it and/or modify
638 | it under the terms of the GNU General Public License as published by
639 | the Free Software Foundation, either version 3 of the License, or
640 | (at your option) any later version.
641 |
642 | This program is distributed in the hope that it will be useful,
643 | but WITHOUT ANY WARRANTY; without even the implied warranty of
644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
645 | GNU General Public License for more details.
646 |
647 | You should have received a copy of the GNU General Public License
648 | along with this program. If not, see .
649 |
650 | Also add information on how to contact you by electronic and paper mail.
651 |
652 | If the program does terminal interaction, make it output a short
653 | notice like this when it starts in an interactive mode:
654 |
655 | Copyright (C)
656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
657 | This is free software, and you are welcome to redistribute it
658 | under certain conditions; type `show c' for details.
659 |
660 | The hypothetical commands `show w' and `show c' should show the appropriate
661 | parts of the General Public License. Of course, your program's commands
662 | might be different; for a GUI interface, you would use an "about box".
663 |
664 | You should also get your employer (if you work as a programmer) or school,
665 | if any, to sign a "copyright disclaimer" for the program, if necessary.
666 | For more information on this, and how to apply and follow the GNU GPL, see
667 | .
668 |
669 | The GNU General Public License does not permit incorporating your program
670 | into proprietary programs. If your program is a subroutine library, you
671 | may consider it more useful to permit linking proprietary applications with
672 | the library. If this is what you want to do, use the GNU Lesser General
673 | Public License instead of this License. But first, please read
674 | .
675 |
--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
1 | # 一站式的图片数据集预处理工具包
2 |
3 | [说明文档](https://github.com/waterminer/SD-DatasetProcessor/blob/preview/doc/doc_cn.md)
4 |
5 | ✅批量处理图片
6 | 包括且不限于:
7 |
8 | * 批量翻转
9 | * 批量随机裁切
10 | * 图片对比度增强
11 |
12 | ✅批量处理标签
13 | 包括且不限于:
14 |
15 | * 批量删除标签
16 | * 批量插入标签
17 | * 批量修改标签
18 |
19 | ✅过滤机制
20 |
21 | ✅易于开发/维护
22 |
23 | ✅自动打标(试验性)
24 |
25 | ✅AI图片放大(试验性)
26 |
27 | ✅子处理(试验性)
28 |
29 | ## TODO
30 |
31 | 🚧智能裁切
32 |
33 | 🚧重构自动打标代码
34 |
35 | 🚧图形化界面
36 |
37 | ## CREADIT
38 |
39 | ### Upscale
40 |
41 | [Real ESRGAN](https://github.com/xinntao/Real-ESRGAN/):一种流行的AI放大方案
42 |
43 | [Real CUGAN](https://github.com/bilibili/ailab/tree/main/Real-CUGAN):更加适合二次元的AI放大方案
44 |
45 | [Real CUGAN-ncnn](https://github.com/Tohrusky/realcugan-ncnn-py):感谢这位作者提供的RealCUGAN工具包
46 |
47 | ### Tagger
48 |
49 | [WD-1.4-Tagger From SmilingWolf](https://huggingface.co/SmilingWolf):自动打标模型
50 |
51 | ---
52 | 如果这个项目为您提供了帮助,不妨点一个⭐star,万分感谢!
53 |
--------------------------------------------------------------------------------
/conf.yaml:
--------------------------------------------------------------------------------
1 | path:
2 | input: "" #输入路径
3 | output: "" #输出路径
4 |
5 | tagger:
6 | active: True #启用自动打标
7 | batch_size: 4 #批次大小
8 | max_data_loader_n_workers: 1 #越大越占内存
9 |
10 | conduct:
11 | - #处理组1 此处示意为将所有大于512的图片进行翻转
12 | filters: #用于定义过滤器
13 | - #过滤器1
14 | filter: img_size
15 | arg: [512,-1]
16 | processor: #用于定义处理器
17 | - #处理器1
18 | method: flip
19 | arg: 512
20 |
21 | #以下为进阶示范
22 | - #处理组2 此处示意为将1024~2048区间大小的图片进行缩放然后进行随机裁切
23 | repeat: 3 #用于循环执行
24 | filters: #用于定义过滤器
25 | - #过滤器1
26 | filter: img_size
27 | arg: [1024,2048]
28 | processor: #用于定义处理器
29 | - #处理器1
30 | method: resize
31 | arg: 0.5
32 | - #处理器2
33 | method: random_crop
34 | arg: 512
--------------------------------------------------------------------------------
/dataset_processor/__init__.py:
--------------------------------------------------------------------------------
1 | from .data import Data
2 | from .filter import Filter
3 | from .processor import Processor, ProcessorError
4 | from .uitl import *
5 | from .tools import *
6 |
--------------------------------------------------------------------------------
/dataset_processor/data.py:
--------------------------------------------------------------------------------
1 | from PIL import Image
2 | import os
3 |
4 |
5 | class Data:
6 |
7 | # 图片读取并初始化
8 | def __init__(self, path: str, name: str, ext: str):
9 | self.token: list[str] = []
10 | self.conduct = ""
11 | self.repeat = 0
12 | self.id = 0
13 | self.name = name
14 | self.ext = ext
15 | self.path = path
16 | # 读取图片
17 | self.img = Image.open(os.path.join(path, name + ext))
18 | self.size = self.img.size
19 |
20 | # 载入标签
21 | def input_token(self, file_name: str, option=None):
22 | clean_tag = False
23 | NO_CHECK = [ # 清洗排除标签
24 | ':)', ';)', ':(', '>:)', '>:(', '\\(^o^)/', # 括号相关
25 | '^_^', '@_@', '>_@', '+_+', '+_-', 'o_o', '0_0', '|_|', '._.', '>_<', '=_=', '_', '<|>_<|>' # 下划线相关
26 | ]
27 | if option.clean_tag:
28 | clean_tag = True
29 | with open(os.path.join(self.path, file_name), "r") as f:
30 | self.token = f.read(-1).split(",")
31 | for tag in self.token:
32 | tag = tag.strip()
33 | if clean_tag:
34 | if tag not in NO_CHECK:
35 | tag = tag.replace("_", " ")
36 | tag = tag.replace("(", "\\(")
37 | tag = tag.replace(")", "\\)")
38 |
39 | # 保存的方法
40 | def save(self, output_dir, option):
41 | # 默认命名方式:id_conduct_repeat.ext 比如"000001_r_0.jpg"
42 | save_name = str(self.id).zfill(6) + self.conduct
43 | if option:
44 | if option.save_source_name or option.save_conduct_id:
45 | save_name = str(self.id).zfill(6)
46 | if option.save_source_name:
47 | save_name = save_name.join('_' + self.name)
48 | if option.save_conduct_id:
49 | save_name = save_name.join(self.conduct)
50 | self.img.save(os.path.join(output_dir, save_name + self.ext))
51 | # print(save_name)
52 | with open(os.path.join(output_dir, save_name + ".txt"), mode="w") as f:
53 | text = ",".join(self.token)
54 | f.write(text)
55 | self.img.close()
56 |
--------------------------------------------------------------------------------
/dataset_processor/filter.py:
--------------------------------------------------------------------------------
1 | from .data import Data
2 |
3 |
4 | class Filter:
5 | """
6 | 这是一个过滤器类,包含所有有关数据过滤的函数
7 | 编写规范如下:
8 | def 过滤器名(data:Data,arg)->bool:
9 | #代码块
10 | return bool
11 | 其中,True表示该数据会被过滤,False则会被保留
12 | """
13 |
14 | def img_size(data: Data, size: list) -> bool:
15 | min, max = tuple(size)
16 | x, y = data.size
17 | if min != -1:
18 | if data.size[0] <= min or data.size[1] <= min:
19 | return True
20 | if max != -1:
21 | if data.size[0] > max and data.size[1] > max:
22 | return True
23 | else:
24 | return False
25 |
26 | def tag_filter(data: Data, tag) -> bool:
27 | if tag in data.token:
28 | return True
29 | else:
30 | return False
31 |
32 | def tag_selector(data: Data, tag) -> bool:
33 | if tag in data.token:
34 | return False
35 | else:
36 | return True
37 |
38 | def tag_is_not_none(data: Data) -> bool:
39 | if data.token:
40 | return False
41 | else:
42 | return True
43 |
44 | def tag_is_none(data: Data) -> bool:
45 | if data.token:
46 | return True
47 | else:
48 | return False
49 |
--------------------------------------------------------------------------------
/dataset_processor/processor.py:
--------------------------------------------------------------------------------
1 | from random import randint as random
2 | from .data import Data
3 | from .tools.tagger import Tagger
4 | from .tools.upscale import UpscaleModel
5 | from PIL import Image
6 | from PIL import ImageEnhance
7 | import numpy as np
8 |
9 |
10 | class Processor:
11 | """
12 | 这是一个处理器类,包含所有有关数据处理的函数
13 | 编写规范如下:
14 | def 处理名(data:Data,args)->Data:
15 | #代码块
16 | return data
17 | """
18 |
19 | def random_crop(data: Data, size) -> Data:
20 | if not (data.size[0] <= size or data.size[1] <= size):
21 | x = random(1, data.size[0] - size)
22 | y = random(1, data.size[1] - size)
23 | box = (x, y, x + size, y + size)
24 | data.img = data.img.crop(box)
25 | data.conduct += f"_rc{data.repeat}"
26 | data.size = data.img.size
27 | else:
28 | raise ImageTooSmallError(data.name + data.ext)
29 | return data
30 |
31 | def flip(data: Data) -> Data:
32 | data.img = data.img.transpose(Image.FLIP_LEFT_RIGHT)
33 | data.conduct += f"_f{data.repeat}"
34 | return data
35 |
36 | def resize(data: Data, proportion: float) -> Data:
37 | size = (int(data.size[0] * proportion), int(data.size[1] * proportion))
38 | data.img = data.img.resize(size)
39 | data.conduct += f"_r{data.repeat}"
40 | data.size = data.img.size
41 | return data
42 |
43 | def force_resize(data: Data, size: list) -> Data:
44 | data.img = data.img.resize(size)
45 | data.conduct += f"_fr{data.repeat}"
46 | data.size = data.img.size
47 | return data
48 |
49 | def offset(data: Data, offset: int) -> Data:
50 | data.img = data.img.offset(offset, 0)
51 | data.conduct += f"_off{data.repeat}"
52 | return data
53 |
54 | def rotation(data: Data, rot: int) -> Data:
55 | data.img = data.img.rotate(rot)
56 | data.conduct += f"_rot{data.repeat}"
57 | return data
58 |
59 | def contrast_enhancement(data: Data) -> Data: # 对比度增强
60 | image = data.img
61 | enh_con = ImageEnhance.Contrast(image)
62 | contrast = 1.5
63 | data.img = enh_con.enhance(contrast)
64 | data.conduct += f"_con_e{data.repeat}"
65 | return data
66 |
67 | def brightness_enhancement(data: Data) -> Data: # 亮度增强
68 | image = data.img
69 | enh_bri = ImageEnhance.Brightness(image)
70 | brightness = 1.5
71 | data.img = enh_bri.enhance(brightness)
72 | data.conduct += f"_bri_e{data.repeat}"
73 | return data
74 |
75 | def color_enhancement(data: Data) -> Data: # 颜色增强
76 | image = data.img
77 | enh_col = ImageEnhance.Color(image)
78 | color = 1.5
79 | data.img = enh_col.enhance(color)
80 | data.conduct += "_col_e"
81 | return data
82 |
83 | def random_enhancement(data: Data) -> Data: # 随机抖动
84 | image = data.img
85 | random_factor = np.random.randint(8, 31) / 10. # 随机因子
86 | color_image = ImageEnhance.Color(image).enhance(random_factor) # 调整图像的饱和度
87 | random_factor = np.random.randint(8, 10) / 10. # 随机因子
88 | brightness_image = ImageEnhance.Brightness(color_image).enhance(random_factor) # 调整图像的亮度
89 | random_factor = np.random.randint(8, 10) / 10. # 随机因子
90 | contrast_image = ImageEnhance.Contrast(brightness_image).enhance(random_factor) # 调整图像对比度
91 | random_factor = np.random.randint(8, 20) / 10. # 随机因子
92 | data.img = ImageEnhance.Sharpness(contrast_image).enhance(random_factor) # 调整图像锐度
93 | data.conduct += f"_ran_e{data.repeat}"
94 | return data
95 |
96 | def none(data: Data) -> Data:
97 | """
98 | 无操作,主要用于一些特殊场景
99 | """
100 | return data
101 |
102 | def append_tag(data: Data, tag: str) -> Data:
103 | data.token.append(tag)
104 | return data
105 |
106 | def remove_tag(data: Data, tag: str) -> Data:
107 | if tag in data.token:
108 | data.token.remove(tag)
109 | else:
110 | raise TagNotExistError(tag, data.name + data.ext)
111 | return data
112 |
113 | def insert_tag(data: Data, tag: str) -> Data:
114 | data.token.insert(0, tag)
115 | return data
116 |
117 | def tag_move_forward(data: Data, tag: str) -> Data:
118 | """
119 | 将匹配项放到开头
120 | """
121 | if tag in data.token:
122 | data.token.remove(tag)
123 | else:
124 | raise TagNotExistError(tag, data.name + data.ext)
125 | data.token.insert(0, tag)
126 | return data
127 |
128 | def rename_tag(data: Data, tags: list[str]) -> Data:
129 | """
130 | 将Atag改名为Btag
131 | """
132 | tag_a = tags[0]
133 | tag_b = tags[1]
134 | if tag_a in data.token:
135 | index = data.token.index(tag_a)
136 | data.token.insert(index, tag_b)
137 | data.token.remove(tag_a)
138 | else:
139 | raise TagNotExistError(tag_a, data.name + data.ext)
140 | return data
141 |
142 | def tag_image(data: Data, tagger: Tagger):
143 | return tagger.tag_data(data)
144 |
145 | def upscale_image(data: Data, upscale: UpscaleModel):
146 | data.img = upscale.upscale_data(data)
147 | data.size = data.img.size
148 | return data
149 |
150 |
151 | # 自定义异常
152 | class ProcessorError(RuntimeError):
153 | def __init__(self, *args: object) -> None:
154 | super().__init__(*args)
155 |
156 |
157 | class ImageTooSmallError(ProcessorError):
158 | def __init__(self, name: str):
159 | print("image " + name + " is too small!")
160 |
161 |
162 | class TagNotExistError(ProcessorError):
163 | def __init__(self, tag, name: str):
164 | print("Tag" + tag + "not exist in" + name + "!")
165 |
--------------------------------------------------------------------------------
/dataset_processor/tools/__init__.py:
--------------------------------------------------------------------------------
1 | from .tagger import Tagger, TaggerOption
2 | from .upscale import UpscaleModel, UpcaleOption
3 |
--------------------------------------------------------------------------------
/dataset_processor/tools/tagger.py:
--------------------------------------------------------------------------------
1 | from huggingface_hub import hf_hub_download
2 | from keras.models import load_model
3 | import torch
4 | from tqdm import tqdm
5 | import numpy as np
6 | import cv2
7 |
8 |
9 | from dataset_processor import Data
10 | from dataclasses import dataclass, field
11 | import os
12 | from enum import Enum
13 | import csv
14 |
15 | IMAGE_SIZE = 448
16 |
17 | class ModelType(Enum):
18 | WD14_MOAT = "wd-v1-4-moat-tagger-v2"
19 | WD14_VIT = "wd-v1-4-vit-tagger-v2"
20 | WD14_SWINV2 = "wd-v1-4-swinv2-tagger-v2"
21 | WD14_CONVNEXT = "wd-v1-4-convnext-tagger-v2"
22 | WD14_CONVNEXT2 = "wd-v1-4-convnextv2-tagger-v2"
23 |
24 |
25 | @dataclass
26 | class TaggerOption:
27 | force_download: bool = field(default=False)
28 | model_type: ModelType = field(default=ModelType.WD14_CONVNEXT)
29 | model_path: str = field(default="./models")
30 | undesired_tags: str = field(default="")
31 | batch_size:int = field(default=1)
32 | max_data_loader_n_workers: int = field(default=None)
33 | remove_underscore:bool = field(default=True)
34 | thresh:float = field(default=0.35)
35 | character_threshold:float = field(default=None)
36 | general_threshold:float = field(default=None)
37 |
38 |
39 | def preprocess_image(image):
40 | image = np.array(image)
41 | image = image[:, :, ::-1] # RGB->BGR
42 |
43 | # pad to square
44 | size = max(image.shape[0:2])
45 | pad_x = size - image.shape[1]
46 | pad_y = size - image.shape[0]
47 | pad_l = pad_x // 2
48 | pad_t = pad_y // 2
49 | image = np.pad(image,
50 | ((pad_t, pad_y - pad_t),
51 | (pad_l, pad_x - pad_l), (0, 0)),
52 | mode="constant",
53 | constant_values=255)
54 |
55 | interp = cv2.INTER_AREA if size > IMAGE_SIZE else cv2.INTER_LANCZOS4
56 | image = cv2.resize(image, (IMAGE_SIZE, IMAGE_SIZE), interpolation=interp)
57 |
58 | image = image.astype(np.float32)
59 | return image
60 |
61 | def collate_fn_remove_corrupted(batch):
62 | """Collate function that allows to remove corrupted examples in the
63 | dataloader. It expects that the dataloader returns 'None' when that occurs.
64 | The 'None's in the batch are removed.
65 | """
66 | # Filter out all the Nones (corrupted examples)
67 | batch = list(filter(lambda x: x is not None, batch))
68 | return batch
69 |
70 | class ImageLoadingPrepDataset(torch.utils.data.Dataset):
71 | def __init__(self,data_list:list[Data]) -> None:
72 | self.dataset = []
73 | for data in data_list:
74 | self.dataset.append({'img':data.img,'sorce_data':data})
75 | def __len__(self):
76 | return len(self.dataset)
77 |
78 | def __getitem__(self,idx):
79 | sorce_data = self.dataset[idx]['sorce_data']
80 | img = self.dataset[idx]['img']
81 | img= preprocess_image(img)
82 | tensor = torch.tensor(img)
83 | return (tensor, sorce_data)
84 |
85 | class Tagger:
86 |
87 | FILES = ["keras_metadata.pb", "saved_model.pb", "selected_tags.csv"]
88 | SUB_DIR = "variables"
89 | SUB_DIR_FILES = ["variables.data-00000-of-00001", "variables.index"]
90 | CSV_FILE = FILES[-1]
91 | MODEL_AUTHOR_ID = "SmilingWolf/"
92 |
93 |
94 | def __init__ (self,option: TaggerOption | None = TaggerOption()):
95 | print("Init tagger...")
96 | self.repo_id = self.MODEL_AUTHOR_ID + option.model_type.value
97 | self.model_path = os.path.join(option.model_path, option.model_type.value)
98 | if not os.path.exists(self.model_path) or option.force_download:
99 | print("Downloading model from huggingface:" + self.repo_id)
100 | os.mkdir(self.model_path)
101 | for file in self.FILES:
102 | hf_hub_download(
103 | self.repo_id,
104 | filename=file,
105 | cache_dir=self.model_path,
106 | force_download=True,
107 | force_filename=file)
108 | for file in self.SUB_DIR_FILES:
109 | hf_hub_download(
110 | self.repo_id,
111 | filename=file,
112 | subfolder=self.SUB_DIR,
113 | cache_dir=os.path.join(self.model_path, self.SUB_DIR),
114 | force_download=True,
115 | force_filename=file,
116 | )
117 | print("Loading model...")
118 | self.model = load_model(self.model_path)
119 | with open(os.path.join(self.model_path, self.CSV_FILE), "r", -1, "utf-8") as f:
120 | reader = csv.reader(f)
121 | line = [row for row in reader]
122 | header = line[0]
123 | rows = line[1:]
124 | assert header[0] == "tag_id" and header[1] == "name" and header[2] == "category", f"unexpected csv format: {header}"
125 | self.general_tags = [row[1] for row in rows[1:] if row[2] == "0"]
126 | self.character_tags = [row[1] for row in rows[1:] if row[2] == "4"]
127 | self.tag_freq = {}
128 | self.undesired_tags = set(option.undesired_tags.split(","))
129 | self.batch_size = option.batch_size
130 | self.max_data_loader_n_workers = option.max_data_loader_n_workers
131 | self.remove_underscore = option.remove_underscore
132 | self.thresh = option.thresh
133 | if option.character_threshold is None:
134 | self.character_threshold = self.thresh
135 | else:
136 | self.character_threshold = option.character_threshold
137 | if option.general_threshold is None:
138 | self.general_threshold = self.thresh
139 | else:
140 | self.general_threshold = option.general_threshold
141 | self.debug = False
142 |
143 |
144 | def run_batch(self,batchs):
145 | imgs = np.array([im for _, im in batchs])
146 |
147 | probs = self.model(imgs, training=False)
148 | probs = probs.numpy()
149 |
150 | for (sorce_data, _), prob in zip(batchs, probs):
151 | # 最初の4つはratingなので無視する
152 | # # First 4 labels are actually ratings: pick one with argmax
153 | # ratings_names = label_names[:4]
154 | # rating_index = ratings_names["probs"].argmax()
155 | # found_rating = ratings_names[rating_index: rating_index + 1][["name", "probs"]]
156 |
157 | # それ以降はタグなのでconfidenceがthresholdより高いものを追加する
158 | # Everything else is tags: pick any where prediction confidence > threshold
159 | combined_tags = []
160 | general_tag_text = ""
161 | character_tag_text = ""
162 | for i, p in enumerate(prob[4:]):
163 | if i < len(self.general_tags) and p >= self.general_threshold:
164 | tag_name = self.general_tags[i]
165 | if self.remove_underscore and len(tag_name) > 3: # ignore emoji tags like >_< and ^_^
166 | tag_name = tag_name.replace("_", " ")
167 |
168 | if tag_name not in self.undesired_tags:
169 | self.tag_freq[tag_name] = self.tag_freq.get(tag_name, 0) + 1
170 | general_tag_text += ", " + tag_name
171 | combined_tags.append(tag_name)
172 | elif i >= len(self.general_tags) and p >= self.character_threshold:
173 | tag_name = self.character_tags[i - len(self.general_tags)]
174 | if self.remove_underscore and len(tag_name) > 3:
175 | tag_name = tag_name.replace("_", " ")
176 |
177 | if tag_name not in self.undesired_tags:
178 | self.tag_freq[tag_name] = self.tag_freq.get(tag_name, 0) + 1
179 | character_tag_text += ", " + tag_name
180 | combined_tags.append(tag_name)
181 |
182 | # 先頭のカンマを取る
183 | if len(general_tag_text) > 0:
184 | general_tag_text = general_tag_text[2:]
185 | if len(character_tag_text) > 0:
186 | character_tag_text = character_tag_text[2:]
187 | tag_text = ", ".join(combined_tags)
188 | sorce_data.token = combined_tags
189 | if self.debug:
190 | print("Character tags: "+
191 | character_tag_text+
192 | "\n General tags: "+
193 | general_tag_text)
194 |
195 |
196 | def tag_data_list(self,data_list:list[Data]):
197 | dataset = ImageLoadingPrepDataset(data_list)
198 | if self.max_data_loader_n_workers is not None:
199 | tensor_data = torch.utils.data.DataLoader(
200 | dataset,
201 | batch_size=self.batch_size,
202 | shuffle=False,
203 | num_workers=self.max_data_loader_n_workers,
204 | collate_fn=collate_fn_remove_corrupted,
205 | drop_last=False
206 | )
207 | else:
208 | tensor_data = [[(None,data)] for data in data_list]
209 | b_imgs = []
210 | for data_entry in tqdm(tensor_data,smoothing=0.0):
211 | for tensor_data in data_entry:
212 | if tensor_data is None:
213 | continue
214 | image, sorce_data = tensor_data
215 | for data in data_list:
216 | if sorce_data.name == data.name:
217 | sorce_data = data
218 | if image is not None:
219 | image = image.detach().numpy()
220 | else:
221 | image = sorce_data.img
222 | if image.mode != "RGB":
223 | image = image.convert("RGB")
224 | image = preprocess_image(image)
225 | b_imgs.append((sorce_data, image))
226 | if len(b_imgs) >= self.batch_size:
227 | b_imgs = [(sorce_data, image) for sorce_data, image in b_imgs] # Convert image_path to string
228 | self.run_batch(b_imgs)
229 | b_imgs.clear()
230 |
231 | if len(b_imgs) > 0:
232 | b_imgs = [(sorce_data, image) for sorce_data, image in b_imgs] # Convert image_path to string
233 | self.run_batch(b_imgs)
234 |
235 | def tag_data(self,data:Data):
236 | img = preprocess_image(data.img)
237 | b_imgs = [(data,img)]
238 | self.run_batch(b_imgs)
239 | return data
240 |
241 |
--------------------------------------------------------------------------------
/dataset_processor/tools/upscale.py:
--------------------------------------------------------------------------------
1 | import numpy as np
2 | from torch import nn as nn
3 | from PIL.Image import Image, fromarray
4 | from basicsr.archs.rrdbnet_arch import RRDBNet
5 | from basicsr.utils.download_util import load_file_from_url
6 | from huggingface_hub import hf_hub_download
7 | from realesrgan import RealESRGANer as RealESRGANModel
8 | from realcugan_ncnn_py import Realcugan as RealcuganModel
9 |
10 | from dataclasses import dataclass, field
11 | import os
12 | from enum import Enum, auto as enumauto
13 |
14 | from dataset_processor import Data
15 |
16 |
17 | class ModelType(Enum):
18 | R_ESRGAN_2X = enumauto()
19 | R_ESRGAN_4X = enumauto()
20 | R_ESRNET_4X = enumauto()
21 | R_ESRGAN_ANIME6B_4X = enumauto()
22 | R_CUGAN_2X_CON = enumauto()
23 | R_CUGAN_2X_ND = enumauto()
24 | R_CUGAN_2X_D1 = enumauto()
25 | R_CUGAN_2X_D2 = enumauto()
26 | R_CUGAN_2X_D3 = enumauto()
27 | R_CUGAN_3X_CON = enumauto()
28 | R_CUGAN_3X_ND = enumauto()
29 | R_CUGAN_3X_D3 = enumauto()
30 | R_CUGAN_4X_CON = enumauto()
31 | R_CUGAN_4X_ND = enumauto()
32 | R_CUGAN_4X_D3 = enumauto()
33 | CUSTOM = enumauto()
34 |
35 |
36 | @dataclass
37 | class UpcaleOption:
38 | force_download: bool = field(default=False)
39 | model_type: ModelType = field(default=ModelType.R_ESRGAN_2X)
40 | model_path: str = field(default="./models")
41 | custom_model_name: str = field(default="")
42 | custom_model: nn.Module = field(default=None)
43 | custom_scale: int = field(default=2)
44 | tile: int = field(default=512)
45 | tile_pad: int = field(default=10)
46 | pre_pad: int = field(default=10)
47 | half: bool = field(default=True)
48 | gpuid: int = field(default=0)
49 |
50 |
51 | class CustomModelError(RuntimeError): ...
52 |
53 |
54 | class UpscaleModel():
55 | REAL_ESRGAN_MODEL = [
56 | ModelType.R_ESRGAN_2X,
57 | ModelType.R_ESRGAN_4X,
58 | ModelType.R_ESRNET_4X,
59 | ModelType.R_ESRGAN_ANIME6B_4X
60 | ]
61 | REAL_CUGAN_MODEL = [
62 | ModelType.R_CUGAN_2X_CON,
63 | ModelType.R_CUGAN_2X_ND,
64 | ModelType.R_CUGAN_2X_D1,
65 | ModelType.R_CUGAN_2X_D2,
66 | ModelType.R_CUGAN_2X_D3,
67 | ModelType.R_CUGAN_3X_CON,
68 | ModelType.R_CUGAN_3X_ND,
69 | ModelType.R_CUGAN_3X_D3,
70 | ModelType.R_CUGAN_4X_CON,
71 | ModelType.R_CUGAN_4X_ND,
72 | ModelType.R_CUGAN_4X_D3
73 | ]
74 |
75 | def __init__(self, option: UpcaleOption | None = UpcaleOption()):
76 | print("Init upscale...")
77 | self.realesrgan = None
78 | self.realcugan = None
79 | match option.model_type.value:
80 | case ModelType.R_ESRGAN_2X.value:
81 | url = 'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth'
82 | file = "RealESRGAN_x2plus.pth"
83 | model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2)
84 | scale = 2
85 | case ModelType.R_ESRGAN_4X.value:
86 | url = 'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth'
87 | file = "RealESRGAN_x4plus.pth"
88 | model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
89 | scale = 4
90 | case ModelType.R_ESRNET_4X.value:
91 | url = "'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/RealESRNet_x4plus.pth'"
92 | file = "RealESRNet_x4plus.pth"
93 | scale = 8
94 | model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
95 | case ModelType.R_ESRGAN_ANIME6B_4X.value:
96 | url = "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth"
97 | file = "RealESRGAN_x4plus_anime_6B.pth"
98 | model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=6, num_grow_ch=32, scale=4)
99 | scale = 4
100 | case ModelType.R_CUGAN_2X_CON.value:
101 | model = "models-se"
102 | noise = -1
103 | scale = 2
104 | case ModelType.R_CUGAN_2X_ND.value:
105 | model = "models-se"
106 | noise = 0
107 | scale = 2
108 | case ModelType.R_CUGAN_2X_D1.value:
109 | model = "models-se"
110 | noise = 1
111 | scale = 2
112 | case ModelType.R_CUGAN_2X_D2.value:
113 | model = "models-se"
114 | noise = 2
115 | scale = 2
116 | case ModelType.R_CUGAN_2X_D3.value:
117 | model = "models-se"
118 | noise = 3
119 | scale = 2
120 | case ModelType.R_CUGAN_3X_CON.value:
121 | model = "models-se"
122 | noise = -1
123 | scale = 3
124 | case ModelType.R_CUGAN_3X_ND.value:
125 | model = "models-se"
126 | noise = 0
127 | scale = 3
128 | case ModelType.R_CUGAN_3X_D3.value:
129 | model = "models-se"
130 | noise = 3
131 | scale = 3
132 | case ModelType.R_CUGAN_4X_CON.value:
133 | model = "models-se"
134 | noise = -1
135 | scale = 4
136 | case ModelType.R_CUGAN_4X_ND.value:
137 | model = "models-se"
138 | noise = 0
139 | scale = 4
140 | case ModelType.R_CUGAN_4X_D3.value:
141 | model = "models-se"
142 | noise = 3
143 | scale = 4
144 | case ModelType.CUSTOM.value:
145 | try:
146 | file = option.custom_model_name
147 | model = option.custom_model
148 | scale = option.custom_scale
149 | if not os.path.exists(os.path.join(option.model_path, file)):
150 | raise ChildProcessError
151 | except CustomModelError:
152 | print("UpcaleOption:custom_model is not exist!")
153 | exit(1)
154 | case _:
155 | raise RuntimeError
156 | print("Loading upscale model...")
157 | if option.model_type in self.REAL_ESRGAN_MODEL: # 这是一个过度办法,将来我会把这些源换成从抱脸下载
158 | if os.path.exists(os.path.join(option.model_path, file)):
159 | model_path = os.path.join(option.model_path, file)
160 | else:
161 | model_path = os.path.join(option.model_path, "real_esrgan")
162 | if option.model_type is not ModelType.CUSTOM.value and (
163 | not os.path.exists(os.path.join(model_path, file)) or option.force_download):
164 | model_path = load_file_from_url(
165 | url=url, model_dir=option.model_path, progress=True, file_name=None)
166 | tile = option.tile
167 | tile_pad = option.tile_pad
168 | pre_pad = option.pre_pad
169 | half = option.half
170 | gpuid = option.gpuid
171 | self.realesrgan = RealESRGANModel(
172 | scale=scale,
173 | model_path=model_path,
174 | model=model,
175 | tile=tile,
176 | tile_pad=tile_pad,
177 | pre_pad=pre_pad,
178 | half=half,
179 | gpu_id=gpuid)
180 | if option.model_type in self.REAL_CUGAN_MODEL:
181 | tile_size = option.tile
182 | gpuid = option.gpuid
183 | self.realcugan = RealcuganModel(gpuid, noise=noise, scale=scale, model=model, tilesize=tile_size)
184 |
185 | def upscale_data(self, data: Data) -> Image:
186 | if self.realesrgan:
187 | np_img = np.array(data.img)
188 | np_img, _ = self.realesrgan.enhance(np_img)
189 | return fromarray(np_img)
190 | if self.realcugan:
191 | return self.realcugan.process_pil(data.img)
192 |
--------------------------------------------------------------------------------
/dataset_processor/uitl.py:
--------------------------------------------------------------------------------
1 | from tqdm import tqdm
2 |
3 | from dataset_processor import Data
4 | from dataset_processor import Filter
5 | from dataset_processor import Processor, ProcessorError
6 | from .tools.tagger import Tagger, TaggerOption, ModelType as TaggerType
7 | from .tools.upscale import UpcaleOption, UpscaleModel, ModelType as UpscaleType
8 | import copy
9 | import os
10 |
11 | # 文件分类
12 | IMG_EXT = [".png", ".jpg"] # 支持的图片格式
13 | TEXT_EXT = [".txt"] # 支持的标签格式
14 |
15 |
16 | def tagger_builder(args: dict) -> Tagger:
17 | option = TaggerOption()
18 | if args.get('model_path'):
19 | option.model_path = args['model_path']
20 | if args.get('model_type'):
21 | try:
22 | option.model_type = TaggerType[args['model_type']]
23 | except KeyError:
24 | print(f"Invalid type:{args['model_type']}")
25 | if args.get('force_download'):
26 | option.force_download = args['force_download']
27 | if args.get('undesired_tags'):
28 | option.undesired_tags = args['undesired_tags']
29 | if args.get('batch_size'):
30 | option.batch_size = args['batch_size']
31 | if args.get('max_data_loader_n_workers'):
32 | option.max_data_loader_n_workers = args['max_data_loader_n_workers']
33 | if args.get('remove_underscore'):
34 | option.remove_underscore = args['remove_underscore']
35 | if args.get('thresh'):
36 | option.thresh = args['thresh']
37 | if args.get('character_threshold'):
38 | option.character_threshold = args['character_threshold']
39 | if args.get('general_threshold'):
40 | option.general_threshold = args['general_threshold']
41 | return Tagger(option)
42 |
43 |
44 | def upscale_model_builder(args: dict) -> UpscaleModel:
45 | option = UpcaleOption()
46 | if args.get('model_path'):
47 | UpcaleOption.model_path = args['model_path']
48 | if args.get('force_download'):
49 | UpcaleOption.force_download = args['force_download']
50 | if args.get('model_type'):
51 | try:
52 | UpcaleOption.model_type = UpscaleType[args['model_type']]
53 | except KeyError:
54 | print(f"Invalid type:{args['model_type']}")
55 | if args.get('tile'):
56 | UpcaleOption.tile = args['tile']
57 | if args.get('tile_pad'):
58 | UpcaleOption.tile_pad = args['tile_pad']
59 | if args.get('pre_pad'):
60 | UpcaleOption.pre_pad = args['pre_pad']
61 | if args.get('half'):
62 | UpcaleOption.half = args['half']
63 | return UpscaleModel(option)
64 |
65 |
66 | class MainOption:
67 | def __init__(self, args={}):
68 | if args.get('save_source_name'):
69 | self.save_source_name = args.get('save_source_name')
70 | else:
71 | self.save_source_name = False
72 |
73 | if args.get('save_conduct_id'):
74 | self.save_conduct_id = args.get('save_conduct_id')
75 | else:
76 | self.save_conduct_id = False
77 |
78 | if args.get('save_sub'):
79 | self.save_sub = args.get('save_sub')
80 | else:
81 | self.save_sub = False
82 |
83 | if args.get('clean_tag'):
84 | self.clean_tag = args.get('clean_tag')
85 | else:
86 | self.clean_tag = True
87 |
88 | if args.get('tag_no_paired_data'):
89 | self.tag_no_paired_data = args.get('tag_no_paired_data')
90 | else:
91 | self.tag_no_paired_data = True
92 |
93 | if args.get('force_tag_all'):
94 | self.force_tag_all = args.get('force_tag_all')
95 | else:
96 | self.force_tag_all = False
97 |
98 |
99 | class DatasetProcessor:
100 | """
101 | 构建DatasetProcessor对象以开始数据处理
102 | """
103 | upscale: UpscaleModel = None
104 | tagger: Tagger = None
105 | option: MainOption = None
106 |
107 | def data_list_builder(self, input_dir: str) -> list[Data]:
108 | ...
109 |
110 | def pair_token(self, token_file_list: list, data_list: list[Data]):
111 | ...
112 |
113 | def __init__(self,
114 | input_dir: str,
115 | output_dir: str,
116 | conduct: dict,
117 | option: dict | None = None,
118 | tagger: dict | None = None,
119 | upscale: dict | None = None
120 | ):
121 | self.input_dir = input_dir
122 | self.conduct = conduct
123 | if not os.path.exists(output_dir):
124 | os.makedirs(output_dir)
125 | self.output_dir = output_dir
126 | if tagger and tagger.get('active'): self.tagger = tagger_builder(tagger)
127 | if upscale and upscale.get('active'): self.upscale = upscale_model_builder(upscale)
128 | if option:
129 | self.option = MainOption(option)
130 | else:
131 | self.option = MainOption()
132 | self.data_list = self.data_list_builder(input_dir)
133 |
134 | # 匹配标签
135 | def pair_token(self, token_file_list: list, data_list: list[Data]):
136 | no_paired_data_list = []
137 | for data in data_list:
138 | for file_name in token_file_list:
139 | splitext = os.path.splitext(file_name)
140 | name = splitext[0]
141 | if name == data.name:
142 | data.input_token(file_name, self.option)
143 | token_file_list.remove(file_name)
144 | if not data.token:
145 | no_paired_data_list.append(data)
146 | return no_paired_data_list
147 |
148 | # 读取文件并建立列表
149 | def data_list_builder(self, input_dir: str) -> list[Data]:
150 | data_list: list[Data] = []
151 | token_list = []
152 | no_paired_data_list = []
153 | count = 0
154 | print("load files...\n开始读取文件...")
155 | for file_name in tqdm(os.listdir(input_dir)):
156 | splitext = os.path.splitext(file_name)
157 | name = splitext[0]
158 | ext = splitext[1]
159 | if ext in IMG_EXT:
160 | img = Data(input_dir, name, ext)
161 | data_list.append(img)
162 | count += 1
163 | if ext in TEXT_EXT:
164 | token_list.append(file_name)
165 | no_paired_data_list = self.pair_token(token_list, data_list)
166 | token_list.clear()
167 | print(
168 | "一共读取" + str(count) + "张图片,其中有" +
169 | str(no_paired_data_list.__len__()) + "张图片没有配对的标签"
170 | )
171 | if self.tagger:
172 | if self.option.tag_no_paired_data and no_paired_data_list != []:
173 | print("已启用对未标签的图片进行打标")
174 | self.tagger.tag_data_list(no_paired_data_list)
175 | if self.option.force_tag_all:
176 | print("已强制对所有图片进行机器标注")
177 | self.tagger.tag_data_list(data_list)
178 | return data_list
179 |
180 | # 过滤器管理
181 | def filter_manager(self, filter_list: list, data: Data) -> bool:
182 | flag = False
183 | for filter in filter_list:
184 | fun = getattr(Filter, filter.get('filter'))
185 | if filter.get('arg'):
186 | if fun(data, filter.get('arg')): return True
187 | else:
188 | if fun(data): return True
189 | return False
190 |
191 | # 处理器管理
192 | def processor_manager(self, processor_list: list, data: Data):
193 | for processor in processor_list:
194 | try:
195 | fun = getattr(Processor, processor.get('method'))
196 | if fun == Processor.tag_image:
197 | if self.tagger is None:
198 | raise NoneTaggerError('tag_image')
199 | data = fun(data, self.tagger)
200 | elif fun == Processor.upscale_image:
201 | if self.upscale is None:
202 | raise NoneUpscaleError('upscale_image')
203 | data = fun(data, self.upscale)
204 | elif bool(processor.get("arg")):
205 | data = fun(data, processor.get("arg"))
206 | else:
207 | data = fun(data)
208 | except ProcessorError:
209 | raise ProcessorError
210 | except AttributeError:
211 | print(f"\nError:Invalid method: {processor.get('method')}\nPlease check the config file")
212 | exit(1)
213 | except NoneUpscaleError as e:
214 | print(f"\nError:{e.name} is faild!")
215 | print("Upscale is not active!Please add this commit in config:")
216 | print("======================")
217 | print("upscale:\n active: True")
218 | print("======================")
219 | exit(1)
220 | except NoneTaggerError as e:
221 | print(f"\nError:{e.name} is faild!")
222 | print("Tagger is not active!Please add this commit in config:")
223 | print("======================")
224 | print("Tagger:\n active: True")
225 | print("======================")
226 | exit(1)
227 | return data
228 |
229 | def conduct_manager(self, conducts: list[dict], data_list: list[Data]) -> list[Data]:
230 | """
231 | 处理行为管理函数,虽然可以接受data_list,但是存在文件名碰撞隐患
232 | 推荐只传入一个data对象
233 | """
234 | return_list = []
235 | output_dir = self.output_dir
236 | for conduct in conducts:
237 | if conduct.get('sub_conduct'):
238 | sub_data_list = [copy.copy(data) for data in data_list]
239 | for data in sub_data_list:
240 | data.conduct += "_sub["
241 | sub_data_list = self.conduct_manager(conduct.get('sub_conduct'), sub_data_list)
242 | if sub_data_list:
243 | for data in sub_data_list:
244 | data.conduct += "]"
245 | data_list = copy.deepcopy(sub_data_list)
246 | if self.option.save_sub:
247 | sub_output = os.path.join(output_dir, "sub")
248 | if not (os.path.exists(sub_output)):
249 | os.mkdir(sub_output)
250 | for sub_data in sub_data_list:
251 | sub_data.save(sub_output, self.option)
252 | for data in data_list:
253 | filters = conduct.get('filters')
254 | if filters:
255 | if self.filter_manager(filters, data): continue
256 | if bool(conduct.get('repeat')):
257 | repeat = conduct.get('repeat')
258 | else:
259 | repeat = 1
260 | for j in range(0, repeat):
261 | data.repeat = j
262 | try:
263 | return_list.append(self.processor_manager(conduct.get('processor'), copy.deepcopy(data)))
264 | except ProcessorError:
265 | break
266 | return return_list
267 |
268 | def main(self):
269 | """
270 | 主入口
271 | """
272 | print("开始图片处理...")
273 | for i in tqdm(range(0, len(self.data_list))):
274 | data = self.data_list.pop()
275 | data.id = i
276 | data_list = self.conduct_manager(self.conduct, [data])
277 | if data_list:
278 | for data in data_list:
279 | data.save(self.output_dir, self.option)
280 |
281 |
282 | class NoneTaggerError(RuntimeError):
283 | def __init__(self, name):
284 | self.name = name
285 |
286 |
287 | class NoneUpscaleError(RuntimeError):
288 | def __init__(self, name):
289 | self.name = name
290 |
--------------------------------------------------------------------------------
/doc/doc_cn.md:
--------------------------------------------------------------------------------
1 | # 说明文档
2 |
3 | ## 安装
4 |
5 | 需要安装依赖
6 |
7 | ```txt
8 | python -m venv ./venv
9 | ./venv/Scripts/activate
10 | pip install -r ./requirements.txt
11 | ```
12 |
13 | 注意:该版本会安装torch相关的一大堆依赖,如果不想要,可以选择激活别的地方的venv后再来运行
14 | *如果你不懂我说的是什么意思那你就照着上面指令用就行了*
15 |
16 | 你也可以选择安装不包含AI处理的[轻量版](https://github.com/waterminer/SD-DatasetProcessor/tree/lite)
17 |
18 | ## 使用方式
19 |
20 | 参考conf文件进行编写
21 |
22 | ```yaml
23 | path:
24 | input: "" #输入路径
25 | output: "" #输出路径
26 |
27 | tagger: #自动打标相关设置
28 | active: True #启用自动打标
29 | batch_size: 4 #批次大小
30 | max_data_loader_n_workers: 1 #越大越占内存
31 |
32 | conduct:
33 | - #处理组1 此处示意为将所有大于512的图片进行翻转
34 | filters: #用于定义过滤器
35 | - #过滤器1
36 | filter: img_filter
37 | arg: [512,-1]
38 | processor: #用于定义处理器
39 | - #处理器1
40 | method: flip
41 | arg: 512
42 |
43 | #以下为进阶示范
44 | - #处理组2 此处示意为将1024~2048区间大小的图片进行缩放然后进行随机裁切
45 | repeat: 3 #用于循环执行(可选)
46 | filters: #用于定义过滤器
47 | - #过滤器1
48 | filter: img_filter
49 | arg: [1024,2048]
50 | processor: #用于定义处理器
51 | - #处理器1
52 | method: resize
53 | arg: 0.5
54 | - #处理器2
55 | method: random_crop
56 | arg: 512
57 | ```
58 |
59 | 在默认情况下,文件保存名称为:`id_处理id_重复次数.格式`
60 | 比如:`000001_f_0.jpg`
61 |
62 | ## 可选项
63 |
64 | 在yaml中加入option来自定义以下选项
65 |
66 | |名称|说明|
67 | |--|--|
68 | |save_source_name|保存原文件名称|
69 | |save_conduct_id|保存处理id|
70 | |save_sub|保存子处理|
71 | |clean_tag|清洗标签(将"_"换成空格,给括号加上"\"),默认开启|
72 | |tag_no_paired_data|自动对没有标签的图片进行打标,需要配置`tagger`,默认开启|
73 | |force_tag_all|强制对所有图片进行打标,需要配置`tagger`|
74 |
75 | 以下是示例:
76 |
77 | ```yaml
78 | option:
79 | save_sorce_name:True
80 | save_sub:True
81 | ```
82 |
83 | ## 处理器说明
84 |
85 | ### 图片处理
86 |
87 | |名称|处理id|说明|参数|
88 | | -- | -- | -- | -- |
89 | |random_crop|_rc|随机裁切矩形图片|图片分辨率(整数)|
90 | |flip|_f|水平翻转图片| - |
91 | |resize|_r|按比例重新调整大小|比例(浮点数)|
92 | |force_resize|_fr|将图片缩放至特定大小|数组,输入格式为[x,y]|
93 | |offset|_off|将图片偏移n个像素|偏移量(整数)|
94 | |rotation|_rot|将图片选择n个角度|角度(整数)|
95 | |contrast_enhancement|_con_e|对比度增强|-|
96 | |brightness_enhancement|_bri_e|亮度增强|-|
97 | |color_enhancement|_col_e|饱和度增强|-|
98 | |random_enhancement|_ran_e|随机增强|-|
99 | |none|-|不做操作(用于特定场合)|-|
100 | |upscale_image|-|放大图片,要使用这个方法,请配置`upscale`|-|
101 |
102 | ### 标签处理
103 |
104 | |名称|说明|参数|
105 | | -- | -- | -- |
106 | |append_tag|在标签组末尾加上标签|标签(文本)|
107 | |remove_tag|删除标签组中符合条件的标签|标签(文本)|
108 | |insert_tag|在标签组开头插入标签|标签(文本)|
109 | |tag_move_forward|将选定标签移到开头|标签(文本)|
110 | |rename_tag|重命名标签,将`A标签`重命名为`B标签`|['A标签','B标签']|
111 | |tag_image|对图片进行打标并覆盖原来的标签,要使用这个方法,请配置`tagger`|-|
112 |
113 | ## 过滤器说明
114 |
115 | 负责过滤符合特定条件的数据
116 |
117 | |名称|说明|参数|
118 | | -- | -- | -- |
119 | |img_size|过虑特定尺寸的图片|数组,输入格式为[max,min],缺省填-1 |
120 | |tag_filter|过滤掉特定标签|标签(文本)|
121 | |tag_selector|须要包含特定标签|标签(文本)|
122 | |tag_is_not_none|只含有带标签的图片|-|
123 | |tag_is_none|只含有不带标签的图片|-|
124 |
125 | ## 子处理说明
126 |
127 | 在配置文件`conduct`项中可以添加`sub_conduct`子处理,在运行中会将子处理的结果作为输入返回主处理
128 |
129 | 子处理的编写方式与主处理的编写方式相同。
130 |
131 | 以下是一种子处理的使用示例:
132 |
133 | ```yaml
134 | conduct:
135 | - sub_conduct: #将图片进行随机裁切
136 | -
137 | filters:
138 | - filter: img_size
139 | arg: [1024,1536]
140 | processor:
141 | - method: random_crop
142 | arg: 1024
143 | -
144 | filters:
145 | - filter: img_size
146 | arg: [1536,2048]
147 | processor:
148 | - method: resize
149 | arg: 0.75
150 | - method: random_crop
151 | arg: 1024
152 | processor:
153 | - method: flip #将所有子处理进行翻转
154 | ```
155 |
156 | 当然,如果你想要的话,你可以在子处理中嵌套子处理,这是完全合法的
157 |
158 | ## 自动打标设置说明
159 |
160 | 在配置中添加添加以下条目即可启用自动打标:
161 |
162 | ``` yaml
163 | Tagger:
164 | active: True
165 | ```
166 |
167 | 如果你已经全部完成打标了,依旧打开此项会大大拖慢速度(花时间读取模型),所以请结合实际情况自行选择是否打开
168 |
169 | 你可以像这样来配置打标设置:
170 |
171 | ``` yaml
172 | Tagger:
173 | active: True
174 | model_type: WD14_MOAT
175 | ```
176 |
177 | 如果你不清楚是什么,请保持默认
178 |
179 | ### 配置项说明
180 |
181 | |名称|说明|参数|
182 | |--|--|--|
183 | |active|启用自动打标|布尔值(True/False)|
184 | |model_path|模型路径,下载的模型都会放在此文件夹内|路径|
185 | |model_type|模型种类,具体看下一章|模型种类|
186 | |force_download|强制下载模型|布尔值|
187 | |undesired_tags|排除标签|标签,以英文半角逗号","分隔|
188 | |remove_underscore|以空格替代下划线"_"|布尔值|
189 | |batch_size|每批大小|整数|
190 | |max_data_loader_n_workers|数据读取大小,越大越占内存|整数|
191 | |thresh|置信度,会排除掉比这个值低的标签,默认是0.35|0~1浮点数|
192 | |character_threshold|角色置信度,如果启用,会以这个值单独设置角色标签的推断|0~1浮点数|
193 | |general_threshold|普通标签置信度,如果启用,会以这个值单独设置普通标签置的推断|0~1浮点数|
194 |
195 | ### 自动打标模型种类
196 |
197 | 默认参数为`WD14_CONVNEXT`可以按照喜好自行选择
198 | |值|链接|P=R: threshold|F1|
199 | |--|--|--|--|
200 | |WD14_MOAT|[链接](https://huggingface.co/SmilingWolf/wd-v1-4-moat-tagger-v2)|0.3771|0.6911|
201 | |WD14_VIT|[链接](https://huggingface.co/SmilingWolf/wd-v1-4-vit-tagger-v2)|0.3537|0.6770|
202 | |WD14_SWINV2|[链接](https://huggingface.co/SmilingWolf/wd-v1-4-swinv2-tagger-v2)|0.3771|0.6854|
203 | |WD14_CONVNEXT|[链接](https://huggingface.co/SmilingWolf/wd-v1-4-convnext-tagger-v2)|0.3685|0.6810|
204 | |WD14_CONVNEXT2|[链接](https://huggingface.co/SmilingWolf/wd-v1-4-convnextv2-tagger-v2)|0.3710|0.6862|
205 |
206 | ## 图片放大说明
207 |
208 | 在配置中添加添加以下条目即可启用图片放大:
209 |
210 | ``` yaml
211 | upscale:
212 | active: True
213 | ```
214 |
215 | 你可以像这样来配置打标设置:
216 |
217 | ``` yaml
218 | upscale:
219 | active: True
220 | model_type: R_CUGAN_2X_CON
221 | ```
222 |
223 | 如果你不清楚是什么,请保持默认
224 |
225 | ### 配置项说明
226 |
227 | |名称|说明|参数|
228 | |--|--|--|
229 | |active|启用自动图片放大|布尔值|
230 | |model_path|模型路径,下载的模型都会放在此文件夹内(仅适用于Real_ESRGAN)|路径|
231 | |model_type|模型种类,具体看下一章|模型种类|
232 | |force_download|强制下载模型(仅适用于Real_ESRGAN)|布尔值|
233 | |tile|切分图片,减少显存占用,0为不裁切,默认是512|每块切片的分辨率(整型)|
234 | |tile_pad|切分pad尺寸,用于减轻合并伪影,默认是10(仅适用于Real_ESRGAN)|pad分辨率(整型)|
235 | |pre_pad|pad填充像素,用于减轻合并伪影,默认是10(仅适用于Real_ESRGAN)|pad填充像素(整型)|
236 | |half|半精度,如果您是20系或者更高,推荐打开来加速(仅适用于Real_ESRGAN)|布尔值|
237 |
238 | ### 放大模型种类
239 |
240 | 默认参数为`R_ESRGAN_2X`可以按照喜好自行选择
241 |
242 | |值|说明|
243 | |--|--|
244 | |R_ESRGAN_2X|Real_ESRGAN算法,2X代表2倍放大,下同|
245 | |R_ESRGAN_4X|-|
246 | |R_ESRGAN_8X|-|
247 | |R_ESRNET_4X|仅在Real_ESRGAN库中支持,作者尚未验证|
248 | |R_ESRGAN_ANIME6B_4X|Real_ESRGAN针对二次元训练的算法,仅有4X放大|
249 | |R_CUGAN_2X_CON|Real_CUGAN算法,针对二次元的AI放大算法,2X代表2倍放大,CON代表保守降噪策略,推荐原图清晰度较高下使用|
250 | |R_CUGAN_2X_ND|同上,ND代表不降噪,推荐原图清晰度非常高的情况下使用|
251 | |R_CUGAN_2X_D3|同上,D3代表3级降噪,等级越高降噪程度越高,仅有2X模型降噪分为三个等级,其余均只有3级降噪,推荐原图清晰度不高的情况下使用|
252 | |R_CUGAN_2X_D2|-|
253 | |R_CUGAN_2X_D1|-|
254 | |R_CUGAN_3X_CON|-|
255 | |R_CUGAN_3X_ND|-|
256 | |R_CUGAN_3X_D3|-|
257 | |R_CUGAN_4X_CON|-|
258 | |R_CUGAN_4X_ND|-|
259 | |R_CUGAN_4X_D3|-|
260 |
--------------------------------------------------------------------------------
/main.py:
--------------------------------------------------------------------------------
1 | from dataset_processor import *
2 |
3 | import yaml
4 | from argparse import ArgumentParser
5 |
6 | if __name__ == "__main__":
7 | parser = ArgumentParser()
8 | parser.add_argument(
9 | '--input_dir',
10 | default=None,
11 | type=str,
12 | help='input dir,if used,it will cover config ''input_dir''//数据集输入路径,如果指定则会覆盖配置文件中的''input_dir'''
13 | )
14 | parser.add_argument(
15 | '--output_dir',
16 | default=None,
17 | type=str,
18 | help='output dir,if used,it will cover config ''output_dir''//数据集输入路径,如果指定则会覆盖配置文件中的''output_dir'''
19 | )
20 | parser.add_argument(
21 | '--config',
22 | default='./conf.yaml',
23 | type=str,
24 | help='yaml config path,default to reading conf.yaml in the root directory//指定yaml配置文件,默认读取根目录下conf.yaml'
25 | )
26 | args = parser.parse_args()
27 | with open(args.config, "r", encoding="utf-8") as f:
28 | config = yaml.load(f.read(), yaml.FullLoader)
29 | # 设置
30 | if args.input_dir:
31 | input_dir = args.input_dir
32 | else:
33 | input_dir = config.get('path').get('input') # 输入目录
34 | if args.input_dir:
35 | output_dir = args.output_dir
36 | else:
37 | output_dir = config.get('path').get('output') # 输出目录
38 | # 参数
39 | conducts = config.get('conduct')
40 | option = config.get('option')
41 | tagger = config.get('tagger')
42 | upscale = config.get('upscale')
43 | DatasetProcessor(input_dir,output_dir,conducts,option,tagger,upscale).main()
--------------------------------------------------------------------------------
/requirements.txt:
--------------------------------------------------------------------------------
1 | torch~=2.0.1
2 | numpy~=1.24.3
3 | opencv-python~=4.8.0.74
4 | keras~=2.13.1
5 | tqdm~=4.65.0
6 | Pillow~=10.0.0
7 | PyYAML~=6.0.1
8 | huggingface_hub
9 | tensorflow
10 | realesrgan
11 | realcugan-ncnn-py
--------------------------------------------------------------------------------
/setup.py:
--------------------------------------------------------------------------------
1 | from setuptools import setup, find_packages
2 | import os
3 |
4 | requires = []
5 | with open('requirements.txt', encoding='utf8') as f:
6 | for x in f.readlines():
7 | requires.append(f'{x.strip()}')
8 |
9 | data_files = [('conf',['conf.yaml'])]
10 |
11 | for f in os.listdir('./doc'):
12 | data_files.append(('doc',['doc/'+f]))
13 |
14 | setup(
15 | name='dataset_processor',
16 | version='0.3.0',
17 | packages=['dataset_processor', 'dataset_processor.tools'],
18 | url='https://github.com/waterminer/SD-DatasetProcessor',
19 | license='GPLv3',
20 | author='Water_miner',
21 | author_email='420773173@qq.com',
22 | description='A dataset preprocess toolkit',
23 | classifiers=[
24 | 'Development Status :: 3 - Alpha',
25 | 'Environment :: GPU :: NVIDIA CUDA :: 11.8',
26 | 'License :: OSI Approved :: GNU General Public License v3 (GPLv3)',
27 | 'Programming Language :: Python :: 3.10',
28 | ],
29 | install_requires=requires,
30 | data_files=data_files
31 | )
32 |
33 |
--------------------------------------------------------------------------------