def build_detection_stack_image(ctx, preprocess=True, smooth=False):
stack_mgr = imgutils.StackedImageManager()
stack_mgr_snr = imgutils.StackedImageManager()
stack_mgr_count = imgutils.StackedImageManager()
for file in ctx.files:
img = ctx.open_file(file)
# post processing and alignwill happens there
results = ctx.detection(img)
stack_mgr.add(img)
stack_mgr_file_snr = imgutils.StackedImageManager()
stack_mgr_file_count = imgutils.StackedImageManager()
for segments in results:
img = segments.get_img()
# img.data = img.data / segments.get_rms_noise()
# img.data[segments.get_labels() == 0] = 0
stack_mgr_file_snr.add(img, action='mean')
img.data = (segments.get_labels() > 0).astype(np.float)
stack_mgr_file_count.add(img, action='max')
stack_mgr_snr.add(stack_mgr_file_snr.get(), action='add')
stack_mgr_count.add(stack_mgr_file_count.get(), action='add')
stack_img = stack_mgr.get()
img_snr = stack_mgr_snr.get()
img_count = stack_mgr_count.get()
if smooth:
img_snr.data = nputils.smooth(img_snr.data, 2, mode='same')
img_count.data = nputils.smooth(img_count.data, 2, mode='same')
return stack_img, img_snr, img_count
def get_mean_ncc_scales(self, smooth_len=3):
mean_global_ncc_scales = dict()
for scale, ncc in self.get_result():
mean_global_ncc_scales[scale] = nputils.smooth(self.agg_fct(
ncc, axis=0),
smooth_len,
mode='same')
return mean_global_ncc_scales
def get_mean_ncc_scales(self, smooth_len=3):
mean_global_ncc_scales = dict()
for scale, ncc in self.get_result():
mean_global_ncc_scales[scale] = nputils.smooth(self.agg_fct(ncc, axis=0), smooth_len, mode='same')
return mean_global_ncc_scales
