[CT404]: Add Lab 2

year4/semester1/CT404: Graphics & Image Processing/assignments/lab2/code/hog.m

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+close all;
+clear all;
+clc;
+
+% Load necessary package
+pkg load image;
+
+% Load and preprocess the image
+img = imread('truck.jpg'); % Replace with the path to your image
+if size(img, 3) == 3
+    img = rgb2gray(img); % Convert to grayscale if the image is in color
+end
+
+% Resize the image to 128x64 for standardization
+img = imresize(img, [128, 64]);
+
+% Gamma normalization
+gamma = 0.9; % Example gamma value
+img = im2double(img) .^ gamma; % Convert to double and apply gamma normalization
+
+figure;
+imshow(img);
+title('Gamma-normalized Image');
+
+% Parameters for HOG
+cell_size = 4;         % Size of each cell (4x4 pixels)
+block_size = 2;        % Number of cells per block (2x2 cells)
+num_bins = 9;          % Number of orientation bins (0° to 180°)
+bin_size = 180 / num_bins;
+
+% Compute gradients using Sobel operator on the entire image
+Gx = imfilter(double(img), [-1 0 1; -2 0 2; -1 0 1], 'conv'); % Horizontal gradient
+Gy = imfilter(double(img), [-1 -2 -1; 0 0 0; 1 2 1], 'conv'); % Vertical gradient
+
+% Compute gradient magnitude and orientation
+magnitude = sqrt(Gx.^2 + Gy.^2);
+orientation = atan2d(Gy, Gx);
+orientation(orientation < 0) += 180; % Convert orientation to [0, 180] range
+
+% Display gradient magnitude and orientation
+figure;
+imshow(magnitude, []);
+title('Gradient Magnitude of the Image');
+
+% Display gradient orientation using quiver
+figure;
+imshow(img); % Show original image as background
+hold on;
+
+% Downsample the quiver plot for better visualization, e.g., every 4th pixel
+step = 4; % Adjust the step to make the plot less dense if needed
+
+% Plot gradient orientations using Gx and Gy as the vector components
+[x, y] = meshgrid(1:step:size(Gx, 2), 1:step:size(Gy, 1));
+quiver(x, y, Gx(1:step:end, 1:step:end), Gy(1:step:end, 1:step:end), 'color', 'r', 'LineWidth', 0.5);
+
+title('Gradient Orientation Visualization Using Quiver');
+hold off;
+
+% Calculate number of cells in each direction
+num_cells_row = floor(size(img, 1) / cell_size);
+num_cells_col = floor(size(img, 2) / cell_size);
+
+% Preallocate the HOG feature array
+hog_features = zeros(1, num_cells_row * num_cells_col * num_bins);
+
+% Calculate HOG features for each cell
+counter = 1;
+for row = 1:cell_size:(size(img, 1) - cell_size + 1)
+    for col = 1:cell_size:(size(img, 2) - cell_size + 1)
+        % Extract cell gradient magnitudes and orientations
+        cell_magnitude = magnitude(row:row+cell_size-1, col:col+cell_size-1);
+        cell_orientation = orientation(row:row+cell_size-1, col:col+cell_size-1);
+
+        % Calculate histogram for the cell manually
+        cell_histogram = zeros(1, num_bins);
+        for bin = 1:num_bins
+            % Define the orientation range for the bin
+            angle_min = (bin - 1) * bin_size;
+            angle_max = bin * bin_size;
+
+            % Find pixels within the bin range
+            mask = (cell_orientation >= angle_min) & (cell_orientation < angle_max);
+
+            % Sum the magnitudes of pixels within the current orientation bin
+            cell_histogram(bin) = sum(cell_magnitude(mask));
+        end
+
+        % Store cell histogram in hog_features
+        hog_features(counter:counter+num_bins-1) = cell_histogram;
+        counter = counter + num_bins;
+    end
+end
+
+% Block normalization: Divide the HOG features into blocks of 2x2 cells and normalize
+block_histograms = [];
+for row = 1:num_cells_row - block_size + 1
+    for col = 1:num_cells_col - block_size + 1
+        % Compute the index in hog_features for the current block
+        start_index = ((row - 1) * num_cells_col + col - 1) * num_bins + 1;
+        end_index = start_index + block_size * block_size * num_bins - 1;
+
+        % Extract the block of HOG features
+        block = hog_features(start_index:end_index);
+
+        % Normalize the block histogram
+        norm_block = block / sqrt(sum(block .^ 2) + 1e-6);
+
+        % Append the normalized block to block_histograms
+        block_histograms = [block_histograms, norm_block];
+    end
+end
+
+% Final HOG descriptor for the entire image
+hog_descriptor = block_histograms;
+
+% Display results
+disp("HOG Descriptor for the entire image:");
+disp(size(hog_descriptor));
+disp("Length of HOG descriptor vector:");
+disp(length(hog_descriptor));
+
+% Visualization of HOG Features on a Blank Canvas
+figure;
+imshow(ones(size(img)), []); % Blank figure with same size as image
+hold on;
+
+% Visualize dominant gradient direction per cell
+for row = 1:cell_size:(size(img, 1) - cell_size + 1)
+    for col = 1:cell_size:(size(img, 2) - cell_size + 1)
+        % Get cell gradient data
+        cell_magnitude = magnitude(row:row+cell_size-1, col:col+cell_size-1);
+        cell_orientation = orientation(row:row+cell_size-1, col:col+cell_size-1);
+
+        % Calculate histogram for the cell manually
+        cell_histogram = zeros(1, num_bins);
+        for bin = 1:num_bins
+            % Define the orientation range for the bin
+            angle_min = (bin - 1) * bin_size;
+            angle_max = bin * bin_size;
+
+            % Find pixels within the bin range
+            mask = (cell_orientation >= angle_min) & (cell_orientation < angle_max);
+
+            % Sum the magnitudes of pixels within the current orientation bin
+            cell_histogram(bin) = sum(cell_magnitude(mask));
+        end
+
+        % Find the dominant orientation
+        [max_value, dominant_bin] = max(cell_histogram);
+        dominant_orientation = (dominant_bin - 1) * bin_size + bin_size / 2; % Center of the bin
+
+        % Convert the orientation to radians for plotting
+        angle_rad = deg2rad(dominant_orientation);
+
+        % Plot the dominant gradient direction using quiver
+        scale_factor = 0.3;
+        quiver(col + cell_size / 2, row + cell_size / 2, ...
+               scale_factor * max_value * cos(angle_rad), ...
+               scale_factor * max_value * sin(angle_rad), ...
+               'color', 'w', 'LineWidth', 1);
+    end
+end
+
+title('HOG Features Visualization for Dominant Orientation per Cell');
+hold off;
+