Integrating Tensor flow API into ASP.net Web applications for Image detection

Introduction

Tensor flow provides advanced Machine Learning API. For the example here we will provide a solution for the Image recognition. The same can be applied to other use cases such as Text recognition or any other data set that needs to solve the problems of AI and Machine Learning such as:

1. Predictive Analytics
2. Cognitive Processes and its Automation e.g. NLP
3. Virtual Agents (Bot’s)
4. Camera Image detection and processing e.g. Industrial Robotics and Drones
5. Text Knowledge Analytics

Application Development Steps

In the first step we create an ASP.NET application for connecting the camera feed or input data.

In the next step Install Tensor flow as per instructions on the website –> Click here

To begin using Tensor flow we need to decide on the model to use for processing input data – There is an option to use either ready-made models or develop on own depending on the problem at hand. For simplicity we will use ready-made model called a deep convolutional neural network.  It can achieve reasonable performance on hard visual recognition tasks.

These model repo’s can be downloaded from the TensorFlow models repo from GitHub.

The steps for loading the model are as shown below:

1. Load the data that is already trained from source URL
2. Converts integer node ID’s to human readable labels
3. Loads a human readable English name for each softmax node
4. Loads mapping from string UID to human-readable string
5. Loads mapping from string UID to integer node ID
6. Loads the final mapping of integer node ID to human-readable string
7. Creates a graph
8. Runs inference on an image by specifying the image file
9. Returns the model probabilities to Application
10. Application displays the inference. 

Results

The result of the application is as shown below with ASP Web App user interface screenshots:

Conclusion

More advanced web applications with different models can be used from Tensor Flow depending on the use case and integrated into the web application as per the requirements.