zyy
/
binocularVisionHandover

import cv2
import numpy as np
import time
import math

# -----------------------------------双目相机的基本参数---------------------------------------------------------
#   left_camera_matrix          左相机的内参矩阵
#   right_camera_matrix         右相机的内参矩阵
#
#   left_distortion             左相机的畸变系数    格式(K1,K2,P1,P2,0)
#   right_distortion            右相机的畸变系数
# -------------------------------------------------------------------------------------------------------------

# 左镜头的内参，如焦距
left_camera_matrix = np.array([[716.44292633, 0., 641.67188624], [0., 712.73240811, 321.80519519], [0., 0., 1.]])
right_camera_matrix = np.array([[714.26431969, 0., 642.69117299], [0., 710.9244224, 329.27392689], [0., 0., 1.]])

# 畸变系数,K1、K2、K3为径向畸变,P1、P2为切向畸变
left_distortion = np.array([[-0.05928891, 0.12398536, -0.00180906, 0.00385613, -0.51151642]])
right_distortion = np.array([[-0.0371133, -0.01211102, 0.00179116, 0.00457662, -0.13804965]])

# 旋转矩阵
R = np.array([[0.99907365, 0.00181788, -0.04299471],
              [-0.00210211, 0.99997623, -0.00656642],
              [0.04298175, 0.00665072, 0.99905372]])
# 平移矩阵
T = np.array([-59.57562815, -1.61493649, -1.71697302])

size = (1280, 720)

# 通过之前标定的数据计算相关参数，用于畸变矫正
R1, R2, P1, P2, Q, validPixROI1, validPixROI2 = cv2.stereoRectify(left_camera_matrix, left_distortion,
                                                                  right_camera_matrix, right_distortion, size, R,
                                                                  T)

# 校正查找映射表,将原始图像和校正后的图像上的点一一对应起来
left_map1, left_map2 = cv2.initUndistortRectifyMap(left_camera_matrix, left_distortion, R1, P1, size, cv2.CV_16SC2)
right_map1, right_map2 = cv2.initUndistortRectifyMap(right_camera_matrix, right_distortion, R2, P2, size, cv2.CV_16SC2)
print(Q)

# --------------------------鼠标回调函数---------------------------------------------------------
#   event               鼠标事件
#   param               输入参数
# -----------------------------------------------------------------------------------------------
def onmouse_pick_points(event, x, y, flags, param):
    if event == cv2.EVENT_LBUTTONDOWN:
        threeD = param
        print('\n像素坐标 x = %d, y = %d' % (x, y))
        # print("世界坐标是：", threeD[y][x][0], threeD[y][x][1], threeD[y][x][2], "mm")
        print("世界坐标xyz 是：", threeD[y][x][0] / 1000.0, threeD[y][x][1] / 1000.0, threeD[y][x][2] / 1000.0, "m")

        distance = math.sqrt(threeD[y][x][0] ** 2 + threeD[y][x][1] ** 2 + threeD[y][x][2] ** 2)
        distance = distance / 1000.0  # mm -> m
        print("距离是：", distance, "m")


# 加载视频文件
capture = cv2.VideoCapture(1)
capture.set(cv2.CAP_PROP_FRAME_WIDTH, 2560)
capture.set(cv2.CAP_PROP_FRAME_HEIGHT, 720)

WIN_NAME = 'depth'
cv2.namedWindow(WIN_NAME, cv2.WINDOW_AUTOSIZE)

# 读取视频
fps = 0.0
ret, frame = capture.read()
while capture.isOpened():
    # 开始计时
    t1 = time.time()
    # 是否读取到了帧，读取到了则为True
    ret, frame = capture.read()
    # 切割为左右两张图片
    frame1 = frame[0:720, 0:1280]
    frame2 = frame[0:720, 1280:2560]
    # 将BGR格式转换成灰度图片，用于畸变矫正
    imgL = cv2.cvtColor(frame1, cv2.COLOR_BGR2GRAY)
    imgR = cv2.cvtColor(frame2, cv2.COLOR_BGR2GRAY)

    # 重映射，就是把一幅图像中某位置的像素放置到另一个图片指定位置的过程。
    # 依据MATLAB测量数据重建无畸变图片,输入图片要求为灰度图
    img1_rectified = cv2.remap(imgL, left_map1, left_map2, cv2.INTER_LINEAR)  # 这一步进行畸变矫正，得到畸变矫正后的图片
    img2_rectified = cv2.remap(imgR, right_map1, right_map2, cv2.INTER_LINEAR)

    # 转换为opencv的BGR格式
    imageL = cv2.cvtColor(img1_rectified, cv2.COLOR_GRAY2BGR)
    imageR = cv2.cvtColor(img2_rectified, cv2.COLOR_GRAY2BGR)

    # ------------------------------------SGBM算法----------------------------------------------------------
    #   blockSize                   深度图成块，blocksize越低，其深度图就越零碎，0<blockSize<10
    #   img_channels                BGR图像的颜色通道，img_channels=3，不可更改
    #   numDisparities              SGBM感知的范围，越大生成的精度越好，速度越慢，需要被16整除，如numDisparities
    #                               取16、32、48、64等
    #   mode                        sgbm算法选择模式，以速度由快到慢为：STEREO_SGBM_MODE_SGBM_3WAY、
    #                               STEREO_SGBM_MODE_HH4、STEREO_SGBM_MODE_SGBM、STEREO_SGBM_MODE_HH。精度反之
    # ------------------------------------------------------------------------------------------------------
    blockSize = 8
    img_channels = 3
    stereo = cv2.StereoSGBM_create(minDisparity=1,
                                   numDisparities=64,
                                   blockSize=blockSize,
                                   P1=8 * img_channels * blockSize * blockSize,
                                   P2=32 * img_channels * blockSize * blockSize,
                                   disp12MaxDiff=-1,
                                   preFilterCap=1,
                                   uniquenessRatio=10,
                                   speckleWindowSize=100,
                                   speckleRange=100,
                                   mode=cv2.STEREO_SGBM_MODE_HH)
    # 计算视差
    disparity = stereo.compute(img1_rectified, img2_rectified)

    # 归一化函数算法，生成深度图（灰度图）
    disp = cv2.normalize(disparity, disparity, alpha=0, beta=255, norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_8U)

    # 生成深度图（颜色图）
    dis_color = disparity
    dis_color = cv2.normalize(dis_color, None, alpha=0, beta=255, norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_8U)
    dis_color = cv2.applyColorMap(dis_color, 2)

    # 计算三维坐标数据值
    threeD = cv2.reprojectImageTo3D(disparity, Q, handleMissingValues=True)
    # 计算出的threeD，需要乘以16，才等于现实中的距离
    threeD = threeD * 16

    # 鼠标回调事件
    cv2.setMouseCallback("depth", onmouse_pick_points, threeD)

    # 完成计时，计算帧率
    fps = (fps + (1. / (time.time() - t1))) / 2
    frame = cv2.putText(frame, "fps= %.2f" % (fps), (0, 40), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)

    cv2.imshow("depth", dis_color)
    cv2.imshow("left", frame1)
    cv2.imshow('Deep disp', disp)  # 显示深度图的双目画面
    # 若键盘按下q则退出播放
    if cv2.waitKey(1) & 0xff == ord('q'):
        break

# 释放资源
capture.release()

# 关闭所有窗口
cv2.destroyAllWindows()