def moments(cls, img, query_size):
"""Calculates the mean and standard deviation for each window of size (query_size x query_size)
in the micrograph.
Args:
img: Micrograph image.
query_size: Size of windows for which to compute mean and std.
Returns:
A matrix of mean intensity and a matrix of variance, each containing a single
entry for each possible (query_size x query_size) window in the micrograph.
"""
filt = xp.ones((query_size, query_size)) / (query_size * query_size)
filt = xp.pad(filt, (0, img.shape[0] - 1),
'constant',
constant_values=(0, 0))
filt_freq = xp.fft2(filt, axes=(0, 1))
pad_img = xp.pad(img, (0, query_size - 1),
'constant',
constant_values=(0, 0))
img_freq = xp.fft2(pad_img, axes=(0, 1))
mean_freq = xp.multiply(img_freq, filt_freq)
mean_all = xp.ifft2(mean_freq, axes=(0, 1)).real
pad_img_square = np.square(pad_img)
img_var_freq = xp.fft2(pad_img_square, axes=(0, 1))
var_freq = xp.multiply(img_var_freq, filt_freq)
var_all = xp.ifft2(var_freq, axes=(0, 1))
var_all = var_all.real - xp.power(mean_all, 2)
std_all = xp.sqrt(xp.maximum(0, var_all))
return mean_all, std_all
def query_score(self, show_progress=True):
"""Calculates score for each query image.
Extracts query images and reference windows. Computes the cross-correlation between these
windows, and applies a threshold to compute a score for each query image.
Args:
show_progress: Whether to show a progress bar
Returns:
Matrix containing a score for each query image.
"""
micro_img = xp.asarray(self.im)
logger.info('Extracting query images')
query_box = PickerHelper.extract_query(micro_img, self.query_size // 2)
logger.info('Extracting query images complete')
query_box = xp.conj(xp.fft2(query_box, axes=(2, 3)))
reference_box = PickerHelper.extract_references(micro_img, self.query_size, self.container_size)
reference_size = PickerHelper.reference_size(micro_img, self.container_size)
conv_map = xp.zeros((reference_size, query_box.shape[0], query_box.shape[1]))
def _work(index):
reference_box_i = xp.fft2(reference_box[index], axes=(0, 1))
window_t = xp.multiply(reference_box_i, query_box)
cc = xp.ifft2(window_t, axes=(2, 3))
return index, cc.real.max((2, 3)) - cc.real.mean((2, 3))
n_works = reference_size
n_threads = config.apple.conv_map_nthreads
pbar = tqdm(total=reference_size, disable=not show_progress)
# Ideally we'd like something like 'SerialExecutor' to enable easy debugging
# but for now do an if-else
if n_threads > 1:
with futures.ThreadPoolExecutor(n_threads) as executor:
to_do = [executor.submit(_work, i) for i in range(n_works)]
for future in futures.as_completed(to_do):
i, res = future.result()
conv_map[i, :, :] = res
pbar.update(1)
else:
for i in range(n_works):
_, conv_map[i, :, :] = _work(i)
pbar.update(1)
pbar.close()
conv_map = xp.transpose(conv_map, (1, 2, 0))
min_val = xp.min(conv_map)
max_val = xp.max(conv_map)
thresh = min_val + (max_val - min_val) / config.apple.response_thresh_norm_factor
return xp.asnumpy(xp.sum(conv_map >= thresh, axis=2))
def _work(index):
reference_box_i = xp.fft2(reference_box[index], axes=(0, 1))
window_t = xp.multiply(reference_box_i, query_box)
cc = xp.ifft2(window_t, axes=(2, 3))
return index, cc.real.max((2, 3)) - cc.real.mean((2, 3))
def testFft2(self):
a = xp.random.random((100, 100))
b = xp.fft2(a)
c = xp.ifft2(b)
self.assertTrue(np.allclose(a, c))