[OpenR8 solution] HttpServer_DeepLearning_Python (Python TensorFlow Keras deep learning server)
  1. Chapter1: HttpServer_DeepLearning_Python

 

The purpose of this solution is to allow users to select images through the web page and obtain a frame of the resulting for object detection. First, the browser selects the picture, sends the picture, and the back end receives the picture and then judges the model result, and then returns the detected object result message to the webpage. The main procedure used is as follows Fig. 1, Fig. 2.

 

The model's main detected objects include airplanes, bicycles, birds, boats, bottles, buses, cars, cats, chairs, cows, dining tables, dogs, horses, motorcycles, people, potted plants, sheep, sofas, trains and TV monitors. The category is shown in Fig. 3.

 

The model is placed in: the archive root directory/solution/HttpServer_DeepLearning_Python/model/data/.

 

By changing different models, you can get different model test results on the web page.

If you want to change the PCB, simply replace the model with a trained PCB model (need to generate additional). For example, the solution to the archive root directory /solution/HttpServer_DeepLearning_Python_PCB differs from this solution as a model.

 

 

Fig. 1.Process.png

Fig. 1.Process.

 

Fig. 2.Schematic diagram of the process.png

Fig. 2.Schematic diagram of the process.

 

Fig. 3.Detecting the category of objects.png

Fig. 3.Detecting the category of objects.

 

 

  1. Chapter2: Instructions for use

 

It takes only three steps to use this solution. as follows:

  1. Loading the solution:

【Run R8_Python3.6_CPU.bat or R8_Python3.6_GPU.bat】=>【File】=>【Open】 =>【Select the HttpServer_DeepLearning_Python folder under solution】 =>【double-click HttpServer_DeepLearning_Python.flow】.As show in Fig. 4, Fig. 5, Fig. 6, Fig. 7.

The loading screen is shown in Fig. 8.

 

Fig. 4. Run R8 .png

Fig. 4.【Run R8.exe】

 

Fig. 5. File Open .png

Fig. 5.【File】=>【Open】

 

Fig. 6. Click solutionHttpServer_DeepLearning_Python .png

Fig. 6.【Click solution/HttpServer_DeepLearning_Python】

 

Fig. 7. double-click HttpServer_DeepLearning_Python .png

Fig. 7.【double-click HttpServer_DeepLearning_Python】

 

Fig. 8.Load HttpServer_DeepLearning_Python.png

Fig. 8.Load HttpServer_DeepLearning_Python.

 

  1. Run the solution, as shown in Fig. 9.

 

Fig. 9.Run the solution.png

Fig. 9.Run the solution.

 

 

  1. Select the image to be tested through the webpage, press submit to get the result of the image detection object. See Fig. 10, Fig. 11.

Test image placed in path: folder root directory/solution/HttpServer_DeepLearning_Python/src/model/pics

 

Fig. 10.Uploading images through webpage.png

Fig. 10.Uploading images through webpage.

 

Fig. 11.Detection result message.png

Fig. 11.Detection result message.


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