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from scipy.interpolate import LinearNDInterpolator
from PIL import Image
import matplotlib.pyplot as plt
def load_data_path(root, file_name_txt, data_type):
with open(file_name_txt, 'r') as f:
data_path = f.readlines()
data_path = [root + x.strip() + data_type for x in data_path]
return data_path
def load_data_path_nu(root, name_list, data_type):
data_path = [root + x.strip() + data_type for x in name_list]
return data_path
def read_paths(filepath):
'''
Reads a newline delimited file containing paths
Arg(s):
filepath : str
path to file to be read
Return:
list[str] : list of paths
'''
path_list = []
with open(filepath) as f:
while True:
path = f.readline().rstrip('\n')
# If there was nothing to read
if path == '':
break
path_list.append(path)
return path_list
def write_paths(filepath, paths):
'''
Stores line delimited paths into file
Arg(s):
filepath : str
path to file to save paths
paths : list[str]
paths to write into file
'''
with open(filepath, 'w') as o:
for idx in range(len(paths)):
o.write(paths[idx] + '\n')
def load_image(path, normalize=False, data_format='HWC'):
'''
Loads an RGB image
Arg(s):
path : str
path to RGB image
normalize : bool
if set, then normalize image between [0, 1]
data_format : str
'CHW', or 'HWC'
Returns:
numpy[float32] : H x W x C or C x H x W image
'''
# Load image
image = Image.open(path).convert('RGB')
# Convert to numpy
image = np.asarray(image, np.float32)
if data_format == 'HWC':
pass
elif data_format == 'CHW':
image = np.transpose(image, (2, 0, 1))
else:
raise ValueError('Unsupported data format: {}'.format(data_format))
# Normalize
image = image / 255.0 if normalize else image #255.0
return image
def load_depth(path, multiplier=256.0, data_format='HW'):
'''
Loads a depth map from a 16-bit PNG file
Arg(s):
path : str
path to 16-bit PNG file
multiplier : float
multiplier for encoding float as 16/32 bit unsigned integer
data_format : str
HW, CHW, HWC
Returns:
numpy[float32] : depth map
'''
# Loads depth map from 16-bit PNG file
z = np.array(Image.open(path), dtype=np.float32)
# Assert 16-bit (not 8-bit) depth map
z = z / multiplier
z[z <= 0] = 0.0
if data_format == 'HW':
pass
elif data_format == 'CHW':
z = np.expand_dims(z, axis=0)
elif data_format == 'HWC':
z = np.expand_dims(z, axis=-1)
else:
raise ValueError('Unsupported data format: {}'.format(data_format))
return z
def save_depth(z, path, multiplier=256.0):
'''
Saves a depth map to a 16-bit PNG file
Arg(s):
z : numpy[float32]
depth map
path : str
path to store depth map
multiplier : float
multiplier for encoding float as 16/32 bit unsigned integer
'''
z = np.uint32(z * multiplier)
z = Image.fromarray(z, mode='I')
z.save(path)
def save_color_depth(z, path):
'''
Saves a color depth map to a 16-bit PNG file
Arg(s):
z : numpy[float32]
depth map
path : str
path to store depth map
multiplier : float
multiplier for encoding float as 16/32 bit unsigned integer
'''
# Normalize depth map to the range [0, 1]
z_normalized = (z - np.min(z)) / (np.max(z) - np.min(z))
# Convert depth map to color
# colormap = plt.cm.jet # Choose a colormap (e.g., jet)
colormap = plt.cm.viridis
z_color = colormap(z_normalized)
# Scale color values to the range [0, 255] and convert to uint8
z_color = np.uint8(z_color * 255)
# Save color depth map as an image
image = Image.fromarray(z_color)
image.save(path)
def load_response(path, multiplier=2**14, data_format='HW'):
'''
Loads a response map from a 16-bit PNG file
Arg(s):
path : str
path to 16-bit PNG file
multiplier : float
multiplier for encoding float as 16/32 bit unsigned integer
data_format : str
HW, CHW, HWC
Returns:
numpy[float32] : response map
'''
# Loads response map from 16-bit PNG file
response = np.array(Image.open(path), dtype=np.float32)
# Convert using encodering multiplier
response = response / multiplier
if data_format == 'HW':
pass
elif data_format == 'CHW':
response = np.expand_dims(response, axis=0)
elif data_format == 'HWC':
response = np.expand_dims(response, axis=-1)
else:
raise ValueError('Unsupported data format: {}'.format(data_format))
return response
def save_response(response, path, multiplier=2**14):
'''
Saves a response map to a 16-bit PNG file
Arg(s):
response : numpy[float32]
depth map
path : str
path to store depth map
multiplier : float
multiplier for encoding float as 16/32 bit unsigned integer
'''
response = np.uint32(response * multiplier)
response = Image.fromarray(response, mode='I')
response.save(path)
def interpolate_depth(depth_map, validity_map, log_space=False):
'''
Interpolate sparse depth with barycentric coordinates
Arg(s):
depth_map : np.float32
H x W depth map
validity_map : np.float32
H x W depth map
log_space : bool
if set then produce in log space
Returns:
np.float32 : H x W interpolated depth map
'''
assert depth_map.ndim == 2 and validity_map.ndim == 2
rows, cols = depth_map.shape
data_row_idx, data_col_idx = np.where(validity_map)
depth_values = depth_map[data_row_idx, data_col_idx]
# Perform linear interpolation in log space
if log_space:
depth_values = np.log(depth_values)
interpolator = LinearNDInterpolator(
# points=Delaunay(np.stack([data_row_idx, data_col_idx], axis=1).astype(np.float32)),
points=np.stack([data_row_idx, data_col_idx], axis=1),
values=depth_values,
fill_value=0 if not log_space else np.log(1e-3))
query_row_idx, query_col_idx = np.meshgrid(
np.arange(rows), np.arange(cols), indexing='ij')
query_coord = np.stack(
[query_row_idx.ravel(), query_col_idx.ravel()], axis=1)
Z = interpolator(query_coord).reshape([rows, cols])
if log_space:
Z = np.exp(Z)
Z[Z < 1e-1] = 0.0
return Z
def interpolate_depth_ZJU(depth_map, validity_map=None, log_space=False, window_size=12):
'''
Interpolate sparse depth with barycentric coordinates
Args:
depth_map : np.float32
H x W depth map
validity_map : np.float32
H x W depth map
log_space : bool
if set then produce in log space
window_size : int
size of the window for checking validity
Returns:
np.float32 : H x W interpolated depth map
'''
assert depth_map.ndim == 2
if validity_map is None:
validity_map = depth_map > 0.0
rows, cols = depth_map.shape
data_row_idx, data_col_idx = np.where(validity_map)
depth_values = depth_map[data_row_idx, data_col_idx]
# Perform linear interpolation in log space
if log_space:
depth_values = np.log(depth_values)
interpolator = LinearNDInterpolator(
points=np.stack([data_row_idx, data_col_idx], axis=1),
values=depth_values,
fill_value=0 if not log_space else np.log(1e-3))
query_row_idx, query_col_idx = np.meshgrid(np.arange(rows), np.arange(cols), indexing='ij')
Z = np.zeros_like(depth_map)
# Create window indices for each query point
query_indices = np.stack([query_row_idx.ravel(), query_col_idx.ravel()], axis=1)
window_indices = np.indices((window_size, window_size)).reshape(2, -1) - window_size // 2
# Calculate window indices for each query point
window_row_indices = np.clip(query_indices[:, 0, None] + window_indices[0], 0, rows - 1)
window_col_indices = np.clip(query_indices[:, 1, None] + window_indices[1], 0, cols - 1)
# Get window values and check validity
window_values = depth_map[window_row_indices, window_col_indices]
valid_indices = np.any(window_values > 0, axis=1)
# Interpolate for valid query points
valid_query_indices = np.where(valid_indices)[0]
valid_query_coords = query_indices[valid_query_indices]
Z.ravel()[valid_query_indices] = interpolator(valid_query_coords)
if log_space:
Z = np.exp(Z)
Z[Z < 1e-1] = 0.0
return Z |