def count_loops(img):
global parent
parent = []
for i in range(0, 50 * 50):
parent.append(i)
img = cv2.resize(img, (50, 50), interpolation=cv2.INTER_CUBIC)
img = pre.otsu_thresh(img)
dir = [[1, 1], [1, 0], [1, -1], [0, 1], [0, -1], [-1, 1], [-1, 0],
[-1, -1]]
r, c = img.shape
for i in range(0, r):
for j in range(0, c):
if img[i, j] == 0:
continue
for k in range(0, 8):
x = i + dir[k][0]
y = j + dir[k][1]
if bounds(x, y, r, c) and img[x, y] != 0:
parenta = find(i * r + j)
parentb = find(x * r + y)
if parenta != parentb:
union(parenta, parentb)
cnt = 0
for i in range(0, r * c):
if find(i) == i and img[int(i / r), i % r] != 0:
cnt += 1
l = []
l.append(cnt)
return l
def histo(img):
# img = abs(255 - img)
img = cv2.resize(img, (25, 25), interpolation=cv2.INTER_CUBIC)
img = pre.otsu_thresh(img)
img[img == 255] = 1
# util.display_image(img)
vec = []
vsum = np.sum(img, axis=0)
hsum = np.sum(img, axis=1)
for i in vsum:
vec.append(i)
for i in hsum:
vec.append(i)
return vec
def pre_init(img):
img = pre.cut_image(img)
aspect = img.shape[0] / img.shape[1]
img = cv2.resize(img, (50, 50), interpolation=cv2.INTER_CUBIC)
img = pre.otsu_thresh(img)
new_img = pre.thin_image(img)
# changes for end-points counting
# img = util.cnvt_bool_to_uint8(img)
# kernel = np.ones((3,3),np.uint8)
# dilation = cv2.dilate(img, kernel, iterations=1)
# dilation[dilation==255]=1
return aspect, img, new_img
def give_me_the_equation(img, model):
if len(img) == 0 or len(img[0]) == 0:
return ""
import preprocess as pre
# util.display_image(img, 'do_ittttt')
# print(path)
# Converting light dark pixels ( <= 30 ) to black ( = 0 )
img = pre.filter_image(img)
# Thresholding OTSU
img = pre.otsu_thresh(img)
ans = ali.align(img, model)
print(ans)
# util.display_image(img)
# characters = seg.split_characters(img)
# # for c in characters:
# # util.display_image(c)
# print("Do itt " + str(len(characters)))
# ans = ""
# idx = 0
# pre = 0
# for c in characters:
# y = util.predict_class(c, model)
# if align[idx] == 0:
# if pre == 0:
# ans = ans + str(y)
# else:
# ans = ans + "}" + str(y)
# elif align[idx] == 1:
# if pre == 0:
# ans = ans + "^{" + str(y)
# else:
# ans = ans + str(y)
# else:
# if pre == 0:
# ans = ans + "_{" + str(y)
# else:
# ans = ans + str(y)
# # print(y)
# pre = align[idx]
# idx = idx + 1
# if pre != 0:
# ans = ans + "}"
# # print ("ADITYA")
# # print(ans)
return tx.tolatex(ans)
res = res + recurse(den, model) + "}^{" + recurse(
num, model) + "}"
else:
res = res + "\\sum_{"
res = res + recurse(den, model) + "}^{" + recurse(
num, model) + "}"
previ = i
if prev == -1:
prev = i
if flag == 0:
res = res + dt.give_me_the_equation(img[:, prev:previ + 1], model)
# print('Recurse exit')
return res
# input the image
path = os.getcwd() + '\\TestEquations\\' + 'eq47.jpg'
# print(path)
img = pre.input_image(path)
util.display_image(img)
# align = ali.align(img)
# print("Result from CNN")
# print(recurse(img, "cnn"))
img = pre.filter_image(img)
img = pre.otsu_thresh(img)
print("Result from ANN")
print(recurse(img, "ann"))
util.display_image(img)
def run(img):
#input image in grayscale
# img = pre.input_image('/home/sarthak/ip/text1.png')
# util.display_image(img, 'original')
#filtering of the input image
img = pre.filter_image(img)
# util.display_image(img, 'filtered')
#smoothening the image
# img = preprocess.gaussian_blur(img)
# utility.display_image(img, 'Smoothened')
#binarization/thresholding of the image
img = pre.otsu_thresh(img)
# util.display_image(img, 'binarized')
#horizontal histogram for line segmentation
lines = hist.line_seg(img)
# print(len(lines))
#displaying lines as recieved
# for sline in lines:
# util.display_image(sline)
#vertical histogram for words segmentation
words = []
for ekline in lines:
# util.display_image(ekline)
curr = hist.word_seg(ekline)
words = words + curr
# break #delete it
# print(len(words))
# displaying words of lines as recieved
# for ekword in words:
# util.display_image(ekword)
chars = []
#separating characters from words
# cnt = 0
for ekword in words:
# util.display_image(ekword)
clist = hist.char_seg(ekword)
chars.append(clist)
# displaying characters as recieved
# for clist in chars:
# for ch in clist:
# util.display_image(ch)
# break
return chars
sum = 0
cnt = 0
maxx = -1
for word in chars:
for charr in word:
cnt += 1
maxx = max(maxx, len(charr[0]))
sum += charr.shape[1]
sum = sum/cnt
print(sum)
print(chars[0][2].shape)
final = []
for word in chars:
new_word = []
for charr in word:
if charr.shape[1] > maxx:
histo = img.sum(axis=0)
minn = 1000
pos = -1
for i in range(0, len(histo)):
if histo[i] < minn:
minn = histo[i]
pos = i
new_word.append(charr[:,:pos+1])
new_word.append(charr[:, pos:])
else:
new_word.append(charr)
final.append(new_word)
# for clist in final:
# for ch in clist:
# util.display_image(ch)
# break
return final
# img = cv2.imread('/home/sarthak/ip/hello.jpg', 0)
# run(img)