def main():
img_path = '../Data/stereo/centre/'
imgs = sorted(os.listdir(img_path))
fx, fy, cx, cy, G_camera_image, LUT = ReadCameraModel('../Data/model')
i = 100
img1 = cv2.imread(img_path + imgs[i], -1)
img1 = cv2.cvtColor(img1, cv2.COLOR_BAYER_GR2BGR)
img1 = UndistortImage(img1, LUT)
img2 = cv2.imread(img_path + imgs[i + 1], -1)
img2 = cv2.cvtColor(img2, cv2.COLOR_BAYER_GR2BGR)
img2 = UndistortImage(img2, LUT)
x1, x2 = utils.getMatchingFeaturePoints(img1, img2)
x1 = np.hstack([x1, np.ones((x1.shape[0], 1))])
x2 = np.hstack([x2, np.ones((x2.shape[0], 1))])
print(x1.shape)
print(x2.shape)
F = EstimateFundamentalMatrix(x1, x2)
print("Calculated")
print(F)
# print(F_n)
print("CV2")
F, _ = cv2.findFundamentalMat(x1, x2)
print(F)
def main():
img_path = '../Data/stereo/centre/'
imgs = sorted(os.listdir(img_path))
fx, fy, cx, cy, G_camera_image, LUT = ReadCameraModel('../Data/model')
i = 100
img1 = cv2.imread(img_path + imgs[i], -1)
img1 = cv2.cvtColor(img1, cv2.COLOR_BAYER_GR2BGR)
img1 = UndistortImage(img1, LUT)
img2 = cv2.imread(img_path + imgs[i + 1], -1)
img2 = cv2.cvtColor(img2, cv2.COLOR_BAYER_GR2BGR)
img2 = UndistortImage(img2, LUT)
x1, x2 = utils.getMatchingFeaturePoints(img1, img2)
x1 = np.hstack([x1, np.ones((x1.shape[0], 1))])
x2 = np.hstack([x2, np.ones((x2.shape[0], 1))])
# print(x1.shape)
# print(x2.shape)
features = np.hstack([x1, x2])
# getInliersRANSAC(features,threshold=(0.07),size=8,num_inliers=0.6*features.shape[0],num_iters=1000)
x1_in, x2_in = RANSAC.getInliersRANSAC(features,
threshold=(0.005),
size=8,
num_inliers=0.6 * features.shape[0],
num_iters=200)
fund_mtx = fundamental.EstimateFundamentalMatrix(x1_in, x2_in)
K = np.array([[fx, 0, cx], [0, fy, cy], [0, 0, 1]])
ess_mtx = essential.EssentialMatrixFromFundamentalMatrix(fund_mtx, K)
print("Essential Matrix")
print(ess_mtx)
C, R = ExtractCameraPose(ess_mtx)
print("Pose Orientation :")
print(R)
E_act = cv2.findEssentialMat(x1_in[:, :2], x2_in[:, :2], K)
# _,R,T,_ = cv2.recoverPose(E_act[0],x1_in[:,:2],x2_in[:,:2])
# print("Pose Position :")
# print(T.T)
# print("Pose Orientation :")
# print(R)
R1, R2, T = cv2.decomposeEssentialMat(E_act[0])
print("OpenCV R1")
print(R1)
print("OpenCV R2")
print(R2)
print("Calculated Pose Position :")
print(C)
print("Opencv T")
print(T.T)
def image_process(H_init, p_0, all_images):
data_points = []
for index in range(20, len(all_images) - 1):
print("image_number = ", index)
img1 = cv2.imread("stereo/centre/" + str(all_images[index]),
0) #loading first image in greyscale
img2 = cv2.imread("stereo/centre/" + str(all_images[index + 1]),
0) #loading second image in greyscale
colorimage1 = cv2.cvtColor(
img1,
cv2.COLOR_BayerGR2BGR) #converting first image from BayerGR to BGR
colorimage2 = cv2.cvtColor(
img2, cv2.COLOR_BayerGR2BGR
) #converting second image from BayerGR to BGR
undistortedimage1 = UndistortImage(
colorimage1, LUT) #undistorting first image using LUT
undistortedimage2 = UndistortImage(
colorimage2, LUT) #undistorting second image using LUT
gray1 = cv2.cvtColor(undistortedimage1,
cv2.COLOR_BGR2GRAY) #converting back to grayscale
gray2 = cv2.cvtColor(undistortedimage2,
cv2.COLOR_BGR2GRAY) #converting back to grayscale
# Finding proper matches between two frames
features1, features2 = SIFT_matches(gray1, gray2)
#using inbuilt function to directly compute F_matrix after RANSAC
FinalFundamentalMatrix, m = cv2.findFundamentalMat(
features1, features2, cv2.FM_RANSAC)
features1 = features1[m.ravel() == 1]
features2 = features2[m.ravel() == 1]
# Calculating the Essential matrix
E_matrix = K.T @ FinalFundamentalMatrix @ K
# Obtaining final Rotational and Translation pose for cameras
retval, rot_mat, T, mask = cv2.recoverPose(E_matrix, features1,
features2, K)
H_init = H_init @ H_matrix(
rot_mat, T
) #matmul between H_init(Ident 4x4) and H_matrix that was calculated using new Rot and Trans matrix
p_projection = H_init @ p_0 #new projection point calculated
data_points.append([p_projection[0][0], -p_projection[2][0]])
plt.scatter(p_projection[0][0], -p_projection[2][0], color='g')
return data_points
def data_prep(data_img):
img_1 = cv2.cvtColor(data_img[x], cv2.COLOR_BAYER_GR2BGR)
img_2 = cv2.cvtColor(data_img[x + 1], cv2.COLOR_BAYER_GR2BGR)
fx, fy, cx, cy, G_C_1, LUT1 = ReadCameraModel('Oxford_dataset/model')
undimg_1 = UndistortImage(img_1, LUT1)
undimg_1 = cv2.cvtColor(undimg_1, cv2.COLOR_BGR2GRAY)
imgeqi_1 = cv2.equalizeHist(undimg_1)
fx, fy, cx, cy, G_C_2, LUT2 = ReadCameraModel('Oxford_dataset/model')
undimg_2 = UndistortImage(img_2, LUT2)
undimg_2 = cv2.cvtColor(undimg_2, cv2.COLOR_BGR2GRAY)
imgeqi_2 = cv2.equalizeHist(undimg_2)
return fx, fy, cx, cy, imgeqi_1, imgeqi_2
def preprocess(self, image, nextimage):
rgb1 = cv2.cvtColor(image, cv2.COLOR_BAYER_GR2BGR)
rgb2 = cv2.cvtColor(nextimage, cv2.COLOR_BAYER_GR2BGR)
undimg_1 = UndistortImage(rgb1, self.LUT1)
undimg_1 = cv2.cvtColor(undimg_1, cv2.COLOR_BGR2GRAY)
undimg_2 = UndistortImage(rgb2, self.LUT2)
undimg_2 = cv2.cvtColor(undimg_2, cv2.COLOR_BGR2GRAY)
imgeqi_1 = cv2.equalizeHist(undimg_1)
# imgeqi_1 = (imgeqi_1[0:800, 0:1280].copy())
imgeqi_2 = cv2.equalizeHist(undimg_2)
# imgeqi_2 = (imgeqi_2[0:800, 0:1280].copy())
return imgeqi_1, imgeqi_2
def preprocess_data(img_path, name, LUT):
im = cv2.imread(os.path.join(img_path, name), 0)
BGR_im = cv2.cvtColor(im, cv2.COLOR_BayerGR2BGR)
un_im = UndistortImage(BGR_im, LUT)
un_im = cv2.cvtColor(un_im, cv2.COLOR_BGR2GRAY)
un_im = un_im[200:650, :]
return un_im
def main():
img_path = '../Data/stereo/centre/'
imgs = sorted(os.listdir(img_path))
fx, fy, cx, cy, G_camera_image, LUT = ReadCameraModel('../Data/model')
i = 100
img1 = cv2.imread(img_path + imgs[i], -1)
img1 = cv2.cvtColor(img1, cv2.COLOR_BAYER_GR2BGR)
img1 = UndistortImage(img1, LUT)
img2 = cv2.imread(img_path + imgs[i + 1], -1)
img2 = cv2.cvtColor(img2, cv2.COLOR_BAYER_GR2BGR)
img2 = UndistortImage(img2, LUT)
x1, x2 = utils.getMatchingFeaturePoints(img1, img2)
x1 = np.hstack([x1, np.ones((x1.shape[0], 1))])
x2 = np.hstack([x2, np.ones((x2.shape[0], 1))])
features = np.hstack([x1, x2])
x1_in, x2_in = RANSAC.getInliersRANSAC(features,
threshold=(0.005),
size=8,
num_inliers=0.6 * features.shape[0],
num_iters=200)
fund_mtx = fundamental.EstimateFundamentalMatrix(x1_in, x2_in)
K = np.array([[fx, 0, cx], [0, fy, cy], [0, 0, 1]])
ess_mtx = essential.EssentialMatrixFromFundamentalMatrix(fund_mtx, K)
C, R = cameraPose.ExtractCameraPose(ess_mtx)
X_tri = []
for j in range(4):
X_tri.append(
linearTriangulation(K, np.zeros(3), C[j], np.eye(3), R[j], x1_in,
x2_in))
X_tri = np.array(X_tri)
print(X_tri.shape)
sys.exit(0)
def main():
img_path = '../Data/stereo/centre/'
imgs = sorted(os.listdir(img_path))
fx, fy, cx, cy, G_camera_image, LUT = ReadCameraModel('../Data/model')
i = 100
img1 = cv2.imread(img_path + imgs[i], -1)
img1 = cv2.cvtColor(img1, cv2.COLOR_BAYER_GR2BGR)
img1 = UndistortImage(img1, LUT)
img2 = cv2.imread(img_path + imgs[i + 1], -1)
img2 = cv2.cvtColor(img2, cv2.COLOR_BAYER_GR2BGR)
img2 = UndistortImage(img2, LUT)
x1, x2 = utils.getMatchingFeaturePoints(img1, img2)
x1 = np.hstack([x1, np.ones((x1.shape[0], 1))])
x2 = np.hstack([x2, np.ones((x2.shape[0], 1))])
# print(x1.shape)
# print(x2.shape)
features = np.hstack([x1, x2])
# getInliersRANSAC(features,threshold=(0.07),size=8,num_inliers=0.6*features.shape[0],num_iters=1000)
x1_in, x2_in = RANSAC.getInliersRANSAC(features,
threshold=(0.005),
size=8,
num_inliers=0.6 * features.shape[0],
num_iters=1000)
fund_mtx = fundamental.EstimateFundamentalMatrix(x1_in, x2_in)
K = np.array([[fx, 0, cx], [0, fy, cy], [0, 0, 1]])
E = EssentialMatrixFromFundamentalMatrix(fund_mtx, K)
print("Essential Matrix")
print(E)
E_act = cv2.findEssentialMat(x1_in[:, :2], x2_in[:, :2], K)
print("E actual")
print(E_act[0])
def preprocess_data(img_path, name, LUT):
im = cv2.imread(os.path.join(img_path, name), 0)
BGR_im = cv2.cvtColor(im, cv2.COLOR_BayerGR2BGR)
un_im = UndistortImage(BGR_im, LUT)
# un_im = un_im[200:650, :]
# cv2.imshow("image", un_im)
# if cv2.waitKey(0) & 0xff == 27:
# cv2.destroyAllWindows()
# h, w = im.shape
# dim = (int(0.6*w), int(0.6*h))
# un_im = cv2.resize(un_im, dim)
return un_im
def undistortImage(path_to_model, path_to_images):
'''
Here, we undistort the dataset images
:param path_to_model:
:param path_to_images:
:return: K - calibration matrix
'''
# Read camera parameters
fx, fy, cx, cy, G_camera_image, LUT = ReadCameraModel(path_to_model)
# iterate through each image, convert to BGR, undistort(function takes all channels in input)
images = glob.glob(path_to_images + "/*.png")
images.sort()
for cnt, image in enumerate(images):
frame = cv2.imread(image, -1)
frame_RGB = cv2.cvtColor(frame, cv2.COLOR_BayerGR2BGR)
undistorted_image = UndistortImage(frame_RGB, LUT)
cv2.imwrite("./undistort/frame" + str(cnt) + ".png", undistorted_image)
def raw2Undistorted(img_path, LUT):
img = cv2.imread(img_path, cv2.IMREAD_UNCHANGED)
img = cv2.cvtColor(img, cv2.COLOR_BayerGR2BGR)
img = UndistortImage(img, LUT)
return img
print(t_f[1,0])
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
for i in range(len(filenames) - 1):
if i<=40:
continue
f = filenames[i]
f1 = filenames[i + 1]
img1 = cv2.imread(f, 0)
img2 = cv2.imread(f1, 0)
img1 = cv2.cvtColor(img1, cv2.COLOR_BAYER_GR2RGB)
img1 = UndistortImage(img1, LUT)
img2 = cv2.cvtColor(img2, cv2.COLOR_BAYER_GR2RGB)
img2 = UndistortImage(img2, LUT)
img1GrayScale = cv2.cvtColor(img1, cv2.COLOR_RGB2GRAY)
img2GrayScale = cv2.cvtColor(img2, cv2.COLOR_RGB2GRAY)
pts1, pts2 = FindCorrespondence(img1GrayScale, img2GrayScale)
corr_list = np.concatenate((pts1, pts2), 1)
F, mI = RANSACforF(pts1, pts2, corr_list)
E = EstimateEssentialMatrix(cameraMatrix, F)
P1, P2, P3, P4, (C1, R1), (C2, R2), (C3, R3), (C4, R4) = EstimateCameraPose(E, cameraMatrix)
h = np.eye(4)
current_pos = np.zeros((3, 1))
current_rot = np.eye(3)
while a < (len(car_images) - 1):
# for i in range(len(car_images)):
#imageRaw1 = cv2.imread(images[i], cv2.IMREAD_GRAYSCALE | cv2.IMREAD_ANYDEPTH)
#rgb1 = cv2.cvtColor(imageRaw1, cv2.COLOR_BAYER_GR2BGR)
rgb1 = cv2.cvtColor(car_images[a], cv2.COLOR_BAYER_GR2BGR)
#imageRaw2 = cv2.imread(frame2, cv2.IMREAD_GRAYSCALE | cv2.IMREAD_ANYDEPTH)
#rgb2 = cv2.cvtColor(imageRaw2, cv2.COLOR_BAYER_GR2BGR)
rgb2 = cv2.cvtColor(car_images[a + 1], cv2.COLOR_BAYER_GR2BGR)
fx, fy, cx, cy, G_camera_image1, LUT1 = ReadCameraModel(
'Oxford_dataset/model')
undistorted_image1 = UndistortImage(rgb1, LUT1)
undistorted_image1 = cv2.cvtColor(undistorted_image1, cv2.COLOR_BGR2GRAY)
eqimage1 = cv2.equalizeHist(undistorted_image1)
fx, fy, cx, cy, G_camera_image2, LUT2 = ReadCameraModel(
'Oxford_dataset/model')
undistorted_image2 = UndistortImage(rgb2, LUT2)
undistorted_image2 = cv2.cvtColor(undistorted_image2, cv2.COLOR_BGR2GRAY)
eqimage2 = cv2.equalizeHist(undistorted_image2)
K = np.array([[fx, 0, cx], [0, fy, cy], [0, 0, 1]])
# Initiate STAR detector
orb = cv2.ORB_create()
def image_process(img):
color_image = cv2.cvtColor(img, cv2.COLOR_BayerGR2BGR)
color_image_undistorted = UndistortImage(color_image, LUT)
gray_image = cv2.cvtColor(color_image_undistorted, cv2.COLOR_BGR2GRAY)
return gray_image
for i in range(0, N):
img_current_frame = cv2.imread(centre_path[i], 0)
img_next_frame = cv2.imread(centre_path[i + 1], 0)
img_current_frame = cv2.rectangle(
img_current_frame,
(np.float32(50), np.float32(np.shape(img_current_frame)[0])),
(np.float32(1250), np.float32(800)), (0, 0, 0), -1)
img_next_frame = cv2.rectangle(
img_next_frame,
(np.float32(50), np.float32(np.shape(img_next_frame)[0])),
(np.float32(1250), np.float32(800)), (0, 0, 0), -1)
color_img = cv2.cvtColor(img_current_frame, cv2.COLOR_BayerGR2RGB)
color_img_next = cv2.cvtColor(img_next_frame, cv2.COLOR_BayerGR2RGB)
undist_img = UndistortImage(color_img, LUT)
undist_img_next = UndistortImage(color_img_next, LUT)
MIN_MATCH_COUNT = 10
# Initiate SIFT detector
sift = cv2.xfeatures2d.SIFT_create()
# find the keypoints and descriptors with SIFT
kp1, des1 = sift.detectAndCompute(undist_img, None)
kp2, des2 = sift.detectAndCompute(undist_img_next, None)
FLANN_INDEX_KDTREE = 0
index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5)
search_params = dict(checks=50)
flann = cv2.FlannBasedMatcher(index_params, search_params)
images = []
# Read and save camera intrinsic parameters
fx, fy, cx, cy, G_camera_image, LUT = ReadCameraModel(modelDirectory)
'''
K = np.array([[fx , 0 , cx],[0 , fy , cy],[0 , 0 , 1]])
f = open('Camera Intrinsics.txt','a')
f.write('K: ' + '\n' + str(K) + '\n\n')
f.write('G_camera_image: ' + '\n' + str(G_camera_image))
f.close()
'''
# Process all images in the image directory
for imageName in os.listdir(imageDirectory):
rawImage = cv2.imread(imageDirectory + imageName)
# Convert raw image to grayscale and demosaic into BGR image
gray = cv2.cvtColor(rawImage, cv2.COLOR_BGR2GRAY)
imageBGR = cv2.cvtColor(gray, cv2.COLOR_BayerGR2BGR)
# Undistort BGR image
undistoredImage = UndistortImage(imageBGR, LUT)
# Save to dataset folder
cv2.imwrite('dataset/' + imageName, undistoredImage)
import os
from UndistortImage import UndistortImage
from ReadCameraModel import ReadCameraModel
img = glob.glob('./stereo/centre/*.png')
img.sort()
print(img)
data = []
final = []
fx, fy, cx, cy, G_camera_image, LUT = ReadCameraModel("./model")
for i in range(len(img)):
temp = cv2.imread(img[i], 0)
data.append(temp)
count = 0
EXTN = ".png"
for j in range(len(data)):
count = count + 1
# if(count>50):
# break
pic = data[j]
print(count)
tempo = cv2.cvtColor(pic, cv2.COLOR_BayerGR2BGR)
temp_img = UndistortImage(tempo, LUT)
filename = str(count).zfill(4) + EXTN
cv2.imwrite("./undistorted_input_images/frame%s" % filename, temp_img)
# print(temp_img)
final.append(temp_img)
cv2.imshow("image", final[0])
cv2.waitKey(0)
cv2.destroyAllWindows()
def undistortImageToGray(img, LUT):
colorimage = cv2.cvtColor(img, cv2.COLOR_BayerGR2BGR)
undistortedimage = UndistortImage(colorimage, LUT)
gray = cv2.cvtColor(undistortedimage, cv2.COLOR_BGR2GRAY)
# equ = cv2.equalizeHist(gray1)
return gray
def main():
img_path = '../Data/stereo/centre/'
imgs = sorted(os.listdir(img_path))
fx, fy, cx, cy, G_camera_image, LUT = ReadCameraModel('../Data/model')
i = 100
pt_old = np.zeros((3, 1))
pt_old_cv = np.zeros((3, 1))
i = 15
while i < len(imgs):
# for i in range(15,len(imgs)):
# for i in range(500,600):
i = i + 3
print(i)
img1 = cv2.imread(img_path + imgs[i])
img1 = cv2.cvtColor(img1, cv2.COLOR_BAYER_GR2BGR)
img1 = UndistortImage(img1, LUT)
img2 = cv2.imread(img_path + imgs[i + 1])
img2 = cv2.cvtColor(img2, cv2.COLOR_BAYER_GR2BGR)
img2 = UndistortImage(img2, LUT)
x1, x2 = utils.getMatchingFeaturePoints(img1, img2)
x1 = np.hstack([x1, np.ones((x1.shape[0], 1))])
x2 = np.hstack([x2, np.ones((x2.shape[0], 1))])
features = np.hstack([x1, x2])
x1_in, x2_in = RANSAC.getInliersRANSAC(features,
threshold=(0.002),
size=8,
num_inliers=0.6 *
features.shape[0],
num_iters=200)
# print("Inliers :",x1_in.shape[0])
fund_mtx = fundamental.EstimateFundamentalMatrix(x1_in, x2_in)
K = np.array([[fx, 0, cx], [0, fy, cy], [0, 0, 1]])
ess_mtx = essential.EssentialMatrixFromFundamentalMatrix(fund_mtx, K)
C, R = cameraPose.ExtractCameraPose(ess_mtx)
X_tri = []
for j in range(4):
X_tri.append(
triangulation.linearTriangulation(K, np.zeros(3), C[j],
np.eye(3), R[j], x1_in,
x2_in))
X_tri = np.array(X_tri)
# print(X_tri.shape)
C_c, R_c = disambiguateCameraPose.disambiguateCameraPose(X_tri, C, R)
C_c = np.reshape(C_c, (3, 1))
if (C_c[2] < 0):
C_c *= -1
# print("Pose Position :")
# print(C_c.T)
# print("Pose Orientation :")
# print(R_c)
pt_new = np.matmul(R_c, pt_old)
pt_new += C_c
if abs(pt_new[0] - pt_old[0]) > 2:
pt_new[0] = copy.copy(pt_old[0])
if abs(pt_new[1] - pt_old[1]) > 2:
pt_new[1] = copy.copy(pt_old[1])
if abs(pt_new[2] - pt_old[2]) > 2:
pt_new[2] = copy.copy(pt_old[2])
print("Old point :", pt_old.T)
print("New point :", pt_new.T)
plt.figure("Calculated")
plt.plot([pt_old[0], pt_new[0]], [pt_old[2], pt_new[2]])
pt_old = copy.copy(pt_new)
# E_act = cv2.findEssentialMat(x1[:,:2],x2[:,:2],K)
# _,R,T,_ = cv2.recoverPose(E_act[0],x1[:,:2],x2[:,:2])
# pt_new_cv = np.matmul(R,pt_old_cv)
# pt_new_cv += T
# if abs(pt_new_cv[0]-pt_old_cv[0]) >0.5:
# pt_new_cv[0] = copy.copy(pt_old_cv[0])
# if abs(pt_new_cv[1]-pt_old_cv[1]) >0.5:
# pt_new_cv[1] = copy.copy(pt_old_cv[1])
# print("Act :",plot_act)
# print("Calc :",plot_calc)
# print("---")
# print("CV2 Pose Position :")
# print(T.T)
# print("CV2 Pose Orientation :")
# print(R)
#
# print("Pose Position cv2:")
# print(T.T)
# print("Pose Orientation cv2 :")
# print(R)
# print("Old point :",pt_old_cv.T)
# print("New point :",pt_new_cv.T)
# plt.figure("OpenCV")
# plt.plot([pt_old_cv[0],pt_new_cv[0]],[pt_old_cv[2],pt_new_cv[2]])
# pt_old_cv = copy.copy(pt_new_cv)
plt.show()
plt.pause(0.00001)
print("!-----------------!")
if Translation_final[2] > 0:
Translation_final = -Translation_final
return Rotation_final, Translation_final
H_Start = np.identity(4)
p_0 = np.array([[0, 0, 0, 1]]).T
flag = 0
data_points = []
for index in range(19, len(frames) - 1):
print(frames[index], index)
img1 = cv2.imread("stereo/centre/" + str(frames[index]), 0)
colorimage1 = cv2.cvtColor(img1, cv2.COLOR_BayerGR2BGR)
undistortedimage1 = UndistortImage(colorimage1, LUT)
gray1 = cv2.cvtColor(undistortedimage1, cv2.COLOR_BGR2GRAY)
img2 = cv2.imread("stereo/centre/" + str(frames[index + 1]), 0)
colorimage2 = cv2.cvtColor(img2, cv2.COLOR_BayerGR2BGR)
undistortedimage2 = UndistortImage(colorimage2, LUT)
gray2 = cv2.cvtColor(undistortedimage2, cv2.COLOR_BGR2GRAY)
grayImage1 = gray1[200:650, 0:1280]
grayImage2 = gray2[200:650, 0:1280]
sift = cv2.xfeatures2d.SIFT_create()
kp1, des1 = sift.detectAndCompute(grayImage1, None)
kp2, des2 = sift.detectAndCompute(grayImage2, None)
def main():
# Preprocess Images
start_t = time.time()
H_init = np.identity(4)
p0 = np.array([[0, 0, 0, 1]]).T
data = []
for i in range(18, len(f) - 1):
print(i)
img1 = cv2.imread(path + str(f[i]), 0)
# cv2.imshow("",img1)
# cv2.waitKey(0)
# Frame 1
bgr_img1 = cv2.cvtColor(img1, cv2.COLOR_BayerGR2BGR)
undist_img1 = UndistortImage(bgr_img1, LUT)
gray_img1 = cv2.cvtColor(undist_img1, cv2.COLOR_BGR2GRAY)
# Frame 2
img2 = cv2.imread(path + str(f[i + 1]), 0)
bgr_img2 = cv2.cvtColor(img2, cv2.COLOR_BayerGR2BGR)
undist_img2 = UndistortImage(bgr_img2, LUT)
gray_img2 = cv2.cvtColor(undist_img2, cv2.COLOR_BGR2GRAY)
gray_img1 = gray_img1[250:600, 0:1280]
gray_img2 = gray_img2[250:600, 0:1280]
# Find Points of Correspondence
f1, f2 = findpts(gray_img1, gray_img2)
for l in range(0, len(f1)):
a = (int(f1[l][0]), int(f1[l][1]))
sift_img = cv2.circle(gray_img2, a, 1, (255, 0, 0), 3)
cv2.imwrite(os.path.join(wrframe, str(i) + '.jpg'), bgr_img1)
#sift_img = plt.imread(sift_img)
# plt.imshow("",sift_img)
# cv2.waitKey(0)
# #print(f2)
# Determine F
F, i1, i2 = findF(f1, f2)
# Determine E
E = findE(K, F)
#print(E)
# Find T and R
R_list, T_list = decomp(E)
# Find Best estimates for R and T
R, T = cheirality_check(R_list, T_list, i1, i2)
# Find Homogeneous Matrix and Camera Centre for Each frame
H = get_homogeneous(R, T)
H_init = H_init @ H
p = H_init @ p0
print('X = ', p[0])
print('Y = ', p[2])
# Write the Co-ordinates in a text file
#data.append([p[0][0], -p[2][0]])
file1.write(str(p[0][0]) + ",")
file1.write(str(-p[2][0]) + "\n")
# Plot the Trajectory
# plt.yticks(np.arange(-200,800,100))
# plt.xticks(np.arange(0,1000,100))
plt.scatter(p[0][0], -p[2][0], color='b')
plt.savefig(os.path.join(pathplot, str(i) + '.png'))
file1.close()
end_t = time.time()
delta = (end_t - start_t) / 60
print("Time taken: ", delta)
plt.show()
images.append(image)
images.sort()
fx, fy, cx, cy, G_camera_image, LUT = ReadCameraModel('model/')
K = np.array([[fx, 0, cx], [0, fy, cy], [0, 0, 1]])
init = np.identity(4)
origin = np.array([[0, 0, 0, 1]]).T
trajectory_points = []
for ind in range(19, len(images) - 1):
print(ind)
img1 = cv2.imread(path + str(images[ind]), 0)
first_image = cv2.cvtColor(img1, cv2.COLOR_BayerGR2BGR)
first_image_undistorted = UndistortImage(first_image, LUT)
first_image_gray = cv2.cvtColor(first_image_undistorted,
cv2.COLOR_BGR2GRAY)
# Reading next image
img2 = cv2.imread(path + str(images[ind + 1]), 0)
next_image = cv2.cvtColor(img2, cv2.COLOR_BayerGR2BGR)
next_image_undistorted = UndistortImage(next_image, LUT)
next_image_gray = cv2.cvtColor(next_image_undistorted, cv2.COLOR_BGR2GRAY)
# Cropping the area of interest from the current image
first_image_final = first_image_gray[200:650, 0:1280]
# Cropping the area of interest from the next frame
next_image_final = next_image_gray[200:650, 0:1280]
sift = cv2.xfeatures2d.SIFT_create()
import cv2
import glob
import numpy as np
from ReadCameraModel import ReadCameraModel
from UndistortImage import UndistortImage
model_dir = 'Oxford_dataset/model/'
fx, fy, cx, cy, G_camera_image, LUT = ReadCameraModel(model_dir)
count = 0
for img in glob.glob('Oxford_dataset/stereo/centre/*.png'):
image = cv2.imread(img,cv2.IMREAD_GRAYSCALE | cv2.IMREAD_ANYDEPTH)
color_image = cv2.cvtColor(image,cv2.COLOR_BayerGR2BGR)
dist = UndistortImage(color_image,LUT)
cv2.imwrite('data/'+'%d.png'%count,dist)
count = count+1
K = np.matrix([[fx,0,cx],[0,fy,cy],[0,0,1]])
def main():
img_path = '../Data/stereo/centre/'
imgs = sorted(os.listdir(img_path))
fx, fy, cx, cy, G_camera_image, LUT = ReadCameraModel('../Data/model')
i = 150
img1 = cv2.imread(img_path + imgs[i], -1)
img1 = cv2.cvtColor(img1, cv2.COLOR_BAYER_GR2BGR)
img1 = UndistortImage(img1, LUT)
img2 = cv2.imread(img_path + imgs[i + 1], -1)
img2 = cv2.cvtColor(img2, cv2.COLOR_BAYER_GR2BGR)
img2 = UndistortImage(img2, LUT)
print(img1.shape)
x1, x2 = utils.getMatchingFeaturePoints(img1, img2)
x1 = np.hstack([x1, np.ones((x1.shape[0], 1))])
x2 = np.hstack([x2, np.ones((x2.shape[0], 1))])
# print(x1.shape)
# print(x2.shape)
features = np.hstack([x1, x2])
# getInliersRANSAC(features,threshold=(0.07),size=8,num_inliers=0.6*features.shape[0],num_iters=1000)
x1_in, x2_in = getInliersRANSAC(features,
threshold=(0.005),
size=8,
num_inliers=0.6 * features.shape[0],
num_iters=500)
print(x1_in.shape)
# drawFeatures(img1,img2,x1,x2,'r',0.3)
# drawFeatures(img1,img2,x1_in,x2_in,'g',1)
# plt.show()
# sys.exit(0)
fund_mtx = fundamental.EstimateFundamentalMatrix(x1_in, x2_in)
# x1 = x1_in
# x2 = x2_in
# print("CV2 Fundamental Matrix :",correct_fund_mtx[0])
print("Fundamental Matrix RANSAC:")
print(fund_mtx)
# img1 =cv2.imread('../Data/1.jpg')
# img2 =cv2.imread('../Data/2.jpg')
pts1 = np.int32(x1[:, :2])
pts2 = np.int32(x2[:, :2])
F, _ = cv2.findFundamentalMat(x1, x2, cv2.RANSAC)
drawFeatures(img1, img2, x1, x2, 'r', 0.1)
drawFeatures(img1, img2, x1_in, x2_in, 'g', 0.5)
plt.show()
print("F")
print(F)
# Find epilines corresponding to points in right image (second image) and
# drawing its lines on left image
lines1 = cv2.computeCorrespondEpilines(pts2.reshape(-1, 1, 2), 2, F)
lines1 = lines1.reshape(-1, 3)
# lines1 = [lines1[15]]
# img5,img6 = drawlines(img1,img2,lines1,pts1,pts2)
img5, img6 = drawlines(img1, img2, lines1[25:30], pts1[25:30], pts2[25:30])
# Find epilines corresponding to points in left image (first image) and
# drawing its lines on right image
lines2 = cv2.computeCorrespondEpilines(pts1.reshape(-1, 1, 2), 1, F)
lines2 = lines2.reshape(-1, 3)
# lines2 = [lines2[25]]
# img3,img4 = drawlines(img2,img1,lines2,pts2,pts1)
img3, img4 = drawlines(img2, img1, lines2[25:30], pts2[25:30], pts1[25:30])
plt.subplot(221), plt.imshow(img5)
plt.subplot(222), plt.imshow(img3)
# plt.show()
# Find epilines corresponding to points in right image (second image) and
# drawing its lines on left image
lines1 = cv2.computeCorrespondEpilines(pts2.reshape(-1, 1, 2), 2, fund_mtx)
lines1 = lines1.reshape(-1, 3)
# lines1 = [lines1[25]]
# img5,img6 = drawlines(img1,img2,lines1,pts1,pts2)
img5, img6 = drawlines(img1, img2, lines1[25:30], pts1[25:30], pts2[25:30])
# Find epilines corresponding to points in left image (first image) and
# drawing its lines on right image
lines2 = cv2.computeCorrespondEpilines(pts1.reshape(-1, 1, 2), 1, fund_mtx)
lines2 = lines2.reshape(-1, 3)
# lines2 = [lines2[25]]
# img3,img4 = drawlines(img2,img1,lines2,pts2,pts1)
img3, img4 = drawlines(img2, img1, lines2[25:30], pts2[25:30], pts1[25:30])
plt.subplot(223), plt.imshow(img5)
plt.subplot(224), plt.imshow(img3)
plt.show()
it += 1
continue
# elif it > 5 and it % 2 != 0:
# it += 1
# continue
elif it > 700:
it += 1
break
print('Iteration: ', it)
# load image
img = cv2.imread(filepath, 0)
img = cv2.cvtColor(img, cv2.COLOR_BayerGR2BGR)
img_orig = UndistortImage(img, LUT)
img = cv2.cvtColor(img_orig, cv2.COLOR_BGR2GRAY)
if (it == 1):
img_orig1 = img_orig
img1 = img
it = 2
orb_kp1, orb_des1 = orb.detectAndCompute(img1, None)
continue
it += 1
img2 = img
img_orig2 = img_orig
# continue
# find orb features
lastH = np.identity(
4) # Initial camera Pose is considered as an identity matrix
origin = np.array([[0, 0, 0, 1]]).T
l = [] # Variable for storing all the trajectory points
for index in range(19, len(images) - 1): # Looping over all the images
img1 = cv2.imread("stereo/centre/" + str(images[index]),
0) # Read current image
colorimage1 = cv2.cvtColor(
img1,
cv2.COLOR_BayerGR2BGR) # Convert image from bayer format to BGR format
undistortedimage1 = UndistortImage(colorimage1,
LUT) # Undistort the current image
gray1 = cv2.cvtColor(
undistortedimage1,
cv2.COLOR_BGR2GRAY) # Convert the undistorted image in grayscale
img2 = cv2.imread("stereo/centre/" + str(images[index + 1]),
0) # Read next image
colorimage2 = cv2.cvtColor(
img2,
cv2.COLOR_BayerGR2BGR) # Convert image from bayer format to BGR format
undistortedimage2 = UndistortImage(colorimage2,
LUT) # Undistort the next image
gray2 = cv2.cvtColor(
undistortedimage2,
cv2.COLOR_BGR2GRAY) # Convert the undistorted image in grayscale
# Main
# Preprocess Images
start_t = time.time()
H_init = np.identity(4)
p0 = np.array([[0, 0, 0, 1]]).T
data = []
for i in range(19, 25):
print(i)
img1 = cv2.imread(path + str(f[i]), 0)
# cv2.imshow("",img1)
# cv2.waitKey(0)
bgr_img1 = cv2.cvtColor(img1, cv2.COLOR_BayerGR2BGR)
undist_img1 = UndistortImage(bgr_img1, LUT)
gray_img1 = cv2.cvtColor(undist_img1, cv2.COLOR_BGR2GRAY)
img2 = cv2.imread(path + str(f[i + 1]), 0)
bgr_img2 = cv2.cvtColor(img2, cv2.COLOR_BayerGR2BGR)
undist_img2 = UndistortImage(bgr_img2, LUT)
gray_img2 = cv2.cvtColor(undist_img2, cv2.COLOR_BGR2GRAY)
gray_img1 = gray_img1[200:650, 0:1280]
gray_img2 = gray_img2[200:650, 0:1280]
# Find Points of Correspondence
f1, f2 = findpts(gray_img1, gray_img2)
F, m = cv2.findFundamentalMat
if Translation_final[2] > 0:
Translation_final = -Translation_final
return Rotation_final, Translation_final
H_Start = np.identity(4)
p_0 = np.array([[0, 0, 0, 1]]).T
flag = 0
data_points = []
for index in range(18, len(frames) - 1):
print(frames[index], index)
img1 = cv2.imread(pathimage + str(frames[index]), 0)
distorted_image1 = cv2.cvtColor(img1, cv2.COLOR_BayerGR2BGR)
undistorted_image1 = UndistortImage(distorted_image1, LUT)
grayscale_1 = cv2.cvtColor(undistorted_image1, cv2.COLOR_BGR2GRAY)
img2 = cv2.imread(pathimage + str(frames[index + 1]), 0)
distorted_image2 = cv2.cvtColor(img2, cv2.COLOR_BayerGR2BGR)
undistorted_image2 = UndistortImage(distorted_image2, LUT)
grayscale_2 = cv2.cvtColor(undistorted_image2, cv2.COLOR_BGR2GRAY)
frame_image1 = grayscale_1[200:650, 0:1280]
frame_image2 = grayscale_2[200:650, 0:1280]
sift = cv2.xfeatures2d.SIFT_create()
keypoints_1, descriptor_1 = sift.detectAndCompute(frame_image1, None)
keypoints_2, descriptor_2 = sift.detectAndCompute(frame_image2, None)
def undistortImg(img):
colorimage = cv2.cvtColor(img, cv2.COLOR_BayerGR2BGR)
undistortedimage = UndistortImage(colorimage, LUT)
gray = cv2.cvtColor(undistortedimage, cv2.COLOR_BGR2GRAY)
return gray
def main():
img_path = '../Data/stereo/centre/'
imgs = sorted(os.listdir(img_path))
fx,fy,cx,cy,G_camera_image,LUT = ReadCameraModel('../Data/model')
i = 100
img1 = cv2.imread(img_path+imgs[i],-1)
img1 = cv2.cvtColor(img1,cv2.COLOR_BAYER_GR2BGR)
img1 = UndistortImage(img1,LUT)
img2 = cv2.imread(img_path+imgs[i+1],-1)
img2 = cv2.cvtColor(img2,cv2.COLOR_BAYER_GR2BGR)
img2 = UndistortImage(img2,LUT)
x1,x2 = utils.getMatchingFeaturePoints(img1,img2)
x1 = np.hstack([x1,np.ones((x1.shape[0],1))])
x2 = np.hstack([x2,np.ones((x2.shape[0],1))])
features = np.hstack([x1,x2])
x1_in,x2_in = RANSAC.getInliersRANSAC(features,threshold=(0.005),size=8,num_inliers=0.6*features.shape[0],num_iters=1000)
print(x1_in.shape)
fund_mtx = fundamental.EstimateFundamentalMatrix(x1_in,x2_in)
K = np.array([[fx,0,cx],[0,fy,cy],[0,0,1]])
ess_mtx = essential.EssentialMatrixFromFundamentalMatrix(fund_mtx,K)
C,R = cameraPose.ExtractCameraPose(ess_mtx)
# print("Available C :")
# print(C)
print("Available R")
print(R)
X_tri = []
for j in range(4):
X_tri.append(triangulation.linearTriangulation(K,np.zeros(3),C[j],np.eye(3),R[j],x1_in,x2_in))
X_tri = np.array(X_tri)
# print(X_tri.shape)
C_c,R_c = disambiguateCameraPose(X_tri,C,R)
print("Pose Position :")
print(C_c)
print("Pose Orientation :")
print(R_c)
# angle = utils.rotationMatrixToEulerAngles(R_c)
# print("Angle :")
# print(angle)
E_act = cv2.findEssentialMat(x1_in[:,:2],x2_in[:,:2],K)
_,R,T,_ = cv2.recoverPose(E_act[0],x1_in[:,:2],x2_in[:,:2])
print("---")
print("CV2 Pose Position :")
print(T.T)
print("CV2 Pose Orientation :")
print(R)
# angle = utils.rotationMatrixToEulerAngles(R)
# print("CV2 Ang;e:")
# print(angle)
print("<----------------->")
