OneEye Changeset - 655956f6ba89

Changeset - 655956f6ba89

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Laman - 6 years ago 2019-05-04 16:43:20

training and testing model

6 files changed with 89 insertions and 12 deletions:

exp/board_detect.py

exp/kerokero/__init__.py

rename

exp/kerokero/prepare_data.py

exp/kerokero/test.py

exp/kerokero/train.py

exp/kerokero/transformation_matrices.py

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exp/board_detect.py

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@@ @@ -225,48 +225,49 @@ class BoardDetector: @@
 		d=2*center-b
 		# abort unfitting sizes
 		if not all(0<=point.x<self._rectW and 0<=point.y<self._rectH for point in (a,b,c,d)):
 			return None
 		if any(g.dist(h)<19*10 for (g,h) in [(a,b),(a,c),(a,d),(b,c),(b,d),(c,d)]):
 			return None
 		(a,b,c,d)=Corners([a,b,c,d])
 		rows=[]
 		cols=[]
 		for j in range(19):
 			rows.append(Line.fromPoints((a*(18-j)+b*j)/18,(d*(18-j)+c*j)/18))
 			cols.append(Line.fromPoints((a*(18-j)+d*j)/18,(b*(18-j)+c*j)/18))
 		return (rows,cols)
 	def _scoreGrid(self,lines):
 		(p,q,r,s)=(lines[0][0],lines[0][-1],lines[-1][0],lines[-1][-1])
 		corners=(p.intersect(r),p.intersect(s),q.intersect(r),q.intersect(s))
 		origCorners=[c.transform(self._inverseMatrix) for c in corners]
 		# must fit
 		if not all(0<=c.x<self._rectW and 0<=c.y<self._rectH for c in origCorners):
 			return 0
 		return sum(self._hough.scoreLine(p.transform(self._inverseMatrix)) for p in itertools.chain(*lines))
 	def detectPosition(self,img):
 		if not self.grid: return None
 		(rows,cols)=self.grid
 		intersections=[[row.intersect(col) for col in cols] for row in rows]
 		position=[[self._detectStoneAt(img,point) for point in row] for row in intersections]
 		log.debug("detected position:\n%s","\n".join("".join(row) for row in position))
 		return position
 	def _detectStoneAt(self,img,intersection):
 		(height,width)=img.img.shape[:2]
 		(x,y)=map(int,intersection)
 		scores=[0,0,0]
 		for xi in range(x-2,x+3):
 			if xi<0 or xi>=width: continue
 			for yi in range(y-2,y+3):
 				if yi<0 or yi>=height: continue
 				scores[img.get(xi,yi)]+=1
 		return sorted(list(zip(scores,"XO.")))[-1][1]
 	def _drawLine(self,img,line,color=None):
 		if not color: color=[0,255,0]
 		(h,w)=img.shape[:2]
 		corners=[EPoint(0,0),EPoint(w,0),EPoint(0,h),EPoint(w,h)] # NW NE SW SE
 		borders=[
 			[Line.fromPoints(corners[0],corners[1]), Line.fromPoints(corners[2],corners[3])], # N S
 			[Line.fromPoints(corners[0],corners[2]), Line.fromPoints(corners[1],corners[3])] # W E

exp/kerokero/__init__.py

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file renamed from exp/keras/__init__.py to exp/kerokero/__init__.py

exp/kerokero/prepare_data.py

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@@ file renamed from exp/keras/prepare_data.py to exp/kerokero/prepare_data.py @@
 import os
 import sys
 import re
 import random
 import itertools
 import numpy as np
 import cv2 as cv
 sys.path.append("../exp")
 sys.path.append("..")
 sys.path.append("../../src")
 from annotations import DataFile,computeBoundingBox,Corners
 from geometry import Line
-from keras.transformation_matrices import getIdentity,getRotation,getTranslation,getScale,getMirroring,getProjection
+from kerokero.transformation_matrices import getIdentity,getRotation,getTranslation,getScale,getMirroring,getProjection
 random.seed(361)
 class Sample:
-	SIDE=256
+	SIDE=224
 	def __init__(self,img,grid):
 		self.img=img
 		self.grid=grid
 	def transform(self):
 		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))
 		grid=Corners(c.transform(m) for c in self.grid)
-		Sample(img,grid).show()
+		return (img,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)
 	def _computeCrop(self,m):
 		grid=Corners(c.transform(m) for c in self.grid)
 		(x1,y1,x2,y2)=computeBoundingBox(grid)
 		(wg,hg)=(x2-x1,y2-y1)
 		(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)
 		for c in self.grid:
 			cv.circle(img,(int(c.x),int(c.y)),3,[0,255,0],-1)
 		show(img)
 def traverseDirs(root):
 	stack=[root]
 	while len(stack)>0:
 		d=stack.pop()
 		contents=sorted(os.scandir(d),key=lambda f: f.name,reverse=True)
 		if any(f.name=="annotations.json.gz" for f in contents):
 			print(d)
 			yield d
 		for f in contents:
 			if f.is_dir(): stack.append(f.path)
 def harvestDir(path):
 	annotations=DataFile(os.path.join(path,"annotations.json.gz"))
 	imgFilter=lambda f: f.is_file() and re.match(r".*\.(jpg|jpeg|png|gif)$", f.name.lower())
 	files=sorted(filter(imgFilter,os.scandir(path)),key=lambda f: f.name)
 	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)
 			sample.transform()
 			(img,label)=sample.transform()
 			yield (img,label)
 def loadDataset(root):
 	testRatio=0.1
 	trainRatio=1-testRatio
 	images=[]
 	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.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__":
 	root=sys.argv[1]
 	for d in traverseDirs(root):
 		harvestDir(d)

exp/kerokero/test.py

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@@ new file 100644 @@
 import argparse
 import numpy as np
 from keras.models import load_model
 from prepare_data import loadDataset,Sample
 from analyzer.epoint import EPoint
 from analyzer.corners import Corners
 parser=argparse.ArgumentParser()
 parser.add_argument("model")
 parser.add_argument("data_dir")
 args=parser.parse_args()
 model=load_model(args.model)
 print("loading data...")
 ((trainImages,trainLabels),(testImages,testLabels))=loadDataset(args.data_dir)
 print("done")
 for img in testImages:
 	label=model.predict(np.reshape(img,(1,224,224)))
 	print(label)
 	points=[]
 	for i in range(4):
 		points.append(EPoint(label[0][i*2],label[0][i*2+1]))
 	corners=Corners(points)
 	sample=Sample(np.uint8(img),corners)
 	sample.show()

exp/kerokero/train.py

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@@ file renamed from exp/keras/train.py to exp/kerokero/train.py @@
 import argparse
 from keras.layers import Conv2D,Dropout,Dense,Flatten
 from keras.models import Sequential
 from keras.models import Sequential,load_model
 from prepare_data import loadDataset
 model = Sequential([
 	Flatten(input_shape=(96,96)),
 parser=argparse.ArgumentParser()
 parser.add_argument("data_dir")
 parser.add_argument("--load_model")
 parser.add_argument("--save_model",default="/tmp/gogo-{0:03}.h5")
 parser.add_argument("--epochs",type=int,default=100)
 parser.add_argument("--initial_epoch",type=int,default=0)
 args=parser.parse_args()
 model=Sequential([
 	Flatten(input_shape=(224,224)),
 	Dense(128, activation="relu"),
 	Dropout(0.1),
 	Dense(64, activation="relu"),
-	Dense(30)
+	Dense(8)
 ])
 model.compile(
 	optimizer='adam',
 	loss='mse',
 	metrics=['mae','accuracy']
+)
 if args.load_model:
 	model=load_model(args.load_model)
 model.fit(X_train,y_train,epochs = 500,batch_size = 128,validation_split = 0.2)
 print("loading data...")
 ((trainImages,trainLabels),(testImages,testLabels))=loadDataset(args.data_dir)
 print("done")
 for i in range(args.initial_epoch,args.epochs//10):
 	model.fit(trainImages,trainLabels,epochs=(i+1)*10,initial_epoch=i*10,batch_size=128,validation_split=1/9)
 	model.save(args.save_model.format(i+1))
 print(model.evaluate(testImages,testLabels))

exp/kerokero/transformation_matrices.py

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@@ @@ -26,31 +26,31 @@ def getTranslation(dx,dy): @@
 		[1,0,dx],
 		[0,1,dy],
 		[0,0,1]
 	])
 def getScale(kx,ky=0):
 	if not ky: ky=kx
 	return np.float32([
 		[kx,0,0],
 		[0,ky,0],
 		[0,0,1]
 	])
 def getMirroring():
 	return np.float32([
 		[random.choice((1,-1)),0,0],
 		[0,1,0],
 		[0,0,1]
 	])
 def getProjection():
 	dx=random.uniform(-0.001,0.001)
 	dy=random.uniform(-0.001,0.001)
 	dx=random.uniform(-0.0005,0.0005)
 	dy=random.uniform(-0.0005,0.0005)
 	return np.float32([
 		[1,0,0],
 		[0,1,0],
 		[dx,dy,1]
 	])

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