navarasu

import numpy as np

def TPS_color_correction(input_color,actual_color,reference):
    NPs = actual.shape[0]
    NPg = reference.shape[0]
    Xp = actual[:, 0].conj().transpose()
    Yp = actual[:, 1].conj().transpose()
    Zp = actual[:, 2].conj().transpose()
    Xg = reference[:, 0].conj().transpose()
    Yg = reference[:, 1].conj().transpose()

navarasu

import numpy as np
import boto3
import os
import joblib

def getModel():
    bucket=os.getenv("BUCKET_NAME")
    S3 = boto3.client('s3')
    model_file=os.getenv("MODEL_FILE_NAME")
    response = S3.download_file(bucket,model_file,model_file)

navarasu

   #Prepare event matrix
    input_products = sorted(PRODUCTS[PRODUCTS !='Product A'])
    event_matrix=generate_event_matrix_index(processed_data,input_products)
    x_train_temp=x_train.apply(lambda x : pd.Series(util.encode_events(event_matrix,x[input_products].dropna().items())) ,axis=1)
    x_train=x_train_temp.join(x_train[['cus_type','cus_point']])

    #Preprocess categorial data
    cus_type = CategoricalDtype(categories=processed_data['cus_type'].unique(), ordered=True)
    x_train['cus_type']=x_train['cus_type'].astype(cus_type)
    x_train=pd.get_dummies(x_train,prefix='cus')

navarasu

import 'package:flutter/material.dart';
import 'package:camera/camera.dart';
import 'package:flutter/services.dart';

class ObjectDetecter extends ValueNotifier<List> {
  ObjectDetecter._() : super(null);

  static CameraController _controller;
  
  static bool _isDetecting = false;

navarasu

package francium.tech.objectdetector;

import android.content.res.AssetFileDescriptor;
import android.os.Bundle;
import android.renderscript.RenderScript;
import java.io.FileInputStream;
import java.io.IOException;
import java.nio.ByteBuffer;
import java.nio.channels.FileChannel;
import java.util.HashMap;

navarasu

package francium.tech.objectdetector;


import android.graphics.Bitmap;
import android.graphics.Matrix;
import android.os.SystemClock;
import android.renderscript.Allocation;
import android.renderscript.Element;
import android.renderscript.RenderScript;
import android.renderscript.ScriptIntrinsicYuvToRGB;

navarasu

import 'dart:async';
import 'dart:convert';
import 'package:flutter/material.dart';
import 'package:flutter/services.dart';
import 'package:camera/camera.dart';
import 'package:object_detector/object_detector.dart';

List<CameraDescription> cameras;
var labels, colors;

navarasu

package francium.tech.objectdetector;

import android.content.res.AssetFileDescriptor;
import android.os.Bundle;
import android.renderscript.RenderScript;
import java.io.FileInputStream;
import java.io.IOException;
import java.nio.ByteBuffer;
import java.nio.channels.FileChannel;
import java.util.Map;

navarasu

package francium.tech.objectdetector;

import android.renderscript.RenderScript;
import android.util.Log;
import org.json.JSONException;
import org.tensorflow.lite.Interpreter;
import java.io.IOException;
import java.nio.ByteBuffer;
import java.nio.ByteOrder;
import java.util.List;

navarasu

import 'dart:async';
import 'dart:convert';
import 'package:flutter/material.dart';
import 'package:flutter/services.dart';
import 'package:camera/camera.dart';

List<CameraDescription> cameras;
var labels, colors;

Future<void> main() async {