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Обратите внимание на 3 и 4 строчки. Они означают, что конкретно с данной активности начнется выполнение приложения на самом деле таковых активностей может быть несколько, но это уникальность. Также обратите внимание на последующую строку:. Можно огласить, это полный «путь» до активности, включая все имена покетов. Конкретно по такому пути вы отыщите ее в исходниках, загруженных в Android Studio.

Открыв исходник данной нам активности в Android Studio, вы заметите его крайнюю простоту. Перебегаем в него и лицезреем ChallengeOneFragment. Он как раз и содержит поле ввода ответа и клавишу для записи файла. Пристально прочитав исходник, замечаем, что в качестве колбэка для данной для нас клавиши употребляется ChallengeOneFragmentOnClickListener, определенный в одноименном файле в каталоге listeners.

Вот мы и взломали crackme, разыскиваемая строка: «poorly-protected-secret». Но постойте, мы же договорились, что представим как будто строчка зашифрована и мы не можем ее узреть. Вот тут нам и нужен дебаггер: соль в том, чтоб поставить брейк-пойнт на вторую строчку приведенного выше листинга и опосля этого запустить приложение.

Когда выполнение приложения дойдет до данной нам точки, оно будет остановлено, а мы с помощью консоли дебаггера сможем прочесть аргумент вызова write который уже должен быть расшифрован перед записью. Итак, открываем исходник в Android Studio, находим подходящую строчку и кликаем рядом с номером данной нам строчки. Строчка подсвечивается красноватым, сигнализируя о установке брейк-пойнта.

Наверное вы не сходу можете установить корректный брейк-пойнт тот, который будет отмечен галочкой , поэтому что исходники, быстрее всего, не будут соответствовать коду приложения. По данной же причине для вас придется действовать вслепую, когда выполнение приложение дойдет до брейк-пойнта. При переходе к последующей строке дебаггер будет кидать вас в совсем неправильные кусочки кода, повсевременно сообщая, что код неправильный.

Выхода из данной для нас ситуации два: или продвигаться на ощупь, или заместо декомпилированного Java-кода загрузить в Android Studio дизассемблированный код smali, который просто технически не может быть неправильным. Дизассемблированный код приложения у нас уже есть. Мы получили его, когда разбирали приложение с помощью apktool. Неувязка лишь в том, что Android Studio в собственном обычном варианте хоть и умеет подсвечивать код smali, но не способна «работать» с сиим кодом.

Иными словами, мы сможем прочесть код smali, но не сможем установить брейк-пойнты. В этот раз код намного длиннее и запутаннее, но, используя ранее приобретенный Java-исходник, вы просто отыщите необходимое место, а конкретно ю строку:. Ставьте на нее брейк-пойнт, запускайте приложение и, когда оно остановится, поглядите на окно дебаггера.

В нем будет состояние текущего и остальных объектов. Нажимая F7, вы заставите дебаггер выполнить последующую строчку с переходом вовнутрь вызываемой функции в нашем случае outputStreamWriter. Оказавшись снутри нее, вы можете просмотреть содержимое ее аргумента, а конкретно разыскиваемую строчку. В данном случае ее видно и до перехода в функцию, но, ежели бы она была зашифрована, этот способ посодействовал бы узнать реальное значение.

В общем, отладчик — чрезвычайно удачный и очень нужный инструмент. Он дозволяет установить последовательность выполнения инструкций приложения и найти сокрытые данные. С помощью отладчика можно учить тонкости работы Android и приложений для него, а также создавать взлом определённого сектора данных.

Материал, представленный в процессе статьи, несёт только ознакомительный нрав. Как взломать приложения для Android при помощи отладчика. In Mobile Tags android , apktool , Jadx , smalidea 17 сентября, Views trinity. ChallengeActivity Можно огласить, это полный «путь» до активности, включая все имена покетов.

Click to rate this post! Post Navigation. Next Post: Как прятать сокрытые файлы в картинах. Related Posts:. Шифрование данных на карте памяти в Android.

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Если при этом добавить в код вызов функции yield , интерфейс никак не пострадает и остается плавным:. Как это возможно? Все дело в том, как Android вызывает код отрисовки интерфейса в основном потоке приложения. Каждые 16 миллисекунд при частоте обновления экрана в 60 герц фреймворк Android добавляет новейший Runnable блок кода с кодом обновления интерфейса в очередь выполнения основного потока приложения. Ежели основной поток не занят в это время иной работой, он исполнит этот код.

В неприятном случае продолжится выполнение текущего кода, а операция обновления будет пропущена. Так выходит пропуск кадра, а пропуск пары кадров попорядку смотрится как фриз интерфейса. Конкретно это обязано было произойти при запуске предшествующего кода. Но не вышло благодаря вызову функции yield. Она приостанавливает выполнение текущей корутины до получения последующего элемента в данном случае числа. Приостановка корутины приводит к перемещению кода обработки последующего элемента в очереди выполнения.

В итоге весь код вычисления факториала разбивается на множество малеханьких блоков, которые помещаются в очередь выполнения вперемешку с кодом обновления экрана. Поток успевает выполнить несколько шагов вычисления факториала, потом код обновления UI , потом еще несколько шагов факториала и так дальше. Это канонический пример того, что именуют словом concurrency в противовес параллельному вычислению.

Мы по максимуму загружаем основной поток работой, при этом позволяя ему быстро переключаться меж задачками. Факториал при таком подходе рассчитывается приблизительно в два раза медлительнее, зато интерфейс остается плавным даже без использования фоновых потоков. 3-я изюминка корутин — то, что именуется structured concurrency о этом отлично написал Роман Елизаров из команды Kotlin. В коде в начале этого раздела корутина запускается с помощью функции CoroutineScope. Это один из разрабов корутин, которые генерируют новейшие корутины.

Не считая самой корутины, этот билдер также делает так именуемый c oroutine scope , представляющий собой собственного рода область деяния данной нам и всех порожденных ей корутин. Смысл существования областей деяния в том, чтоб не отдать корутинам утечь. Чтоб показать возможность таковой утечки, возьмите последующий код. Он делает две корутины, используя билдер async. Эти сопрограммы сразу выполняют фоновую загрузку 2-ух изображений, а потом результаты их работы употребляются в родительской сопрограмме:.

Прекрасно и комфортно, но есть неувязка. Ежели при загрузке изображения возникает исключение и одна из корутин завершает свою работу, 2-ая корутина продолжит работу, хотя она больше не нужна. Сейчас обе корутины находятся в одной области видимости, и ежели одна из запущенных снутри корутин выдает исключение, все остальные корутины в данной нам области будут автоматом завершены.

Сейчас обе корутины находятся в одной области, и ежели одна из запущенных корутин запускает исключение, все другие корутины в данной для нас области будут автоматом закрыты. Мысль structured concurrency заключается в том, что разраб делает новейшие «области» для всех связанных блоков кода. В Android это может быть модель просмотра, вариант использования, код пуска сети и т. Вы сможете сделать CoroutineScope как свойство класса и употреблять его во всем классе:.

Есть три главных метода завершения корутин. 1-ый — через завершение coroutine scope , которому принадлежат корутины:. Kotlin не лишь упрощает написание многопоточного кода, но и делает его наиболее надежным. Его создатели учли ошибки Java и сделали все, чтоб избежать заморочек с разделением данных и блокировкой, которые присущи классическому подходу к написанию многопоточных приложений.

Во-1-х, Kotlin и его обычная библиотека верно различают модифицируемые и немодифицируемые данные, а компилятор и среда разработки постоянно указывают, что переменная может стать постоянной. Все это приводит к написанию кода в многофункциональном стиле, когда любая функция выполняет лишь одну задачку, не изменяет входные данные и не изменяет поля класса. Неплохой многопоточный код Kotlin — это конвейер с источником данных в начале, функциями обработки в середине и потребителем в конце.

Но у этого подхода есть проблема: он подступает лишь для задач, где каждый элемент конвейера возвращает всю коллекцию данных полностью, а сама коллекция не чрезвычайно крупная. Но что, ежели наш поток зависает в фоновом режиме на время жизни приложения и время от времени получает от сервера данные, которые каким-то образом необходимо обрабатывать в остальных потоках?

Классическим решением данной препядствия в стиле Java было бы создание специального объекта, к которому все потоки могут обращаться для записи либо чтения данных. И все это принесет с собой те же задачи с блокировкой, решив задачи синхронизации потоков. Kotlin дает иной путь: стиль программирования CSP , аналогичный используемому в языке Go. Мысль проста: мы не передаем данные меж потоками, но канал связи и поток записывают данные в него, в то время как остальные читают их.

Этот код делает канал, а потом запускает корутину, которая записывает 5 цифр в строчку для этого канала, а потом закрывает ее. Иная корутина считывает данные из трубки и показывает их. И сходу опосля этого будет запущена корутина, которая считывает данные.

Каналы — действенное средство синхронизации и передачи данных. Создатели Kotlin приложили много усилий, чтоб сделать их неблокирующими и высокопроизводительными. Но каналы подступают не для всех ситуаций. Они отлично работают, когда корутина, дающая данные, живет собственной своей жизнью, а сами данные могут появляться без спроса. Но ежели все, что для вас необходимо сделать, это начать сбор ограниченного количества данных, которые могут поступать по частям, тогда для вас следует направить внимание на flow.

Допустим, у нас есть несколько фрагментов данных, которые можно вводить через определенные промежутки времени. Чтоб получить эти данные, обработать их и выслать далее, мы можем написать функцию, которая делает поток по сущности, «поток данных» :.

В этом случае функция «выпускает» 5 объектов Int в поток данных с интервалом в миллисекунд. Обратите внимание, что flow — это s uspend-функция , которая может запускать остальные suspend-функции в данном случае delay. Но сама функция doSomething не приостановлена. Это принципиально. Тут мы вызываем функцию doSomething вне корутины, но функцию collect — снутри корутины.

Все дело в том, что при вызове doSomething ничего не происходит, функция сходу возвращает управление. И лишь опосля вызова collect начинает работать определенный снутри функции код. Иными словами, вызов collect запускает код функции doSomething в рамках текущей корутины, а опосля получения крайнего элемента возвращает управление. Завершают поток данных несколькими методами — от вызова collect до способов типа first , fold , toList , знакомых для тебя по работе с коллекциями.

Поток данных можно трансформировать, чтоб получить новейший поток данных с помощью способов map , filter , take. Можно объединить несколько потоков в один с помощью способов zip , merge и combine и поменять контекст выполнения к примеру, диспетчер с помощью flowOn :.

Это код функции поиска из настоящего приложения. Она берет несколько источников данных для поиска searchSources — это перечень объектов , отфильтровывает отключенные источники, а потом запускает на каждом из их функцию поиска search , которая возвращает Flow.

Дальше несколько потоков данных объединяются с помощью merge. В конце потоки запускаются, а их результаты показываются на экране. Благодаря использованию функции flowOn вся обработка потоков данных происходит в фоне, но результаты будут показаны в UI-потоке приложения. В конце концов, давайте побеседуем о корутинах в StateFlow , совсем новеньком механизме для хранения состояний на базе потока. StateFlow дозволяет разным компонентам приложения изменять состояние и реагировать на конфигурации в этом состоянии.

Приведу тут выжимку кода. Сейчас давайте сделаем действие, используя эту модель. Действие будет состоять из TextView с числом и 2-мя клавишами для роста и уменьшения этого числа:. Это все. Нажатие клавиши с номером конфигурации изменит состояние ViewModel , что, в свою очередь, приведет к автоматическому изменению TextView. И все это работает с жизненным циклом бизнеса с помощью lifecycleScope.

Функции в Kotlin наиболее массивные, чем в Java в особенности когда мы говорим о Java 7 и ниже. Так же функции могут быть объявлены вне классов, вложены друг в друга, иметь предопределенные значения аргументов и принимать остальные функции в качестве аргументов. Данные функции можно объявлять с помощью главного слова inline, потому для увеличения производительности тело функции будет интегрированным, а не вызовом.

Также функции могут быть функциями фрейма и расширения. Мы сосредоточимся на крайних 2-ух типах функций, как на более увлекательных и недопонятых. Функции расширения — это способы, которые вы сможете добавить к хоть какому классу, независимо от того, есть ли у вас доступ к его начальному коду либо нет.

Функции-расширения делают лучше читаемость кода. Строчка string. Они разрешают сделать класс легче и удобнее для чтения и осознания. Ежели, к примеру, некий набор приватных способов класса нужен лишь одному общественному способу, их совместно с общественным способом можно вынести в расширения.

Они упрощают написание кода, так как IDE будет автоматом давать подсказку, какие способы и функции-расширения есть у класса. Но при неверном использовании функции- расширения могут вызвать множество заморочек. Большая часть этих заморочек возникает, когда создатели пробуют употреблять функции расширения в собственных классах заместо сотворения обычных способов.

Любопытно, что функциями-расширениями в Kotlin также могут быть лямбды, они именуются лямбдами с ресивером. К примеру, последующая функция воспринимает в качестве аргумента лямбду:. Лямбды с ресивером активно употребляются в обычной библиотеке Kotlin. К примеру, функция-расширение apply употребляет лямбду с ресивером, чтоб запустить код лямбды в контексте объекта, для которого была вызвана функция apply:.

Еще наиболее увлекательный пример — функция use , которую можно вызвать для объектов, реализующих интерфейс Closable, чтоб автоматом вызвать способ close опосля выполнения кода лямбды:. В данном случае close будет вызван сходу опосля записи строчки Hello world в файл. Наверняка, самый успешный пример внедрения инфиксных функций был приведен в статье Kotlin: When if-else is too mainstream. Это короткая заметка о том, как сделать наиболее удачный аналог оператора if-else:.

Функции then и elze в данном примере инфиксные. По сущности, это обыденные функции-расширения, которые можно вызвать, используя пробел заместо точки:. Kotlin дозволяет вызывать объект как функцию. Все, что необходимо, — добавить в код его класса способ invoke.

К огорчению либо к счастью , функции в Kotlin не могут возвращать два значения. Но тут есть парные и тройные классы, чтоб упаковать несколько значений в один класс. Парадигма аспектно-ориентированного программирования АОП дозволяет помещать код в отдельные модули, что проблематично для разделения с иными подходами к программированию: ведение журнальчика, обработка исключений, кэширование и контроль доступа. Вся эта функциональность именуется сквозной.

Обычные фреймворки аспектно нацеленного программирования, такие как AspectJ , разрешают вынести такую функциональность в отдельные модули, используя аннотации:. В этом примере customer. Единственная неувязка с данной нам аннотацией заключается в том, что она не так очевидна и проста в использовании, как рядовая функция. К счастью, Kotlin дозволяет получить некие достоинства АОП без использования особых фреймворков и аннотаций:. Как и функции, классы в Kotlin имеют наиболее широкие способности в сопоставлении с аналогами из Java.

Классы можно вызывать на манер обыденных функций, их можно делать инлайновыми, чтоб компилятор развернул их на месте использования. Тут есть data-классы для хранения данных для их будут автоматом сделаны способы toString , hashCode и copy , также изолированные sealed классы, которые совершенно подступают для хранения состояния. Sealed-класс в Kotlin — собственного рода enum на стероидах. Он может иметь ограниченное количество потомков, все из которых должны быть объявлены в том же файле.

Взглянем на последующий пример:. Тут объявляются sealed-класс NetworkResult и два его потомка: Success и Failure. Потом они употребляются для проверки результата операции и принятия мер на базе этого результата. Мы можем быть убеждены, что данные не могут быть ничем другим, не считая Success и Failure , они даже не могут быть NetworkResult, так как сам sealed-класс постоянно является абстрактным.

Sealed-классы безупречны для хранения состояний. Они также подступают для реализации самих конвейеров обработки инфы Railway Oriented Programming , о которых мы говорили в разделе о сопрограммах:. Это пример с GitHub-страницы kotlin-result, крошечной библиотеки, реализующей тип Result на базе sealed-классов. В этом примере любая следующая команда возвращает значение sealed-класса Result ошибка либо значение , но ошибки обрабатываются лишь на крайнем шаге выполнения.

Один из главных постулатов современного ООП-программирования гласит: предпочитайте делегирование наследованию. Это означает, что заместо наследования от какого-нибудь класса лучше включить инстанс этого класса в иной класс и вызывать его способы при вызове одноименных способов этого класса:. Для чего это нужно? Для того, чтоб избежать заморочек, когда способы класса-родителя вызывают друг друга. Ежели method1 вызывает method2 , то, переопределив 2-ой способ, мы сломаем работу первого.

Agriculture is the primary occupation of villagers and a large sustaining population is dependent on agriculture. From the advent of agriculture, there has been much mechanical and chemical advancement that has occurred to improve the yield and help farmers tackle issues like agriculture and crop diseases. But there has been little to less digitization done in this field. With the boom of IOT, there is a hope for creating a digital system for agriculture which will help the farmer make informed decisions about his farm and help him tackle some undesired situations in advance.

So, it will help to improve the quality of crops and also it will be beneficial for farmers. Early Detection of Disease which is a great challenge in agriculture field. An earlier large team of experts are called by the farmers to chalk out the diseases or any harm which occurred to plants, even this practice is not known to every farmer and therefore the experts cost much and also it is time consuming.

Whereas Automatic detection is more beneficial than this long process of observations by the experts, Automation technique of the disease detection where the result comes out to just monitoring the change in plant. Creating Hydra: OpenVino based Plant Disease and climatic factors monitoring and autonomous watering system. Taking into consideration these drawbacks faced during manual crop monitoring, I decided to create an autonomous monitoring system.

In manual methods of agriculture, due to insufficient crop data, there is inaccurate crop monitoring, This leads to low crop yield production. In Apple plant production, stability of constant soil PH levels are required along with constant temperature and humidity for accurate high yield productions. Depending on the Diseases faced by the Apple plant, the plant array has to be provided with the suitable conditions.

For example: If an Apple plant disease is detected where in the calcium levels of the plant is low, the plant has to be provided with the similar nutrients for which it is facing deficiency. Based on this conclusion, I decided to create a Vision Learning model which accuratly detects the diseases faced in Apple plantations and provide geo spatial analysis of this data across the farm along with Timely Data Trends. This Pie chart displays the percentage-wise distribution of commonly faced diseases in Apple Agriculture.

Taking into consideration these commonly faced diseases, I decided to create a Computer Vision Model based on OpenVino deployed on the Raspberry Pi for detection and classification of these diseases. For this purpose, I have taken into consideration the following most commonly faced Diseases:. Some spots turn grayish brown, but most lesions may coalesce or undergo secondary enlargement and become irregular and much darker, acquiring a "frog-eye" appearance.

When lesions occur on petioles, the leaves turn yellow and 50 percent or more defoliation may occur. Severe defoliation leads to premature fruit drop. Fruit infections result in small, dark, raised lesions associated with the lenticel. Frogeye leaf spot usually appears earlier in the season and is associated with nearby dead wood or fruit mummies.

Captan spot spray injury occurs when captan fungicide is applied under wet conditions and associated with 2 to 4 leaves on terminals, representing a spray event. Alternaria leaf blotch tends to be uniformly distributed throughout the tree. Cedar-apple rust is the most common of the three fungal rust diseases and attacks susceptible cultivars of apples and crabapples. It infects the leaves, fruit, and, occasionally, young twigs. The alternate host plant, Eastern red cedar Juniperus virginiana , is necessary for the survival of the fungus.

Fire blight is a common and very destructive bacterial disease of apples and pears Figure 1. The disease is caused by the bacterium Erwinia amylovora , which can infect and cause severe damage to many plants in the rose Rosaceae family Table 1. On apples and pears, the disease can kill blossoms, fruit, shoots, twigs, branches and entire trees.

While young trees can be killed in a single season, older trees can survive several years, even with continuous dieback. Powdery mildew of apples, caused by the fungus Podosphaera leucotricha, forms a dense white fungal growth mycelium on the host tissue and affects:1 leaves2 buds3 shoots4 fruits.

The disease stunts the growth of trees and is found wherever apples are grown. Pest description and crop damage Several species of leafrollers [family: Tortricidae] are pests of tree fruits. These species use native host plants as well as fruit trees. The different species of leafroller cause similar damage to apple trees but differ in appearance and life cycle. The principal leafroller pests of fruit trees can be divided into single-generation moths, such as the fruittree leafroller and the European leafroller, and two-generation moths, such as the obliquebanded leafroller and pandemis leafroller.

Using the above set of most commonly faced diseases in Apple plants, I have trained a computer vision Tensorflow Model that helps in pre prediction of diseases in apple plants to prevent loss of Crop yield. The Disease prediction and detection computer vision model was meant to detect diseases in Apple Plants.

Besides this, plant and soil health monitoring along with Climate Tracking Models are essential for analysis for Apple plants. The Hydra Tool consists of a framework which analyses the following models to perform inference and provide visual Analysis of the Data:. The Neural Compute Stick 2 offers plug-and-play simplicity, support for common frameworks and out-of-the-box sample applications. Use any platform with a USB port to prototype and operate without cloud compute dependence. The Intel NCS 2 delivers 4 trillion operations per second with an 8X performance boost compared to previous generations.

Since the sensors and camera modules for all the plants in an array cumulatively send data to a single Raspberry Pi for processing, Video Processing Units are important to speed up the task as well as reduce the load on Processors. This is the area where Intel Neural Compute Stick 2 comes into picture.

This helps in Inferencing, Processing, classification of Video data inputs from over 6 sources in an array at a time which the Raspberry Pi Processor is not capable of. This solution is comparatively quite cost efficient and affordable to be deployed in Apple Farms rather than sending data to the cloud for computation. Sending Video data to the Cloud requires high availability of Internet in remote areas as well as a huge server to store Video data input.

Thus resulting in increased Expenses during deploying. Hence, Here I have used a solution which inferences processes data at the Edge. Automatic and accurate estimation of disease severity is essential for food security, disease management, and yield loss prediction. Deep learning, the latest breakthrough in computer vision, is promising for fine-grained disease severity classification, as the method avoids the labor-intensive feature engineering and threshold-based segmentation.

Using the apple disease images in the custom dataset, which are further annotated by botanists with four severity stages as ground truth, a series of deep convolutional neural networks are trained to diagnose the severity of the disease. Since this model is built to classify Apple Diseases of 6kinds, there was no open Dataset available for all the diseases. Taking this into consideration, I decided to use Google Open Dataset for training the model.

You only look once, or YOLO, is one of the faster object detection algorithms out there. Though it is no longer the most accurate object detection algorithm, it is a very good choice when you need real-time detection, without loss of too much accuracy. For the task of detection, 53 more layers are stacked onto it, giving us a layer fully convolutional underlying architecture for YOLO v3.

Here is how the architecture of YOLO now looks like. The most salient feature of v3 is that it makes detections at three different scales. YOLO is a fully convolutional network and its eventual output is generated by applying a 1 x 1 kernel on a feature map. In YOLO v3, the detection is done by applying 1 x 1 detection kernels on feature maps of three different sizes at three different places in the network.

The feature map produced by this kernel has identical height and width of the previous feature map, and has detection attributes along the depth as described above. YOLO v3 makes prediction at three scales, which are precisely given by downsampling the dimensions of the input image by 32, 16 and 8 respectively.

The first detection is made by the 82nd layer. For the first 81 layers, the image is down sampled by the network, such that the 81st layer has a stride of In this model, we have an image of x , the resultant feature map would be of size 13 x One detection is made here using the 1 x 1 detection kernel, giving us a detection feature map of 13 x 13 x Then, the feature map from layer 79 is subjected to a few convolutional layers before being up sampled by 2x to dimensions of 26 x This feature map is then depth concatenated with the feature map from layer Then the combined feature maps is again subjected a few 1 x 1 convolutional layers to fuse the features from the earlier layer Then, the second detection is made by the 94th layer, yielding a detection feature map of 26 x 26 x A similar procedure is followed again, where the feature map from layer 91 is subjected to few convolutional layers before being depth concatenated with a feature map from layer Like before, a few 1 x 1 convolutional layers follow to fuse the information from the previous layer We make the final of the 3 at th layer, yielding feature map of size 52 x 52 x The upsampled layers concatenated with the previous layers help preserve the fine grained features which help in detecting small objects.

LabelImg is a graphical image annotation tool. It is written in Python and uses Qt for its graphical interface. Besides, it also supports YOLO format. Since this dataset needs to be extremely accurate with evasion of background data, use of pre-built Object detection frameworks like Teachable Machine cannot be used for training accurate models which YOLOV3 can perform. This Image is categorised under "fresh" category which detects fresh shrubs of Apple Plants which are just growing or are still in the process of bearing Apples.

These are the Images under the category "ripe". These images are labelled to detect all the Ripe Apples which are ready for harvesting. Hence, during training these Images, it is better to use suitable backgrounds representing the colour of the Ripe Apple under the category "ripe". Due to these reasons, the model is trained using RGB and not GrayScale as a basis of differentiation.

These Images are categorised under the category of "Raw Apples". The main factor of differentiation in these Images is the green colour of the Apples. If these Images would have been taken in a leafy background, edge classification of these Images would have not been so accurate. The model would not accurately differentiate between leaves and Green Apples. For this purpose, the images in this category have been labelled in a white background to perform accurate edge detection of Apples.

If we take into consideration raw apples in a white background, excluding the Green Nature of the Apples, the complete background is white. In this Image, the colour of the background changes drastically giving the model a factor of differentiation of the Raw Apple. These Images are categorised under the category "leaf rollers". Leaf rollers are the most likely found pests on an Apple plant as well as source of many diseases for plants.

In this category, most of the Images were taken with leaf rollers present on leaves. In this category, the classification is done on the basis of the leaf-roller shape and hence, the background does not contribute to a large extent in decreasing the category of the class. To accurately detect leaf-rollers during implementation of the model, the background was assumed to be the background while carrying out actual inference of the model.

For this purpose, the images were taken as representation of actual on-sight leaf-rollers. The above image is categorised under the class "flowering" which detects the Apple Flowers. This category is used for sending alerts mentioning that since flowers are observed on the plant, It is required to take more care of the plant. In this category, the main factor of differentiation of the object from other categories is the shape as well as the colour of the flower.

The colour of the flower stands to be the major factor of classification in this category. In this category, two types of flowers are taken into consideration which are White flowers buds of the plants , and the Purple flowers Fully grown flowers of the plant. Considering the above parameters, and classes and the basis of differentiation of these classes, I decided to go with YOLOv3 framework for object detection.

These parameters mentioned above make YOLOv3 an accurate framework in comparison with RetinaNet - 50 and RetinaNet - and make it significantly faster than these Frameworks. Even after these parameters, which make YOLOv3 easier to deploy on the edge, it is still far heavy to be deployed on Microcontrollers like Raspberry PI. For this purpose, OpenVino is used which quantizes the model further. Note: Syntaxes may be different as compared to terminal because this is in a Jupyter Notebook format.

Darknet is a convolutional neural network that acts as a backbone for the YOLOv3 object detection approach. The improvements upon its predecessor Darknet include the use of residual connections, as well as more layers. The below code defines all the helper functions which are required throughout the training process:. Besides this, an input file function and file path function has been defined to take file inputs and allow downloading the file path.

Before going ahead with the next steps; the requirements for YOLOv3 need to be downloaded. After having these files downloaded, we can go ahead and follow the next steps:. After the environment and variables are set up, I compressed the trained YOLOv3 dataset with images and labels and uploaded it to my drive. The zip folder with Training and Testing dataset is now uploaded to github. The cfg file is the most important while training the hydra model.

These variables vary according to the number of classes in the model. Finally after changing these variables, I uploaded the cfg file to the Colab Notebook to go ahead and train the model:. The obj. Out of these 9 classes, 4 are states of the plant and the rest 5 are diseases of plants. After configuring these files, I copied both the files to the Colab Notebook:.

The next step is to upload image paths to a. By using these weights it helps my object detector to be way more accurate and not have to train as long. Its not necessary to use these weights but it speeds up the process and makes the model accurate. After setting up these requirements, I went ahead to train my model using the following command:. This process took around 6 to 7 hours to complete and completely train the model until the model could be used.

After training the model to iterations and reaching a loss of 2. The mAP of the model was Classes like flowering and Fungal did not perform extremely well in the mAP but during generating the output process, they could predict the classes with a minimum threshold of 0.

This completes the model training process and to check the model results, I took various images of Apple Plants and some images with diseases to perform inference using the command:. After using this command, I generated output for 6 images which are displayed here:. In this image nearly 13 ripe apples have been detected and a fresh plant in the background is detected which shows a newly growing plant which does not bear fruits or flowers.

This image displays the plant from a close-up but if the leaf-rollers are located at a distant location, the model detects the leaf-roller with a confidence score of 0. The drop in the confidence score is because of the black background which was not trained in the model. The cedar rust was trained with green natural background and hence on taking an image with a black background, the confidence rating has dropped. On performing the detection with a green background, the confidence increases to 0.

Thus, this model performs really well in real life environment than demo images. All the leaves diagnosed with fire-blight in the image are detected by the Model. Towards the left, the leaf in the pre-stage of fire-blight is detected as well which serves as a warning to the forthcoming diseases. In a few cases, the model classified ripe apples to be raw, but in most of the cases, Apples were detected accurately. The confidence rating of the instances started from 0.

Using these 9 classes of model training, all the conditions of the Apple Plant can be detected from performing Extremely well to performing Critically Bad. It is a toolkit provided by Intel to facilitate faster inference of deep learning models.

It helps developers to create cost-effective and robust computer vision applications. It supports a large number of deep learning models out of the box. Model optimizer is a cross-platform command line tool that facilitates the transition between the training and deployment environment.

It adjusts the deep learning models for optimal execution on end-point target devices. Model Optimizer loads a model into memory, reads it, builds the internal representation of the model, optimizes it, and produces the Intermediate Representation. Intermediate Representation is the only format that the Inference Engine accepts and understands. The Model Optimizer does not infer models. It is an offline tool that runs before the inference takes place.

It is an important step in the optimization process. Most deep learning models generally use the FP32 format for their input data. The FP32 format consumes a lot of memory and hence increases the inference time. So, intuitively we may think, that we can reduce our inference time by changing the format of our input data. There are various other formats like FP16 and INT8 which we can use, but we need to be careful while performing quantization as it can also result in loss of accuracy.

So, we essentially perform hybrid execution where some layers use FP32 format whereas some layers use INT8 format. There is a separate layer which handles theses conversions. Calibrate laye r handles all these intricate type conversions. The way it works is as follows —. After using the Model Optimizer to create an intermediate representation IR , we use the Inference Engine to infer input data. The heterogeneous execution of the model is possible because of the Inference Engine.

It uses different plug-ins for different devices. The following components are installed by default:. You must update several environment variables before you can compile and run OpenVINO toolkit applications. Run the following script to temporarily set the environment variables:. As an option, you can permanently set the environment variables as follows:. To test your change, open a new terminal. You will see the following:. Add the current Linux user to the users group:.

Log out and log in for it to take effect. After the Installation is complete the Raspberry Pi is set up to perform inference. If you want to use your model for inference, the model must be converted to the. Originally, YOLOv3 model includes feature extractor called Darknet with three branches at the end that make detections at three different scales. Region layer was first introduced in the DarkNet framework. Other frameworks, including TensorFlow, do not have the Region implemented as a single layer, so every author of public YOLOv3 model creates it using simple layers.

This badly affects performance. For this reason, the main idea of YOLOv3 model conversion to IR is to cut off these custom Region -like parts of the model and complete the model with the Region layers where required. These commands have been deployed on a Google Colab Notebook where the Apple diseases.

After this is created, we get an. After Deploying this command, this activates the camera module deployed on the Raspberry Pi is activated and the inference on the module begins:. This is the timelapse video of a duration of 4 days reduced to 2 seconds.

During actual inference of video input, this data is recorded in real time and accordingly real time notifications are updated. These notifications do not change quite frequently because the change in Video data is not a lot. After I have successfully configured and generated the output video, detection of the video data wont be enough. In that case, I decided to send this video output data to a web-frontend dashboard for other Data-Visualization.

The output generator is as follows:. Deploying unoptimised Tensorflow Lite model on Raspberry Pi:. Tensorflow Lite is an open-source framework created to run Tensorflow models on mobile devices, IoT devices, and embedded devices. It optimizes the model so that it uses a very low amount of resources from your phone or edge devices like Raspberry Pi.

Furthermore, on embedded systems with limited memory and compute, the Python frontend adds substantial overhead to the system and makes inference slow. TensorFlow Lite provides faster execution and lower memory usage compared to vanilla TensorFlow. By default, Tensorflow Lite interprets a model once it is in a Flatbuffer file format. Before this can be done, we need to convert the darknet model to the Tensorflow supported Protobuf file format.

I have already converted the file in the above conversion and the link to the pb file is: YOLOv3 file. To perform this conversion, you need to identify the name of the input, dimensions of the input, and the name of the output of the model. This generates a file called yolov3-tiny. Then, create the "tflite1-env" virtual environment by issuing:.

This will create a folder called tflite1-env inside the tflite1 directory. The tflite1-env folder will hold all the package libraries for this environment. Next, activate the environment by issuing:. You can tell when the environment is active by checking if tflite1-env appears before the path in your command prompt, as shown in the screenshot below.

Step 1c. OpenCV is not needed to run TensorFlow Lite, but the object detection scripts in this repository use it to grab images and draw detection results on them. Initiate a shell script that will automatically download and install all the packages and dependencies. Run it by issuing:. Step 1d. Set up TensorFlow Lite detection model. Before running the command, make sure the tflite1-env environment is active by checking that tflite1-env appears in front of the command prompt.

Getting Inferencing results and comparing them:. These are the inferencing results of deploying tensorflow and tflite to Raspberry Pi respectively. Even though the inferencing time in tflite model is less than tensorflow, it is comparitively high to be deployed. While deploying the unoptimised model on Raspberry Pi, the CPU Temperature rises drastically and results in poor execution of the model:.

Tensorflow Lite uses 15Mb of memory and this usage peaks to 45mb when the temperature of the CPU rises after performing continuous execution:. Power Consumption while performing inference: In order to reduce the impact of the operating system on the performance, the booting process of the RPi does not start needless processes and services that could cause the processor to waste power and clock cycles in other tasks.

Under these conditions, when idle, the system consumes around 1. This shows significant jump from 0. This increases the model performance by a significant amount which is nearly 12 times. This increment in FPS and model inferencing is useful when deploying the model on drones using hyperspectral Imaging.

Temperature Difference in 2 scenarios in deploying the model:. This image shows that the temperature of the core microprocessor rises to a tremendous extent. This is the prediction of the scenario while the model completed 21 seconds after being deployed on the Raspberry Pi.

After seconds of running the inference, the model crashed and the model had to be restarted again after 4mins of being idle. This image was taken after disconnecting power peripherals and NCS2 from the Raspberry Pi 6 seconds after inferencing.

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YOLOv4 TFLite Object Detection Android App Tutorial Using YOLOv4 Tiny, YOLOv4, and YOLOv4 Custom

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