In [2]:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import skvideo.io
import cv2
import scipy.ndimage
from matplotlib import animation, rc
import fisheye
%matplotlib inline
In [61]:
df = pd.read_csv('R:/output.csv')
In [62]:
df.orientation_x.apply(lambda x: x if (x < 180.0) else x - 360.0).plot()
Out[62]:
<matplotlib.axes._subplots.AxesSubplot at 0x1d6537b4f28>
In [3]:
filename = 'C:/Users/corey/Desktop/output.mp4'
videodata = skvideo.io.vread(filename)
print(videodata.shape)
(6203, 480, 640, 3)
In [9]:
checkerboard_filename = 'C:/Users/corey/Desktop/checkerboard2.mp4'
checkerboard_videodata = skvideo.io.vread(checkerboard_filename)
print(checkerboard_videodata.shape)
(252, 480, 640, 3)
In [66]:
plt.imshow(videodata[200])
Out[66]:
<matplotlib.image.AxesImage at 0x209a04e6518>
In [67]:
img = videodata[200]
color = ('r','g','b')
for i,col in enumerate(color):
histr = cv2.calcHist([img],[i],None,[256],[1,256])
plt.plot(histr,color = col)
plt.xlim([1,256])
plt.show()
In [44]:
plt.imshow(img[:][:][1])
Out[44]:
<matplotlib.image.AxesImage at 0x1d5eb317780>
In [59]:
df.steering_servo_voltage.plot()
Out[59]:
<matplotlib.axes._subplots.AxesSubplot at 0x1d6536d1b70>
In [60]:
pd.DataFrame({'enc0': df.encoder0_count.cumsum(), 'enc1': df.encoder1_count.cumsum()}).plot()
Out[60]:
<matplotlib.axes._subplots.AxesSubplot at 0x1d6536dd198>
In [66]:
pd.DataFrame({'enc0': df.encoder0_count.cumsum(), 'enc1': df.encoder1_count.cumsum()}).plot()
Out[66]:
<matplotlib.axes._subplots.AxesSubplot at 0x1d5eb1f8f28>
In [69]:
pd.DataFrame({'x': df.acceleration_x.cumsum().cumsum(), 'y': df.acceleration_y.cumsum().cumsum(), 'z': df.acceleration_z.cumsum().cumsum()}).plot()
Out[69]:
<matplotlib.axes._subplots.AxesSubplot at 0x1d5eb4432b0>
In [71]:
pd.DataFrame({'x': df.acceleration_x}).plot()
Out[71]:
<matplotlib.axes._subplots.AxesSubplot at 0x1d5ef400438>
In [72]:
plt.imshow(img)
Out[72]:
<matplotlib.image.AxesImage at 0x1d5f0c1c160>
In [79]:
edges = cv2.Canny(img,100,110)
plt.subplot(121),plt.imshow(img,cmap = 'gray')
plt.title('Original Image'), plt.xticks([]), plt.yticks([])
plt.subplot(122),plt.imshow(edges,cmap = 'gray')
plt.title('Edge Image'), plt.xticks([]), plt.yticks([])
plt.show()
In [26]:
hsv = cv2.cvtColor(img, cv2.COLOR_RGB2HSV)
yuv = cv2.cvtColor(img, cv2.COLOR_RGB2YUV)
In [59]:
plt.imshow(yuv[:,:,2]<65)
Out[59]:
<matplotlib.image.AxesImage at 0x2099e369ba8>
In [50]:
np.max(yuv[:,:,1])
Out[50]:
181
In [76]:
img = videodata[260]
kernel = np.ones((5,5),np.float32)/25
sharpen = np.array((
[0, -1, 0],
[-1, 5, -1],
[0, -1, 0]), dtype="int")
dst = cv2.filter2D(img,-1,sharpen)
plt.figure(figsize=(20,10))
plt.subplot(121),plt.imshow(img),plt.title('Original')
plt.xticks([]), plt.yticks([])
plt.subplot(122),plt.imshow(dst),plt.title('Sharpen')
plt.xticks([]), plt.yticks([])
plt.show()
In [10]:
plt.figure(figsize=(10,10))
plt.imshow(checkerboard_videodata[200])
Out[10]:
<matplotlib.image.AxesImage at 0x2b6b53d8080>
In [8]:
#img = checkerboard_videodata[201]
img = scipy.ndimage.imread('R:/marv/bin/experiments/calib/output14.jpg')
resized_img = cv2.resize(img, (640, 480))
gray = cv2.cvtColor(img,cv2.COLOR_RGB2GRAY)
plt.imshow(gray)
Out[8]:
<matplotlib.image.AxesImage at 0x1fdc9d93f98>
In [9]:
found, corners = cv2.findChessboardCorners(gray, (9,6))
found, corners
Out[9]:
(True, array([[[ 387.44219971, 268.0340271 ]],
[[ 391.41043091, 243.48387146]],
[[ 395.67733765, 218.22341919]],
[[ 399.10699463, 194.78514099]],
[[ 403.46136475, 171.52355957]],
[[ 408.42980957, 149.73109436]],
[[ 412.70275879, 128.1264801 ]],
[[ 416.98709106, 107.19924927]],
[[ 422.31445312, 87.37377167]],
[[ 412.9715271 , 271.77844238]],
[[ 416.71731567, 246.59608459]],
[[ 420.33361816, 222.90267944]],
[[ 424.14169312, 199.06938171]],
[[ 427.97311401, 176.79702759]],
[[ 432.49575806, 154.10198975]],
[[ 436.6272583 , 132.62072754]],
[[ 440.78225708, 112.44959259]],
[[ 445.11633301, 92.59114075]],
[[ 438.40213013, 275.12524414]],
[[ 441.66421509, 250.65486145]],
[[ 444.50204468, 226.92904663]],
[[ 448.43789673, 203.8258667 ]],
[[ 452.1628418 , 180.51913452]],
[[ 456.23260498, 159.25093079]],
[[ 459.38140869, 137.66705322]],
[[ 464.06546021, 117.53083038]],
[[ 467.55648804, 97.6620636 ]],
[[ 463.19403076, 279.06933594]],
[[ 465.69662476, 254.70510864]],
[[ 469.2593689 , 231.51441956]],
[[ 472.66159058, 207.79263306]],
[[ 476.0241394 , 185.54328918]],
[[ 479.47949219, 164.09933472]],
[[ 483.29983521, 142.60305786]],
[[ 486.08319092, 122.61242676]],
[[ 489.65936279, 103.49211121]],
[[ 487.43414307, 282.99273682]],
[[ 490.02850342, 259.05764771]],
[[ 493.04766846, 235.56634521]],
[[ 495.67828369, 212.64128113]],
[[ 498.97409058, 190.50820923]],
[[ 502.1880188 , 168.79162598]],
[[ 505.36965942, 148.11380005]],
[[ 508.36398315, 128.21234131]],
[[ 511.58361816, 108.76273346]],
[[ 511.45184326, 287.16229248]],
[[ 513.41583252, 263.13803101]],
[[ 516.04724121, 240.18229675]],
[[ 518.60461426, 218.03375244]],
[[ 521.75994873, 195.74468994]],
[[ 524.70935059, 174.0627594 ]],
[[ 527.32373047, 153.73588562]],
[[ 530.07501221, 134.00009155]],
[[ 533.08062744, 114.60292816]]], dtype=float32))
In [344]:
plt.figure(figsize=(10,10))
plt.imshow(cv2.drawChessboardCorners(gray, patternSize=(6,7), corners=corners, patternWasFound=found))
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-344-d409493cb364> in <module>()
1 plt.figure(figsize=(10,10))
----> 2 plt.imshow(cv2.drawChessboardCorners(gray, patternSize=(6,7), corners=corners, patternWasFound=found))
NameError: name 'found' is not defined
<matplotlib.figure.Figure at 0x2cd8b0ab9b0>
In [341]:
img = scipy.ndimage.imread('D:/Downloads/opencv-3.3.0/samples/data/left01.jpg')
gray = img
In [342]:
plt.imshow(img)
Out[342]:
<matplotlib.image.AxesImage at 0x2cd8b07ca90>
In [343]:
objp = np.zeros((6*7,3), np.float32)
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)
objp[:,:2] = np.mgrid[0:7,0:6].T.reshape(-1,2)
objpoints = []
imgpoints = []
cv2.cornerSubPix(gray,corners,(11,11),(-1,-1),criteria)
objpoints.append(objp)
imgpoints.append(corners)
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1],None,None)
h, w = img.shape[:2]
newcameramtx, roi=cv2.getOptimalNewCameraMatrix(mtx,dist,(w,h),1,(w,h))
print(roi)
plt.imshow(gray)
---------------------------------------------------------------------------
error Traceback (most recent call last)
<ipython-input-343-3b523d3772b4> in <module>()
7 objpoints.append(objp)
8 imgpoints.append(corners)
----> 9 ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1],None,None)
10 h, w = img.shape[:2]
11 newcameramtx, roi=cv2.getOptimalNewCameraMatrix(mtx,dist,(w,h),1,(w,h))
error: ..\..\..\modules\calib3d\src\calibration.cpp:3064: error: (-215) ni == ni1 in function cv::collectCalibrationData
In [66]:
dst = cv2.undistort(img, mtx, dist, None, newcameramtx)
x,y,w,h = roi
print(x, y, w, h)
dst = dst[y:y+h, x:x+w]
plt.imshow(dst)
0 0 0 0
C:\Users\corey\Anaconda2\envs\rustler\lib\site-packages\matplotlib\axes\_base.py:2917: UserWarning: Attempting to set identical left==right results
in singular transformations; automatically expanding.
left=-0.5, right=-0.5
'left=%s, right=%s') % (left, right))
C:\Users\corey\Anaconda2\envs\rustler\lib\site-packages\matplotlib\axes\_base.py:3193: UserWarning: Attempting to set identical bottom==top results
in singular transformations; automatically expanding.
bottom=-0.5, top=-0.5
'bottom=%s, top=%s') % (bottom, top))
Out[66]:
<matplotlib.image.AxesImage at 0x2b6c7b3c780>
In [67]:
mapx,mapy = cv2.initUndistortRectifyMap(mtx,dist,None,newcameramtx,(1280,960),5)
dst = cv2.remap(img,mapx,mapy,cv2.INTER_LINEAR)
x,y,w,h = roi
dst = dst[y:y+h, x:x+w]
plt.imshow(dst)
C:\Users\corey\Anaconda2\envs\rustler\lib\site-packages\matplotlib\axes\_base.py:2917: UserWarning: Attempting to set identical left==right results
in singular transformations; automatically expanding.
left=-0.5, right=-0.5
'left=%s, right=%s') % (left, right))
C:\Users\corey\Anaconda2\envs\rustler\lib\site-packages\matplotlib\axes\_base.py:3193: UserWarning: Attempting to set identical bottom==top results
in singular transformations; automatically expanding.
bottom=-0.5, top=-0.5
'bottom=%s, top=%s') % (bottom, top))
Out[67]:
<matplotlib.image.AxesImage at 0x2b6c7ba0f60>
In [3]:
import numpy as np
import cv2
import glob
# termination criteria
criteria = (cv2.TERM_CRITERIA_EPS + cv2.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((6*9,3), np.float32)
objp[:,:2] = np.mgrid[0:9,0:6].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.
images = glob.glob('R:/marv/bin/experiments/calib/*.jpg')
for fname in images:
print(fname)
img = cv2.imread(fname)
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:
print('found')
objpoints.append(objp)
cv2.cornerSubPix(gray,corners,(11,11),(-1,-1),criteria)
imgpoints.append(corners)
# Draw and display the corners
cv2.drawChessboardCorners(img, (9,6), corners,ret)
plt.imshow(img)
#cv2.waitKey(500)
#cv2.destroyAllWindows()
R:/marv/bin/experiments/calib\output13.jpg
found
R:/marv/bin/experiments/calib\output07.jpg
found
R:/marv/bin/experiments/calib\output17.jpg
found
R:/marv/bin/experiments/calib\output03.jpg
found
R:/marv/bin/experiments/calib\output16.jpg
found
R:/marv/bin/experiments/calib\output15.jpg
found
R:/marv/bin/experiments/calib\output09.jpg
found
R:/marv/bin/experiments/calib\output01.jpg
found
R:/marv/bin/experiments/calib\output10.jpg
found
R:/marv/bin/experiments/calib\output00.jpg
R:/marv/bin/experiments/calib\output08.jpg
found
R:/marv/bin/experiments/calib\output02.jpg
found
R:/marv/bin/experiments/calib\output04.jpg
found
R:/marv/bin/experiments/calib\output14.jpg
found
R:/marv/bin/experiments/calib\output18.jpg
found
R:/marv/bin/experiments/calib\output06.jpg
found
R:/marv/bin/experiments/calib\output19.jpg
R:/marv/bin/experiments/calib\output05.jpg
found
R:/marv/bin/experiments/calib\output11.jpg
found
R:/marv/bin/experiments/calib\output12.jpg
found
In [274]:
fname = 'R:/marv/bin/experiments/output.jpg'
img = cv2.imread(fname)
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1],None,None)
h, w = img.shape[:2]
newcameramtx, roi=cv2.getOptimalNewCameraMatrix(mtx,dist,(w,h),1,(w,h))
print(roi)
plt.imshow(gray)
(1028, 544, 384, 252)
Out[274]:
<matplotlib.image.AxesImage at 0x2cd8ae00a58>
In [18]:
img.shape[:2]
Out[18]:
(1440, 1920)
In [345]:
# undistort
img = cv2.imread('R:/marv/bin/experiments/calib/output01.jpg')
dst = cv2.undistort(img, mtx, dist, None, newcameramtx)
plt.figure(figsize=(10, 10))
# crop the image
x,y,w,h = roi
dst = dst[y:y+h, x:x+w]
cv2.imwrite('calibresult.png',dst)
plt.imshow(dst)
Out[345]:
<matplotlib.image.AxesImage at 0x2cd27c38be0>
In [346]:
img = cv2.imread(fname)
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
ret, mtx, dist, rvecs, tvecs = cv2.fisheye.calibrate(objpoints, imgpoints, gray.shape[::-1],None,None)
h, w = img.shape[:2]
newcameramtx, roi=cv2.fisheye.estimateNewCameraMatrixForUndistortRectify(mtx,dist,(w,h),1,(w,h))
print(roi)
plt.imshow(gray)
---------------------------------------------------------------------------
error Traceback (most recent call last)
<ipython-input-346-4a2c002af7ac> in <module>()
1 img = cv2.imread(fname)
2 gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
----> 3 ret, mtx, dist, rvecs, tvecs = cv2.fisheye.calibrate(objpoints, imgpoints, gray.shape[::-1],None,None)
4 h, w = img.shape[:2]
5 newcameramtx, roi=cv2.fisheye.estimateNewCameraMatrixForUndistortRectify(mtx,dist,(w,h),1,(w,h))
error: ..\..\..\modules\calib3d\src\fisheye.cpp:695: error: (-215) objectPoints.type() == CV_32FC3 || objectPoints.type() == CV_64FC3 in function cv::fisheye::calibrate
In [31]:
objpoints, imgpoints
Out[31]:
([array([[ 0., 0., 0.],
[ 1., 0., 0.],
[ 2., 0., 0.],
[ 3., 0., 0.],
[ 4., 0., 0.],
[ 5., 0., 0.],
[ 6., 0., 0.],
[ 7., 0., 0.],
[ 8., 0., 0.],
[ 0., 1., 0.],
[ 1., 1., 0.],
[ 2., 1., 0.],
[ 3., 1., 0.],
[ 4., 1., 0.],
[ 5., 1., 0.],
[ 6., 1., 0.],
[ 7., 1., 0.],
[ 8., 1., 0.],
[ 0., 2., 0.],
[ 1., 2., 0.],
[ 2., 2., 0.],
[ 3., 2., 0.],
[ 4., 2., 0.],
[ 5., 2., 0.],
[ 6., 2., 0.],
[ 7., 2., 0.],
[ 8., 2., 0.],
[ 0., 3., 0.],
[ 1., 3., 0.],
[ 2., 3., 0.],
[ 3., 3., 0.],
[ 4., 3., 0.],
[ 5., 3., 0.],
[ 6., 3., 0.],
[ 7., 3., 0.],
[ 8., 3., 0.],
[ 0., 4., 0.],
[ 1., 4., 0.],
[ 2., 4., 0.],
[ 3., 4., 0.],
[ 4., 4., 0.],
[ 5., 4., 0.],
[ 6., 4., 0.],
[ 7., 4., 0.],
[ 8., 4., 0.],
[ 0., 5., 0.],
[ 1., 5., 0.],
[ 2., 5., 0.],
[ 3., 5., 0.],
[ 4., 5., 0.],
[ 5., 5., 0.],
[ 6., 5., 0.],
[ 7., 5., 0.],
[ 8., 5., 0.]], dtype=float32), array([[ 0., 0., 0.],
[ 1., 0., 0.],
[ 2., 0., 0.],
[ 3., 0., 0.],
[ 4., 0., 0.],
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[ 0., 1., 0.],
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[ 0., 3., 0.],
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[ 3., 3., 0.],
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[ 5., 3., 0.],
[ 6., 3., 0.],
[ 7., 3., 0.],
[ 8., 3., 0.],
[ 0., 4., 0.],
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In [392]:
import fisheye
import os
import glob
import cv2
base_path = r'R:/marv/bin/experiments/calib640'
NX, NY = 9, 6
imgs_paths = glob.glob(os.path.join(base_path, '*.jpg'))
fe = fisheye.FishEye(nx=NX, ny=NY, verbose=True)
rms, K, D, rvecs, tvecs = fe.calibrate(
imgs_paths,
show_imgs=True
)
fe.save('./calib.dat')
In [353]:
img = cv2.imread(fname)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
undist_img = fe.undistort(img, undistorted_size=(1920, 1440))
plt.imsave('C:/Users/corey/Desktop/undist_img.jpg', undist_img)
plt.figure(figsize=(20,20))
plt.subplot(1,2,1)
plt.imshow(img)
plt.subplot(1,2,2)
plt.imshow(undist_img)
Out[353]:
<matplotlib.image.AxesImage at 0x2cd90f159b0>
In [30]:
yuv = cv2.cvtColor(undist_img, cv2.COLOR_RGB2YUV)
plt.imshow(yuv[:,:,2], cmap="hot")
Out[30]:
<matplotlib.image.AxesImage at 0x2cd345502b0>
In [33]:
gray = np.float32(gray)
dst = cv2.cornerHarris(gray,2,3,0.04)
dst = cv2.dilate(dst,None)
ret, dst = cv2.threshold(dst,0.01*dst.max(),255,0)
dst = np.uint8(dst)
plt.figure(figsize=(20,20))
plt.subplot(1,2,1)
plt.imshow(gray)
plt.subplot(1,2,2)
plt.imshow(dst)
Out[33]:
<matplotlib.image.AxesImage at 0x2cd30eef518>
In [76]:
gray = cv2.bilateralFilter(yuv[:,:,2], 11, 17, 17)
edged = cv2.Canny(gray, 30, 70)
plt.figure(figsize=(20,20))
plt.subplot(1,2,1)
plt.imshow(gray)
plt.subplot(1,2,2)
plt.imshow(edged)
Out[76]:
<matplotlib.image.AxesImage at 0x2cd54dedb38>
In [81]:
im2, contours, hierarchy = cv2.findContours(edged.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
im3 = cv2.drawContours(im2, contours, 0, (0,255,0), 3)
plt.imshow(im3)
Out[81]:
<matplotlib.image.AxesImage at 0x2cd53a3f860>
In [57]:
result[2]
Out[57]:
array([[[ 1, -1, -1, -1],
[ 2, 0, -1, -1],
[ 3, 1, -1, -1],
[-1, 2, -1, -1]]], dtype=int32)
In [218]:
M = np.array([[4.0, 4.0, -4500.0],
[0.0, 15.0, -8000.0],
[0.0, 0.0004, 1.0]])
warped = cv2.warpPerspective(gray, M, (gray.shape[1],gray.shape[0]))
plt.figure(figsize=(20,20))
plt.subplot(1,2,1)
plt.imshow(gray)
plt.subplot(1,2,2)
plt.imshow(warped)
Out[218]:
<matplotlib.image.AxesImage at 0x2cd80125978>
In [364]:
src_pts = np.array([[719,836],
[904,471],
[1000,471],
[1254,836]])
dst_pts = np.array([[850,1900],
[850,100],
[1110,100],
[1110,1900]])
M2, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC,5.0)
In [365]:
M2
Out[365]:
array([[ -5.52614876e-01, -2.45299280e+00, 1.48148740e+03],
[ 5.65036628e-15, -5.86330789e+00, 2.74121377e+03],
[ 3.00666152e-18, -2.55635332e-03, 1.00000000e+00]])
In [366]:
warped = cv2.warpPerspective(undist_img, M2, (2000,2000))
plt.figure(figsize=(12,12))
plt.subplot(1,2,1)
plt.imshow(img)
plt.subplot(1,2,2)
plt.imshow(warped)
Out[366]:
<matplotlib.image.AxesImage at 0x2cdae9207f0>
In [372]:
warped_hsv = cv2.cvtColor(warped, cv2.COLOR_RGB2HSV)
plt.figure(figsize=(12,12))
plt.subplot(1,3,1)
plt.imshow(warped_hsv[:,:,0])
plt.subplot(1,3,2)
plt.imshow(warped_hsv[:,:,1])
plt.subplot(1,3,3)
plt.imshow(warped_hsv[:,:,2])
Out[372]:
<matplotlib.image.AxesImage at 0x2cdbac51940>
In [325]:
warped_hsv[1000,1500,0]
Out[325]:
21
In [371]:
warped_cmy = cv2.cvtColor(warped, cv2.COLOR_RGB2YCR_CB)
plt.figure(figsize=(12,12))
plt.subplot(1,3,1)
plt.imshow(warped_cmy[:,:,0])
plt.subplot(1,3,2)
plt.imshow(warped_cmy[:,:,1])
plt.subplot(1,3,3)
plt.imshow(warped_cmy[:,:,2])
Out[371]:
<matplotlib.image.AxesImage at 0x2cdbab0c8d0>
In [379]:
plt.imshow(warped_cmy[:,:,2]<98)
Out[379]:
<matplotlib.image.AxesImage at 0x2cdbc7c0e48>
In [389]:
plt.imshow((warped_hsv[:,:,1]>200)&(warped_cmy[:,:,2]<98))
Out[389]:
<matplotlib.image.AxesImage at 0x2cdbf310c88>
In [ ]:
Content source: coreyabshire/marv
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