shubham0204

Certifications

Amazon Web Services

Badges - Getting Started

• Intro to Cloud Computing 101
• AWS EC2
• AWS Storage / S3
• AWS VPC

shubham0204

import streamlit as st
import cv2

vid = cv2.VideoCapture( 'http://<network_ip_Address>:8080/video' )

st.title( 'Using Mobile Camera with Streamlit' )
frame_window = st.image( [] )
take_picture_button = st.button( 'Take Picture' )

while True:

shubham0204

import streamlit as st
import cv2
import numpy as np
import requests

st.title( 'Mobile Camera Preview in Streamlit' )
frame_window = st.image( [] )
take_picture_button = st.button( 'Take Picture' )

while True:

shubham0204

while True:
    # Request the image from the server
    response = requests.get(url="http://<network_ip_address>:<port>/photo.jpg")
    imgNp = np.array(bytearray(response.content), dtype=np.uint8)
    frame = cv2.imdecode(imgNp, cv2.IMREAD_UNCHANGED ) 
    # As OpenCV decodes images in BGR format, we'd convert it to the RGB format
    frame = cv2.cvtColor( frame , cv2.COLOR_BGR2RGB )

    frame_window.image(frame)

shubham0204

import streamlit as st
import cv2
import numpy as np
import requests

# Title of the app
st.title( 'Mobile Camera Preview in Streamlit' )

# An empty image container that will hold the frames we'll fetch from the server
frame_window = st.image( [] )

shubham0204

Demystifying Mathematics - Shubham Panchal

• Kernels: Everything You Need to Know
• Principal Component Analysis: Everything You Need To Know
• Demystifying Monte Carlo Integration
• Demystifying Linear Independence
• Demystifying Probability Distributions ( 1 / 3 )
• Demystifying Probability Distributions ( 2 / 3 )
• [Demystifying Probability Distributions ( 3 / 3 )](https://www.canto

shubham0204

import random 

a = 0
b = 1
N = 1000

def f( x ):
    return 2 * x**5

def uniform():

shubham0204

// ML Model to detect the age of a person, hosted on Heroku
class AgeDetectionModel {

    private val herokuModelPredictURL = "https://age-detection-tf-app.herokuapp.com/predict"
    private val mediaType = "application/json".toMediaType()
    private val okHttpClient = OkHttpClient()

    interface PredictionCallback {
        fun onResult( age : Int )
        fun onError( error : String )

shubham0204

// Detect faces in the given image and crop them.
// Pass the cropped faces to the AgeDetectionModel
private fun detectFaces(image: Bitmap) {
    val inputImage = InputImage.fromBitmap(image, 0)
    firebaseFaceDetector.process(inputImage)
        .addOnSuccessListener { faces ->
            if ( faces.size != 0 ) {
                progressDialog.apply {
                    dismiss()
                    setMessage( "📍 Posting to server ...")

shubham0204

dependencies {
    implementation 'androidx.core:core-ktx:1.7.0'
    implementation 'androidx.appcompat:appcompat:1.4.1'
    implementation 'com.google.android.material:material:1.5.0'
    implementation 'androidx.constraintlayout:constraintlayout:2.1.3'

    // 1. MLKit Face Detection
    implementation 'com.google.android.gms:play-services-mlkit-face-detection:17.0.0'
    
    // 2. OkHttp is a HTTP client for Android