Add multiple images to video fluent-ffmpeg - node.js

I manage to add a single image to a video starting at a certain time and ending at a certain time, but I cannot find a way to do this for multiple images. I use fluent-ffmpeg.
This is the code I have:
ffmpeg('../videos/testklein.mp4')
.input('../src/test.png')
.input('../src/0.png')
.input('../src/1.png')
.addOptions([
"-strict -2"
])
.complexFilter([
{
filter: 'overlay',
options: {
x: 200,
y: 200,
enable: 'between(t,1,3)',
}}, {
filter: 'overlay',
options: {
x: 200,
y: 200,
enable: 'between(t,3,5)',
}},
{
filter: 'overlay',
options: {
x: 200,
y: 200,
enable: 'between(t,5,7)',
}
},
])
I suppose I need to explicitly state what filter should take what file, but I am not sure about the syntax for that.

This is the right syntax:
The first one add inputs [0:v][1:v] and outputs ['tmp']
Then add ['tmp'] as inputs for the next one. For 2 images:
I can add as many images as I want.
Pass the 'tmp' as 2nd argument to complexFilter!
(You can ofcourse change tmp to any string)
.complexFilter(
[
{
"filter": "overlay",
"options": {
"enable": "between(t,2,4)",
"x": "810",
"y": "465"
},
"inputs": "[0:v][1:v]",
"outputs": "tmp"
},
{
"filter": "overlay",
"options": {
"enable": "between(t,13,14)",
"x": "810",
"y": "465"
},
"inputs": "[tmp][2:v]",
"outputs": "tmp"
}
], 'tmp')

i was searching for adding multiple images in a simple complexFilter then i found this code working but after rendering the video the sound was being removed. After making a lot of changes I found a solution for audio.
add an outputOption in your code
.outputOptions(["-map 0:a"])
Sharing this if anyone is having the audio problem.

Related

LLRP for Zebra FX7500 with llrpjs doesn't read tags

Using the llrpjs library for Node.js, we are attempting to read tags from the Zebra FX7500 (Motorola?). This discussion points to the RFID Reader Software Interface Control Guide pages 142-144, but does not indicate potential values to set up the device.
From what we can gather, we should issue a SET_READER_CONFIG with a custom parameter (MotoDefaultSpec = VendorIdentifier: 161, ParameterSubtype: 102, UseDefaultSpecForAutoMode: true). Do we need to include the ROSpec and/or AccessSpec values as well (are they required)? After sending the SET_READER_CONFIG message, do we still need to send the regular LLRP messages (ADD_ROSPEC, ENABLE_ROSPEC, START_ROSPEC)? Without the MotoDefaultSpec, even after sending the regular LLRP messages, sending a GET_REPORT does not retrieve tags nor does a custom message with MOTO_GET_TAG_EVENT_REPORT. They both trigger a RO_ACCESS_REPORT event message, but the tagReportData is null.
The README file for llrpjs lists "Vendor definitions support" as a TODO item. While that is somewhat vague, is it possible that the library just hasn't implemented custom LLRP extension (messages/parameters) support, which is why none of our attempts are working? The MotoDefaultSpec parameter and MOTO_GET_TAG_EVENT_REPORT are custom to the vendor/chipset. The MOTO_GET_TAG_EVENT_REPORT custom message seems to trigger a RO_ACCESS_REPORT similar to the base LLRP GET_REPORT message, so we assume that part is working.
It is worth noting that Zebra's 123RFID Desktop setup and optimization tool connects and reads tags as expected, so the device and antenna are working (reading tags).
Could these issues be related to the ROSPEC file we are using (see below)?
{
"$schema": "https://llrpjs.github.io/schema/core/encoding/json/1.0/llrp-1x0.schema.json",
"id": 1,
"type": "ADD_ROSPEC",
"data": {
"ROSpec": {
"ROSpecID": 123,
"Priority": 1,
"CurrentState": "Disabled",
"ROBoundarySpec": {
"ROSpecStartTrigger": {
"ROSpecStartTriggerType": "Immediate"
},
"ROSpecStopTrigger": {
"ROSpecStopTriggerType": "Null",
"DurationTriggerValue": 0
}
},
"AISpec": {
"AntennaIDs": [1, 2, 3, 4],
"AISpecStopTrigger": {
"AISpecStopTriggerType": "Null",
"DurationTrigger": 0
},
"InventoryParameterSpec": {
"InventoryParameterSpecID": 1234,
"ProtocolID": "EPCGlobalClass1Gen2"
}
},
"ROReportSpec": {
"ROReportTrigger": "Upon_N_Tags_Or_End_Of_ROSpec",
"N": 1,
"TagReportContentSelector": {
"EnableROSpecID": true,
"EnableAntennaID": true,
"EnableFirstSeenTimestamp": true,
"EnableLastSeenTimestamp": true,
"EnableSpecIndex": false,
"EnableInventoryParameterSpecID": false,
"EnableChannelIndex": false,
"EnablePeakRSSI": false,
"EnableTagSeenCount": true,
"EnableAccessSpecID": false
}
}
}
}
}
For anyone having a similar issue, we found that attempting to configure more antennas than the Zebra device has connected caused the entire spec to fail. In our case, we had two antennas connected, so including antennas 3 and 4 in the spec was causing the problem.
See below for the working ROSPEC. The extra antennas in the data.AISpec.AntennaIDs property were removed and allowed our application to connect and read tags.
We are still having some issues with llrpjs when trying to STOP_ROSPEC because it sends an RO_ACCESS_REPORT response without a resName value. See the issue on GitHub for more information.
That said, our application works without sending the STOP_ROSPEC command.
{
"$schema": "https://llrpjs.github.io/schema/core/encoding/json/1.0/llrp-1x0.schema.json",
"id": 1,
"type": "ADD_ROSPEC",
"data": {
"ROSpec": {
"ROSpecID": 123,
"Priority": 1,
"CurrentState": "Disabled",
"ROBoundarySpec": {
"ROSpecStartTrigger": {
"ROSpecStartTriggerType": "Null"
},
"ROSpecStopTrigger": {
"ROSpecStopTriggerType": "Null",
"DurationTriggerValue": 0
}
},
"AISpec": {
"AntennaIDs": [1, 2],
"AISpecStopTrigger": {
"AISpecStopTriggerType": "Null",
"DurationTrigger": 0
},
"InventoryParameterSpec": {
"InventoryParameterSpecID": 1234,
"ProtocolID": "EPCGlobalClass1Gen2",
"AntennaConfiguration": {
"AntennaID": 1,
"RFReceiver": {
"ReceiverSensitivity": 0
},
"RFTransmitter": {
"HopTableID": 1,
"ChannelIndex": 1,
"TransmitPower": 170
},
"C1G2InventoryCommand": {
"TagInventoryStateAware": false,
"C1G2RFControl": {
"ModeIndex": 23,
"Tari": 0
},
"C1G2SingulationControl": {
"Session": 1,
"TagPopulation": 32,
"TagTransitTime": 0,
"C1G2TagInventoryStateAwareSingulationAction": {
"I": "State_A",
"S": "SL"
}
}
}
}
}
},
"ROReportSpec": {
"ROReportTrigger": "Upon_N_Tags_Or_End_Of_AISpec",
"N": 1,
"TagReportContentSelector": {
"EnableROSpecID": true,
"EnableAntennaID": true,
"EnableFirstSeenTimestamp": true,
"EnableLastSeenTimestamp": true,
"EnableTagSeenCount": true,
"EnableSpecIndex": false,
"EnableInventoryParameterSpecID": false,
"EnableChannelIndex": false,
"EnablePeakRSSI": false,
"EnableAccessSpecID": false
}
}
}
}
}

Google Speech API transcribe email

I'm having trouble transcribing email using Google Speech REST API. The best I can get is most of the email address, however Google Speech ignores "dot" and "dot com". For example first.last#gmail.com returns "First Last at gmail". If I say "period" instead of "dot" I at least get "First. Last at gmail." I'm using the following:
{
"config": {
"encoding": "MULAW",
"sampleRateHertz": 8000,
"languageCode": "en-US",
"maxAlternatives": 0,
"profanityFilter": true,
"enableWordTimeOffsets": false,
"model": "phone_call",
"useEnhanced": true
},
"audio": {
"content":"&&NameBase64&&"
}
}
I've tried add "dot" as a speech context with no changes. ".", ".com", "com", and "kom" also didn't change the results.
{
"config": {
"encoding": "MULAW",
"sampleRateHertz": 8000,
"languageCode": "en-US",
"maxAlternatives": 1,
"profanityFilter": true,
"enableWordTimeOffsets": false,
"model": "phone_call",
"useEnhanced": true,
"speechContexts": [{
"phrases": ["dot"],
}],
},
"audio": {
"content":"Base64Recording"
}
}
I've tried adding alphanumberic speech contexts and spelling it out but the results were pretty bad.
Any thoughts on how I can get "." or "dot" and "com" to show up in the transcription would be greatly appreciated.
Have you tried providing a boost value for the phrase? I'm facing the same issue and I noticed that increasing the boost value helped in identifying the word "dot".
Boost values are usually between 0 and 20, but applying anything above 10 helped in recognizing the "dot".
Here's an example:-
"config": {
"encoding": "MULAW",
"sampleRateHertz": 8000,
"languageCode": "en-US",
"maxAlternatives": 1,
"profanityFilter": true,
"enableWordTimeOffsets": false,
"model": "phone_call",
"useEnhanced": true,
"speechContexts": [{
"phrases": ["dot"],
"boots": 15.0
}],
},
"audio": {
"content":"Base64Recording"
}
}
You can also have multiple key value pairs in the context, each with different boost values. For example, this is what I use to detect email addresses:-
[{
phrases: ["$OOV_CLASS_ALPHANUMERIC_SEQUENCE"],
boost: 14.0
},
{
phrases: ["gmail.com","yahoo.com","aol.com","outlook.com"],
boost:5.0
},
{
phrases: ["com",".","c o m",".com","dotcom","dot com","dot","at","at the rate","#"],
boost: 10.0
},
{
phrases: ["org","io","dot org","dot io","gov","dot gov","net","dot net","co","dot co"],
boost:8.0
},
{
phrases: ["$OOV_CLASS_DIGIT_SEQUENCE","8","naught","z","zed","zee","zz","d","aa","ae","ee","oo","ii","ay","eh","ahh","ah","ze","dee",
"1","2","3","4","5","6","7","8","9","0","zero",],
boost: -20.0
}
]
Notice, the phrases with negative boost values will help weed out words that are often misunderstood.

How to include two analyzers into a single SEARCH statement?

I have a feeds collection with documents like this:
{
"created": 1510000000,
"find": [
"title of the document",
"body of the document"
],
"filter": [
"/example.com",
"-en"
]
}
created contains an epoch timestamp
find contains an array of fulltext snippets, e.g. the title and the body of a text
filter is an array with further search tokens, such as hashtags, domains, locales
Problem is that find contains fulltext snippets, which we want to tokenize, e.g. with a text analyzer, but filter contains final tokens which we want to compare as a whole, e.g. with the identity analyzer.
Goal is to combine find and filter into a single custom analyzer or to combine two analyzers using two SEARCH statements or something to that end.
I did manage to query by either find or by filter successfully, but do not manage to query by both. This is how I query by filter:
I created a feeds_search view:
{
"writebufferIdle": 64,
"type": "arangosearch",
"links": {
"feeds": {
"analyzers": [
"identity"
],
"fields": {
"find": {},
"filter": {},
"created": {}
},
"includeAllFields": false,
"storeValues": "none",
"trackListPositions": false
}
},
"consolidationIntervalMsec": 10000,
"writebufferActive": 0,
"primarySort": [],
"writebufferSizeMax": 33554432,
"consolidationPolicy": {
"type": "tier",
"segmentsBytesFloor": 2097152,
"segmentsBytesMax": 5368709120,
"segmentsMax": 10,
"segmentsMin": 1,
"minScore": 0
},
"cleanupIntervalStep": 2,
"commitIntervalMsec": 1000,
"id": "362444",
"globallyUniqueId": "hD6FBD6EE239C/362444"
}
and I created a sample query:
FOR feed IN feeds_search
SEARCH ANALYZER(feed.created < 9990000000 AND feed.created > 1500000000
AND (feed.find == "title of the document")
AND (feed.`filter` == "/example.com" OR feed.`filter` == "-uk"), "identity")
SORT feed.created
LIMIT 20
RETURN feed
The sample query works, because find contains the full text (identity analyzer). As soon as I switch to a text analyzer, single word tokens work for find, but filter no longer works.
I tried using a combination of SEARCH and FILTER, which gives me the desired result, but I assume it probably performs worse than having the SEARCH analyzer do the whole thing. I see that analyzers is an array in the view syntax, but I seem not to be able to set individual fields for each analyzer.
The analyzers can be added as a property to each field in fields. What is specified in analyzers is the default and is used in case a more specific analyzer is not set for a given field.
"analyzers": [
"identity"
],
"fields": {
"find": {
"analyzers": [
"text_en"
]
},
"filter": {},
"created": {}
},
Credits: Simran at ArangoDB

Couchdb 2 _find query not using index

I'm struggling with something that should be easy but it's making no sense to me, I have these 2 documents in a database:
{ "name": "foo", "type": "typeA" },
{ "name": "bar", "type": "typeB" }
And I'm posting this to _find:
{
"selector": {
"type": "typeA"
},
"sort": ["name"]
}
Which works as expected but I get a warning that there's no matching index, so I've tried posting various combinations of the following to _index which makes no difference:
{
"index": {
"fields": ["type"]
}
}
{
"index": {
"fields": ["name"]
}
}
{
"index": {
"fields": ["name", "type"]
}
}
If I remove the sort by name and only index the type it works fine except it's not sorted, is this a limitation with couchdbs' mango implementation or am I missing something?
Using a view and map function works fine but I'm curious what mango is/isn't doing here.
With just the type index, I think it will normally be almost as efficient unless you have many documents of each type (as it has to do the sorting stage in memory.)
But since fields are ordered, it would be necessary to do:
{
"index": {
"fields": ["type", "name"]
}
}
to have a contiguous slice of this index for each type that is already ordered by name. But the query planner may not determine that this index applies.
As an example, the current pouchdb-find (which should be similar) needs the more complicated but equivalent query:
{
selector: {type: 'typeA', name: {$gte: null} },
sort: ['type','name']
}
to choose this index and build a plan that doesn't resort to building in memory for any step.

Elasticsearch query_string combined with match_phrase

I think it's best if I describe my intent and try to break it down to code.
I want users to have the ability of complex queries should they choose to that query_string offers. For example 'AND' and 'OR' and '~', etc.
I want to have fuzziness in effect, which has made me do things I feel dirty about like "#{query}~" to the sent to ES, in other words I am specifying fuzzy query on the user's behalf because we offer transliteration which could be difficult to get the exact spelling.
At times, users search a number of words that are suppose to be in a phrase. query_string searches them individually and not as a phrase. For example 'he who will' should bring me the top match to be when those three words are in that order, then give me whatever later.
Current query:
{
"indices_boost": {},
"aggregations": {
"by_ayah_key": {
"terms": {
"field": "ayah.ayah_key",
"size": 6236,
"order": {
"average_score": "desc"
}
},
"aggregations": {
"match": {
"top_hits": {
"highlight": {
"fields": {
"text": {
"type": "fvh",
"matched_fields": [
"text.root",
"text.stem_clean",
"text.lemma_clean",
"text.stemmed",
"text"
],
"number_of_fragments": 0
}
},
"tags_schema": "styled"
},
"sort": [
{
"_score": {
"order": "desc"
}
}
],
"_source": {
"include": [
"text",
"resource.*",
"language.*"
]
},
"size": 5
}
},
"average_score": {
"avg": {
"script": "_score"
}
}
}
}
},
"from": 0,
"size": 0,
"_source": [
"text",
"resource.*",
"language.*"
],
"query": {
"bool": {
"must": [
{
"query_string": {
"query": "inna alatheena",
"fuzziness": 1,
"fields": [
"text^1.6",
"text.stemmed"
],
"minimum_should_match": "85%"
}
}
],
"should": [
{
"match": {
"text": {
"query": "inna alatheena",
"type": "phrase"
}
}
}
]
}
}
}
Note: alatheena searched without the ~ will not return anything although I have allatheena in the indices. So I must do a fuzzy search.
Any thoughts?
I see that you're doing ES indexing of Qur'anic verses, +1 ...
Much of your problem domain, if I understood it correctly, can be solved simply by storing lots of transliteration variants (and permutations of their combining) in a separate field on your Aayah documents.
First off, you should make a char filter that replaces all double letters with single letters [aa] => [a], [ll] => [l]
Maybe also make a separate field containing all of [a, e, i] (because of their "vocative"/transcribal ambiguity) replaced with € or something similar, and do the same while querying in order to get as many matches as possible...
Also, TH in "allatheena" (which as a footnote may really be Dhaal, Thaa, Zhaa, Taa+Haa, Taa+Hhaa, Ttaa+Hhaa transcribed ...) should be replaced by something, or both the Dhaal AND the Thaa should be transcribed multiple times.
Then, because it's Qur'anic script, all Alefs without diacritics, Hamza, Madda, etc should be treated as Alef (or Hamzat) ul-Wasl, and that should also be considered when indexing / searching, because of Waqf / Wasl in reading arabic. (consider all the Wasl`s in the first Aayah of Surat Al-Alaq for example)
Dunno if this is answering your question in any way, but I hope it's of some assistance in implementing your application nontheless.
You should use Dis Max Query to achieve that.
A query that generates the union of documents produced by its
subqueries, and that scores each document with the maximum score for
that document as produced by any subquery, plus a tie breaking
increment for any additional matching subqueries.
This is useful when searching for a word in multiple fields with
different boost factors (so that the fields cannot be combined
equivalently into a single search field). We want the primary score to
be the one associated with the highest boost.
Quick example how to use it:
POST /_search
{
"query": {
"dis_max": {
"tie_breaker": 0.7,
"boost": 1.2,
"queries": [
{
"match": {
"text": {
"query": "inna alatheena",
"type": "phrase",
"boost": 5
}
}
},
{
"match": {
"text": {
"query": "inna alatheena",
"type": "phrase",
"fuzziness": "AUTO",
"boost": 3
}
}
},
{
"query_string": {
"default_field": "text",
"query": "inna alatheena"
}
}
]
}
}
}
It will run all of your queries, and the one, which scored highest compared to others, will be taken. So just define your rules using it. You should achieve what you wanted.

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