def read_image_sequence(data_dir, prefix, num_images, start_index=0, image_format='png', zero_padding=0, crop=None, verbose=False):
'''Read a series of images into a list of numpy arrays.
:param str data_dir: directory where the image files are located.
:param str prefix: a string to construct the image file names.
:param int num_images: the number of images to read.
:param int start_index: the index to start loading the images (0 by default).
:param str image_format: can be tif or png (png by default).
:param int zero_padding: number of zero to use in zero padding (0 by default).
:param list crop: bounds to crop the images (None by default)
:param bool verbose: activate verbose mode (False by default).
:return: the list of the images read from the disk.
'''
# build the numbering pattern
pat = '0%dd' % zero_padding
fmt = '{0:s}{1:' + pat + '}.{2:s}'
image_stack = []
if image_format == 'tif':
from pymicro.external.tifffile import TiffFile
elif image_format == 'png':
from matplotlib import pyplot as plt
for i in range(start_index, start_index + num_images):
image_path = os.path.join(data_dir, fmt.format(prefix, i, image_format))
if verbose:
print('loading image %s' % image_path)
if image_format == 'tif':
im = TiffFile(image_path).asarray()
elif image_format == 'png':
im = (np.mean(plt.imread(image_path)[:, :, :3], axis=2) * 255).astype(np.uint8)
if crop:
im = im[crop[2]:crop[3], crop[0]:crop[1]]
image_stack.append(im.T)
return image_stack
def load_image(self, image_path):
print('loading image from %s' % image_path)
# read image depending on file extension
if image_path.endswith('.edf'):
image = edf_read(image_path).transpose()
elif image_path.endswith('.png'):
image = mpimg.imread(image_path)
elif image_path.endswith('.tif'):
image = TiffFile(image_path).asarray() # should we transpose here?
else:
print('Only png, tif and edf images are supported for the moment')
image = np.zeros((5, 5), dtype=np.uint8) # dummy image
return image
def load_image(self, image_path):
print('loading image %s' % image_path)
self.image_path = image_path
if image_path.endswith('.tif'):
self.data = TiffFile(image_path).asarray().T.astype(np.float32)
elif image_path.endswith('.raw'):
self.data = HST_read(self.image_path, data_type=self.data_type,
dims=(self.get_size_px()[0], self.get_size_px()[1], 1))[:, :, 0].astype(np.float32)
else:
print('unrecognized file format: %s' % image_path)
return None
assert self.data.shape == self.size
self.compute_corrected_image()
tsr_points[5] = [209, 675]
from pymicro.view.vol_utils import compute_affine_transform
# compute the affine transform from the point set
translation, transformation = compute_affine_transform(ref_points, tsr_points)
invt = np.linalg.inv(transformation)
offset = -np.dot(invt, translation)
print(translation)
print(transformation)
invt
from pymicro.external.tifffile import TiffFile
sem = TiffFile(
'Slice%d.tif' %
slice).asarray()[:2048, :].T # imaged with scaling already applied
print('SEM shape', sem.shape)
compositeScan = OimScan.zeros_like(sem.T, resolution=(pixel_size, pixel_size))
compositeScan.sampleId = '%s_%03d' % (prefix, slice)
plt.imshow(sem.T, cmap=cm.gray)
plt.show()
from scipy import ndimage
# register ebsd
iq_reg = ndimage.interpolation.affine_transform(iq.T,
invt,
output_shape=sem.shape,
offset=offset).T
eu0_reg = ndimage.interpolation.affine_transform(scan.euler[:, 0].T,