OneEye Changeset - ecf98a415d97

Changeset - ecf98a415d97

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Laman - 6 years ago 2019-05-09 22:15:27

a different CNN architecture

3 files changed with 34 insertions and 69 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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 import os
 import sys
 import re
 import random
 import itertools
 import numpy as np
 import cv2 as cv
 sys.path.append("..")
 sys.path.append("../../src")
 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: an image as a 3D np.uint8, channels-last
+		""":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=cv.cvtColor(img,cv.COLOR_BGR2RGB)
+		img=np.float32(img)/128-1
 		grid=Corners(c.transform(m) for c in self.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)
 	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)
+		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):
 	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)[::3]
 	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.show()
 			(transformedImg,transformedGrid,label)=sample.transform()
 			# Sample(transformedImg,map(lambda c: (c+EPoint(1,1))*Sample.SIDE/2,transformedGrid)).show()
+			# Sample(np.uint8((transformedImg+1)*128),map(lambda c: (c+EPoint(1,1))*Sample.SIDE/2,transformedGrid)).show()
 			yield (transformedImg,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 (
 		(images[:m],np.float32(labels[:m])),
 		(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)
 	cv.destroyAllWindows()
 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
+	)

exp/kerokero/test.py

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 import argparse
 import logging as log
 import numpy as np
 from keras.models import load_model
 from keras.applications.inception_v3 import preprocess_input
 from prepare_data import loadDataset,Sample
 from analyzer.epoint import EPoint
 from analyzer.corners import Corners
 import config as cfg
 parser=argparse.ArgumentParser()
 parser.add_argument("model")
 parser.add_argument("data")
 args=parser.parse_args()
 model=load_model(args.model)
 model.summary()
 log.info("loading data...")
 with np.load(args.data) as data:
 	testImages=data["testImages"]
 	testLabels=data["testLabels"]
 log.info("done")
-log.info(model.evaluate(preprocess_input(testImages).reshape((-1,224,224,3)),testLabels))
+log.info(model.evaluate(testImages.reshape((-1,224,224,1)),testLabels))
 for img in testImages:
-	label=model.predict(preprocess_input(np.reshape(img,(1,224,224,3))))
+	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]+1)*112,(label[0][i*2+1]+1)*112))
 	corners=Corners(points)
 	sample=Sample(img,corners)
+	sample=Sample(np.uint8((img+1)*128),corners)
 	sample.show()

exp/kerokero/train.py

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 import os
 from time import time
 import argparse
 import logging as log
 import numpy as np
 from keras.layers import Conv2D,Dropout,Dense,Flatten,MaxPooling2D,GlobalAveragePooling2D,BatchNormalization
 from keras.models import Sequential,load_model,Model
 from keras.optimizers import SGD
 from keras.layers import Conv2D,Dropout,Dense,Flatten,MaxPooling2D,BatchNormalization,GlobalAveragePooling2D
 from keras.models import Sequential,load_model
 from keras.callbacks import TensorBoard
 from keras.applications.inception_v3 import InceptionV3,preprocess_input
 import config as cfg
 import ftp
 parser=argparse.ArgumentParser()
 parser.add_argument("data")
 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)
 parser.add_argument("--log_dir",default="/tmp/tflogs")
 args=parser.parse_args()
 def createFullyConnected():
 	model=Sequential([
 		Flatten(input_shape=(224,224)),
 		Dense(128, activation="relu"),
 		Dropout(0.1),
 		Dense(64, activation="relu"),
 		Dense(8)
 	])
 	model.compile(
 		optimizer='adam',
 		loss='mse',
 		metrics=['mae','accuracy']
+	)
 	return model
 def createCNN():
 	model=Sequential()
 	model.add(Conv2D(filters=16,kernel_size=2,padding="same",activation="relu",input_shape=(224,224,1)))
 	model.add(Dropout(0.1))
 	model.add(MaxPooling2D(pool_size=(2,2),strides=(2,2),padding="valid"))
 	model.add(BatchNormalization())
 	model.add(BatchNormalization(input_shape=(224,224,1)))
 	model.add(Conv2D(32,(5,5),activation="relu"))
 	model.add(MaxPooling2D(pool_size=(2,2),strides=(2,2),padding="valid"))
 	model.add(Dropout(0.2))
 	model.add(BatchNormalization())
 	model.add(Conv2D(24,(5,5),border_mode="same",init="he_normal",activation="relu",input_shape=(224,224,1),dim_ordering="tf"))
 	model.add(MaxPooling2D(pool_size=(2,2),strides=(2,2),border_mode="valid"))
 	model.add(Conv2D(36,(5,5),activation="relu"))
 	model.add(MaxPooling2D(pool_size=(2,2),strides=(2,2),border_mode="valid"))
 	model.add(Conv2D(64,(5,5),activation="relu"))
 	model.add(MaxPooling2D(pool_size=(2,2),strides=(2,2),padding="valid"))
 	model.add(BatchNormalization())
 	model.add(Conv2D(48,(5,5),activation="relu"))
 	model.add(MaxPooling2D(pool_size=(2,2),strides=(2,2),border_mode="valid"))
 	model.add(Conv2D(128,(3,3),activation="relu"))
 	model.add(MaxPooling2D(pool_size=(2,2),strides=(2,2),padding="valid"))
 	model.add(Dropout(0.4))
 	model.add(BatchNormalization())
 	model.add(Conv2D(64,(3,3),activation="relu"))
 	model.add(MaxPooling2D(pool_size=(2,2),strides=(2,2),border_mode="valid"))
 	model.add(Flatten())
 	model.add(Conv2D(64,(3,3),activation="relu"))
 	model.add(GlobalAveragePooling2D())
 	model.add(Dense(500,activation="relu"))
 	model.add(Dropout(0.1))
 	model.add(Dense(128,activation="relu"))
 	model.add(Dropout(0.1))
 	model.add(Dense(90,activation="relu"))
 	model.add(Dense(8))
-	model.compile(optimizer='adam',loss='mse',metrics=['mae','accuracy'])
+	model.compile(optimizer="rmsprop",loss="mse",metrics=["mae","accuracy"])
 	return model
 def createPretrained():
 	base=InceptionV3(weights="imagenet",include_top=False,input_shape=(224,224,3))
 	x=base.output
 	x=GlobalAveragePooling2D()(x)
 	x=Dense(1024,activation="relu")(x)
 	predictions=Dense(8)(x)
 	model=Model(inputs=base.input,outputs=predictions)
 	for layer in base.layers:
 		layer.trainable=False
 	model.compile(optimizer='adam',loss='mse',metrics=['mae','accuracy'])
 	return model
 model=createCNN()
 if args.load_model:
 	model=load_model(args.load_model)
 else:
 	model=createPretrained()
 log.info("loading data...")
 with np.load(args.data) as data:
-	trainImages=preprocess_input(data["trainImages"])
 	trainImages=data["trainImages"]
 	trainLabels=data["trainLabels"]
-	testImages=preprocess_input(data["testImages"])
 	testImages=data["testImages"]
 	testLabels=data["testLabels"]
 log.info("done")
 tensorboard = TensorBoard(log_dir=os.path.join(args.log_dir,"{}".format(time())))
 if not args.load_model:
 	model.fit(trainImages.reshape((-1,224,224,3)),trainLabels,epochs=10,batch_size=128,validation_split=0.2,callbacks=[tensorboard])
 for layer in model.layers[:249]:
 	layer.trainable = False
 for layer in model.layers[249:]:
 	layer.trainable = True
 model.compile(optimizer=SGD(lr=0.0001,momentum=0.9),loss='mse')
 tensorboard = TensorBoard(log_dir=os.path.join(cfg.thisDir,"../logs","{}".format(time())))
 BIG_STEP=20
 for i in range(args.initial_epoch//BIG_STEP,args.epochs//BIG_STEP):
-	model.fit(trainImages.reshape((-1,224,224,3)),trainLabels,epochs=(i+1)*BIG_STEP,initial_epoch=i*BIG_STEP,batch_size=128,validation_split=0.2,callbacks=[tensorboard])
+	model.fit(trainImages.reshape((-1,224,224,1)),trainLabels,epochs=(i+1)*BIG_STEP,initial_epoch=i*BIG_STEP,batch_size=20,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)
-log.info(model.evaluate(testImages.reshape((-1,224,224,3)),testLabels))
+log.info(model.evaluate(testImages.reshape((-1,224,224,1)),testLabels))

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