#import <AssertMacros.h>
#import <AssetsLibrary/AssetsLibrary.h>
#import <CoreImage/CoreImage.h>
#import <ImageIO/ImageIO.h>
#import "CameraExampleViewController.h"
#include <sys/time.h>
#include "tensorflow_utils.h"
// If you have your own model, modify this to the file name, and make sure
// you've added the file to your app resources too.
static NSString* model_file_name = @"tensorflow_inception_graph";
static NSString* model_file_type = @"pb";
// This controls whether we'll be loading a plain GraphDef proto, or a
// file created by the convert_graphdef_memmapped_format utility that wraps a
// GraphDef and parameter file that can be mapped into memory from file to
// reduce overall memory usage.
const bool model_uses_memory_mapping = false;
// If you have your own model, point this to the labels file.
static NSString* labels_file_name = @"imagenet_comp_graph_label_strings";
static NSString* labels_file_type = @"txt";
// These dimensions need to match those the model was trained with.
// 以下尺寸需要和模型训练时相匹配
const int wanted_input_width =299;// 224;
const int wanted_input_height = 299;//224;
const int wanted_input_channels = 3;
const float input_mean = 128.0f;//117.0f;
const float input_std = 128.0f;//1.0f;
const std::string input_layer_name = "Mul";//"input";
const std::string output_layer_name = "final_result";//"softmax1";
static void *AVCaptureStillImageIsCapturingStillImageContext =&AVCaptureStillImageIsCapturingStillImageContext;
@interface CameraExampleViewController (InternalMethods)
- (void)setupAVCapture;
- (void)teardownAVCapture;
@end
@implementation CameraExampleViewController
- (void)setupAVCapture {NSError *error = nil;session = [AVCaptureSession new];if ([[UIDevice currentDevice] userInterfaceIdiom] ==UIUserInterfaceIdiomPhone)[session setSessionPreset:AVCaptureSessionPreset640x480];else[session setSessionPreset:AVCaptureSessionPresetPhoto];AVCaptureDevice *device =[AVCaptureDevice defaultDeviceWithMediaType:AVMediaTypeVideo];AVCaptureDeviceInput *deviceInput =[AVCaptureDeviceInput deviceInputWithDevice:device error:&error];assert(error == nil);isUsingFrontFacingCamera = NO;if ([session canAddInput:deviceInput]) [session addInput:deviceInput];stillImageOutput = [AVCaptureStillImageOutput new];[stillImageOutputaddObserver:selfforKeyPath:@"capturingStillImage"options:NSKeyValueObservingOptionNewcontext:(void *)(AVCaptureStillImageIsCapturingStillImageContext)];if ([session canAddOutput:stillImageOutput])[session addOutput:stillImageOutput];videoDataOutput = [AVCaptureVideoDataOutput new];NSDictionary *rgbOutputSettings = [NSDictionarydictionaryWithObject:[NSNumber numberWithInt:kCMPixelFormat_32BGRA]forKey:(id)kCVPixelBufferPixelFormatTypeKey];[videoDataOutput setVideoSettings:rgbOutputSettings];[videoDataOutput setAlwaysDiscardsLateVideoFrames:YES];videoDataOutputQueue =dispatch_queue_create("VideoDataOutputQueue", DISPATCH_QUEUE_SERIAL);[videoDataOutput setSampleBufferDelegate:self queue:videoDataOutputQueue];if ([session canAddOutput:videoDataOutput])[session addOutput:videoDataOutput];[[videoDataOutput connectionWithMediaType:AVMediaTypeVideo] setEnabled:YES];previewLayer = [[AVCaptureVideoPreviewLayer alloc] initWithSession:session];[previewLayer setBackgroundColor:[[UIColor blackColor] CGColor]];[previewLayer setVideoGravity:AVLayerVideoGravityResizeAspect];CALayer *rootLayer = [previewView layer];[rootLayer setMasksToBounds:YES];[previewLayer setFrame:[rootLayer bounds]];[rootLayer addSublayer:previewLayer];[session startRunning];if (error) {NSString *title = [NSString stringWithFormat:@"Failed with error %d", (int)[error code]];UIAlertController *alertController =[UIAlertController alertControllerWithTitle:titlemessage:[error localizedDescription]preferredStyle:UIAlertControllerStyleAlert];UIAlertAction *dismiss =[UIAlertAction actionWithTitle:@"Dismiss" style:UIAlertActionStyleDefault handler:nil];[alertController addAction:dismiss];[self presentViewController:alertController animated:YES completion:nil];[self teardownAVCapture];}
}
- (void)teardownAVCapture {[stillImageOutput removeObserver:self forKeyPath:@"isCapturingStillImage"];[previewLayer removeFromSuperlayer];
}
- (void)observeValueForKeyPath:(NSString *)keyPathofObject:(id)objectchange:(NSDictionary *)changecontext:(void *)context {if (context == AVCaptureStillImageIsCapturingStillImageContext) {BOOL isCapturingStillImage =[[change objectForKey:NSKeyValueChangeNewKey] boolValue];if (isCapturingStillImage) {// do flash bulb like animationflashView = [[UIView alloc] initWithFrame:[previewView frame]];[flashView setBackgroundColor:[UIColor whiteColor]];[flashView setAlpha:0.f];[[[self view] window] addSubview:flashView];[UIView animateWithDuration:.4fanimations:^{[flashView setAlpha:1.f];}];} else {[UIView animateWithDuration:.4fanimations:^{[flashView setAlpha:0.f];}completion:^(BOOL finished) {[flashView removeFromSuperview];flashView = nil;}];}}
}
- (AVCaptureVideoOrientation)avOrientationForDeviceOrientation:(UIDeviceOrientation)deviceOrientation {AVCaptureVideoOrientation result =(AVCaptureVideoOrientation)(deviceOrientation);if (deviceOrientation == UIDeviceOrientationLandscapeLeft)result = AVCaptureVideoOrientationLandscapeRight;else if (deviceOrientation == UIDeviceOrientationLandscapeRight)result = AVCaptureVideoOrientationLandscapeLeft;return result;
}
- (IBAction)takePicture:(id)sender {if ([session isRunning]) {[session stopRunning];[sender setTitle:@"Continue" forState:UIControlStateNormal];flashView = [[UIView alloc] initWithFrame:[previewView frame]];[flashView setBackgroundColor:[UIColor whiteColor]];[flashView setAlpha:0.f];[[[self view] window] addSubview:flashView];[UIView animateWithDuration:.2fanimations:^{[flashView setAlpha:1.f];}completion:^(BOOL finished) {[UIView animateWithDuration:.2fanimations:^{[flashView setAlpha:0.f];}completion:^(BOOL finished) {[flashView removeFromSuperview];flashView = nil;}];}];} else {[session startRunning];[sender setTitle:@"Freeze Frame" forState:UIControlStateNormal];}
}
+ (CGRect)videoPreviewBoxForGravity:(NSString *)gravityframeSize:(CGSize)frameSizeapertureSize:(CGSize)apertureSize {CGFloat apertureRatio = apertureSize.height / apertureSize.width;CGFloat viewRatio = frameSize.width / frameSize.height;CGSize size = CGSizeZero;if ([gravity isEqualToString:AVLayerVideoGravityResizeAspectFill]) {if (viewRatio > apertureRatio) {size.width = frameSize.width;size.height =apertureSize.width * (frameSize.width / apertureSize.height);} else {size.width =apertureSize.height * (frameSize.height / apertureSize.width);size.height = frameSize.height;}} else if ([gravity isEqualToString:AVLayerVideoGravityResizeAspect]) {if (viewRatio > apertureRatio) {size.width =apertureSize.height * (frameSize.height / apertureSize.width);size.height = frameSize.height;} else {size.width = frameSize.width;size.height =apertureSize.width * (frameSize.width / apertureSize.height);}} else if ([gravity isEqualToString:AVLayerVideoGravityResize]) {size.width = frameSize.width;size.height = frameSize.height;}CGRect videoBox;videoBox.size = size;if (size.width < frameSize.width)videoBox.origin.x = (frameSize.width - size.width) / 2;elsevideoBox.origin.x = (size.width - frameSize.width) / 2;if (size.height < frameSize.height)videoBox.origin.y = (frameSize.height - size.height) / 2;elsevideoBox.origin.y = (size.height - frameSize.height) / 2;return videoBox;
}
- (void)captureOutput:(AVCaptureOutput *)captureOutput
didOutputSampleBuffer:(CMSampleBufferRef)sampleBufferfromConnection:(AVCaptureConnection *)connection {CVPixelBufferRef pixelBuffer = CMSampleBufferGetImageBuffer(sampleBuffer);CFRetain(pixelBuffer);[self runCNNOnFrame:pixelBuffer];CFRelease(pixelBuffer);
}
- (void)runCNNOnFrame:(CVPixelBufferRef)pixelBuffer {assert(pixelBuffer != NULL);OSType sourcePixelFormat = CVPixelBufferGetPixelFormatType(pixelBuffer);int doReverseChannels;if (kCVPixelFormatType_32ARGB == sourcePixelFormat) {doReverseChannels = 1;} else if (kCVPixelFormatType_32BGRA == sourcePixelFormat) {doReverseChannels = 0;} else {assert(false);  // Unknown source format
  }const int sourceRowBytes = (int)CVPixelBufferGetBytesPerRow(pixelBuffer);const int image_width = (int)CVPixelBufferGetWidth(pixelBuffer);const int fullHeight = (int)CVPixelBufferGetHeight(pixelBuffer);CVPixelBufferLockFlags unlockFlags = kNilOptions;CVPixelBufferLockBaseAddress(pixelBuffer, unlockFlags);unsigned char *sourceBaseAddr =(unsigned char *)(CVPixelBufferGetBaseAddress(pixelBuffer));int image_height;unsigned char *sourceStartAddr;if (fullHeight <= image_width) {image_height = fullHeight;sourceStartAddr = sourceBaseAddr;} else {image_height = image_width;const int marginY = ((fullHeight - image_width) / 2);sourceStartAddr = (sourceBaseAddr + (marginY * sourceRowBytes));}const int image_channels = 4;assert(image_channels >= wanted_input_channels);tensorflow::Tensor image_tensor(tensorflow::DT_FLOAT,tensorflow::TensorShape({1, wanted_input_height, wanted_input_width, wanted_input_channels}));auto image_tensor_mapped = image_tensor.tensor<float, 4>();tensorflow::uint8 *in = sourceStartAddr;float *out = image_tensor_mapped.data();for (int y = 0; y < wanted_input_height; ++y) {float *out_row = out + (y * wanted_input_width * wanted_input_channels);for (int x = 0; x < wanted_input_width; ++x) {const int in_x = (y * image_width) / wanted_input_width;const int in_y = (x * image_height) / wanted_input_height;tensorflow::uint8 *in_pixel =in + (in_y * image_width * image_channels) + (in_x * image_channels);float *out_pixel = out_row + (x * wanted_input_channels);for (int c = 0; c < wanted_input_channels; ++c) {out_pixel[c] = (in_pixel[c] - input_mean) / input_std;}}}CVPixelBufferUnlockBaseAddress(pixelBuffer, unlockFlags);if (tf_session.get()) {std::vector<tensorflow::Tensor> outputs;tensorflow::Status run_status = tf_session->Run({{input_layer_name, image_tensor}}, {output_layer_name}, {}, &outputs);if (!run_status.ok()) {LOG(ERROR) << "Running model failed:" << run_status;} else {tensorflow::Tensor *output = &outputs[0];auto predictions = output->flat<float>();NSMutableDictionary *newValues = [NSMutableDictionary dictionary];for (int index = 0; index < predictions.size(); index += 1) {const float predictionValue = predictions(index);if (predictionValue > 0.05f) {std::string label = labels[index % predictions.size()];NSString *labelObject = [NSString stringWithUTF8String:label.c_str()];NSNumber *valueObject = [NSNumber numberWithFloat:predictionValue];[newValues setObject:valueObject forKey:labelObject];}}dispatch_async(dispatch_get_main_queue(), ^(void) {[self setPredictionValues:newValues];});}}CVPixelBufferUnlockBaseAddress(pixelBuffer, 0);
}
- (void)dealloc {[self teardownAVCapture];
}
// use front/back camera
- (IBAction)switchCameras:(id)sender {AVCaptureDevicePosition desiredPosition;if (isUsingFrontFacingCamera)desiredPosition = AVCaptureDevicePositionBack;elsedesiredPosition = AVCaptureDevicePositionFront;for (AVCaptureDevice *d in[AVCaptureDevice devicesWithMediaType:AVMediaTypeVideo]) {if ([d position] == desiredPosition) {[[previewLayer session] beginConfiguration];AVCaptureDeviceInput *input =[AVCaptureDeviceInput deviceInputWithDevice:d error:nil];for (AVCaptureInput *oldInput in [[previewLayer session] inputs]) {[[previewLayer session] removeInput:oldInput];}[[previewLayer session] addInput:input];[[previewLayer session] commitConfiguration];break;}}isUsingFrontFacingCamera = !isUsingFrontFacingCamera;
}
- (void)didReceiveMemoryWarning {[super didReceiveMemoryWarning];
}
- (void)viewDidLoad {[super viewDidLoad];square = [UIImage imageNamed:@"squarePNG"];synth = [[AVSpeechSynthesizer alloc] init];labelLayers = [[NSMutableArray alloc] init];oldPredictionValues = [[NSMutableDictionary alloc] init];tensorflow::Status load_status;if (model_uses_memory_mapping) {load_status = LoadMemoryMappedModel(model_file_name, model_file_type, &tf_session, &tf_memmapped_env);} else {load_status = LoadModel(model_file_name, model_file_type, &tf_session);}if (!load_status.ok()) {LOG(FATAL) << "Couldn't load model: " << load_status;}tensorflow::Status labels_status =LoadLabels(labels_file_name, labels_file_type, &labels);if (!labels_status.ok()) {LOG(FATAL) << "Couldn't load labels: " << labels_status;}[self setupAVCapture];
}
- (void)viewDidUnload {[super viewDidUnload];
}
- (void)viewWillAppear:(BOOL)animated {[super viewWillAppear:animated];
}
- (void)viewDidAppear:(BOOL)animated {[super viewDidAppear:animated];
}
- (void)viewWillDisappear:(BOOL)animated {[super viewWillDisappear:animated];
}
- (void)viewDidDisappear:(BOOL)animated {[super viewDidDisappear:animated];
}
- (BOOL)shouldAutorotateToInterfaceOrientation:(UIInterfaceOrientation)interfaceOrientation {return (interfaceOrientation == UIInterfaceOrientationPortrait);
}
- (BOOL)prefersStatusBarHidden {return YES;
}
- (void)setPredictionValues:(NSDictionary *)newValues {const float decayValue = 0.75f;const float updateValue = 0.25f;const float minimumThreshold = 0.01f;NSMutableDictionary *decayedPredictionValues =[[NSMutableDictionary alloc] init];for (NSString *label in oldPredictionValues) {NSNumber *oldPredictionValueObject =[oldPredictionValues objectForKey:label];const float oldPredictionValue = [oldPredictionValueObject floatValue];const float decayedPredictionValue = (oldPredictionValue * decayValue);if (decayedPredictionValue > minimumThreshold) {NSNumber *decayedPredictionValueObject =[NSNumber numberWithFloat:decayedPredictionValue];[decayedPredictionValues setObject:decayedPredictionValueObjectforKey:label];}}oldPredictionValues = decayedPredictionValues;for (NSString *label in newValues) {NSNumber *newPredictionValueObject = [newValues objectForKey:label];NSNumber *oldPredictionValueObject =[oldPredictionValues objectForKey:label];if (!oldPredictionValueObject) {oldPredictionValueObject = [NSNumber numberWithFloat:0.0f];}const float newPredictionValue = [newPredictionValueObject floatValue];const float oldPredictionValue = [oldPredictionValueObject floatValue];const float updatedPredictionValue =(oldPredictionValue + (newPredictionValue * updateValue));NSNumber *updatedPredictionValueObject =[NSNumber numberWithFloat:updatedPredictionValue];[oldPredictionValues setObject:updatedPredictionValueObject forKey:label];}NSArray *candidateLabels = [NSMutableArray array];for (NSString *label in oldPredictionValues) {NSNumber *oldPredictionValueObject =[oldPredictionValues objectForKey:label];const float oldPredictionValue = [oldPredictionValueObject floatValue];if (oldPredictionValue > 0.05f) {NSDictionary *entry = @{@"label" : label,@"value" : oldPredictionValueObject};candidateLabels = [candidateLabels arrayByAddingObject:entry];}}NSSortDescriptor *sort =[NSSortDescriptor sortDescriptorWithKey:@"value" ascending:NO];NSArray *sortedLabels = [candidateLabelssortedArrayUsingDescriptors:[NSArray arrayWithObject:sort]];const float leftMargin = 10.0f;const float topMargin = 10.0f;const float valueWidth = 48.0f;const float valueHeight = 26.0f;const float labelWidth = 246.0f;const float labelHeight = 26.0f;const float labelMarginX = 5.0f;const float labelMarginY = 5.0f;[self removeAllLabelLayers];int labelCount = 0;for (NSDictionary *entry in sortedLabels) {NSString *label = [entry objectForKey:@"label"];NSNumber *valueObject = [entry objectForKey:@"value"];const float value = [valueObject floatValue];const float originY =(topMargin + ((labelHeight + labelMarginY) * labelCount));const int valuePercentage = (int)roundf(value * 100.0f);const float valueOriginX = leftMargin;NSString *valueText = [NSString stringWithFormat:@"%d%%", valuePercentage];[self addLabelLayerWithText:valueTextoriginX:valueOriginXoriginY:originYwidth:valueWidthheight:valueHeightalignment:kCAAlignmentRight];const float labelOriginX = (leftMargin + valueWidth + labelMarginX);[self addLabelLayerWithText:[label capitalizedString]originX:labelOriginXoriginY:originYwidth:labelWidthheight:labelHeightalignment:kCAAlignmentLeft];if ((labelCount == 0) && (value > 0.5f)) {[self speak:[label capitalizedString]];}labelCount += 1;if (labelCount > 4) {break;}}
}
- (void)removeAllLabelLayers {for (CATextLayer *layer in labelLayers) {[layer removeFromSuperlayer];}[labelLayers removeAllObjects];
}
- (void)addLabelLayerWithText:(NSString *)textoriginX:(float)originXoriginY:(float)originYwidth:(float)widthheight:(float)heightalignment:(NSString *)alignment {CFTypeRef font = (CFTypeRef) @"Menlo-Regular";const float fontSize = 20.0f;const float marginSizeX = 5.0f;const float marginSizeY = 2.0f;const CGRect backgroundBounds = CGRectMake(originX, originY, width, height);const CGRect textBounds =CGRectMake((originX + marginSizeX), (originY + marginSizeY),(width - (marginSizeX * 2)), (height - (marginSizeY * 2)));CATextLayer *background = [CATextLayer layer];[background setBackgroundColor:[UIColor blackColor].CGColor];[background setOpacity:0.5f];[background setFrame:backgroundBounds];background.cornerRadius = 5.0f;[[self.view layer] addSublayer:background];[labelLayers addObject:background];CATextLayer *layer = [CATextLayer layer];[layer setForegroundColor:[UIColor whiteColor].CGColor];[layer setFrame:textBounds];[layer setAlignmentMode:alignment];[layer setWrapped:YES];[layer setFont:font];[layer setFontSize:fontSize];layer.contentsScale = [[UIScreen mainScreen] scale];[layer setString:text];[[self.view layer] addSublayer:layer];[labelLayers addObject:layer];
}
- (void)setPredictionText:(NSString *)text withDuration:(float)duration {if (duration > 0.0) {CABasicAnimation *colorAnimation =[CABasicAnimation animationWithKeyPath:@"foregroundColor"];colorAnimation.duration = duration;colorAnimation.fillMode = kCAFillModeForwards;colorAnimation.removedOnCompletion = NO;colorAnimation.fromValue = (id)[UIColor darkGrayColor].CGColor;colorAnimation.toValue = (id)[UIColor whiteColor].CGColor;colorAnimation.timingFunction =[CAMediaTimingFunction functionWithName:kCAMediaTimingFunctionLinear];[self.predictionTextLayer addAnimation:colorAnimationforKey:@"colorAnimation"];} else {self.predictionTextLayer.foregroundColor = [UIColor whiteColor].CGColor;}[self.predictionTextLayer removeFromSuperlayer];[[self.view layer] addSublayer:self.predictionTextLayer];[self.predictionTextLayer setString:text];
}
- (void)speak:(NSString *)words {if ([synth isSpeaking]) {return;}AVSpeechUtterance *utterance =[AVSpeechUtterance speechUtteranceWithString:words];utterance.voice = [AVSpeechSynthesisVoice voiceWithLanguage:@"en-US"];utterance.rate = 0.75 * AVSpeechUtteranceDefaultSpeechRate;[synth speakUtterance:utterance];
}
@end

android_sdk_repository(name = "androidsdk",api_level = 25,build_tools_version = "25.0.2",# Replace with path to Android SDK on your systempath = "~/Library/Android/sdk"
)
android_ndk_repository(name = "androidndk",api_level = 23,path = "~/Downloads/android-ndk-r13b"
)

package org.tensorflow.demo;
import android.content.res.AssetManager;
import android.graphics.Bitmap;
import android.os.Trace;
import android.util.Log;
import java.io.BufferedReader;
import java.io.IOException;
import java.io.InputStreamReader;
import java.util.ArrayList;
import java.util.Comparator;
import java.util.List;
import java.util.PriorityQueue;
import java.util.Vector;
import org.tensorflow.Operation;
import org.tensorflow.contrib.android.TensorFlowInferenceInterface;
/** A classifier specialized to label images using TensorFlow. */
public class TensorFlowImageClassifier implements Classifier {private static final String TAG = "TensorFlowImageClassifier";// Only return this many results with at least this confidence.private static final int MAX_RESULTS = 3;private static final float THRESHOLD = 0.1f;// Config values.private String inputName;private String outputName;private int inputSize;private int imageMean;private float imageStd;// Pre-allocated buffers.private Vector<String> labels = new Vector<String>();private int[] intValues;private float[] floatValues;private float[] outputs;private String[] outputNames;private boolean logStats = false;private TensorFlowInferenceInterface inferenceInterface;private TensorFlowImageClassifier() {}/*** Initializes a native TensorFlow session for classifying images.** @param assetManager The asset manager to be used to load assets.* @param modelFilename The filepath of the model GraphDef protocol buffer.* @param labelFilename The filepath of label file for classes.* @param inputSize The input size. A square image of inputSize x inputSize is assumed.* @param imageMean The assumed mean of the image values.* @param imageStd The assumed std of the image values.* @param inputName The label of the image input node.* @param outputName The label of the output node.* @throws IOException*/public static Classifier create(AssetManager assetManager,String modelFilename,String labelFilename,int inputSize,int imageMean,float imageStd,String inputName,String outputName) {TensorFlowImageClassifier c = new TensorFlowImageClassifier();c.inputName = inputName;c.outputName = outputName;// Read the label names into memory.// TODO(andrewharp): make this handle non-assets.String actualFilename = labelFilename.split("file:///android_asset/")[1];Log.i(TAG, "Reading labels from: " + actualFilename);BufferedReader br = null;try {br = new BufferedReader(new InputStreamReader(assetManager.open(actualFilename)));String line;while ((line = br.readLine()) != null) {c.labels.add(line);}br.close();} catch (IOException e) {throw new RuntimeException("Problem reading label file!" , e);}c.inferenceInterface = new TensorFlowInferenceInterface(assetManager, modelFilename);// The shape of the output is [N, NUM_CLASSES], where N is the batch size.final Operation operation = c.inferenceInterface.graphOperation(outputName);final int numClasses = (int) operation.output(0).shape().size(1);Log.i(TAG, "Read " + c.labels.size() + " labels, output layer size is " + numClasses);// Ideally, inputSize could have been retrieved from the shape of the input operation.  Alas,// the placeholder node for input in the graphdef typically used does not specify a shape, so it// must be passed in as a parameter.c.inputSize = inputSize;c.imageMean = imageMean;c.imageStd = imageStd;// Pre-allocate buffers.c.outputNames = new String[] {outputName};c.intValues = new int[inputSize * inputSize];c.floatValues = new float[inputSize * inputSize * 3];c.outputs = new float[numClasses];return c;}@Overridepublic List<Recognition> recognizeImage(final Bitmap bitmap) {// Log this method so that it can be analyzed with systrace.Trace.beginSection("recognizeImage");Trace.beginSection("preprocessBitmap");// Preprocess the image data from 0-255 int to normalized float based// on the provided parameters.bitmap.getPixels(intValues, 0, bitmap.getWidth(), 0, 0, bitmap.getWidth(), bitmap.getHeight());for (int i = 0; i < intValues.length; ++i) {final int val = intValues[i];floatValues[i * 3 + 0] = (((val >> 16) & 0xFF) - imageMean) / imageStd;floatValues[i * 3 + 1] = (((val >> 8) & 0xFF) - imageMean) / imageStd;floatValues[i * 3 + 2] = ((val & 0xFF) - imageMean) / imageStd;}Trace.endSection();// Copy the input data into TensorFlow.Trace.beginSection("feed");inferenceInterface.feed(inputName, floatValues, 1, inputSize, inputSize, 3);Trace.endSection();// Run the inference call.Trace.beginSection("run");inferenceInterface.run(outputNames, logStats);Trace.endSection();// Copy the output Tensor back into the output array.Trace.beginSection("fetch");inferenceInterface.fetch(outputName, outputs);Trace.endSection();// Find the best classifications.PriorityQueue<Recognition> pq =new PriorityQueue<Recognition>(3,new Comparator<Recognition>() {@Overridepublic int compare(Recognition lhs, Recognition rhs) {// Intentionally reversed to put high confidence at the head of the queue.return Float.compare(rhs.getConfidence(), lhs.getConfidence());}});for (int i = 0; i < outputs.length; ++i) {if (outputs[i] > THRESHOLD) {pq.add(new Recognition("" + i, labels.size() > i ? labels.get(i) : "unknown", outputs[i], null));}}final ArrayList<Recognition> recognitions = new ArrayList<Recognition>();int recognitionsSize = Math.min(pq.size(), MAX_RESULTS);for (int i = 0; i < recognitionsSize; ++i) {recognitions.add(pq.poll());}Trace.endSection(); // "recognizeImage"return recognitions;}@Overridepublic void enableStatLogging(boolean logStats) {this.logStats = logStats;}@Overridepublic String getStatString() {return inferenceInterface.getStatString();}@Overridepublic void close() {inferenceInterface.close();}
}