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
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
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
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
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
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
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