TensorFlow का उपयोग करके एंड्रॉइड पर वस्तुओं को पहचानना: डिवाइस पर लॉन्च करने के लिए डेटा तैयारी से

पैटर्न मान्यता में प्रशिक्षण तंत्रिका नेटवर्क एक लंबी और संसाधन-गहन प्रक्रिया है। खासकर जब हाथ में केवल एक सस्ता लैपटॉप है, और शक्तिशाली ग्राफिक्स कार्ड वाला कंप्यूटर नहीं है। इस मामले में, Google कोलैबोरेटरी बचाव में आएगा , जो टेस्ला K80 स्तर के GPU को बिल्कुल मुफ्त ( अधिक ) का उपयोग करने की पेशकश करता है ।

इस लेख में Google कोलैबोरेटरी में डेटा तैयार करने, टेन्सरफ़्लो मॉडल को प्रशिक्षित करने और एंड्रॉइड डिवाइस पर इसके लॉन्च की प्रक्रिया का वर्णन किया गया है।

डेटा तैयारी

एक उदाहरण के रूप में, एक काले रंग की पृष्ठभूमि पर सफेद पासा को पहचानने के लिए एक तंत्रिका नेटवर्क को प्रशिक्षित करने का प्रयास करें। तदनुसार, शुरुआत के लिए, आपको प्रशिक्षण के लिए पर्याप्त डेटा सेट बनाने की आवश्यकता है (अभी के लिए, चलो ~ 100 फ़ोटो पर रोकें)।

प्रशिक्षण के लिए, हम Tensorflow Object Detection API का उपयोग करेंगे । हम एक लैपटॉप पर प्रशिक्षण के लिए आवश्यक सभी डेटा तैयार करेंगे। हमें एक कॉन्डा पर्यावरण और निर्भरता प्रबंधन प्रबंधक की आवश्यकता है । यहां इंस्टॉलेशन निर्देश ।

आइए काम के लिए माहौल बनाएं:

conda create -n object_detection_prepare pip python=3.6

और इसे सक्रिय करें:

conda activate object_detection_prepare

हमें निर्भरताएँ सेट करें जिनकी हमें आवश्यकता है:

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/