Я пытаюсь использовать метод Оцу, чтобы отделить фон от переднего плана этой картинки. Я делаю это, разбивая изображение на его цветовые компоненты, а затем нахожу пороговые значения для зеленого и красного компонентов. Позже я решу, является ли что-то на переднем плане, увидев, имеет ли оно достаточно высокое значение зеленого, но достаточно низкое значение красного. Проблема в том, что я продолжаю получать пороговые значения около 245, которые, как мне кажется, не такие, какими они должны быть, если вы посмотрите на мою гистограмму. 
Я чувствую, что пороги должны быть до 2 зеленых пиков в районе 150?
Спасибо-вы
def otsu_helper(counts):
'''
Does the arithmetic of the otsu method
:param counts: An array recording how many pixels are in each bin
:return: A threshold found by minimizing the weighted sum of group variances σ^(2)w(t)
'''
# variables for otsu's method
m1t = 0 # mean prob for first group
q1t = 0 # prob that a given greylevel < t
s21t = 0 # variace for the group of values less that t
tot = 0 #keep track of the total amount of pixels captured to bins so far
pix = sum(counts) #The total number of pixels
print(counts)
group1 = np.zeros(shape=(256,2))
#Calculate the mean probability and variances for group1 for all values of t
for i in range(0,256):
j = i+1
bin = counts\[i\]
#print(i,":",bin)
tot += bin
pi = bin/pix
q1t += tot/pix
if (q1t != 0):
m1t += (j * pi )/q1t
s21t += ( (j - m1t)**2 * pi )/q1t
group1\[i\] = \[q1t,s21t\] # Store the probability and variance for later use in finding the sum of group variances
else:
group1\[i\] = \[None,None\]
tot = 0
q2t = 0 # prob that a given greylevel > t
m2t = 0 # mean prob for second group
s22t = 0 # variace for the group of values greater than t
group2 = np.zeros(shape=(256,2))
group2\[255\] = \[None,None\] #At threshold 256, there are no values greater than t
# Calculate the mean probability and variances for group2 for all values of t
for i in range(254, -1, -1):
j = i+1
bin = counts\[j\] #Because all the values from t+1 to G are summed
tot += bin
pi = (bin / pix)
q2t += tot / pix
if (q2t != 0):
m2t += (j*pi)/q2t
s22t += ((j - m2t)**2 * pi)/q2t
group2\[i\] = \[q2t, s22t\]
else:
group2\[i\] = \[None, None\]
s2w = 999999999999 # Weight sum of group variances
t = 0 #The optimal threshold value
#Calculate the weighted sum of group variances for all values of t and save the minimum
for i in range(0,256):
if (not np.isnan(group1\[i,1\]) and not np.isnan(group2\[i:1\])):
s2wt = group1\[i,0\]*group1\[i,1\] + group2\[i,0\]*group2\[i,1\]
print(i,":","{0:.{1}e}".format(s2wt, 1))
if s2w > s2wt:
s2w = s2wt
t = i
return t
def otsu(I):
'''
Finds the optimal threshold of an image
:param I: Input image to find the threshold
:return:A tuple of values that minimizes weighted sum of group variances σ^(2)w(t) for the red and green channel
'''
#Isolating the colour channels and placing them in bins
red = 256 * I\[:, :, 0\] # Zero out contribution from green
green = 256 * I\[:, :, 1\]
blue = 256 * I\[:, :, 2\]
#red, green, blue = np.moveaxis(I, -1, 0)
bins = np.array(range(0,257))
g_counts,pixels = np.histogram(green,bins)
r_counts,pixels = np.histogram(red, bins)
b_counts,pixels = np.histogram(blue,bins)
print('gt = otsu_helper(g_counts)')
gt = otsu_helper(g_counts)
print('rt = otsu_helper(r_counts)')
rt = otsu_helper(r_counts)
# return (gt,rt)
# Creating a histogram of the green colour channel
pixels = pixels\[:-1\]
plt.bar(pixels, g_counts, align='center')
plt.xlim(-1, 256)
plt.show()
return (gt,rt)
