def make_kitti_data_loader(size=(128, 128), norm="norm"):
# print ('dataset:',DATASET_PATH)
path_dir = os.listdir(PATH_CAM)
print(f"path_dir: {path_dir}")
'''
ct = 0
for path in path_dir:
path_left = os.path.join(PATH_CAM, path , 'image_00/data/')
path_right = os.path.join(PATH_CAM, path , 'image_01/data/')
if not (os.path.isdir(path_left) and os.path.isdir(path_right)):
continue
left_filenames = os.listdir(path_left)
right_filenames = os.listdir(path_right)
for l_path, r_path in zip(left_filenames, right_filenames):
left_path = os.path.join(path_left, l_path)
right_path = os.path.join(path_right, r_path)
if not (os.path.isfile(left_path) and os.path.isfile(right_path)):
continue
# print ('pre-processing image: ', left_path)
F, pts1, pts2 = get_FMat(left_path, right_path)
if F is None:
continue
ct += 1
'''
N = 0
print('path_dir: ', path_dir)
for path in path_dir:
path_new = os.path.join(PATH_CAM, path, 'image_00/data/')
if not os.path.isdir(path_new):
continue
print('path new: ', path_new)
filenames = os.listdir(path_new)
N += len(filenames)
W, H = size
W, H = 1392, 512 # size before rectification @Eason
# N = len(filenames)
print("How many imgs:", N)
N = 2000
print('N: ', N)
X = np.zeros(
(N, H, W, 2)) # H, W interchanged here since numpy takes H,W as input
Y = np.zeros((N, 9))
P1_lst, P2_lst = [], []
kpts_cnt = 1000 # max 100 kpts
i = 0
for path in path_dir:
path_left = os.path.join(PATH_CAM, path, 'image_00/data/')
print(f"path_left: {path_left}")
path_right = os.path.join(PATH_CAM, path, 'image_01/data/')
print(f"path_right: {path_right}")
if not (os.path.isdir(path_left) and os.path.isdir(path_right)):
continue
left_filenames = os.listdir(path_left)
right_filenames = os.listdir(path_right)
for l_path, r_path in zip(left_filenames, right_filenames):
left_path = os.path.join(path_left, l_path)
right_path = os.path.join(path_right, r_path)
if not (os.path.isfile(left_path) and os.path.isfile(right_path)):
continue
print('pre-processing image: ', left_path, 'i: ', i)
F, pts1, pts2 = get_FMat(
left_path, right_path
) # without method input means get the F_GT from camera parameters
if F is None or pts1 is None or pts2 is None:
continue
left_img = img_prep(left_path, target_size=(W, H))
right_img = img_prep(right_path, target_size=(W, H))
# print ('left image: ', left_img.shape)
imgs = np.zeros(
(2, H,
W)) # H, W interchanged here since numpy takes H,W as input
imgs[0, :, :] = left_img
imgs[1, :, :] = right_img
X[i, :] = np.moveaxis(imgs, [0, 1, 2], [2, 0, 1])
# print ('X shape: ', X[i,:].shape)
Y[i, :] = np.resize(F, (1, 9))
# pts1, pts2 = note['kpts']
kpts_cnt = min(len(pts1), kpts_cnt)
P1_lst.append(np.array(pts1))
P2_lst.append(np.array(pts2))
i += 1
if i >= N:
break
if i >= N:
break
# X = X.astype(np.float32)
X, Y = X.astype(np.float32), Y.astype(np.float32)
tot_len = len(P1_lst)
# cut the data by the length of matching points list's length ?
# P1_lst is a [nums of imgs, nums of points per img, 2]
X, Y = X[:tot_len, :], Y[:tot_len, :]
#
# changed here
# Normalize F-matrices
if norm == "abs":
print("[data loader] Use max abs value to normalize the F-matrix")
Y = Y / (np.abs(Y).max(axis=1)[:, np.newaxis] + 1e-8)
elif norm == "norm":
print("[data loader] Use L2 norm to normalize the F-matrix")
Y = Y / (np.linalg.norm(Y, axis=1)[:, np.newaxis] + 1e-8)
elif norm == "last":
print("[data loader] Use last index to normalize the F-matrix")
Y = Y / (Y[:, -1].reshape(-1)[np.newaxis, 1] + 1e-8)
else:
raise Exception("Unrecognized normalization methods:%s" % norm)
P1_lst = [x[:kpts_cnt, :].reshape((1, kpts_cnt, 2)) for x in P1_lst]
P2_lst = [x[:kpts_cnt, :].reshape((1, kpts_cnt, 2)) for x in P2_lst]
P1, P2 = np.concatenate(P1_lst, axis=0), np.concatenate(P2_lst, axis=0)
print(X.shape)
print(Y.shape)
print(P1.shape)
print(P2.shape)
X /= 255. # Normalize to [0,1]
# return X
return (lambda: data_spliter([X, Y, P1, P2]))
def make_aloi_data_loader(size=(128, 128), norm="norm"):
filenames = os.listdir(PATH_ALOI)
W, H = size
# W, H = 768, 576
W, H = 342, 256
N = len(filenames)
# N = 10
# testing code for total valid files
ct = 0
for i in range(N):
left_path = os.path.join(PATH_ALOI,
'%s/%s_l.png' % (filenames[i], filenames[i]))
right_path = os.path.join(PATH_ALOI,
'%s/%s_r.png' % (filenames[i], filenames[i]))
if not (os.path.isfile(left_path) and os.path.isfile(right_path)):
continue
# print ('pre-processing image: ', filenames[i])
F, pts1, pts2 = get_FMat(left_path, right_path)
if F is None:
continue
ct += 1
print('total data: ', N)
X = np.zeros(
(ct, H, W, 2)) # H, W interchanged here since numpy takes H,W as input
Y = np.zeros((ct, 9))
P1_lst, P2_lst = [], []
kpts_cnt = 200 # max 100 kpts
ind = 0
for i in range(N):
left_path = os.path.join(PATH_ALOI,
'%s/%s_l.png' % (filenames[i], filenames[i]))
right_path = os.path.join(PATH_ALOI,
'%s/%s_r.png' % (filenames[i], filenames[i]))
if not (os.path.isfile(left_path) and os.path.isfile(right_path)):
continue
print('pre-processing image: ', filenames[i])
F, pts1, pts2 = get_FMat(left_path, right_path)
if F is None:
continue
left_img = img_prep(left_path, target_size=(W, H))
right_img = img_prep(right_path, target_size=(W, H))
# print ('left image: ', left_img.shape)
imgs = np.zeros(
(2, H, W)) # H, W interchanged here since numpy takes H,W as input
imgs[0, :, :] = left_img
imgs[1, :, :] = right_img
X[ind, :] = np.moveaxis(imgs, [0, 1, 2], [2, 0, 1])
# print ('X shape: ', X[i,:].shape)
Y[ind, :] = np.resize(F, (1, 9))
# pts1, pts2 = note['kpts']
kpts_cnt = min(len(pts1), kpts_cnt)
P1_lst.append(np.array(pts1))
P2_lst.append(np.array(pts2))
ind += 1
X, Y = X.astype(np.float32), Y.astype(np.float32)
# Normalize F-matrices
if norm == "abs":
print("[data loader] Use max abs value to normalize the F-matrix")
Y = Y / (np.abs(Y).max(axis=1)[:, np.newaxis] + 1e-8)
elif norm == "norm":
print("[data loader] Use L2 norm to normalize the F-matrix")
Y = Y / (np.linalg.norm(Y, axis=1)[:, np.newaxis] + 1e-8)
elif norm == "last":
print("[data loader] Use last index to normalize the F-matrix")
Y = Y / (Y[:, -1].reshape(-1)[np.newaxis, 1] + 1e-8)
else:
raise Exception("Unrecognized normalization methods:%s" % norm)
P1_lst = [x[:kpts_cnt, :].reshape((1, kpts_cnt, 2)) for x in P1_lst]
P2_lst = [x[:kpts_cnt, :].reshape((1, kpts_cnt, 2)) for x in P2_lst]
P1, P2 = np.concatenate(P1_lst, axis=0), np.concatenate(P2_lst, axis=0)
# print(X.shape)
# print(Y.shape)
# print(P1.shape)
# print(P2.shape)
X /= 255. # Normalize to [0,1]
return (lambda: data_spliter([X, Y, P1, P2]))
def make_mvs_data_loader(size=(128, 128), norm="norm"):
path_dir = os.listdir(PATH_MVS)
N = 0
for path in path_dir:
filenames = os.listdir(os.path.join(PATH_MVS, path))
N += len(filenames)
W, H = size
W, H = 342, 256
# N = 10
X = np.zeros(
(N, H, W, 2)) # H, W interchanged here since numpy takes H,W as input
Y = np.zeros((N, 9))
P1_lst, P2_lst = [], []
kpts_cnt = 1000 # max 100 kpts
i = 0
for path in path_dir:
filenames = os.listdir(os.path.join(PATH_MVS, path))
filenames.sort()
for j in range(
8): #since there are total of 8 different lighting conditions
left_path = os.path.join(PATH_MVS, path, filenames[j])
right_path = os.path.join(PATH_MVS, path, filenames[j + 8])
if not (os.path.isfile(left_path) and os.path.isfile(right_path)):
continue
print('pre-processing image: ', left_path)
F, pts1, pts2 = get_FMat(left_path, right_path)
if F is None:
continue
# print ('F:', F.shape, 'pts1: ', len(pts1), 'pts2: ', len(pts2))
left_img = img_prep(left_path, target_size=(W, H))
right_img = img_prep(right_path, target_size=(W, H))
# print ('left image: ', left_img.shape)
imgs = np.zeros(
(2, H,
W)) # H, W interchanged here since numpy takes H,W as input
imgs[0, :, :] = left_img
imgs[1, :, :] = right_img
X[i, :] = np.moveaxis(imgs, [0, 1, 2], [2, 0, 1])
# print ('X shape: ', X[i,:].shape)
Y[i, :] = np.resize(F, (1, 9))
# pts1, pts2 = note['kpts']
kpts_cnt = min(len(pts1), kpts_cnt)
P1_lst.append(np.array(pts1))
P2_lst.append(np.array(pts2))
i += 1
if i >= N:
break
if i >= N:
break
X, Y = X.astype(np.float32), Y.astype(np.float32)
tot_len = len(P1_lst)
X, Y = X[:tot_len, :], Y[:tot_len, :]
# Normalize F-matrices
if norm == "abs":
print("[data loader] Use max abs value to normalize the F-matrix")
Y = Y / (np.abs(Y).max(axis=1)[:, np.newaxis] + 1e-8)
elif norm == "norm":
print("[data loader] Use L2 norm to normalize the F-matrix")
Y = Y / (np.linalg.norm(Y, axis=1)[:, np.newaxis] + 1e-8)
elif norm == "last":
print("[data loader] Use last index to normalize the F-matrix")
Y = Y / (Y[:, -1].reshape(-1)[np.newaxis, 1] + 1e-8)
else:
raise Exception("Unrecognized normalization methods:%s" % norm)
P1_lst = [x[:kpts_cnt, :].reshape((1, kpts_cnt, 2)) for x in P1_lst]
P2_lst = [x[:kpts_cnt, :].reshape((1, kpts_cnt, 2)) for x in P2_lst]
P1, P2 = np.concatenate(P1_lst, axis=0), np.concatenate(P2_lst, axis=0)
# print(X.shape)
# print(Y.shape)
# print(P1.shape)
# print(P2.shape)
X /= 255. # Normalize to [0,1]
return (lambda: data_spliter([X, Y, P1, P2]))