图片卷积运算

一个图片有三个通道RGB，每个通道就是一层数据
以一个图片为例子，从图片数据，再由数据到图片转化过程，理解数据与图形以及表示的关系

兔子

from PIL import Image
#打开图片
im = Image.open('tuzi.jpg')
#导入像素
pix = im.load()
#获取宽度
width = im.size[0]
print '图片宽%s'%width
#获取高度
height = im.size[1]
print '图片高%s'%height
# tuzi保存每个像素点值
tuzi = []
for x in range(height):   
    for y in range(width):       
        r, g, b = pix[y, x]
        # 每个点像素包含rgb三个通道
        # 注意这里读取顺序，我是横着读取，加到数组中，
        # 如果竖着读取会把图搞反了，可以自己脑补下。
        tuzi+=[r,g,b]

我们将tuzi,reshape成图片的样子

tuziArr = np.array(tuzi).reshape([height,width,3])

from matplotlib.font_manager import FontProperties  
  
def getChineseFont():  
    return FontProperties(fname='/System/Library/Fonts/PingFang.ttc')  

%matplotlib inline
from PIL import Image
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
matplotlib.rcParams['figure.figsize'] = (20.0, 20.0)
# channel表示图片通道，取某一个通道数据方法[:,:,x]
def imshow(channel, ax, picture_data):
    tt = np.array([0]*rt.shape[0]*rt.shape[1]*3).reshape(
            [rt.shape[0],rt.shape[1], 3])
    if channel == 3:
        # 全部通道
        tt = picture_data
    else:
        tt[:,:,channel]=picture_data[:,:,channel]
    tt_img = np.array(tt, dtype='uint8')
    ax.imshow(tt_img)
    #help(ax)

plt.figure()
N = 4
fig, axes = plt.subplots(1,N)
for i in range(N):
    imshow(i, axes[i], tuziArr)
plt.show()

# 生成0-26序列，reshape成[1,3,3,3],大小3X3，3通道，每个点就
# 是RGB三个值如[0,1,2]代表三个点像素值,看到点就是下面排列
# (0,1,2)    (3,4,5)    (6,7,8)
# (9 10 11)  (12 13 14) (15 16 17 )
# (18 19 20) (21 22 23) (24 25 26)

用numpy来操作一下

temp = np.array(xrange(27)).reshape([1,3,3,3])
print temp

取0行1列所有通道值

temp[0,0,1,:]

取第二个通道值

temp[0,:,:,1]

下面用tensorflow来运算卷积

• filter只输出一个通道情况

# 生成0-26序列，reshape成[1,3,3,3],大小3X3，3通道，每个点就
# 是RGB三个值如[0,1,2]代表三个点像素值
# (0,1,2)    (3,4,5)    (6,7,8)

# (9 10 11)  (12 13 14) (15 16 17 )
# (18 19 20) (21 22 23) (24 25 26)

#  一个图片，3X3,通道3

input = tf.constant(np.array(xrange(27)).reshape([1,3,3,3]), 
                dtype = tf.float32)

#  高1，宽1，输入通道3，输出通道1
filter = tf.constant(np.array(xrange(3)).reshape([1,1,3,1]), 
                dtype=tf.float32)

op = tf.nn.conv2d(input, filter, strides = [1,1,1,1],padding ='VALID')

init = tf.global_variables_initializer()
with tf.Session() as sess:
    sess.run(init)
    input,filter, result = sess.run([input,filter, op])
    print '------filter------'
    print filter
    print '------第0个通道------'
    print input[0,:,:,0]
    print '-----卷积结果------'
    print(result)

输出是filter扫过

# (0,1,2)    (3,4,5)    (6,7,8)
# (9 10 11)  (12 13 14) (15 16 17 )
# (18 19 20) (21 22 23) (24 25 26)

运算结果

• 输出两个通道情况

input = tf.constant(np.array(xrange(27)).reshape([1,3,3,3]), 
                dtype = tf.float32)

#  高1，宽1，输入通道3，输出通道2
filter = tf.constant(np.array(xrange(6)).reshape([1,1,3,2]), 
                dtype=tf.float32)

op = tf.nn.conv2d(input, filter, strides = [1,1,1,1],padding ='VALID')

init = tf.global_variables_initializer()
with tf.Session() as sess:
    sess.run(init)
    input,filter, result = sess.run([input,filter, op])
    print '------filter------'
    print filter
    print '------第0个通道------'
    print input[0,:,:,0]
    print '-----卷积结果------'
    print(result)

输出是2个通道的filter扫过

# (0,1,2)    (3,4,5)    (6,7,8)
# (9 10 11)  (12 13 14) (15 16 17 )
# (18 19 20) (21 22 23) (24 25 26)

的结果

现在加深一个难度,之前的卷积核filter是[1，1，3，1]和[1,1,3,2]

现在使用卷积核[2,2,3,1]

input

0,1,2	3,4,5	6,7,8
9,10,11	12,13,14	15,16,17
18,19,20	21,22,23	24,25,26

filter

0,1,2	3,4,5
6,7,8	9,10,11

#  一个图片，3X3,通道3

input = tf.constant(np.array(xrange(27)).reshape([1,3,3,3]), 
                dtype = tf.float32)

#  高2，宽2，输入通道3，输出通道1
filter = tf.constant(np.array(xrange(12)).reshape([2,2,3,1]), 
                dtype=tf.float32)

op = tf.nn.conv2d(input, filter, strides = [1,1,1,1],padding ='VALID')

init = tf.global_variables_initializer()
with tf.Session() as sess:
    sess.run(init)
    input,filter, result = sess.run([input,filter, op])
    print '------filter------'
    print filter
    print '-----卷积结果------'
    print(result)

659	857
1253	1451

现在使用卷积核[2,2,3,2]

input = tf.constant(np.array(xrange(18)).reshape([1,3,3,2]), 
                dtype = tf.float32)

#  高2，宽2，输入通道3，输出通道1
filter = tf.constant(np.array(xrange(32)).reshape([2,2,2,4]), 
                dtype=tf.float32)

op = tf.nn.conv2d(input, filter, strides = [1,1,1,1],padding ='VALID')

init = tf.global_variables_initializer()
with tf.Session() as sess:
    sess.run(init)
    input,filter, result = sess.run([input,filter, op])
    print input
    print '------filter------'
    print filter[:,:,:,0]
    #print '------第0个通道------'
    #print input[0,:,:,0]
    print '-----卷积结果------'
    print(result)

# input
# (0,1)  (2,3)  (4,5)
# (6,7)  (9,9)  (10,11)
# (12,13)(14,15)(16,17)

# filter
# [(0,1,2,3)   (4,5,6,7)] [(8,9,10,11) (12,13,14,15)]
# [(16,17,18,19)(20,21,22,23)] [(24,25,26,27)(28,29,30,31)]