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import numpy as np
_color_map_errors = np.array([
[149, 54, 49], #0: log2(x) = -infinity
[180, 117, 69], #0.0625: log2(x) = -4
[209, 173, 116], #0.125: log2(x) = -3
[233, 217, 171], #0.25: log2(x) = -2
[248, 243, 224], #0.5: log2(x) = -1
[144, 224, 254], #1.0: log2(x) = 0
[97, 174, 253], #2.0: log2(x) = 1
[67, 109, 244], #4.0: log2(x) = 2
[39, 48, 215], #8.0: log2(x) = 3
[38, 0, 165], #16.0: log2(x) = 4
[38, 0, 165] #inf: log2(x) = inf
]).astype(float)
def color_error_image(errors, scale=1, mask=None, BGR=True):
"""
Color an input error map.
Arguments:
errors -- HxW numpy array of errors
[scale=1] -- scaling the error map (color change at unit error)
[mask=None] -- zero-pixels are masked white in the result
[BGR=True] -- toggle between BGR and RGB
Returns:
colored_errors -- HxWx3 numpy array visualizing the errors
"""
errors_flat = errors.flatten()
errors_color_indices = np.clip(np.log2(errors_flat / scale + 1e-5) + 5, 0, 9)
i0 = np.floor(errors_color_indices).astype(int)
f1 = errors_color_indices - i0.astype(float)
colored_errors_flat = _color_map_errors[i0, :] * (1-f1).reshape(-1,1) + _color_map_errors[i0+1, :] * f1.reshape(-1,1)
if mask is not None:
colored_errors_flat[mask.flatten() == 0] = 255
if not BGR:
colored_errors_flat = colored_errors_flat[:,[2,1,0]]
return colored_errors_flat.reshape(errors.shape[0], errors.shape[1], 3).astype(np.int)
_color_map_depths = np.array([
[0, 0, 0], # 0.000
[0, 0, 255], # 0.114
[255, 0, 0], # 0.299
[255, 0, 255], # 0.413
[0, 255, 0], # 0.587
[0, 255, 255], # 0.701
[255, 255, 0], # 0.886
[255, 255, 255], # 1.000
[255, 255, 255], # 1.000
]).astype(float)
_color_map_bincenters = np.array([
0.0,
0.114,
0.299,
0.413,
0.587,
0.701,
0.886,
1.000,
2.000, # doesn't make a difference, just strictly higher than 1
])
def color_depth_map(depths, scale=None):
"""
Color an input depth map.
Arguments:
depths -- HxW numpy array of depths
[scale=None] -- scaling the values (defaults to the maximum depth)
Returns:
colored_depths -- HxWx3 numpy array visualizing the depths
"""
# if scale is None:
# scale = depths.max() / 1.5
scale = 50
values = np.clip(depths.flatten() / scale, 0, 1)
# for each value, figure out where they fit in in the bincenters: what is the last bincenter smaller than this value?
lower_bin = ((values.reshape(-1, 1) >= _color_map_bincenters.reshape(1,-1)) * np.arange(0,9)).max(axis=1)
lower_bin_value = _color_map_bincenters[lower_bin]
higher_bin_value = _color_map_bincenters[lower_bin + 1]
alphas = (values - lower_bin_value) / (higher_bin_value - lower_bin_value)
colors = _color_map_depths[lower_bin] * (1-alphas).reshape(-1,1) + _color_map_depths[lower_bin + 1] * alphas.reshape(-1,1)
return colors.reshape(depths.shape[0], depths.shape[1], 3).astype(np.uint8) |