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
1
exp/kerokero/__init__.py
rename
exp/kerokero/prepare_data.py
30
5
exp/kerokero/test.py
30
exp/kerokero/train.py
26
5
exp/kerokero/transformation_matrices.py
2
2
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exp/board_detect.py
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@@ -246,6 +246,7 @@ class BoardDetector:
 
		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]

			




        

    


    
    

    

        

                

                    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

			




	
	
		
 
@@ -2,20 +2,22 @@ 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

			




	
	
		
 
@@ -33,7 +35,7 @@ class Sample:

			




	
	
	
		
 
		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

			




	
	
		
 
@@ -76,9 +78,32 @@ 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)

			




	
	
	
		
 
			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"):

			




        

    


    
    

    

        

                

                    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(

			




	
	
		
 
@@ -15,5 +27,14 @@ model.compile(

			




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

			




	
	
		
 
@@ -47,8 +47,8 @@ def getMirroring():

			




	
	
	
		
 

			




	
	
	
		
 

			




	
	
	
		
 
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],

			




        

    



    


    



    



      

      





    
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