How to spearate the output of vl_hog() into 6*6*31 blocks and use reshape() to convert every block to a row vector?

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Pooyan Mobtahej
Pooyan Mobtahej 2020-11-26
评论： Pooyan Mobtahej 2020-11-29
How to spearate the output of vl_hog() into 6*6*31 blocks,
use reshape() to convert every block to a row vector,
save each vector to features_neg, and increase idx by 1
in the following code:
step=feature_params.hog_cell_size;
idx=0;
for i=1:num_images
im=imread([non_face_scn_path '/' image_files(i).name]);
im=single(im);
hog = vl_hog(im, feature_parames.hog_cell_size, 'verbose');
for m=1:step:size(im,1)
for n=1:step:size(im,2)
%%%%% spearate the output of vl_hog() into 6*6*31 blocks,
%%%%% use reshape() to convert every block to a row vector,
%%%%% save each vector to features_neg, and increase idx by 1
%%%%%
features_neg(idx,:)=reshape(hog,1, []);
idx=idx+1
  2 个评论
Walter Roberson
Walter Roberson 2020-11-26
Do you mean you want each block to be 6 x 6 x 31 ? Or do you mean that you want the code to figure out what block size to use such that you can fit 6 blocks down by 6 blocks across by 31 blocks tall ? Does hog start out as 3 dimensional, or does it start out as a vector? What do you want done if the size of the first dimension is not an exact multiple of 6?
For example if hog is input as 512 x 512, then how many blocks of what size are you expecting as output?
Pooyan Mobtahej
Pooyan Mobtahej 2020-11-26
It is face detection using sliding window and i am trying to fill the function for get random negative features. The size is diffrenet and we use the followng code for sliding window and I want to fill the part that I mentioned, I put my code but don't know if it is correct, you can take a look:
% Starter code prepared by James Hays, Brown University
% This function should return negative training examples (non-faces) from
% any images in 'non_face_scn_path'. Images should be converted to
% grayscale, because the positive training data is only available in
% grayscale. For best performance, you should sample random negative
% examples at multiple scales.
function features_neg = get_random_negative_features(non_face_scn_path, feature_params, num_samples)
% 'non_face_scn_path' is a string. This directory contains many images
% which have no faces in them.
% 'feature_params' is a struct, with fields
% feature_params.template_size (probably 36), the number of pixels
% spanned by each train / test template and
% feature_params.hog_cell_size (default 6), the number of pixels in each
% HoG cell. template size should be evenly divisible by hog_cell_size.
% Smaller HoG cell sizes tend to work better, but they make things
% slower because the feature dimensionality increases and more
% importantly the step size of the classifier decreases at test time.
% 'num_samples' is the number of random negatives to be mined, it's not
% important for the function to find exactly 'num_samples' non-face
% features, e.g. you might try to sample some number from each image, but
% some images might be too small to find enough.
% 'features_neg' is N by D matrix where N is the number of non-faces and D
% is the template dimensionality, which would be
% (feature_params.template_size / feature_params.hog_cell_size)^2 * 31
% if you're using the default vl_hog parameters
% Useful functions:
% vl_hog, HOG = VL_HOG(IM, CELLSIZE)
% http://www.vlfeat.org/matlab/vl_hog.html (API)
% http://www.vlfeat.org/overview/hog.html (Tutorial)
% rgb2gray
image_files = dir(fullfile(non_face_scn_path, '*.jpg'));
num_images = length(image_files);
% placeholder to be deleted
features_neg = rand(100, (feature_params.template_size / feature_params.hog_cell_size)^2 * 31);
step=feature_params.hog_cell_size;
idx=0;
for i=1:num_images
im=imread([non_face_scn_path '/' image_files(i).name]);
%%%%% please follow the steps below to compute HOG features for each image.
%%%%% 1. use single() function to convert input image to SINGLE class;
%%%%% 2. call vl_hog() function, two parameters: one is the image, the
%%%%% other is the feature_parames.hog_cell_size;
%%%%%
im=single(im);
hog = vl_hog(im, feature_parames.hog_cell_size, 'verbose');
for m=1:step:size(im,1)
for n=1:step:size(im,2)
%%%%% spearate the output of vl_hog() into 6*6*31 blocks,
%%%%% use reshape() to convert every block to a row vector,
%%%%% save each vector to features_neg, and increase idx by 1
%%%%% Your code here!
features_neg(idx,:)=reshape(hog, [1, 6*6*31]);
idx=idx+1
end
end
disp(['idex is: ' num2str(idx)]);
disp(['case is: ' num2str(i)]);
end
idx

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采纳的回答

Walter Roberson
Walter Roberson 2020-11-26
编辑：Walter Roberson 2020-11-26
If you have a cell array of blocks, then
vector_blocks_cell = cellfun(@(B) reshape(B,1,[]), non_vector_blocks_cell, 'uniform', 0);
  10 个评论
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Pooyan Mobtahej
Pooyan Mobtahej 2020-11-27
I am trying to modify face detection project which I will attach the project code for you to check! The i shall get positive and negative random featuresusing HOG and then do SVM classification so please check if I am doing write in codes besides the one that I mentioned
Project:
% Sliding window face detection with linear SVM.
% All code by James Hays, except for pieces of evaluation code from Pascal
% VOC toolkit. Images from CMU+MIT face database, CalTech Web Face
% Database, and SUN scene database.
% Code structure:
% project.m <--- You code parts of this
% + get_positive_features.m <--- You code this
% + get_random_negative_features.m <--- You code this
% [classifier training] <--- You code this
% + report_accuracy.m
% + run_detector.m <--- You code this
% + non_max_supr_bbox.m
% + evaluate_all_detections.m
% + VOCap.m
% + visualize_detections_by_image.m
% + visualize_detections_by_image_no_gt.m
% + visualize_detections_by_confidence.m
% Other functions. You don't need to use any of these unless you're trying
% to modify or build a test set:
% Training and Testing data related functions:
% test_scenes/visualize_cmumit_database_landmarks.m
% test_scenes/visualize_cmumit_database_bboxes.m
% test_scenes/cmumit_database_points_to_bboxes.m %This function converts
% from the original MIT+CMU test set landmark points to Pascal VOC
% annotation format (bounding boxes).
% caltech_faces/caltech_database_points_to_crops.m %This function extracts
% training crops from the Caltech Web Face Database. The crops are
% intentionally large to contain most of the head, not just the face. The
% test_scene annotations are likewise scaled to contain most of the head.
% set up paths to VLFeat functions.
% See http://www.vlfeat.org/matlab/matlab.html for VLFeat Matlab documentation
% This should work on 32 and 64 bit versions of Windows, MacOS, and Linux
close all
clear all
%%%%% JZ: You may need to change the directory below to the directory where you installed VLFeat
run('/Users/pooyan/Documents/computer Vision/vlfeat-0.9.21 3/toolbox/vl_setup')
[~,~,~] = mkdir('visualizations');
%%%%% JZ: You don't need to change anything below (line 47-52)
data_path = '/Users/pooyan/Documents/projectcv/data/'; %change if you want to work with a network copy
train_path_pos = fullfile(data_path, 'caltech_faces/Caltech_CropFaces'); %Positive training examples. 36x36 head crops
non_face_scn_path = fullfile(data_path, 'train_non_face_scenes'); %We can mine random or hard negatives from here
test_scn_path = fullfile(data_path,'test_scenes/test_jpg'); %CMU+MIT test scenes
label_path = fullfile(data_path,'test_scenes/ground_truth_bboxes.txt'); %the ground truth face locations in the test set
%test_scn_path = fullfile(data_path,'test_scenes/test_class'); %Bonus scenes
%label_path = fullfile(data_path,'test_scenes/ground_truth_class_bboxes.txt'); %the ground truth face locations in the test set
%The faces are 36x36 pixels, which works fine as a template size. You could
%add other fields to this struct if you want to modify HoG default
%parameters such as the number of orientations, but that does not help
%performance in our limited test.
feature_params = struct('template_size', 36, 'hog_cell_size', 6);
% Step 1. Load positive training crops and random negative examples
%%%%% JZ: Please open get_positive_features.m file and complete it.
features_pos = get_positive_features( train_path_pos, feature_params );
%%%% JZ: you may change num_negative_examples to see the performance.
num_negative_examples = 10000; %Higher will work strictly better, but you should start with 10000 for debugging
%%%% JZ: Please open get_random_negative_features.m and complete it.
features_neg = get_random_negative_features( non_face_scn_path, feature_params, num_negative_examples);
% step 2. Train Classifier
% Use vl_svmtrain on your training features to get a linear classifier
% specified by 'w' and 'b'
% [w b] = vl_svmtrain(X, Y, lambda)
% http://www.vlfeat.org/sandbox/matlab/vl_svmtrain.html
% 'lambda' is an important parameter, try many values. Small values seem to
% work best e.g. 0.0001, but you can try other values
%
% (1) num_examples is a variable that defines the number of positive examples (face) and negative examples (non-face).
% (2) randomly select num_examples positive examples and num_examples negative examples
% (3) F is the training dataset containing positive and negative examples. Totol 2*num_examples examples.
% (4) Label is the class of each examples in F. Positive examples have class 1 and negative examples have class -1.
num_examples = length(features_pos);
selectedcase = randperm(length(features_pos),num_examples);
features_pos=features_pos(selectedcase,:);
selectedcase = randperm(length(features_neg),num_examples);
features_neg=features_neg(selectedcase,:);
Label=[ones(length(features_pos),1); ones(length(features_neg),1)*-1]';
F=[features_pos; features_neg]';
%%%%% YOUR CODE HERE FOR SVMTRAIN!
%X = [features_pos',features_neg'];
%Y = [ones(size(features_pos,1),1);-ones(size(features_neg,1),1)];
lambda=0.0001;
[w b] = vl_svmtrain(F, Labels, lambda);
%% step 3. Examine learned classifier
%%%%% Don't change this step!
% You don't need to modify anything in this section. The section first
% evaluates _training_ error, which isn't ultimately what we care about,
% but it is a good sanity check. Your training error should be very low.
fprintf('Initial classifier performance on train data:\n')
confidences = [features_pos; features_neg]*w + b;
label_vector = [ones(size(features_pos,1),1); -1*ones(size(features_neg,1),1)];
[tp_rate, fp_rate, tn_rate, fn_rate] = report_accuracy( confidences, label_vector );
% Visualize how well separated the positive and negative examples are at
% training time. Sometimes this can idenfity odd biases in your training
% data, especially if you're trying hard negative mining. This
% visualization won't be very meaningful with the placeholder starter code.
non_face_confs = confidences( label_vector < 0);
face_confs = confidences( label_vector > 0);
figure(2);
plot(sort(face_confs), 'g'); hold on
plot(sort(non_face_confs),'r');
plot([0 size(non_face_confs,1)], [0 0], 'b');
hold off;
% Visualize the learned detector. This would be a good thing to include in
% your writeup!
n_hog_cells = sqrt(length(w) / 31); %specific to default HoG parameters
imhog = vl_hog('render', single(reshape(w, [n_hog_cells n_hog_cells 31])), 'verbose') ;
figure(3); imagesc(imhog) ; colormap gray; set(3, 'Color', [.988, .988, .988])
pause(0.1) %let's ui rendering catch up
hog_template_image = frame2im(getframe(3));
% getframe() is unreliable. Depending on the rendering settings, it will
% grab foreground windows instead of the figure in question. It could also
% return a partial image.
imwrite(hog_template_image, 'visualizations/hog_template.png')
%% Step 4. Run detector on test set.
% YOU CODE 'run_detector'. Make sure the outputs are properly structured!
% They will be interpreted in Step 6 to evaluate and visualize your
% results. See run_detector.m for more details.
%%%% JZ: Please open run_detector.m file and complete it.
[bboxes, confidences, image_ids] = run_detector(test_scn_path, w, b, feature_params);
% run_detector will have (at least) two parameters which can heavily
% influence performance -- how much to rescale each step of your multiscale
% detector, and the threshold for a detection. If your recall rate is low
% and your detector still has high precision at its highest recall point,
% you can improve your average precision by reducing the threshold for a
% positive detection.
%% Step 5. Evaluate and Visualize detections
% These functions require ground truth annotations, and thus can only be
% run on the CMU+MIT face test set. Use visualize_detectoins_by_image_no_gt
% for testing on extra images (it is commented out below).
% Don't modify anything in 'evaluate_detections'!
[gt_ids, gt_bboxes, gt_isclaimed, tp, fp, duplicate_detections] = ...
evaluate_detections(bboxes, confidences, image_ids, label_path);
visualize_detections_by_image(bboxes, confidences, image_ids, tp, fp, test_scn_path, label_path)
% visualize_detections_by_image_no_gt(bboxes, confidences, image_ids, test_scn_path)
% visualize_detections_by_confidence(bboxes, confidences, image_ids, test_scn_path, label_path);
% performance to aim for
% random (stater code) 0.001 AP
% single scale ~ 0.2 to 0.4 AP
% multiscale, 6 pixel step ~ 0.83 AP
% multiscale, 4 pixel step ~ 0.89 AP
% multiscale, 3 pixel step ~ 0.92 AP
Positive Feature:
% Starter code prepared by James Hays, Brown University
% This function should return all positive training examples (faces) from
% 36x36 images in 'train_path_pos'. Each face should be converted into a
% HoG template according to 'feature_params'. For improved performance, try
% mirroring or warping the positive training examples.
function features_pos = get_positive_features(train_path_pos, feature_params)
% 'train_path_pos' is a string. This directory contains 36x36 images of
% faces
% 'feature_params' is a struct, with fields
% feature_params.template_size (probably 36), the number of pixels
% spanned by each train / test template and
% feature_params.hog_cell_size (default 6), the number of pixels in each
% HoG cell. template size should be evenly divisible by hog_cell_size.
% Smaller HoG cell sizes tend to work better, but they make things
% slower because the feature dimensionality increases and more
% importantly the step size of the classifier decreases at test time.
% 'features_pos' is N by D matrix where N is the number of faces and D
% is the template dimensionality, which would be
% (feature_params.template_size / feature_params.hog_cell_size)^2 * 31
% if you're using the default vl_hog parameters
% Useful functions:
% vl_hog, HOG = VL_HOG(IM, CELLSIZE)
% http://www.vlfeat.org/matlab/vl_hog.html (API)
% http://www.vlfeat.org/overview/hog.html (Tutorial)
% rgb2gray
image_files = dir( fullfile(train_path_pos, '*.jpg') ); %Caltech Faces stored as .jpg
num_images = length(image_files); % number of images in the dataset
idx=0;
for i=1:num_images
im=imread([train_path_pos '/' image_files(i).name]);
%%%%% please follow the steps below to compute HOG features for each image.
%%%%% 1. use single() function to convert input image to SINGLE class;
%%%%% 2. call vl_hog() function, two parameters: one is the image, the
%%%%% other is the feature_parames.hog_cell_size;
%%%%% 3. use reshape() function to convert the output of vl_hog() to a
%%%%% row vector;
%%%%% 4. add the row vector to features_pos and increase idx by 1;
%%%%% 5. use fliplr() function to flip the input image and repeat steps
%%%%% 2, 3, 4;
%%%%% YOUR CODE HERE!
im=single(im);
hog = vl_hog(im, feature_parames.hog_cell_size, 'verbose') ;
%idx=idx+1;
features_pos(idx,:)=reshape(hog,1,[]);
idx=idx+1;
im=fliplr(im);
hog = vl_hog(im, feature_parames.hog_cell_size, 'verbose') ;
features_pos(idx,:)=reshape(hog,1,[]);
end
Negative:
% Starter code prepared by James Hays, Brown University
% This function should return negative training examples (non-faces) from
% any images in 'non_face_scn_path'. Images should be converted to
% grayscale, because the positive training data is only available in
% grayscale. For best performance, you should sample random negative
% examples at multiple scales.
function features_neg = get_random_negative_features(non_face_scn_path, feature_params, num_samples)
% 'non_face_scn_path' is a string. This directory contains many images
% which have no faces in them.
% 'feature_params' is a struct, with fields
% feature_params.template_size (probably 36), the number of pixels
% spanned by each train / test template and
% feature_params.hog_cell_size (default 6), the number of pixels in each
% HoG cell. template size should be evenly divisible by hog_cell_size.
% Smaller HoG cell sizes tend to work better, but they make things
% slower because the feature dimensionality increases and more
% importantly the step size of the classifier decreases at test time.
% 'num_samples' is the number of random negatives to be mined, it's not
% important for the function to find exactly 'num_samples' non-face
% features, e.g. you might try to sample some number from each image, but
% some images might be too small to find enough.
% 'features_neg' is N by D matrix where N is the number of non-faces and D
% is the template dimensionality, which would be
% (feature_params.template_size / feature_params.hog_cell_size)^2 * 31
% if you're using the default vl_hog parameters
% Useful functions:
% vl_hog, HOG = VL_HOG(IM, CELLSIZE)
% http://www.vlfeat.org/matlab/vl_hog.html (API)
% http://www.vlfeat.org/overview/hog.html (Tutorial)
% rgb2gray
image_files = dir(fullfile(non_face_scn_path, '*.jpg'));
num_images = length(image_files);
% placeholder to be deleted
features_neg = rand(100, (feature_params.template_size / feature_params.hog_cell_size)^2 * 31);
step=feature_params.hog_cell_size;
idx=0;
for i=1:num_images
im=imread([non_face_scn_path '/' image_files(i).name]);
%%%%% please follow the steps below to compute HOG features for each image.
%%%%% 1. use single() function to convert input image to SINGLE class;
%%%%% 2. call vl_hog() function, two parameters: one is the image, the
%%%%% other is the feature_parames.hog_cell_size;
%%%%% Your code here!
im=single(im);
hog = vl_hog(im, feature_parames.hog_cell_size, 'verbose');
for m=1:step:size(im,1)
for n=1:step:size(im,2)
%%%%% spearate the output of vl_hog() into 6*6*31 blocks,
%%%%% use reshape() to convert every block to a row vector,
%%%%% save each vector to features_neg, and increase idx by 1
%%%%% Your code here!
features_neg(idx,:)=reshape(hog, [1, 6*6*31]);
idx=idx+1
end
end
disp(['idex is: ' num2str(idx)]);
disp(['case is: ' num2str(i)]);
end
idx

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更多回答（2 个）

Pooyan Mobtahej
Pooyan Mobtahej 2020-11-28
i have modified as follows and got error again :
idx=0;
for i=1:num_images
im=imread([non_face_scn_path '/' image_files(i).name]);
%%%%% JZ: please follow the steps below to compute HOG features for each image.
%%%%% 1. use single() function to convert input image to SINGLE class;
%%%%% 2. call vl_hog() function, two parameters: one is the image, the
%%%%% other is the feature_parames.hog_cell_size;
%%%%% JZ: Your code here!
im=single(im);
hog = vl_hog(im, feature_params.hog_cell_size);
for m=1:step:size(im,1)
for n=1:step:size(im,2)
%%%%% JZ: spearate the output of vl_hog() into 6*6*31 blocks,
%%%%% use reshape() to convert every block to a row vector,
%%%%% save each vector to features_neg, and increase idx by 1
%%%%% JZ: Your code here!
idx=idx+1
% feature_neg(idx,:)=reshape(hog,6,6,[]);
% features_neg(idx,:) = hog(:);
feature_neg(idx,:) = reshape(hog(m-step+1:m, n-step+1:n, :),[],1);
%idx=idx+1
  1 个评论
Walter Roberson
Walter Roberson 2020-11-28
Your m starts at 1 and your n starts at 1. Suppose step is 6. Then you try to index hog as hog(1-6+1:1, 1-6+1:1, :) which would be hog(-4:1, -4:1, :) which would use invalid negative indices.
I told you to use + not - and the order was reversed hog(m:m+step-1, n:n+step-1, :) which would be hog(1:1+6-1, 1:1+6-1,:) which would be hog(1:6, 1:6, :)
for m=1:step:size(im,1)
You need to stop at the point where m+step-1 does not exceed size(im,1) .
m+step-1 <= size(im,1)
so m <= size(im,1) - step + 1

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Pooyan Mobtahej
Pooyan Mobtahej 2020-11-28
I revised that
im=single(im);
hog = vl_hog(im, feature_params.hog_cell_size);
for m=1:step:size(im,1)
for n=1:step:size(im,2)
%%%%% JZ: spearate the output of vl_hog() into 6*6*31 blocks,
%%%%% use reshape() to convert every block to a row vector,
%%%%% save each vector to features_neg, and increase idx by 1
%%%%% JZ: Your code here!
idx=idx+1
% feature_neg(idx,:)=reshape(hog,6,6,[]);
% features_neg(idx,:) = hog(:);
feature_neg(idx,:) = reshape(hog(m:m+step-1, n:n+step-1, :),[],1);
%idx=idx+1
end
end
disp(['idex is: ' num2str(idx)]);
disp(['case is: ' num2str(i)]);
end
idx
still this error:
Index in position 2 exceeds array bounds (must not exceed 43).
Error in get_random_negative_features (line 66)
feature_neg(idx,:) = reshape(hog(m:m+step-1, n:n+step-1, :),[],1);
Error in project (line 71)
features_neg = get_random_negative_features( non_face_scn_path, feature_params, num_negative_examples);
  2 个评论
Walter Roberson
Walter Roberson 2020-11-28
As I wrote,
You need to stop at the point where m+step-1 does not exceed size(im,1) .
And I posted the logic that shows you how to calculate the upper bound.
Pooyan Mobtahej
Pooyan Mobtahej 2020-11-29
can once again explain how to :
stop at the point where m+step-1 does not exceed size(im,1) .
should I modify loop? or anything in reshape part?

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