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Mood analyzer
@Sherrrylst
Hello!
I’m Sherry List
Azure Developer Engagement Lead, Microsoft
You can find me at @SherrryLst
http://bit.ly/mood-analyzer
Machine
Learning 101
@Sherrrylst
Data
Machine Learning 101
@Sherrrylst
Data
Patterns
Machine Learning 101
@Sherrrylst
Data
Patterns
Algorithm
(Deep learning, clustering, …)
Machine Learning 101
@Sherrrylst
Data
Patterns
Algorithm
(Deep learning, clustering, …)
Find patterns
Machine Learning 101
@Sherrrylst
Data
Patterns
Model
Algorithm
(Deep learning, clustering, …)
Find patterns
Machine Learning 101
@Sherrrylst
Data
Patterns
Model
Algorithm
(Deep learning, clustering, …)
Find patterns Recognizes patterns
Machine Learning 101
@Sherrrylst
Data
Patterns
Model
Algorithm
(Deep learning, clustering, …)
Find patterns Recognizes patterns
Training
Machine Learning 101
@Sherrrylst
Data
Patterns
Model
Algorithm
(Deep learning, clustering, …)
Find patterns Recognizes patterns Application
Training
Machine Learning 101
Challenges
@Sherrrylst
Data
Patterns
Model
Algorithm
(Deep learning, clustering, …)
Find patterns Recognizes patterns Application
Challenges
Data preparation
@Sherrrylst
Data
Patterns
Model
Algorithm
(Deep learning, clustering, …)
Find patterns Recognizes patterns Application
Challenges
Data preparation Create and test algorithms
@Sherrrylst
Algorithm
(Deep learning, clustering, …)
Find patterns
Challenges
Create and test algorithms
Chihuahua or muffin?
@Sherrrylst
Data
Patterns
Model
Algorithm
(Deep learning, clustering, …)
Find patterns Recognizes patterns Application
Challenges
Data preparation Create and test algorithms Expose the model to app
Is there an
easier way?
Azure
Cognitive
Services
@Sherrrylst
Application
The easier way
Azure Cognitive
Services
REST API
@Sherrrylst
Application
The easier way
Azure Cognitive
Services
REST API
Cognitive Services
Decision
Speech
Language
Vision
Web search
@Sherrrylst
Cognitive Services
Decision
Speech
Language
Vision
Web search
@Sherrrylst
Cognitive Services - Vision
• Computer vision
• Video Indexer
• Face
• Form Recognizer
• Custom Vision
@Sherrrylst
Cognitive Services - Vision
• Computer vision
• Video Indexer
• Face
• Form Recognizer
• Custom Vision
@Sherrrylst
@Sherrrylst
Face Detection
Face API
Let’s do it!
@Sherrrylst
Create a Cognitive Services account in the
Azure portal
@Sherrrylst
@Sherrrylst
Cognitive Services account
💻</code>
@Sherrrylst
@Sherrrylst
Capture the picture
<div class="p-1">
<video #video autoplay></video>
</div>
<div class="pb-2">
<button (click)="capture()">🤳</button>
</div>
<div>
<canvas #canvas id="flatten"></canvas>
</div>
Source code
@Sherrrylst
Start the camera
export class CameraComponent implements OnInit {
@ViewChild("video", { static: true }) videoElement: ElementRef;
@ViewChild("canvas", { static: true }) canvas: ElementRef;
constraints = {
video: {
facingMode: "user",
width: { ideal: 400 },
height: { ideal: 400 }
}
};
constructor(private renderer: Renderer2) { }
ngOnInit(){
this.startCamera();
}
Source code
@Sherrrylst
Start the camera
export class CameraComponent implements OnInit {
@ViewChild("video", { static: true }) videoElement: ElementRef;
@ViewChild("canvas", { static: true }) canvas: ElementRef;
constraints = {
video: {
facingMode: "user",
width: { ideal: 400 },
height: { ideal: 400 }
}
};
constructor(private renderer: Renderer2) { }
ngOnInit(){
this.startCamera();
}
Source code
@Sherrrylst
Start the camera
export class CameraComponent implements OnInit {
@ViewChild("video", { static: true }) videoElement: ElementRef;
@ViewChild("canvas", { static: true }) canvas: ElementRef;
constraints = {
video: {
facingMode: "user",
width: { ideal: 400 },
height: { ideal: 400 }
}
};
constructor(private renderer: Renderer2) { }
ngOnInit(){
this.startCamera();
}
Source code
@Sherrrylst
Start the camera
startCamera() {
if (!!(navigator.mediaDevices && navigator.mediaDevices.getUserMedia)) {
navigator.mediaDevices
.getUserMedia(this.constraints)
.then(this.attachVideo.bind(this))
.catch(this.handleError);
} else {
alert("Sorry, camera not available.");
}
}
Source code
@Sherrrylst
Start the camera
startCamera() {
if (!!(navigator.mediaDevices && navigator.mediaDevices.getUserMedia)) {
navigator.mediaDevices
.getUserMedia(this.constraints)
.then(this.attachVideo.bind(this))
.catch(this.handleError);
} else {
alert("Sorry, camera not available.");
}
}
Source code
@Sherrrylst
Start the camera
attachVideo(stream) {
this.renderer.setProperty(
this.videoElement.nativeElement,
"srcObject",
stream
);
this.renderer.listen(this.videoElement.nativeElement, "play", event => {
this.videoHeight = this.videoElement.nativeElement.videoHeight;
this.videoWidth = this.videoElement.nativeElement.videoWidth;
});
}
Source code
@Sherrrylst
Start the camera
Source code
@Sherrrylst
Start the camera
attachVideo(stream) {
this.renderer.setProperty(
this.videoElement.nativeElement,
"srcObject",
stream
);
this.renderer.listen(this.videoElement.nativeElement, "play", event => {
this.videoHeight = this.videoElement.nativeElement.videoHeight;
this.videoWidth = this.videoElement.nativeElement.videoWidth;
});
}
Source code
🤳 Snap
@Sherrrylst
@Sherrrylst
Capture the picture
capture() {
this.renderer.setProperty(
this.canvas.nativeElement,
"width",
this.videoWidth
);
this.renderer.setProperty(
this.canvas.nativeElement,
"height",
this.videoHeight
);
this.canvas.nativeElement
.getContext("2d")
.drawImage(this.videoElement.nativeElement, 0, 0);
...
}
Source code
@Sherrrylst
Capture the picture
capture() {
this.renderer.setProperty(
this.canvas.nativeElement,
"width",
this.videoWidth
);
this.renderer.setProperty(
this.canvas.nativeElement,
"height",
this.videoHeight
);
this.canvas.nativeElement
.getContext("2d")
.drawImage(this.videoElement.nativeElement, 0, 0);
...
}
Source code
@Sherrrylst
Capture the picture
capture() {
...
this.selfie = this.canvas.nativeElement.toDataURL("img/png");
let file = this.cameraService.dataURItoBlob(this.selfie);
// Mojify this
this.detectEmotions(file);
}
Source code
@Sherrrylst
Capture the picture
capture() {
...
this.selfie = this.canvas.nativeElement.toDataURL("img/png");
let file = this.cameraService.dataURItoBlob(this.selfie);
// Mojify this
this.detectEmotions(file);
}
Source code
@Sherrrylst
Capture the picture
capture() {
...
this.selfie = this.canvas.nativeElement.toDataURL("img/png");
let file = this.cameraService.dataURItoBlob(this.selfie);
// Mojify this
this.detectEmotions(file);
}
Source code
@Sherrrylst
Capture the picture
async detectEmotions(file: Blob) {
const headerDict = {
"Content-Type": "application/octet-stream",
"Ocp-Apim-Subscription-Key": this.apiKey
};
const requestOptions = {
headers: new HttpHeaders(headerDict)
};
this.http
.post(this.faceAPI, file, requestOptions)
.subscribe((res: FaceData[]) => {
this.emotions = this.cameraService.getEmotionData(res);
// let's drwa the emoji on canvas
this.draw(this.emotions);
});
}
Source code
@Sherrrylst
Capture the picture
async detectEmotions(file: Blob) {
const headerDict = {
"Content-Type": "application/octet-stream",
"Ocp-Apim-Subscription-Key": this.apiKey
};
const requestOptions = {
headers: new HttpHeaders(headerDict)
};
this.http
.post(this.faceAPI, file, requestOptions)
.subscribe((res: FaceData[]) => {
this.emotions = this.cameraService.getEmotionData(res);
// let's drwa the emoji on canvas
this.draw(this.emotions);
});
}
Source code
@Sherrrylst
Capture the picture
async detectEmotions(file: Blob) {
const headerDict = {
"Content-Type": "application/octet-stream",
"Ocp-Apim-Subscription-Key": this.apiKey
};
const requestOptions = {
headers: new HttpHeaders(headerDict)
};
this.http
.post(this.faceAPI, file, requestOptions)
.subscribe((res: FaceData[]) => {
this.emotions = this.cameraService.getEmotionData(res);
// let's drwa the emoji on canvas
this.draw(this.emotions);
});
}
Source code
📚 Service
@Sherrrylst
@Sherrrylst
Capture the picture
async detectEmotions(file: Blob) {
const headerDict = {
"Content-Type": "application/octet-stream",
"Ocp-Apim-Subscription-Key": this.apiKey
};
const requestOptions = {
headers: new HttpHeaders(headerDict)
};
this.http
.post(this.faceAPI, file, requestOptions)
.subscribe((res: FaceData[]) => {
this.emotions = this.cameraService.getEmotionData(res);
// let's drwa the emoji on canvas
this.draw(this.emotions);
});
}
Source code
@Sherrrylst
Capture the picture
async detectEmotions(file: Blob) {
const headerDict = {
"Content-Type": "application/octet-stream",
"Ocp-Apim-Subscription-Key": this.apiKey
};
const requestOptions = {
headers: new HttpHeaders(headerDict)
};
this.http
.post(this.faceAPI, file, requestOptions)
.subscribe((res: FaceData[]) => {
this.emotions = this.cameraService.getEmotionData(res);
// let's drwa the emoji on canvas
this.draw(this.emotions);
});
}
Source code
@Sherrrylst
🤳 Draw
@Sherrrylst
Capture the picture
draw(faces: Face[]) {
const cameraCanvas = this.canvas.nativeElement.getContext("2d");
for (let face of faces) {
let multiplier = face.faceRectangle.height / face.faceRectangle.width;
let fontSize = face.faceRectangle.width * multiplier;
let stringFont = fontSize + "px Arial";
cameraCanvas.font = stringFont;
cameraCanvas.fillText(
face.mojiIcon,
face.faceRectangle.left * multiplier - 20,
face.faceRectangle.top * multiplier + face.faceRectangle.height / 2 + 20
);
}
}
Source code
@Sherrrylst
Capture the picture
draw(faces: Face[]) {
const cameraCanvas = this.canvas.nativeElement.getContext("2d");
for (let face of faces) {
let multiplier = face.faceRectangle.height / face.faceRectangle.width;
let fontSize = face.faceRectangle.width * multiplier;
let stringFont = fontSize + "px Arial";
cameraCanvas.font = stringFont;
cameraCanvas.fillText(
face.mojiIcon,
face.faceRectangle.left * multiplier - 20,
face.faceRectangle.top * multiplier + face.faceRectangle.height / 2 + 20
);
}
}
Source code
@Sherrrylst
Capture the picture
draw(faces: Face[]) {
const cameraCanvas = this.canvas.nativeElement.getContext("2d");
for (let face of faces) {
let multiplier = face.faceRectangle.height / face.faceRectangle.width;
let fontSize = face.faceRectangle.width * multiplier;
let stringFont = fontSize + "px Arial";
cameraCanvas.font = stringFont;
cameraCanvas.fillText(
face.mojiIcon,
face.faceRectangle.left * multiplier - 20,
face.faceRectangle.top * multiplier + face.faceRectangle.height / 2 + 20
);
}
}
Source code
@Sherrrylst
Capture the picture
draw(faces: Face[]) {
const cameraCanvas = this.canvas.nativeElement.getContext("2d");
for (let face of faces) {
let multiplier = face.faceRectangle.height / face.faceRectangle.width;
let fontSize = face.faceRectangle.width * multiplier;
let stringFont = fontSize + "px Arial";
cameraCanvas.font = stringFont;
cameraCanvas.fillText(
face.mojiIcon,
face.faceRectangle.left * multiplier - 20,
face.faceRectangle.top * multiplier + face.faceRectangle.height / 2 + 20
);
}
}
Source code
@Sherrrylst
Capture the picture
draw(faces: Face[]) {
const cameraCanvas = this.canvas.nativeElement.getContext("2d");
for (let face of faces) {
let multiplier = face.faceRectangle.height / face.faceRectangle.width;
let fontSize = face.faceRectangle.width * multiplier;
let stringFont = fontSize + "px Arial";
cameraCanvas.font = stringFont;
cameraCanvas.fillText(
face.mojiIcon,
face.faceRectangle.left * multiplier - 20,
face.faceRectangle.top * multiplier + face.faceRectangle.height / 2 + 20
);
}
}
Source code
Tell me, how are you?
http://bit.ly/mood-analyzer
# ngPoland
@Sherrrylst
AI.lab@Sherrrylst
The Learner
aka.ms/azure.heroes
#azureHeroes
Join the
community!
azureheroes.community
@Sherrrylst
Thank you!
https://aka.ms/mood-analyzer
Resources
• Microsoft Azure Cognitive Services: The Big Picture
• The Mojifier (MS Learn)
• The Mojifier (Github)
• Where's Chewie? Object detection with Azure Custom Vision by Goran Vuksic
• What does the Computer Vision see? Analyse a local image with JavaScript by Goran Vuksic
• Azure Cognitive Services API — I need your clothes, boots and your motorcycle by Chris Noring
• Add conversational intelligence to your apps by using LUIS (MS Learn)
• Discover sentiment in text with the Text Analytics API (MS Learn)
• Create Intelligent Bots with the Azure Bot Service (MS Learn)
• Capturing Camera Images with Angular by Chad Upton
Artificial Intelligence
AI is the simulation of human intelligence
processes by machines. These processes
include, reasoning, remembering, learning
and self-correction.
@Sherrrylst
Machine Learning
Machine learning is a field of computer
science that gives computers the ability to
learn without being explicitly programmed.
Arthur Samuel, 1959
@Sherrrylst
Machine learning
techniques
• Artificial Neural Networks
• Deep Learning
• Bayesian Networks
• Clustering
@Sherrrylst
Artificial Intelligence (AI)
The bigger picture
Machine
Learning
Artificial Neural Networks
Deep Learning
Bayesian Networks
Clustering
@Sherrrylst
AI Services
Cognitive Services Bot Service Azure ML, Databricks, HDInsight
Pre-build AI Conversational AI Custom AI
Azure Infrastructure
CPU, FPGA, GPU
Cosmos
DB
SQL DB Data Lake IOT EdgeDSVMSparkBatch AISQL DW
Deep learning Frameworks
Cognitive Toolkits Tensorflow Azure ML, Databricks, HDInsight
Pre-build AI Conversational AI Custom AI
Deep learning Frameworks
CPU, FPGA, GPU
Cosmos
DB
SQL DB Data Lake IOT EdgeDSVMSparkBatch AISQL DW
• Speech to Text
• Text to Speech
• Speaker recognition (Preview)
• Speech Translation
@Sherrrylst
Cognitive Services - Speech
• Speech to Text
• Text to Speech
• Speaker recognition (Preview)
• Speech Translation
@Sherrrylst
Cognitive Services - Speech
• Language Understanding
• Bing Spell Check
• Translator Text
• Content Moderator
• Text Analytics
@Sherrrylst
Cognitive Services - Language
• Language Understanding
• Bing Spell Check
• Translator Text
• Content Moderator
• Text Analytics
@Sherrrylst
Cognitive Services - Language
Cognitive Services - Search
@Sherrrylst
• Bing Custom Search
• Bing Web Search
• Bing Video Search
• Bing Image Search
• Bing Local Business Search (Preview)
• Bing Visual Search
• Bing Entity Search
• Bing News Search
• Bing Auto Suggest

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Mood analyzer-ng poland

Notas del editor

  1. Demo http://bit.ly/mood-analyzer #devconmu
  2. You use Machine learning to analyze the data
  3. Normally we have some data
  4. Which contains a pattern. Like Dog’s pictures
  5. You analyze this data with Machine learning algorithm
  6. To find patterns
  7. The result is called model. So, machine knows how a dog look like
  8. Model is the thing to recognizes the patterns
  9. Now application can enter data to see if it can recognize a pattern.
  10. Now application can enter data to see if it can recognize a pattern.
  11. You use Machine learning to analyze the data
  12. Preparing a set of data with diversity and covers the edge cases
  13. Creating the algorithms and choosing the techniques can be challenging. Also testing the outcome and making sure we get the right result is also super challenging.
  14. This is not the most difficult way, but still it’s challenging to find a secure way with having the performance in mind
  15. Cognitive Services are RESTful APIs that exposes ML models to the outside world.
  16. Cognitive Services are RESTful APIs that exposes ML models to the outside world.
  17. Computer vision -> Analyze the data and return information like 4 storm troopers standing and sky is blue Video Indexer -> Analyze the video and extract the text and recognizes things that are in the video Face -> Detect faces and extract information about the face Form -> Extract text, key-value pairs, and tables from documents. Custom vision -> Customize image recognition to fit your business needs. Custom Vision -> Upload different pictures of Princess Lea for training, so it can recognizes princess Lea
  18. Detect one or more human faces along with attributes such as: age, emotion, pose, smile and facial hair, including 27 landmarks for each face in the image.
  19. You can even create a guest account to try it without providing Credit card info and no data will be saved after trial is over (7 days)
  20. https://github.com/sazimi/ng-mood-analyzer
  21. https://github.com/sazimi/ng-mood-analyzer
  22. https://github.com/sazimi/ng-mood-analyzer
  23. https://github.com/sazimi/ng-mood-analyzer
  24. https://github.com/sazimi/ng-mood-analyzer
  25. https://github.com/sazimi/ng-mood-analyzer
  26. https://github.com/sazimi/ng-mood-analyzer
  27. add a listener to store the video’s height and width when the video starts.  And update the native element
  28. Set the with and height of canvas element
  29. Draw the video element content to canvas element
  30. Create a png file from the canvas content
  31. Convert it to blob since Face API only accept octet stream
  32. https://beam.enjin.io/claim/a912505fb950f3739999a8eb62686c4e
  33. https://aka.ms/mood-analyzer
  34. https://docs.microsoft.com/en-us/learn/modules/replace-faces-with-emojis-matching-emotion https://dev.to/stratiteq/where-s-chewie-object-detection-with-azure-custom-vision-lne https://dev.to/azure/getting-started-with-azure-cognitive-services-cma https://docs.microsoft.com/en-us/learn/modules/classify-user-feedback-with-the-text-analytics-api/?wt.mc_id=cognitiveservices-nativescriptdeveloperdays-shlist
  35. Devices that can remember, learn, understand and recognize things
  36. ML is all about the ability to learn. Applications that can learn without hardcoding different scenarios.
  37. ML is used in many applications to detect the patterns, Is this a cat or a dog. In order to detect these patterns, you need to use different techniques. ANN: Mimics the way that human brain works DL: Learn from many layers of analysis where each layer has the input from the previous layer
  38. AI is the overall concept to make computers intelligent
  39. Speaker recognition -> Identify people based on their speech Speech Translation -> Listen and translate to text
  40. Speaker recognition -> Identify people based on their speech Speech Translation -> Listen and translate to text
  41. Language Understanding -> You feeding it with the command and train it to what it means and after that it understands Text Analytics: Analyze a text in order to get the sentiment of a text (Positive/Negative), detect the language, extract the key phrase from a piece of text and retrieve the topics
  42. Language Understanding -> You feeding it with the command and train it to what it means and after that it understands Text Analytics: Analyze a text in order to get the sentiment of a text (Positive/Negative), detect the language, extract the key phrase from a piece of text and retrieve the topics
  43. Bing Custom Search -> Corporate search engine Bing Entity Search -> Detects pictures or persons to enrich the result