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Code Packages Releases Wiki Activity

Merge branch 4.x

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This commit is contained in:
Alexander Alekhin
2021-10-15 15:59:36 +00:00
parent 91a7a4523a 0e86e292e4
commit 7ba26ada12
537 changed files with 39768 additions and 10712 deletions
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samples/python/camera_calibration_show_extrinsics.py
+1 -1
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@@ -188,7 +188,7 @@ def main():
fig = plt.figure()
ax = fig.gca(projection='3d')
ax.set_aspect("equal")
ax.set_aspect("auto")
cam_width = args.cam_width
cam_height = args.cam_height
samples/python/gaussian_mix.py
+2 -2
View File
@@ -28,11 +28,11 @@ def make_gaussians(cluster_n, img_size):
return points, ref_distrs
def draw_gaussain(img, mean, cov, color):
x, y = np.int32(mean)
x, y = mean
w, u, _vt = cv.SVDecomp(cov)
ang = np.arctan2(u[1, 0], u[0, 0])*(180/np.pi)
s1, s2 = np.sqrt(w)*3.0
cv.ellipse(img, (x, y), (s1, s2), ang, 0, 360, color, 1, cv.LINE_AA)
cv.ellipse(img, (int(x), int(y)), (int(s1), int(s2)), ang, 0, 360, color, 1, cv.LINE_AA)
def main():
samples/python/hist.py
+2 -2
View File
@@ -46,9 +46,9 @@ def hist_lines(im):
im = cv.cvtColor(im,cv.COLOR_BGR2GRAY)
hist_item = cv.calcHist([im],[0],None,[256],[0,256])
cv.normalize(hist_item,hist_item,0,255,cv.NORM_MINMAX)
hist=np.int32(np.around(hist_item))
hist = np.int32(np.around(hist_item))
for x,y in enumerate(hist):
cv.line(h,(x,0),(x,y),(255,255,255))
cv.line(h,(x,0),(x,y[0]),(255,255,255))
y = np.flipud(h)
return y
samples/python/kalman.py
+49 -48
View File
@@ -1,14 +1,18 @@
#!/usr/bin/env python
"""
Tracking of rotating point.
Rotation speed is constant.
Point moves in a circle and is characterized by a 1D state.
state_k+1 = state_k + speed + process_noise N(0, 1e-5)
The speed is constant.
Both state and measurements vectors are 1D (a point angle),
Measurement is the real point angle + gaussian noise.
The real and the estimated points are connected with yellow line segment,
the real and the measured points are connected with red line segment.
Measurement is the real state + gaussian noise N(0, 1e-1).
The real and the measured points are connected with red line segment,
the real and the estimated points are connected with yellow line segment,
the real and the corrected estimated points are connected with green line segment.
(if Kalman filter works correctly,
the yellow segment should be shorter than the red one).
Pressing any key (except ESC) will reset the tracking with a different speed.
the yellow segment should be shorter than the red one and
the green segment should be shorter than the yellow one).
Pressing any key (except ESC) will reset the tracking.
Pressing ESC will stop the program.
"""
# Python 2/3 compatibility
@@ -21,8 +25,7 @@ if PY3:
import numpy as np
import cv2 as cv
from math import cos, sin, sqrt
import numpy as np
from math import cos, sin, sqrt, pi
def main():
img_height = 500
@@ -30,64 +33,62 @@ def main():
kalman = cv.KalmanFilter(2, 1, 0)
code = long(-1)
cv.namedWindow("Kalman")
num_circle_steps = 12
while True:
state = 0.1 * np.random.randn(2, 1)
kalman.transitionMatrix = np.array([[1., 1.], [0., 1.]])
kalman.measurementMatrix = 1. * np.ones((1, 2))
kalman.processNoiseCov = 1e-5 * np.eye(2)
kalman.measurementNoiseCov = 1e-1 * np.ones((1, 1))
kalman.errorCovPost = 1. * np.ones((2, 2))
kalman.statePost = 0.1 * np.random.randn(2, 1)
img = np.zeros((img_height, img_width, 3), np.uint8)
state = np.array([[0.0],[(2 * pi) / num_circle_steps]]) # start state
kalman.transitionMatrix = np.array([[1., 1.], [0., 1.]]) # F. input
kalman.measurementMatrix = 1. * np.eye(1, 2) # H. input
kalman.processNoiseCov = 1e-5 * np.eye(2) # Q. input
kalman.measurementNoiseCov = 1e-1 * np.ones((1, 1)) # R. input
kalman.errorCovPost = 1. * np.eye(2, 2) # P._k|k KF state var
kalman.statePost = 0.1 * np.random.randn(2, 1) # x^_k|k KF state var
while True:
def calc_point(angle):
return (np.around(img_width/2 + img_width/3*cos(angle), 0).astype(int),
np.around(img_height/2 - img_width/3*sin(angle), 1).astype(int))
return (np.around(img_width / 2. + img_width / 3.0 * cos(angle), 0).astype(int),
np.around(img_height / 2. - img_width / 3.0 * sin(angle), 1).astype(int))
img = img * 1e-3
state_angle = state[0, 0]
state_pt = calc_point(state_angle)
# advance Kalman filter to next timestep
# updates statePre, statePost, errorCovPre, errorCovPost
# k-> k+1, x'(k) = A*x(k)
# P'(k) = temp1*At + Q
prediction = kalman.predict()
predict_angle = prediction[0, 0]
predict_pt = calc_point(predict_angle)
measurement = kalman.measurementNoiseCov * np.random.randn(1, 1)
predict_pt = calc_point(prediction[0, 0]) # equivalent to calc_point(kalman.statePre[0,0])
# generate measurement
measurement = kalman.measurementNoiseCov * np.random.randn(1, 1)
measurement = np.dot(kalman.measurementMatrix, state) + measurement
measurement_angle = measurement[0, 0]
measurement_pt = calc_point(measurement_angle)
# plot points
def draw_cross(center, color, d):
cv.line(img,
(center[0] - d, center[1] - d), (center[0] + d, center[1] + d),
color, 1, cv.LINE_AA, 0)
cv.line(img,
(center[0] + d, center[1] - d), (center[0] - d, center[1] + d),
color, 1, cv.LINE_AA, 0)
img = np.zeros((img_height, img_width, 3), np.uint8)
draw_cross(np.int32(state_pt), (255, 255, 255), 3)
draw_cross(np.int32(measurement_pt), (0, 0, 255), 3)
draw_cross(np.int32(predict_pt), (0, 255, 0), 3)
cv.line(img, state_pt, measurement_pt, (0, 0, 255), 3, cv.LINE_AA, 0)
cv.line(img, state_pt, predict_pt, (0, 255, 255), 3, cv.LINE_AA, 0)
# correct the state estimates based on measurements
# updates statePost & errorCovPost
kalman.correct(measurement)
improved_pt = calc_point(kalman.statePost[0, 0])
process_noise = sqrt(kalman.processNoiseCov[0,0]) * np.random.randn(2, 1)
state = np.dot(kalman.transitionMatrix, state) + process_noise
# plot points
cv.drawMarker(img, measurement_pt, (0, 0, 255), cv.MARKER_SQUARE, 5, 2)
cv.drawMarker(img, predict_pt, (0, 255, 255), cv.MARKER_SQUARE, 5, 2)
cv.drawMarker(img, improved_pt, (0, 255, 0), cv.MARKER_SQUARE, 5, 2)
cv.drawMarker(img, state_pt, (255, 255, 255), cv.MARKER_STAR, 10, 1)
# forecast one step
cv.drawMarker(img, calc_point(np.dot(kalman.transitionMatrix, kalman.statePost)[0, 0]),
(255, 255, 0), cv.MARKER_SQUARE, 12, 1)
cv.line(img, state_pt, measurement_pt, (0, 0, 255), 1, cv.LINE_AA, 0) # red measurement error
cv.line(img, state_pt, predict_pt, (0, 255, 255), 1, cv.LINE_AA, 0) # yellow pre-meas error
cv.line(img, state_pt, improved_pt, (0, 255, 0), 1, cv.LINE_AA, 0) # green post-meas error
# update the real process
process_noise = sqrt(kalman.processNoiseCov[0, 0]) * np.random.randn(2, 1)
state = np.dot(kalman.transitionMatrix, state) + process_noise # x_k+1 = F x_k + w_k
cv.imshow("Kalman", img)
code = cv.waitKey(100)
code = cv.waitKey(1000)
if code != -1:
break
samples/python/lk_homography.py
+3 -3
View File
@@ -77,8 +77,8 @@ class App:
for (x0, y0), (x1, y1), good in zip(self.p0[:,0], self.p1[:,0], status[:,0]):
if good:
cv.line(vis, (x0, y0), (x1, y1), (0, 128, 0))
cv.circle(vis, (x1, y1), 2, (red, green)[good], -1)
cv.line(vis, (int(x0), int(y0)), (int(x1), int(y1)), (0, 128, 0))
cv.circle(vis, (int(x1), int(y1)), 2, (red, green)[good], -1)
draw_str(vis, (20, 20), 'track count: %d' % len(self.p1))
if self.use_ransac:
draw_str(vis, (20, 40), 'RANSAC')
@@ -86,7 +86,7 @@ class App:
p = cv.goodFeaturesToTrack(frame_gray, **feature_params)
if p is not None:
for x, y in p[:,0]:
cv.circle(vis, (x, y), 2, green, -1)
cv.circle(vis, (int(x), int(y)), 2, green, -1)
draw_str(vis, (20, 20), 'feature count: %d' % len(p))
cv.imshow('lk_homography', vis)
samples/python/lk_track.py
+1 -1
View File
@@ -65,7 +65,7 @@ class App:
if len(tr) > self.track_len:
del tr[0]
new_tracks.append(tr)
cv.circle(vis, (x, y), 2, (0, 255, 0), -1)
cv.circle(vis, (int(x), int(y)), 2, (0, 255, 0), -1)
self.tracks = new_tracks
cv.polylines(vis, [np.int32(tr) for tr in self.tracks], False, (0, 255, 0))
draw_str(vis, (20, 20), 'track count: %d' % len(self.tracks))
samples/python/morphology.py
+5 -2
View File
@@ -50,8 +50,11 @@ def main():
cur_str_mode = str_modes.next()
def update(dummy=None):
sz = cv.getTrackbarPos('op/size', 'morphology')
iters = cv.getTrackbarPos('iters', 'morphology')
try: # do not get trackbar position while trackbar is not created
sz = cv.getTrackbarPos('op/size', 'morphology')
iters = cv.getTrackbarPos('iters', 'morphology')
except:
return
opers = cur_mode.split('/')
if len(opers) > 1:
sz = sz - 10
samples/python/stitching_detailed.py
+2 -1
View File
@@ -450,7 +450,8 @@ def main():
cameras[i].focal *= compose_work_aspect
cameras[i].ppx *= compose_work_aspect
cameras[i].ppy *= compose_work_aspect
sz = (full_img_sizes[i][0] * compose_scale, full_img_sizes[i][1] * compose_scale)
sz = (int(round(full_img_sizes[i][0] * compose_scale)),
int(round(full_img_sizes[i][1] * compose_scale)))
K = cameras[i].K().astype(np.float32)
roi = warper.warpRoi(sz, K, cameras[i].R)
corners.append(roi[0:2])
samples/python/video_v4l2.py
+2 -2
View File
@@ -30,7 +30,7 @@ def main():
color = (0, 255, 0)
cap = cv.VideoCapture(0)
cap.set(cv.CAP_PROP_AUTOFOCUS, False) # Known bug: https://github.com/opencv/opencv/pull/5474
cap.set(cv.CAP_PROP_AUTOFOCUS, 0) # Known bug: https://github.com/opencv/opencv/pull/5474
cv.namedWindow("Video")
@@ -67,7 +67,7 @@ def main():
break
elif k == ord('g'):
convert_rgb = not convert_rgb
cap.set(cv.CAP_PROP_CONVERT_RGB, convert_rgb)
cap.set(cv.CAP_PROP_CONVERT_RGB, 1 if convert_rgb else 0)
print('Done')