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US: Laptop suggestions for web browsing and occasional remote work (Budget: $2500 USD)

  • **Total budget (in local currency) and country of purchase. Please do not use USD unless purchasing in the US:**Up to $2500 USD, but would like to save money if possible. Edit: emphasizing that I have that money, but want to save it!! A lot of people have already added their "tHEy WAnT tO SpENd $2500 fOR SImS??????" comments, so any additional is pretty pointless imo. It is my money that I am lucky to have to spend, but I want to be smart about it hence why I came here for suggestions. Jeez. Edit 2: I also get a lot of discounts, 60% off Dell, $200 off Apple, up to 45% off HP, $150 off Alienware etc.
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  • Leave any finishing thoughts here that you may feel are necessary and beneficial to the discussion.
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Edit 4: I also really want a laptop with longevity. I try to take really good care of my laptops so that they last, and do know that some maintenance is required for that. However, I have had friends who used Macbooks for ~8 years (obviously not the case with everyone, and heavily depends on how well you take care of it). Conversely, some of those cheap laptops need to be replaced or fixed often. Yeah, I could buy a $400 laptop, but if it's only lasting 2 years max and results in a ton more maintenance and I have to buy a new laptop more frequently...why not just get a better laptop upfront? That is a more cost-effective plan imo since I do have the money. That doesn't mean I need to spend $2500, but I would like a quality piece that lasts.
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submitted by Buehr to SuggestALaptop

American Sign Language (ASL) using HUAWEI ML Kit's Hand key point Detection Capability

American Sign Language (ASL) using HUAWEI ML Kit's Hand key point Detection Capability
Introduction:
ML Kit provides a hand keypoint detection capability, which can be used to detect sign language. Handle Key point detection currently provides 21 points in the hand to detect the signs. Here we will use the direction of each finger and compare that with ASL rules to find the alphabet.
Application scenarios:
Sign language can used by deaf and speech impaired people. Sign language is a collection of hand gestures involving motions and signs which are used in daily interaction.
Using ML kit you can create an intelligent signed alphabet recognizer can work as an aiding agent to translate the signs to words (and also sentences) and vice versa.
What I am trying here is American Sign Language (ASL) alphabets from hand gesture. The classification is based on the position of the joints, fingers and wrists. I have attempted to gather alphabets for the word “HELLO” from hand gestures.
https://preview.redd.it/6byn3x3g3dy51.png?width=694&format=png&auto=webp&s=b0e244b413141c60237108b593842e6c145b0ab2
Development Practice
1. Preparations
You can find detailed information about the preparations you need to make on the HUAWEI Developers-Development Process URL https://developer.huawei.com/consumeen/doc/development/HMS-Guides/ml-process-4
Here, we will just look at the most important procedures.
Step 1 Enable ML Kit
In HUAWEI Developer AppGallery Connect, choose Develop > Manage APIs. Make sure ML Kit is activated
Step 2 Configure the Maven Repository Address in the Project-Level build.gradle File
buildscript { 
repositories { ... maven {url 'https://developer.huawei.com/repo/'} } } dependencies { ... classpath 'com.huawei.agconnect:agcp:1.3.1.301' } allprojects { repositories { ... maven {url 'https://developer.huawei.com/repo/'} } }
Step 3 Add SDK Dependencies to the App-Level build.gradle File
apply plugin: 'com.android.application' 
apply plugin: 'com.huawei.agconnect'
dependencies{ 
// Import the base SDK. implementation 'com.huawei.hms:ml-computer-vision-handkeypoint:2.0.2.300' // Import the hand keypoint detection model package. implementation 'com.huawei.hms:ml-computer-vision-handkeypoint-model:2.0.2.300' }
Step 4 Add related meta-data tags into your AndroidManifest.xml.
 
Step 5 Apply for Camera Permission and Local File Reading Permission
Camera permission 
Read permission
2. Coding Steps:
Step 1 Create SurfaceView for Camera Preview and also create surfaceView for the result.
At present we are showing theresult only in the UI, but you can extend and read the result using the TTS recognition.
 mSurfaceHolderCamera.addCallback(surfaceHolderCallback) private val surfaceHolderCallback = object : SurfaceHolder.Callback { override fun surfaceCreated(holder: SurfaceHolder) { createAnalyzer() } override fun surfaceChanged(holder: SurfaceHolder, format: Int, width: Int, height: Int) { prepareLensEngine(width, height) mLensEngine.run(holder) } override fun surfaceDestroyed(holder: SurfaceHolder) { mLensEngine.release() } } 
Step 2 Create a Hand Keypoint Analyzer
//Creates MLKeyPointAnalyzer with MLHandKeypointAnalyzerSetting. val settings = MLHandKeypointAnalyzerSetting.Factory() .setSceneType(MLHandKeypointAnalyzerSetting.TYPE_ALL) .setMaxHandResults(2) .create() // Set the maximum number of hand regions that can be detected within an image. A maximum of 10 hand regions can be detected by default mAnalyzer = MLHandKeypointAnalyzerFactory.getInstance().getHandKeypointAnalyzer(settings) mAnalyzer.setTransactor(mHandKeyPointTransactor) 
Step 3 Create the HandKeypointTransactor class for processing detection results
Below class implements the MLAnalyzer.MLTransactor API and uses the transactResult method in this class to obtain the detection results and implement specific services.
class HandKeyPointTransactor(surfaceHolder: SurfaceHolder? = null): MLAnalyzer.MLTransactor { override fun transactResult(result: MLAnalyzer.Result?) { var foundCharacter = findTheCharacterResult(result) if (foundCharacter.isNotEmpty() && !foundCharacter.equals(lastCharacter)) { lastCharacter = foundCharacter displayText.append(lastCharacter) } canvas.drawText(displayText.toString(), paddingleft, paddingRight, Paint().also { it.style = Paint.Style.FILL it.color = Color.YELLOW }) } 
Step 4 Create an Instance of the Lens Engine
LensEngine lensEngine = new LensEngine.Creator(getApplicationContext(), analyzer) setLensType(LensEngine.BACK_LENS) applyDisplayDimension(width, height) // adjust width and height depending on the orientation applyFps(5f) enableAutomaticFocus(true) create(); 
Step 5 Run the LensEngine
private val surfaceHolderCallback = object : SurfaceHolder.Callback { // run the LensEngine in surfaceChanged() override fun surfaceChanged(holder: SurfaceHolder, format: Int, width: Int, height: Int) { createLensEngine(width, height) mLensEngine.run(holder) } } 
Step 6 Need the analyzer stop it and release resources
 fun stopAnalyzer() { mAnalyzer.stop() } 
Step 7. Process the transactResult() to detect character
You can use the transactResult method in HandKeypointTransactor class to obtain the detection results and implement specific services. In addition to the coordinate information of each hand keypoint, the detection result includes a confidence value of the palm and of each keypoint. The palm and hand keypoints that are incorrectly recognized can be filtered out based on the confidence values. In actual scenarios, a threshold can be flexibly set based on tolerance of misrecognition
Step 7.1 Find the direction of the finger:
Let us start consider possible vector slope for finger is in two axis x-axis and y-axis
private const val X_COORDINATE = 0 private const val Y_COORDINATE = 1 
We have Fingers possible in five vectros. The direction of any finger at any time can be categorised as UP, DOWN, DOWN_UP, UP_DOWN, NEUTRAL.
enum class FingerDirection { VECTOR_UP, VECTOR_DOWN, VECTOR_UP_DOWN, VECTOR_DOWN_UP, VECTOR_UNDEFINED } enum class Finger { THUMB, FIRST_FINGER, MIDDLE_FINGER, RING_FINGER, LITTLE_FINGER } 
First separate the corresponding keyupoints from the result to different fingers's key point array like this
var firstFinger = arrayListOf() var middleFinger = arrayListOf() var ringFinger = arrayListOf() var littleFinger = arrayListOf() var thumb = arrayListOf() 
Each keypoint in the finger corresponds to the joint in the finger. By finding the distance of the joints from from the average position value of the finger, you can find the slope. Check the x and y co-ordinates in the key point with respect to the x and y co-ordinates of the nearby keypoint.
For example :
Take two samples keypoints for H letter
int[] datapointSampleH1 = {623, 497, 377, 312, 348, 234, 162, 90, 377, 204, 126, 54, 383, 306, 413, 491, 455, 348, 419, 521 }; int [] datapointSampleH2 = {595, 463, 374, 343, 368, 223, 147, 78, 381, 217, 110, 40, 412, 311, 444, 526, 450, 406, 488, 532}; 
You can calculate the vector using average of the coordinates of the finger
//For ForeFinger - 623, 497, 377, 312 double avgFingerPosition = (datapoints[0].getX()+datapoints[1].getX()+datapoints[2].getX()+datapoints[3].getX())/4; // find the average and subract it from the value of x double diff = datapointSampleH1 [position] .getX() - avgFingerPosition ; //vector either positive or negative representing the direction int vector = (int)((diff *100)/avgFingerPosition ) ; 
Resulting vector - will be in negative or positive, if it is positive it is in the direction of x-axis positive quad and if it negative it is viceversa. Using this approrach do the vector mapping for all alphabets. Once you all the vectors we can use for our programming.
https://preview.redd.it/6jad4kxy3dy51.png?width=1087&format=png&auto=webp&s=a35b56b2d0166648765fb6ed7c78993c5a88ed09
Using above vector direction we can categorise into the vectors we defined first as FingerDirection enum .
private fun getSlope(keyPoints: MutableList, coordinate: Int): FingerDirection { when (coordinate) { X_COORDINATE -> { if (keyPoints[0].pointX > keyPoints[3].pointX && keyPoints[0].pointX > keyPoints[2].pointX) return FingerDirection.VECTOR_DOWN if (keyPoints[0].pointX > keyPoints[1].pointX && keyPoints[3].pointX > keyPoints[2].pointX) return FingerDirection.VECTOR_DOWN_UP if (keyPoints[0].pointX < keyPoints[1].pointX && keyPoints[3].pointX < keyPoints[2].pointX) return FingerDirection.VECTOR_UP_DOWN if (keyPoints[0].pointX < keyPoints[3].pointX && keyPoints[0].pointX < keyPoints[2].pointX) return FingerDirection.VECTOR_UP } Y_COORDINATE -> { if (keyPoints[0].pointY > keyPoints[1].pointY && keyPoints[2].pointY > keyPoints[1].pointY && keyPoints[3].pointY > keyPoints[2].pointY) return FingerDirection.VECTOR_UP_DOWN if (keyPoints[0].pointY > keyPoints[3].pointY && keyPoints[0].pointY > keyPoints[2].pointY) return FingerDirection.VECTOR_UP if (keyPoints[0].pointY < keyPoints[1].pointY && keyPoints[3].pointY < keyPoints[2].pointY) return FingerDirection.VECTOR_DOWN_UP if (keyPoints[0].pointY < keyPoints[3].pointY && keyPoints[0].pointY < keyPoints[2].pointY) return FingerDirection.VECTOR_DOWN } } return FingerDirection.VECTOR_UNDEFINED 
Get the directions of each finger and store in an array
xDirections[Finger.FIRST_FINGER] = getSlope(firstFinger, X_COORDINATE) yDirections[Finger.FIRST_FINGER] = getSlope(firstFinger, Y_COORDINATE ) 
https://preview.redd.it/kfwh91j24dy51.png?width=980&format=png&auto=webp&s=edd3a8496b18e6e6e13afa78343244256b8fe131
https://preview.redd.it/je4jpz544dy51.png?width=980&format=png&auto=webp&s=d1419d0c518b194bcd452b6cc6093d5fb156847f
https://preview.redd.it/5ocxxlk54dy51.png?width=980&format=png&auto=webp&s=bb789cb7e6428633bd95f0217477b9c912ee7418

Step 7.2 Find the character from the finger directions:
Currenlty we taken only for Word "HELLO", it required aplphabets H, E, L, O. Their corresponding vectors against x-axis and y-aaxis is shown the table.
Assumptions:
  1. The orientation of the hand always portrait.
  2. Make palm and wrist parallel to phone, that is always 90-degree to the X-axis.
  3. Hold position for alteast 3-secods to record the character.
Start mapping the vectors with character to find the string.
// Alphabet H if (xDirections[Finger.LITTLE_FINGER] == FingerDirection.VECTOR_DOWN_UP && xDirections [Finger.RING_FINGER] == FingerDirection.VECTOR_DOWN_UP && xDirections [Finger.MIDDLE_FINGER] == FingerDirection.VECTOR_DOWN && xDirections [Finger.FIRST_FINGER] == FingerDirection.VECTOR_DOWN && xDirections [Finger.THUMB] == FingerDirection.VECTOR_DOWN) return "H" //Alphabet E if (yDirections[Finger.LITTLE_FINGER] == FingerDirection.VECTOR_UP_DOWN && yDirections [Finger.RING_FINGER] == FingerDirection.VECTOR_UP_DOWN && yDirections [Finger.MIDDLE_FINGER] == FingerDirection.VECTOR_UP_DOWN && yDirections [Finger.FIRST_FINGER] == FingerDirection.VECTOR_UP_DOWN && xDirections [Finger.THUMB] == FingerDirection.VECTOR_DOWN) return "E" if (yDirections[Finger.LITTLE_FINGER] == FingerDirection.VECTOR_UP_DOWN && yDirections [Finger.RING_FINGER] == FingerDirection.VECTOR_UP_DOWN && yDirections [Finger.MIDDLE_FINGER] == FingerDirection.VECTOR_UP_DOWN && yDirections [Finger.FIRST_FINGER] == FingerDirection.VECTOR_UP && yDirections [Finger.THUMB] == FingerDirection.VECTOR_UP) return "L" if (xDirections[Finger.LITTLE_FINGER] == FingerDirection.VECTOR_UP && xDirections [Finger.RING_FINGER] == FingerDirection.VECTOR_UP && yDirections [Finger.THUMB] == FingerDirection.VECTOR_UP) return "O" 
3. Screenshots and Results
https://preview.redd.it/rtm0j4x94dy51.png?width=133&format=png&auto=webp&s=6a6870064130bcc51538059542c95369c7e6b27a
https://preview.redd.it/vlwfjo6a4dy51.png?width=134&format=png&auto=webp&s=f86a69f5ed446b39bec75a97d7d83d6bed019c2d
https://preview.redd.it/3u3u2dia4dy51.png?width=133&format=png&auto=webp&s=51d882f5a4dc046914a65962cfe69a0fd6aa0c60
https://preview.redd.it/5xgchnra4dy51.png?width=133&format=png&auto=webp&s=7994f5ebb72bfbf154303ba9b479dc253069f8ba
4. More Tips and Tricks
  1. When extending for 26 alphabets, corelation error will be more. For more accuracy scan for 2-3 secods, find characters and calculate most probable character from 2-3 seconds. This can reduce the corelation errors of the alphabets.
  2. For supporting all orienation increase vector support more than x-y axis, may be 8 or more direction. First find the degree of the fingers and correspoding vectods of the fingers.
Conclusion:
This try was brute force cor-ordinates technique and this can be extended to all 26 alphabets after generatig the vector mappings. Orientation also can be extended to all 8 direction. That would 26*8*5fingers = 1040 vectors. For better approach instead using vector , we can apply first derivative fn of the fingers and make it simple.
Other enchancement can, instead creating vector, we can use image claasification and train the a model and use the custome model. This excericse was to check the feasibility use key point handling feature of Huawei ML kit.
References:
1. https://en.wikipedia.org/wiki/American\_Sign\_Language
2. https://forums.developer.huawei.com/forumPortal/en/topic/0202369245767250343?fid=0101187876626530001
3. https://forums.developer.huawei.com/forumPortal/en/topic/0202366784184930320?fid=0101187876626530001
submitted by helloworddd to HuaweiDevelopers

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