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Sumin
Advisor
Advisor
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Building deep learning model within SAP BTP


Dear readers, 

This is a blog series from the Meter Reading project and we attempted to discover the possibilities for ML models in a web browser. This long journey has provided many lessons learned in terms of deep learning and deployment in web browsers. As a beginner in computer vision, I would like to share the lessons I learned from this project. I would like to special thank you for our project members - aditi.arora16, vriddhishetty, gunteralbrecht, anoop.mittal Roopa Prabhu Nagavara,, namagupta and S, Deepak Chethan.

In the previous AI on mobile: Powering your field force – Part 2 of 5, anoop.mittal, we read about the high-level overview on basic design principles.

Then, how can we extract digits from a captured image📷  ?

We can build the deep learning model on the SAP Data Intelligence and write the model to the SAP HANA Cloud. To build the deep learning model for digit recognition, we have five steps as follows. Are you ready to start meter reading journey with SAP BTP?


Understand Scenarios


In this posting, we will build the deep learning model with the meter reading scenario on SAP BTP.




Automatic Meter Reading (AMR) is a technology that automatically collects consumption and status data from a water meter or energy metering devices (gas, electric).


AMR has a two-step approach for object detection and object recognition. Region of interest (ROI) is the detection of digits, and many studies have adopted YOLO, a CNN-based object detection system, to detect digits.


In this scenario, we developed a front-end application with a bounded region that captures a region of interest (ROI), thus we decided to consider only an object recognition approach. For the object recognition, we used a deep learning model, which is part of a broader machine learning model using neural network in the SAP BTP ecosystem.


 


Check Requirements




python == 3.7.0


tensorflow==2.5.0


opencv-python == 4.5.5.62


numpy==1.19.5






import os
import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.layers import LSTM #python ==3.7 mandatory, (python==3.8.12, not mandatory)
from PIL import ImageFont, ImageOps, Image
import json
import cv2
import random
from hdfs import InsecureClient
import io
from io import BytesIO
from PIL import Image
# Converting to TFLite
from tensorflow import lite


 

Create Training Pipeline


SAP Data Intelligence


We used SAP Data Intelligence for training pipeline and used Python Producer template in SAP Data Intelligence.


Training pipeline


 

Prepare data


For the data preparation, we create annotated images as captured by mobile application and this is the example images. We stored our image datasets in the data lake in SAP Data Intelligence.


Example of dataset




Split data


In the SAP Data Intelligence, we split the dataset into training and validation sets.
def split_data(images, labels, train_size=0.9, shuffle=True):
# 1. Get the total size of the dataset
size = len(images)
# 2. Make an indices array and shuffle it, if required
indices = np.arange(size)
if shuffle:
np.random.shuffle(indices)
# 3. Get the size of training samples
train_samples = int(size * train_size)
# 4. Split data into training and validation sets
x_train, y_train = images[indices[:train_samples]], labels[indices[:train_samples]]
x_valid, y_valid = images[indices[train_samples:]], labels[indices[train_samples:]]

return x_train, x_valid, y_train, y_valid

 

Preprocessing


In the SAP Data Intelligence, we get 'image path' from the data lake, so we need to client.read and please refer to the example python code. This is different from the local python code!
# to send metrics to the Submit Metrics operator, create a Python dictionary of key-value pairs
client = InsecureClient('http://datalake:50070')
directory='/shared/ml/images/'

 

For image preprocessing, we decode and resize the image, convert the dtype and adjust the image brightness, sharpness and quality before training as follows:
def encode_single_sample(img_path, label):
img_path = tf.get_static_value(img_path).decode('utf-8')
label = tf.get_static_value(label).decode('utf-8')

with client.read(img_path) as reader:
# 1. Read image
img_arr = reader.read()
img = np.array(img_arr)
# 2. Decode or convert to grayscale
img = tf.image.decode_png(img, channels=3)
# 3. Convert to float32 in [0, 1] range
img = tf.image.convert_image_dtype(img, tf.float32)
# 4. Resize to the desired size
img = tf.image.resize(img, [img_height, img_width])
# 4.1. brightness
img = tf.image.adjust_brightness(img, delta=0.1)
# 4.2 sharpness
img = tf.image.random_contrast(img, 1.2, 1.5)
# 4.3 increase quality
img = tf.image.adjust_jpeg_quality(img, 75)
# 5. Transpose the image because we want the time
# dimension to correspond to the width of the image.
img = tf.transpose(img, perm=[1, 0, 2])
# 6. Map the characters in label to numbers
label = char_to_num(tf.strings.unicode_split(label, input_encoding="UTF-8"))
# 7. Return a dict as our model is expecting two inputs
return {"image": img, "label": label}

 

Train dataset


We use the batch training with batch size=16 with tf.data.Dataset for efficient training.
batch_size = 16
train_dataset = tf.data.Dataset.from_tensor_slices((np.array(dataset_train['image']), np.array(dataset_train['label'])))
train_dataset = (
train_dataset.map(
encode_single_sample, num_parallel_calls=tf.data.experimental.AUTOTUNE)
.batch(batch_size)
.prefetch(buffer_size=tf.data.experimental.AUTOTUNE)
)

 

Build Model


When capturing the images, recognizing the sequence of digits is important. We use Convolutional Recurrent Neural Network (CRNN) to build deep learning model. Since CRNN models are network architectures specifically designed to recognize sequence-like objects in images, CRNN is suitable for our AMR scenario. CRNN is effective with smaller model and remarkable performance without predefined lexicon, compared to conventional methods such as CNN and RNN based algorithms.


 


CRNN architecture(reference link to here)


 

By using the maximum length of the captcha in the meter reading image instead of a predefined dictionary, this approach produces predictions of varying lengths.
# Maximum length of any captcha in the dataset
max_length = max([len(label) for label in train_labels])

# Create characters
characters = ['0','1', '2', '3', '4', '5', '6', '7', '8', '9']

# Mapping characters to integers
char_to_num = layers.experimental.preprocessing.StringLookup(
vocabulary=list(characters), num_oov_indices=0, mask_token=None
)

 

We define that the size of the input image is 350 X 50 and channel is 3 as a tensor.
Model: "model_v1"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
image (InputLayer) [(None, 350, 50, 3)] 0
__________________________________________________________________________________________________
Conv1 (Conv2D) (None, 350, 50, 32) 896 image[0][0]
__________________________________________________________________________________________________
pool1 (MaxPooling2D) (None, 175, 25, 32) 0 Conv1[0][0]
__________________________________________________________________________________________________
Conv2 (Conv2D) (None, 175, 25, 64) 18496 pool1[0][0]
__________________________________________________________________________________________________
pool2 (MaxPooling2D) (None, 87, 12, 64) 0 Conv2[0][0]
__________________________________________________________________________________________________
reshape (Reshape) (None, 87, 768) 0 pool2[0][0]
__________________________________________________________________________________________________
dense1 (Dense) (None, 87, 64) 49216 reshape[0][0]
__________________________________________________________________________________________________
dropout_4 (Dropout) (None, 87, 64) 0 dense1[0][0]
__________________________________________________________________________________________________
bidirectional_8 (Bidirectional) (None, 87, 256) 197632 dropout_4[0][0]
__________________________________________________________________________________________________
bidirectional_9 (Bidirectional) (None, 87, 128) 164352 bidirectional_8[0][0]
__________________________________________________________________________________________________
label (InputLayer) [(None, None)] 0
__________________________________________________________________________________________________
dense2 (Dense) (None, 87, 11) 1419 bidirectional_9[0][0]
__________________________________________________________________________________________________
ctc_loss (CTCLayer) (None, 87, 11) 0 label[0][0]
dense2[0][0]
==================================================================================================
Total params: 432,011
Trainable params: 432,011
Non-trainable params: 0
__________________________________________________________________________________________________

 

Training model


### Get the model
model = build_model()

# Get the prediction model by extracting layers till the output layer
prediction_model = keras.models.Model(
model.get_layer(name="image").input, model.get_layer(name="dense2").output
)


 

Convert to TFLite


We convert to the TFLite to integrate with the PWA app.
#Convert to TFLite 
converter = lite.TFLiteConverter.from_keras_model(prediction_model)
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.experimental_new_converter=True
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS
,tf.lite.OpsSet.SELECT_TF_OPS]

tfmodel = converter.convert()


 

Save model


This is the step to save artifact to SAP Data Intelligence Data Lake. We send the model blob to the output port and Artifact Producer operator will use this to persist the model and create an artifact ID. The interesting lessons learned was that the TFLite model itself is a byte type, so we don't need to change it.
metrics_dict = {"kpi1": "1"}
# send the metrics to the output port - Submit Metrics operator will use this to persist the metrics
api.send("metrics", api.Message(metrics_dict))

# create & send the model blob to the output port
model_blob = tfmodel
#model_blob = bytes(tfmodel, 'utf-8') # error
api.send("modelBlob", model_blob)

 

Write to HANA


To read the SAP Data Intelligence artifact, we need pipeline to write to SAP HANA.

Create consumer pipeline in SAP Data Intelligence


To create writing to HANA pipeline, we use Python Consumer template in SAP Data Intelligence.


Writing to HANA pipeline


 

Lesson's learned


  Check the data type

Checking the data type is really important. Please note the datatypes as each library support different datatypes.

  Check the requirements

I've been struggling with different types of errors. And I'd like to ask you to check your python version and tensorflow version to minimize errors.
import tensorflow as tf
print(tf.__version__)

  Collaboration

We tried to find a suitable approach for running an inference TFLite model on the web browser using C++ and WebAssembly backend. Discovering the appropriate architecture with new and innovative technologies is an amazing collaboration, and I have learned to collaborate with other areas.

 

Blog series


In the AI on mobile: Powering your field force – Part 4 of 5, aditi.arora16 will explain how to load a TensorFlow Lite Machine Learning model in C++ and run inference on the web browser with the help of WebAssembly backend.

  1. In Part 1, vriddhishetty describes the context of the meter reading projects

  2. In Part 2, anoop.mittal describes the solution backend, particularly how the service was created with CAP & Spring boot

  3. In Part 3lsm1401 describes the intuition and steps behind creation of the ML model that will be referenced by the backend at run time

  4. In Part 4aditi.arora16 describes how she created the web assembly binary and how she loaded the model in browser for inference

  5. In Part 5namagupta describes how he built the front end that stitches all working parts together, particularly how the PWA app was built with Angular




References


Shi, B., Bai, X., & Yao, C. (2016). An end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition. IEEE transactions on pattern analysis and machine intelligence39(11), 2298-2304.

Laroca, R., Barroso, V., Diniz, M. A., Gonçalves, G. R., Schwartz, W. R., & Menotti, D. (2019). Convolutional neural networks for automatic meter reading. Journal of Electronic Imaging28(1), 013023.

Salomon, G., Laroca, R., & Menotti, D. (2020, July). Deep learning for image-based automatic dial meter reading: Dataset and baselines. In 2020 International Joint Conference on Neural Networks (IJCNN) (pp. 1-8). IEEE.

https://blogs.sap.com/2022/03/16/computer-vision-with-sap-data-intelligence/

https://en.wikipedia.org/wiki/Automatic_meter_reading

https://levity.ai/blog/difference-machine-learning-deep-learning

https://keras.io/examples/vision/handwriting_recognition/

 
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