"""
Walk-Assistant : Recognizing sidewalk for the visually impaired
Copyright (C) 2018 Yoongi Kim (devlifecode@outlook.com)
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
"""
import cv2
import numpy as np
class Filter:
def __init__(self, n_cluster=16, zone_h=17, zone_w=31):
self.n_cluster = n_cluster
self.colors = None
self.zone_h = zone_h
self.zone_w = zone_w
def filter_sidewalk(self, img, show=False, roi_w=100, roi_h=50):
# 이미지를 작게 해서 처리속도 향상
img = cv2.resize(img, (480, 270))
blur = Filter.blur(img)
labels = Filter.color_quantization(blur, self.n_cluster, 1)
crop = Filter.roi(labels, roi_w, roi_h, img, show=show)
# cv2.imshow('crop', crop)
self.colors = set(crop.flatten())
main_colors = self.get_main_colors(crop)
# print(main_colors)
match = Filter.binary_match(labels, main_colors)
match = Filter.remove_small_objects(match, 3000)
if show:
cv2.imshow('match', match)
activation = cv2.resize(match, (self.zone_w, self.zone_h))
if show:
cv2.imshow('result', cv2.resize(activation, (480, 270)))
if cv2.waitKey(1) & 0xFF == ord('q'):
print('User Interrupted')
exit(1)
# cv2.waitKey(0)
np_arr = np.array(activation).reshape(self.zone_h, self.zone_w, 1)
np_arr = np_arr / 255.0
return np_arr
def get_main_colors(self, img):
count = [0 for i in range(self.n_cluster)]
for i in img.flatten():
count[i] += 1
avg = np.average(count)
main_colors = []
for i in range(len(count)):
if count[i] > avg:
main_colors.append(i)
return main_colors
@staticmethod
def remove_small_objects(img, min_size=150):
# find all your connected components (white blobs in your image)
nb_components, output, stats, centroids = cv2.connectedComponentsWithStats(img, connectivity=8)
# connectedComponentswithStats yields every seperated component with information on each of them, such as size
# the following part is just taking out the background which is also considered a component, but most of the time we don't want that.
sizes = stats[1:, -1]
nb_components = nb_components - 1
# your answer image
img2 = img
# for every component in the image, you keep it only if it's above min_size
for i in range(0, nb_components):
if sizes[i] < min_size:
img2[output == i + 1] = 0
return img2
@staticmethod
def binary_match(img, search_list, mask=255):
h, w, c = img.shape
match = np.zeros(img.shape, dtype=np.uint8)
for row in range(h):
for col in range(w):
if img[row][col][0] in search_list:
match[row][col] = mask
return match
@staticmethod
def color_quantization(img, n_cluster, iteration, epsilon=1.0):
Z = img.reshape((-1, 3))
Z = np.float32(Z)
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, iteration, epsilon)
ret, label, center = cv2.kmeans(Z, n_cluster, None, criteria, iteration, cv2.KMEANS_PP_CENTERS)
labels = label.reshape((img.shape[0], img.shape[1], 1))
# center = np.uint(center)
# visual = center[label.flatten()]
# visual = visual.reshape(img.shape)
# visual = np.uint8(visual)
return labels
@staticmethod
def blur(img):
return cv2.bilateralFilter(img, 9, 75, 75)
@staticmethod
def roi(labels, width, height, original_img, show=False):
h, w, c = labels.shape
x_center = int(w/2)
roi_size = width
x_start = int(x_center - roi_size/2)
x_end = int(x_center + roi_size/2)
if show:
rect = cv2.rectangle(original_img.copy(), (x_start, h - height), (x_end, h), (0, 255, 255), 2)
cv2.imshow('roi', rect)
crop = labels[h - height:h, x_start:x_end]
return crop
if __name__ == '__main__':
img = cv2.imread('data/test.png')
filter = Filter()
activation = filter.filter_sidewalk(img)
cv2.imshow('result', cv2.resize(activation, (480, 270)))
cv2.waitKey(0)
