diff --git a/exp/board_detect.py b/exp/board_detect.py
--- a/exp/board_detect.py
+++ b/exp/board_detect.py
@@ -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]
diff --git a/exp/keras/__init__.py b/exp/kerokero/__init__.py
rename from exp/keras/__init__.py
rename to exp/kerokero/__init__.py
diff --git a/exp/keras/prepare_data.py b/exp/kerokero/prepare_data.py
rename from exp/keras/prepare_data.py
rename to exp/kerokero/prepare_data.py
--- a/exp/keras/prepare_data.py
+++ b/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"):
diff --git a/exp/kerokero/test.py b/exp/kerokero/test.py
new file mode 100644
--- /dev/null
+++ b/exp/kerokero/test.py
@@ -0,0 +1,30 @@
+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()
diff --git a/exp/keras/train.py b/exp/kerokero/train.py
rename from exp/keras/train.py
rename to exp/kerokero/train.py
--- a/exp/keras/train.py
+++ b/exp/kerokero/train.py
@@ -1,13 +1,25 @@
+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))
diff --git a/exp/keras/transformation_matrices.py b/exp/kerokero/transformation_matrices.py
rename from exp/keras/transformation_matrices.py
rename to exp/kerokero/transformation_matrices.py
--- a/exp/keras/transformation_matrices.py
+++ b/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],