From 20a087ff96cd982d3da3e0eec1b61f1daaba9f0a Mon Sep 17 00:00:00 2001 From: Aaron Markham Date: Mon, 10 Sep 2018 16:53:34 -0700 Subject: [PATCH] update C++ example so it is easier to run (#12397) * update example so it is easier to run * updated per feedback; removed CPP prerequisite --- .../predict-cpp/README.md | 54 +++++++++++-------- 1 file changed, 32 insertions(+), 22 deletions(-) diff --git a/example/image-classification/predict-cpp/README.md b/example/image-classification/predict-cpp/README.md index b3433ff2f824..69f63d706006 100644 --- a/example/image-classification/predict-cpp/README.md +++ b/example/image-classification/predict-cpp/README.md @@ -1,13 +1,23 @@ -# Image Classification Example of C++ -This is a simple predictor which shows how to use c api for image classfication. +# Image Classification Example Using the C Predict API +This is a simple predictor which shows how to use the MXNet C Predict API for image classification with a pre-trained ImageNet model. -It uses opencv for image reading +## Prerequisites -# How to Use +* OpenCV for image processing: `USE_OPENCV` is set to true by default when [building from source](https://mxnet.incubator.apache.org/install/build_from_source.html) -## Build -1. Edit image-classification-predict.cc file, change the following lines to your model paths: - ```bash +## How to Use this Example + +### Download the Model Artifacts +1. You will need the model artifacts for the Inception ImageNet model. You can download these from http://data.mxnet.io/mxnet/models/imagenet/inception-bn/ +2. Place them into a `model/Inception/` subfolder, or if not, you will need to edit the source file and update the paths in the Build step. + +* [model/Inception/Inception-BN-symbol.json](http://data.mxnet.io/mxnet/models/imagenet/inception-bn/Inception-BN-symbol.json) +* [model/Inception/Inception-BN-0126.params](http://data.mxnet.io/mxnet/models/imagenet/inception-bn/Inception-BN-0126.params) +* [model/Inception/synset.txt](http://data.mxnet.io/mxnet/models/imagenet/synset.txt) + +### Build +1. If using a different location for the model artifacts, edit `image-classification-predict.cc` file, and change the following lines to your artifacts' paths: + ```c // Models path for your model, you have to modify it std::string json_file = "model/Inception/Inception-BN-symbol.json"; std::string param_file = "model/Inception/Inception-BN-0126.params"; @@ -16,41 +26,43 @@ It uses opencv for image reading ``` 2. You may also want to change the image size and channels: - ```bash + ```c // Image size and channels int width = 224; int height = 224; int channels = 3; ``` - + 3. Simply just use our Makefile to build: ```bash make ``` -## Usage -Run: +### Run +Run the example by passing it an image that you want to classify. If you don't have one handy, run the following to get one: + ```bash - ./image-classification-predict apple.jpg + wget https://upload.wikimedia.org/wikipedia/commons/thumb/f/f4/Honeycrisp.jpg/1920px-Honeycrisp.jpg + ``` + +Then run the `image-classification-predict` program, passing the image as the argument. + + ```bash + ./image-classification-predict 1920px-Honeycrisp.jpg ``` -The only parameter is the path of the test image. ## Tips -* The model used in the sample can be downloaded here: -http://pan.baidu.com/s/1sjXKrqX -or here: -http://data.mxnet.io/mxnet/models/imagenet/ -* If you don't run it in the mxnet root path, maybe you will need to copy lib folder here. +* If you don't run it in the MXNet root path, you may need to copy the `lib` folder here. -# Author +## Author * **Xiao Liu** * E-mail: liuxiao@foxmail.com * Homepage: [www.liuxiao.org](http://www.liuxiao.org/) -# Thanks +## Thanks * pertusa (for Makefile and image reading check) * caprice-j (for reading function) @@ -58,5 +70,3 @@ http://data.mxnet.io/mxnet/models/imagenet/ * sofiawu (for sample model) * piiswrong and tqchen (for useful coding suggestions) - -