# -*- coding: utf-8 -*-



import cv2
import numpy as np
from matplotlib import pyplot as plt

img = cv2.imread('lena_gray.jpg',0)
ret,thresh1 = cv2.threshold(img,127,255,cv2.THRESH_BINARY)
ret,thresh2 = cv2.threshold(img,127,255,cv2.THRESH_BINARY_INV)
ret,thresh3 = cv2.threshold(img,127,255,cv2.THRESH_TRUNC)
ret,thresh4 = cv2.threshold(img,127,255,cv2.THRESH_TOZERO)
ret,thresh5 = cv2.threshold(img,127,255,cv2.THRESH_TOZERO_INV)

titles = ['Original Image','BINARY','BINARY_INV','TRUNC','TOZERO','TOZERO_INV']
images = [img, thresh1, thresh2, thresh3, thresh4, thresh5]

for i in xrange(6):
    plt.subplot(2,3,i+1),plt.imshow(images[i],'gray')
    plt.title(titles[i])
    plt.xticks([]),plt.yticks([])

plt.show()

import cv2
import numpy as np
from matplotlib import pyplot as plt
img = cv2.imread('2.jpg',0)
# 中值滤波
img = cv2.medianBlur(img,5)
ret,th1 = cv2.threshold(img,127,255,cv2.THRESH_BINARY)
#11 为 Block size, 2 为 C 值
th2 = cv2.adaptiveThreshold(img,255,cv2.ADAPTIVE_THRESH_MEAN_C,\
cv2.THRESH_BINARY,11,2)
th3 = cv2.adaptiveThreshold(img,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,\
cv2.THRESH_BINARY,11,2)
titles = ['Original Image', 'Global Thresholding (v = 127)',
'Adaptive Mean Thresholding', 'Adaptive Gaussian Thresholding']
images = [img, th1, th2, th3]
for i in xrange(4):
	plt.subplot(2,2,i+1),plt.imshow(images[i],'gray')
	plt.title(titles[i])
	plt.xticks([]),plt.yticks([])
plt.show()

import cv2
import numpy as np
from matplotlib import pyplot as plt
img = cv2.imread('2.jpg',0)
#img = cv2.imread('lena_gray.jpg',0)
# global thresholding
ret1,th1 = cv2.threshold(img,127,150,cv2.THRESH_BINARY)
# Otsu's thresholding
ret2,th2 = cv2.threshold(img,0,100,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
# Otsu's thresholding after Gaussian filtering
#（5,5）为高斯核的大小， 0 为标准差
blur = cv2.GaussianBlur(img,(5,5),0)
# 阈值一定要设为 0！
ret3,th3 = cv2.threshold(blur,0,100,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
# plot all the images and their histograms
images = [img, 0, th1,
			img, 0, th2,
			blur, 0, th3]
titles = ['Original Noisy Image','Histogram','Global Thresholding (v=127)',
			'Original Noisy Image','Histogram',"Otsu's Thresholding",
			'Gaussian filtered Image','Histogram',"Otsu's Thresholding"]
# 这里使用了 pyplot 中画直方图的方法， plt.hist, 要注意的是它的参数是一维数组
# 所以这里使用了（numpy）ravel 方法，将多维数组转换成一维，也可以使用 flatten 方法
#ndarray.flat 1-D iterator over an array.
#ndarray.flatten 1-D array copy of the elements of an array in row-major order.
for i in xrange(3):
	plt.subplot(3,3,i*3+1),plt.imshow(images[i*3],'gray')
	plt.title(titles[i*3]), plt.xticks([]), plt.yticks([])
	plt.subplot(3,3,i*3+2),plt.hist(images[i*3].ravel(),256)
	plt.title(titles[i*3+1]), plt.xticks([]), plt.yticks([])
	plt.subplot(3,3,i*3+3),plt.imshow(images[i*3+2],'gray')
	plt.title(titles[i*3+2]), plt.xticks([]), plt.yticks([])
plt.show()