双目视觉三维重建代码 matlab
时间: 2023-09-06 10:14:21 浏览: 54
以下是一个简单的双目视觉三维重建的 MATLAB 代码示例:
```matlab
% 读取左右相机图像
left_img = imread('left_image.jpg');
right_img = imread('right_image.jpg');
% 定义视差搜索窗口大小和最大视差值
window_size = 15;
max_disparity = 64;
% 计算左右相机的灰度图像
left_gray = rgb2gray(left_img);
right_gray = rgb2gray(right_img);
% 根据视差搜索窗口大小计算视差图
disparity_map = zeros(size(left_gray));
for i = 1:size(left_gray, 1)
for j = 1:size(left_gray, 2)
% 定义视差搜索窗口的左上角和右下角坐标
left_top = max(1, i - window_size);
left_bottom = min(size(left_gray, 1), i + window_size);
left_left = max(1, j - window_size);
left_right = min(size(left_gray, 2), j + window_size);
% 在右图中搜索匹配块
match_block = right_gray(left_top:left_bottom, left_left:left_right);
best_match = Inf;
best_disparity = 0;
for d = 0:max_disparity
% 计算右图中的搜索窗口位置
right_top = left_top;
right_bottom = left_bottom;
right_left = max(1, j - window_size - d);
right_right = min(size(right_gray, 2), j + window_size - d);
% 计算块之间的差异度
if (right_right - right_left) > 0 && (right_bottom - right_top) > 0 && (left_right - left_left) > 0 && (left_bottom - left_top) > 0
candidate_block = right_gray(right_top:right_bottom, right_left:right_right);
diff = sum(sum((candidate_block - match_block).^2));
if diff < best_match
best_match = diff;
best_disparity = d;
end
end
end
% 将最佳匹配的视差值存储在视差图中
disparity_map(i, j) = best_disparity;
end
end
% 根据相机参数和视差图计算三维点云
focal_length = 100; % 相机焦距
baseline = 10; % 相机基线长度
point_cloud = zeros(size(left_gray, 1), size(left_gray, 2), 3);
for i = 1:size(left_gray, 1)
for j = 1:size(left_gray, 2)
disparity = disparity_map(i, j);
if disparity > 0
depth = focal_length * baseline / disparity;
x = (j - size(left_gray, 2) / 2) * depth / focal_length;
y = (i - size(left_gray, 1) / 2) * depth / focal_length;
z = depth;
point_cloud(i, j, :) = [x, y, z];
end
end
end
% 可视化三维点云
figure;
scatter3(point_cloud(:,:,1), point_cloud(:,:,2), point_cloud(:,:,3), '.');
xlabel('X');
ylabel('Y');
zlabel('Z');
```
此代码展示了如何读取左右相机图像、计算视差图以及根据相机参数和视差图计算三维点云,并可视化结果。请注意,此代码仅作为示例,实际应用中需要根据具体情况进行修改和优化。