def LoadImageBatch(path, img_format):
"""
Generic function to be used in multi-threaded batch read for input images.
"""
image = misc.imread(path)
if image.shape[2] == 4:
image = image[:, :, 0:3]
if img_format == "HEDab":
dec = deconv.Deconvolution()
dec.params['image_type'] = 'HEDab'
np_img = np.array(image)
dec_img = dec.colorDeconv(np_img[:, :, :3])
image = dec_img.astype('uint8')
elif img_format == "HE":
dec = deconv.Deconvolution()
dec.params['image_type'] = 'HEDab'
np_img = np.array(image)
dec_img = dec.colorDeconv(np_img[:, :, :3])
image = dec_img.astype('uint8')
return image
def LoadImage(self, path):
"""
Loads one single input image with certain image format.
This image format can be 'RGB', 'HEDab' and 'HE'.
"""
if not hasattr(self, "img_format"):
self.img_format = "RGB"
image = misc.imread(path)
if image.shape[2] == 4:
image = image[:, :, 0:3]
if self.img_format == "HEDab":
dec = deconv.Deconvolution()
dec.params['image_type'] = 'HEDab'
np_img = np.array(image)
dec_img = dec.colorDeconv(np_img[:, :, :3])
image = dec_img.astype('uint8')
elif self.img_format == "HE":
dec = deconv.Deconvolution()
dec.params['image_type'] = 'HEDab'
np_img = np.array(image)
dec_img = dec.colorDeconv(np_img[:, :, :3])
image = dec_img.astype('uint8')
return image
def _apply_(self, *image):
res = ()
n_img = 0
for img in image:
if n_img == 0:
### transform image into HE
dec = deconv.Deconvolution()
dec.params['image_type'] = 'HEDab'
np_img = np.array(img)
dec_img = dec.colorDeconv(np_img)
#pdb.set_trace()
dec_img = dec_img.astype('uint8')
### perturbe each channel H, E, Dab
for i in range(3):
k_i = self.params['k'][i]
b_i = self.params['b'][i]
val = np.max(dec_img[:, :, i])
dec_img[:, :, i] = GreyValuePerturbation(dec_img[:, :, i],
k_i,
b_i,
MIN=0,
MAX=255)
val -= np.max(dec_img[:, :, i])
dec_img[:, :, i] += val
sub_res = dec.colorDeconvHE(dec_img).astype('uint8')
### Have to implement deconvolution of the deconvolution
else:
sub_res = img
res += (sub_res, )
n_img += 1
return res