imageNet.h

/*
 * http://github.com/dusty-nv/jetson-inference
 */
 
#ifndef __IMAGE_NET_H__
#define __IMAGE_NET_H__


#include "tensorNet.h"


/**
 * Image recognition with GoogleNet/Alexnet or custom models, using TensorRT.
 * @ingroup deepVision
 */
class imageNet : public tensorNet
{
public:
	/**
	 * Network choice enumeration.
	 */
	enum NetworkType
	{
		ALEXNET,
		GOOGLENET
	};

	/**
	 * Load a new network instance
	 */
	static imageNet* Create( NetworkType networkType=GOOGLENET, uint32_t maxBatchSize=2 );
	
	/**
	 * Load a new network instance
	 * @param prototxt_path File path to the deployable network prototxt
	 * @param model_path File path to the caffemodel
	 * @param mean_binary File path to the mean value binary proto
	 * @param class_info File path to list of class name labels
	 * @param input Name of the input layer blob.
	 * @param maxBatchSize The maximum batch size that the network will support and be optimized for.
	 */
	static imageNet* Create( const char* prototxt_path, const char* model_path, const char* mean_binary,
							 const char* class_labels, const char* input="data", const char* output="prob",
							 uint32_t maxBatchSize=2 );
	
	/**
	 * Destroy
	 */
	virtual ~imageNet();
	
	/**
	 * Determine the maximum likelihood image class.
	 * @param rgba float4 input image in CUDA device memory.
	 * @param width width of the input image in pixels.
	 * @param height height of the input image in pixels.
	 * @param confidence optional pointer to float filled with confidence value.
	 * @returns Index of the maximum class, or -1 on error.
	 */
	int Classify( float* rgba, uint32_t width, uint32_t height, float* confidence=NULL );

	/**
	 * Retrieve the number of image recognition classes (typically 1000)
	 */
	inline uint32_t GetNumClasses() const						{ return mOutputClasses; }
	
	/**
	 * Retrieve the description of a particular class.
	 */
	inline const char* GetClassDesc( uint32_t index )	const		{ return mClassDesc[index].c_str(); }
	
	/**
	 * Retrieve the class synset category of a particular class.
	 */
	inline const char* GetClassSynset( uint32_t index ) const		{ return mClassSynset[index].c_str(); }
	
	/**
	 * Retrieve the network type (alexnet or googlenet)
	 */
	inline NetworkType GetNetworkType() const					{ return mNetworkType; }

	/**
 	 * Retrieve a string describing the network name.
	 */
	inline const char* GetNetworkName() const					{ return (mNetworkType == GOOGLENET ? "googlenet" : "alexnet"); }

protected:
	imageNet();
	
	bool init( NetworkType networkType, uint32_t maxBatchSize );
	bool init(const char* prototxt_path, const char* model_path, const char* mean_binary, const char* class_path, const char* input, const char* output, uint32_t maxBatchSize );
	bool loadClassInfo( const char* filename );
	
	uint32_t mOutputClasses;
	
	std::vector<std::string> mClassSynset;	// 1000 class ID's (ie n01580077, n04325704)
	std::vector<std::string> mClassDesc;

	NetworkType mNetworkType;
};


#endif