OneEye Changeset - 5f42b982809c

Changeset - 5f42b982809c

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Laman - 6 years ago 2019-05-24 11:31:52

deferred data preprocessing saving space

3 files changed with 6 insertions and 6 deletions:

exp/kerokero/prepare_data.py

exp/kerokero/test.py

exp/kerokero/train.py

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exp/kerokero/prepare_data.py

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@@ @@ -28,25 +28,25 @@ class Sample: @@
 		""":return: (img, grid), where img is a 2D np.float32 with values in (0,1),
 		grid [(float) x, (float) y, ...], with x, y in (-1,1)"""
 		center=self._getCenter()
 		m=getIdentity()
 		t1=getTranslation(-center.x,-center.y)
 		proj=getProjection()
 		rot=getRotation()
 		mir=getMirroring()
 		for mi in [t1,mir,proj,rot]:
 			m=np.matmul(mi,m)
 		m=np.matmul(self._computeCrop(m),m)
 		img=cv.warpPerspective(self.img,m,(self.SIDE,self.SIDE))
-		img=np.float32(img)/128-1
+		img=np.uint8(img)
 		grid=Corners(c.transform(m) for c in self.grid)
 		grid=list(map(lambda p: list(2*p/self.SIDE-EPoint(1,1)), grid))
 		return (img,grid)
 	def _getCenter(self):
 		(a,b,c,d)=self.grid
 		p=Line.fromPoints(a,c)
 		q=Line.fromPoints(b,d)
 		return p.intersect(q)
 	def _computeCrop(self,m):
 		grid=Corners(c.transform(m) for c in self.grid)
@@ @@ -99,26 +99,26 @@ def loadDataset(root): @@
 	labels=[]
 	for d in traverseDirs(root):
 		for (img,label) in harvestDir(d):
 			images.append(img)
 			labels.append(label)
 	n=len(images)
 	keys=list(range(n))
 	random.shuffle(keys)
 	images=[images[k] for k in keys]
 	labels=[labels[k] for k in keys]
 	m=int(n*trainRatio)
 	return (
 		(np.float32(images[:m]),np.float32(labels[:m])),
 		(np.float32(images[m:]),np.float32(labels[m:]))
 		(np.uint8(images[:m]),np.float32(labels[:m])),
 		(np.uint8(images[m:]),np.float32(labels[m:]))
+	)
 def show(img,filename="x"):
 	cv.imshow(filename,img)
 	cv.waitKey(0)
 	cv.destroyAllWindows()
 if __name__=="__main__":
 	((trainImages,trainLabels),(testImages,testLabels))=loadDataset(sys.argv[1])
 	np.savez_compressed(

exp/kerokero/test.py

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@@ @@ -15,25 +15,25 @@ keras.losses.averageDistance=averageDist @@
 keras.metrics.averageDistance=averageDistance
 parser=argparse.ArgumentParser()
 parser.add_argument("model")
 parser.add_argument("data")
 args=parser.parse_args()
 model=load_model(args.model)
 model.summary()
 log.info("loading data...")
 with np.load(args.data) as data:
 	testImages=data["testImages"].reshape((-1,224,224,1))
+	testImages=(np.float32(data["testImages"])/128-1).reshape((-1,224,224,1))
 	testLabels=data["testLabels"].reshape((-1,4,2))
 log.info("done")
 log.info(model.evaluate(testImages,testLabels))
 for img in testImages:
 	label=model.predict(np.reshape(img,(1,224,224,1)))
 	print(label)
 	points=[]
 	for i in range(4):
 		points.append(EPoint((label[0][i][0]+1)*112,(label[0][i][1]+1)*112))
 	corners=Corners(points)

exp/kerokero/train.py

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@@ @@ -70,27 +70,27 @@ def createCNN(): @@
 	model.add(Reshape((4,2)))
 	model.compile(optimizer="rmsprop",loss=averageDistance,metrics=["mae","accuracy"])
 	return model
 model=createCNN()
 if args.load_model:
 	model=load_model(args.load_model)
 log.info("loading data...")
 with np.load(args.data) as data:
 	trainImages=data["trainImages"].reshape((-1,224,224,1))
+	trainImages=(np.float32(data["trainImages"])/128-1).reshape((-1,224,224,1))
 	trainLabels=data["trainLabels"].reshape((-1,4,2))
 	testImages=data["testImages"].reshape((-1,224,224,1))
+	testImages=(np.float32(data["testImages"])/128-1).reshape((-1,224,224,1))
 	testLabels=data["testLabels"].reshape((-1,4,2))
 log.info("done")
 n=len(trainImages)
 k=round(n*0.9)
 n_=n-k
 (trainImages,valImages)=(np.float32(trainImages[:k]),np.float32(trainImages[k:]))
 (trainLabels,valLabels)=(np.float32(trainLabels[:k]),np.float32(trainLabels[k:]))
 tensorboard=TensorBoard(log_dir=os.path.join(args.log_dir,"{}".format(time())))
 checkpoint=ModelCheckpoint(args.save_model,monitor="val_loss",period=10)

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