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207 lines
8.1 KiB
Markdown
207 lines
8.1 KiB
Markdown
[](https://travis-ci.org/dsoprea/go-exif)
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[](https://codecov.io/gh/dsoprea/go-exif)
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[](https://goreportcard.com/report/github.com/dsoprea/go-exif/v3)
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[](https://godoc.org/github.com/dsoprea/go-exif/v3)
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# Overview
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This package provides native Go functionality to parse an existing EXIF block, update an existing EXIF block, or add a new EXIF block.
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# Getting
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To get the project and dependencies:
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```
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$ go get -t github.com/dsoprea/go-exif/v3
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```
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# Scope
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This project is concerned only with parsing and encoding raw EXIF data. It does
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not understand specific file-formats. This package assumes you know how to
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extract the raw EXIF data from a file, such as a JPEG, and, if you want to
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update it, know how to write it back. File-specific formats are not the concern
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of *go-exif*, though we provide
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[exif.SearchAndExtractExif][search-and-extract-exif] and
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[exif.SearchFileAndExtractExif][search-file-and-extract-exif] as brute-force
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search mechanisms that will help you explore the EXIF information for newer
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formats that you might not yet have any way to parse.
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That said, the author also provides the following projects to support the
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efficient processing of the corresponding image formats:
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- [go-jpeg-image-structure](https://github.com/dsoprea/go-jpeg-image-structure)
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- [go-png-image-structure](https://github.com/dsoprea/go-png-image-structure)
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- [go-tiff-image-structure](https://github.com/dsoprea/go-tiff-image-structure)
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- [go-heic-exif-extractor](https://github.com/dsoprea/go-heic-exif-extractor)
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See the [SetExif example in go-jpeg-image-structure][jpeg-set-exif] for
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practical information on getting started with JPEG files.
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# Usage
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The package provides a set of [working examples][examples] and is covered by
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unit-tests. Please look to these for getting familiar with how to read and write
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EXIF.
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Create an instance of the `Exif` type and call `Scan()` with a byte-slice, where
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the first byte is the beginning of the raw EXIF data. You may pass a callback
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that will be invoked for every tag or `nil` if you do not want one. If no
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callback is given, you are effectively just validating the structure or parsing
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of the image.
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Obviously, it is most efficient to properly parse the media file and then
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provide the specific EXIF data to be parsed, but there is also a heuristic for
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finding the EXIF data within the media blob, directly. This means that, at least
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for testing or curiosity, **you do not have to parse or even understand the
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format of image or audio file in order to find and decode the EXIF information
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inside of it.** See the usage of the `SearchAndExtractExif` method in the
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example.
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The library often refers to an IFD with an "IFD path" (e.g. IFD/Exif,
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IFD/GPSInfo). A "fully-qualified" IFD-path is one that includes an index
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describing which specific sibling IFD is being referred to if not the first one
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(e.g. IFD1, the IFD where the thumbnail is expressed per the TIFF standard).
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There is an "IFD mapping" and a "tag index" that must be created and passed to
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the library from the top. These contain all of the knowledge of the IFD
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hierarchies and their tag-IDs (the IFD mapping) and the tags that they are
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allowed to host (the tag index). There are convenience functions to load them
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with the standard TIFF information, but you, alternatively, may choose
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something totally different (to support parsing any kind of EXIF data that does
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not follow or is not relevant to TIFF at all).
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# Standards and Customization
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This project is configuration driven. By default, it has no knowledge of tags
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and IDs until you load them prior to using (which is incorporated in the
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examples). You are just as easily able to add additional custom IFDs and custom
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tags for them. If desired, you could completely ignore the standard information
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and load *totally* non-standard IFDs and tags.
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This would be useful for divergent implementations that add non-standard
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information to images. It would also be useful if there is some need to just
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store a flat list of tags in an image for simplified, proprietary usage.
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# Reader Tool
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There is a runnable reading/dumping tool included:
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```
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$ go get github.com/dsoprea/go-exif/v3/command/exif-read-tool
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$ exif-read-tool --filepath "<media file-path>"
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```
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Example output:
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```
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IFD-PATH=[IFD] ID=(0x010f) NAME=[Make] COUNT=(6) TYPE=[ASCII] VALUE=[Canon]
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IFD-PATH=[IFD] ID=(0x0110) NAME=[Model] COUNT=(22) TYPE=[ASCII] VALUE=[Canon EOS 5D Mark III]
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IFD-PATH=[IFD] ID=(0x0112) NAME=[Orientation] COUNT=(1) TYPE=[SHORT] VALUE=[1]
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IFD-PATH=[IFD] ID=(0x011a) NAME=[XResolution] COUNT=(1) TYPE=[RATIONAL] VALUE=[72/1]
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IFD-PATH=[IFD] ID=(0x011b) NAME=[YResolution] COUNT=(1) TYPE=[RATIONAL] VALUE=[72/1]
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IFD-PATH=[IFD] ID=(0x0128) NAME=[ResolutionUnit] COUNT=(1) TYPE=[SHORT] VALUE=[2]
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IFD-PATH=[IFD] ID=(0x0132) NAME=[DateTime] COUNT=(20) TYPE=[ASCII] VALUE=[2017:12:02 08:18:50]
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...
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```
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You can also print the raw, parsed data as JSON:
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```
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$ exif-read-tool --filepath "<media file-path>" -json
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```
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Example output:
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```
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[
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{
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"ifd_path": "IFD",
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"fq_ifd_path": "IFD",
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"ifd_index": 0,
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"tag_id": 271,
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"tag_name": "Make",
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"tag_type_id": 2,
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"tag_type_name": "ASCII",
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"unit_count": 6,
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"value": "Canon",
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"value_string": "Canon"
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},
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{
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"ifd_path": "IFD",
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...
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```
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# Testing
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The traditional method:
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```
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$ go test github.com/dsoprea/go-exif/v3/...
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```
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# Release Notes
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## v3 Release
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This release primarily introduces an interchangeable data-layer, where any
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`io.ReadSeeker` can be used to read EXIF data rather than necessarily loading
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the EXIF blob into memory first.
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Several backwards-incompatible clean-ups were also included in this release. See
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[releases][releases] for more information.
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## v2 Release
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Features a heavily reflowed interface that makes usage much simpler. The
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undefined-type tag-processing (which affects most photographic images) has also
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been overhauled and streamlined. It is now complete and stable. Adoption is
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strongly encouraged.
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# *Contributing*
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EXIF has an excellently-documented structure but there are a lot of devices and
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manufacturers out there. There are only so many files that we can personally
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find to test against, and most of these are images that have been generated only
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in the past few years. JPEG, being the largest implementor of EXIF, has been
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around for even longer (but not much). Therefore, there is a lot of
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compatibility to test for.
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**If you are able to help by running the included reader-tool against all of the
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EXIF-compatible files you have, it would be deeply appreciated. This is mostly
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going to be JPEG files (but not all variations). If you are able to test a large
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number of files (thousands or millions) then please post an issue mentioning how
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many files you have processed. If you had failures, then please share them and
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try to support efforts to understand them.**
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If you are able to test 100K+ files, I will give you credit on the project. The
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further back in time your images reach, the higher in the list your name/company
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will go.
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# Contributors/Testing
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Thank you to the following users for solving non-trivial issues, supporting the
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project with solving edge-case problems in specific images, or otherwise
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providing their non-trivial time or image corpus to test go-exif:
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- [philip-firstorder](https://github.com/philip-firstorder) (200K images)
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- [matchstick](https://github.com/matchstick) (102K images)
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In addition to these, it has been tested on my own collection, north of 478K
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images.
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[search-and-extract-exif]: https://godoc.org/github.com/dsoprea/go-exif/v3#SearchAndExtractExif
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[search-file-and-extract-exif]: https://godoc.org/github.com/dsoprea/go-exif/v3#SearchFileAndExtractExif
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[jpeg-set-exif]: https://godoc.org/github.com/dsoprea/go-jpeg-image-structure#example-SegmentList-SetExif
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[examples]: https://godoc.org/github.com/dsoprea/go-exif/v3#pkg-examples
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[releases]: https://github.com/dsoprea/go-exif/releases
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