原文代碼在python3.0或者opencv版本不同下跑不通。

我這里做一些改動，自行參考，文筆有限。

改動點1：圖像輸入自定義，不再固定名稱

改動點2：解決找不到sift、svm組件類型問題

改動點3：解決svm訓(xùn)練的標(biāo)簽錯誤問題

改動點4：解決導(dǎo)入不了svm模型問題

等

注意：SVM.train中標(biāo)簽一定要為整數(shù)類型

代碼如下：

import cv2

import numpy as np

import os

TrainSetInfo = {

"car":40,

"city":20,

"dog":30,

"earth":15,

"fireworks":20,

"flowers":20,

"fruits":20,

"glass":20,

"gold":15,

"gun":20,

"plane":40,

"sky":30,

"worldcup":40

}

TestSetInfo = {

"car":119,

"city":59,

"dog":49,

"earth":24,

"fireworks":54,

"flowers":63,

"fruits":78,

"glass":52,

"gold":44,

"gun":44,

"plane":102,

"sky":78,

"worldcup":131

}

def calcSiftFeature(img):

gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

sift = cv2.xfeatures2d.SIFT_create(200) # max number of SIFT points is 200

kp, des = sift.detectAndCompute(gray, None)

return des

def calcFeatVec(features, centers):

featVec = np.zeros((1, 50))

for i in range(0, features.shape[0]):

fi = features[i]

diffMat = np.tile(fi, (50, 1)) - centers

sqSum = (diffMat**2).sum(axis=1)

dist = sqSum**0.5

sortedIndices = dist.argsort()

idx = sortedIndices[0] # index of the nearest center

featVec[0][idx] += 1

return featVec

def initFeatureSet():

for name, count in TrainSetInfo.items():

# dir = "D:/GJAI_data/gongjingai_pre/train_1-2-3/" + name + "/"

dir = "TrainSet/" + name + "/"

featureSet = np.float32([]).reshape(0,128)

print("Extract features from training set " + name + "...")

pathDir = os.listdir(dir)

for i in pathDir:

filename = os.path.join('%s%s' % (dir, i))

img = cv2.imread(filename)

des = calcSiftFeature(img)

featureSet = np.append(featureSet, des, axis=0)

featCnt = featureSet.shape[0]

print(str(featCnt) + " features in " + str(count) + " images\n")

# save featureSet to file

filename = "Temp1/features/" + name + ".npy"

np.save(filename, featureSet)

def learnVocabulary():

wordCnt = 50

for name, count in TrainSetInfo.items():

filename = "Temp1/features/" + name + ".npy"

features = np.load(filename)

print("Learn vocabulary of " + name + "...")

# use k-means to cluster a bag of features

criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 20, 0.1)

flags = cv2.KMEANS_RANDOM_CENTERS

compactness, labels, centers = cv2.kmeans(features, wordCnt,None, criteria, 20, flags)

# save vocabulary(a tuple of (labels, centers)) to file

filename = "Temp1/vocabulary/" + name + ".npy"

np.save(filename, (labels, centers))

print("Done\n")

def trainClassifier():

trainData = np.float32([]).reshape(0, 50)

response = np.int32([])

dictIdx = 0

for name, count in TrainSetInfo.items():

# dir = "D:/GJAI_data/gongjingai_pre/train_1-2-3/" + name + "/"

dir = "TrainSet/" + name + "/"

labels, centers = np.load("Temp1/vocabulary/" + name + ".npy")

print("Init training data of " + name + "...")

pathDir = os.listdir(dir)

for i in pathDir:

filename = os.path.join('%s%s' % (dir, i))

img = cv2.imread(filename)

features = calcSiftFeature(img)

featVec = calcFeatVec(features, centers)

trainData = np.append(trainData, featVec, axis=0)

res = np.repeat(dictIdx, count)

response = np.append(response, res)

dictIdx += 1

print("Done\n")

print("Now train svm classifier...")

trainData = np.float32(trainData)

response = response.reshape(-1, 1)

svm = cv2.ml.SVM_create()

svm.setKernel(cv2.ml.SVM_LINEAR)

# svm.train_auto(trainData, response, None, None, None) # select best params

svm.train(trainData,cv2.ml.ROW_SAMPLE,response)

svm.save("svm.clf")

print("Done\n")

def classify():

# svm = cv2.SVM()

# svm = cv2.ml.SVM_create()

svm = cv2.ml.SVM_load("svm.clf")

total = 0; correct = 0; dictIdx = 0

for name, count in TestSetInfo.items():

crt = 0

# dir = "D:/GJAI_data/gongjingai_pre/validation_1-2-3/" + name + "/"

dir = "TestSet/" + name + "/"

labels, centers = np.load("Temp1/vocabulary/" + name + ".npy")

print("Classify on TestSet " + name + ":")

pathDir = os.listdir(dir)

for i in pathDir:

filename = os.path.join('%s%s' % (dir, i))

img = cv2.imread(filename)

features = calcSiftFeature(img)

featVec = calcFeatVec(features, centers)

case = np.float32(featVec)

dict_svm = svm.predict(case)

dict_svm = int(dict_svm[1])

if (dictIdx == dict_svm):

crt += 1

print("Accuracy: " + str(crt) + " / " + str(count) + "\n")

total += count

correct += crt

dictIdx += 1

print("Total accuracy: " + str(correct) + " / " + str(total))

if __name__ == "__main__":

initFeatureSet()

learnVocabulary()

trainClassifier()

classify()

如有疑問，請評論。

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