def roiCorners(roi):
#returns all corners of a ROI
p = roi.pos()
s = roi.size()
a = roi.angle()
q = np.empty((4,2))
q[0] = p
q[1] = p[0]+s[0],p[1]
q[2] = p[0]+s[0],p[1]+s[1]
q[3] = p[0] ,p[1]+s[1]
return rotatePolygon(q, radians(a), q[0].copy())
def roiCorners(roi):
# returns all corners of a ROI
p = roi.pos()
s = roi.size()
a = roi.angle()
q = np.empty((4, 2))
q[0] = p
q[1] = p[0] + s[0], p[1]
q[2] = p[0] + s[0], p[1] + s[1]
q[3] = p[0], p[1] + s[1]
return rotatePolygon(q, radians(a), q[0].copy())
def rotate(line, angle):
x0, y0, x1, y1 = line
p = rotatePolygon(array(((x0, y0), (x1, y1))), angle)
return [p[0, 0], p[0, 1], p[1, 0], p[1, 1]]
def simulateSytematicError(N_SAMPLES=5, N_IMAGES=10,
SHOW_DETECTED_PATTERN=True, # GRAYSCALE=False,
HEIGHT=500, PLOT_RESULTS=True, PLOT_ERROR_ARRAY=True,
CAMERA_PARAM=None, PERSPECTIVE=True, ROTATION=True,
RELATIVE_PATTERN_SIZE=0.5, POSITION=True,
NOISE=25, BLUR=(3, 3), PATTERNS=None):
'''
Simulates a lens calibration using synthetic images
* images are rendered under the given HEIGHT resolution
* noise and smoothing is applied
* perspective and position errors are applied
* images are deformed using the given CAMERA_PARAM
* the detected camera parameters are used to calculate the error to the given ones
simulation
-----------
N_IMAGES -> number of images to take for a camera calibration
N_SAMPLES -> number of camera calibrations of each pattern type
output
--------
SHOW_DETECTED_PATTERN: print each image and detected pattern to screen
PLOT_RESULTS: plot boxplots of the mean error and std of the camera parameters
PLOT_ERROR_ARRAY: plot position error for the lens correction
pattern
--------
this simulation tests the openCV standard patterns: chess board, asymmetric and symmetric circles
GRAYSCALE: whether to load the pattern as gray scale
RELATIVE_PATTERN_SIZE: the relative size of the pattern within the image (0.4->40%)
PERSPECTIVE: [True] -> enable perspective distortion
ROTATION: [True] -> enable rotation of the pattern
BLUR: False or (sizex,sizey), like (3,3)
CAMERA_PARAM: camera calibration parameters as [fx,fy,cx,cy,k1,k2,k3,p1,p2]
'''
print(
'calculate systematic error of the implemented calibration algorithms')
# LOCATION OF PATTERN IMAGES
folder = MEDIA_PATH
if PATTERNS is None:
PATTERNS = ('Chessboard', 'Asymmetric circles', 'Symmetric circles')
patterns = OrderedDict(( # n of inner corners
('Chessboard', ((6, 9), 'chessboard_pattern_a3.svg')),
('Asymmetric circles', ((4, 11), 'acircles_pattern_a3.svg')),
('Symmetric circles', ((8, 11), 'circles_pattern_a3.svg')),
))
# REMOVE PATTERNS THAT ARE NOT TO BE TESTED:
[patterns.pop(key) for key in patterns if key not in PATTERNS]
if SHOW_DETECTED_PATTERN:
cv2.namedWindow('Pattern', cv2.WINDOW_NORMAL)
# number of positive detected patterns:
success = []
# list[N_SAMPLES] of random camera parameters
fx, fy, cx, cy, k1, k2, k3, p1, p2 = [], [], [], [], [], [], [], [], []
# list[Method, N_SAMPLES] of given-detected parameters:
errl, fxl, fyl, cxl, cyl, k1l, k2l, k3l, p1l, p2l = [
], [], [], [], [], [], [], [], [], []
# list[Method, N_SAMPLES] of magnitude(difference of displacement vector
# array):
dxl = []
dyl = []
# maintain aspect ratio of din a4, a3...:
aspect_ratio_DIN = 2.0**0.5
width = int(round(HEIGHT / aspect_ratio_DIN))
if CAMERA_PARAM is None:
CAMERA_PARAM = [
HEIGHT, HEIGHT, HEIGHT / 2, width / 2, 0.0, 0.01, 0.1, 0.01, 0.001]
# ???CREATE N DIFFERENT RANDOM LENS ERRORS:
for n in range(N_SAMPLES):
# TODO: RANDOMIZE CAMERA ERROR??
fx.append(CAMERA_PARAM[0]) # * np.random.uniform(1, 2) )
fy.append(CAMERA_PARAM[1]) # * np.random.uniform(1, 2) )
cx.append(CAMERA_PARAM[2]) # * np.random.uniform(0.9, 1.1) )
cy.append(CAMERA_PARAM[3]) # * np.random.uniform(0.9, 1.1) )
k1.append(CAMERA_PARAM[4]) # + np.random.uniform(-1, 1)*0.1)
k2.append(CAMERA_PARAM[5]) # + np.random.uniform(-1, 1)*0.01)
p1.append(CAMERA_PARAM[6]) # + np.random.uniform(0, 1)*0.1)
p2.append(CAMERA_PARAM[7]) # + np.random.uniform(0, 1)*0.01)
k3.append(CAMERA_PARAM[8]) # + np.random.uniform(0, 1)*0.001)
L = LensDistortion()
# FOR EVERY METHOD:
for method, (board_size, filename) in patterns.items():
f = folder.join(filename)
# LOAD THE SVG FILE, AND SAVE IT WITH NEW RESOLUTION:
svg = QtSvg.QSvgRenderer(f)
image = QtGui.QImage(width * 4, HEIGHT * 4, QtGui.QImage.Format_ARGB32)
image.fill(QtCore.Qt.white)
# Get QPainter that paints to the image
painter = QtGui.QPainter(image)
svg.render(painter)
# Save, image format based on file extension
# f = "rendered.png"
# image.save(f)
#
# if GRAYSCALE:
# img = cv2.imread(f, cv2.IMREAD_GRAYSCALE)
# else:
# img = cv2.imread(f)
img = qImageToArray(image)
success.append([])
fxl.append([])
errl.append([])
fyl.append([])
cxl.append([])
cyl.append([])
k1l.append([])
k2l.append([])
k3l.append([])
p1l.append([])
p2l.append([])
dxl.append([])
dyl.append([])
imgHeight, imgWidth = img.shape[0], img.shape[1]
for n in range(N_SAMPLES):
L.calibrate(board_size, method)
print('SET PARAMS:', fx[n], fy[n], cx[n],
cy[n], k1[n], k2[n], k3[n], p1[n], p2[n])
L.setCameraParams(
fx[n], fy[n], cx[n], cy[n], k1[n], k2[n], k3[n], p1[n], p2[n])
L._coeffs['shape'] = (imgHeight, imgWidth)
hw = imgWidth * 0.5
hh = imgHeight * 0.5
for m in range(N_IMAGES):
pts1 = np.float32([[hw, hh + 100],
[hw - 100, hh - 100],
[hw + 100, hh - 100]])
pts2 = pts1.copy()
if ROTATION:
rotatePolygon(pts2, np.random.randint(0, 2 * np.pi))
if PERSPECTIVE:
# CREATE A RANDOM PERSPECTIVE:
pts2 += np.random.randint(-hw *
0.05, hh * 0.05, size=(3, 2))
# MAKE SURE THAT THE PATTERN IS FULLY WITHIN THE IMAGE:
pts2 *= RELATIVE_PATTERN_SIZE
# MOVE TO THE CENTER
pts2[:, 0] += hw * (1 - RELATIVE_PATTERN_SIZE)
pts2[:, 1] += hh * (1 - RELATIVE_PATTERN_SIZE)
if POSITION:
f = ((2 * np.random.rand(2)) - 1)
pts2[:, 0] += hw * 0.7 * f[0] * (1 - RELATIVE_PATTERN_SIZE)
pts2[:, 1] += hh * 0.7 * f[1] * (1 - RELATIVE_PATTERN_SIZE)
# EXEC PERSPECTICE, POSITION, ROTATION:
M = cv2.getAffineTransform(pts1, pts2)
img_warped = cv2.warpAffine(
img, M, (imgWidth, imgHeight), borderValue=(230, 230, 230))
# DOWNSCALE IMAGE AGAIN - UPSCALING AND DOWNSCALING SHOULD BRING THE ERRROR
# WARPING DOWN
img_warped = cv2.resize(img_warped, (width, HEIGHT))
# CREATE THE LENS DISTORTION:
mapx, mapy = L.getDistortRectifyMap(width, HEIGHT)
# print 664, mapx.shape
img_distorted = cv2.remap(
img_warped, mapx, mapy, cv2.INTER_LINEAR, borderValue=(230, 230, 230))
# img_distorted[img_distorted==0]=20
# img_distorted[img_distorted>100]=230
if BLUR:
img_distorted = cv2.blur(img_distorted, BLUR)
if NOISE:
# soften, black and white more gray, and add noise
img_distorted = img_distorted.astype(np.int16)
img_distorted += (np.random.rand(*img_distorted.shape) *
NOISE).astype(img_distorted.dtype)
img_distorted = np.clip(
img_distorted, 0, 255).astype(np.uint8)
# plt.imshow(img_distorted)
# plt.show()
found = L.addImg(img_distorted)
if SHOW_DETECTED_PATTERN and found:
img_distorted = L.drawChessboard(img_distorted)
cv2.imshow('Pattern', img_distorted)
cv2.waitKey(1)
success[-1].append(L.findCount)
try:
L._coeffs = None
errl[-1].append(L.coeffs['reprojectionError'])
L.correct(img_distorted)
c = L.getCameraParams()
print('GET PARAMS:', c)
fxl[-1].append(fx[n] - c[0])
fyl[-1].append(fy[n] - c[1])
cxl[-1].append(cx[n] - c[2])
cyl[-1].append(cy[n] - c[3])
k1l[-1].append(k1[n] - c[4])
k2l[-1].append(k2[n] - c[5])
k3l[-1].append(k3[n] - c[6])
p1l[-1].append(p1[n] - c[7])
p2l[-1].append(p2[n] - c[8])
if PLOT_ERROR_ARRAY:
dx = (mapx - L.mapx) / 2
dy = (mapy - L.mapy) / 2
dxl[-1].append(dx)
dyl[-1].append(dy)
except NothingFound:
print(
"Couldn't create a calibration because no patterns were detected")
del painter
# AVERAGE SAMPLES AND GET STD
dx_std, dx_mean = [], []
dy_std, dy_mean = [], []
mag = []
std = []
for patterndx, patterndy in zip(dxl, dyl):
x = np.mean(patterndx, axis=0)
dx_mean.append(x)
y = np.mean(patterndy, axis=0)
dy_mean.append(y)
x = np.std(patterndx, axis=0)
mag.append((x**2 + y**2)**0.5)
dx_std.append(x)
y = np.std(patterndy, axis=0)
dy_std.append(y)
std.append((x**2 + y**2)**0.5)
# PLOT
p = len(patterns)
if PLOT_RESULTS:
fig, axs = plt.subplots(nrows=2, ncols=5)
axs = np.array(axs).ravel()
for ax, typ, tname in zip(axs,
(success, fxl, fyl, cxl, cyl,
k1l, k2l, k3l, p1l, p2l),
('Success rate', 'fx', 'fy', 'cx',
'cy', 'k1', 'k2', 'k3', 'p1', 'p2')
):
ax.set_title(tname)
# , showmeans=True, meanline=True)#labels=patterns.keys())
ax.boxplot(typ, notch=0, sym='+', vert=1, whis=1.5)
# , ha=ha[n])
ax.set_xticklabels(patterns.keys(), rotation=40, fontsize=8)
if PLOT_ERROR_ARRAY:
mmin = np.min(mag)
mmax = np.max(mag)
smin = np.min(std)
smax = np.max(std)
plt.figure()
for n, pattern in enumerate(patterns.keys()):
plt.subplot(int('2%s%s' % (p, n + 1)), axisbg='g')
plt.title(pattern)
plt.imshow(mag[n], origin='upper', vmin=mmin, vmax=mmax)
if n == p - 1:
plt.colorbar(label='Average')
plt.subplot(int('2%s%s' % (p, n + p + 1)), axisbg='g')
plt.title(pattern)
plt.imshow(std[n], origin='upper', vmin=smin, vmax=smax)
if n == p - 1:
plt.colorbar(label='Standard deviation')
fig = plt.figure()
fig.suptitle('Individually scaled')
for n, pattern in enumerate(patterns.keys()):
# downscale - show max 30 arrows each dimension
sy, sx = dx_mean[n].shape
ix = int(sx / 15)
if ix < 1:
ix = 1
iy = int(sy / 15)
if iy < 1:
iy = 1
Y, X = np.meshgrid(np.arange(0, sy, iy), np.arange(0, sx, ix))
plt.subplot(int('2%s%s' % (p, n + 1)), axisbg='g')
plt.title(pattern)
plt.imshow(mag[n], origin='upper')
plt.colorbar()
plt.quiver(
X, Y, dy_mean[n][::ix, ::iy] * 20, dx_mean[n][::ix, ::iy] * 20)
plt.subplot(int('2%s%s' % (p, n + p + 1)), axisbg='g')
plt.title(pattern)
plt.imshow(std[n], origin='upper')
plt.colorbar()
# plt.quiver(X,Y,dx_std[n][::ix,::iy]*50, dy_std[n][::ix,::iy]*10)
#############################################
fig = plt.figure()
fig.suptitle('Spatial uncertainty + deflection')
for n, pattern in enumerate(patterns.keys()):
L.calibrate(board_size, method)
# there is alot of additional calc thats not necassary:
L.setCameraParams(
fx[0], fy[0], cx[0], cy[0], k1[0], k2[0], k3[0], p1[0], p2[0])
L._coeffs['shape'] = (imgHeight, imgWidth)
L._coeffs['reprojectionError'] = np.mean(errl[n])
# deflection_x, deflection_y = L.getDeflection(width, HEIGHT)
# deflection_x += dx_mean[n]
# deflection_y += dy_mean[n]
ux, uy = L.standardUncertainties()
plt.subplot(int('2%s%s' % (p, n + 1)), axisbg='g')
plt.title(pattern)
plt.imshow(mag[n], origin='upper')
plt.colorbar()
# DEFLECTION
plt.subplot(int('2%s%s' % (p, n + p + 1)), axisbg='g')
plt.title(pattern)
plt.imshow(np.linalg.norm([ux, uy], axis=0), origin='upper')
plt.colorbar()
# DEFL: VECTORS
# downscale - show max 30 arrows each dimension
sy, sx = dx_mean[n].shape
ix = int(sx / 15)
if ix < 1:
ix = 1
iy = int(sy / 15)
if iy < 1:
iy = 1
Y, X = np.meshgrid(np.arange(0, sy, iy), np.arange(0, sx, ix))
plt.quiver(X, Y, ux[::ix, ::iy] * 20, uy[::ix, ::iy] * 20)
if PLOT_ERROR_ARRAY or PLOT_RESULTS:
plt.show()
return dx_mean, dy_mean
def simulateSytematicError(
N_SAMPLES=5,
N_IMAGES=10,
SHOW_DETECTED_PATTERN=True, # GRAYSCALE=False,
HEIGHT=500,
PLOT_RESULTS=True,
PLOT_ERROR_ARRAY=True,
CAMERA_PARAM=None,
PERSPECTIVE=True,
ROTATION=True,
RELATIVE_PATTERN_SIZE=0.5,
POSITION=True,
NOISE=25,
BLUR=(3, 3),
PATTERNS=None):
'''
Simulates a lens calibration using synthetic images
* images are rendered under the given HEIGHT resolution
* noise and smoothing is applied
* perspective and position errors are applied
* images are deformed using the given CAMERA_PARAM
* the detected camera parameters are used to calculate the error to the given ones
simulation
-----------
N_IMAGES -> number of images to take for a camera calibration
N_SAMPLES -> number of camera calibrations of each pattern type
output
--------
SHOW_DETECTED_PATTERN: print each image and detected pattern to screen
PLOT_RESULTS: plot boxplots of the mean error and std of the camera parameters
PLOT_ERROR_ARRAY: plot position error for the lens correction
pattern
--------
this simulation tests the openCV standard patterns: chess board, asymmetric and symmetric circles
GRAYSCALE: whether to load the pattern as gray scale
RELATIVE_PATTERN_SIZE: the relative size of the pattern within the image (0.4->40%)
PERSPECTIVE: [True] -> enable perspective distortion
ROTATION: [True] -> enable rotation of the pattern
BLUR: False or (sizex,sizey), like (3,3)
CAMERA_PARAM: camera calibration parameters as [fx,fy,cx,cy,k1,k2,k3,p1,p2]
'''
print(
'calculate systematic error of the implemented calibration algorithms')
# LOCATION OF PATTERN IMAGES
folder = MEDIA_PATH
if PATTERNS is None:
PATTERNS = ('Chessboard', 'Asymmetric circles', 'Symmetric circles')
patterns = OrderedDict(( # n of inner corners
('Chessboard', ((6, 9), 'chessboard_pattern_a3.svg')),
('Asymmetric circles', ((4, 11), 'acircles_pattern_a3.svg')),
('Symmetric circles', ((8, 11), 'circles_pattern_a3.svg')),
))
# REMOVE PATTERNS THAT ARE NOT TO BE TESTED:
[patterns.pop(key) for key in patterns if key not in PATTERNS]
if SHOW_DETECTED_PATTERN:
cv2.namedWindow('Pattern', cv2.WINDOW_NORMAL)
# number of positive detected patterns:
success = []
# list[N_SAMPLES] of random camera parameters
fx, fy, cx, cy, k1, k2, k3, p1, p2 = [], [], [], [], [], [], [], [], []
# list[Method, N_SAMPLES] of given-detected parameters:
errl, fxl, fyl, cxl, cyl, k1l, k2l, k3l, p1l, p2l = [
], [], [], [], [], [], [], [], [], []
# list[Method, N_SAMPLES] of magnitude(difference of displacement vector
# array):
dxl = []
dyl = []
# maintain aspect ratio of din a4, a3...:
aspect_ratio_DIN = 2.0**0.5
width = int(round(HEIGHT / aspect_ratio_DIN))
if CAMERA_PARAM is None:
CAMERA_PARAM = [
HEIGHT, HEIGHT, HEIGHT / 2, width / 2, 0.0, 0.01, 0.1, 0.01, 0.001
]
# ???CREATE N DIFFERENT RANDOM LENS ERRORS:
for n in range(N_SAMPLES):
# TODO: RANDOMIZE CAMERA ERROR??
fx.append(CAMERA_PARAM[0]) # * np.random.uniform(1, 2) )
fy.append(CAMERA_PARAM[1]) # * np.random.uniform(1, 2) )
cx.append(CAMERA_PARAM[2]) # * np.random.uniform(0.9, 1.1) )
cy.append(CAMERA_PARAM[3]) # * np.random.uniform(0.9, 1.1) )
k1.append(CAMERA_PARAM[4]) # + np.random.uniform(-1, 1)*0.1)
k2.append(CAMERA_PARAM[5]) # + np.random.uniform(-1, 1)*0.01)
p1.append(CAMERA_PARAM[6]) # + np.random.uniform(0, 1)*0.1)
p2.append(CAMERA_PARAM[7]) # + np.random.uniform(0, 1)*0.01)
k3.append(CAMERA_PARAM[8]) # + np.random.uniform(0, 1)*0.001)
L = LensDistortion()
# FOR EVERY METHOD:
for method, (board_size, filename) in patterns.items():
f = folder.join(filename)
# LOAD THE SVG FILE, AND SAVE IT WITH NEW RESOLUTION:
svg = QtSvg.QSvgRenderer(f)
image = QtGui.QImage(width * 4, HEIGHT * 4, QtGui.QImage.Format_ARGB32)
image.fill(QtCore.Qt.white)
# Get QPainter that paints to the image
painter = QtGui.QPainter(image)
svg.render(painter)
# Save, image format based on file extension
# f = "rendered.png"
# image.save(f)
#
# if GRAYSCALE:
# img = cv2.imread(f, cv2.IMREAD_GRAYSCALE)
# else:
# img = cv2.imread(f)
img = qImageToArray(image)
success.append([])
fxl.append([])
errl.append([])
fyl.append([])
cxl.append([])
cyl.append([])
k1l.append([])
k2l.append([])
k3l.append([])
p1l.append([])
p2l.append([])
dxl.append([])
dyl.append([])
imgHeight, imgWidth = img.shape[0], img.shape[1]
for n in range(N_SAMPLES):
L.calibrate(board_size, method)
print('SET PARAMS:', fx[n], fy[n], cx[n], cy[n], k1[n], k2[n],
k3[n], p1[n], p2[n])
L.setCameraParams(fx[n], fy[n], cx[n], cy[n], k1[n], k2[n], k3[n],
p1[n], p2[n])
L._coeffs['shape'] = (imgHeight, imgWidth)
hw = imgWidth * 0.5
hh = imgHeight * 0.5
for m in range(N_IMAGES):
pts1 = np.float32([[hw, hh + 100], [hw - 100, hh - 100],
[hw + 100, hh - 100]])
pts2 = pts1.copy()
if ROTATION:
rotatePolygon(pts2, np.random.randint(0, 2 * np.pi))
if PERSPECTIVE:
# CREATE A RANDOM PERSPECTIVE:
pts2 += np.random.randint(-hw * 0.05,
hh * 0.05,
size=(3, 2))
# MAKE SURE THAT THE PATTERN IS FULLY WITHIN THE IMAGE:
pts2 *= RELATIVE_PATTERN_SIZE
# MOVE TO THE CENTER
pts2[:, 0] += hw * (1 - RELATIVE_PATTERN_SIZE)
pts2[:, 1] += hh * (1 - RELATIVE_PATTERN_SIZE)
if POSITION:
f = ((2 * np.random.rand(2)) - 1)
pts2[:, 0] += hw * 0.7 * f[0] * (1 - RELATIVE_PATTERN_SIZE)
pts2[:, 1] += hh * 0.7 * f[1] * (1 - RELATIVE_PATTERN_SIZE)
# EXEC PERSPECTICE, POSITION, ROTATION:
M = cv2.getAffineTransform(pts1, pts2)
img_warped = cv2.warpAffine(img,
M, (imgWidth, imgHeight),
borderValue=(230, 230, 230))
# DOWNSCALE IMAGE AGAIN - UPSCALING AND DOWNSCALING SHOULD BRING THE ERRROR
# WARPING DOWN
img_warped = cv2.resize(img_warped, (width, HEIGHT))
# CREATE THE LENS DISTORTION:
mapx, mapy = L.getDistortRectifyMap(width, HEIGHT)
# print 664, mapx.shape
img_distorted = cv2.remap(img_warped,
mapx,
mapy,
cv2.INTER_LINEAR,
borderValue=(230, 230, 230))
# img_distorted[img_distorted==0]=20
# img_distorted[img_distorted>100]=230
if BLUR:
img_distorted = cv2.blur(img_distorted, BLUR)
if NOISE:
# soften, black and white more gray, and add noise
img_distorted = img_distorted.astype(np.int16)
img_distorted += (np.random.rand(*img_distorted.shape) *
NOISE).astype(img_distorted.dtype)
img_distorted = np.clip(img_distorted, 0,
255).astype(np.uint8)
# plt.imshow(img_distorted)
# plt.show()
found = L.addImg(img_distorted)
if SHOW_DETECTED_PATTERN and found:
img_distorted = L.drawChessboard(img_distorted)
cv2.imshow('Pattern', img_distorted)
cv2.waitKey(1)
success[-1].append(L.findCount)
try:
L._coeffs = None
errl[-1].append(L.coeffs['reprojectionError'])
L.correct(img_distorted)
c = L.getCameraParams()
print('GET PARAMS:', c)
fxl[-1].append(fx[n] - c[0])
fyl[-1].append(fy[n] - c[1])
cxl[-1].append(cx[n] - c[2])
cyl[-1].append(cy[n] - c[3])
k1l[-1].append(k1[n] - c[4])
k2l[-1].append(k2[n] - c[5])
k3l[-1].append(k3[n] - c[6])
p1l[-1].append(p1[n] - c[7])
p2l[-1].append(p2[n] - c[8])
if PLOT_ERROR_ARRAY:
dx = (mapx - L.mapx) / 2
dy = (mapy - L.mapy) / 2
dxl[-1].append(dx)
dyl[-1].append(dy)
except NothingFound:
print(
"Couldn't create a calibration because no patterns were detected"
)
del painter
# AVERAGE SAMPLES AND GET STD
dx_std, dx_mean = [], []
dy_std, dy_mean = [], []
mag = []
std = []
for patterndx, patterndy in zip(dxl, dyl):
x = np.mean(patterndx, axis=0)
dx_mean.append(x)
y = np.mean(patterndy, axis=0)
dy_mean.append(y)
x = np.std(patterndx, axis=0)
mag.append((x**2 + y**2)**0.5)
dx_std.append(x)
y = np.std(patterndy, axis=0)
dy_std.append(y)
std.append((x**2 + y**2)**0.5)
# PLOT
p = len(patterns)
if PLOT_RESULTS:
fig, axs = plt.subplots(nrows=2, ncols=5)
axs = np.array(axs).ravel()
for ax, typ, tname in zip(
axs, (success, fxl, fyl, cxl, cyl, k1l, k2l, k3l, p1l, p2l),
('Success rate', 'fx', 'fy', 'cx', 'cy', 'k1', 'k2', 'k3', 'p1',
'p2')):
ax.set_title(tname)
# , showmeans=True, meanline=True)#labels=patterns.keys())
ax.boxplot(typ, notch=0, sym='+', vert=1, whis=1.5)
# , ha=ha[n])
ax.set_xticklabels(patterns.keys(), rotation=40, fontsize=8)
if PLOT_ERROR_ARRAY:
mmin = np.min(mag)
mmax = np.max(mag)
smin = np.min(std)
smax = np.max(std)
plt.figure()
for n, pattern in enumerate(patterns.keys()):
plt.subplot(int('2%s%s' % (p, n + 1)), axisbg='g')
plt.title(pattern)
plt.imshow(mag[n], origin='upper', vmin=mmin, vmax=mmax)
if n == p - 1:
plt.colorbar(label='Average')
plt.subplot(int('2%s%s' % (p, n + p + 1)), axisbg='g')
plt.title(pattern)
plt.imshow(std[n], origin='upper', vmin=smin, vmax=smax)
if n == p - 1:
plt.colorbar(label='Standard deviation')
fig = plt.figure()
fig.suptitle('Individually scaled')
for n, pattern in enumerate(patterns.keys()):
# downscale - show max 30 arrows each dimension
sy, sx = dx_mean[n].shape
ix = int(sx / 15)
if ix < 1:
ix = 1
iy = int(sy / 15)
if iy < 1:
iy = 1
Y, X = np.meshgrid(np.arange(0, sy, iy), np.arange(0, sx, ix))
plt.subplot(int('2%s%s' % (p, n + 1)), axisbg='g')
plt.title(pattern)
plt.imshow(mag[n], origin='upper')
plt.colorbar()
plt.quiver(X, Y, dy_mean[n][::ix, ::iy] * 20,
dx_mean[n][::ix, ::iy] * 20)
plt.subplot(int('2%s%s' % (p, n + p + 1)), axisbg='g')
plt.title(pattern)
plt.imshow(std[n], origin='upper')
plt.colorbar()
# plt.quiver(X,Y,dx_std[n][::ix,::iy]*50, dy_std[n][::ix,::iy]*10)
#############################################
fig = plt.figure()
fig.suptitle('Spatial uncertainty + deflection')
for n, pattern in enumerate(patterns.keys()):
L.calibrate(board_size, method)
# there is alot of additional calc thats not necassary:
L.setCameraParams(fx[0], fy[0], cx[0], cy[0], k1[0], k2[0], k3[0],
p1[0], p2[0])
L._coeffs['shape'] = (imgHeight, imgWidth)
L._coeffs['reprojectionError'] = np.mean(errl[n])
# deflection_x, deflection_y = L.getDeflection(width, HEIGHT)
# deflection_x += dx_mean[n]
# deflection_y += dy_mean[n]
ux, uy = L.standardUncertainties()
plt.subplot(int('2%s%s' % (p, n + 1)), axisbg='g')
plt.title(pattern)
plt.imshow(mag[n], origin='upper')
plt.colorbar()
# DEFLECTION
plt.subplot(int('2%s%s' % (p, n + p + 1)), axisbg='g')
plt.title(pattern)
plt.imshow(np.linalg.norm([ux, uy], axis=0), origin='upper')
plt.colorbar()
# DEFL: VECTORS
# downscale - show max 30 arrows each dimension
sy, sx = dx_mean[n].shape
ix = int(sx / 15)
if ix < 1:
ix = 1
iy = int(sy / 15)
if iy < 1:
iy = 1
Y, X = np.meshgrid(np.arange(0, sy, iy), np.arange(0, sx, ix))
plt.quiver(X, Y, ux[::ix, ::iy] * 20, uy[::ix, ::iy] * 20)
if PLOT_ERROR_ARRAY or PLOT_RESULTS:
plt.show()
return dx_mean, dy_mean
