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eval-all.py
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eval-all.py
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import numpy as np
import os
import cv2
import cv2.cv as cv
from skimage import transform as tf
from PIL import Image, ImageDraw
import threading
from time import ctime,sleep
import time
import sklearn
import matplotlib.pyplot as plt
import skimage
import sklearn.metrics.pairwise as pw
import triplet._init_paths
import triplet.config as cfg
from triplet.sampledata import sampledata
from utils.timer import Timer
import caffe
from caffe.proto import caffe_pb2
import google.protobuf as pb2
import argparse
import glob
from sklearn.metrics import confusion_matrix
import pandas as pd
####
####Define Recognizer
####
global filelist_path
filelist_path='./filelist/'
global extension
extension='.txt'
global filenames
filenames = ['c0','c1','c2','c3','c4','c5','c6','c7','c8','c9']
global filecount
filecount = [2489,2267,2317,2346,2326,2312,2325,2002,1911,2129]
global feature_size
feature_size=1024
global class_size
class_size=10
global model_name
model_name = 'triplet-loss'#'batch-triplet-loss'#'softmax'#
global accuracy_path
accuracy_path = './accuracy/'
def plot_confusion_matrix(df_confusion, title='Confusion matrix', cmap=plt.cm.gray_r):
plt.matshow(df_confusion, cmap=cmap) # imshow
#plt.title(title)
plt.colorbar()
tick_marks = np.arange(len(df_confusion.columns))
plt.xticks(tick_marks, df_confusion.columns, rotation=45)
plt.yticks(tick_marks, df_confusion.index)
#plt.tight_layout()
plt.ylabel(df_confusion.index.name)
plt.xlabel(df_confusion.columns.name)
class Recognizer(caffe.Net):
"""
Recognizer extends Net for image class prediction
by scaling, center cropping, or oversampling.
Parameters
----------
image_dims : dimensions to scale input for cropping/sampling.
Default is to scale to net input size for whole-image crop.
mean, input_scale, raw_scale, channel_swap: params for
preprocessing options.
"""
def __init__(self, model_file, pretrained_file, mean_file=None,
image_dims=(227, 227),
raw_scale=255,
channel_swap=(2,1,0),
input_scale=None):
#set GPU mode
caffe.set_mode_gpu()
#init net
caffe.Net.__init__(self, model_file, pretrained_file, caffe.TEST)
# configure pre-processing
in_ = self.inputs[0]
self.transformer = caffe.io.Transformer(
{in_: self.blobs[in_].data.shape})
self.transformer.set_transpose(in_, (2, 0, 1))
if mean_file is not None:
proto_data = open(mean_file, "rb").read()
mean_blob = caffe.io.caffe_pb2.BlobProto.FromString(proto_data)
mean = caffe.io.blobproto_to_array(mean_blob)[0]
self.transformer.set_mean(in_, mean)
if input_scale is not None:
self.transformer.set_input_scale(in_, input_scale)
if raw_scale is not None:
self.transformer.set_raw_scale(in_, raw_scale)
if channel_swap is not None:
self.transformer.set_channel_swap(in_, channel_swap)
self.crop_dims = np.array(self.blobs[in_].data.shape[2:])
if not image_dims:
image_dims = self.crop_dims
self.image_dims = image_dims
def alex_predict(self, oversample=True):
"""
Predict classification probabilities of inputs.
Parameters
----------
inputs : iterable of (H x W x K) input ndarrays.
oversample : boolean
average predictions across center, corners, and mirrors
when True (default). Center-only prediction when False.
Returns
-------
predictions: (N x C) ndarray of class probabilities for N images and C
classes.
"""
#load files
input_dir='/media/frank/Data/Database/ImageNet/Kaggle/train/c9'
inputs =[caffe.io.load_image(im_f)
for im_f in glob.glob(input_dir + '/*.jpg')]
# Scale to standardize input dimensions.
input_ = np.zeros((len(inputs),
self.image_dims[0],
self.image_dims[1],
inputs[0].shape[2]),
dtype=np.float32)
for ix, in_ in enumerate(inputs):
input_[ix] = caffe.io.resize_image(in_, self.image_dims)
if oversample:
# Generate center, corner, and mirrored crops.
input_ = caffe.io.oversample(input_, self.crop_dims)
else:
# Take center crop.
center = np.array(self.image_dims) / 2.0
crop = np.tile(center, (1, 2))[0] + np.concatenate([
-self.crop_dims / 2.0,
self.crop_dims / 2.0
])
crop = crop.astype(int)
input_ = input_[:, crop[0]:crop[2], crop[1]:crop[3], :]
# Classify
caffe_in = np.zeros(np.array(input_.shape)[[0, 3, 1, 2]],
dtype=np.float32)
for ix, in_ in enumerate(input_):
caffe_in[ix] = self.transformer.preprocess(self.inputs[0], in_)
out = self.forward_all(**{self.inputs[0]: caffe_in})
predictions = out[self.outputs[0]]
# For oversampling, average predictions across crops.
if oversample:
predictions = predictions.reshape((len(predictions) / 10, 10, -1))
predictions = predictions.mean(1)
return predictions
def read_imagelist(self,filelist):
fid=open(filelist)
lines=fid.readlines()
test_num=len(lines)
fid.close()
X=np.empty((test_num,3,self.image_dims[0],self.image_dims[1]))
i =0
for line in lines:
word=line.split('\n')
filename=word[0]
im1=skimage.io.imread(filename,as_grey=False)
image =skimage.transform.resize(im1,(self.image_dims[0], self.image_dims[1]))*255
if image.ndim<3:
print 'gray:'+filename
X[i,0,:,:]=image[:,:]
X[i,1,:,:]=image[:,:]
X[i,2,:,:]=image[:,:]
else:
X[i,0,:,:]=image[:,:,2]
X[i,1,:,:]=image[:,:,0]
X[i,2,:,:]=image[:,:,1]
i=i+1
return X
def read_labels(labelfile):
fin=open(labelfile)
lines=fin.readlines()
labels=np.empty((len(lines),))
k=0;
for line in lines:
labels[k]=int(line)
k=k+1;
fin.close()
return labels
def draw_roc_curve(fpr,tpr,title='cosine',save_name='roc_lfw'):
plt.figure()
plt.plot(fpr, tpr)
plt.plot([0, 1], [0, 1], 'k--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.0])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver operating characteristic using: '+title)
plt.legend(loc="lower right")
plt.show()
plt.savefig(save_name+'.png')
#predict_test_alexnet
def test_alex(self):
class_index = 0
image_index = 0
total_count = 0.0
accept_sum = 0
actual = []
predict = []
for filename in filenames:
#query-feature
X=self.read_imagelist(filelist_path + filename + extension)
test_num=np.shape(X)[0]
out = self.forward_all(data=X)
predicts=out[self.outputs[0]]
predicts=np.reshape(predicts,(test_num,10))
confusion_array = np.zeros((class_size), dtype = np.int)
for i in range(test_num):
actual.append(class_index)
for j in range(class_size):
if np.max(predicts[i]) == predicts[i][j]:
confusion_array[j] += 1
predict.append(j)
image_index += 1
#print(confusion_array)
total_count += test_num
accept_sum += confusion_array[class_index]
class_index += 1
print 'total:%d' % (round(total_count))
print 'accept:%d' % (accept_sum)
print 'reject:%d' % (round(total_count) - accept_sum)
print 'accuray:%.4f' % (accept_sum / total_count)
#conf_mat = confusion_matrix(actual,predict)
#print(conf_mat)
#actual = np.array(actual)
#predict = np.array(predict)
#y_actual = pd.Series(actual, name='Actual')
#y_predict = pd.Series(predict, name='Predicted')
#df_confusion = pd.crosstab(y_actual,y_predict, rownames=['Actual'], colnames=['Predicted'], margins=True)
#print(df_confusion)
#plot_confusion_matrix(df_confusion)
return (accept_sum / total_count)
#process a text file
def evaluate(self,metric='cosine'):
#sample-feature
X=self.read_imagelist(filelist_sample)
sample_num=np.shape(X)[0]
out = self.forward_all(data=X)
feature1=np.float64(out['deepid'])
feature1=np.reshape(feature1,(sample_num,feature_size))
#np.savetxt('feature1.txt', feature1, delimiter=',')
class_index = 0
image_index = 0
total_count = 0.0
accept_sum = 0
actual = []
predict = []
for filename in filenames:
#query-feature
X=self.read_imagelist(filelist_path + filename + extension)
test_num=np.shape(X)[0]
out = self.forward_all(data=X)
feature2=np.float64(out['deepid'])
feature2=np.reshape(feature2,(test_num,feature_size))
#np.savetxt('feature2.txt', feature2, delimiter=',')
#mt=pw.pairwise_distances(feature2, feature1, metric=metric)
mt=pw.cosine_similarity(feature2, feature1)
false=0
for i in range(test_num):
actual.append(class_index)
for j in range(sample_num):
if np.max(mt[i]) == mt[i][j]:
confusion_array[j] += 1
predict.append(j)
image_index += 1
total_count += test_num
accept_sum += confusion_array[class_index]
class_index += 1
print 'total:%d' % (round(total_count))
print 'accept:%d' % (accept_sum)
print 'reject:%d' % (round(total_count) - accept_sum)
print 'accuray:%.4f' % (accept_sum / total_count)
#conf_mat = confusion_matrix(actual,predict)
#print(conf_mat)
actual = np.array(actual)
predict = np.array(predict)
y_actual = pd.Series(actual, name='Actual')
y_predict = pd.Series(predict, name='Predicted')
df_confusion = pd.crosstab(y_actual,y_predict, rownames=['Actual'], colnames=['Predicted'], margins=True)
print(df_confusion)
plot_confusion_matrix(df_confusion)
return (accept_sum / total_count)
#process a text file
def evaluate2(self,metric='cosine'):
feature1=np.fromfile('./features/' + model_name +'-features.dat',dtype=np.float64)
feature1=np.reshape(feature1,(class_size,feature_size))
#np.savetxt('feature1.txt', feature1, delimiter=',')
class_index = 0
image_index = 0
total_count = 0.0
accept_sum = 0
actual = []
predict = []
for filename in filenames:
#query-feature
X=self.read_imagelist(filelist_path + filename + extension)
test_num=np.shape(X)[0]
out = self.forward_all(data=X)
feature2=np.float64(out['deepid'])
feature2=np.reshape(feature2,(test_num,feature_size))
#np.savetxt('feature2.txt', feature2, delimiter=',')
#mt=pw.pairwise_distances(feature2, feature1, metric=metric)
mt=pw.cosine_similarity(feature2, feature1)
false=0
for i in range(test_num):
actual.append(class_index)
for j in range(class_size):
if np.max(mt[i]) == mt[i][j]:
confusion_array[j] += 1
predict.append(j)
image_index += 1
total_count += test_num
accept_sum += confusion_array[class_index]
class_index += 1
print 'total:%d' % (round(total_count))
print 'accept:%d' % (accept_sum)
print 'reject:%d' % (round(total_count) - accept_sum)
print 'accuray:%.4f' % (accept_sum / total_count)
#conf_mat = confusion_matrix(actual,predict)
#print(conf_mat)
#actual = np.array(actual)
#predict = np.array(predict)
#y_actual = pd.Series(actual, name='Actual')
#y_predict = pd.Series(predict, name='Predicted')
#df_confusion = pd.crosstab(y_actual,y_predict, rownames=['Actual'], colnames=['Predicted'], margins=True)
#print(df_confusion)
#plot_confusion_matrix(df_confusion)
return (accept_sum / total_count)
#process a text file
def evaluate3(self,metric='cosine'):
feature1=np.fromfile('./features/' + model_name +'-features.dat',dtype=np.float64)
feature1=np.reshape(feature1,(class_size,feature_size))
class_index = 0
image_index = 0
total_count = 0.0
accept_sum = 0
top5_accept_sum = 0
actual = []
predict = []
for filename in filenames:
#query-feature
#X=self.read_imagelist(filelist_path + filename + extension)
test_num = filecount[class_index]#np.shape(X)[0]
feature2=np.fromfile('./features/' + model_name +'-features-c' + str(class_index) + '.dat',dtype=np.float64)
feature2=np.reshape(feature2,(test_num,feature_size))
mt=pw.cosine_similarity(feature2, feature1)
top5_accept = 0
confusion_array = np.zeros((class_size), dtype = np.int)
for i in range(test_num):
actual.append(class_index)
sort_array = np.zeros((class_size), dtype = np.float64)
for j in range(class_size):
sort_array[j] = mt[i][j]
if np.max(mt[i]) == mt[i][j]:
confusion_array[j] += 1
predict.append(j)
break
#print(sort_array)
sort_array.sort()
#print(sort_array)
for j in range((class_size - 5),class_size):
if sort_array[j] == mt[i][class_index]:
top5_accept += 1
break
image_index += 1
total_count += test_num
accept_sum += confusion_array[class_index]
top5_accept_sum += top5_accept
class_index += 1
print 'total:%d' % (round(total_count))
print 'accept:%d' % (accept_sum)
print 'reject:%d' % (round(total_count) - accept_sum)
print 'top 1 accuray:%.4f' % (accept_sum / total_count)
print 'top 5 accuray:%.4f' % (top5_accept_sum / total_count)
#conf_mat = confusion_matrix(actual,predict)
#print(conf_mat)
actual = np.array(actual)
predict = np.array(predict)
y_actual = pd.Series(actual, name='Actual')
y_predict = pd.Series(predict, name='Predicted')
df_confusion = pd.crosstab(y_actual,y_predict, rownames=['Actual'], colnames=['Predicted'], margins=True)
print(df_confusion)
#plot_confusion_matrix(df_confusion)
result = []
result.append(accept_sum / total_count)
result.append(top5_accept_sum / total_count)
return result
#save features
def saveFeature(self):
averages=np.zeros((class_size,feature_size),dtype=np.float64)
i=0
for filename in filenames:
#query-feature
X=self.read_imagelist(filelist_path + filename + extension)
test_num=np.shape(X)[0]
out = self.forward_all(data=X)
feature2=np.float64(out['deepid'])
feature2.tofile('./features/' + model_name + '-features-c' + str(i) + '.dat')
feature2=np.reshape(feature2,(test_num,feature_size))
average=np.zeros((feature_size),dtype=np.float64)
for j in range(test_num):
average[:] = average[:] + feature2[j,:]
average[:]=average[:]/test_num
averages[i,:]=average[:]
i=i+1
averages.tofile('./features/' + model_name + '-features.dat')
#process an image file
def getFeature2(self,imgfile):
img=skimage.io.imread(imgfile,as_grey=False)
resized =skimage.transform.resize(img,(self.image_dims[0], self.image_dims[1]))*255
X=np.empty((1,3,self.image_dims[0],self.image_dims[1]))
X[0,0,:,:]=resized[:,:,2]
X[0,1,:,:]=resized[:,:,0]
X[0,2,:,:]=resized[:,:,1]
test_num=np.shape(X)[0]
out = self.forward_all(data=X)
#extract feature
feature = np.float64(out['deepid'])
feature=np.reshape(feature,(test_num,feature_size))
return feature
def compare_pic(self,feature1,feature2):
predicts=pw.pairwise_distances(feature2, feature1,'cosine')
#predicts=pw.cosine_similarity(feature1, feature2)
return predicts
def compare_pic2(self,path1,path2):
feature1 = self.getFeature2(path1)
feature2 = self.getFeature2(path2)
predicts = self.compare_pic(feature1,feature2)
return predicts
def classify(self,path):
feature1 = self.getFeature2(path)
fid=open('./filelist/sample.txt')
lines=fid.readlines()
test_num=len(lines)
fid.close()
i =0
msg='out:'
for line in lines:
word=line.split('\n')
filename=word[0]
feature2 = self.getFeature2(filename)
predicts = self.compare_pic(feature1,feature2)
tmp='(%d,%f)'%(i,predicts)
msg=msg+tmp
i=i+1
print msg
if __name__ == '__main__':
step = 2
top1_accuracy = []
top5_accuracy = []
for i in range(30,30 + 1):
iteration = str(i * step)
tripletnet= Recognizer('./models/deploy.prototxt',
'./data/models/triplet/alexnet_triplet_iter_' + iteration + '.caffemodel',
'./data/models/softmax/mean.binaryproto')
#alexnet= Recognizer('/home/frank/triplet-master/data/models/softmax/deploy.prototxt',
# '/home/frank/digits/digits/jobs/20170429-175608-c101/snapshot_iter_' + iteration + '.caffemodel',
# '/home/frank/triplet-master/data/models/softmax/mean.binaryproto')
##ALEXNET TEST
#start = time.time()
#model_accuracy = alexnet.test_alex()
##TRIPLET TEST
#tripletnet.saveFeature()
model_accuracy = tripletnet.evaluate3()
top1_accuracy.append(model_accuracy[0])
top5_accuracy.append(model_accuracy[1])
#with open(accuracy_path + model_name + '-top1-' +iteration + '.txt', 'w') as file:
# file.write(str(model_accuracy[0]))
#with open(accuracy_path + model_name + '-top5-' +iteration + '.txt', 'w') as file:
# file.write(str(model_accuracy[1]))
top1_accuracy = np.array(top1_accuracy)
#top1_accuracy.tofile(accuracy_path + model_name + '-top1.dat')
#np.savetxt(accuracy_path + model_name + '-top1.out',top1_accuracy,delimiter=',')
top5_accuracy = np.array(top5_accuracy)
#top5_accuracy.tofile(top5_accuracy + model_name + '-top5.dat')
#np.savetxt(accuracy_path + model_name + '-top5.out',top5_accuracy,delimiter=',')
#print(accuracy)