garbage_picture

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This sample provides reference for you to learn the Ascend AI Software Stack and cannot be used for commercial purposes.

This README file provides only guidance for running the sample in command line (CLI) mode.

Garbage Sorting Sample

For details about the training, see Waste Sorting with MobileNetV2.
Function: Classify input images by using the MobileNetV2 model. Input: a source JPG image.
Output: JPG image after inference.

Prerequisites

Check whether the following requirements are met. If not, perform operations according to the remarks. If the CANN version is upgraded, check whether the third-party dependencies need to be reinstalled. (The third-party dependencies for 5.0.4 and later versions are different from those for earlier versions.)

Item	Requirement	Remarks
CANN version	≥5.0.4	Install the CANN by referring to Sample Deployment in the About Ascend Samples Repository. If the CANN version is earlier than the required version, switch to the samples repository specific to the CANN version. See Release Notes.
Hardware	Atlas200DK/Atlas300(AI1s)	Currently, the Atlas 200 DK and Atlas 300 have passed the test. For details about the product description, see Hardware Platform. For other products, adaptation may be required.
Third-party dependency	python-acllite	Select required dependencies. See Third-Party Dependency Installation Guide (Python Sample).

Sample Preparation

1. Obtain the source package.

   You can download the source code in either of the following ways:

   • Command line (The download takes a long time, but the procedure is simple.)

     # In the development environment, run the following commands as a non-root user to download the source repository:   
     cd ${HOME}     
     git clone https://github.com/Ascend/samples.git
     

     Note: To switch to another tag (for example, v0.5.0), run the following command:

     git checkout v0.5.0
     

   • Compressed package (The download takes a short time, but the procedure is complex.)
     Note: If you want to download the code of another version, switch the branch of the samples repository according to the prerequisites.

      # 1. Click Clone or Download in the upper right corner of the samples repository and click Download ZIP.   
      # 2. Upload the .zip package to the home directory of a common user in the development environment, for example, ${HOME}/ascend-samples-master.zip.    
      # 3. In the development environment, run the following commands to unzip the package:    
      cd ${HOME}    
      unzip ascend-samples-master.zip
     

2. Obtain the source model required by the application.

   Model	Description	How to Obtain
   mobilenetV2	Image classification inference model. It is a MobileNetV2 model based on MindSpore.	Download the model and weight files by referring to the links in README.md in the ATC_mobilenetv2_mindspore_AE directory of the ModelZoo repository.

   # To facilitate download, the commands for downloading the original model and converting the model are provided here. You can directly copy and run the commands(If you are converting on a 310B chip, you need to modify the parameter -- soc_version=Ascend310B1). You can also refer to the above table to download the model from ModelZoo and manually convert it.    
   cd ${HOME}/samples/python/contrib/garbage_picture/model    
   wget https://obs-9be7.obs.cn-east-2.myhuaweicloud.com:443/003_Atc_Models/AE/ATC%20Model/garbage/mobilenetv2.air   
   wget https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/models/garbage_picture/insert_op_yuv.cfg
   atc --model=./mobilenetv2.air --framework=1 --output=garbage_yuv --soc_version=Ascend310 --insert_op_conf=./insert_op_yuv.cfg --input_shape="data:1,3,224,224" --input_format=NCHW
   

3. Obtain the test images required by the sample.

   # Run the following commands to go to the data folder of the sample and download the corresponding test images:
   cd $HOME/samples/python/contrib/garbage_picture
   mkdir data
   cd data
   wget https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/models/garbage_picture/newspaper.jpg
   wget https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/models/garbage_picture/bottle.jpg    
   wget https://obs-9be7.obs.cn-east-2.myhuaweicloud.com/models/garbage_picture/dirtycloth.jpg 
   cd ../src  
   

Sample Running

Note: If the development environment and operating environment are set up on the same server, skip step 1 and go to step 2 directly.

1. Run the following commands to upload the garbage_picture directory in the development environment to any directory in the operating environment, for example, /home/HwHiAiUser, and log in to the operating environment (host) as the running user (HwHiAiUser):

   # In the following information, xxx.xxx.xxx.xxx is the IP address of the operating environment. The IP address of Atlas 200 DK is 192.168.1.2 when it is connected over the USB port, and that of Atlas 300 (AI1s) is the corresponding public IP address.
   scp -r $HOME/samples/python/contrib/garbage_picture HwHiAiUser@xxx.xxx.xxx.xxx:/home/HwHiAiUser
   ssh HwHiAiUser@xxx.xxx.xxx.xxx
   cd ${HOME}/garbage_picture/src    
   

2. Run the executable file.

   python3 classify_test.py ../data/
   

Result Viewing

After the execution is complete, find the result JPG image in the out directory.

Common Errors

For details about how to rectify the errors, see Troubleshooting. If an error is not included in Wiki, submit an issue to the samples repository.