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import numpy as np
from scipy.ndimage.filters import gaussian_filter, median_filter
from atoms_detection.fast_filters import median_filter_parallel
from PIL import Image
def preprocess_jpg(np_img: np.ndarray) -> np.ndarray:
return np_img[:, :, 0]
def dl_prepro_image(np_img: np.ndarray, ruler_units=None, clip=1):
# np_bg = gaussian_filter(np_img, sigma=20)
if len(np_img.shape) == 3:
np_img = preprocess_jpg(np_img)
scale_factor = None
if ruler_units is not None:
try:
ruler_size = get_ruler_size(np_img)
np_img, scale_factor = rescale_img_to_target_pxls_nm(
np_img, ruler_size, ruler_units
)
except Exception:
pass
print("WARNING, MANUAL CLIP USAGE")
clip = 0.999
max_allowed = np.quantile(np_img, q=clip)
np_img = np.clip(np_img, a_min=0, a_max=max_allowed)
try:
np_bg = median_filter_parallel(np_img, 40, splits=4)
except Exception as e:
print(e)
print("Median filter failed, using slower scipy version")
np_bg = median_filter(np_img, 40)
np_clean = np_img - np_bg
np_clean[np_clean < 0] = 0
np_normed = (np_clean - np_clean.min()) / (np_clean.max() - np_clean.min())
# np_normed = (np_img - np_img.min()) / (np_img.max() - np_img.min())
from matplotlib import pyplot as plt
if scale_factor is not None:
return np_normed, scale_factor
return np_normed
def cv_prepro_image(img: np.ndarray) -> np.ndarray:
bg_img = gaussian_filter(img, sigma=10)
clean_img = img - bg_img
normed_img = (clean_img - clean_img.min()) / (clean_img.max() - clean_img.min())
return normed_img
def get_ruler_size(img: np.ndarray) -> int:
ruler_start_location_percent = 0.0625 # empirically located here in samples
ruler_start_coords = int(
img.shape[0] * (1 - ruler_start_location_percent) - 1
), int(img.shape[1] * ruler_start_location_percent)
if img[ruler_start_coords] != img.max():
print("Ruler start position verification failed, skipping rescaling")
raise Exception
else:
ruler_iterator = ruler_start_coords
while img[ruler_iterator] == img[ruler_start_coords]:
ruler_iterator = ruler_iterator[0], ruler_iterator[1] + 1
return ruler_iterator[1] - ruler_start_coords[1]
def rescale_img_to_target_pxls_nm(
img: np.ndarray, ruler_pixels: int, ruler_units: int, atom_prior=None
):
target_scale = (
512 / 15
) # original images were 512x512 and labelled 15nm across, 34 pixels per nano
pixels_per_nanometer = ruler_pixels / ruler_units # current pixels per nano
scaling_factor = target_scale / pixels_per_nanometer
new_dimensions = int(img.shape[0] * scaling_factor), int(
img.shape[1] * scaling_factor
)
if atom_prior is None:
return np.array(Image.fromarray(img).resize(new_dimensions)), scaling_factor
else:
raise NotImplementedError
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