import numpy as np
__all__ = [
"transparency",
"adapt_michelson_contrast",
"adapt_rms_contrast",
"adapt_normalized_rms_contrast",
"adapt_intensity_range",
"adapt_michelson_contrast_dict",
"adapt_rms_contrast_dict",
"adapt_normalized_rms_contrast_dict",
"adapt_intensity_range_dict",
]
[docs]
def transparency(img, mask=None, alpha=0.5, tau=0.2):
"""Applies a transparency to image at specified (mask) location if provided
Parameters
----------
img : np.array
image to which transparency will be applied
mask : np.array or None (default)
if not None, transparency will be provided at non-zero locations
provided in this mask
alpha : Number
alpha of transparency (i.e. how transparent the medium is), default 0.2
tau : Number
tau of transparency (i.e. value of transparent medium), default 0.5
Returns
-------
img with the applied transparency
"""
if mask is None:
mask = np.ones(img.shape)
img = np.where(mask, alpha * img + (1 - alpha) * tau, img)
return img
[docs]
def adapt_michelson_contrast(img, michelson_contrast, mean_luminance=None):
"""
Adapt Michelson contrast of image
Parameters
----------
img : np.ndarray
stimulus array
michelson_contrast : float
desired Michelson contrast
mean_luminance : float
desired mean luminance; if None (default), dont change mean luminance
Returns
----------
img : np.ndarray
image with adapted michelson contrast and mean luminance if passed
"""
if mean_luminance is None:
mean_luminance = img.mean()
# Adapt Michelson contrast
img = (img - img.min()) / (img.max() - img.min())
img = img * michelson_contrast * 2.0 * mean_luminance
img += mean_luminance - michelson_contrast * mean_luminance
return img
[docs]
def adapt_rms_contrast(img, rms_contrast, mean_luminance=None):
"""
Adapt rms contrast of image (std)
Parameters
----------
img : np.ndarray
stimulus array
rms_contrast : float
desired rms contrast (std divided by mean intensity)
mean_luminance : float
desired mean luminance; if None (default), dont change mean luminance
Returns
----------
img : np.ndarray
image with adapted rms contrast and mean luminance if passed
"""
if mean_luminance is None:
mean_luminance = img.mean()
# Adapt rms contrast
img = img - img.mean()
img = img / img.std() * rms_contrast + mean_luminance
return img
[docs]
def adapt_normalized_rms_contrast(img, rms_contrast, mean_luminance=None):
"""
Adapt normalized rms contrast of image (std divided by mean)
Parameters
----------
img : np.ndarray
stimulus array
rms_contrast : float
desired rms contrast (std divided by mean intensity)
mean_luminance : float
desired mean luminance; if None (default), dont change mean luminance
Returns
----------
img : np.ndarray
image with adapted rms contrast and mean luminance if passed
"""
if mean_luminance is None:
mean_luminance = img.mean()
img = img - img.mean()
img = img / img.std() * rms_contrast * mean_luminance + mean_luminance
return img
[docs]
def adapt_intensity_range(img, intensity_min=0.0, intensity_max=1.0):
"""
Adapt intensity range of image
Parameters
----------
img : np.ndarray
stimulus array
intensity_min : float
new minimal intensity value
intensity_max : float
new maximal intensity value
Returns
----------
img : np.ndarray
image with adapted intensity range
"""
img = (img - img.min()) / (img.max() - img.min())
img = img * (intensity_max - intensity_min) + intensity_min
return img
[docs]
def adapt_michelson_contrast_dict(stim, michelson_contrast, mean_luminance=None):
"""
Adapt Michelson contrast of image in dict
Parameters
----------
stim : dict
stimulus dictionary containing at least key "img"
michelson_contrast : float
desired Michelson contrast
mean_luminance : float
desired mean luminance; if None (default), dont change mean luminance
Returns
----------
dict[str, Any]
dict with the stimulus (key: "img"),
Michelson contrast (key: "michelson_contrast"),
mean luminance ("mean_luminance")
and additional keys containing stimulus parameters
"""
# Adapt Michelson contrast of image
img = adapt_michelson_contrast(stim["img"], michelson_contrast, mean_luminance)
stim["img"] = img
stim["michelson_contrast"] = michelson_contrast
stim["mean_luminance"] = mean_luminance
return stim
[docs]
def adapt_rms_contrast_dict(stim, rms_contrast, mean_luminance=None):
"""
Adapt rms contrast of image (std)
Parameters
----------
stim : dict
stimulus dictionary containing at least key "img"
rms_contrast : float
desired rms contrast (std divided by mean intensity)
mean_luminance : float
desired mean luminance; if None (default), dont change mean luminance
Returns
----------
dict[str, Any]
dict with the stimulus (key: "img"),
RMS contrast (key: "rms_contrast"),
mean luminance ("mean_luminance")
and additional keys containing stimulus parameters
"""
# Adapt rms_contrast of image
img = adapt_rms_contrast(stim["img"], rms_contrast, mean_luminance)
stim["img"] = img
stim["rms_contrast"] = rms_contrast
stim["mean_luminance"] = mean_luminance
return stim
[docs]
def adapt_normalized_rms_contrast_dict(stim, rms_contrast, mean_luminance=None):
"""
Adapt normalized rms contrast of image (std divided by mean)
Parameters
----------
stim : dict
stimulus dictionary containing at least key "img"
rms_contrast : float
desired rms contrast (std divided by mean intensity)
mean_luminance : float
desired mean luminance; if None (default), dont change mean luminance
Returns
----------
dict[str, Any]
dict with the stimulus (key: "img"),
RMS contrast (key: "rms_contrast"),
mean luminance ("mean_luminance")
and additional keys containing stimulus parameters
"""
# Adapt normalized rms contrast
img = adapt_normalized_rms_contrast(stim["img"], rms_contrast, mean_luminance)
stim["img"] = img
stim["rms_contrast"] = rms_contrast
stim["mean_luminance"] = mean_luminance
return stim
[docs]
def adapt_intensity_range_dict(stim, intensity_min=0.0, intensity_max=1.0):
"""
Adapt intensity range of image
Parameters
----------
stim : dict
stimulus dictionary containing at least key "img"
intensity_min : float
new minimal intensity value
intensity_max : float
new maximal intensity value
Returns
----------
dict[str, Any]
dict with the stimulus (key: "img"),
intensity range (key: "intensity_range"),
and additional keys containing stimulus parameters
"""
img = adapt_intensity_range(stim["img"], intensity_min, intensity_max)
stim["img"] = img
stim["intensity_range"] = (intensity_min, intensity_max)
return stim
