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https://github.com/ruhyadi/siliragungdet-android


https://github.com/ruhyadi/siliragungdet-android

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README 

        
# TensorFlow Lite Object Detection Android Demo



### Overview



This is a camera app that continuously detects the objects (bounding boxes and

classes) in the frames seen by your device's back camera, using a quantized

[MobileNet SSD](https://github.com/tensorflow/models/tree/master/research/object_detection)

model trained on the [COCO dataset](http://cocodataset.org/). These instructions

walk you through building and running the demo on an Android device.



The model files are downloaded via Gradle scripts when you build and run. You

don't need to do any steps to download TFLite models into the project

explicitly.



Application can run either on device or emulator.



## Build the demo using Android Studio



### Prerequisites



*   If you don't have already, install

    **[Android Studio](https://developer.android.com/studio/index.html)**,

    following the instructions on the website.



*   You need an Android device and Android development environment with minimum

    API 21.



*   Android Studio 3.2 or later.



### Building



*   Open Android Studio, and from the Welcome screen, select Open an existing

    Android Studio project.



*   From the Open File or Project window that appears, navigate to and select

    the tensorflow-lite/examples/object_detection/android directory from

    wherever you cloned the TensorFlow Lite sample GitHub repo. Click OK.



*   If it asks you to do a Gradle Sync, click OK.



*   You may also need to install various platforms and tools, if you get errors

    like "Failed to find target with hash string 'android-21'" and similar.

    Click the `Run` button (the green arrow) or select `Run > Run 'android'`

    from the top menu. You may need to rebuild the project using `Build >

    Rebuild` Project.



*   If it asks you to use Instant Run, click Proceed Without Instant Run.



*   Also, you need to have an Android device plugged in with developer options

    enabled at this point. See

    **[here](https://developer.android.com/studio/run/device)** for more details

    on setting up developer devices.



#### Switch between inference solutions (Task library vs TFLite Interpreter)



This object detection Android reference app demonstrates two implementation

solutions:



(1)

[`lib_task_api`](https://github.com/tensorflow/examples/tree/master/lite/examples/nl_classification/android/lib_task_api)

that leverages the out-of-box API from the

[TensorFlow Lite Task Library](https://www.tensorflow.org/lite/inference_with_metadata/task_library/object_detector);



(2)

[`lib_interpreter`](https://github.com/tensorflow/examples/tree/master/lite/examples/text_classification/android/lib_interpreter)

that creates the custom inference pipleline using the

[TensorFlow Lite Interpreter Java API](https://www.tensorflow.org/lite/guide/inference#load_and_run_a_model_in_java).



The [`build.gradle`](app/build.gradle) inside `app` folder shows how to change

`flavorDimensions "tfliteInference"` to switch between the two solutions.



Inside **Android Studio**, you can change the build variant to whichever one you

want to build and run—just go to `Build > Select Build Variant` and select one

from the drop-down menu. See

[configure product flavors in Android Studio](https://developer.android.com/studio/build/build-variants#product-flavors)

for more details.



For gradle CLI, running `./gradlew build` can create APKs for both solutions

under `app/build/outputs/apk`.



*Note: If you simply want the out-of-box API to run the app, we recommend

`lib_task_api` for inference. If you want to customize your own models and

control the detail of inputs and outputs, it might be easier to adapt your model

inputs and outputs by using `lib_interpreter`.*



### Model used



Downloading, extraction and placing it in assets folder has been managed

automatically by download.gradle.



If you explicitly want to download the model, you can download from

**[here](http://storage.googleapis.com/download.tensorflow.org/models/tflite/coco_ssd_mobilenet_v1_1.0_quant_2018_06_29.zip)**.

Extract the zip to get the .tflite and label file.



### Custom model used



This example shows you how to perform TensorFlow Lite object detection using a

custom model. * Clone the TensorFlow models GitHub repository to your computer.

`git clone https://github.com/tensorflow/models/` * Build and install this

repository. `cd models/research python3 setup.py build && python3 setup.py

install` * Download the MobileNet SSD trained on

**[Open Images v4](https://storage.googleapis.com/openimages/web/factsfigures_v4.html)**

**[here](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf1_detection_zoo.md)**.

Extract the pretrained TensorFlow model files. * Go to `models/research`

directory and execute this code to get the frozen TensorFlow Lite graph.

`python3 object_detection/export_tflite_ssd_graph.py \ --pipeline_config_path

object_detection/samples/configs/ssd_mobilenet_v2_oid_v4.config \

--trained_checkpoint_prefix /model.ckpt \ --output_directory

exported_model` * Convert the frozen graph to the TFLite model. `tflite_convert

\ --input_shape=1,300,300,3 \ --input_arrays=normalized_input_image_tensor \

--output_arrays=TFLite_Detection_PostProcess,TFLite_Detection_PostProcess:1,TFLite_Detection_PostProcess:2,TFLite_Detection_PostProcess:3

\ --allow_custom_ops \ --graph_def_file=exported_model/tflite_graph.pb \

--output_file=/lite/examples/object_detection/android/app/src/main/assets/detect.tflite`

`input_shape=1,300,300,3` because the pretrained model works only with that

input shape.



`allow_custom_ops` is necessary to allow TFLite_Detection_PostProcess operation.



`input_arrays` and `output_arrays` can be drawn from the visualized graph of the

example detection model. `bazel run //tensorflow/lite/tools:visualize \

"/lite/examples/object_detection/android/app/src/main/assets/detect.tflite"

\ detect.html`



*   Get `labelmap.txt` from the second column of

    **[class-descriptions-boxable](https://storage.googleapis.com/openimages/2018_04/class-descriptions-boxable.csv)**.

*   In `DetectorActivity.java` set `TF_OD_API_IS_QUANTIZED` to `false`.



### Additional Note



_Please do not delete the assets folder content_. If you explicitly deleted the

files, then please choose *Build*->*Rebuild* from menu to re-download the

deleted model files into assets folder.