def calibrateTwo(leftCameraParam, rightCameraParam, lmap, rmap, leftImg,
rightImg):
img1 = cv2.imread(leftImg)
img2 = cv2.imread(rightImg)
F, H_left, H_right = rectify_pair(img1, img2, True)
R1 = np.linalg.inv(
leftCameraParam['mtx']) * H_left * leftCameraParam['mtx']
R2 = np.linalg.inv(
rightCameraParam['mtx']) * H_right * rightCameraParam['mtx']
rms, C1, dist1, C2, dist2, R, T, E, F = cv2.stereoCalibrate(
leftCameraParam['obj_points'],
leftCameraParam['img_points'],
rightCameraParam['img_points'],
leftCameraParam['mtx'],
leftCameraParam['dist'],
rightCameraParam['mtx'],
rightCameraParam['dist'],
leftCameraParam['size'],
flags=cv2.CALIB_USE_INTRINSIC_GUESS)
Q = cv2.stereoRectify(C1,
dist1,
C2,
dist2,
leftCameraParam['size'],
R,
T,
alpha=1)
#Computes rectification transforms for each head of a calibrated stereo camera.
left_map1, left_map2 = cv2.initUndistortRectifyMap(
C1, dist1, R1, leftCameraParam['newmtx'], leftCameraParam['size'],
cv2.CV_16SC2)
#计算矫正参数
right_map1, right_map2 = cv2.initUndistortRectifyMap(
C2, dist2, R2, rightCameraParam['newmtx'], leftCameraParam['size'],
cv2.CV_16SC2)
print('calibrate two camera success')
lmap['lm1'] = left_map1
lmap['lm2'] = left_map2
rmap['rm1'] = right_map1
rmap['rm2'] = right_map2
lmap['Q'] = Q
lmap['R1'] = R1
rmap['R2'] = R2
cv2.waitKey(-1)
def get_rect_maps(cameras, rect, proj):
"""Computes rectification maps."""
# empty_mat = np.empty(tuple(frame_size))
# map1 = [empty_mat for __ in range(len(cam_mats))]
# map2 = [empty_mat for __ in range(len(cam_mats))]
map1 = [np.empty(cameras[0].size) for __ in range(len(cameras))]
map2 = [np.empty(cameras[0].size) for __ in range(len(cameras))]
for i, (camera, r, p) in enumerate(zip(cameras, rect, proj)):
map1[i], map2[i] = cv2.initUndistortRectifyMap(camera.cam_mat,
camera.dist_coeff, r, p,
camera.size,
cv2.CV_32FC1)
return map1, map2
print("mtx:\n",mtx) # 内参数矩阵
print("dist畸变值:\n",dist ) # 畸变系数 distortion cofficients = (k_1,k_2,p_1,p_2,k_3)
print("rvecs旋转（向量）外参:\n",rvecs) # 旋转向量 # 外参数
print("tvecs平移（向量）外参:\n",tvecs ) # 平移向量 # 外参数
newcameramtx, roi = cv2.getOptimalNewCameraMatrix(mtx, dist, (u, v), 0, (u, v))
print('newcameramtx外参',newcameramtx)
#打开摄像机
camera=cv2.VideoCapture(0)
while True:
(grabbed,frame)=camera.read()
h1, w1 = frame.shape[:2]
newcameramtx, roi = cv2.getOptimalNewCameraMatrix(mtx, dist, (u, v), 0, (u, v))
# 纠正畸变
dst1 = cv2.undistort(frame, mtx, dist, None, newcameramtx)
#dst2 = cv2.undistort(frame, mtx, dist, None, newcameramtx)
mapx,mapy=cv2.initUndistortRectifyMap(mtx,dist,None,newcameramtx,(w1,h1),5)
dst2=cv2.remap(frame,mapx,mapy,cv2.INTER_LINEAR)
# 裁剪图像，输出纠正畸变以后的图片
x, y, w1, h1 = roi
dst1 = dst1[y:y + h1, x:x + w1]
#cv2.imshow('frame',dst2)
#cv2.imshow('dst1',dst1)
cv2.imshow('dst2', dst2)
if cv2.waitKey(1) & 0xFF == ord('q'): # 按q保存一张图片
cv2.imwrite("../u4/frame.jpg", dst1)
break
camera.release()
cv2.destroyAllWindows()
right_distortion = np.array([[-0.4983, 0.5503, -0.0020, 0.0026, -0.5252]])
R = np.array([[1.0000, -0.0024, 0.0073], [0.0024, 1.0000, -0.0061],
[-0.0073, 0.0062, 1.0000]]) # 旋转关系向量
#R = cv2.Rodrigues(om)[0] # 使用Rodrigues变换将om变换为R
T = np.array([-114.6016, -1.9783, 4.3376]) # 平移关系向量
size = (640, 480) # 图像尺寸
# 进行立体更正
R1, R2, P1, P2, Q, validPixROI1, validPixROI2 = cv2.stereoRectify(
left_camera_matrix, left_distortion, right_camera_matrix, right_distortion,
size, R, T)
# 计算更正map
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)
cv2.namedWindow("left")
cv2.namedWindow("right")
cv2.namedWindow("leftt")
cv2.namedWindow("rightt")
cv2.namedWindow("depth")
cv2.moveWindow("left", 0, 0)
cv2.moveWindow("right", 640, 0)
cv2.moveWindow("leftt", 0, 480)
cv2.moveWindow("rightt", 640, 480)
#cv2.createTrackbar("num", "depth", 0, 10, lambda x: None)
# If found, add object points, image points (after refining them)
if ret == True:
objpoints.append(objp)
corners2 = cv.cornerSubPix(gray, corners, (11, 11), (-1, -1), criteria)
imgpoints.append(corners)
# Draw and display the corners
cv.drawChessboardCorners(img, (11, 8), corners2, ret)
ret, mtx, dist, rvecs, tvecs = cv.calibrateCamera(
objpoints, imgpoints, gray.shape[::-1], None, None)
h, w = img.shape[:2]
newcameramtx, roi = cv.getOptimalNewCameraMatrix(
mtx, dist, (w, h), 1, (w, h))
# print(roi)
mapx, mapy = cv.initUndistortRectifyMap(mtx, dist, None, newcameramtx,
(w, h), 5)
dst = cv.remap(img, mapx, mapy, cv.INTER_LINEAR)
x, y, w, h = roi
dst = dst[y:y + h, x:x + w]
npz = np.load('./output.npz')
print(npz.files)
cv.imwrite('calibresult.png', dst)
plt_img = img[:, :, ::-1]
plt.figure(fname)
plt.imshow(plt_img)
plt.show()