python 이미지 처리의sift-kmeans-SVM 이미지 분류

판권 성명: 본고는 블로거의 오리지널 문장으로 작가와 출처를 밝혀 주십시오.https://blog.csdn.net/xq920831/article/details/85052318
인터넷에는 많은 자료가 있는데 참고:https://blog.csdn.net/u012874151/article/details/45457085/
원본 코드는python3.0 또는 오픈cv 버전이 다르면 뛸 수 없습니다.
내가 있는 이곳은 약간의 변경을 하였으니 스스로 참고하면 문필에 한계가 있다.
변동점 1: 이미지 입력 사용자 정의, 이름 고정 해제
변경점 2:sift,svm 구성 요소 형식을 찾을 수 없음 문제 해결
개선점3:svm훈련의 라벨 오류 해결
변동점 4:svm모델을 가져올 수 없는 문제 해결
등.
참고: SVM.train에서 태그는 정수 형식이어야 합니다
부동 소수점 수형 데이터는 정수형으로 강제로 변환할 수 없으며 데이터 오류가 발생할 수 있습니다. 참고:https://blog.csdn.net/smileyuping/article/details/22100743
코드는 다음과 같습니다.

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
")
		
		# 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
")

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
")

	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
")
	
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) + "
")
		total += count
		correct += crt
		dictIdx += 1
		
	print("Total accuracy: " + str(correct) + " / " + str(total))

if __name__ == "__main__":	
	initFeatureSet()
	learnVocabulary()
	trainClassifier()
	classify()

궁금한 점이 있으면 평론해 주십시오.