def calibrate():
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)
Nx_cor = 9
Ny_cor = 6
objp = np.zeros((Nx_cor * Ny_cor, 3), np.float32)
objp[:, :2] = np.mgrid[0:Nx_cor, 0:Ny_cor].T.reshape(-1, 2)
objpoints = [] # 3d points in real world space
imgpoints = [] # 2d points in image plane.
count = 0 # count 用来标志成功检测到的棋盘格画面数量
while (True):
ret, frame = cap.read()
if cv2.waitKey(1) & 0xFF == ord(' '):
# Our operations on the frame come here
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
ret, corners = cv2.findChessboardCorners(gray, (Nx_cor, Ny_cor),
None) # Find the corners
# If found, add object points, image points
if ret == True:
corners = cv2.cornerSubPix(gray, corners, (5, 5), (-1, -1),
criteria)
objpoints.append(objp)
imgpoints.append(corners)
cv2.drawChessboardCorners(frame, (Nx_cor, Ny_cor), corners,
ret)
count += 1
if count > 20:
break
# Display the resulting frame
cv2.imshow('frame', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
global mtx, dist
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints,
gray.shape[::-1], None,
None)
print(mtx, dist)
mean_error = 0
for i in range(len(objpoints)):
imgpoints2, _ = cv2.projectPoints(objpoints[i], rvecs[i], tvecs[i],
mtx, dist)
error = cv2.norm(imgpoints[i], imgpoints2,
cv2.NORM_L2) / len(imgpoints2)
mean_error += error
print("total error: ", mean_error / len(objpoints))
# # When everything done, release the capture
np.savez('calibrate.npz', mtx=mtx, dist=dist[0:4])
def calibrateOne(img_path, cameraParam):
criteria = (cv2.TERM_CRITERIA_MAX_ITER | cv2.TERM_CRITERIA_EPS, 30, 0.001)
#设置寻找亚像素角点的参数，采用的停止准则是最大循环次数30和最大误差容限0.001
#获取标定板角点的位置
objp = np.zeros((4 * 7, 3), np.float32)
objp[:, :2] = np.mgrid[0:7, 0:4].T.reshape(-1, 2) #将世界坐标系建在标定板上
obj_points = [] #3D points
img_points = [] #2D points
images = glob.glob(img_path)
i = 0
for fname in images:
img = cv2.imread(fname)
#print(fname)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
size = gray.shape[::-1]
h, w = gray.shape[:2]
ret, corners = cv2.findChessboardCorners(gray, (7, 4), None)
if ret:
obj_points.append(objp)
corners2 = cv2.cornerSubPix(gray, corners, (5, 5), (-1, -1),
criteria)
#在原角点的基础上寻找亚像素点
#print(corners2)
if [corners2]:
img_points.append(corners2)
else:
img_points.append(corners)
cv2.drawChessboardCorners(img, (7, 4), corners, ret)
i += 1
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(obj_points, img_points,
size, None, None,
criteria)
#dist——>dist coffients(就是k、p的值)
newcameramtx, roi = cv2.getOptimalNewCameraMatrix(
mtx, dist, (w, h), 1, (w, h)) #显示更大范围的图片（正常重映射之后会删掉一部分图像）
#将fx，fy（世界坐标系的焦距）投射到fu，fv（图像坐标系的焦距）上，立体矫正时会用
#dst = cv2.undistort(img,mtx,dist,None,newcameramtx)
#x,y,w,h = roi
cameraParam['ret'] = ret
cameraParam['mtx'] = mtx
cameraParam['dist'] = dist
cameraParam['rvecs'] = rvecs
cameraParam['tvecs'] = tvecs
cameraParam['img_points'] = img_points
cameraParam['obj_points'] = obj_points
cameraParam['size'] = size
cameraParam['newmtx'] = newcameramtx
print("Calibrate One Camera Success")
def find_corners(images, windows, chessboard):
"""Display chessboard corners on input images."""
retvals = [[False] for __ in range(len(images))]
corners = [[None] for __ in range(len(images))]
for i, (image, window) in enumerate(zip(images, windows)):
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
retvals[i], corners[i] = cv2.findChessboardCorners(
image, chessboard.dims)
cv2.drawChessboardCorners(image, chessboard.dims, corners[i],
retvals[i])
cv2.imshow(window, image)
return retvals, corners
def _update_img_obj_points(self, index: int, original_image: np.ndarray,
gray_scale_img: np.ndarray, is_dev: bool):
found_corners, corners = cv2.findChessboardCorners(
gray_scale_img, self.pattern_size, None)
calibrated_image = copy.copy(original_image)
if found_corners is True:
self._objpoints.append(self._objp)
self._imgpoints.append(corners)
cv2.drawChessboardCorners(calibrated_image, self.pattern_size,
corners, found_corners)
cv2.imwrite('camera_cal_output/calibrated-' + str(index) + '.jpg',
calibrated_image)
if is_dev is True:
_, (ax1, ax2) = plt.subplots(1, 2, figsize=(20, 10))
ax1.set_title('Original Image')
ax1.imshow(original_image)
ax2.set_title('Image clibration')
ax2.imshow(calibrated_image)
def findcorners(self):
plt.close('all')
# termination criteria
criteria = (cv.TERM_CRITERIA_EPS + cv.TERM_CRITERIA_MAX_ITER, 30,
0.001)
# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)
objp = np.zeros((11 * 8, 3), np.float32)
objp[:, :2] = np.mgrid[0:11, 0:8].T.reshape(-1, 2)
# Arrays to store object points and image points from all the images.
objpoints = [] # 3d point in real world space
imgpoints = [] # 2d points in image plane.
for i in range(1, 16):
img_name = str(i) + '.bmp'
fname = os.path.join('Datasets/Q2_Image', img_name)
img = cv.imread(fname)
gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
# Find the chess board corners
ret, corners = cv.findChessboardCorners(gray, (11, 8), 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)
plt_img = img[:, :, ::-1]
plt.figure(fname)
plt.imshow(plt_img)
ret, mtx, dist, rvecs, tvecs = cv.calibrateCamera(
objpoints, imgpoints, gray.shape[::-1], None, None)
np.savez('output.npz', mtx, dist, rvecs, tvecs)
np.savez('img_obj_points.npz', imgpoints, objpoints)
plt.show()
h1, w1 = img.shape[0], img.shape[1]
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
u, v = img.shape[:2]
# 找到棋盘格角点
ret, corners = cv2.findChessboardCorners(gray, (w,h),None)
# 如果找到足够点对，将其存储起来
if ret == True:
print("i:", i)
i = i+1
# 在原角点的基础上寻找亚像素角点
cv2.cornerSubPix(gray,corners,(11,11),(-1,-1),criteria)
#追加进入世界三维点和平面二维点中
objpoints.append(objp)
imgpoints.append(corners)
# 将角点在图像上显示
cv2.drawChessboardCorners(img, (w,h), corners, ret)
cv2.namedWindow('findCorners', cv2.WINDOW_NORMAL)
cv2.resizeWindow('findCorners', 640, 480)
cv2.imshow('findCorners',img)
cv2.waitKey(200)
cv2.destroyAllWindows()
#%% 标定
print('正在计算')
#标定
ret, mtx, dist, rvecs, tvecs = \
cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1], None, None)
print("ret:",ret )
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)
t = str(i)
print(t)
originL = cv2.imread(PATH_CALIBRATION_IMAGE_CAM1 + t + '.png')
originR = cv2.imread(PATH_CALIBRATION_IMAGE_CAM2 + t + '.png')
ChessImaL = cv2.imread(PATH_CALIBRATION_IMAGE_CAM1 + t + '.png',
0) # Left side
ChessImaR = cv2.imread(PATH_CALIBRATION_IMAGE_CAM2 + t + '.png',
0) # Right side
retR, cornersR = cv2.findChessboardCorners(
ChessImaR, (ChessWidth, ChessHeight),
None) # Define the number of chees corners we are looking for
retL, cornersL = cv2.findChessboardCorners(ChessImaL,
(ChessWidth, ChessHeight),
None) # Left side
cv2.drawChessboardCorners(originR, (ChessWidth, ChessHeight), cornersR,
retR)
cv2.drawChessboardCorners(originL, (ChessWidth, ChessHeight), cornersL,
retL)
while True:
cv2.imshow('Corner', np.hstack((originL, originR)))
if (cv2.waitKey(1) & 0xFF == ord('n')):
print("Couple with " + t + " has been added")
if (True == retR) & (True == retL):
objpoints.append(objp)
cv2.cornerSubPix(ChessImaR, cornersR, (11, 11), (-1, -1),
criteria)
cv2.cornerSubPix(ChessImaL, cornersL, (11, 11), (-1, -1),
criteria)
imgpointsR.append(cornersR)
imgpointsL.append(cornersL)
def calibration(images):
# termination criteria
criteria = (cv.TERM_CRITERIA_EPS + cv.TERM_CRITERIA_MAX_ITER, 30, 0.001)
# prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)
objp = np.zeros((7 * 10, 3), np.float32)
objp[:, :2] = np.mgrid[0:7, 0:10].T.reshape(-1, 2)
# Arrays to store object points and image points from all the images.
objpoints = [] # 3d point in real world space
imgpoints = [] # 2d points in image plane.
for fname in images:
img = cv.imread(fname)
gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
# Find the chess board corners
ret, corners = cv.findChessboardCorners(gray, (7, 10), 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, (7, 10), corners2, ret)
img_resized = cv.resize(img, (1400, 700))
cv.imshow('img', img_resized)
cv.waitKey(10)
cv.destroyAllWindows()
ret, K, dist, rvecs, tvecs, std_int, std_ext, pVE = \
cv.calibrateCameraExtended(objpoints, imgpoints, gray.shape[::-1], None, None)
mean_error = []
error_vecs = np.zeros(
(70 * len(images),
2)) # vertical stack of [x, y] errors for all points in all pictures
for i in range(len(objpoints)): # calculating errors
imgpoints2, _ = cv.projectPoints(objpoints[i], rvecs[i], tvecs[i], K,
dist)
error = cv.norm(imgpoints[i], imgpoints2, cv.NORM_L2) / len(imgpoints2)
mean_error.append(error)
imgpoints2 = np.array(imgpoints2)
imgpoints2 = imgpoints2[:, 0, :]
imgpoints1 = np.array(imgpoints[i])
imgpoints1 = imgpoints1[:, 0, :]
error_vecs[i * 70:(i + 1) * 70, :] = imgpoints1 - imgpoints2
fig = plt.figure(1) # mean reprojection error plot
img_nr = [f"{i+1}" for i in range(len(images))]
plt.bar(img_nr, mean_error)
plt.ylabel("mean reprojection error")
plt.xlabel("image number")
plt.savefig("Calibration_errors")
plt.show()
fig2 = plt.figure(2)
plt.scatter(error_vecs[:, 0], error_vecs[:, 1])
plt.ylabel("y error")
plt.xlabel("x error")
plt.savefig("Reprojection_scatter")
plt.show()
print('Standard errors')
print('Focal length and principal point')
print('--------------------------------')
print('fx: %g +/- %g' % (K[0, 0], std_int[0]))
print('fy: %g +/- %g' % (K[1, 1], std_int[1]))
print('cx: %g +/- %g' % (K[0, 2], std_int[2]))
print('cy: %g +/- %g' % (K[1, 2], std_int[3]))
print('Distortion coefficients')
print('--------------------------------')
print('k1: %g +/- %g' % (dist[0, 0], std_int[4]))
print('k2: %g +/- %g' % (dist[0, 1], std_int[5]))
print('p1: %g +/- %g' % (dist[0, 2], std_int[6]))
print('p2: %g +/- %g' % (dist[0, 3], std_int[7]))
print('k3: %g +/- %g' % (dist[0, 4], std_int[8]))
np.savetxt('cam_matrix.txt', K)
np.savetxt('dist.txt', dist)
np.savetxt('stdInt.txt', std_int)
np.savetxt('stdExt.txt', std_ext)
# 提取角点
ret, corners = cv2.findChessboardCorners(gray, (8, 6), None)
# print("ret = ", ret)
# print(corners)
if ret:
obj_points.append(objp)
# 找寻亚像素角点
corners2 = cv2.cornerSubPix(gray, corners, (5, 5), (-1, -1),
criteria) # 在原角点的基础上寻找亚像素角点
# print(corners2)
if [corners2]:
img_points.append(corners2)
else:
img_points.append(corners)
# 把角点画出来
cv2.drawChessboardCorners(img, (8, 6), corners,
ret) # 记住，OpenCV的绘制函数一般无返回值
# cv2.imwrite('c-'+str(0).zfill(4)+'.png', img) # 文件命名补齐函数zfill
# 保存绘制角度后的图像
# cv2.imwrite(image_name +".png", img)
# print(len(img_points))
# cv2.destroyAllWindows()
# 标定
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(obj_points, img_points,
size, None, None)
# print("ret:", ret)
# print("mtx:\n", mtx) # 内参数矩阵
# print("dist:\n", dist) # 畸变系数 distortion cofficients = (k_1,k_2,p_1,p_2,k_3)
# print("rvecs:\n", rvecs) # 旋转向量 # 外参数
# print("tvecs:\n", tvecs ) # 平移向量 # 外参数
# print("-----------------------------------------------------")
while (cap.isOpened()):
ret, frame = cap.read()
if ret == True:
frame = skip_frame(cap)
r_frame = cv2_helpers.get_resized(frame, 40)
r_frame_bw = cv2_helpers.get_resized_and_bw(frame, 40)
cv2.imshow("something", r_frame)
found_corners, corners = cv2.findChessboardCorners(
r_frame_bw, (nx, ny), None)
if found_corners:
frames_with_corners += 1
objpoints.append(objp)
corners2 = cv2.cornerSubPix(r_frame_bw, corners, (11, 11),
(-1, -1), criteria)
imgpoints.append(corners2)
new_img = cv2.drawChessboardCorners(r_frame, (nx, ny), corners,
ret)
cv2.imshow("test", new_img)
if frames_with_corners == 16:
break
cap.release()
cv2.destroyAllWindows()
print("callibrating the given video...")
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints,
r_frame_bw.shape[::-1],
None, None)
print("ret: ", ret)
print("mtx: ", mtx)
print("dist: ", dist)
images = glob.glob('chessboards/*.png')
for fname in images:
img = cv2.imread(fname)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# Find the chess board corners
ret, corners = cv2.findChessboardCorners(gray, (7, 6), None)
# If found, add object points, image points (after refining them)
if ret:
objpoints.append(objp)
corners2 = cv2.cornerSubPix(gray, corners, (11, 11), (-1, -1),
criteria)
imgpoints.append(corners2)
# Draw and display the corners
img = cv2.drawChessboardCorners(img, (7, 7), corners2, ret)
cv2.imshow('img', img)
cv2.waitKey(500)
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints,
gray.shape[::-1], None,
None)
print(rvecs)
print(tvecs)
cv2.destroyAllWindows()
def calibrate():
#棋盘角点数col*row
col = 13
row = 6
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)
objp = np.zeros((row * col, 3), np.float32)
# 用于标定的棋盘每个方格边长为22mm
objp[:, :2] = 22*np.mgrid[0:col, 0:row].T.reshape(-1, 2)
objpoints = [] # 世界坐标系下的点坐标
imgpoints = [] # 像素平面坐标系下的点坐标
print("请选择标定用到的照片所在的文件夹", "\n")
root = tkinter.Tk()
root.withdraw()
global path # 用于标定的照片所在目录
path = tkinter.filedialog.askdirectory(
title="选择标定用到的照片所在的文件夹") # 选择标定用到的照片所在的文件夹
images = glob.glob(path+"/*.jpg")
found = 0 # 记录用于标定的图像数目
for k, fname in enumerate(images):
img = cv2.imread(fname)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
ret, corners = cv2.findChessboardCorners(gray, (col, row), None)
# 角点检测
if ret is True:
print("读取", fname)
objpoints.append(objp)
# 角点检测精度会影响标定的精度
corners2 = cv2.cornerSubPix(
gray, corners, (11, 11), (-1, -1), criteria)#亚像素角点位置
# corners2=corners
#_,corners2=cv2.find4QuadCornerSubpix(gray, corners, (11, 11))
imgpoints.append(corners2)
img = cv2.drawChessboardCorners(img, (col, row), corners2, ret)#标记角点
found += 1
if len(images) < 16: # 图片过多时，不在UI中展示，避免弹窗过多
cv2.namedWindow('press any key to continue', cv2.WINDOW_NORMAL)
cv2.imshow('press any key to continue', img)
cv2.waitKey(0)
#image_name = path2 + "//corner"+str(found) + '.png'
#cv2.imwrite(image_name, img)
#存储已标出角点的照片
global path2 # 存放结果的目录（含记录相机参数的文件，和畸变矫正后的照片，3-D box照片）
path2 = tkinter.filedialog.askdirectory(
title="选择结果存放的文件夹(应与用于标定的照片所在的文件夹不同)") # 选择结果存放的文件夹
print("Number of images used for calibration: ", found)
# 相机标定
ret2, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints,
gray.shape[::-1], None, None)
print("reprojection error:", ret2)
print("内参矩阵:", mtx)
print("畸变系数：", dist)
print("旋转向量：", rvecs)
print("平移向量：", tvecs)
images = glob.glob(path+"//*.jpg")
for i, fname in enumerate(images):
img = cv2.imread(fname)
if img is None:
continue
h, w = img.shape[:2]
newcameramtx, roi = cv2.getOptimalNewCameraMatrix(mtx, dist, (w, h), 1,
(w, h))
dst = cv2.undistort(img, mtx, dist, None, newcameramtx) # 矫正畸变
x, y, w, h = roi
dst = dst[y:y + h, x:x + w]#裁剪
outpath = path2+"//tianyi_gao_undistorted" + str(i + 1) + ".jpg"
cv2.imwrite(outpath, dst)
print("新内参矩阵：", newcameramtx)
data = {
'camera_matrix': np.asarray(mtx).tolist(),
'dist_coeff': np.asarray(dist).tolist(),
'new_camera_matrix': np.asarray(newcameramtx).tolist(),
'rvecs': np.asarray(rvecs).tolist(),
'tvecs': np.asarray(tvecs).tolist(),
'reprojection_error': np.asarray(ret2).tolist()
}
# 存储相机参数(yaml)
with open(path2+"//calibration_parameters.yaml", "w") as f:
yaml.dump(data, f)
# 存储相机参数(txt)
with open(path2+"//tianyi_gao_cam.txt", "w") as f2:
name = list(data.keys())
value = list(data.values())
for i in range(len(name)):
f2.write(name[i] + ":" + "\n" + str(value[i]) + "\n")
print('Calibrate Done')
cv2.destroyAllWindows()
return mtx, dist, rvecs, tvecs, ret2, path2
img = cv2.imread('/calib_images/imgS'+(str)(i)+'.png')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# Find the chess board corners
ret, corners = cv2.findChessboardCorners(gray, (9, 6), None)
# If found, add object points, image points (after refining them)
if ret == True:
objpoints.append(objp)
corners2 = cv2.cornerSubPix(
gray, corners, (11, 11), (-1, -1), criteria)
imgpoints.append(corners2)
# Draw and display the corners
img = cv2.drawChessboardCorners(img, (cbcol, cbrow), corners2, ret)
cv2.imshow('img', img)
cv2.waitKey(WAIT_TIME)
cv2.destroyAllWindows()
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(
objpoints, imgpoints, gray.shape[::-1], None, None)
# ---------- Saving the calibration -----------------
cv_file = cv2.FileStorage("calib_images/test.yaml", cv2.FILE_STORAGE_WRITE)
cv_file.write("camera_matrix", mtx)
cv_file.write("dist_coeff", dist)
# note you *release* you don't close() a FileStorage object
cv_file.release()
for i in range(1, 2):
img_name = str(i) + '.bmp'
fname = os.path.join('Datasets/Q2_Image', img_name)
img = cv.imread(fname)
gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
# Find the chess board corners
ret, corners = cv.findChessboardCorners(gray, (11, 8), 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]