README
¶
# rescribe.xyz/bookpipeline package
This package contains various tools and functions for the OCR of
books, with a focus on distributed OCR using short-lived virtual
servers.
This is a Go package, and can be installed in the standard go way,
by running `go get rescribe.xyz/bookpipeline/...`
## Commands
The commands in the cmd/ directory are at the heart of this
package. For more details on their usage, use `go doc` or read
doc.go in the package repository.
The key commands for the virtual server side are:
- bookpipeline : processes items from queues, doing preprocessing,
ocr and postprocessing, and moving items on to
the next queue step on completion. this is the
core command of the package.
- booktopipeline : uploads a book to the pipeline and adds it to the
appropriate queue.
- getpipelinebook : downloads the pipeline results for a book.
- lspipeline : prints useful information about the status of the
pipeline.
- mkpipeline : sets up storage buckets and queues for use by the
pipeline.
- spotme : starts up a short-lived virtual server running
bookpipeline.
There are also some commands which are more useful in a standalone
setting:
- confgraph : creates a graph showing average word confidence of
each page of hOCR in a directory
- pagegraph : creates a graph showing average confidence of each
word in a page of hOCR
- pdfbook : creates a searchable PDF from a directory of hOCR
and image files
## Contributions
Any and all comments, bug reports, patches or pull requests would
be very welcomely received. Please email them to <nick@rescribe.xyz>.
## License
This package is licensed under the GPLv3. See the LICENSE file for
more details.
Documentation
¶
Overview ¶
Package bookpipeline contains various tools and functions for the OCR of books, with a focus on distributed OCR using short-lived virtual servers. It also contains several tools that are useful standalone; read the accompanying README for more details.
Introduction ¶
The book pipeline is a way to split the different processes that for book OCR into small jobs, which can be processed when a computer is ready for them. It is currently implemented with Amazon's AWS cloud systems, and can scale from zero to many computers, with jobs being processed faster when more servers are available.
Central to the bookpipeline in terms of software is the bookpipeline command, which is part of the rescribe.xyz/bookpipeline package. Presuming you have the go tools installed, you can install it, and useful tools to control the system, with this command:
go get -u rescribe.xyz/bookpipeline/...
All of the tools provided in the bookpipeline package will give information on what they do and how they work with the '-h' flag, so for example to get usage information on the booktopipeline tool simply run the following:
booktopipeline -h
To get the pipeline tools to work for you, you'll need to change the settings in cloudsettings.go, and set up your ~/.aws/credentials appropriately.
Managing servers ¶
Most of the time the bookpipeline is expected to be run from potentially short-lived servers on Amazon's EC2 system. EC2 provides servers which have no guaranteed of stability (though in practice they seem to be), called "Spot Instances", which we use for bookpipeline. bookpipeline can handle a process or server being suddenly destroyed without warning (more on this later), so Spot Instances are perfect for us. We have set up a machine image with bookpipeline preinstalled which will launch at bootup, which is all that's needed to launch an bookpipeline instance. Presuming the bookpipeline package has been installed on your computer (see above), the spot instance can be started with the command:
spotme
You can keep an eye on the servers (spot or otherwise) that are running, and the jobs left to do and in progress, with the "lspipeline" tool (which is also part of the bookpipeline package). It's recommended to use this with the ssh private key for the servers, so that it can also report on what each server is currently doing, but it can run successfully without it. It takes a little while to run, so be patient. It can be run with the command:
lspipeline -i key.pem
Spot instances can be terminated with ssh, using their ip address which can be found with lspipeline, like so:
ssh -i key.pem admin@<ip-address> sudo poweroff
The bookpipeline program is run as a service managed by systemd on the servers. The system is fully resiliant in the face of unexpected failures. See the section "How the pipeline works" for details on this. bookpipeline can be managed like any other systemd service. A few examples:
# show all logs for bookpipeline: ssh -i key.pem admin@<ip-address> journalctl -n all -u bookpipeline # restart bookpipeline ssh -i key.pem admin@<ip-address> systemctl restart bookpipeline
Using the pipeline ¶
Books can be added to the pipeline using the "booktopipeline" tool. This takes a directory of page images as input, and uploads them all to S3, adding a job to the pipeline queue to start processing them. So it can be used like this:
booktopipeline -v ExcellentBook/
Getting a finished book ¶
Once a book has been finished, it can be downloaded using the "getpipelinebook" tool. This has several options to download specific parts of a book, but the default case will download the best hOCR for each page, PDFs, and the best, conf and graph.png files. Use it like this:
getpipelinebook ExcellentBook
To get the plain text from the book, use the hocrtotxt tool, which is part of the rescribe.xyz/utils package. You can get the package, and run the tool, like this:
go get -u rescribe.xyz/utils/... hocrtotext ExcellentBook/0010_bin0.2.hocr > ExcellentBook/0010_bin0.2.txt
How the pipeline works ¶
The central part of the book pipeline is several SQS queues, which contain jobs which need to be done by a server running bookpipeline. The exact content of the SQS messages vary according to each queue, as some jobs need more information than others. Each queue is checked at least once every couple of minutes on any server that isn't currently processing a job.
When a job is taken from the queue by a process, it is hidden from the queue for 2 minutes so that no other process can take it. Once per minute when processing a job the process sends a message updating the queue, to tell it to keep the job hidden for two minutes. This is called the "heartbeat", as if the process fails for any reason the heartbeat will stop, and in 2 minutes the job will reappear on the queue for another process to have a go at. Once a job is completed successfully it is deleted from the queue.
Queues ¶
Queue names are defined in cloudsettings.go.
queuePreProc
Each message in the queuePreProc queue is a bookname, optionally followed by a space and the name of the training to use. Each page of the bookname will be binarised with several different parameters, and then wiped, with each version uploaded to S3, with the path of the preprocessed page, plus the training name if it was provided, will be added to the queueOcrPage queue. The pages are binarised with different parameters as it can be difficult to determine which binarisation level will be best prior to OCR, so several different options are used, and in the queueAnalyse step the best one is chosen, based on the confidence of the OCR output.
example message: APolishGentleman_MemoirByAdamKruczkiewicz example message: APolishGentleman_MemoirByAdamKruczkiewicz rescribelatv7
queueWipeOnly
This queue works the same as queuePreProc, except that it doesn't binarise the pages, only runs the wiper. Hence it is designed for books which have been prebinarised.
example message: APolishGentleman_MemoirByAdamKruczkiewicz example message: APolishGentleman_MemoirByAdamKruczkiewicz rescribefrav2
queueOcrPage
This queue contains the path of individual pages, optionally followed by a space and the name of the training to use. Each page is OCRed, and the results are uploaded to S3. After each page is OCRed, a check is made to see whether all pages that look like they were preprocessed have corresponding .hocr files. If so, the bookname is added to the queueAnalyse queue.
example message: APolishGentleman_MemoirByAdamKruczkiewicz/00162_bin0.0.png example message: APolishGentleman_MemoirByAdamKruczkiewicz/00162_bin0.0.png rescribelatv7
queueAnalyse
A message on the queueAnalyse queue contains only a book name. The confidences for each page are calculated and saved in the 'conf' file, and the best version of each page is decided upon and saved in the 'best' file. PDFs are then generated, and the confidence graph is generated.
example message: APolishGentleman_MemoirByAdamKruczkiewicz
Queue manipulation ¶
The queues should generally only be messed with by the bookpipeline and booktopipeline tools, but if you're feeling ambitious you can take a look at the `addtoqueue` tool.
Remember that messages in a queue are hidden for a few minutes when they are read, so for example you couldn't straightforwardly delete a message which was currently being processed by a server, as you wouldn't be able to see it.
Page naming ¶
At present the bookpipeline has some silly limitations of file names for book pages to be recognised. This is something which will be fixed in due course.
Pages that are to be fully processed: *[0-9]{4}.jpg$
Pages that are to be wiped only: *[0-9]{6}(.bin)?.png$
Index ¶
- Constants
- func Graph(confs map[string]*Conf, bookname string, w io.Writer) error
- func GraphOpts(confs map[string]*Conf, bookname string, xaxis string, guidelines bool, ...) error
- type AwsConn
- func (a *AwsConn) AddToQueue(url string, msg string) error
- func (a *AwsConn) AnalyseQueueId() string
- func (a *AwsConn) CheckQueue(url string, timeout int64) (Qmsg, error)
- func (a *AwsConn) CreateBucket(name string) error
- func (a *AwsConn) CreateQueue(name string) error
- func (a *AwsConn) DelFromQueue(url string, handle string) error
- func (a *AwsConn) Download(bucket string, key string, path string) error
- func (a *AwsConn) GetInstanceDetails() ([]InstanceDetails, error)
- func (a *AwsConn) GetLogger() *log.Logger
- func (a *AwsConn) GetQueueDetails(url string) (string, string, error)
- func (a *AwsConn) Init() error
- func (a *AwsConn) ListObjectPrefixes(bucket string) ([]string, error)
- func (a *AwsConn) ListObjects(bucket string, prefix string) ([]string, error)
- func (a *AwsConn) ListObjectsWithMeta(bucket string, prefix string) ([]ObjMeta, error)
- func (a *AwsConn) Log(v ...interface{})
- func (a *AwsConn) MinimalInit() error
- func (a *AwsConn) MkPipeline() error
- func (a *AwsConn) OCRPageQueueId() string
- func (a *AwsConn) PreQueueId() string
- func (a *AwsConn) QueueHeartbeat(msg Qmsg, qurl string, duration int64) (Qmsg, error)
- func (a *AwsConn) StartInstances(n int) error
- func (a *AwsConn) Upload(bucket string, key string, path string) error
- func (a *AwsConn) WIPStorageId() string
- func (a *AwsConn) WipeQueueId() string
- type Conf
- type Fpdf
- type GraphConf
- type InstanceDetails
- type ObjMeta
- type Qmsg
Constants ¶
const PreprocPattern = `_bin[0-9].[0-9].png`
Variables ¶
This section is empty.
Functions ¶
Types ¶
type AwsConn ¶
type AwsConn struct {
// these need to be set before running Init()
Region string
Logger *log.Logger
// contains filtered or unexported fields
}
func (*AwsConn) CreateBucket ¶
CreateBucket creates a new S3 bucket
func (*AwsConn) CreateQueue ¶
CreateQueue creates a new SQS queue Note the queue attributes are currently hardcoded; it may make sense to specify them as arguments in the future.
func (*AwsConn) GetInstanceDetails ¶
func (a *AwsConn) GetInstanceDetails() ([]InstanceDetails, error)
func (*AwsConn) GetQueueDetails ¶
GetQueueDetails gets the number of in progress and available messages for a queue. These are returned as strings.
func (*AwsConn) Init ¶
Init initialises aws services, also finding the urls needed to address SQS queues directly.
func (*AwsConn) ListObjectPrefixes ¶
func (*AwsConn) ListObjects ¶
func (*AwsConn) ListObjectsWithMeta ¶
func (*AwsConn) Log ¶ added in v0.2.0
func (a *AwsConn) Log(v ...interface{})
Log records an item in the with the Logger. Arguments are handled as with fmt.Println.
func (*AwsConn) MinimalInit ¶
MinimalInit does the bare minimum to initialise aws services
func (*AwsConn) MkPipeline ¶ added in v0.2.0
mkpipeline sets up necessary buckets and queues for the pipeline TODO: also set up the necessary security group and iam stuff
func (*AwsConn) QueueHeartbeat ¶
QueueHeartbeat updates the visibility timeout of a message. This ensures that the message remains "in flight", meaning that it cannot be seen by other processes, but if this process fails the timeout will expire and it will go back to being available for any other process to retrieve and process.
SQS only allows messages to be "in flight" for up to 12 hours, so this will detect if the request for an update to visibility timeout fails, and if so will attempt to find the message on the queue, and return it, as the handle will have changed.
type Fpdf ¶
type Fpdf struct {
// contains filtered or unexported fields
}
func (*Fpdf) AddPage ¶
AddPage adds a page to the pdf with an image and (invisible) text from an hocr file
Source Files
Directories