def process(file):
path = pathlib.Path(file)
filename = path.stem
extension = path.suffix
print("processing {:}".format(filename))
try:
image = colour.cctf_decoding(colour.io.read_image(os.path.join(
IN_DIR, file),
method='Imageio'),
function='sRGB')
corrected_image = numpy.clip(
ColorCorrect.color_correct(image,
SpyderCHECKR24.colour_checker,
method='Finlayson 2015'), 0.0, 1.0)
output_filename = os.path.join(OUT_DIR,
filename + '_corrected' + extension)
colour.io.write_image(colour.cctf_encoding(corrected_image,
function='sRGB'),
output_filename,
bit_depth='uint8',
method='Imageio')
except:
print("error processing {:}".format(filename))
def demosaicking(image: np.ndarray,
method: str = "bilinear",
pattern: str = "RGGB") -> np.ndarray:
"""Returns the demosaicked image given a method.
Parameters
-------------------
image: np.ndarray,
The image to be demosaicked.
method: str,
Demosaicking method to be applied.
pattern: str,
Arrangement of the colour filters on the pixel array.
Possible patterns are: {RGGB, BGGR, GRBG, GBRG}.
Raises
------------------
ValueError,
If given method does not exist.
Returns
-------------------
Returns the demosaicked image.
"""
image_rgb = cv2.cvtColor(image, cv2.COLOR_GRAY2RGB)
image_cfa = mosaicing_CFA_Bayer(image_rgb, pattern=pattern) / 255
if method == 'bilinear':
image_demo = ((cctf_encoding(
demosaicing_CFA_Bayer_bilinear(image_cfa, pattern=pattern))) *
255).astype(np.uint8)
elif method == 'malvar':
image_demo = ((cctf_encoding(
demosaicing_CFA_Bayer_Malvar2004(image_cfa, pattern=pattern))) *
255).astype(np.uint8)
elif method == 'menon':
image_demo = ((cctf_encoding(
demosaicing_CFA_Bayer_Menon2007(image_cfa, pattern=pattern))) *
255).astype(np.uint8)
else:
raise ValueError(
'Given method \'{}\' does not belong to possible methods. '
'Valid methods are: \'bilinear\', \'malvar\' and \'menon\'.'.
format(method))
return cv2.cvtColor(image_demo, cv2.COLOR_RGB2GRAY)
def readCurrentImage(self):
img = self.__images[self.__current_image]
if img['pds_data'] is None:
# 读入pds label
img['pds_data'] = pds4_tools.read('%s/%s' %(img['path'], img['filename']))
# 将图像数据提取出来
img['raw_data'] = np.asanyarray(img['pds_data'][0].data)
print(img['raw_data'].shape, img['raw_data'].ndim)
img['raw_data'] = img['raw_data'] / 1023 #10位的图像数据归一化
# de-bayer
rgb_data = colour.cctf_encoding(colour_demosaicing.demosaicing_CFA_Bayer_bilinear(img['raw_data'], 'RGGB'))
print(rgb_data.shape, rgb_data.ndim)
img['rgb_data'] = rgb_data
## 直方图拉伸
#lower, upper = np.percentile(rgb_data, (0.2,99.8))
#print(lower, upper)
#scale_data = exposure.rescale_intensity(rgb_data, in_range=(lower, upper))
return img
"resources")
colour_style()
ISHIHARA_CBT_3_IMAGE = colour.cctf_decoding(
colour.read_image(
os.path.join(RESOURCES_DIRECTORY,
"Ishihara_Colour_Blindness_Test_Plate_3.png")),
function="sRGB",
)
message_box("Colour Blindness Plots")
message_box('Displaying "Ishihara Colour Blindness Test - Plate 3".')
plot_image(
colour.cctf_encoding(ISHIHARA_CBT_3_IMAGE),
text_kwargs={
"text": "Normal Trichromat",
"color": "black"
},
)
print("\n")
message_box(
'Simulating average "Protanomaly" on '
'"Ishihara Colour Blindness Test - Plate 3" with Machado (2010) model and '
"pre-computed matrix.")
plot_cvd_simulation_Machado2009(
ISHIHARA_CBT_3_IMAGE,
"Protanomaly",
from colour.utilities.verbose import message_box
RESOURCES_DIRECTORY = os.path.join(os.path.dirname(os.path.abspath(__file__)),
'resources')
colour_style()
ISHIHARA_CBT_3_IMAGE = colour.cctf_decoding(colour.read_image(
os.path.join(RESOURCES_DIRECTORY,
'Ishihara_Colour_Blindness_Test_Plate_3.png')),
function='sRGB')
message_box('Colour Blindness Plots')
message_box('Displaying "Ishihara Colour Blindness Test - Plate 3".')
plot_image(colour.cctf_encoding(ISHIHARA_CBT_3_IMAGE),
text_parameters={
'text': 'Normal Trichromat',
'color': 'black'
})
print('\n')
message_box('Simulating average "Protanomaly" on '
'"Ishihara Colour Blindness Test - Plate 3" with Machado (2010) '
'model and pre-computed matrix.')
plot_cvd_simulation_Machado2009(ISHIHARA_CBT_3_IMAGE,
'Protanomaly',
0.5,
text_parameters={
'text': 'Protanomaly - 50%',
def apply_cctf_encoding(RGB_input,
transfer_function_name="Gamma 2.2",
exponent=None):
return colour.cctf_encoding(RGB_input,
function=transfer_function_name,
negative_number_handling="Clamp")
def apply_odt_cctf(self, in_rgb):
cctf_func = ODT_DICO.get(self.odt)[0]
result_cctf = colour.cctf_encoding(in_rgb, cctf_func)
return result_cctf
-----------------------
<BLANKLINE>
Dimensions : 3
Domain : [[ 0. 0. 0.]
[ 1. 1. 1.]]
Size : (33, 33, 33, 3)
"""
size = self.size
LUT_inverse = colour.LUT3D(size=size, name='Inverted Format')
indexes = LUT_inverse.table
tree = cKDTree(self.table.reshape(-1, 3))
query = tree.query(indexes)[-1]
LUT_inverse.table = indexes.reshape(-1, 3)[query].reshape(
[size, size, size, 3])
return LUT_inverse
RGB = [0.18, 0.18, 0.18]
LUT = colour.LUT3D()
LUT.table = colour.cctf_encoding(LUT.table)
RGB_a = LUT.apply(RGB)
LUT_inverse = invert(LUT)
RGB_i = LUT_inverse.apply(RGB_a)
print(RGB)
print(RGB_a)
print(RGB_i)
