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predict(12).py
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predict(12).py
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import os
import glob
import cv2
import numpy as np
#import matplotlib.pyplot as plt
#import matplotlib.gridspec as gridspec
#目标：将test图片切分成不重合的小块，分别进行：
# 正、左、右、下和它们的镜像8种操作，
# 再进入模型预测，结果还原求最大概率:
# 结果求和，>=4置1，<4置0
def Mirror_x(img): #关于x轴镜像
new = cv2.flip(img, 0)
return new
def Mirror_y(img): #关于y轴镜像
new = cv2.flip(img, 1)
return new
def AntiClock90(img): #逆时针旋转90度
trans = cv2.transpose(img) #矩阵转置
new = cv2.flip(trans, 0) #关于x轴镜像
return new
def Clock90(img): #顺时针旋转90度
trans = cv2.transpose(img) #矩阵转置
new = cv2.flip(trans, 1) #关于y轴镜像
return new
def png_generator(file_path, file_savepath):
print(' <.tif to .png >'+file_path+'-->'+file_savepath+':')
i=0
for files in glob.glob(file_path+"/*.tif"): #遍历image每个文件
i+=1
filepath,filename = os.path.split(files)
print(filename+' '+'%d'%(i-1)+".png")
x = cv2.imread(files, -1)
m = x.max()
q = (x*255)/m #标准化
cv2.imwrite(file_savepath+'/'+'%d'%(i-1)+".png", q) #转png
print(' new_png_number = '+'%d'%i)
return i
#将图片(500,600)转为(256,256)2*3=6张
def test_cut(img, savepath):
print('test_cut')
print(img+'-->'+savepath+':')
k=0
a = [0, 244]
b = [0, 214, 344]
for p in range(0,2): #竖着2张
for q in range(0,3): #横着3张
k+=1
#filepath,filename = os.path.split(files)
i = a[p]
j = b[q]
x = cv2.imread(img, -1)
m = x[i : (256+i), j : (256+j) ]
cv2.imwrite(savepath+"/"+'%d'%(k-1)+".png", m)
print(' number = '+'%d'%k) #2*3 = 6
def test_generator(imgpath, savepath): #6---->(6*12)
k=0
for tt in range (0,6):
file = imgpath+"/%d"%tt+".png"
filepath,filename = os.path.split(file)
print(filename)
#filepath,filename = os.path.split(files)
img = cv2.imread(file, -1)
cv2.imwrite(savepath+"/"+"%d"%(0 + 12*k)+".png", img)
cv2.imwrite(savepath+"/"+"%d"%(1 + 12*k)+".png", Mirror_x(img))
cv2.imwrite(savepath+"/"+"%d"%(2 + 12*k)+".png", Mirror_y(img))
img = AntiClock90(img)
cv2.imwrite(savepath+"/"+"%d"%(3 + 12*k)+".png", img)
cv2.imwrite(savepath+"/"+"%d"%(4 + 12*k)+".png", Mirror_x(img))
cv2.imwrite(savepath+"/"+"%d"%(5 + 12*k)+".png", Mirror_y(img))
img = AntiClock90(img)
cv2.imwrite(savepath+"/"+ "%d"%(6 + 12*k)+".png", img)
cv2.imwrite(savepath+"/"+"%d"%(7 + 12*k)+".png", Mirror_x(img))
cv2.imwrite(savepath+"/"+"%d"%(8 + 12*k)+".png", Mirror_y(img))
img = AntiClock90(img)
cv2.imwrite(savepath+"/"+"%d"%(9 + 12*k)+".png", img)
cv2.imwrite(savepath+"/"+"%d"%(10 + 12*k)+".png", Mirror_x(img))
cv2.imwrite(savepath+"/"+"%d"%(11 + 12*k)+".png", Mirror_y(img))
k+= 1
print(' number = '+'%d'%(k*12)) #12*6 = 72
from model import *
from data import *
def test_result(path, savepath, n = 72):
# python test.py
"""
注：
A: target_size()为图片尺寸，要求测试集图像尺寸设置和model输入图像尺寸保持一致，
如果不设置图片尺寸，会对输入图片做resize为处理，输入网络和输出图像尺寸默认均为（256,256），
B: 且要求图片位深为8位，24/32的会报错！！
C: 测试集数据名称需要设置为：0.png……
D：model.predict_generator( ,n, ):n为测试集中样本数量，需要手动设置，不然会报错！！
"""
# 输入测试数据集，
testGene = testGenerator(path , n,target_size = (256,256)) # data
# 导入模型
model = unet(input_size = (256,256,1)) # model
# 导入训练好的模型
model.load_weights("unet_model_300.hdf5")
# 预测数据
results = model.predict_generator(testGene, n, verbose=1) # keras
#print(results)
saveResult(savepath, results) # data
print("over")
def mask_together(maskpath, savepath): # (6*12)---->6
k = 0
a = [ ]
for k in range (0,72):
file = maskpath+"/%d"%k+".png"
i = k%12
filepath,filename = os.path.split(file)
print(filename)
x = cv2.imread(file, -1)
a.append(x) #图片进表
if (i==11) : #将图片回归原方向
a[1] = Mirror_x(a[1])
a[2] = Mirror_y(a[2])
a[3] = Clock90(a[3])
a[4] = Mirror_x(a[4])
a[4] = Clock90(a[4])
a[5] = Mirror_y(a[5])
a[5] = Clock90(a[5])
a[6] = Clock90(a[6])
a[6] = Clock90(a[6])
a[7] = Mirror_x(a[7])
a[7] = Clock90(a[7])
a[7] = Clock90(a[7])
a[8] = Mirror_y(a[8])
a[8] = Clock90(a[8])
a[8] = Clock90(a[8])
a[9] = AntiClock90(a[9])
a[10] = Mirror_x(a[10])
a[10] = AntiClock90(a[10])
a[11] = Mirror_y(a[11])
a[11] = AntiClock90(a[11])
for o in range(0,12):
u = a[o]
cv2.imwrite("predict/out12change"+"/"+ "%d"%(o+k-11)+".png", a[o]) #中间过程
u =a[o]
u = u/(u.max()) #每张图最大值归一化
#u[u<=0.5] *= 0.5
#u[u>0.5] *= 1.5
u = u-0.5 #为了求和黑的更黑白的更白
a[o] = u
#u[u>=0] = 1 #此除优先二值化，可以保留细节特征，但是整体图像效果下降
#u[u<0] = -1
mask = a[0]+a[1]+a[2]+a[3]+a[4]+a[5]+a[6]+a[7]+a[8]+a[9]+a[10]+a[11]
mask = mask/12 + 0.5 #取平均，恢复（0,1）
maskmax = mask.max() #最大值归一化
mask = mask/maskmax
write1 = mask*255
name = (k-11)/12 #文件名
cv2.imwrite(savepath+"/"+ "%d"%name+"_ori.png", write1)
mask[mask>0.5]=255
mask[mask<=0.5]=0
cv2.imwrite(savepath+"/"+ "%d"%name+"_bi.png", mask)
a = [ ] #列表更新
print('\n')
print(' finish '+'%d'%(name)+'/6')
def result(maskpath, savepath, typename, namenumber): #6---->1
i = 0
x = [ ]
for k in range(0,6):
file = maskpath+"/%d"%k+typename+".png"
filepath,filename = os.path.split(file)
print(filename)
x.append(cv2.imread(file, -1))
i+=1
y = x[1]
x[1] = y[: , 42 : 130]
#左右合并
line1 = np.concatenate([x[0], x[1]], axis = 1)
line1 = np.concatenate([line1, x[2]], axis = 1)
line1 = line1[0 : 244, :]
y = x[4]
x[4] = y[: , 42 : 130]
#左右合并
line2 = np.concatenate([x[3], x[4]], axis = 1)
line2 = np.concatenate([line2, x[5]], axis = 1)
mask = np.vstack((line1, line2)) #上下合并
print(mask.shape)
cv2.imwrite(savepath+"/%d"%namenumber +typename+ ".png", mask)
print('\n')
#将二值png转tif
def tif_generator(file_path, file_savepath):
print(' <.png to .tif >'+file_path+'-->'+file_savepath+':')
i=0
for files in glob.glob(file_path+"/*_bi.png"): #遍历image每个文件,后缀随前程序为_ori
i+=1
filepath,filename = os.path.split(files)
x = cv2.imread(files, -1)/255
x[x>=0.5] = 1
x[x<0.5] = 0
cv2.imwrite(file_savepath+'/'+filename+".tif", x) #转png
print(' new_tif_number = '+'%d'%i)
#路径是数据文件夹相对路径
tifpath1 = r"predict/tif_question" #存放原始卫星tif文件夹
in1 = r"predict/in1" #
in6 = r"predict/in6"
in12 = r"predict/in12"
out12 = r"predict/out12"
out6 = r"predict/out6"
out1 = r"predict/out1"
tifpath2 = r"predict/tif_result" #存放标注结果tif文件夹
n = png_generator(tifpath1, in1 )#tif图片数目
for number in range(0,n):
test_cut(in1+'/%d.png'%number, in6)
test_generator(in6, in12)
test_result(in12, out12, 72)
mask_together(out12, out6)
result(out6, out1, '_ori', number) #灰度图结果
result(out6, out1, '_bi', number) #二值图预测结果
print('finish_predict %d / %d'%(number+1, n))
tif_generator(out1, tifpath2)