OneEye Changeset - fad34516870e

Changeset - fad34516870e

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Laman - 6 years ago 2019-05-07 19:52:46

normed pixel values, normed coordinates

3 files changed with 20 insertions and 12 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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@@ @@ -6,39 +6,45 @@ import itertools @@
 import numpy as np
 import cv2 as cv
 sys.path.append("..")
 sys.path.append("../../src")
 from annotations import DataFile,computeBoundingBox,Corners
+from annotations import DataFile,computeBoundingBox,Corners,EPoint
 from geometry import Line
 from kerokero.transformation_matrices import getIdentity,getRotation,getTranslation,getScale,getMirroring,getProjection
 random.seed(361)
 class Sample:
 	SIDE=224
 	def __init__(self,img,grid):
 		""":param img: a greyscale image as a 2D np.uint8
 		:param grid: iterable of 4 EPoints, ie. Corners"""
 		self.img=img
 		self.grid=grid
 	def transform(self):
 		""":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)/255
 		grid=Corners(c.transform(m) for c in self.grid)
 		return (img,list(itertools.chain.from_iterable(grid)))
 		grid=list(map(lambda p: 2*p/self.SIDE-EPoint(1,1), grid))
 		return (img,grid,list(itertools.chain.from_iterable(grid)))
 	def _getCenter(self):
 		(a,b,c,d)=self.grid
 		p=Line.fromPoints(a,c)
 		q=Line.fromPoints(b,d)
 		return p.intersect(q)
@@ @@ -50,13 +56,13 @@ class Sample: @@
 		(left,top,right,bottom)=[random.uniform(0.05,0.2) for i in range(4)]
 		t2=getTranslation(left*wg-x1, top*hg-y1)
 		scale=getScale(self.SIDE/(wg*(1+left+right)), self.SIDE/(hg*(1+top+bottom)))
 		return np.matmul(scale,t2)
 	def show(self):
-		img=np.copy(self.img)
+		img=cv.cvtColor(self.img,cv.COLOR_GRAY2BGR)
 		for c in self.grid:
 			cv.circle(img,(int(c.x),int(c.y)),3,[0,255,0],-1)
 		show(img)
 def traverseDirs(root):
@@ @@ -78,13 +84,14 @@ def harvestDir(path): @@
 	boards=annotations["."]
 	for f in files:
 		img=cv.imread(f.path)
 		img=cv.cvtColor(img,cv.COLOR_BGR2GRAY)
 		for b in boards:
 			sample=Sample(img,b.grid)
 			(transformedImg,label)=sample.transform()
 			(transformedImg,transformedGrid,label)=sample.transform()
 			# Sample(np.uint8(transformedImg*255),map(lambda c: (c+EPoint(1,1))*Sample.SIDE/2,transformedGrid)).show()
 			yield (transformedImg,label)
 def loadDataset(root):
 	testRatio=0.1
 	trainRatio=1-testRatio
@@ @@ -98,14 +105,14 @@ def loadDataset(root): @@
 	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.uint8(images[:m]),np.float32(labels[:m])),
 		(np.uint8(images[m:]),np.float32(labels[m:]))
 		(np.float32(images[:m]),np.float32(labels[:m])),
 		(np.float32(images[m:]),np.float32(labels[m:]))
+	)
 def show(img,filename="x"):
 	cv.imshow(filename,img)
 	cv.waitKey(0)

exp/kerokero/test.py

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@@ @@ -29,10 +29,10 @@ log.info(model.evaluate(testImages.resha @@
 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*2],label[0][i*2+1]))
+		points.append(EPoint((label[0][i*2]+1)*112,(label[0][i*2+1]+1)*112))
 	corners=Corners(points)
 	sample=Sample(np.uint8(img),corners)
+	sample=Sample(np.uint8(img*255),corners)
 	sample.show()

exp/kerokero/train.py

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@@ @@ -82,13 +82,14 @@ with np.load(args.data) as data: @@
 	trainLabels=data["trainLabels"]
 	testImages=data["testImages"]
 	testLabels=data["testLabels"]
 log.info("done")
 tensorboard = TensorBoard(log_dir=os.path.join(cfg.thisDir,"../logs","{}".format(time())))
 for i in range(args.initial_epoch//10,args.epochs//10):
 	model.fit(trainImages.reshape((-1,224,224,1)),trainLabels,epochs=(i+1)*10,initial_epoch=i*10,batch_size=128,validation_split=0.2,callbacks=[tensorboard])
 	path=args.save_model.format((i+1)*10)
 BIG_STEP=50
 for i in range(args.initial_epoch//BIG_STEP,args.epochs//BIG_STEP):
 	model.fit(trainImages.reshape((-1,224,224,1)),trainLabels,epochs=(i+1)*BIG_STEP,initial_epoch=i*BIG_STEP,batch_size=128,validation_split=0.2,callbacks=[tensorboard])
 	path=args.save_model.format((i+1)*BIG_STEP)
 	log.info("saving model...")
 	model.save(path)
 	ftp.push(path)
+	# ftp.push(path)
 log.info(model.evaluate(testImages.reshape((-1,224,224,1)),testLabels))

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