This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
import PIL.ImageOps | |
import numpy as np | |
import cv2 | |
from PIL import Image | |
from PIL.ImageOps import invert | |
from scipy.misc import imread | |
from matplotlib import pyplot as plt | |
from scipy.misc import imsave | |
def simple_invert(image): | |
image = np.array(image) | |
return 1 - image[:, :, :3] / 255. | |
def connected_components_with_black_background(image, bw_threshold=255 / 2): | |
bw_image = np.array(image.convert('L')) | |
to_invert = (bw_image > bw_threshold).astype(np.uint8) | |
num_labels, labels, stats, centroids = cv2.connectedComponentsWithStats( | |
to_invert, connectivity=4) | |
if labels[np.where(labels == 0)].mean() == 1: | |
to_invert = (bw_image < bw_threshold).astype(np.uint8) | |
num_labels, labels, stats, centroids = cv2.connectedComponentsWithStats( | |
to_invert, connectivity=4) | |
return num_labels, labels, stats, centroids | |
def get_top_stat_indexes(image, stats, threshold=0.05): | |
top_area_indexes = np.argsort(stats[:, 4])[::-1] | |
top_areas = stats[top_area_indexes, 4] | |
top_area_percents = top_areas / np.prod(image.size) | |
area_indexes_above_threshold = top_area_indexes[:np.argmin(top_area_percents > threshold)] | |
return area_indexes_above_threshold | |
def invert_bright_connected_components(image, boundary_thickness=2, component_pct=0.02): | |
np_image = np.array(image)[:, :, :3] | |
num_labels, labels, stats, centroids = connected_components_with_black_background(image) | |
top_stat_indexes = get_top_stat_indexes(image, stats, component_pct) | |
for idx in range(top_stat_indexes.shape[0]): | |
if top_stat_indexes[idx] == 0: | |
# ignore background | |
continue | |
search_labels = labels == top_stat_indexes[idx] | |
_, contours, _ = cv2.findContours( | |
search_labels.astype(np.uint8) * 255, | |
cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) | |
search_labels = search_labels.astype(np.float) | |
cv2.fillPoly(search_labels, [contours[0]], 1) | |
np_image[np.where(search_labels)] = 255 - np_image[np.where(search_labels)] | |
if boundary_thickness > 0: | |
_, full_contours, _ = cv2.findContours( | |
search_labels.astype(np.uint8) * 255, | |
cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE) | |
contour_im = cv2.drawContours(search_labels, full_contours[0], -1, 0.5, boundary_thickness) | |
np_image[np.where(contour_im == 0.5)] = 255 | |
return np_image |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment