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final_test.py
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# -*- coding: utf-8 -*-
"""
Created on Wed Jan 16 09:52:29 2019
@author: wen
"""
import numpy as np
import cv2
from skimage.transform import resize
from keras.models import load_model
import pandas as pd
from sklearn.metrics import accuracy_score
from collections import Counter
#载入测试数据
path = './test_image_cnngru/'
test = pd.read_csv("test_cnngru.csv")
#保存预测结果
image = []
b = []
array = []
c2d = []
c3d = []
count = 1
#混合预测测试集，1行为采用2dcnn
for image_name in test[test.Image_name1.notnull()].Image_name1:
#导入数据
#img = Image.open(path + 'frame%d.jpg' % i).convert('L')
img = cv2.imread(path + image_name, 0)
equ = cv2.equalizeHist(img) #使图片的像素趋近于高斯分布
img = cv2.GaussianBlur(equ, (5, 5), 0)
#ret, binary = cv2.threshold(image, 127, 255, cv2.THRESH_BINARY)
a = resize(img, preserve_range = True, output_shape = (112, 112, 1)).astype(int)
c = resize(img, preserve_range = True, output_shape = (224, 224, 1)).astype(int)
img = a / 255 #数据归一化
image = c / 255
b.append(img)
image = np.expand_dims(image, 0)
array.append(image)
if count% 5 == 0:
#增加一个维度
b = np.expand_dims(b, 0)
c3d.append(b)
c2d.append(array)
b = []
array = []
count += 1
# np.array(c2d).shape
#2dcnn预测
#载入模型
model = load_model('model_cnn_short_threshold.h5')
#测试模型
result1 = []
for i in c2d:
temp = []
for j in i:
pred = model.predict(j)
temp.append(np.argmax(pred))
# Counter(temp).most_common(1):返回出现频率最高的一个数
temp = Counter(temp).most_common(1)[0][0]
result1.append(temp)
#3dcnn预测
#载入模型
model = load_model('model_3dcnn.h5')
#测试模型
result = []
for i in c3d:
pred = model.predict(i)
result.append(np.argmax(pred))
#对比，如果2dCNN为1的而3dCNN对应位置不为1的，则相应位置将会变为1
for i in range(len(result)):
if result1[i] == 1 and result[i] != 1:
result[i] = 1
if result1[i] == 2 and result[i] != 2:
result[i] = 2
label = test[test.Class1.notnull()].Class1
#输出测试集的准确率
accuracy = accuracy_score(np.array(label), np.array(result))
print("The accuracy of the model in test is:", accuracy)
#混合预测整个测试集，1行为采用2dcnn
for image_name in test.Image_name:
#导入数据
#img = Image.open(path + 'frame%d.jpg' % i).convert('L')
img = cv2.imread(path + image_name, 0)
equ = cv2.equalizeHist(img) #使图片的像素趋近于高斯分布
img = cv2.GaussianBlur(equ, (5, 5), 0)
#ret, binary = cv2.threshold(image, 127, 255, cv2.THRESH_BINARY)
a = resize(img, preserve_range = True, output_shape = (112, 112, 1)).astype(int)
c = resize(img, preserve_range = True, output_shape = (224, 224, 1)).astype(int)
img = a / 255 #数据归一化
image = c / 255
b.append(img)
image = np.expand_dims(image, 0)
array.append(image)
if count% 5 == 0:
#增加一个维度
b = np.expand_dims(b, 0)
c3d.append(b)
c2d.append(array)
b = []
array = []
count += 1
# np.array(c2d).shape
#2dcnn预测
#载入模型
model = load_model('model_cnn_short_threshold.h5')
#测试模型
result1 = []
for i in c2d:
temp = []
for j in i:
pred = model.predict(j)
temp.append(np.argmax(pred))
# Counter(temp).most_common(1):返回出现频率最高的一个数
temp = Counter(temp).most_common(1)[0][0]
result1.append(temp)
#3dcnn预测
#载入模型
model = load_model('model_3dcnn.h5')
#测试模型
result = []
for i in c3d:
pred = model.predict(i)
result.append(np.argmax(pred))
#对比，如果2dCNN为1的而3dCNN对应位置不为1的，则相应位置将会变为1
for i in range(len(result)):
if result1[i] == 1 and result[i] != 1:
result[i] = 1
if result1[i] == 2 and result[i] != 2:
result[i] = 2
label = test[test.Class.notnull()].Class
#输出测试集的准确率
accuracy = accuracy_score(np.array(label), np.array(result))
print("The accuracy of the model in test is:", accuracy)
# The accuracy of the model in test is: 0.9210526315789473