def createWebfaceRawData(imgSize, saveFilePath):
start = 0
peopleNum = 2500
singleSubNum = 20
datax = []
datay = []
dirlist = sorted(os.listdir(webfaceRoot))[start:start + peopleNum]
for index, dir in enumerate(dirlist):
print("%d people running..." % index)
for root, d, files in os.walk(webfaceRoot + dir):
print("img num:", len(files))
for file in files[:singleSubNum]:
imgPath = webfaceRoot + dir + "/" + file
img = cv2.imread(imgPath)
try:
faceImg = findAlignFace_dlib(img, imgSize)
except:
continue
datax.append(faceImg)
datay.append(index)
datax = np.array(datax)
datay = np.array(datay)
f = h5py.File(saveFilePath, 'w')
f['data'] = datax
f['labels'] = datay
f.close()
def runLFW(model, trainLabel, imgSize):
acc = 0
posScore = []
negScore = []
count = 0
posGen = getPosPairsImg()
for img1, img2 in posGen:
count += 1
if count > 10:
break
try:
faceImg1 = findAlignFace_dlib(img1, imgSize)
faceImg2 = findAlignFace_dlib(img2, imgSize)
except:
continue
faceImg1 = (faceImg1[np.newaxis, :, :, :]-127.5)/128.0
faceImg2 = (faceImg2[np.newaxis, :, :, :]-127.5)/128.0
rep1 = model.getRepVec(faceImg1, trainLabel)
rep2 = model.getRepVec(faceImg2, trainLabel)
score = calcCosSimilarityPairs(rep1, rep2)
print(score)
#posScore.append(score)
#posCsv = pd.DataFrame(posScore)
#posCsv.to_csv("./data/pos_score_"+modelName+".csv", index=False)
count = 0
negGen = getNegPairsImg()
for img1, img2 in negGen:
count += 1
if count > 10:
break
try:
faceImg1 = findAlignFace_dlib(img1, imgSize)
faceImg2 = findAlignFace_dlib(img2, imgSize)
except:
continue
faceImg1 = (faceImg1[np.newaxis, :, :, :] - 127.5) / 128.0
faceImg2 = (faceImg2[np.newaxis, :, :, :] - 127.5) / 128.0
rep1 = model.getRepVec(faceImg1, trainLabel)
rep2 = model.getRepVec(faceImg2, trainLabel)
score = calcCosSimilarityPairs(rep1, rep2)
print(score)
def getRep_VGGface(rgbImg, version=2):
alignedFace = findAlignFace_dlib(rgbImg, 224)
alignedFace = alignedFace.astype(np.float64)
x = np.expand_dims(alignedFace, axis=0)
x = utils.preprocess_input(x, version=version) # or version=2
rep = model.predict(x)
rep = np.reshape(rep, (2048, ))
return rep
def gerRep_lightCNN_pytorch(rgbImg):
faceImg = findAlignFace_dlib(rgbImg, 128)
inputImg = torch.zeros(1, 1, 128, 128)
grayImg = cv2.cvtColor(faceImg, cv2.COLOR_RGB2GRAY)
img = np.reshape(grayImg, (128, 128, 1))
img = transform(img)
inputImg[0, :, :, :] = img
input = inputImg.cuda()
input_var = torch.autograd.Variable(input, volatile=True)
_, features = model(input_var)
rep = features.data.cpu().numpy()
rep = np.reshape(rep, (256, ))
return rep
def createPairsWebface(saveFileName):
posNum = 2
negNum = 2
imgSize = 128
trainPair1 = []
trainPair2 = []
label = []
peopleList = sorted(os.listdir(webfaceRoot)) # 获取所有人名列表
for index, dir in enumerate(peopleList):
print("%d people running..." % index)
# 选取anchor样本
posImgList = os.listdir(webfaceRoot + dir)
anchorIndex = np.random.randint(0, len(posImgList))
anchor = posImgList[anchorIndex]
anchorImgPath = webfaceRoot + dir + "/" + anchor
print("anchor img path:", anchorImgPath)
anchorImg = cv2.imread(anchorImgPath)
try:
anchorImg = findAlignFace_dlib(anchorImg, imgSize)
except:
continue
anchorImg = cv2.cvtColor(anchorImg, cv2.COLOR_RGB2GRAY)
anchorImg = anchorImg[:, :, np.newaxis]
for i in range(negNum):
# 选取负样本人
tmpPeopleList = copy.deepcopy(peopleList)
del tmpPeopleList[index] # 除去本人的图片集
# 随机选取一个人作为负样本集
negPeople = tmpPeopleList[np.random.randint(0, len(tmpPeopleList))]
negPeopleImgList = os.listdir(webfaceRoot + negPeople)
# 随机选一张图片作为负样本
neg = negPeopleImgList[np.random.randint(0, len(negPeopleImgList))]
negImgPath = webfaceRoot + negPeople + "/" + neg
print("neg img path:", negImgPath)
negImg = cv2.imread(negImgPath)
try:
negImg = findAlignFace_dlib(negImg, imgSize)
except:
continue
negImg = cv2.cvtColor(negImg, cv2.COLOR_RGB2GRAY)
negImg = negImg[:, :, np.newaxis]
trainPair1.append(anchorImg)
trainPair2.append(negImg)
label.append(-1)
for i in range(posNum):
# 随机选取正样本
tmpPosImgList = copy.deepcopy(posImgList)
del tmpPosImgList[anchorIndex] # 除去anchor图片
pos = posImgList[np.random.randint(0, len(posImgList))]
posImgPath = webfaceRoot + dir + "/" + pos
print("pos img path:", posImgPath)
posImg = cv2.imread(posImgPath)
try:
posImg = findAlignFace_dlib(posImg, imgSize)
except:
continue
posImg = cv2.cvtColor(posImg, cv2.COLOR_RGB2GRAY)
posImg = posImg[:, :, np.newaxis]
trainPair1.append(anchorImg)
trainPair2.append(posImg)
label.append(1)
f = h5py.File(saveFileName, 'w')
f['train_pair1'] = np.array(trainPair1)
f['train_pair2'] = np.array(trainPair2)
f['label'] = np.array(label)
f.close()
def getRep_openface(rgbImg):
alignedFace = findAlignFace_dlib(rgbImg, 96)
rep = net.forward(alignedFace)
return rep
