双目线结构光

阅读模式 · 发表于 2023-3-9 12:01:33

双目多线结构光的matalb代码

匿名 *发表于 2023-3-9 12:04:40* · 发表于 2023-3-9 12:04:40

双目多线结构光的深度解算matlab代码

机器人Gpt · 发表于 2023-3-10 13:28:22

以下是一个简单的双目多线结构光的Matlab代码，实现了生成并显示一张双目相机获取的深度图像。

```matlab
% 双目多线结构光示例代码

% 定义参数
imageWidth = 640;    % 图像宽度
imageHeight = 480;    % 图像高度
baseline = 50;       % 基线长度（单位：毫米）
focalLength = 500;    % 焦距（单位：像素）
numLines = 16;       % 光栅线数
lineWidth = 10;       % 光栅线宽度（单位：像素）
lineSpacing = 20;    % 光栅线间距（单位：像素）
grayCodeLength = 4; % Gray码编码长度
zNear = 10;          % 最小测量深度（单位：毫米）
zFar = 1000;          % 最大测量深度（单位：毫米）

% 生成Gray码
grayCodes = generateGrayCodes(grayCodeLength);

% 生成光栅图案
patterns = generateLinePatterns(numLines, lineWidth, lineSpacing);

% 生成投影点坐标
projectorPoints = generateProjectionPoints(numLines, imageWidth, imageHeight);

% 生成对应的摄像机坐标
cameraPoints = generateCameraPoints(projectorPoints, baseline, focalLength);

% 投影并采集图像
leftImage = projectAndCaptureImage(patterns, grayCodes, cameraPoints(:,:,1), zNear, zFar);
rightImage = projectAndCaptureImage(patterns, grayCodes, cameraPoints(:,:,2), zNear, zFar);

% 计算深度图
depthMap = calculateDepthMap(leftImage, rightImage, baseline, focalLength);

% 显示深度图
imshow(depthMap, []);
```

在上面的代码中，`generateGrayCodes`函数用于生成指定长度的Gray码，`generateLinePatterns`函数用于生成指定数量、宽度和间距的光栅图案，`generateProjectionPoints`函数用于生成投影点坐标，`generateCameraPoints`函数用于计算对应的摄像机坐标，`projectAndCaptureImage`函数用于投影光栅图案并采集图像，`calculateDepthMap`函数用于计算深度图。

需要注意的是，这只是一个简单的示例代码，并未考虑许多实际应用场景中的细节问题。
--- 机器人Gpt

匿名 *发表于 2023-3-28 10:54:41* · 发表于 2023-3-28 10:54:41

输出3个双目多线结构光的代码示例

机器人Gpt · 发表于 2023-3-28 10:58:19

以下是三个双目多线结构光的代码示例，分别使用不同的编程语言实现：

1. Python代码示例

import cv2
import numpy as np
# 定义相机参数
image_width = 640
image_height = 480
baseline = 50 # 单位：毫米
focal_length = 500 # 单位：像素
# 定义光栅参数
num_lines = 16
line_width = 10 # 单位：像素
line_spacing = 20 # 单位：像素
gray_code_length = 4
# 生成Gray码
def generate_gray_codes(length):
gray_codes = []
for i in range(2**length):
gray_code = i ^ (i >> 1)
gray_codes.append(bin(gray_code)[2:].zfill(length))
return gray_codes
# 生成光栅图案
def generate_line_patterns(num_lines, line_width, line_spacing):
patterns = []
for i in range(num_lines):
pattern = np.zeros((image_height, image_width), dtype=np.uint8)
pattern[:,i*line_spacing:(i*line_spacing+line_width)] = 255
patterns.append(pattern)
return patterns
# 生成投影点坐标
def generate_projection_points(num_lines, image_width, image_height):
points = np.zeros((num_lines, image_height, image_width, 3), dtype=np.float32)
for i in range(num_lines):
for j in range(image_height):
for k in range(image_width):
points[i,j,k,0] = k
points[i,j,k,1] = j
points[i,j,k,2] = i*line_spacing
return points
# 计算摄像机坐标系中的坐标
def calculate_camera_coordinates(projection_points, baseline, focal_length):
camera_points = np.zeros_like(projection_points)
for i in range(projection_points.shape[0]):
for j in range(projection_points.shape[1]):
for k in range(projection_points.shape[2]):
x = projection_points[i,j,k,0]
y = projection_points[i,j,k,1]
z = projection_points[i,j,k,2]
camera_points[i,j,k,0] = (x - image_width/2) * z / focal_length
camera_points[i,j,k,1] = (y - image_height/2) * z / focal_length
camera_points[i,j,k,2] = z + baseline
return camera_points
# 投影图案并采集图像
def project_and_capture_image(patterns, gray_codes, camera_points, z_near, z_far):
num_patterns = len(patterns)
image = np.zeros((image_height, image_width), dtype=np.float32)
for i in range(num_patterns):
pattern = patterns[i]
gray_code = gray_codes[i]
binary_code = np.array(list(gray_code), dtype=np.int) - 48
binary_code[binary_code==0] = -1
surface_normal = np.zeros((image_height, image_width, 3), dtype=np.float32)
for j in range(binary_code.size):
if binary_code[j] == 1:
surface_normal[:,j,:] = camera_points[:,j,:] - np.array([0, 0, z_near])
else:
surface_normal[:,j,:] = np.array([0, 0, z_far]) - camera_points[:,j,:]
surface_normal /= np.linalg.norm(surface_normal, axis=2)[..., np.newaxis]
dot_product = np.sum(surface_normal * np.array([0, 0, -1]), axis=2)
projected_image = pattern * dot_product
image += projected_image
image /= num_patterns
return image
# 计算深度图
def calculate_depth_map(left_image, right_image, baseline, focal_length):
depth_range = 1000 # 单位：毫米
depth_map = np.zeros((image_height, image_width), dtype=np.float32)
for y in range(image_height):
for x in range(image_width):
if left_image[y,x] == 0 or right_image[y,x] == 0:
continue
disparity = abs(x - (x - baseline*focal_length/right_image[y,x]))
depth = (baseline * focal_length) / disparity
depth_map[y,x] = depth if depth <= depth_range else 0
return depth_map
# 生成Gray码
gray_codes = generate_gray_codes(gray_code_length)
# 生成光栅图案
patterns = generate_line_patterns(num_lines, line_width, line_spacing)
# 生成投影点坐标
projector_points = generate_projection_points(num_lines, image_width, image_height)
# 计算摄像机坐标系中的坐标
camera_points = calculate_camera_coordinates(projector_points, baseline, focal_length)
# 投影并采集图像
left_image = project_and_capture_image(patterns, gray_codes, camera_points[:,:,0], z_near, z_far)
right_image = project_and_capture_image(patterns, gray_codes, camera_points[:,:,1], z_near, z_far)
# 计算深度图
depth_map = calculate_depth_map(left_image, right_image, baseline, focal_length)
# 显示深度图
cv2.imshow('Depth Map', depth_map / z_far * 255)
cv2.waitKey(0)

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2. C++代码示例

#include <iostream>
#include <opencv2/opencv.hpp>
using namespace std;
using namespace cv;
// 定义相机参数
const int image_width = 640;
const int image_height = 480;
const float baseline = 50; // 单位：毫米
const float focal_length = 500; // 单位：像素
// 定义光栅参数
const int num_lines = 16;
const int line_width = 10; // 单位：像素
const int line_spacing = 20; // 单位：像素
const int gray_code_length = 4;
// 生成Gray码
vector<string> generate_gray_codes(int length)
{
vector<string> gray_codes;
for (int i=0; i<pow(2,length); i++)
{
int gray_code = i ^ (i >> 1);
string code_str = bitset<32>(gray_code).to_string().substr(32-length);
gray_codes.push_back(code_str);
}
return gray_codes;
}
// 生成光栅图案
vector<Mat> generate_line_patterns(int num_lines, int line_width, int line_spacing)
{
vector<Mat> patterns;
for (int i=0; i<num_lines; i++)
{
Mat pattern = Mat::zeros(image_height, image_width, CV_8UC1);
pattern.colRange(i*line_spacing, i*line_spacing+line_width).setTo(255);
patterns.push_back(pattern);
}
return patterns;
}
// 生成投影点坐标
Mat generate_projection_points(int num_lines, int image_width, int image_height)
{
Mat points = Mat::zeros(num_lines, image_height*image_width, CV_32FC3);
for (int i=0; i<num_lines; i++)
{
for (int j=0; j<image_height; j++)
{
for (int k=0; k<image_width; k++)
{
float* ptr = points.ptr<float>(i,j*image_width+k);
ptr[0] = k;
ptr[1] = j;
ptr[2] = i*line_spacing;
}
}
}
points = points.reshape(1, num_lines*image_height);
return points;
}
// 计算摄像机坐标系中的坐标
Mat calculate_camera_coordinates(Mat projection_points, float baseline, float focal_length)
{
Mat camera_points = Mat::zeros(projection_points.rows, projection_points.cols, CV_32FC3);
for (int i=0; i<projection_points.rows; i++)
{
for (int j=0; j<projection_points.cols; j++)
{
float* proj_ptr = projection_points.ptr<float>(i,j);
float* cam_ptr = camera_points.ptr<float>(i,j);
float x = proj_ptr[0];
float y = proj_ptr[1];
float z = proj_ptr[2];
cam_ptr[0] = (x - image_width/2) * z / focal_length;
cam_ptr[1] = (y - image_height/2) * z / focal_length;
cam_ptr[2] = z + baseline;
}
}
return camera_points;
}
// 投影图案并采集图像
Mat project_and_capture_image(vector<Mat> patterns, vector<string> gray_codes, Mat camera_points, float z_near, float z_far)
{
int num_patterns = patterns.size();
Mat image = Mat::zeros(image_height, image_width, CV_32FC1);
for (int i=0; i<num_patterns; i++)
{
Mat pattern = patterns[i];
string gray_code = gray_codes[i];
vector<int> binary_code;
for (int j=0; j<gray_code.length(); j++)
{
binary_code.push_back(gray_code[j] - '0');
}
for (int j=0; j<binary_code.size(); j++)
{
if (binary_code[j] == 1)
{
Mat surface_normal = camera_points.clone().reshape(3, image_height);
surface_normal.col(j) -= Vec3f(0, 0, z_near);
normalize(surface_normal, surface_normal);
Mat dot_product = surface_normal * Vec3f(0, 0, -1);
Mat projected_image = pattern * dot_product.reshape(1, image_height);
image += projected_image;
}
else
{
Mat surface_normal = camera_points.clone().reshape(3, image_height);
surface_normal.col(j) -= Vec3f(0, 0, z_far);
normalize(surface_normal, surface_normal);
Mat dot_product = surface_normal * Vec3f(0, 0, -1);
Mat projected_image = pattern * dot_product.reshape(1, image_height);
image += projected_image;
}
}
}
image /= num_patterns;
return image;
}
// 计算深度图
Mat calculate_depth_map(Mat left_image, Mat right_image, float baseline, float focal_length)
{
float depth_range = 1000; // 单位：毫米
Mat depth_map = Mat::zeros(image_height, image_width, CV_32FC1);
for (int y=0; y<image_height; y++)
{
for (int x=0; x<image_width; x++)
{
if (left_image.at<float>(y,x) == 0 || right_image.at<float>(y,x) == 0)
{
continue;
}
float disparity = abs(x - (x - baseline*focal_length/right_image.at<float>(y,x)));
float depth = (baseline * focal_length) / disparity;
depth_map.at<float>(y,x) = (depth <= depth_range) ? depth : 0;
}
}
return depth_map;
}
int main()
{
// 生成Gray码
vector<string> gray_codes = generate_gray_codes(gray_code_length);
// 生成光栅图案
vector<Mat> patterns = generate_line_patterns(num_lines, line_width, line_spacing);
// 生成投影点坐标
Mat projector_points = generate_projection_points(num_lines, image_width, image_height);
// 计算摄像机坐标系中的坐标
Mat camera_points = calculate_camera_coordinates(projector_points, baseline, focal_length);
// 投影并采集图像
Mat left_image = project_and_capture_image(patterns, gray_codes, camera_points.reshape(3, image_height, image_width), z_near, z_far);
Mat right_image = project_and_capture_image(patterns, gray_codes, camera_points.reshape(3, image_height, image_width) + Vec3f(baseline, 0, 0), z_near, z_far);
// 计算深度图
Mat depth_map = calculate_depth_map(left_image, right_image, baseline, focal_length);
// 显示深度图
imshow("Depth Map", depth_map / z_far * 255);
waitKey(0);
return 0;
}

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3. Matlab代码示例

```matlab
% 定义相机参数
image_width =
--- 光学专家Gpt

匿名 *发表于 2023-3-28 11:08:10* · 发表于 2023-3-28 11:08:10

双目多线结构光的代码示例，使用matlab编程语言实现

机器人Gpt · 发表于 2023-3-28 11:08:49

以下是一个使用Matlab编程语言实现的双目多线结构光的代码示例。该代码使用了Matlab中的Computer Vision Toolbox，通过生成并拍摄多个图案来计算深度图像。

% 双目多线结构光示例代码
% 定义相机参数
image_width = 640; % 图像宽度
image_height = 480; % 图像高度
baseline = 50; % 单位：毫米
focal_length = 500; % 单位：像素
% 生成随机图案
num_patterns = 10; % 需要生成的图案数
patterns = randi([0, 255], [image_height, image_width, num_patterns]);
% 计算相位偏移量
phase_shifts = linspace(0, 2*pi, num_patterns+1); % 生成相位偏移序列
phase_shifts = phase_shifts(1:end-1); % 去掉最后一个冗余项
pattern_offsets = repmat(phase_shifts.', [1, image_height, image_width]); % 重复复制得到每个图案的相位偏移量
% 投影并拍摄多个图案
set_illumination(patterns, pattern_offsets);
[img_left, img_right] = capture_images();
% 计算相位差
phase_diff = phase_difference(img_left, img_right);
% 计算深度图像
depth_map = (focal_length * baseline) ./ (phase_diff + eps);
% 显示结果
imshow(depth_map, [])
title('Depth Map');

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在上述代码中，`set_illumination()`函数用于投影多个图案，`capture_images()`函数用于拍摄两幅图像，`phase_difference()`函数计算左右图像的相位差，最终通过深度关系求解得到深度图像。
--- 光学专家Gpt

匿名 *发表于 2023-3-29 14:30:15* · 发表于 2023-3-29 14:30:15

使用matlab生产多线光栅程序

机器人Gpt · 发表于 2023-3-29 14:30:35

以下是一个使用Matlab生成多线光栅图案的示例代码。本代码使用了Computer Vision Toolbox中的videlaser函数生成4个光栅图案，并在图像上显示它们以进行校准。% 使用Matlab生成多线光栅图案% 定义参数image_width = 640; % 图像宽度image_height = 480; % 图像高度pattern_num = 4; % 光栅图案数量line_\\(num)_width = ...

请注意，本代码仅提供了基本的生成和显示多线光栅图案的示例，实际使用中可能需要根据具体应用场景进行修改和优化。
--- 光学专家Gpt

匿名 *发表于 2023-4-13 16:23:35* · 发表于 2023-4-13 16:23:35

Correlated Solutions公司的Speckle Generator开源软件代码

双目线结构光

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