Reconocer objetos en Android usando TensorFlow: desde la preparación de datos hasta el lanzamiento en el dispositivo

La capacitación de redes neuronales en el reconocimiento de patrones es un proceso largo y que requiere muchos recursos. Especialmente cuando solo hay una computadora portátil económica, y no una computadora con una tarjeta gráfica potente. En este caso, Google Colaboratory vendrá al rescate , que ofrece usar la GPU de nivel Tesla K80 totalmente gratis ( más ).

Este artículo describe el proceso de preparación de datos, la capacitación del modelo de flujo de tensor en Google Colaboratory y su lanzamiento en un dispositivo Android.

Preparación de datos

Como ejemplo, intentemos entrenar una red neuronal para reconocer los dados blancos sobre un fondo negro. En consecuencia, para empezar, debe crear un conjunto de datos suficiente para el entrenamiento (por ahora, detengámonos en ~ 100 fotos).

Para la capacitación, utilizaremos la API de detección de objetos de Tensorflow . Prepararemos todos los datos necesarios para la capacitación en una computadora portátil. Necesitamos un entorno conda y un administrador de gestión de dependencias . Instrucciones de instalación aquí .

Creemos un entorno para el trabajo:

conda create -n object_detection_prepare pip python=3.6

Y activarlo:

conda activate object_detection_prepare

Establezca las dependencias que necesitamos:

pip install --ignore-installed --upgrade tensorflow==1.14
pip install --ignore-installed pandas
pip install --ignore-installed Pillow
pip install lxml
conda install pyqt=5

object_detection, object_detection/images.

Google Colab , , "tcmalloc: large alloc....".

object_detection/preprocessing .

:

python ./object_detection/preprocessing/image_resize.py -i ./object_detection/images --imageWidth=800 --imageHeight=600

, 800x600 object_detection/images/resized. object_detection/images.

labelImg.

labelImg object_detection

labelImg

cd [FULL_PATH]/object_detection/labelImg 

:

pyrcc5 -o libs/resources.py resources.qrc

( ):

python labelImg.py

“Open dir” object_detection/images , . (1, 2, 3, 4, 5, 6). ( *.xml) .

object_detection/training_demo, Google Colab .

( ) 80/20 object_detection/training_demo/images/train object_detection/training_demo/images/test.

object_detection/training_demo/annotations, . label_map.pbtxt, . :

item {
    id: 1
    name: '1'
}

item {
    id: 2
    name: '2'
}

item {
    id: 3
    name: '3'
}

item {
    id: 4
    name: '4'
}

item {
    id: 5
    name: '5'
}

item {
    id: 6
    name: '6'
}

, ? , TFRecord. [1].

: xml -> csv csv -> record

preprocessing :

cd [FULL_PATH]\object_detection\preprocessing

1. xml csv

:

python xml_to_csv.py -i [FULL_PATH]/object_detection/training_demo/images/train -o [FULL_PATH]/object_detection/training_demo/annotations/train_labels.csv

:

python xml_to_csv.py -i [FULL_PATH]/object_detection/training_demo/images/test -o [FULL_PATH]/object_detection/training_demo/annotations/test_labels.csv

2. csv record

:

python generate_tfrecord.py --label_map_path=[FULL_PATH]\object_detection\training_demo\annotations\label_map.pbtxt --csv_input=[FULL_PATH]\object_detection\training_demo\annotations\train_labels.csv --output_path=[FULL_PATH]\object_detection\training_demo\annotations\train.record --img_path=[FULL_PATH]\object_detection\training_demo\images\train

:

python generate_tfrecord.py --label_map_path=[FULL_PATH]\object_detection\training_demo\annotations\label_map.pbtxt --csv_input=[FULL_PATH]\object_detection\training_demo\annotations\test_labels.csv --output_path=[FULL_PATH]\object_detection\training_demo\annotations\test.record --img_path=[FULL_PATH]\object_detection\training_demo\images\test

, , .

.

ssdlite_mobilenet_v2_coco, android .

object_detection/training_demo/pre-trained-model.

-
object_detection/training_demo/pre-trained-model/ssdlite_mobilenet_v2_coco_2018_05_09

pipeline.config object_detection/training_demo/training ssdlite_mobilenet_v2_coco.config.

, :

1.

model.ssd.num_classes: 6

2. ( ), ,

train_config.batch_size: 18
train_config.num_steps: 20000
train_config.fine_tune_checkpoint:"./training_demo/pre-trained-model/ssdlite_mobilenet_v2_coco_2018_05_09/model.ckpt"

3. (object_detection/training_demo/images/train)

eval_config.num_examples: 64

4.

train_input_reader.label_map_path: "./training_demo/annotations/label_map.pbtxt"
train_input_reader.tf_record_input_reader.input_path:"./training_demo/annotations/train.record"

5.

eval_input_reader.label_map_path: "./training_demo/annotations/label_map.pbtxt"
eval_input_reader.tf_record_input_reader.input_path:"./training_demo/annotations/test.record"

, .

training_demo training_demo.zip Google Drive.

, .

Google Drive training_demo.zip, Get shareable link id :

drive.google.com/open?id=[YOUR_FILE_ID_HERE]

Google Colab — Google Drive.

CPU. GPU, runtime.

.

:

1. TensorFlow Models:

!git clone https://github.com/tensorflow/models.git                                                

2. protobuf object_detection:

!apt-get -qq install libprotobuf-java protobuf-compiler                                               
%cd ./models/research/
!protoc object_detection/protos/*.proto --python_out=.
%cd ../..

3. PYTHONPATH:

import os
os.environ['PYTHONPATH'] += ":/content/models/research/"
os.environ['PYTHONPATH'] += ":/content/models/research/slim"
os.environ['PYTHONPATH'] += ":/content/models/research/object_detection"
os.environ['PYTHONPATH'] += ":/content/models/research/object_detection/utils"

4. Google Drive PyDrive :

!pip install -U -q PyDrive

from pydrive.auth import GoogleAuth
from pydrive.drive import GoogleDrive
from google.colab import auth
from oauth2client.client import GoogleCredentials

auth.authenticate_user()
gauth = GoogleAuth()
gauth.credentials = GoogleCredentials.get_application_default()
drive = GoogleDrive(gauth)

5. ( id ) :

drive_file_id="[YOUR_FILE_ID_HERE]"

training_demo_zip = drive.CreateFile({'id': drive_file_id})
training_demo_zip.GetContentFile('training_demo.zip')

!unzip training_demo.zip
!rm training_demo.zip

6. :

!python ./models/research/object_detection/legacy/train.py --logtostderr --train_dir=./training_demo/training --pipeline_config_path=./training_demo/training/ssdlite_mobilenet_v2_coco.config

7. frozen graph, :

!python /content/models/research/object_detection/export_inference_graph.py --input_type image_tensor --pipeline_config_path /content/training_demo/training/ssdlite_mobilenet_v2_coco.config --trained_checkpoint_prefix /content/training_demo/training/model.ckpt-[CHECKPOINT_NUMBER]
 --output_directory /content/training_demo/training/output_inference_graph_v1.pb

. ~20000 .

8. tflite.
tensorflow lite tflite. frozen graph tflite ( export_inference_graph.py):

!python /content/models/research/object_detection/export_tflite_ssd_graph.py --pipeline_config_path /content/training_demo/training/ssdlite_mobilenet_v2_coco.config --trained_checkpoint_prefix /content/training_demo/training/model.ckpt-[CHECKPOINT_NUMBER] --output_directory /content/training_demo/training/output_inference_graph_tf_lite.pb

tflite , output_inference_graph_tf_lite.pb:

Netron. .

pb tflite :

!tflite_convert --output_file=/content/training_demo/training/model_q.tflite  --graph_def_file=/content/training_demo/training/output_inference_graph_tf_lite_v1.pb/tflite_graph.pb --input_arrays=normalized_input_image_tensor  --output_arrays='TFLite_Detection_PostProcess','TFLite_Detection_PostProcess:1','TFLite_Detection_PostProcess:2','TFLite_Detection_PostProcess:3' --input_shapes=1,300,300,3 --enable_select_tf_ops --allow_custom_ops  --inference_input_type=QUANTIZED_UINT8 --inference_type=FLOAT --mean_values=128 --std_dev_values=128

Google Drive:

!zip -r ./training_demo/training.zip ./training_demo/training/

training_result = drive.CreateFile({'title': 'training_result.zip'})
training_result.SetContentFile('training_demo/training.zip')
training_result.Upload()

Invalid client secrets file, google drive.

android

android object detection, kotlin CameraX. .

CameraX ImageAnalysis. ObjectDetectorAnalyzer.

:

1. YUV . RGB :

val rgbArray = convertYuvToRgb(image)

2. (, , , 300x300), Bitmap :

val rgbBitmap = getRgbBitmap(rgbArray, image.width, image.height)
val transformation =  getTransformation(rotationDegrees, image.width, image.height)
Canvas(resizedBitmap).drawBitmap(rgbBitmap, transformation, null)

3. bitmap , :

ImageUtil.storePixels(resizedBitmap, inputArray)
val objects = detect(inputArray)

4. RecognitionResultOverlayView :

val scaleFactorX = measuredWidth / result.imageWidth.toFloat()
val scaleFactorY = measuredHeight / result.imageHeight.toFloat()

result.objects.forEach { obj ->
    val left = obj.location.left * scaleFactorX
    val top = obj.location.top * scaleFactorY
    val right = obj.location.right * scaleFactorX
    val bottom = obj.location.bottom * scaleFactorY

    canvas.drawRect(left, top, right, bottom, boxPaint)
    canvas.drawText(obj.text, left, top - 25f, textPaint)
}

, assets training_demo/training/model_q.tflite ( detect.tflite) labelmap.txt, :

1
2
3
4
5
6

SSD Mobilenet V1, 1, 0, labelOffset 1 0 collectDetectionResult ObjectDetector.

.
, Xiaomi Redmi 4X :

:

• https://tensorflow-object-detection-api-tutorial.readthedocs.io
• https://github.com/EdjeElectronics/TensorFlow-Object-Detection-API-Tutorial-Train-Multiple-Objects-Windows-10
• https://lutzroeder.imtqy.com/netron/