Have you ever tried to cast hand shadows on a wall? It is the easiest thing in the world, and yet to do it well requires practice and just the right setup. To cultivate your #cottagecore aesthetic, try going into a completely dark room with just one lit candle, and casting hand shadows on a plain wall. The effect is startlingly dramatic. What fun!
Even a tea light suffices to create a great effect
In 2020, and now into 2021, many folks are reverting back to basics as they look around their houses, reopening dusty corners of attics and basements and remembering the simple crafts that they used to love. Papermaking, anyone? All you need is a few tools and torn up, recycled paper. Pressing flowers? All you need is newspaper, some heavy books, and patience. And hand shadows? Just a candle.
This TikTok creator has thousands of views for their handshadow tutorials
But what's a developer to do when trying to capture that #cottagecore vibe in a web app?
High Tech for the Cottage
While exploring the art of hand shadows, I wondered whether some of therecent workI had done for body poses might be applicable to hand poses. What if you could tell a story on the web using your hands, and somehow save a video of the show and the narrative behind it, and send it to someone special? In lockdown, what could be more amusing than sharing shadow stories between friends or relatives, all virtually?
Hand shadow casting is a folk art probably originating in China; if you go to tea houses with stage shows, you might be lucky enough to view one like this!
A Show Of Hands
When you start researching hand poses, it's striking how much content there is on the web on the topic. There has been work since at least 2014 on creating fully articulated hands within the research, simulation, and gaming sphere:
MSR throwing hands
There are dozens of handpose libraries already on GitHub:
There are many applications where tracking hands is a useful activity:
• Gaming • Simulations / Training • "Hands free" uses for remote interactions with things by moving the body • Assistive technologies • TikTok effects :trophy: • Useful things likeAccordion Hands apps
One of the more interesting new libraries,handsfree.js, offers an excellent array of demos in its effort to move to a hands free web experience:
Handsfree.js, a very promising project
As it turns out, hands are pretty complicated things. Theyeachinclude 21 keypoints (vs PoseNet's 17 keypoints for an entire body). Building a model to support inference for such a complicated grouping of keypoints has provenn challenging.
There are two main libraries available to the web developer when incorporating hand poses into an app: TensorFlow.js's handposes, and MediaPipe's. HandsFree.js uses both, to the extent that they expose APIs. As it turns out, neither TensorFlow.js nor MediaPipe's handposes are perfect for our project. We will have to compromise.
TensorFlow.js's handposesallow access to each hand keypoint and the ability to draw the hand to canvas as desired. HOWEVER, it only currently supports single hand poses, which is not optimal for good hand shadow shows.
MediaPipe's handpose models(which are used by TensorFlow.js) do allow for dual hands BUT its API does not allow for much styling of the keypoints so that drawing shadows using it is not obvious.
One other library,fingerposes, is optimized for finger spelling in a sign language context and is worth a look.
Since it's more important to use the Canvas API to draw custom shadows, we are obliged to use TensorFlow.js, hoping that either it will soon support multiple hands OR handsfree.js helps push the envelope to expose a more styleable hand.
Let's get to work to build this app.
Scaffold a Static Web App
As a Vue.js developer, I always use the Vue CLI to scaffold an app usingvue create my-appand creating a standard app. I set up a basic app with two routes: Home and Show. Since this is going to be deployed as an Azure Static Web App, I follow my standard practice of including my app files in a folder namedappand creating anapifolder to include an Azure function to store a key (more on this in a minute).
In my package.json file, I import the important packages for using TensorFlow.js and the Cognitive Services Speech SDK in this app. Note that TensorFlow.js has divided its imports into individual packages:
We will draw an image of a hand, as detected by TensorFlow.js, onto a canvas, superimposed onto a video suppled by a webcam. In addition, we will redraw the hand to a second canvas (shadowCanvas), styled like shadows:
Working asynchronously, load the Handpose model. Once the backend is setup and the model is loaded, load the video via the webcam, and start watching the video's keyframes for hand poses. It's important at these steps to ensure error handling in case the model fails to load or there's no webcam available.
asyncmounted(){awaittf.setBackend(this.backend);//async load model, then load video, then pass it to start landmarkingthis.model=awaithandpose.load();this.message="Model is loaded! Now loading video";letwebcam;try{webcam=awaitthis.loadVideo();}catch(e){this.message=e.message;throwe;}this.landmarksRealTime(webcam);},
Setup the Webcam
Still working asynchronously, set up the camera to provide a stream of images
asyncsetupCamera(){if(!navigator.mediaDevices||!navigator.mediaDevices.getUserMedia){thrownewError("Browser API navigator.mediaDevices.getUserMedia not available");}this.video=this.$refs.video;conststream=awaitnavigator.mediaDevices.getUserMedia({video:{facingMode:"user",width:VIDEO_WIDTH,height:VIDEO_HEIGHT,},});returnnewPromise((resolve)=>{this.video.srcObject=stream;this.video.onloadedmetadata=()=>{resolve(this.video);};});},
Design a Hand to Mirror the Webcam's
Now the fun begins, as you can get creative in drawing the hand on top of the video. This landmarking function runs on every keyframe, watching for a hand to be detected and drawing lines onto the canvas - red on top of the video, and black on top of the shadowCanvas. Since the shadowCanvas background is white, the hand is drawn as white as well and the viewer only sees the offset shadow, in fuzzy black with rounded corners. The effect is rather spooky!
asynclandmarksRealTime(video){//start showing landmarksthis.videoWidth=video.videoWidth;this.videoHeight=video.videoHeight;//set up skeleton canvasthis.canvas=this.$refs.output;...//set up shadowCanvasthis.shadowCanvas=this.$refs.shadowCanvas;...this.ctx=this.canvas.getContext("2d");this.sctx=this.shadowCanvas.getContext("2d");...//paint to mainthis.ctx.clearRect(0,0,this.videoWidth,this.videoHeight);this.ctx.strokeStyle="red";this.ctx.fillStyle="red";this.ctx.translate(this.shadowCanvas.width,0);this.ctx.scale(-1,1);//paint to shadow boxthis.sctx.clearRect(0,0,this.videoWidth,this.videoHeight);this.sctx.shadowColor="black";this.sctx.shadowBlur=20;this.sctx.shadowOffsetX=150;this.sctx.shadowOffsetY=150;this.sctx.lineWidth=20;this.sctx.lineCap="round";this.sctx.fillStyle="white";this.sctx.strokeStyle="white";this.sctx.translate(this.shadowCanvas.width,0);this.sctx.scale(-1,1);//now you've set up the canvases, now you can frame its landmarksthis.frameLandmarks();},
For Each Frame, Draw Keypoints
As the keyframes progress, the model predict new keypoints for each of the hand's elements, and both canvases are cleared and redrawn.
Since TensorFlow.js allows you direct access to the keypoints of the hand and the hand's coordinates, you can manipulate them to draw a more lifelike hand. Thus we can redraw the palm to be a polygon, rather than resembling a garden rake with points culminating in the wrist.
With the models and video loaded, keyframes tracked, and hands and shadows drawn to canvas, we can implement a speech-to-text SDK so that you can narrate and save your shadow story.
To do this, get a key from the Azure portal forSpeech Servicesby creating a Service:
You can connect to this service by importing the sdk:
import * as sdk from "microsoft-cognitiveservices-speech-sdk";
...and start audio transcription after obtaining an API key which is stored in an Azure function in the/apifolder. This function gets the key stored in the Azure portal in the Azure Static Web App where the app is hosted.
asyncstartAudioTranscription(){try{//get the keyconstresponse=awaitaxios.get("/api/getKey");this.subKey=response.data;//sdkletspeechConfig=sdk.SpeechConfig.fromSubscription(this.subKey,"eastus");letaudioConfig=sdk.AudioConfig.fromDefaultMicrophoneInput();this.recognizer=newsdk.SpeechRecognizer(speechConfig,audioConfig);this.recognizer.recognized=(s,e)=>{this.text=e.result.text;this.story.push(this.text);};this.recognizer.startContinuousRecognitionAsync();}catch(error){this.message=error;}},
In this function, the SpeechRecognizer gathers text in chunks that it recognizes and organizes into sentences. That text is printed into a message string and displayed on the front end.
Display the Story
In this last part, the output cast onto the shadowCanvas is saved as a stream and recorded using the MediaRecorder API:
This app can be deployed as an Azure Static Web App using the excellentAzure plugin for Visual Studio Code. And once it's live, you can tell durable shadow stories!
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Creating Hand Shadow stories with Azure Speech and TensorFlow.js Handposes","id":"message:2166579","revisionNum":1,"author":{"__ref":"User:user:381821"},"depth":0,"hasGivenKudo":false,"board":{"__ref":"Blog:board:Azure-AI-Services-blog"},"conversation":{"__ref":"Conversation:conversation:2166579"},"messagePolicies":{"__typename":"MessagePolicies","canPublishArticleOnEdit":{"__typename":"PolicyResult","failureReason":{"__typename":"FailureReason","message":"error.lithium.policies.forums.policy_can_publish_on_edit_workflow_action.accessDenied","key":"error.lithium.policies.forums.policy_can_publish_on_edit_workflow_action.accessDenied","args":[]}},"canModerateSpamMessage":{"__typename":"PolicyResult","failureReason":{"__typename":"FailureReason","message":"error.lithium.policies.feature.moderation_spam.action.moderate_entity.allowed.accessDenied","key":"error.lithium.policies.feature.moderation_spam.action.moderate_entity.allowed.accessDenied","args":[]}}},"contentWorkflow":{"__typename":"ContentWorkflow","state":"PUBLISH","scheduledPublishTime":null,"scheduledTimezone":null,"userContext":{"__typename":"MessageWorkflowContext","canSubmitForReview":null,"canEdit":false,"canRecall":null,"canSubmitForPublication":null,"canReturnToAuthor":null,"canPublish":null,"canReturnToReview":null,"canSchedule":null},"shortScheduledTimezone":null},"readOnly":false,"editFrozen":false,"moderationData":{"__ref":"ModerationData:moderation_data:2166579"},"teaser":"
This is an article for all those big siblings who sneaked flashlights into their bedroom to cast scary shadows onto the wall, and for all the little kids, or the grownups who are still kids at heart, who were thus deliciously entertained.
","body":"
\n
Have you ever tried to cast hand shadows on a wall? It is the easiest thing in the world, and yet to do it well requires practice and just the right setup. To cultivate your #cottagecore aesthetic, try going into a completely dark room with just one lit candle, and casting hand shadows on a plain wall. The effect is startlingly dramatic. What fun!
\n
\n
\n
\n
\n
Even a tea light suffices to create a great effect
\n
\n
In 2020, and now into 2021, many folks are reverting back to basics as they look around their houses, reopening dusty corners of attics and basements and remembering the simple crafts that they used to love. Papermaking, anyone? All you need is a few tools and torn up, recycled paper. Pressing flowers? All you need is newspaper, some heavy books, and patience. And hand shadows? Just a candle.
\n
\n
\n
\n
\n
This TikTok creator has thousands of views for their handshadow tutorials
\n
\n
But what's a developer to do when trying to capture that #cottagecore vibe in a web app?
\n
High Tech for the Cottage
\n
While exploring the art of hand shadows, I wondered whether some of therecent workI had done for body poses might be applicable to hand poses. What if you could tell a story on the web using your hands, and somehow save a video of the show and the narrative behind it, and send it to someone special? In lockdown, what could be more amusing than sharing shadow stories between friends or relatives, all virtually?
\n
\n\n
\n
\n
\n
Hand shadow casting is a folk art probably originating in China; if you go to tea houses with stage shows, you might be lucky enough to view one like this!
\n
\n
A Show Of Hands
\n
When you start researching hand poses, it's striking how much content there is on the web on the topic. There has been work since at least 2014 on creating fully articulated hands within the research, simulation, and gaming sphere:
\n
\n
\n
\n
\n
MSR throwing hands
\n
\n
There are dozens of handpose libraries already on GitHub:
There are many applications where tracking hands is a useful activity:
\n
\n
• Gaming • Simulations / Training • \"Hands free\" uses for remote interactions with things by moving the body • Assistive technologies • TikTok effects :trophy: • Useful things likeAccordion Hands apps
\n
\n
One of the more interesting new libraries,handsfree.js, offers an excellent array of demos in its effort to move to a hands free web experience:
\n
\n
\n
\n
\n
Handsfree.js, a very promising project
\n
\n
As it turns out, hands are pretty complicated things. Theyeachinclude 21 keypoints (vs PoseNet's 17 keypoints for an entire body). Building a model to support inference for such a complicated grouping of keypoints has provenn challenging.
\n
\n
\n
\n
There are two main libraries available to the web developer when incorporating hand poses into an app: TensorFlow.js's handposes, and MediaPipe's. HandsFree.js uses both, to the extent that they expose APIs. As it turns out, neither TensorFlow.js nor MediaPipe's handposes are perfect for our project. We will have to compromise.
\n
\n
\n
\n
TensorFlow.js's handposesallow access to each hand keypoint and the ability to draw the hand to canvas as desired. HOWEVER, it only currently supports single hand poses, which is not optimal for good hand shadow shows.
\n
\n
\n
MediaPipe's handpose models(which are used by TensorFlow.js) do allow for dual hands BUT its API does not allow for much styling of the keypoints so that drawing shadows using it is not obvious.
\n
\n
\n
\n
One other library,fingerposes, is optimized for finger spelling in a sign language context and is worth a look.
\n
\n
Since it's more important to use the Canvas API to draw custom shadows, we are obliged to use TensorFlow.js, hoping that either it will soon support multiple hands OR handsfree.js helps push the envelope to expose a more styleable hand.
\n
\n
Let's get to work to build this app.
\n
Scaffold a Static Web App
\n
As a Vue.js developer, I always use the Vue CLI to scaffold an app usingvue create my-appand creating a standard app. I set up a basic app with two routes: Home and Show. Since this is going to be deployed as an Azure Static Web App, I follow my standard practice of including my app files in a folder namedappand creating anapifolder to include an Azure function to store a key (more on this in a minute).
\n
\n
In my package.json file, I import the important packages for using TensorFlow.js and the Cognitive Services Speech SDK in this app. Note that TensorFlow.js has divided its imports into individual packages:
We will draw an image of a hand, as detected by TensorFlow.js, onto a canvas, superimposed onto a video suppled by a webcam. In addition, we will redraw the hand to a second canvas (shadowCanvas), styled like shadows:
Working asynchronously, load the Handpose model. Once the backend is setup and the model is loaded, load the video via the webcam, and start watching the video's keyframes for hand poses. It's important at these steps to ensure error handling in case the model fails to load or there's no webcam available.
\n
\n
\n
asyncmounted(){\n awaittf.setBackend(this.backend);\n //async load model, then load video, then pass it to start landmarking\n this.model=awaithandpose.load();\n this.message=\"Model is loaded! Now loading video\";\n letwebcam;\n try{\n webcam=awaitthis.loadVideo();\n }catch(e){\n this.message=e.message;\n throwe;\n }\n\n this.landmarksRealTime(webcam);\n },\n
\n
\n
\n
\n
\n
Setup the Webcam
\n
Still working asynchronously, set up the camera to provide a stream of images
Now the fun begins, as you can get creative in drawing the hand on top of the video. This landmarking function runs on every keyframe, watching for a hand to be detected and drawing lines onto the canvas - red on top of the video, and black on top of the shadowCanvas. Since the shadowCanvas background is white, the hand is drawn as white as well and the viewer only sees the offset shadow, in fuzzy black with rounded corners. The effect is rather spooky!
\n
\n
\n
asynclandmarksRealTime(video){\n //start showing landmarks\n this.videoWidth=video.videoWidth;\n this.videoHeight=video.videoHeight;\n\n //set up skeleton canvas\n this.canvas=this.$refs.output;\n ...\n\n //set up shadowCanvas\n this.shadowCanvas=this.$refs.shadowCanvas;\n ...\n\n this.ctx=this.canvas.getContext(\"2d\");\n this.sctx=this.shadowCanvas.getContext(\"2d\");\n\n ...\n\n //paint to main\n\n this.ctx.clearRect(0,0,this.videoWidth, \n this.videoHeight);\n this.ctx.strokeStyle=\"red\";\n this.ctx.fillStyle=\"red\";\n this.ctx.translate(this.shadowCanvas.width,0);\n this.ctx.scale(-1,1);\n\n //paint to shadow box\n\n this.sctx.clearRect(0,0,this.videoWidth,this.videoHeight);\n this.sctx.shadowColor=\"black\";\n this.sctx.shadowBlur=20;\n this.sctx.shadowOffsetX=150;\n this.sctx.shadowOffsetY=150;\n this.sctx.lineWidth=20;\n this.sctx.lineCap=\"round\";\n this.sctx.fillStyle=\"white\";\n this.sctx.strokeStyle=\"white\";\n\n this.sctx.translate(this.shadowCanvas.width,0);\n this.sctx.scale(-1,1);\n\n //now you've set up the canvases, now you can frame its landmarks\n this.frameLandmarks();\n },\n
\n
\n
\n
\n
\n
For Each Frame, Draw Keypoints
\n
\n
As the keyframes progress, the model predict new keypoints for each of the hand's elements, and both canvases are cleared and redrawn.
Since TensorFlow.js allows you direct access to the keypoints of the hand and the hand's coordinates, you can manipulate them to draw a more lifelike hand. Thus we can redraw the palm to be a polygon, rather than resembling a garden rake with points culminating in the wrist.
With the models and video loaded, keyframes tracked, and hands and shadows drawn to canvas, we can implement a speech-to-text SDK so that you can narrate and save your shadow story.
\n
To do this, get a key from the Azure portal forSpeech Servicesby creating a Service:
\n
\n
\n
\n
You can connect to this service by importing the sdk:
\n
\n
import * as sdk from \"microsoft-cognitiveservices-speech-sdk\";
\n
\n
...and start audio transcription after obtaining an API key which is stored in an Azure function in the/apifolder. This function gets the key stored in the Azure portal in the Azure Static Web App where the app is hosted.
In this function, the SpeechRecognizer gathers text in chunks that it recognizes and organizes into sentences. That text is printed into a message string and displayed on the front end.
\n
Display the Story
\n
In this last part, the output cast onto the shadowCanvas is saved as a stream and recorded using the MediaRecorder API:
This app can be deployed as an Azure Static Web App using the excellentAzure plugin for Visual Studio Code. And once it's live, you can tell durable shadow stories!
Have you ever tried to cast hand shadows on a wall? It is the easiest thing in the world, and yet to do it well requires practice and just the right setup. To cultivate your #cottagecore aesthetic, try going into a completely dark room with just one lit candle, and casting hand shadows on a plain wall. The effect is startlingly dramatic. What fun!
\n
\n
\n
\n
\n
Even a tea light suffices to create a great effect
\n
\n
In 2020, and now into 2021, many folks are reverting back to basics as they look around their houses, reopening dusty corners of attics and basements and remembering the simple crafts that they used to love. Papermaking, anyone? All you need is a few tools and torn up, recycled paper. Pressing flowers? All you need is newspaper, some heavy books, and patience. And hand shadows? Just a candle.
\n
\n
\n
\n
\n
This TikTok creator has thousands of views for their handshadow tutorials
\n
\n
But what's a developer to do when trying to capture that #cottagecore vibe in a web app?
\n
High Tech for the Cottage
\n
While exploring the art of hand shadows, I wondered whether some of therecent workI had done for body poses might be applicable to hand poses. What if you could tell a story on the web using your hands, and somehow save a video of the show and the narrative behind it, and send it to someone special? In lockdown, what could be more amusing than sharing shadow stories between friends or relatives, all virtually?
\n
\n\n
\n
\n
\n
Hand shadow casting is a folk art probably originating in China; if you go to tea houses with stage shows, you might be lucky enough to view one like this!
\n
\n
A Show Of Hands
\n
When you start researching hand poses, it's striking how much content there is on the web on the topic. There has been work since at least 2014 on creating fully articulated hands within the research, simulation, and gaming sphere:
\n
\n
\n
\n
\n
MSR throwing hands
\n
\n
There are dozens of handpose libraries already on GitHub:
There are many applications where tracking hands is a useful activity:
\n
\n
• Gaming • Simulations / Training • \"Hands free\" uses for remote interactions with things by moving the body • Assistive technologies • TikTok effects • Useful things likeAccordion Hands apps
\n
\n
One of the more interesting new libraries,handsfree.js, offers an excellent array of demos in its effort to move to a hands free web experience:
\n
\n
\n
\n
\n
Handsfree.js, a very promising project
\n
\n
As it turns out, hands are pretty complicated things. Theyeachinclude 21 keypoints (vs PoseNet's 17 keypoints for an entire body). Building a model to support inference for such a complicated grouping of keypoints has provenn challenging.
\n
\n
\n
\n
There are two main libraries available to the web developer when incorporating hand poses into an app: TensorFlow.js's handposes, and MediaPipe's. HandsFree.js uses both, to the extent that they expose APIs. As it turns out, neither TensorFlow.js nor MediaPipe's handposes are perfect for our project. We will have to compromise.
\n
\n
\n
\n
TensorFlow.js's handposesallow access to each hand keypoint and the ability to draw the hand to canvas as desired. HOWEVER, it only currently supports single hand poses, which is not optimal for good hand shadow shows.
\n
\n
\n
MediaPipe's handpose models(which are used by TensorFlow.js) do allow for dual hands BUT its API does not allow for much styling of the keypoints so that drawing shadows using it is not obvious.
\n
\n
\n
\n
One other library,fingerposes, is optimized for finger spelling in a sign language context and is worth a look.
\n
\n
Since it's more important to use the Canvas API to draw custom shadows, we are obliged to use TensorFlow.js, hoping that either it will soon support multiple hands OR handsfree.js helps push the envelope to expose a more styleable hand.
\n
\n
Let's get to work to build this app.
\n
Scaffold a Static Web App
\n
As a Vue.js developer, I always use the Vue CLI to scaffold an app usingvue create my-appand creating a standard app. I set up a basic app with two routes: Home and Show. Since this is going to be deployed as an Azure Static Web App, I follow my standard practice of including my app files in a folder namedappand creating anapifolder to include an Azure function to store a key (more on this in a minute).
\n
\n
In my package.json file, I import the important packages for using TensorFlow.js and the Cognitive Services Speech SDK in this app. Note that TensorFlow.js has divided its imports into individual packages:
We will draw an image of a hand, as detected by TensorFlow.js, onto a canvas, superimposed onto a video suppled by a webcam. In addition, we will redraw the hand to a second canvas (shadowCanvas), styled like shadows:
Working asynchronously, load the Handpose model. Once the backend is setup and the model is loaded, load the video via the webcam, and start watching the video's keyframes for hand poses. It's important at these steps to ensure error handling in case the model fails to load or there's no webcam available.
\n
\n
\n
asyncmounted(){\n awaittf.setBackend(this.backend);\n //async load model, then load video, then pass it to start landmarking\n this.model=awaithandpose.load();\n this.message=\"Model is loaded! Now loading video\";\n letwebcam;\n try{\n webcam=awaitthis.loadVideo();\n }catch(e){\n this.message=e.message;\n throwe;\n }\n\n this.landmarksRealTime(webcam);\n },\n
\n
\n
\n
\n
\n
Setup the Webcam
\n
Still working asynchronously, set up the camera to provide a stream of images
Now the fun begins, as you can get creative in drawing the hand on top of the video. This landmarking function runs on every keyframe, watching for a hand to be detected and drawing lines onto the canvas - red on top of the video, and black on top of the shadowCanvas. Since the shadowCanvas background is white, the hand is drawn as white as well and the viewer only sees the offset shadow, in fuzzy black with rounded corners. The effect is rather spooky!
\n
\n
\n
asynclandmarksRealTime(video){\n //start showing landmarks\n this.videoWidth=video.videoWidth;\n this.videoHeight=video.videoHeight;\n\n //set up skeleton canvas\n this.canvas=this.$refs.output;\n ...\n\n //set up shadowCanvas\n this.shadowCanvas=this.$refs.shadowCanvas;\n ...\n\n this.ctx=this.canvas.getContext(\"2d\");\n this.sctx=this.shadowCanvas.getContext(\"2d\");\n\n ...\n\n //paint to main\n\n this.ctx.clearRect(0,0,this.videoWidth, \n this.videoHeight);\n this.ctx.strokeStyle=\"red\";\n this.ctx.fillStyle=\"red\";\n this.ctx.translate(this.shadowCanvas.width,0);\n this.ctx.scale(-1,1);\n\n //paint to shadow box\n\n this.sctx.clearRect(0,0,this.videoWidth,this.videoHeight);\n this.sctx.shadowColor=\"black\";\n this.sctx.shadowBlur=20;\n this.sctx.shadowOffsetX=150;\n this.sctx.shadowOffsetY=150;\n this.sctx.lineWidth=20;\n this.sctx.lineCap=\"round\";\n this.sctx.fillStyle=\"white\";\n this.sctx.strokeStyle=\"white\";\n\n this.sctx.translate(this.shadowCanvas.width,0);\n this.sctx.scale(-1,1);\n\n //now you've set up the canvases, now you can frame its landmarks\n this.frameLandmarks();\n },\n
\n
\n
\n
\n
\n
For Each Frame, Draw Keypoints
\n
\n
As the keyframes progress, the model predict new keypoints for each of the hand's elements, and both canvases are cleared and redrawn.
Since TensorFlow.js allows you direct access to the keypoints of the hand and the hand's coordinates, you can manipulate them to draw a more lifelike hand. Thus we can redraw the palm to be a polygon, rather than resembling a garden rake with points culminating in the wrist.
With the models and video loaded, keyframes tracked, and hands and shadows drawn to canvas, we can implement a speech-to-text SDK so that you can narrate and save your shadow story.
\n
To do this, get a key from the Azure portal forSpeech Servicesby creating a Service:
\n
\n
\n
\n
You can connect to this service by importing the sdk:
\n
\n
import * as sdk from \"microsoft-cognitiveservices-speech-sdk\";
\n
\n
...and start audio transcription after obtaining an API key which is stored in an Azure function in the/apifolder. This function gets the key stored in the Azure portal in the Azure Static Web App where the app is hosted.
In this function, the SpeechRecognizer gathers text in chunks that it recognizes and organizes into sentences. That text is printed into a message string and displayed on the front end.
\n
Display the Story
\n
In this last part, the output cast onto the shadowCanvas is saved as a stream and recorded using the MediaRecorder API:
This app can be deployed as an Azure Static Web App using the excellentAzure plugin for Visual Studio Code. And once it's live, you can tell durable shadow stories!
This is an article for all those big siblings who sneaked flashlights into their bedroom to cast scary shadows onto the wall, and for all the little kids, or the grownups who are still kids at heart, who were thus deliciously entertained.
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