基于python的双目立体视觉及三维重建源码+项目说明+代码注释.zip

# -*- coding: utf-8 -*- import cv2 import numpy as np import stereoconfig # 导入相机标定的参数 import pcl import pcl.pcl_visualization # 预处理 def preprocess(img1, img2): # 彩色图->灰度图 if (img1.ndim == 3): # 判断为三维数组 img1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY) # 通过OpenCV加载的图像通道顺序是BGR if (img2.ndim == 3): img2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY) # 直方图均衡 img1 = cv2.equalizeHist(img1) img2 = cv2.equalizeHist(img2) return img1, img2 # 消除畸变 def undistortion(image, camera_matrix, dist_coeff): undistortion_image = cv2.undistort(image, camera_matrix, dist_coeff) return undistortion_image # 获取畸变校正和立体校正的映射变换矩阵、重投影矩阵 # @param：config是一个类，存储着双目标定的参数:config = stereoconfig.stereoCamera() def getRectifyTransform(height, width, config): # 读取内参和外参 left_K = config.cam_matrix_left right_K = config.cam_matrix_right left_distortion = config.distortion_l right_distortion = config.distortion_r R = config.R T = config.T # 计算校正变换 R1, R2, P1, P2, Q, roi1, roi2 = cv2.stereoRectify(left_K, left_distortion, right_K, right_distortion, (width, height), R, T, alpha=0) map1x, map1y = cv2.initUndistortRectifyMap(left_K, left_distortion, R1, P1, (width, height), cv2.CV_32FC1) map2x, map2y = cv2.initUndistortRectifyMap(right_K, right_distortion, R2, P2, (width, height), cv2.CV_32FC1) return map1x, map1y, map2x, map2y, Q # 畸变校正和立体校正 def rectifyImage(image1, image2, map1x, map1y, map2x, map2y): rectifyed_img1 = cv2.remap(image1, map1x, map1y, cv2.INTER_AREA) rectifyed_img2 = cv2.remap(image2, map2x, map2y, cv2.INTER_AREA) return rectifyed_img1, rectifyed_img2 # 立体校正检验----画线 def draw_line(image1, image2): # 建立输出图像 height = max(image1.shape[0], image2.shape[0]) width = image1.shape[1] + image2.shape[1] output = np.zeros((height, width, 3), dtype=np.uint8) output[0:image1.shape[0], 0:image1.shape[1]] = image1 output[0:image2.shape[0], image1.shape[1]:] = image2 # 绘制等间距平行线 line_interval = 50 # 直线间隔：50 for k in range(height // line_interval): cv2.line(output, (0, line_interval * (k + 1)), (2 * width, line_interval * (k + 1)), (0, 255, 0), thickness=2, lineType=cv2.LINE_AA) return output # 视差计算 def stereoMatchSGBM(left_image, right_image, down_scale=False): # SGBM匹配参数设置 if left_image.ndim == 2: img_channels = 1 else: img_channels = 3 blockSize = 3 paraml = {'minDisparity': 0, #最小视差 'numDisparities': 64, #视差的搜索范围，16的整数倍 'blockSize': blockSize, 'P1': 8 * img_channels * blockSize ** 2, #值越大，视差越平滑，相邻像素视差+/-1的惩罚系数 'P2': 32 * img_channels * blockSize ** 2, #同上，相邻像素视差变化值>1的惩罚系数 'disp12MaxDiff': 1, #左右一致性检测中最大容许误差值 'preFilterCap': 63, #映射滤波器大小，默认15 'uniquenessRatio': 15, #唯一检测性参数，匹配区分度不够，则误匹配(5-15) 'speckleWindowSize': 100, #视差连通区域像素点个数的大小（噪声点）(50-200)或用0禁用斑点过滤 'speckleRange': 1, #认为不连通(1-2) 'mode': cv2.STEREO_SGBM_MODE_SGBM_3WAY } # 构建SGBM对象 left_matcher = cv2.StereoSGBM_create(**paraml) paramr = paraml paramr['minDisparity'] = -paraml['numDisparities'] right_matcher = cv2.StereoSGBM_create(**paramr) # 计算视差图 size = (left_image.shape[1], left_image.shape[0]) if down_scale == False: disparity_left = left_matcher.compute(left_image, right_image) disparity_right = right_matcher.compute(right_image, left_image) else: left_image_down = cv2.pyrDown(left_image) right_image_down = cv2.pyrDown(right_image) factor = left_image.shape[1] / left_image_down.shape[1] disparity_left_half = left_matcher.compute(left_image_down, right_image_down) disparity_right_half = right_matcher.compute(right_image_down, left_image_down) disparity_left = cv2.resize(disparity_left_half, size, interpolation=cv2.INTER_AREA) disparity_right = cv2.resize(disparity_right_half, size, interpolation=cv2.INTER_AREA) disparity_left = factor * disparity_left disparity_right = factor * disparity_right # 真实视差（因为SGBM算法得到的视差是×16的） trueDisp_left = disparity_left.astype(np.float32) / 16. trueDisp_right = disparity_right.astype(np.float32) / 16. return trueDisp_left, trueDisp_right # 视差计算+wls滤波 def stereoMatchSGBM2(left_image, right_image, down_scale=False): # SGBM匹配参数设置 if left_image.ndim == 2: img_channels = 1 else: img_channels = 3 blockSize = 3 paraml = {'minDisparity': 0, #最小视差 'numDisparities': 64, #视差的搜索范围，16的整数倍 'blockSize': blockSize, 'P1': 8 * img_channels * blockSize ** 2, #值越大，视差越平滑，相邻像素视差+/-1的惩罚系数 'P2': 32 * img_channels * blockSize ** 2, #同上，相邻像素视差变化值>1的惩罚系数 'disp12MaxDiff': 1, #左右一致性检测中最大容许误差值 'preFilterCap': 63, #映射滤波器大小，默认15 'uniquenessRatio': 15, #唯一检测性参数，匹配区分度不够，则误匹配(5-15) 'speckleWindowSize': 100, #视差连通区域像素点个数的大小（噪声点）(50-200)或用0禁用斑点过滤 'speckleRange': 1, #认为不连通(1-2) 'mode': cv2.STEREO_SGBM_MODE_SGBM_3WAY } # 构建SGBM对象 left_matcher = cv2.StereoSGBM_create(**paraml) paramr = paraml paramr['minDisparity'] = -paraml['numDisparities'] right_matcher = cv2.StereoSGBM_create(**paramr) # 计算视差图 size = (left_image.shape[1], left_image.shape[0]) if down_scale == False: disparity_left = left_matcher.compute(left_image, right_image) disparity_right = right_matcher.compute(right_image, left_image) else: left_image_down = cv2.pyrDown(left_image) right_image_down = cv2.pyrDown(right_image) factor = left_image.shape[1] / left_image_down.shape[1] disparity_left_half = left_matcher.compute(left_image_down, right_image_down) disparity_right_half = right_matcher.compute(right_image_down, left_image_down) disparity_left = cv2.resize(disparity_left_half, size, interpolation=cv2.INTER_AREA) disparity_right = cv2.resize(disparity_right_half, size, interpolation=cv2.INTER_AREA) disparity_left = factor * disparity_left disparity_right = factor * disparity_right # 真实视差（因为SGBM算法得到的视差是×16的） trueDisp_left = disparity_left.astype(np.float32) / 16. trueDisp_right = disparity_right.astype(np.float32) / 16. wls_filter = cv2.ximgproc.createDisparityWLSFilter(left_matcher) # sigmaColor典型范围值为0.8-2.0 wls_filter.setLambda(8000.) wls_filter.setSigmaColor(2.0) wls_filter.setLRCthresh(24) wls_filter.setDepthDiscontinuityRadius(3