AWS Rekognition Javascript API声明对于 rekognition.compareFaces(params,...)方法，SourceImageandTargetImage可以采用Bytesor S3Object。我想用Byteswhich 可以
“字节——（缓冲区、类型化数组、Blob、字符串）”
最多 5 MB 的图像字节 Blob。
当我传递Base64图像的编码字符串时，JS SDK 再次重新编码（即双重编码）。因此服务器响应错误说
{"__type":"InvalidImageFormatException","Message":"无效的图像编码"}
有没有人设法使用 base64 编码图像（不是）使用compareFaces JS SDK APIS3Object？Bytes或任何使用param的 JavaScript 示例都会有所帮助。
此AWS Rekognition JS SDK Invalid image encoding 错误线程中的技术有效。
将 base64 图像编码转换为ArrayBuffer：
function getBinary(base64Image) {
var binaryImg = atob(base64Image);
var length = binaryImg.length;
var ab = new ArrayBuffer(length);
var ua = new Uint8Array(ab);
for (var i = 0; i < length; i++) {
ua[i] = binaryImg.charCodeAt(i);
}
return ab;
}
传入rekognition作为Bytes参数：
var data = canvas.toDataURL('image/jpeg');
var base64Image = data.replace(/^data:image\/(png|jpeg|jpg);base64,/, '');
var imageBytes = getBinary(base64Image);
var rekognitionRequest = {
CollectionId: collectionId,
Image: {
Bytes: imageBytes
}
};
根据@Sean 提供的答案，我想添加另一种方法来使用axios从 URL 请求中获取字节并传递给- 或Amazon Rekognitionrekognition.detectLabels()的其他各种检测方法。
我继续创建了一个承诺，fs.readFile它应该与 async/await 结构一起使用。然后一些正则表达式来确定您是否需要获取 URL 或读取文件作为后备。
我还为标签添加了检查Gray和检查。World Of Warcraft不确定是否有其他人经历过这种情况，但lorempixel似乎每隔一段时间就会抛出这些标签。我之前也曾在全黑图像上看到过它们。
/* jshint esversion: 6, node:true, devel: true, undef: true, unused: true */
const AWS = require('aws-sdk'),
axios = require('axios'),
fs = require('fs'),
path = require('path');
// Get credentials from environmental variables.
const {S3_ACCESS_KEY, S3_SECRET_ACCESS_KEY, S3_REGION} = process.env;
// Set AWS credentials.
AWS.config.update({
accessKeyId: S3_ACCESS_KEY,
secretAccessKey: S3_SECRET_ACCESS_KEY,
region: S3_REGION
});
const rekognition = new AWS.Rekognition({
apiVersion: '2016-06-27'
});
startDetection();
// ----------------
async function startDetection() {
let found = {};
found = await detectFromPath(path.join(__dirname, 'test.jpg'));
console.log(found);
found = await detectFromPath('https://upload.wikimedia.org/wikipedia/commons/9/96/Bill_Nye%2C_Barack_Obama_and_Neil_deGrasse_Tyson_selfie_2014.jpg');
console.log(found);
found = await detectFromPath('http://placekitten.com/g/200/300');
console.log(found);
found = await detectFromPath('https://loremflickr.com/g/320/240/text');
console.log(found);
found = await detectFromPath('http://lorempixel.com/400/200/sports/');
console.log(found);
// Sometimes 'Grey' and 'World Of Warcraft' are the only labels...
if (found && found.labels.length === 2 && found.labels.some(i => i.Name === 'Gray') && found.labels.some(i => i.Name === 'World Of Warcraft')) {
console.log('⚠️', '\n\tMaybe this is a bad image...`Gray` and `World Of Warcraft`???\n');
}
}
// ----------------
/**
* @param {string} path URL or filepath on your local machine.
* @param {Number} maxLabels
* @param {Number} minConfidence
* @param {array} attributes
*/
async function detectFromPath(path, maxLabels, minConfidence, attributes) {
// Convert path to base64 Buffer data.
const bytes = (/^https?:\/\//gm.exec(path)) ?
await getBase64BufferFromURL(path) :
await getBase64BufferFromFile(path);
// Invalid data.
if (!bytes)
return {
path,
faces: [],
labels: [],
text: [],
celebs: [],
moderation: []
};
// Pass buffer to rekognition methods.
let labels = await detectLabelsFromBytes(bytes, maxLabels, minConfidence),
text = await detectTextFromBytes(bytes),
faces = await detectFacesFromBytes(bytes, attributes),
celebs = await recognizeCelebritiesFromBytes(bytes),
moderation = await detectModerationLabelsFromBytes(bytes, minConfidence);
// Filter out specific values.
labels = labels && labels.Labels ? labels.Labels : [];
faces = faces && faces.FaceDetails ? faces.FaceDetails : [];
text = text && text.TextDetections ? text.TextDetections.map(i => i.DetectedText) : [];
celebs = celebs && celebs.CelebrityFaces ? celebs.CelebrityFaces.map(i => ({
Name: i.Name,
MatchConfidence: i.MatchConfidence
})) : [];
moderation = moderation && moderation.ModerationLabels ? moderation.ModerationLabels.map(i => ({
Name: i.Name,
Confidence: i.Confidence
})) : [];
// Return collection.
return {
path,
faces,
labels,
text,
celebs,
moderation
};
}
/**
* https://nodejs.org/api/fs.html#fs_fs_readfile_path_options_callback
*
* @param {string} filename
*/
function getBase64BufferFromFile(filename) {
return (new Promise(function(resolve, reject) {
fs.readFile(filename, 'base64', (err, data) => {
if (err) return reject(err);
resolve(new Buffer(data, 'base64'));
});
})).catch(error => {
console.log('[ERROR]', error);
});
}
/**
* https://github.com/axios/axios
*
* @param {string} url
*/
function getBase64BufferFromURL(url) {
return axios
.get(url, {
responseType: 'arraybuffer'
})
.then(response => new Buffer(response.data, 'base64'))
.catch(error => {
console.log('[ERROR]', error);
});
}
/**
* https://docs.aws.amazon.com/rekognition/latest/dg/labels.html
* https://docs.aws.amazon.com/AWSJavaScriptSDK/latest/AWS/Rekognition.html#detectLabels-property
*
* @param {Buffer} bytes
* @param {Number} maxLabels
* @param {Number} minConfidence
*/
function detectLabelsFromBytes(bytes, maxLabels, minConfidence) {
return rekognition
.detectLabels({
Image: {
Bytes: bytes
},
MaxLabels: typeof maxLabels !== 'undefined' ? maxLabels : 1000,
MinConfidence: typeof minConfidence !== 'undefined' ? minConfidence : 50.0
})
.promise()
.catch(error => {
console.error('[ERROR]', error);
});
}
/**
* https://docs.aws.amazon.com/rekognition/latest/dg/text-detection.html
* https://docs.aws.amazon.com/AWSJavaScriptSDK/latest/AWS/Rekognition.html#detectText-property
*
* @param {Buffer} bytes
*/
function detectTextFromBytes(bytes) {
return rekognition
.detectText({
Image: {
Bytes: bytes
}
})
.promise()
.catch(error => {
console.error('[ERROR]', error);
});
}
/**
* https://docs.aws.amazon.com/rekognition/latest/dg/celebrities.html
* https://docs.aws.amazon.com/AWSJavaScriptSDK/latest/AWS/Rekognition.html#recognizeCelebrities-property
*
* @param {Buffer} bytes
*/
function recognizeCelebritiesFromBytes(bytes) {
return rekognition
.recognizeCelebrities({
Image: {
Bytes: bytes
}
})
.promise()
.catch(error => {
console.error('[ERROR]', error);
});
}
/**
* https://docs.aws.amazon.com/rekognition/latest/dg/moderation.html
* https://docs.aws.amazon.com/AWSJavaScriptSDK/latest/AWS/Rekognition.html#detectModerationLabels-property
*
* @param {Buffer} bytes
* @param {Number} minConfidence
*/
function detectModerationLabelsFromBytes(bytes, minConfidence) {
return rekognition
.detectModerationLabels({
Image: {
Bytes: bytes
},
MinConfidence: typeof minConfidence !== 'undefined' ? minConfidence : 60.0
})
.promise()
.catch(error => {
console.error('[ERROR]', error);
});
}
/**
* https://docs.aws.amazon.com/rekognition/latest/dg/faces.html
* https://docs.aws.amazon.com/AWSJavaScriptSDK/latest/AWS/Rekognition.html#detectFaces-property
*
* @param {Buffer} bytes
* @param {array} attributes Attributes can be "ALL" or "DEFAULT". "DEFAULT" includes: BoundingBox, Confidence, Landmarks, Pose, and Quality.
*/
function detectFacesFromBytes(bytes, attributes) {
return rekognition
.detectFaces({
Image: {
Bytes: bytes
},
Attributes: typeof attributes !== 'undefined' ? attributes : ['ALL']
})
.promise()
.catch(error => {
console.error('[ERROR]', error);
});
}
/**
* https://docs.aws.amazon.com/rekognition/latest/dg/API_CompareFaces.html
* https://docs.aws.amazon.com/AWSJavaScriptSDK/latest/AWS/Rekognition.html#compareFaces-property
*
* @param {Buffer} sourceBytes
* @param {Buffer} targetBytes
* @param {Number} similarityThreshold
*/
function compareFaces(sourceBytes, targetBytes, similarityThreshold) {
return rekognition
.detectModerationLabels({
SourceImage: {
Bytes: sourceBytes
},
TargetImage: {
Bytes: targetBytes
},
SimilarityThreshold: typeof similarityThreshold !== 'undefined' ? similarityThreshold : 0.0
})
.promise()
.catch(error => {
console.error('[ERROR]', error);
});
}
资源：
AWS JavaScript 开发工具包参考：
参考：
在将 Node 中的文件作为字节数组缓冲区读取并将其发送到 Rekognition 时，我遇到了类似的问题。
我通过读取 base64 表示来解决它，然后将其转换为如下所示的缓冲区：
const aws = require('aws-sdk');
const fs = require('fs');
var rekognition = new aws.Rekognition({
apiVersion: '2016-06-27'
});
// pull base64 representation of image from file system (or somewhere else)
fs.readFile('./test.jpg', 'base64', (err, data) => {
// create a new buffer out of the string passed to us by fs.readFile()
const buffer = Buffer.from(data, 'base64');
// now that we have things in the right type, send it to rekognition
rekognition.detectLabels({
Image: {
Bytes: buffer
}
}).promise()
.then((res) => {
// print out the labels that rekognition sent back
console.log(res);
});
});
这也可能与人们收到：Expected params.Image.Bytes to be a string, Buffer, Stream, Blob, or typed array object消息有关。
似乎将字符串转换为缓冲区的工作方式更加一致，但很难找到有关它的文档。
对于 Node，您可以使用它来转换字符串中的参数（确保将数据删除......直到“，”。
var params = {
CollectionId: collectionId,
Image: {
Bytes: new Buffer(imageBytes, 'base64')
}
};
在普通 JS 中，您希望可以使用 atob 进行转换并使用以下代码传递数组缓冲区：
function getBinary(base64Image) {
var binaryImg = Buffer.from(base64Image, 'base64').toString();
var length = binaryImg.length;
var ab = new ArrayBuffer(length);
var ua = new Uint8Array(ab);
for (var i = 0; i < length; i++) {
ua[i] = binaryImg.charCodeAt(i);
}
return ab;
}
我遇到了同样的问题，我会告诉你我是如何解决的。
Amazon Rekognition 支持的图像类型为JPEG 和 PNG
这意味着如果您输入使用其他格式（如 webp）编码的图像文件，您总是会遇到同样的错误。
将不使用 jpeg 或 png 编码的图像格式更改为 jpeg 后，我可以解决该问题。
希望你能解决这个问题！