def __init__(self, filepaths, imageSize):
self.__trainData = []
self.__trainLabel = []
self.__testData = []
self.__testLabel = []
for filepath in filepaths:
ds = datasetVOC(filepath, imageSize)
train, test = ds.getDataset()
trainData, trainLabel = train._datasets
testData, testLabel = test._datasets
self.__trainData.extend(trainData)
self.__trainLabel.extend(trainLabel)
self.__testData.extend(testData)
self.__testLabel.extend(testLabel)
def run(datasetPath, resultPath, modelPath="", resumePath=""):
# set dataset
if isinstance(datasetPath, str):
ds = datasetVOC(datasetPath, 32)
elif isinstance(datasetPath, list):
ds = datasetVOCs(datasetPath, 32)
else:
raise Exception("データセットパスの型が不正です。")
train, test = ds.getDataset()
# set model
model = chainer.links.Classifier(Alex())
if os.path.isfile(modelPath):
chainer.serializers.load_npz(modelPath, model)
optimizer = chainer.optimizers.Adam()
optimizer.setup(model)
# set evaluation model
eval_model = model.copy()
eval_model.train = False
# train and test
train_iter = chainer.iterators.SerialIterator(train, BATCH_SIZE)
test_iter = chainer.iterators.SerialIterator(test, BATCH_SIZE, repeat=False, shuffle=False)
updater = chainer.training.StandardUpdater(train_iter, optimizer, device=-1)
trainer = chainer.training.Trainer(updater, (EPOCH, "epoch"), out=resultPath)
trainer.extend(extensions.Evaluator(test_iter, eval_model, device=-1))
trainer.extend(extensions.LogReport())
trainer.extend(
extensions.PrintReport(
["epoch", "main/loss", "validation/main/loss", "main/accuracy", "validation/main/accuracy"]
)
)
trainer.extend(extensions.ProgressBar(update_interval=5))
trainer.extend(extensions.snapshot(filename="snapshot_epoch_{.updater.epoch}"))
trainer.extend(extensions.snapshot_object(model, filename="model_epoch_{.updater.epoch}"))
trainer.extend(extensions.dump_graph("main/loss"))
if os.path.isfile(resumePath):
chainer.serializers.load_npz(resumePath, trainer)
trainer.run()
def run(PATH):
# set PATH
Common.mkDir(PATH['RESULT'])
# read model
model = chainer.links.Classifier(Alex())
model.train = False
if not os.path.isfile(PATH['MODEL']):
raise Exception("モデルが読み込めませんでした")
chainer.serializers.load_npz(PATH['MODEL'], model)
# set test dataset
ds= datasetVOC(PATH['DATASET'], 32)
tests, rawImages, GTbbBndBoxs, names = ds.getTestDataFromBING(PATH['DATASET-BING'], ".csv", 20000);
# output summary
CARNUM = []
IDENTIFICATION_CARNUM = []
PPNUM = []
PNNUM = []
NPNUM = []
NNNUM = []
# test
for (i, name) in enumerate(names):
# output detail
PP = [] # 車両データを車両と認識したときTRUE
PN = [] # 背景データを車両と認識したときTURE
NP = [] # 車両データを背景と認識したときTRUE
NN = [] # 背景データを背景と認識したときTRUE
XMIN = [] # 矩形の最小X
YMIN = [] # 矩形の最小Y
XMAX = [] # 矩形の最大X
YMAX = [] # 矩形の最大Y
GROUNDTRUTH = [] # 正解ラベル
PREDICTION = [] # モデルが算出したラベル
images, labels, points = tests[i]._datasets
rawImage = rawImages[i]
bndboxs = GTbbBndBoxs[i]
predictions = testModel(model, images, labels)
identificationCar = getIdentificationBndboxFromProposal(bndboxs, points)
for j in range(0, len(predictions)):
PP.append((predictions[j] == 1 and labels[j] == 1))
PN.append((predictions[j] == 1 and labels[j] == 0))
NP.append((predictions[j] == 0 and labels[j] == 1))
NN.append((predictions[j] == 0 and labels[j] == 0))
XMIN.append(points[j][0])
YMIN.append(points[j][1])
XMAX.append(points[j][2])
YMAX.append(points[j][3])
GROUNDTRUTH.append(labels[j])
PREDICTION.append(predictions[j])
# output summary
CARNUM.append(len(bndboxs))
IDENTIFICATION_CARNUM.append(len(identificationCar))
PPNUM.append(PP.count(True))
PNNUM.append(PN.count(True))
NPNUM.append(NP.count(True))
NNNUM.append(NN.count(True))
Common.writeCSV(PATH["RESULT"] + "/" + name + ".csv",
xmin=XMIN, ymin=YMIN, xmax=XMAX, ymax=YMAX,
groudtruth=GROUNDTRUTH, prediction=PREDICTION)
Common.writeCSV(PATH["RESULT"] + "/summary.csv",
name=names, carnum=CARNUM, pp=PPNUM,pn=PNNUM,np=NPNUM,nn=NNNUM,identificationcarnum=IDENTIFICATION_CARNUM)
def run(PATH):
logging.debug("function start")
# set PATH
Common.mkDir(PATH['RESULT'])
# read model
model = chainer.links.Classifier(Alex())
model.train = False
if not os.path.isfile(PATH['MODEL']):
raise Exception("モデルが読み込めませんでした")
chainer.serializers.load_npz(PATH['MODEL'], model)
# output summary
CARNUM = []
IDENTIFICATION_CARNUM = []
TPnum = []
FPnum = []
FNnum = []
TNnum = []
TP_15num = []
FP_15num = []
TP_20num = []
FP_20num = []
TP_25num = []
FP_25num = []
TP_30num = []
FP_30num = []
# set test dataset
bs = bingTestset(datasetVOC(PATH['DATASET'], 32, False), PATH['BING'])
names = bs.getTestIdList()
for name in names:
logging.debug("start: " + name)
# read data
images, labels, points, rawImage, bndboxs = bs.getTestDataByIndex(name)
# test
predictions = testModel(model, images, labels)
# output
TP = [] # 車両データを車両と認識した座標
FP = [] # 背景データを車両と認識した座標
FN = [] # 車両データを背景と認識した座標
TN = [] # 背景データを背景と認識した座標
for j in range(0, len(predictions)):
if (predictions[j] == 1 and labels[j] == 1):
TP.append(points[j])
elif(predictions[j] == 1 and labels[j] == 0):
FP.append(points[j])
elif(predictions[j] == 0 and labels[j] == 1):
FN.append(points[j])
elif(predictions[j] == 0 and labels[j] == 0):
TN.append(points[j])
# TP結果と正解データから出力してる。
identificationCar = Common.getIdentificationBndboxFromProposal(bndboxs, TP)
# TPとFPに対して，重複矩形の削除を行う。
TP_015 = Common.removeOverlapRect(TP, 0.15)
TP_020 = Common.removeOverlapRect(TP, 0.20)
TP_025 = Common.removeOverlapRect(TP, 0.25)
TP_030 = Common.removeOverlapRect(TP, 0.30)
FP_015 = Common.removeOverlapRect(FP, 0.15)
FP_020 = Common.removeOverlapRect(FP, 0.20)
FP_025 = Common.removeOverlapRect(FP, 0.25)
FP_030 = Common.removeOverlapRect(FP, 0.30)
# output summary
CARNUM.append(len(bndboxs))
IDENTIFICATION_CARNUM.append(len(identificationCar))
TPnum.append(len(TP))
FPnum.append(len(FP))
FNnum.append(len(FN))
TNnum.append(len(TN))
TP_15num.append(len(TP_015))
FP_15num.append(len(FP_015))
TP_20num.append(len(TP_020))
FP_20num.append(len(FP_020))
TP_25num.append(len(TP_025))
FP_25num.append(len(FP_025))
TP_30num.append(len(TP_030))
FP_30num.append(len(FP_030))
# write file
if(len(TP)):
Common.writeCSV(PATH["RESULT"] + "/" + name + "_TP.csv", xmin=np.array(TP).T[0], ymin=np.array(TP).T[1], xmax=np.array(TP).T[2], ymax=np.array(TP).T[3])
if(len(FP)):
Common.writeCSV(PATH["RESULT"] + "/" + name + "_FP.csv", xmin=np.array(FP).T[0], ymin=np.array(FP).T[1], xmax=np.array(FP).T[2], ymax=np.array(FP).T[3])
if(len(FN)):
Common.writeCSV(PATH["RESULT"] + "/" + name + "_FN.csv", xmin=np.array(FN).T[0], ymin=np.array(FN).T[1], xmax=np.array(FN).T[2], ymax=np.array(FN).T[3])
if(len(TN)):
Common.writeCSV(PATH["RESULT"] + "/" + name + "_TN.csv", xmin=np.array(TN).T[0], ymin=np.array(TN).T[1], xmax=np.array(TN).T[2], ymax=np.array(TN).T[3])
if(len(TP_015)):
Common.writeCSV(PATH["RESULT"] + "/" + name + "_TP(0.15).csv", xmin=np.array(TP_015).T[0], ymin=np.array(TP_015).T[1], xmax=np.array(TP_015).T[2], ymax=np.array(TP_015).T[3])
if(len(FP_015)):
Common.writeCSV(PATH["RESULT"] + "/" + name + "_FP(0.15).csv", xmin=np.array(FP_015).T[0], ymin=np.array(FP_015).T[1], xmax=np.array(FP_015).T[2], ymax=np.array(FP_015).T[3])
if(len(TP_020)):
Common.writeCSV(PATH["RESULT"] + "/" + name + "_TP(0.20).csv", xmin=np.array(TP_020).T[0], ymin=np.array(TP_020).T[1], xmax=np.array(TP_020).T[2], ymax=np.array(TP_020).T[3])
if(len(FP_020)):
Common.writeCSV(PATH["RESULT"] + "/" + name + "_FP(0.20).csv", xmin=np.array(FP_020).T[0], ymin=np.array(FP_020).T[1], xmax=np.array(FP_020).T[2], ymax=np.array(FP_020).T[3])
if(len(TP_025)):
Common.writeCSV(PATH["RESULT"] + "/" + name + "_TP(0.25).csv", xmin=np.array(TP_025).T[0], ymin=np.array(TP_025).T[1], xmax=np.array(TP_025).T[2], ymax=np.array(TP_025).T[3])
if(len(FP_025)):
Common.writeCSV(PATH["RESULT"] + "/" + name + "_FP(0.25).csv", xmin=np.array(FP_025).T[0], ymin=np.array(FP_025).T[1], xmax=np.array(FP_025).T[2], ymax=np.array(FP_025).T[3])
if(len(TP_030)):
Common.writeCSV(PATH["RESULT"] + "/" + name + "_TP(0.30).csv", xmin=np.array(TP_030).T[0], ymin=np.array(TP_030).T[1], xmax=np.array(TP_030).T[2], ymax=np.array(TP_030).T[3])
if(len(FP_030)):
Common.writeCSV(PATH["RESULT"] + "/" + name + "_FP(0.30).csv", xmin=np.array(FP_030).T[0], ymin=np.array(FP_030).T[1], xmax=np.array(FP_030).T[2], ymax=np.array(FP_030).T[3])
random.shuffle(TP)
random.shuffle(FP)
random.shuffle(FN)
random.shuffle(TN)
Common.outputImage(PATH["RESULT"] + "/" + name + "_TP.jpg", rawImage, TP[0:min(2000, len(TP))])
Common.outputImage(PATH["RESULT"] + "/" + name + "_FP.jpg", rawImage, FP[0:min(2000, len(FP))])
Common.outputImage(PATH["RESULT"] + "/" + name + "_FN.jpg", rawImage, FN[0:min(2000, len(FN))])
Common.outputImage(PATH["RESULT"] + "/" + name + "_TN.jpg", rawImage, TN[0:min(2000, len(TN))])
Common.outputImage(PATH["RESULT"] + "/" + name + "_TP(0.15).jpg", rawImage, TP_015)
Common.outputImage(PATH["RESULT"] + "/" + name + "_FP(0.15).jpg", rawImage, FP_015)
Common.outputImage(PATH["RESULT"] + "/" + name + "_TP(0.20).jpg", rawImage, TP_020)
Common.outputImage(PATH["RESULT"] + "/" + name + "_FP(0.20).jpg", rawImage, FP_020)
Common.outputImage(PATH["RESULT"] + "/" + name + "_TP(0.25).jpg", rawImage, TP_025)
Common.outputImage(PATH["RESULT"] + "/" + name + "_FP(0.25).jpg", rawImage, FP_025)
Common.outputImage(PATH["RESULT"] + "/" + name + "_TP(0.30).jpg", rawImage, TP_030)
Common.outputImage(PATH["RESULT"] + "/" + name + "_FP(0.30).jpg", rawImage, FP_030)
Common.writeCSV(PATH["RESULT"] + "/summary.csv",
name=names, carnum=CARNUM,
tp=TPnum,fp=FPnum,fn=FNnum,tn=TNnum,
tp15=TP_15num, fp15=FP_15num,
tp20=TP_20num, fp20=FP_20num,
tp25=TP_25num, fp25=FP_25num,
tp30=TP_30num, fp30=FP_30num,
identificationcarnum=IDENTIFICATION_CARNUM
)
