Python adjustIMG 예제들

예제 #1

파일: NNimg2.py 프로젝트: u3paka/umi_bot

def convIMG(address, DIR = "_imgswork"):
	imgaddress = DIR+address
	print(imgaddress)
	recogresult = openCVmod.FaceRecognition(imgaddress, isShow = False, saveStyle = '', workDIR = 'work', through = True)
	img = recogresult[0]
	img = openCVmod.adjustIMG(img, K = 0, isHC = True, size = (28, 28))
	return img.flatten().astype(np.float32)/255.0

예제 #2

파일: TFimgCNN.py 프로젝트: masaMikam/Umi_bot

def convIMG(address, DIR = "/Users/masaMikam/Dropbox/Project/umiA/Data/imgs/_imgswork"):
	imgaddress = DIR+address
	print(imgaddress)
	# データを読み込んで28x28に縮小
	img, altfilename, frame, flag = openCVmod.FaceRecognition(imgaddress, isShow = False, saveStyle = '', workDIR = '')
	img = openCVmod.adjustIMG(img, K = 64, size = (28, 28))
	return img.flatten().astype(np.float32)/255.0

예제 #3

파일: TFimgCNN.py 프로젝트: u3paka/umi_bot

def convIMG(address, DIR = "_imgswork"):
	imgaddress = DIR+address
	print(imgaddress)
	# データを読み込んで28x28に縮小
	img, altfilename, frame, flag = openCVmod.FaceRecognition(imgaddress, isShow = False, saveStyle = '', workDIR = '')
	img = openCVmod.adjustIMG(img, K = 64, size = (28, 28))
	return img.flatten().astype(np.float32)/255.0

예제 #4

파일: NNimg2.py 프로젝트: masaMikam/Umi_bot

def convIMG(address, DIR = "/Users/masaMikam/Dropbox/Project/umiA/Data/imgs/_imgswork"):
	imgaddress = DIR+address
	print(imgaddress)
	recogresult = openCVmod.FaceRecognition(imgaddress, isShow = False, saveStyle = '', workDIR = 'work', through = True)
	img = recogresult[0]
	img = openCVmod.adjustIMG(img, K = 0, isHC = True, size = (28, 28))
	return img.flatten().astype(np.float32)/255.0

예제 #5

파일: TFimgCNN.py 프로젝트: masaMikam/Umi_bot

def predictAns(filename  = "/Users/masaMikam/Dropbox/Project/umiA/Data/imgs/rin/show.png", isShow = True, model = '/Users/masaMikam/Dropbox/Project/umiA/Data/lib/DNNmodel', workDIR = '', label =  ['チノちゃん', '絵里', '花陽', '穂乃果', 'ことり', '真姫', 'にこ', '希', '凛', '私']):
	classifier = skflow.TensorFlowEstimator.restore(model)
	# imgaddress = "/Users/masaMikam/Dropbox/Project/umiA/Data/imgs/rin/images-10.jpeg"
	# imgaddress = '/Users/masaMikam/Dropbox/Project/umiA/Data/twimgs/20160204152357.jpg'
	img, altfilename, frame, FACEflag = openCVmod.FaceRecognition(filename, isShow = isShow, saveStyle = 'whole', workDIR = '')
	img = openCVmod.adjustIMG(img, isHC = True, K = 0, size = (28, 28))
	result = classifier.predict(img)
	anslabel = label[result]
	return anslabel, FACEflag, altfilename

예제 #6

def predictAns(filename  = "rin/show.png", isShow = True, model = '/Users/xxxx']):
	classifier = skflow.TensorFlowEstimator.restore(model)
	# imgaddress = "rin/images-10.jpeg"
	# imgaddress = '/Users/xxxx'
	img, altfilename, frame, FACEflag = openCVmod.FaceRecognition(filename, isShow = isShow, saveStyle = 'whole', workDIR = '')
	img = openCVmod.adjustIMG(img, isHC = True, K = 0, size = (28, 28))
	result = classifier.predict(img)
	anslabel = label[result]
	return anslabel, FACEflag, altfilename

예제 #7

파일: NNimg2.py 프로젝트: u3paka/umi_bot

def predictAns(filename  = "", isShow = True, model = '', workDIR = '', label =['ことり', 'にこ', '凛', '希', '海未', '真姫', '穂乃果', '絵里', '花陽']):
	classifier = skflow.TensorFlowEstimator.restore(model)
	
	img, altfilename, frame, FACEflag = openCVmod.FaceRecognition(filename, isShow = isShow, saveStyle = 'whole', workDIR = '')
	img = openCVmod.adjustIMG(img, isHC = True, K = 0, size = (28, 28))
	result = classifier.predict(img)
	print(result)
	anslabel = label[result]
	return anslabel, FACEflag, altfilename