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 getEulerAngle(rotationVector):
# calculate rotation angles
theta = cv2.norm(rotationVector, cv2.NORM_L2)
# transformed to quaterniond
w = math.cos(theta / 2)
x = math.sin(theta / 2) * rotationVector[0] / theta
y = math.sin(theta / 2) * rotationVector[1] / theta
z = math.sin(theta / 2) * rotationVector[2] / theta
ysqr = y * y
# pitch (x-axis rotation)
t0 = 2.0 * (w * x + y * z)
t1 = 1.0 - 2.0 * (x * x + ysqr)
# print('t0:{}, t1:{}'.format(t0, t1))
pitch = math.atan2(t0, t1)
# yaw (y-axis rotation)
t2 = 2.0 * (w * y - z * x)
if t2 > 1.0:
t2 = 1.0
if t2 < -1.0:
t2 = -1.0
yaw = math.asin(t2)
# roll (z-axis rotation)
t3 = 2.0 * (w * z + x * y)
t4 = 1.0 - 2.0 * (ysqr + z * z)
roll = math.atan2(t3, t4)
# print('pitch:{}, yaw:{}, roll:{}'.format(pitch, yaw, roll))
# convert to degree
Y = int((pitch / math.pi) * 180)
X = int((yaw / math.pi) * 180)
Z = int((roll / math.pi) * 180)
return 0, Y, X, Z
def cos_dist(a, b):
return 1 - np.dot(a, b) / (norm(a) * norm(b))
np.save("./camera_params/dist", dist)
np.save("./camera_params/rvecs", rvecs)
np.save("./camera_params/tvecs", tvecs)
#Get exif data in order to get focal length.
exif_img = PIL.Image.open(calibration_paths[0])
stuff = exif_img.getexif().items()
exif_data = {
PIL.ExifTags.TAGS[k]:v
for k, v in exif_img.getexif().items()
if k in PIL.ExifTags.TAGS}
#Get focal length in tuple form
focal_length_exif = exif_data['FocalLength']
#Get focal length in decimal form
focal_length = focal_length_exif[0]/focal_length_exif[1]
#Save focal length
np.save("./camera_params/FocalLength", focal_length)
#Calculate projection error.
mean_error = 0
for i in range(len(obj_points)):
img_points2, _ = cv2.projectPoints(obj_points[i],rvecs[i],tvecs[i], K, dist)
error = cv2.norm(img_points[i], img_points2, cv2.NORM_L2)/len(img_points2)
mean_error += error
total_error = mean_error/len(obj_points)
print (total_error)
from cv2 import cv2 as cv
import numpy as np
img = cv.imread("Practica/Input/lake.jpg", 1)
#Ecualización Conjunta
gris = cv.cvtColor(img, cv.COLOR_BGR2GRAY) #conversion gris
hist = cv.calcHist(images = [gris],
channels = [0],
mask = None,
histSize = [256],
ranges = [0, 256])
hist *= 255.0/cv.norm(hist, cv.NORM_L1)
# MAT CV_8UC1
lut = np.zeros((1, 256), dtype = "uint8")
acum = 0.0
for i in range (0,256):
lut[0][i] = acum
acum += hist[i]
res = cv.LUT(img,lut)
cv.imshow("Entrada", img)
cv.imshow("Ecualizada", res)
cv.waitKey(0)
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)