matlab2016 trainACFObjectDetector
原文链接：https://cn.mathworks.com/help/vision/ref/trainacfobjectdetector.html
trainACFObjectDetector
Syntax
detector = trainACFObjectDetector(trainingData)
detector = trainACFObjectDetector(trainingData,Name,Value)
Description
detector =
trainACFObjectDetector(trainingData)trainingData table
and automatically collects negative instances from the images during training. To create a ground truth table, use the Image
Labeler app.
detector =
trainACFObjectDetector(trainingData,Name,Value)detector object with additional options specified by one or more Name,Value pair
arguments.
Examples
Train a Stop Sign Detector Using an ACF Object Detector
Use the trainACFObjectDetector with training images to create an ACF object detector
that can detect stop signs. Test the detector with a separate image.
Load the training data.
load('stopSignsAndCars.mat')
Select the ground truth for stop signs. These ground truth is the set of known locations of stop signs in the images.
stopSigns = stopSignsAndCars(:,1:2);
Add the full path to the image files.
stopSigns.imageFilename = fullfile(toolboxdir('vision'),... 'visiondata',stopSigns.imageFilename);
Train the ACF detector. You can turn off the training progress output by specifying 'Verbose',false as
a Name,Value pair.
acfDetector = trainACFObjectDetector(stopSigns,'NumStages',3);
ACF Object Detector Training The training will take 3 stages. The model size is 34x31. Sample positive examples(~100% Completed) Compute approximation coefficients...Completed. Compute aggregated channel features...Completed. -------------------------------------------- Stage 1: Sample negative examples(~100% Completed) Compute aggregated channel features...Completed. Train classifier with 42 positive examples and 210 negative examples...Completed. The trained classifier has 19 weak learners. -------------------------------------------- Stage 2: Sample negative examples(~100% Completed) Found 210 new negative examples for training. Compute aggregated channel features...Completed. Train classifier with 42 positive examples and 210 negative examples...Completed. The trained classifier has 38 weak learners. -------------------------------------------- Stage 3: Sample negative examples(~100% Completed) Found 210 new negative examples for training. Compute aggregated channel features...Completed. Train classifier with 42 positive examples and 210 negative examples...Completed. The trained classifier has 71 weak learners. -------------------------------------------- ACF object detector training is completed. Elapsed time is 35.539 seconds.
Test the ACF detector on a test image.
img = imread('stopSignTest.jpg');
[bboxes,scores] = detect(acfDetector,img);
Display the detection results and insert the bounding boxes for objects into the image.
for i = 1:length(scores) annotation = sprintf('Confidence = %.1f',scores(i)); img = insertObjectAnnotation(img,'rectangle',bboxes(i,:),annotation); end figure imshow(img)
Input Arguments
trainingData — Labeled ground truth images
table
Labeled ground truth images, specified as a table with two or more columns. The first column must contain paths and file names to grayscale or truecolor (RGB) images. Although, ACF-based detectors work best with truecolor images. The remaining columns must contain bounding boxes related to the corresponding image. Each column represents a single object class, such as a car, dog, flower, or stop sign.
Each bounding box must be in the format [x,y,width,height]. The format specifies the upper-left corner location and the size of the object in the corresponding image. The table variable name defines the object class name. To create the ground truth table, use the Image Labeler app.
Name-Value Pair Arguments
Specify optional comma-separated pairs of Name,Value arguments. Name is
the argument name and Value is the corresponding value. Name must
appear inside single quotes (' '). You can specify several name and value pair arguments
in any order as Name1,Value1,...,NameN,ValueN.
'ObjectTrainingSize', [100
100]