2019b warnings popping up never had this before with 2019a

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Matpar 2020-2-12

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https://ww2.mathworks.cn/matlabcentral/answers/505159-2019b-warnings-popping-up-never-had-this-before-with-2019a

评论： Guillaume 2020-2-13

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Hi all,

I have been running my code and processing stuff normally with matlab until I upgraded to 2019b

can some one help me solve this error please?

Warning: PNG library warning: iCCP: profile 'ICC Profile': 0h: PCS illuminant is not D50. 

Everything worked perfect until i hit the upgrade button!

now everything is arguing!

Help Please!

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Guillaume 2020-2-12

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Comment by Matpar originally posted as an answer moved here:

Hi Guillaume,

It is the augmented step 10 that is causing the issue can you guide me please? this in 2019a work fine? now this is causing the argument!

%% Extract region proposals with selective search
%% Conducting Feature Extraction With RCNN
%% Classifing Features With SVM
%% Improving The Bounding Box
clc
clearvars
clear
close all
%% Step 1 Creating Filenames /Loading Data
anet = alexnet
load('Wgtruth.mat');
load('anet.mat');
save Wgtruth.mat Wgtruth; 
save test16.mat;
save anet.mat anet;
load('test16.mat', 'Wgtruth', 'anet');
%% Step 2 Highlighting Image Input Size
inputSize = anet.Layers(1).InputSize;
anet.Layers;
total_images = size(Wgtruth,1);
%% Step 3 Adding Image Directory For Path To Image Data
imDir = '/Users/mmgp/Documents/MATLAB/2020/RCNN/Wgtruth'
addpath(imDir)
% imDir = fullfile(matlabroot, 'toolbox', 'vision', 'visiondata','Wgtruth');
% addpath(imDir);
 
%% Step 4 Accessing Contents Of Folder TrainingSet Using Datastore
imds =imageDatastore(imDir,'IncludeSubFolders',true,'LabelSource','Foldernames');
%% Step 5 Splitting Inputs Into Training and Testing Sets
[imdsTrain,imdsValidation] = splitEachLabel(imds,0.7,'randomized');
%% Step 6 Replacing Final Layer/Last 3 Configure For Network classes
% Complex Architecture Layers Has Inputs/Outputs From Multiple Layers
% Finetuning These 3 Layers For New Classification
% Extracting All Layers Except The Last 3
layersTransfer = anet.Layers(1:end-3)
%% Step 7 Specifying Image Categories/Clases From 1000 to Gun(One Class):
numClasses = numel(categories(imdsTrain.Labels));
Tlayers = [
    layersTransfer
    fullyConnectedLayer(numClasses,'Name','fc8','WeightLearnRateFactor',10,'BiasLearnRateFactor',10);
    softmaxLayer('name', 'Softmax')
    classificationLayer('Name','ClassfLay')]
 
%% Step 8 Displaying and Visualising Layer Features Of FC8
% layer(16) = maxPooling2dLayer(5,'stride',2)
% disp(Tlayers)
% layer = 22;
% channels = 1:30;
% I = deepDreamImage(net,layer,channels,'PyramidLevels',1);
% figure
% I = imtile(I,'ThumbnailSize',[64 64]);
% imshow(I)
% name = net.Layers(layer).Name;
% title(['Layer ',name,' Features'])
%% Warp Image & Pixel Labels
%  Creates A Randomized 2-D Affine Transformation From A Combination Of Rotation,
%  Translation, Scaling (Resizing), Reflection, And Shearing
%  Rotate Input Properties By An Angle Selected Randomly From Range [-50,50] Degrees.
%% Step 9 Setting Output Function(images may have size variation resizing for consistency with pretrain net)
pixelRange = [-70 70]
imageAugmenter = imageDataAugmenter('RandRotation',[-70 70],...
'RandXReflection',true,...
'RandYReflection',true,...
'RandXShear',pixelRange,...
'RandYShear',pixelRange,...
'RandXTranslation',pixelRange, ... 
'RandYTranslation',pixelRange),...
augimdsTrain = augmentedImageDatastore(inputSize(1:2),imdsTrain, ... 
'DataAugmentation',imageAugmenter);
%% Step 10 Resizing Images, Assists With Preventing Overfitting 
%  Utilising Data Augmentation For Resizing Validation Data
%  Implemented Without Specifying Overfit Prevention Procedures
%  By Not Specifying These Procedures The System Will Be Precise Via
%  Predicitons Data Augmentation Prevent The Network From
%  Overfitting/ MemorizingExact Details Of Training Images
augmentedTrainingSet = augmentedImageDatastore(inputSize ,imdsTrain,'ColorPreprocessing', 'gray2rgb')
augimdsValidation = augmentedImageDatastore(inputSize,imdsValidation,'ColorPreprocessing', 'gray2rgb')
%% Step 11 Specifying Training Options
%  Keep features from earlier layers of pretrained networked for transfer learning
%  Specify epoch training cycle, the mini-batch size and validation data
%  Validate the network for each iteration during training.
%  (SGDM)groups the full dataset into disjoint mini-batches This reaches convergence faster 
%  as it updates the network's weight value more frequently and increases the
%  computationl speed
%  Implementing **WITH** The RCNN Object Detector
options = trainingOptions('sgdm',... 
    'Momentum',0.9,... 
    'InitialLearnRate', 1e-4,... 
    'LearnRateSchedule', 'piecewise', ... 
    'LearnRateDropFactor', 0.1, ... 
    'Shuffle','every-epoch', ... 
    'LearnRateDropPeriod', 8, ... 
    'L2Regularization', 1e-4, ... 
    'MaxEpochs', 10,... 
    'MiniBatchSize',80,...
    'Verbose', true)
 
%% Step 12 Training network Consisting Of Transferred & New Layers.  
netTransfer = trainNetwork(augmentedTrainingSet,Tlayers,options)
rcnn = trainRCNNObjectDetector(Wgtruth, netTransfer, options, 'NegativeOverlapRange', [0 0.3]);
save('rcnn.mat', 'rcnn')
%% Predicitng Validation Image Accuracy
predictedLabels = classify(netTransfer,augimdsValidation)
accuracy =mean(predictedLabels== imdsValidation.Labels)
%% Step 13 Testing R-CNN Detector On Test Image.
img = imread('11.jpg');
[bbox, score, label] = detect(rcnn, img, 'MiniBatchSize', 80)
% numObservations = 4;
% images = repelem({img},numObservations,1)
% bboxes = repelem({bbox},numObservations,1)
% labels = repelem({label},numObservations,1)
%% Step 14 Displaying Strongest Detection Results.
[score, idx] = max(score)
bbox = bbox(idx, :)
annotation = sprintf('%s: (Confidence = %f)', label(idx), score)
detectedImg = insertObjectAnnotation(img, 'rectangle', bbox, annotation);
figure
imshow(detectedImg)
%  

Guillaume 2020-2-13

编辑：Guillaume 2020-2-13

在 MATLAB Online 中打开

Comment by Matpar originally posted as an answer moved here:

Hi all,

these warnings are fiercely persistant!

I am trying to get the classification process completed but this is my error for which I am having some challenges interpreting!

Warning: Unable to find any region proposals to use as positive training samples. Lower the first value of PositiveOverlapRange to increase the number of positive region proposals. 
> In vision.internal.rcnn.BBoxTrainingDataDispatcher (line 127)
  In rcnnObjectDetector.trainBBoxRegressor (line 279)
  In rcnnObjectDetector.train (line 261)
  In trainRCNNObjectDetector (line 268)
  In test17 (line 137) 

Can a professional help me solve the highlighted?

Thanx in advance for acknowledging my novice approach!

Guillaume 2020-2-13

You won't get any professional help here I'm afraid, we're all volunteers.

This is a completely different warning. The previous warning was issued when you loaded an image with some unexpected metadata. Unless you were using the embedded ICC profile (you'd know if you were), you can safely ignore/disable it.

This new warning looks a lot more serious. It looks like it's telling you that one of your training doesn't have any recognisable training data, so most likely you need to review your training data and/or your detection algorithm. I don't have the required toolbox so can't help you any further.

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Answer 1

Sean de Wolski 2020-2-12

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链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/505159-2019b-warnings-popping-up-never-had-this-before-with-2019a#answer_415316

I've seen this warning in 19b too! Not sure if there is a change to the PNG library being used or if my image is corrupt/out of spec. Please contact support with the offending image.