Changeset - 29f28718a69b
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Laman - 6 years ago 2019-09-09 20:58:21

transitional data processing
1 file changed with 46 insertions and 44 deletions:
exp/kerokero/prepare_data.py
46
44
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exp/kerokero/prepare_data.py
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@@ -7,6 +7,8 @@ import logging as log
 

			




	
	
	
		
 
import numpy as np

			




	
	
	
		
 
import cv2 as cv

			




	
	
	
		
 
import PIL.Image

			




	
	
	
		
 
import PIL.ImageDraw

			




	
	
	
		
 

			




	
	
	
		
 
import config as cfg

			




	
	
	
		
 
sys.path.append("..")

			




	
	
		
 
@@ -44,10 +46,10 @@ class Sample:

			




	
	
	
		
 
			m=np.matmul(mi,m)

			




	
	
	
		
 
		m=np.matmul(self._computeCrop(m),m)

			




	
	
	
		
 
		img=cv.warpPerspective(self.img,m,(self.SIDE,self.SIDE))

			




	
	
	
		
 
		img=np.uint8(img)

			




	
	
	
		
 
		img=PIL.Image.fromarray(img[:,:,::-1])

			




	
	
	
		
 
		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)

			




	
	
	
		
 
		mask=self._createMask(img,grid)

			




	
	
	
		
 
		return (img,mask)

			




	
	
	
		
 

			




	
	
	
		
 
	def rectify(self):

			




	
	
	
		
 
		x1=self.SIDE*0.1

			




	
	
		
 
@@ -62,29 +64,6 @@ class Sample:

			




	
	
	
		
 
		grid=list(map(lambda p: list(2*p/self.SIDE-EPoint(1,1)), grid))

			




	
	
	
		
 
		return (img,grid)

			




	
	
	
		
 

			




	
	
	
		
 
	def cut(self):

			




	
	
	
		
 
		width=max(p.x for p in self.grid)-min(p.x for p in self.grid)

			




	
	
	
		
 
		height=max(p.y for p in self.grid)-min(p.y for p in self.grid)

			




	
	
	
		
 
		kx=width/4

			




	
	
	
		
 
		ky=height/4

			




	
	
	
		
 
		n=self.SIDE

			




	
	
	
		
 
		for p in self.grid:

			




	
	
	
		
 
			shift=self._createNoise(0.2)

			




	
	
	
		
 
			abcd=[[p.x-kx,p.y-ky],[p.x-kx,p.y+ky],[p.x+kx,p.y+ky],[p.x+kx,p.y-ky]]

			




	
	
	
		
 
			abcd_=[[shift.x,shift.y],[shift.x,n+shift.y],[n+shift.x,n+shift.y],[n+shift.x,shift.y]]

			




	
	
	
		
 
			m=cv.getPerspectiveTransform(np.float32(abcd),np.float32(abcd_))

			




	
	
	
		
 
			t1=getTranslation(-n/2,-n/2)

			




	
	
	
		
 
			mir=getMirroring()

			




	
	
	
		
 
			proj=getProjection()

			




	
	
	
		
 
			rot=getRotation()

			




	
	
	
		
 
			t2=getTranslation(n/2,n/2)

			




	
	
	
		
 
			for mi in [t1,mir,proj,rot,t2]:

			




	
	
	
		
 
				m=np.matmul(mi,m)

			




	
	
	
		
 
			img=cv.warpPerspective(self.img,m,(self.SIDE,self.SIDE))

			




	
	
	
		
 
			img=np.uint8(img)

			




	
	
	
		
 
			point=p.transform(m)*2/self.SIDE-EPoint(1,1)

			




	
	
	
		
 
			yield (img,[point.x,point.y])

			




	
	
	
		
 

			




	
	
	
		
 
	def _getCenter(self):

			




	
	
	
		
 
		(a,b,c,d)=self.grid

			




	
	
	
		
 
		p=Line.fromPoints(a,c)

			




	
	
		
 
@@ -100,9 +79,15 @@ class Sample:

			




	
	
	
		
 
		scale=getScale(self.SIDE/(wg*(1+left+right)), self.SIDE/(hg*(1+top+bottom)))

			




	
	
	
		
 
		return np.matmul(scale,t2)

			




	
	
	
		
 

			




	
	
	
		
 
	def _createNoise(self,mag=0.05):

			




	
	
	
		
 
	def _createMask(self,image,grid):

			




	
	
	
		
 
		img=PIL.Image.new("L",image.size)

			




	
	
	
		
 
		draw=PIL.ImageDraw.Draw(img)

			




	
	
	
		
 
		draw.polygon([tuple(p) for p in grid],255,255)

			




	
	
	
		
 
		return img

			




	
	
	
		
 

			




	
	
	
		
 
	def _createNoise(self):

			




	
	
	
		
 
		alpha=random.uniform(0,math.pi*2)

			




	
	
	
		
 
		d=random.uniform(0,self.SIDE*mag)

			




	
	
	
		
 
		d=random.uniform(0,self.SIDE*0.05)

			




	
	
	
		
 
		return EPoint(math.cos(alpha)*d, math.sin(alpha)*d)

			




	
	
	
		
 

			




	
	
	
		
 
	def show(self):

			




	
	
		
 
@@ -133,17 +118,12 @@ def harvestDir(path):

			




	
	
	
		
 
	for f in files:

			




	
	
	
		
 
		grade=annotations.get(f.name,[Board()])[0].grade

			




	
	
	
		
 
		Stats.counts[grade]+=1

			




	
	
	
		
 
		if not Board.UNSET<grade<=Board.GOOD: continue

			




	
	
	
		
 
		if not Board.UNSET<grade<=Board.POOR: continue

			




	
	
	
		
 
		img=cv.imread(f.path)

			




	
	
	
		
 
		img=cv.cvtColor(img,cv.COLOR_BGR2GRAY)

			




	
	
	
		
 
		for b in boards:

			




	
	
	
		
 
			sample=Sample(img,b.grid)

			




	
	
	
		
 
			# sample.show()

			




	
	
	
		
 
			# (transformedImg,label)=sample.transform()

			




	
	
	
		
 
			# (transformedImg,label)=sample.rectify()

			




	
	
	
		
 
			for (transformedImg,label) in sample.cut():

			




	
	
	
		
 
				Sample(np.uint8(transformedImg),[(EPoint(*label)+EPoint(1,1))*Sample.SIDE/2]).show()

			




	
	
	
		
 
				yield (transformedImg,label)

			




	
	
	
		
 
			(transformedImg,mask)=sample.transform()

			




	
	
	
		
 
			yield (transformedImg,mask)

			




	
	
	
		
 

			




	
	
	
		
 

			




	
	
	
		
 
def loadDataset(root):

			




	
	
		
 
@@ -172,6 +152,35 @@ def loadDataset(root):

			




	
	
	
		
 
	)

			




	
	
	
		
 

			




	
	
	
		
 

			




	
	
	
		
 
def prepareDataset(root,dest):

			




	
	
	
		
 
	i=0

			




	
	
	
		
 
	train=[]

			




	
	
	
		
 
	test=[]

			




	
	
	
		
 
	for d in traverseDirs(root):

			




	
	
	
		
 
		for (image,mask) in harvestDir(d):

			




	
	
	
		
 
			i+=1

			




	
	
	
		
 
			if random.random()<0.9:

			




	
	
	
		
 
				image.save(os.path.join(dest,"train/{0}.jpg".format(i)))

			




	
	
	
		
 
				mask.save(os.path.join(dest,"train_masks/{0}_mask.png".format(i)))

			




	
	
	
		
 
				train.append(str(i)+".jpg")

			




	
	
	
		
 
			else:

			




	
	
	
		
 
				image.save(os.path.join(dest,"test/{0}.jpg".format(i)))

			




	
	
	
		
 
				test.append(str(i)+".jpg")

			




	
	
	
		
 
	with open(os.path.join(dest,"train_masks.csv"),mode="w") as f:

			




	
	
	
		
 
		f.write("img,rle_mask\n")

			




	
	
	
		
 
		for file in train:

			




	
	
	
		
 
			f.write('{0},""\n'.format(file))

			




	
	
	
		
 
	with open(os.path.join(dest,"test_masks.csv"),mode="w") as f:

			




	
	
	
		
 
		f.write("img,rle_mask\n")

			




	
	
	
		
 
		for file in test:

			




	
	
	
		
 
			f.write('{0},""\n'.format(file))

			




	
	
	
		
 
	log.info("clear images: %s",Stats.counts[1])

			




	
	
	
		
 
	log.info("good images: %s",Stats.counts[2])

			




	
	
	
		
 
	log.info("poor images: %s",Stats.counts[3])

			




	
	
	
		
 
	log.info("unset images: %s",Stats.counts[0])

			




	
	
	
		
 
	log.info("total: %s",sum(Stats.counts))

			




	
	
	
		
 

			




	
	
	
		
 

			




	
	
	
		
 
def show(img,filename="x"):

			




	
	
	
		
 
	cv.imshow(filename,img)

			




	
	
	
		
 
	cv.waitKey(0)

			




	
	
		
 
@@ -179,11 +188,4 @@ def show(img,filename="x"):

			




	
	
	
		
 

			




	
	
	
		
 

			




	
	
	
		
 
if __name__=="__main__":

			




	
	
	
		
 
	((trainImages,trainLabels),(testImages,testLabels))=loadDataset(sys.argv[1])

			




	
	
	
		
 
	np.savez_compressed(

			




	
	
	
		
 
		sys.argv[2],

			




	
	
	
		
 
		trainImages=trainImages,

			




	
	
	
		
 
		trainLabels=trainLabels,

			




	
	
	
		
 
		testImages=testImages,

			




	
	
	
		
 
		testLabels=testLabels

			




	
	
	
		
 
	)

			




	
	
	
		
 
	prepareDataset(sys.argv[1],sys.argv[2])

			




        

    



    


    



    



      

      





    
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