使用MATLAB语言_利用HOG特征和svm分类器来实现行人的检测_matlab

function H = getHistogram(magnitudes, angles, numBins) % GETHISTOGRAM Computes a histogram for the supplied gradient vectors. % H = getHistogram(magnitudes, angles, numBins) % % This function takes the supplied gradient vectors and places them into a % histogram with 'numBins' based on their unsigned orientation. % % "Unsigned" orientation means that, for example, a vector with angle % -3/4 * pi will be treated the same as a vector with angle 1/4 * pi. % % Each gradient vector's contribution is split between the two nearest bins, % in proportion to the distance between the two nearest bin centers. % % A gradient's contribution to the histogram is equal to its magnitude; % the magnitude is divided between the two nearest bin centers. % % Parameters: % magnitudes - A column vector storing the magnitudes of the gradient % vectors. % angles - A column vector storing the angles in radians of the % gradient vectors (ranging from -pi to pi) % numBins - The number of bins to place the gradients into. % Returns: % A row vector of length 'numBins' containing the histogram. % $Author: ChrisMcCormick $ $Date: 2013/12/04 22:00:00 $ $Revision: 1.2 $ % Revision Notes: % v1.2 % - Expanded '+=' since this gave Matlab users trouble. % v1.1 % - The function was actually hardcoded to 9 bins; it now properly supports % specifying 'numBins'. % - It now returns an unsigned histogram. This has been shown to improve % accuracy for person detection. % Compute the bin size in radians. 180 degress = pi. binSize = pi / numBins; % The angle values will range from 0 to pi. minAngle = 0; % Make the angles unsigned by adding pi (180 degrees) to all negative angles. angles(angles < 0) = angles(angles < 0) + pi; % The gradient angle for each pixel will fall between two bin centers. % For each pixel, we split the bin contributions between the bin to the left % and the bin to the right based on how far the angle is from the bin centers. % For each pixel's gradient vector, determine the indeces of the bins to the % left and right of the vector's angle. % % The histogram needs to wrap around at the edges--vectors on the far edges of % the histogram (i.e., close to -pi or pi) will contribute partly to the bin % at that edge, and partly to the bin on the other end of the histogram. % For vectors with an orientation close to 0 radians, leftBinIndex will be 0. % Likewise, for vectors with an orientation close to pi radians, rightBinIndex % will be numBins + 1. We will fix these indeces after we calculate the bin % contribution amounts. leftBinIndex = round((angles - minAngle) / binSize); rightBinIndex = leftBinIndex + 1; % For each pixel, compute the center of the bin to the left. leftBinCenter = ((leftBinIndex - 0.5) * binSize) - minAngle; % For each pixel, compute the fraction of the magnitude % to contribute to each bin. rightPortions = angles - leftBinCenter; leftPortions = binSize - rightPortions; rightPortions = rightPortions / binSize; leftPortions = leftPortions / binSize; % Before using the bin indeces, we need to fix the '0' and '10' values. % Recall that the histogram needs to wrap around at the edges--bin "0" % contributions, for example, really belong in bin 9. % Replace index 0 with 9 and index 10 with 1. leftBinIndex(leftBinIndex == 0) = numBins; rightBinIndex(rightBinIndex == (numBins + 1)) = 1; % Create an empty row vector for the histogram. H = zeros(1, numBins); % For each bin index... for (i = 1:numBins) % Find the pixels with left bin == i pixels = (leftBinIndex == i); % For each of the selected pixels, add the gradient magnitude to bin 'i', % weighted by the 'leftPortion' for that pixel. H(1, i) = H(1, i) + sum(leftPortions(pixels)' * magnitudes(pixels)); % Find the pixels with right bin == i pixels = (rightBinIndex == i); % For each of the selected pixels, add the gradient magnitude to bin 'i', % weighted by the 'rightPortion' for that pixel. H(1, i) = H(1, i) + sum(rightPortions(pixels)' * magnitudes(pixels)); end end