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classindex | Index classes , do not scan | Build Tool library

 by   atteo Java Version: Current License: Apache-2.0

 by   atteo Java Version: Current License: Apache-2.0

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kandi X-RAY | classindex Summary

classindex is a Java library typically used in Utilities, Build Tool applications. classindex has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub, Maven.
ClassIndex is an index of classes which you can query for:.
Support
Support
Quality
Quality
Security
Security
License
License
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kandi-support Support

  • classindex has a low active ecosystem.
  • It has 213 star(s) with 36 fork(s). There are 15 watchers for this library.
  • It had no major release in the last 12 months.
  • There are 14 open issues and 38 have been closed. On average issues are closed in 34 days. There are 1 open pull requests and 0 closed requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of classindex is current.
classindex Support
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classindex Support
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quality kandi Quality

  • classindex has 0 bugs and 0 code smells.
classindex Quality
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classindex Quality
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securitySecurity

  • classindex has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
  • classindex code analysis shows 0 unresolved vulnerabilities.
  • There are 0 security hotspots that need review.
classindex Security
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classindex Security
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license License

  • classindex is licensed under the Apache-2.0 License. This license is Permissive.
  • Permissive licenses have the least restrictions, and you can use them in most projects.
classindex License
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classindex License
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buildReuse

  • classindex releases are not available. You will need to build from source code and install.
  • Deployable package is available in Maven.
  • Build file is available. You can build the component from source.
  • Installation instructions, examples and code snippets are available.
  • It has 1983 lines of code, 136 functions and 64 files.
  • It has low code complexity. Code complexity directly impacts maintainability of the code.
classindex Reuse
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classindex Reuse
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Top functions reviewed by kandi - BETA

kandi has reviewed classindex and discovered the below as its top functions. This is intended to give you an instant insight into classindex implemented functionality, and help decide if they suit your requirements.

  • Iterate through the classes and collect the annotations .
  • Read old index file .
  • Reads the class index file .
  • Process a resource .
  • Store repeatable annotation .
  • Finds the classes in the given package .
  • Relocate the class if needed .
  • Returns a filter that matches any given predicates .
  • Recursively searches for classes .
  • Remove all service entries from the JsOutputStream .

classindex Key Features

the list of classes annotated by a given annotation (see: getAnnotated()),

the list of classes implementing a given interface (see: getSubclasses()),

the list of subclasses of a given class (see: getSubclasses()),

the list of classes from a given package (see: getPackageClasses()).

Javadoc summary (see: getClassSummary())

and more

is as fast as reading a file, it is not impacted by the usual performance penalty of the classpath scanning,

is based on standard APIs provided by Java, like annotation processing, it does not assume any inner workings of the ClassLoaders, it does not analyse bytecode of a compiled classes,

supports incremental compilation in IntelliJ, NetBeans and Eclipse,

is compatible with Java modules,

is compatible with ServiceLoader,

is compatible with jaxb.index,

How to use it?

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<dependency>
    <groupId>org.atteo.classindex</groupId>
    <artifactId>classindex</artifactId>
    <version>3.4</version>
</dependency>

Class Indexing

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@IndexAnnotated
public @interface Entity {
}
 
@Entity
public class Car {
}
 
...
 
for (Class<?> klass : ClassIndex.getAnnotated(Entity.class)) {
    System.out.println(klass.getName());
}

Javadoc storage

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@IndexAnnotated(storeJavadoc = true)
public @interface Entity {
}
 
/**
 * This is car.
 * Detailed car description follows.
 */
@Entity
public class Car {
}
 
...
 
assertEquals("This is car", ClassIndex.getClassSummary(Car.class));

Class filtering

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ClassFilter.only()
	.topLevel()
	.from(ClassIndex.getAnnotated(SomeAnnotation.class));

Indexing when annotations cannot be used

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public class MyImportantClassIndexProcessor extends ClassIndexProcessor {
    public MyImportantClassIndexProcessor() {
        indexAnnotations(Entity.class);
    }
}

Making shaded jar

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<build>
    <plugins>
        <plugin>
            <artifactId>maven-shade-plugin</artifactId>
            <version>@maven-shade-plugin.version@</version>
            <executions>
                <execution>
                    <phase>package</phase>
                    <goals>
                        <goal>shade</goal>
                    </goals>
                    <configuration>
                        <transformers>
                            <transformer implementation="org.atteo.classindex.ClassIndexTransformer"/>
                        </transformers>
                    </configuration>
                </execution>
            </executions>
            <dependencies>
                <dependency>
                    <groupId>org.atteo.classindex</groupId>
                    <artifactId>classindex-transformer</artifactId>
                    <version>@class index version@</version>
                </dependency>
            </dependencies>
        </plugin>
    </plugins>
</build>

How to run tf.lite model on raspery-pi instead of saved keras model

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# model is your keras model
tflite_model = tf.lite.TFLiteConverter.from_keras_model(model).convert()

with open('model.tflite', 'wb') as f:
    f.write(tflite_model)
# instead of `model = tensorflow.keras.models.load_model("yourmodel.tflite")`
# use this code to load tflite model
interpreter = tf.lite.Interpreter(model_path="model.tflite")
interpreter.allocate_tensors()
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()

# replace `predictions = model.predict(img)` with this code
interpreter.set_tensor(input_details[0]['index'], img)
interpreter.invoke()
predictions = interpreter.get_tensor(output_details[0]['index'])

-----------------------
# model is your keras model
tflite_model = tf.lite.TFLiteConverter.from_keras_model(model).convert()

with open('model.tflite', 'wb') as f:
    f.write(tflite_model)
# instead of `model = tensorflow.keras.models.load_model("yourmodel.tflite")`
# use this code to load tflite model
interpreter = tf.lite.Interpreter(model_path="model.tflite")
interpreter.allocate_tensors()
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()

# replace `predictions = model.predict(img)` with this code
interpreter.set_tensor(input_details[0]['index'], img)
interpreter.invoke()
predictions = interpreter.get_tensor(output_details[0]['index'])

Memory leak in Tensorflow.js: How to manage memory for a large dataset created using tf.data.generator?

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        return tf.node.decodeJpeg(buffer, 3)
            .resizeNearestNeighbor([128, 128])
            .toFloat()
            .div(tf.scalar(255.0))
const decoded = tf.node.decodeJpeg(buffer, 3)
const resized = decoded.resizeNearestNeighbor([128, 128])
const casted = resized.toFloat();
const normalized = casted.div(tf.scalar(255.0))
return normalized;
tf.dispose([decoded, resized, casted]);
   #generateTensor = tf.tidy(imagePath) => {
        const buffer = fs.readFileSync(imagePath);
        return tf.node.decodeJpeg(buffer, 3)
            .resizeNearestNeighbor([128, 128])
            .toFloat()
            .div(tf.scalar(255.0))
    }
-----------------------
        return tf.node.decodeJpeg(buffer, 3)
            .resizeNearestNeighbor([128, 128])
            .toFloat()
            .div(tf.scalar(255.0))
const decoded = tf.node.decodeJpeg(buffer, 3)
const resized = decoded.resizeNearestNeighbor([128, 128])
const casted = resized.toFloat();
const normalized = casted.div(tf.scalar(255.0))
return normalized;
tf.dispose([decoded, resized, casted]);
   #generateTensor = tf.tidy(imagePath) => {
        const buffer = fs.readFileSync(imagePath);
        return tf.node.decodeJpeg(buffer, 3)
            .resizeNearestNeighbor([128, 128])
            .toFloat()
            .div(tf.scalar(255.0))
    }
-----------------------
        return tf.node.decodeJpeg(buffer, 3)
            .resizeNearestNeighbor([128, 128])
            .toFloat()
            .div(tf.scalar(255.0))
const decoded = tf.node.decodeJpeg(buffer, 3)
const resized = decoded.resizeNearestNeighbor([128, 128])
const casted = resized.toFloat();
const normalized = casted.div(tf.scalar(255.0))
return normalized;
tf.dispose([decoded, resized, casted]);
   #generateTensor = tf.tidy(imagePath) => {
        const buffer = fs.readFileSync(imagePath);
        return tf.node.decodeJpeg(buffer, 3)
            .resizeNearestNeighbor([128, 128])
            .toFloat()
            .div(tf.scalar(255.0))
    }
-----------------------
        return tf.node.decodeJpeg(buffer, 3)
            .resizeNearestNeighbor([128, 128])
            .toFloat()
            .div(tf.scalar(255.0))
const decoded = tf.node.decodeJpeg(buffer, 3)
const resized = decoded.resizeNearestNeighbor([128, 128])
const casted = resized.toFloat();
const normalized = casted.div(tf.scalar(255.0))
return normalized;
tf.dispose([decoded, resized, casted]);
   #generateTensor = tf.tidy(imagePath) => {
        const buffer = fs.readFileSync(imagePath);
        return tf.node.decodeJpeg(buffer, 3)
            .resizeNearestNeighbor([128, 128])
            .toFloat()
            .div(tf.scalar(255.0))
    }

How do I resolve 'expected struct, found reference` for borrowed value?

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#[derive(Clone)]
struct MethodMatch {
    selector: usize,
    function: Box<MethodType>,
}
`fn addMethodToTable(....,method: &Box<MethodType>)
-----------------------
#[derive(Clone)]
struct MethodMatch {
    selector: usize,
    function: Box<MethodType>,
}
`fn addMethodToTable(....,method: &Box<MethodType>)

count boolean values that equal between two strings

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import weka.classifiers.Classifier;
import weka.classifiers.Evaluation;
import weka.classifiers.bayes.NaiveBayes;
import weka.core.Instances;
import weka.core.converters.ConverterUtils.DataSource;

public class ForClassifier {

  public static void main(String[] args) throws Exception {
    // load dataset
    Instances train = DataSource.read(args[0]);
    train.setClassIndex(train.numAttributes() - 1);

    // build classifier
    Classifier model = new NaiveBayes();
    model.buildClassifier(train);

    // 1. manual evaluation
    System.out.println("manual evaluation");
    int correct = 0;
    int incorrect = 0;
    for (int i = 0; i < train.numInstances(); i++) {
      double actual = train.instance(i).classValue();
      double predicted = model.classifyInstance(train.get(i));
      if (actual == predicted)
        correct++;
      else
        incorrect++;
    }
    System.out.println("- correct: " + correct);
    System.out.println("- incorrect: " + incorrect);

    // 2. using Weka's Evaluation class
    System.out.println("Weka's Evaluation");
    Evaluation eval = new Evaluation(train);
    eval.evaluateModel(model, train);
    System.out.println("- correct: " + eval.correct());
    System.out.println("- incorrect: " + eval.incorrect());
  }
}

How to make text flow in counter clockwise direction javascript code

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var deg = 90 / txt.length,
    origin = 321;

  txt.forEach((ea) => {
    ea = `<p style='height:${radius+6}px;position:absolute;top:93px;z-index:99;
    transform:rotate(${origin}deg);transform-origin:0 -63%'>${ea}</p>`;
-----------------------
 var deg = 90 / txt.length,
    origin = 325;

  txt.forEach((ea) => {
    ea = `<p style='height:${radius}px;position:absolute;z-index:99;
                        transform:rotate(${origin}deg)translateY(95px);transform-origin:0 100%'>${ea}</p>`;
    classIndex.innerHTML += ea;
    origin += deg;

XGBoost4J-Spark Error - object dmlc is not a member of package org.apache.spark.ml

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spark-submit --packages ml.dmlc:xgboost4j-spark_2.11:1.1.0-SNAPSHOT other options
      <plugin>
        <groupId>org.apache.maven.plugins</groupId>
        <artifactId>maven-assembly-plugin</artifactId>
        <version>3.2.0</version>
        <configuration>
          <descriptorRefs>
            <descriptorRef>jar-with-dependencies</descriptorRef>
          </descriptorRefs>
        </configuration>
        <executions>
          <execution>
            <phase>package</phase>
            <goals>
              <goal>single</goal>
            </goals>
          </execution>
        </executions>
      </plugin>
-----------------------
spark-submit --packages ml.dmlc:xgboost4j-spark_2.11:1.1.0-SNAPSHOT other options
      <plugin>
        <groupId>org.apache.maven.plugins</groupId>
        <artifactId>maven-assembly-plugin</artifactId>
        <version>3.2.0</version>
        <configuration>
          <descriptorRefs>
            <descriptorRef>jar-with-dependencies</descriptorRef>
          </descriptorRefs>
        </configuration>
        <executions>
          <execution>
            <phase>package</phase>
            <goals>
              <goal>single</goal>
            </goals>
          </execution>
        </executions>
      </plugin>

Converting JSON coordinates to numpy array

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import matplotlib.pyplot as plt
import numpy as np
import mahotas.polygon as mp

json_dict = {
  "firstEditDate": "2019-12-02T19:05:45.393Z",
  "lastEditDate": "2020-06-30T13:21:33.371Z",
  "folder": "/Pictures/poly",
  "objects": [{
    "classIndex": 1,
    "layer": 0,
    "polygon": [
      {"x": 170, "y": 674},
      {"x": 70, "y": 674},
      {"x": 70, "y": 1120},
      {"x": 870, "y": 1120},
      {"x": 870, "y": 674},
      {"x": 770, "y": 674},
      {"x": 770, "y": 1020},
      {"x": 170, "y": 1020},
    ],
  }, {
    "classIndex": 2,
    "layer": 0,
    "polygon": [
      {"x": 220, "y": 870},
      {"x": 220, "y": 970},
      {"x": 720, "y": 970},
      {"x": 720, "y": 870},
    ]
  }, {
    "classIndex": 3,
    "layer": 0,
    "polygon": [
      {"x": 250, "y": 615},
      {"x": 225, "y": 710},
      {"x": 705, "y": 840},
      {"x": 730, "y": 745},
    ]
  }, {
    "classIndex": 4,
    "layer": 0,
    "polygon": [
      {"x": 350, "y": 380},
      {"x": 300, "y": 465},
      {"x": 730, "y": 710},
      {"x": 780, "y": 630},
    ]
  }, {
    "classIndex": 5,
    "layer": 0,
    "polygon": [
      {"x": 505, "y": 180},
      {"x": 435, "y": 250},
      {"x": 790, "y": 605},
      {"x": 855, "y": 535},
    ]
  }, {
    "classIndex": 6,
    "layer": 0,
    "polygon": [
      {"x": 700, "y": 30},
      {"x": 615, "y": 80},
      {"x": 870, "y": 515},
      {"x": 950, "y": 465},
    ]
  }]
}

canvas = np.zeros((1000,1150))
for obj in json_dict["objects"]:
  pts = [(round(p["x"]),round(p["y"])) for p in obj["polygon"]]
  mp.fill_polygon(pts, canvas, obj["classIndex"])
plt.imshow(canvas.transpose())
plt.colorbar()
plt.show()

Trying to build a table of unique values in LUA

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local unique_values = {}

for i = 1, 100 do
  local random_value = math.random(10)
  unique_values[random_value] = true
end

for k,v in pairs(unique_values) do print(k) end

Training of Kmeans algorithm failed on Spark

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+--------------+-----------------------------------------------------------+----------+
|CategoryVec   |feature_Norm                                               |prediction|
+--------------+-----------------------------------------------------------+----------+
|(13,[0],[1.0])|[0.2574383611739353,0.6931032800836721,0.6733003292241385] |1         |
|(13,[0],[1.0])|[0.22614412777205142,0.6989909403863407,0.6784323833161543]|1         |
|(13,[0],[1.0])|[0.24551225268848764,0.675158694893341,0.6956180492840484] |1         |
|(13,[0],[1.0])|[0.2420417625303279,0.7059551407134563,0.6656148469584017] |1         |
|(13,[0],[1.0])|[0.24214029368137852,0.6860641654305725,0.6860641654305725]|1         |
|(13,[0],[1.0])|[0.24214029368137852,0.6860641654305725,0.6860641654305725]|1         |
|(13,[0],[1.0])|[0.2540244987629046,0.683912112053974,0.683912112053974]   |1         |
|(13,[0],[1.0])|[0.2388089256503974,0.6766252893427926,0.6965260331469925] |1         |
|(13,[0],[1.0])|[0.2574383611739353,0.6733003292241385,0.6931032800836721] |1         |
|(13,[0],[1.0])|[0.2572366859677566,0.652985433610459,0.7123477457568644]  |1         |
+--------------+-----------------------------------------------------------+----------+

+--------------+------------------------------------------------------------+----------+
|CategoryVec   |feature_Norm                                                |prediction|
+--------------+------------------------------------------------------------+----------+
|(13,[5],[1.0])|[0.4673452175282961,0.5098311463945049,0.7222607907255486]  |0         |
|(13,[5],[1.0])|[0.4673452175282961,0.5098311463945049,0.7222607907255486]  |0         |
|(13,[5],[1.0])|[0.46105396573580254,0.48899663032585117,0.7404806116362889]|0         |
|(13,[5],[1.0])|[0.4369231823814617,0.5214889596165833,0.7329034027043874]  |0         |
|(13,[5],[1.0])|[0.45146611838648026,0.5078993831847903,0.7336324423780305] |0         |
|(13,[5],[1.0])|[0.4561664027908625,0.5131872031397203,0.7270152044479371]  |0         |
|(13,[5],[1.0])|[0.4561664027908625,0.5131872031397203,0.7270152044479371]  |0         |
|(13,[5],[1.0])|[0.45789190653985307,0.49951844349802155,0.7354021529276429]|0         |
|(13,[5],[1.0])|[0.4658526940598004,0.4940861906694853,0.7340709118518067]  |0         |
|(13,[5],[1.0])|[0.4625915702820905,0.5046453493986442,0.7289321713535972]  |0         |
+--------------+------------------------------------------------------------+----------+

scalaVersion := "2.11.10"

// https://mvnrepository.com/artifact/org.apache.spark/spark-mllib
libraryDependencies += "org.apache.spark" %% "spark-mllib" % "2.2.0"
libraryDependencies += "org.apache.spark" %% "spark-core" % "2.2.0"
libraryDependencies += "org.apache.spark" % "spark-sql_2.11" % "2.2.0"
import org.apache.spark.ml.Pipeline
import org.apache.spark.ml.clustering.KMeans
import org.apache.spark.ml.feature.{Normalizer, OneHotEncoderEstimator, StringIndexer, VectorAssembler}
import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.types.DoubleType
-----------------------
+--------------+-----------------------------------------------------------+----------+
|CategoryVec   |feature_Norm                                               |prediction|
+--------------+-----------------------------------------------------------+----------+
|(13,[0],[1.0])|[0.2574383611739353,0.6931032800836721,0.6733003292241385] |1         |
|(13,[0],[1.0])|[0.22614412777205142,0.6989909403863407,0.6784323833161543]|1         |
|(13,[0],[1.0])|[0.24551225268848764,0.675158694893341,0.6956180492840484] |1         |
|(13,[0],[1.0])|[0.2420417625303279,0.7059551407134563,0.6656148469584017] |1         |
|(13,[0],[1.0])|[0.24214029368137852,0.6860641654305725,0.6860641654305725]|1         |
|(13,[0],[1.0])|[0.24214029368137852,0.6860641654305725,0.6860641654305725]|1         |
|(13,[0],[1.0])|[0.2540244987629046,0.683912112053974,0.683912112053974]   |1         |
|(13,[0],[1.0])|[0.2388089256503974,0.6766252893427926,0.6965260331469925] |1         |
|(13,[0],[1.0])|[0.2574383611739353,0.6733003292241385,0.6931032800836721] |1         |
|(13,[0],[1.0])|[0.2572366859677566,0.652985433610459,0.7123477457568644]  |1         |
+--------------+-----------------------------------------------------------+----------+

+--------------+------------------------------------------------------------+----------+
|CategoryVec   |feature_Norm                                                |prediction|
+--------------+------------------------------------------------------------+----------+
|(13,[5],[1.0])|[0.4673452175282961,0.5098311463945049,0.7222607907255486]  |0         |
|(13,[5],[1.0])|[0.4673452175282961,0.5098311463945049,0.7222607907255486]  |0         |
|(13,[5],[1.0])|[0.46105396573580254,0.48899663032585117,0.7404806116362889]|0         |
|(13,[5],[1.0])|[0.4369231823814617,0.5214889596165833,0.7329034027043874]  |0         |
|(13,[5],[1.0])|[0.45146611838648026,0.5078993831847903,0.7336324423780305] |0         |
|(13,[5],[1.0])|[0.4561664027908625,0.5131872031397203,0.7270152044479371]  |0         |
|(13,[5],[1.0])|[0.4561664027908625,0.5131872031397203,0.7270152044479371]  |0         |
|(13,[5],[1.0])|[0.45789190653985307,0.49951844349802155,0.7354021529276429]|0         |
|(13,[5],[1.0])|[0.4658526940598004,0.4940861906694853,0.7340709118518067]  |0         |
|(13,[5],[1.0])|[0.4625915702820905,0.5046453493986442,0.7289321713535972]  |0         |
+--------------+------------------------------------------------------------+----------+

scalaVersion := "2.11.10"

// https://mvnrepository.com/artifact/org.apache.spark/spark-mllib
libraryDependencies += "org.apache.spark" %% "spark-mllib" % "2.2.0"
libraryDependencies += "org.apache.spark" %% "spark-core" % "2.2.0"
libraryDependencies += "org.apache.spark" % "spark-sql_2.11" % "2.2.0"
import org.apache.spark.ml.Pipeline
import org.apache.spark.ml.clustering.KMeans
import org.apache.spark.ml.feature.{Normalizer, OneHotEncoderEstimator, StringIndexer, VectorAssembler}
import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.types.DoubleType
-----------------------
+--------------+-----------------------------------------------------------+----------+
|CategoryVec   |feature_Norm                                               |prediction|
+--------------+-----------------------------------------------------------+----------+
|(13,[0],[1.0])|[0.2574383611739353,0.6931032800836721,0.6733003292241385] |1         |
|(13,[0],[1.0])|[0.22614412777205142,0.6989909403863407,0.6784323833161543]|1         |
|(13,[0],[1.0])|[0.24551225268848764,0.675158694893341,0.6956180492840484] |1         |
|(13,[0],[1.0])|[0.2420417625303279,0.7059551407134563,0.6656148469584017] |1         |
|(13,[0],[1.0])|[0.24214029368137852,0.6860641654305725,0.6860641654305725]|1         |
|(13,[0],[1.0])|[0.24214029368137852,0.6860641654305725,0.6860641654305725]|1         |
|(13,[0],[1.0])|[0.2540244987629046,0.683912112053974,0.683912112053974]   |1         |
|(13,[0],[1.0])|[0.2388089256503974,0.6766252893427926,0.6965260331469925] |1         |
|(13,[0],[1.0])|[0.2574383611739353,0.6733003292241385,0.6931032800836721] |1         |
|(13,[0],[1.0])|[0.2572366859677566,0.652985433610459,0.7123477457568644]  |1         |
+--------------+-----------------------------------------------------------+----------+

+--------------+------------------------------------------------------------+----------+
|CategoryVec   |feature_Norm                                                |prediction|
+--------------+------------------------------------------------------------+----------+
|(13,[5],[1.0])|[0.4673452175282961,0.5098311463945049,0.7222607907255486]  |0         |
|(13,[5],[1.0])|[0.4673452175282961,0.5098311463945049,0.7222607907255486]  |0         |
|(13,[5],[1.0])|[0.46105396573580254,0.48899663032585117,0.7404806116362889]|0         |
|(13,[5],[1.0])|[0.4369231823814617,0.5214889596165833,0.7329034027043874]  |0         |
|(13,[5],[1.0])|[0.45146611838648026,0.5078993831847903,0.7336324423780305] |0         |
|(13,[5],[1.0])|[0.4561664027908625,0.5131872031397203,0.7270152044479371]  |0         |
|(13,[5],[1.0])|[0.4561664027908625,0.5131872031397203,0.7270152044479371]  |0         |
|(13,[5],[1.0])|[0.45789190653985307,0.49951844349802155,0.7354021529276429]|0         |
|(13,[5],[1.0])|[0.4658526940598004,0.4940861906694853,0.7340709118518067]  |0         |
|(13,[5],[1.0])|[0.4625915702820905,0.5046453493986442,0.7289321713535972]  |0         |
+--------------+------------------------------------------------------------+----------+

scalaVersion := "2.11.10"

// https://mvnrepository.com/artifact/org.apache.spark/spark-mllib
libraryDependencies += "org.apache.spark" %% "spark-mllib" % "2.2.0"
libraryDependencies += "org.apache.spark" %% "spark-core" % "2.2.0"
libraryDependencies += "org.apache.spark" % "spark-sql_2.11" % "2.2.0"
import org.apache.spark.ml.Pipeline
import org.apache.spark.ml.clustering.KMeans
import org.apache.spark.ml.feature.{Normalizer, OneHotEncoderEstimator, StringIndexer, VectorAssembler}
import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.types.DoubleType

How to overlay correctly in html tables

copy iconCopydownload iconDownload
const layerArray = ["layer1", "layer2", "layer3", "layer4"];

var arrLen = layerArray.length;


$(function() {
  $("td").click(function() {
    $(this).children().toggleClass('red');
    let index = $("td").index(this);
    let layerChange = $(this).children().hasClass('red') ? 1 : -1;
    
    $("td").slice(index + 1, index + 20).each(function() {
      let classIndex = $(this).data('layer');
      classIndex += layerChange;

      if (layerChange === 1 && classIndex - 1 < arrLen) {
        $(this).addClass(layerArray[classIndex - 1])
      } else if (layerChange === -1 && classIndex >= 0) {
        $(this).removeClass(layerArray[classIndex])
      }

      $(this).data('layer', classIndex);
    });
  });
});
td {
  transition-duration: 0.5s;
  border: solid black 1px;
  cursor: pointer;
}

div {
  padding: 5px;
}

table {
  border-collapse: collapse;
}

.red {
  background-color: red;
}

.layer1 {
  background-color: hsl(60, 90%, 90%);
}

.layer2 {
  background-color: hsl(40, 90%, 90%);
}

.layer3 {
  background-color: hsl(20, 90%, 90%);
}

.layer4 {
  background-color: hsl(0, 90%, 90%);
}
<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.3.1/jquery.min.js"></script>

<div id=calendar></div>


<script>
  let html = ''
  html += '<table>';
  let i = 0;
  for (let w = 0; w < 10; w++) {
    html += '<tr>';
    for (let d = 0; d < 10; d++) {
      i = i + 1;
      html += '<td data-layer=0>' + '<div>' + i + '</div>' + '</td>'
    }
    html += '</tr>';
  }
  html += '</table>'
  document.querySelector('#calendar').innerHTML = html;
</script>
-----------------------
const layerArray = ["layer1", "layer2", "layer3", "layer4"];

var arrLen = layerArray.length;


$(function() {
  $("td").click(function() {
    $(this).children().toggleClass('red');
    let index = $("td").index(this);
    let layerChange = $(this).children().hasClass('red') ? 1 : -1;
    
    $("td").slice(index + 1, index + 20).each(function() {
      let classIndex = $(this).data('layer');
      classIndex += layerChange;

      if (layerChange === 1 && classIndex - 1 < arrLen) {
        $(this).addClass(layerArray[classIndex - 1])
      } else if (layerChange === -1 && classIndex >= 0) {
        $(this).removeClass(layerArray[classIndex])
      }

      $(this).data('layer', classIndex);
    });
  });
});
td {
  transition-duration: 0.5s;
  border: solid black 1px;
  cursor: pointer;
}

div {
  padding: 5px;
}

table {
  border-collapse: collapse;
}

.red {
  background-color: red;
}

.layer1 {
  background-color: hsl(60, 90%, 90%);
}

.layer2 {
  background-color: hsl(40, 90%, 90%);
}

.layer3 {
  background-color: hsl(20, 90%, 90%);
}

.layer4 {
  background-color: hsl(0, 90%, 90%);
}
<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.3.1/jquery.min.js"></script>

<div id=calendar></div>


<script>
  let html = ''
  html += '<table>';
  let i = 0;
  for (let w = 0; w < 10; w++) {
    html += '<tr>';
    for (let d = 0; d < 10; d++) {
      i = i + 1;
      html += '<td data-layer=0>' + '<div>' + i + '</div>' + '</td>'
    }
    html += '</tr>';
  }
  html += '</table>'
  document.querySelector('#calendar').innerHTML = html;
</script>
-----------------------
const layerArray = ["layer1", "layer2", "layer3", "layer4"];

var arrLen = layerArray.length;


$(function() {
  $("td").click(function() {
    $(this).children().toggleClass('red');
    let index = $("td").index(this);
    let layerChange = $(this).children().hasClass('red') ? 1 : -1;
    
    $("td").slice(index + 1, index + 20).each(function() {
      let classIndex = $(this).data('layer');
      classIndex += layerChange;

      if (layerChange === 1 && classIndex - 1 < arrLen) {
        $(this).addClass(layerArray[classIndex - 1])
      } else if (layerChange === -1 && classIndex >= 0) {
        $(this).removeClass(layerArray[classIndex])
      }

      $(this).data('layer', classIndex);
    });
  });
});
td {
  transition-duration: 0.5s;
  border: solid black 1px;
  cursor: pointer;
}

div {
  padding: 5px;
}

table {
  border-collapse: collapse;
}

.red {
  background-color: red;
}

.layer1 {
  background-color: hsl(60, 90%, 90%);
}

.layer2 {
  background-color: hsl(40, 90%, 90%);
}

.layer3 {
  background-color: hsl(20, 90%, 90%);
}

.layer4 {
  background-color: hsl(0, 90%, 90%);
}
<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.3.1/jquery.min.js"></script>

<div id=calendar></div>


<script>
  let html = ''
  html += '<table>';
  let i = 0;
  for (let w = 0; w < 10; w++) {
    html += '<tr>';
    for (let d = 0; d < 10; d++) {
      i = i + 1;
      html += '<td data-layer=0>' + '<div>' + i + '</div>' + '</td>'
    }
    html += '</tr>';
  }
  html += '</table>'
  document.querySelector('#calendar').innerHTML = html;
</script>

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QUESTION

How to run tf.lite model on raspery-pi instead of saved keras model

Asked 2021-Dec-01 at 16:19

I am tring to classify traffic sings by using raspery-pi, for this i trained and saved a keras model that is .h5 file, but it consume too much cpu so i convert it to .tflite model and tried to run. However it gives that error OSError: SavedModel file does not exist at: yourmodel.tflite/{saved_model.pbtxt|saved_model.pb} i checked the path, here is my code. Also i just changed that line: model = tensorflow.keras.models.load_model("my_model.h5") to model = tensorflow.keras.models.load_model("yourmodel.tflite")

import numpy as np
import cv2
import tensorflow
from tensorflow import keras
from tensorflow.keras.preprocessing import image
 
#############################################
frameWidth= 600         # CAMERA RESOLUTION
frameHeight = 480
brightness = 180
threshold = 0.75         # PROBABLITY THRESHOLD
font = cv2.FONT_HERSHEY_SIMPLEX
##############################################
 
# SETUP THE VIDEO CAMERA
cap = cv2.VideoCapture(0)
cap.set(3, frameWidth)
cap.set(4, frameHeight)
cap.set(10, brightness)
cap.set(cv2.CAP_PROP_FPS, 3)

# IMPORT THE TRANNIED MODEL
model = tensorflow.keras.models.load_model("yourmodel.tflite")
#model = load_model('best_model.h5')

def equalize(img):
    img = cv2.equalizeHist(img)
    return img
def grayscale(img):
    img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    return img
def preprocessing(img):
    img = grayscale(img)
    img = equalize(img)
    img = img/255
    return img
 
def getCalssName(classNo):
    if   classNo == 0: return 'Speed Limit 20 km/h'
    
    elif classNo == 9: return 'No passing'

    elif classNo == 12: return 'Priority road'
    elif classNo == 13: return 'Yield'
    elif classNo == 14: return 'Stop'

    elif classNo == 38: return 'Keep right'
    elif classNo == 39: return 'Keep left'
    
while True:
    success, imgOrignal = cap.read()
    img = np.asarray(imgOrignal)
    #img = cv2.resize(img, (32, 32))
    img = preprocessing(img)
    cv2.imshow("Processed Image", img)
    img = img.reshape(1, 32, 32, 1)
    cv2.putText(imgOrignal, "CLASS: " , (20, 35), font, 0.75, (0, 0, 255), 2, cv2.LINE_AA)
    cv2.putText(imgOrignal, "PROBABILITY: ", (20, 75), font, 0.75, (0, 0, 255), 2, cv2.LINE_AA)
    
    # PREDICT IMAGE
    predictions = model.predict(img)
    classIndex = model.predict_classes(img)
    probabilityValue =np.amax(predictions)
    if probabilityValue > threshold:
        print(getCalssName(classIndex))
        #cv2.rectangle(image, coordinate[0],coordinate[1], (0, 255, 0), 1)
        cv2.putText(imgOrignal,str(classIndex)+" "+str(getCalssName(classIndex)), (120, 35), font, 0.75, (0, 0, 255), 2, cv2.LINE_AA)
        cv2.putText(imgOrignal, str(round(probabilityValue*100,2) )+"%", (180, 75), font, 0.75, (0, 0, 255), 2, cv2.LINE_AA)
        cv2.imshow("Result", imgOrignal)
        
        if cv2.waitKey(1) and 0xFF == ord('q'):
            break

cap.release()

cv2.destroyAllWindows()

ANSWER

Answered 2021-Dec-01 at 16:19

Try to save your keras model using this code

# model is your keras model
tflite_model = tf.lite.TFLiteConverter.from_keras_model(model).convert()

with open('model.tflite', 'wb') as f:
    f.write(tflite_model)

To load and use it you will need tf.lite.Interpreter

# instead of `model = tensorflow.keras.models.load_model("yourmodel.tflite")`
# use this code to load tflite model
interpreter = tf.lite.Interpreter(model_path="model.tflite")
interpreter.allocate_tensors()
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()

# replace `predictions = model.predict(img)` with this code
interpreter.set_tensor(input_details[0]['index'], img)
interpreter.invoke()
predictions = interpreter.get_tensor(output_details[0]['index'])

Source https://stackoverflow.com/questions/70167070

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install classindex

You can download the library from here or use the following Maven dependency:.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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