OpenVINO API Daisi

This web service is an implementation of Intel OpenVINO API on Daisi Platform which provide optimized deep learning inference.

It provides OpenVINO Models as a service in order to present the capabilities of these models for ML applications.

At this time It contains 8 different deployed models which available on Open Model Zoo for OpenVINO Toolkit.

This implementation in its initial phase and still need alot of work.

Check this demo for more info about the project.

Models

Currently, Deployed OpenVINO Models:

Animal Classification

Animal Class: 'cheetah, chetah, Acinonyx jubatus'
Road Segmentation
Optical Character Detection(OCD)
Super Resolution
Vehicle Recognition

Named Entity Recognition(NER)

source_text = "Intel was founded in Mountain View, California, " \
                "in 1968 by Gordon E. Moore, a chemist, and Robert Noyce, " \
                "a physicist and co-inventor of the integrated circuit."

{
  'Extraction': [
      {'Entity': 'Intel', 'Type': 'company', 'Score': '0.98'},
      {
          'Entity': 'Gordon E. Moore, a chemist, and Robert Noyce',
          'Type': 'persons',
          'Score': '0.83'
      },
      {'Entity': 'Mountain View', 'Type': 'city', 'Score': '0.79'},
      {'Entity': 'California', 'Type': 'state', 'Score': '0.98'}
  ]
}

Handwritten OCR (Chinese & Japanese)

Recognized Text: '人有悲欢离合，月有阴睛圆缺，此事古难全。'

Interactive Question Answering

sources = ["https://en.wikipedia.org/wiki/OpenVINO"]
question = "What does OpenVINO refers to?"

{
    'Question': 'What does OpenVINO refers to?',
    'Answer': 'Open Visual Inference and Neural network Optimization',
    'Score': '0.85'
}

How to call it

-First load OpenVINO API Daisi

import pydaisi as pyd
openvino_api_v3 = pyd.Daisi("oghli/OpenVINO API v3")

Animal Classification Model

Call the cv_animal_classify end point, passing the image_source to classify it, you can pass image source either from data/animal_classify/ directory or from valid url of the image
```
img = "data/animal_classify/test.jpg"
result = openvino_api_v3.cv_animal_classify(img).value
result
```
Road Segmentation Model

Call the cv_road_segmentation end point, passing the image_source to segment roads in it, you can pass image source either from data/road_segmentation/ directory or from valid url of the image
```
img = "data/road_segmentation/empty_road_mapillary.jpg"
result = openvino_api_v3.cv_road_segmentation(img).value
```
To show the result image import matplotlib then use plt.imshow method
```
import matplotlib.pyplot as plt
plt.figure(figsize=(15, 5))
plt.imshow(result)
```
Optical Character Detection Model

Call the cv_ocd end point, passing the image_source to detect text in it, you can pass image source either from data/ocr/ directory or from valid url of the image
```
img = "data/ocr/intel_rnb.jpg"
result = openvino_api_v3.cv_ocd(img).value
plt.figure(figsize=(20, 10))
plt.imshow(result)
```
Super Resolution Model

Call the cv_superresolution end point, passing the image_source to enhance it, you can pass image source either from a directory path or from valid url of the image
```
img = "https://i.imgur.com/R5ovXDO.jpg"
result = openvino_api_v3.cv_superresolution(img).value
plt.figure(figsize=(20, 10))
plt.imshow(result[1])
```
Vehicle Recognition Model

Call the cv_vehicle_rec end point, passing the image_source to recognize Vehicles in it, you can pass image source either from a directory path or from valid url of the image
```
img = "https://i.imgur.com/IvwQdz5.jpg"
result = openvino_api_v3.cv_vehicle_rec(img).value
plt.figure(figsize=(20, 10))
plt.imshow(result)
```

Named Entity Recognition Model

Call the analyze_entities end point, passing the source_text to analyze entities in it

source_text = "Intel was founded in Mountain View, California, " \
                  "in 1968 by Gordon E. Moore, a chemist, and Robert Noyce, " \
                  "a physicist and co-inventor of the integrated circuit."
result = openvino_api_v3.analyze_entities(source_text).value
result

Handwritten OCR Model

Call the handwritten_ocr end point, passing the image_source and the selected language lang to recognize text in it, you can pass image source either from data/handwritten_ocr/ directory or from valid url of the image
```
img = "data/handwritten_ocr/handwritten_chinese_test.jpg"
result = openvino_api_v3.handwritten_ocr(img, "ch").value
result
```
Set lang using "ch" for chinesse language or "jap" for japanese language
```
img = "data/handwritten_ocr/handwritten_japanese_test.png"
result = openvino_api_v3.handwritten_ocr(img, "jap").value
result 
```

Interactive Question Answering Model

Call the question_answering end point, passing the sources as array and the question to answer it using the provided sources, you can pass sources either as string text or from valid url for the information resource

sources = ["https://en.wikipedia.org/wiki/OpenVINO"]
question = "What does OpenVINO refers to?"
result = openvino_api_v3.question_answering(sources, question).value
result

sources = ["Computational complexity theory is a branch of the theory of computation in theoretical computer "
           "science that focuses on classifying computational problems according to their inherent difficulty, "
           "and relating those classes to each other. A computational problem is understood to be a task that "
           "is in principle amenable to being solved by a computer, which is equivalent to stating that the "
           "problem may be solved by mechanical application of mathematical steps, such as an algorithm."]

question = "what branch of the theory of computation in theoretical computer science that focuses on classifying computational problems?"
result = openvino_api_v3.question_answering(sources, example_question=question).value
result

Please support me by starring the repository.

Contributor: Mohamad Oghli.

mohammedkassem / OpenVINO-API

OpenVINO API Daisi

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