I am having a program to classify pollen in 2 types: sugi and hinoki. The teaching data are 100x100 images of only 1 pollen, and the real data are bigger images with lots of pollen inside (which I used opencv to mark circle in each pollen, with suitable color according to its type).
Teacher asked me to change the program into a new version using vgg (16 or 19), but I really dont know how. I have looked up in the internet, and most of versions use image_net weight, not trainable one. There is only this version seem to be useful but I dont know how to change it to fit my program . I am really new to neuron network and tensorflow, so please help me.
Here is my program.
``# coding: UTF-8
#opencv bgr
#rgb
import numpy as np
import tensorflow as tf
import os
import sys
import csv
import random
from datetime import datetime
from PIL import Image
from matplotlib import pylab as plt
from time import sleep
import cv2
#import cv2.cv as cv
DataShape = (100,100,3)
Ratio = 1#2000.0
TestNum = 100
def show_img(dataDir):
data = np.load(dataDir)#.astype(float)
#label = int(row[LabelIdx])
#data += np.random.normal(0,0.1*np.max(data),(512,512))
#data -= np.min(data)
#data = data*255/np.max(data)
#print name
#print data.shape
#if name=="os10-020_cancer_full.npy":
for i in range(data.shape[0]):
print (i)
#lt.imshow(data[i])
#plt.show()
#plt.imshow(data[data.shape[0]-ZIdx])
#plt.show()
#pilImg = Image.fromarray(np.uint8(data[15]))
#pilImg.show()
#sleep(1)
def show_npy(dataList):
#data = np.load(dataDir)#.astype(float)
#label = int(row[LabelIdx])
#data += np.random.normal(0,0.1*np.max(data),(512,512))
#data -= np.min(data)
#data = data*255/np.max(data)
#print name
#print data.shape
#if name=="os10-020_cancer_full.npy":
for i in range(len(dataList[0])):
#print i
#plt.imshow(dataList[2][i])
#plt.show()
pilImg = Image.fromarray(np.uint8(dataList[2][i]*255.0))
pilImg.show()
sleep(1)
#plt.imshow(data[data.shape[0]-ZIdx])
#plt.show()
#pilImg = Image.fromarray(np.uint8(data[15]))
#pilImg.show()
#sleep(1)
# nameList : list of each data name
# labelList : list of teaching data (written by 0 or 1 and 3 calams)
# dataList : 4 dimensionary data
def GetData():
sugi_data = np.load('sugi.npy')/255.0
hinoki_data = np.load('hinoki.npy')/255.0
nameList = []
labelList = []
dataList = []
print (sugi_data.shape)
print (hinoki_data.shape)
#sys.exit()
repeat = np.min((len(sugi_data),len(hinoki_data)))%100
for i_repeat in range(repeat):
for i,data in enumerate(sugi_data[i_repeat*100:(i_repeat+1)*100]):
name = 'sugi_'+str(i)
data = np.reshape(data,[DataShape[0],DataShape[1],DataShape[2]])
label = 0
l = np.zeros(2)
l[label] += 1
labelList.append(l)
nameList.append([name])
dataList.append(data)
print (len(sugi_data))
for i,data in enumerate(hinoki_data[i_repeat*100:(i_repeat+1)*100]):
name = 'hinoki_'+str(i)
data = np.reshape(data,[DataShape[0],DataShape[1],DataShape[2]])
label = 1
l = np.zeros(2)
l[label] += 1
labelList.append(l)
nameList.append([name])
dataList.append(data)
#print len(hinoki_data)
#print np.array(dataList).shape
#print np.max(dataList)
#data_shuffle = zip(nameList,labelList,dataList)
#random.shuffle(data_shuffle)
#return zip(*data_shuffle)
return nameList,labelList,dataList
# dataList : 4 dimensionary data
# testIdxList : 1=test data , 0 = training data
def GetDataset(dataList,testIdxList):
teDataList = []
trDataList = []
for i in range(len(dataList)):
if i in testIdxList:
teDataList.append(dataList[i])
else:
trDataList.append(dataList[i])
return np.vstack([teDataList]),np.vstack([trDataList])
def Train(alldata,testIdxList,resultName):
print ("start train initialize")
os.mkdir("output/" + resultName)
print ("get dataset")
ten,trn = GetDataset(alldata[0], testIdxList)
tel,trl = GetDataset(alldata[1], testIdxList)
ted,trd = GetDataset(alldata[2], testIdxList)
#tef,trf = GetDataset(alldata[3], testIdxList)
#print trd.shape
#sys.exit()
#mizumasi
#data_num = len(trd)
#nlist = []
#llist = []
#dlist = []
#flist = []
#nlist.extend(trn)
#llist.extend(trl)
#dlist.extend(trd)
#flist.extend(trf)
#print data_num
#label_num = np.zeros(3).astype(np.int32)
#for i in range(data_num):
# for n in range(3):
# if trl[i][n]==1 :
# label_num[n]+=1
#print label_num
#for n in range(3):
# for j in range(100-label_num[n]):
# flag=1
# while(flag==1):
# i = random.randint(0,data_num-1)
# if trl[i][n]==1:
# flag=0 #
# nlist.append(trn[i])
# llist.append(trl[i])
# data = trd[i] + np.random.normal(0,0.1,(DataShape[0],DataShape[1],DataShape[2],1))
# dlist.append(data)
#flist.append(trf[i])
#for i in range(data_num):
# for j in range(10):
# nlist.append(trn[i])
# llist.append(trl[i])
# data = trd[i] + np.random.normal(0,0.1,(DataShape[0],DataShape[1],DataShape[2],1))
# dlist.append(data)
#flist.append(trf[i])
#trn = np.array(nlist)
#trl = np.array(llist)
#trd = np.array(dlist)
print ("generate cnn model")
x = tf.placeholder(tf.float32, [None,DataShape[0],DataShape[1],DataShape[2]])
y = tf.placeholder(tf.float32, [None,2])
conv_W0 = tf.Variable(tf.truncated_normal([5,5,3,32],stddev=0.01), name="conv_W0")
conv_b0 = tf.Variable(tf.zeros([32]), name="conv_b0")
conv_z0 = tf.nn.conv2d(x, conv_W0, [1,1,1,1], "SAME") + conv_b0
conv_u0 = tf.nn.relu(conv_z0)
pool0 = tf.nn.max_pool(conv_u0, [1,2,2,1], [1,2,2,1], "SAME")
norm0 = pool0#tf.nn.lrn(pool0)
conv_W1 = tf.Variable(tf.truncated_normal([5,5,32,64],stddev=0.01), name="conv_W1")
conv_b1 = tf.Variable(tf.zeros([64]), name="conv_b1")
conv_z1 = tf.nn.conv2d(norm0, conv_W1, [1,1,1,1], "SAME") + conv_b1
conv_u1 = tf.nn.relu(conv_z1)
pool1 = tf.nn.max_pool(conv_u1, [1,2,2,1], [1,2,2,1], "SAME")
norm1 = pool1#tf.nn.lrn(pool1, 4, bias=1.0, alpha=0.001 / 9.0, beta = 0.75, name='norm1')
reshapeVal = 25*25*64
flat = tf.nn.dropout(tf.reshape(norm1,[-1,reshapeVal]),0.5)
full_W0 = tf.Variable(tf.truncated_normal([reshapeVal,200],stddev=0.01), name="full_W0")
full_b0 = tf.Variable(tf.zeros([200]), name="full_b0")
full_z0 = tf.matmul(flat, full_W0) + full_b0
full_u0 = tf.nn.relu(full_z0)
full_W = tf.Variable(tf.truncated_normal([200,2],stddev=0.01), name="full_W")
full_b = tf.Variable(tf.zeros([2]), name="full_b")
full_z = tf.matmul(full_u0, full_W) + full_b
full_u = tf.nn.softmax(full_z)
#conv_L2 = tf.nn.l2_loss(conv_W0) + tf.nn.l2_loss(full_W)
loss = -tf.reduce_mean(y*tf.log(tf.clip_by_value(full_u,1e-10,1.0))) #+ 0.001*conv_L2
trainstep = tf.train.AdamOptimizer(1e-5).minimize(loss)
#trainstep = tf.train.GradientDescentOptimizer(0.0001).minimize(loss)
#trainstep = tf.train.FtrlOptimizer(0.0001).minimize(loss)
collect_prediction = tf.equal(tf.argmax(full_u, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(collect_prediction,tf.float32))
print ("server define")
sess = tf.Session()
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver()
#saver.restore(sess, "/home/miyake/Desktop/kahun_ohira/finalmodel.ckpt")
f = open("output/" + resultName+"/LearningResult.csv","w")
writer = csv.writer(f)
writer.writerow(["loop","train loss","test accuracy"])
print ("start learning")
for i in range(1000001):
#for i in range(101):
idxList = []
for j in range(10):
idxList.append(random.randint(0,trd.shape[0]-1))
if i % 10 == 0:
l = sess.run(loss,feed_dict={x:trd[idxList], y:trl[idxList]})
acc = sess.run(accuracy,feed_dict={x:ted,y:tel})
writer.writerow([i,l,acc])
print (str(i) + ": loss = " + str(l) + ", acc = " + str(acc))
if l<0.0001 :
saver.save(sess, datetime.now().strftime('%s')+"finalmodel.ckpt")
break
if i % 1000 == 0:
saver.save(sess, "model.ckpt")
sess.run(trainstep,feed_dict={x:trd[idxList], y:trl[idxList]})
f.close()
print ("output result")
f = open("output/"+resultName+"/TestResult.csv","w")
writer = csv.writer(f)
writer.writerow(["Name","IsCollect"])
isCollect,answer,output,output_data = sess.run((collect_prediction,tf.argmax(y, 1),tf.argmax(full_u, 1),full_u),feed_dict={x:ted,y:tel})
for i in range(len(output)):
writer.writerow([ten[i][0],int(isCollect[i]),answer[i],output[i],output_data[i][0],output_data[i][1]])
tf.reset_default_graph()
def Test(image_path,label,filename,file_path):
limit_data = []
limit_label = []
limit_circles = []
img = cv2.imread(image_path,cv2.IMREAD_COLOR)
img_array = np.array(img)
cimg = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
cimg = cv2.medianBlur(cimg,5)
#_,cimg = cv2.threshold(cimg,0,255,cv2.THRESH_BINARY| cv2.THRESH_OTSU)
#cv2.imwrite(datetime.now().strftime('%s')+"binary.jpg",cimg)
#sys.exit()
#circles = cv2.HoughCircles(cimg,cv.CV_HOUGH_GRADIENT,1,10,param1=10,param2=18,minRadius=10,maxRadius=25)
circles = cv2.HoughCircles(cimg,cv2.HOUGH_GRADIENT,1,20,param1=15,param2=20,minRadius=10,maxRadius=25)
circles = np.uint16(np.around(circles))[0,:]
print (len(circles))
for i in circles:
half = DataShape[0]//2
zoom_data = img_array[i[1]-half:i[1]+half,i[0]-half:i[0]+half,:]/255.0
if zoom_data.shape!=DataShape : continue
limit_data.append(zoom_data)
l = np.zeros(2)
l[label] += 1
limit_label.append(l)
limit_circles.append(i)
#cv2.circle(img,(i[0],i[1]),i[2],(0,255,0),2)
#v2.circle(img,(i[0],i[1]),2,(0,0,255),3)
#print img_array[i[0]-half:i[0]+half,i[1]-half:i[1]+half,:].shape
limit_data = np.array(limit_data)
limit_label = np.array(limit_label)
limit_circles = np.array(limit_circles)
label_num = limit_data.shape[0]
#cv2.imwrite(datetime.now().strftime('%s')+"output.jpg",img)
#sys.exit()
x = tf.placeholder(tf.float32, [None,DataShape[0],DataShape[1],DataShape[2]])
y = tf.placeholder(tf.float32, [None,2])
conv_W0 = tf.Variable(tf.truncated_normal([5,5,3,32],stddev=0.01), name="conv_W0")
conv_b0 = tf.Variable(tf.zeros([32]), name="conv_b0")
conv_z0 = tf.nn.conv2d(x, conv_W0, [1,1,1,1], "SAME") + conv_b0
conv_u0 = tf.nn.relu(conv_z0)
pool0 = tf.nn.max_pool(conv_u0, [1,2,2,1], [1,2,2,1], "SAME")
norm0 = pool0#tf.nn.lrn(pool0)
conv_W1 = tf.Variable(tf.truncated_normal([5,5,32,64],stddev=0.01), name="conv_W1")
conv_b1 = tf.Variable(tf.zeros([64]), name="conv_b1")
conv_z1 = tf.nn.conv2d(norm0, conv_W1, [1,1,1,1], "SAME") + conv_b1
conv_u1 = tf.nn.relu(conv_z1)
pool1 = tf.nn.max_pool(conv_u1, [1,2,2,1], [1,2,2,1], "SAME")
norm1 = pool1#tf.nn.lrn(pool1, 4, bias=1.0, alpha=0.001 / 9.0, beta = 0.75, name='norm1')
reshapeVal = 25*25*64
flat = tf.nn.dropout(tf.reshape(norm1,[-1,reshapeVal]),0.5)
full_W0 = tf.Variable(tf.truncated_normal([reshapeVal,200],stddev=0.01), name="full_W0")
full_b0 = tf.Variable(tf.zeros([200]), name="full_b0")
full_z0 = tf.matmul(flat, full_W0) + full_b0
full_u0 = tf.nn.relu(full_z0)
full_W = tf.Variable(tf.truncated_normal([200,2],stddev=0.01), name="full_W")
full_b = tf.Variable(tf.zeros([2]), name="full_b")
full_z = tf.matmul(full_u0, full_W) + full_b
full_u = tf.nn.softmax(full_z)
#conv_L2 = tf.nn.l2_loss(conv_W0) + tf.nn.l2_loss(full_W)
loss = -tf.reduce_mean(y*tf.log(tf.clip_by_value(full_u,1e-10,1.0))) #+ 0.001*conv_L2
trainstep = tf.train.AdamOptimizer(1e-5).minimize(loss)
#trainstep = tf.train.GradientDescentOptimizer(0.0001).minimize(loss)
#trainstep = tf.train.FtrlOptimizer(0.0001).minimize(loss)
collect_prediction = tf.equal(tf.argmax(full_u, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(collect_prediction,tf.float32))
sess = tf.Session()
#sess.run(tf.global_variables_initializer())
saver = tf.train.Saver()
saver.restore(sess, "model.ckpt")
#for i in range(label_num):
output = sess.run(tf.argmax(full_u, 1),feed_dict={x:limit_data,y:limit_label})
for i in range(label_num):
if output[i]==label :
cv2.circle(img,(circles[i][0],circles[i][1]),circles[i][2],(0,255,0),2)
else:
cv2.circle(img,(circles[i][0],circles[i][1]),circles[i][2],(0,0,255),2)
if not os.path.exists("output/"+ file_path):
os.mkdir("output/"+ file_path)
cv2.imwrite("output/"+ file_path + "/" + datetime.now().strftime('%s') + filename,img)
sleep(0.1)
#cv2.waitKey(0)
#cv2.destroyAllWindows()
#f = open(resultName+"/TestResult.csv","w")
#writer = csv.writer(f)
#writer.writerow(["Name","IsCollect"])
#isCollect,answer,output,output_data = sess.run((collect_prediction,tf.argmax(y, 1),tf.argmax(full_u, 1),full_u),feed_dict={x:data,y:label})
#for i in range(TestNum):
# writer.writerow([ten[i][0],int(isCollect[i]),answer[i],output[i],output_data[i][0],output_data[i][1]])
tf.reset_default_graph()
def learning_data_print(dataList):
data_num = len(dataList[0])
for i in range(0,data_num,200):
for j in range(200):
path = "image/"+str(i/200)+"/"+ dataList[0][i+j][0] +".png"
if not os.path.exists(path) :
plt.imshow(dataList[2][i+j])
plt.savefig(path)
plt.clf()
#pilImg = Image.fromarray(np.uint8(dataList[2][i+j]))
#pilImg.save("image/"+str(i/200)+"/"+ dataList[0][j][0] +".png")
#print dataList[2][i]
#plt.show()
if __name__ == '__main__':
#show_img("/home/miyake/Desktop/kahun_ohira/sugi.npy")
#Test('/home/miyake/Desktop/kahun_ohira/original_data/test/hinoki/IMG_3782.JPG',1)
#Test('/home/miyake/Desktop/kahun_ohira/original_data/test/hinoki/IMG_3785.JPG',1)
#Test('/home/miyake/Desktop/kahun_ohira/original_data/test/hinoki/IMG_3792.JPG',1)
#Test('/home/miyake/Desktop/kahun_ohira/original_data/test/sugi/IMG_3806.JPG',0)
#Test('/home/miyake/Desktop/kahun_ohira/original_data/test/sugi/IMG_3808.JPG',0)
#Test('/home/miyake/Desktop/kahun_ohira/original_data/test/sugi/IMG_3806.JPG',0)
#Test('/home/miyake/Desktop/kahun_ohira/original_data/mix/IMG_3828.JPG',0)
#sys.exit()
alldata = GetData()
#learning_data_print(alldata)
#show_npy(alldata)
data_num = len(alldata[0])
now = datetime.now()
for i in range(0,data_num,200):
testidxList = []
for j in range(200):
testidxList.append(i+j)
resultName = now.strftime('%s')+"_Result_"+str(i)+"_"+str(i+200)
Train(alldata,testidxList,resultName)
for path, subdirs, files in os.walk("check/"):
for filename in files:
f = os.path.join(path, filename)
Test(str(f),0,filename,path)
#Test('/home/miyake/Desktop/kahun_ohira/original_data/test/sugi/IMG_3802.JPG',0)
#Test('/home/miyake/Desktop/kahun_ohira/original_data/mix/IMG_3814.JPG',0)
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