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simple_search.py
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simple_search.py
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#filename : simple_search.py
#author : PRAJWAL T R
#date last modified : Mon SEPT 04 11:09:55 2020
#comments :
'''
simpleSearch performs a DFS search to find connected strokes, which is useful of stroke segmentation
and obtaining Lcon for local model while running in real time, given the start pixel and size of image(search space)
'''
# imports
import copy as cp
import numpy as np
stack = []
visited = {}
# Constants
PROCESSING = 1
VISITED = 2
UNVISITED = 0
threshold = 0
def getSliceWindow(current_xy):
# cordinates for slicing 3*3 window
slice_begin = [0, 0] # pass by reference sucks some times
slice_begin[1] = current_xy[1] - 1
slice_begin[0] = current_xy[0] - 1
return slice_begin
def findAdjacent(current_xy, img, threshold):
neighbourers = []
# changes made here stays here !
img = cp.deepcopy(img)
# get search window
slice_begin = getSliceWindow(current_xy)
img[current_xy[1], current_xy[0]] = 0 # exclude current point from searching
img_slice = img[slice_begin[1] : slice_begin[1] + 3, slice_begin[0] : slice_begin[0] + 3]
# get cordinates of neighbourers above threshold
cord = np.where(img_slice > threshold)
# offset withing search window
delta = [[i - 1, j - 1] for i, j in zip(cord[1], cord[0])] # inverted axis x,y = y,x
# add delta to current_xy to get global coordinate
for d in delta:
neighbourers.append([current_xy[0] + d[0], current_xy[1] + d[1]])
return neighbourers
def simpleSearch(start, img, size):
connected_points = []
# check if start cordinate is false start
if(img[start[1]][start[0]] == 0):
# no possible connected stroke can be found
connected_points.append(list(start))
return connected_points
# prepare visited structure
for i in range(size):
for j in range(size):
visited[[i, j].__repr__()] = UNVISITED
visited[list(start).__repr__()] = PROCESSING
stack.append(list(start))
# DFS search the image for connected strokes
while(len(stack) != 0):
current_xy = stack.pop()
visited[current_xy.__repr__()] = VISITED
connected_points.append(current_xy)
neighbourers = findAdjacent(current_xy, img, threshold)
for neighbour in neighbourers:
if visited[neighbour.__repr__()] == UNVISITED:
visited[neighbour.__repr__()] = PROCESSING
stack.append(neighbour)
# search complete
return connected_points
# test working of algorithm
if __name__ == "__main__":
import cv2 as cv
from skimage.morphology import skeletonize
import matplotlib.pyplot as plt
file = "./test_dir/kanji_samples/0f9a8.png"
thresh_val = 127
thresh = cv.THRESH_BINARY_INV
img = cv.imread(file) # 2 - RBG -> GRAYSCALE
# img = cv.resize(img, (100, 100), cv.INTER_CUBIC)
# blur image to reduce noise
img = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
# img = cv.GaussianBlur(img,(3,3),0) # 1 * 1 kernel
_, img = cv.threshold(img, thresh_val, 255, thresh)
img[np.where(img > 0)] = 1
img = skeletonize(img, method='lee')
# plt.imshow(img, cmap = "Greys_r")
plt.savefig("./test_dir/image.png")
plt.close()
start = (10, 47) # this is given by global model during run time
points = simpleSearch(list(start), img, img.shape[0])
draw_img = np.zeros(img.shape)
# mark points obtained by search algorithm, which are connected
for point in points:
draw_img[point[1], point[0]] = 1
# plt.imshow(draw_img, cmap = "Greys_r")
plt.savefig("./test_dir/connected.png")
plt.close()