def testName(self): # read data data = reader.read("../data/log_1_fixed.txt", jstartline=15000, maxlines=5000) # preprocess data preprocess.preproc(data) # initialize model layers = [13, 8, 1] activf = [activation.linear(), activation.tanh(), activation.sigmoid()] # , activation.tanh(1.75, 3./2.), net = ffnet.FFNet(layers, activf) net.initw(0.1) # create training options opts = trainb.options() # write function f = open("../output/trainb_test.txt", "w+") writefcn = lambda s: f.write(s) # training trainb.train(data, opts, net, writefcn) # close file f.close()
def testName(self): # read data data = reader.read("../data/log_1_fixed.txt", jstartline=15000, maxlines=5000) # preprocess data preprocess.preproc(data) # initialize model layers = [13, 8, 1] activf = [ activation.linear(), activation.tanh(), activation.sigmoid() ] # activation.tanh(1.75, 3./2.), net = ffnet.FFNet(layers, activf) net.initw(0.1) # create training options opts = trainsg.options() # write function f = open("../output/trainsg_test.txt", "w+") writefcn = lambda s: f.write(s) # training trainsg.train(data, opts, net, writefcn) # close file f.close()
def run(_): if config.mode == "train": train(config, device) elif config.mode == "preprocess": preproc(config) elif config.mode == "debug": config.epochs = 1 config.batch_size = 3 config.val_batch_size = 20 config.checkpoint = 1 config.period = 1 train(config, device) elif config.mode == "test": pass elif config.mode == "dev": dev(config, device) else: print("Unknown mode") exit(0)
def main(): # load training data data = reader.read("../data/log_1_fixed.txt") # preprocess preprocess.preproc(data) # shuffle data perm = range(len(data)) random.shuffle(perm) # train a network ntrain = 100000 dtrain = la.idxview(data, perm[:ntrain]) net = train(0, dtrain) # evaluate on training data evaluate(dtrain, net) # evaluate on test data devaluate = la.idxview(data, perm[ntrain:2 * ntrain]) evaluate(devaluate, net)
def main(): # load training data data = reader.read("../data/log_1_fixed.txt") # preprocess preprocess.preproc(data) # shuffle data perm = range(len(data)) random.shuffle(perm) # train a network ntrain = 100000 dtrain = la.idxview(data, perm[:ntrain]) net = train(0, dtrain) # evaluate on training data evaluate(dtrain, net) # evaluate on test data devaluate = la.idxview(data, perm[ntrain:2*ntrain]) evaluate(devaluate, net)
epochs=EPOCHS, steps_per_epoch=dtgen.steps['train'], validation_data=dtgen.next_valid_batch(), validation_steps=dtgen.steps['valid'], callbacks=callbacks, shuffle=True, verbose=1 ) model.save(f"saved_model/Flor/{INPUT_SOURCE_NAME}_filter") # Predict PREDICT_IMAGE_SRC = "hello.png" tokenizer = Tokenizer(chars=CHARSET_BASE, max_text_length=MAX_TEXT_LENGTH) img = preproc(PREDICT_IMAGE_SRC, input_size=INPUT_SHAPE) x_test = normalization([img]) STEPS = 1 out = model.predict( x=x_test, batch_size=None, verbose=False, steps=STEPS, callbacks=None, max_queue_size=10, workers=1, use_multiprocessing=False )
from balance_dataloader import BalancedBatchSampler from densenet import densenet121, densenet201, densenet161 from preprocess import preproc from ArcMarginModel import ArcMarginModel_AutoMargin from FocalLoss import FocalLoss import timm os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' training_csv = './main/train.csv' validate_csv = './main/val.csv' data = './data/ISIC2018_Task3_Training_Input' labels_names = ['MEL', 'NV', 'BCC', 'AKIEC', 'BKL', 'DF', 'VASC'] training = dataloader(training_csv, data, preproc(), 'training') validation = dataloader(validate_csv, data, preproc(), 'validate') device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") dataloaders = {'train': training, 'val': validation} def visualizing(phase, epoch, step, epoch_loss, epoch_acc): # training visualizing if epoch == 0 and step == 0: writer.add_scalar(f'{phase} loss', epoch_loss, 0) writer.add_scalar(f'{phase} accuracy', 0, 0) ###################### else: writer.add_scalar(f'{phase} loss',
import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D # import cadastro_usuarios as cadastro import preprocess as pp #diretorio2='/home/caroline/Documentos/iotprogram diretorio2 = 'iotprograms' ### xxxxxxxxxxxxxxxxxx ESTAGIO DE RECONHECIMENTO xxxxxxxxxxxxxxxxxx ### # Imagem Teste path_teste = diretorio2 + '/fotosteste/Teste.pgm' face_teste = cv2.imread(path_teste, 0) ImgTeste = pp.preproc(face_teste) cv2.imshow('img', ImgTeste) cv2.waitKey(0) cv2.destroyAllWindows() print('\n\n') size_rows = 92 size_cols = 80 size_img = size_rows * size_cols ImgRe = np.zeros((size_img, 1)) ImgIm = np.zeros((size_img, 1)) ImgDFT = np.zeros((size_img, 1, 2)) # ImgDFT = np.zeros((size_rows, size_cols, 2))
if novo_indice.all() == 0: print 'Theta < 10 para todas as fotos.' k = False else: minimo_indice = novo_indice[minimo_indice] indice = novo_indice loop = 0 print(novo_indice) print('---------------') if k==True: if loop == 0 and minimo_indice!=0: # V e o vetor referencia para calculo do cosseno pathV = '/home/caroline/Documentos/Smart_room/BancoDeDados/Treinamento/'+str(name)+'/'+nome_img+str(minimo_indice)+'p.pgm' V = cv2.imread(pathV, cv2.CV_LOAD_IMAGE_GRAYSCALE) print(pathV) imgV = pp.preproc(V) rows,cols = imgV.shape tripaU = np.zeros((rows*cols,1)) tripaV = np.zeros((rows*cols,1)) novo_indice = np.array([0]) ind = j = i = 0 while j<cols: while i<rows: tripaV[ind][0] = imgV[i][j] i = i +1 ind = ind +1 j = j +1 elif indice[loop-1]!=0:
matX = np.zeros((size_img, total_img, 2)) matXtr = np.zeros((total_img, size_img, 2)) matReX = np.zeros((size_img, total_img)) matImX = np.zeros((size_img, total_img)) matXpow = np.zeros((size_img, total_img)) vetD = np.zeros((size_img, 1)) matY = np.zeros((size_img, total_img, 2)) H = np.zeros((1, size_img, 2)) while num_img < total_img: path = '/home/caroline/Documentos/Smart_room/BancoDeDados/Filtros/' + name + '/foto_' + str( num_img + 1) + '.pgm' face = cv2.imread(path, cv2.CV_LOAD_IMAGE_GRAYSCALE) img = pp.preproc(face) # 1.3 > img > -1.0 # cv2.imshow('img', img) # cv2.waitKey(0) # cv2.destroyAllWindows() rows = 0 # 2D image --->>> 1D image for j in xrange(0, size_cols): for i in xrange(0, size_rows): matRe[rows] = img[i, j] rows = rows + 1 # matRe = matRe - avg(matRe) matReAvg = cv2.mean(matRe)
from dataset import dataloader from preprocess import preproc import pickle warnings.filterwarnings("ignore", category=SourceChangeWarning) os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' # data and model paths test_data = '../../data/ISIC2018_Task3_Training_Input' model_path = './weights/densenet121_ArcMargin_2021-01-22_1-12_epoch99.tar' labels_names = ['MEL', 'NV', 'BCC', 'AKIEC', 'BKL', 'DF', 'VASC'] # dataloader test_loader = dataloader(None, test_data, preproc(), 'test') device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") def full_crop(image): img_t = [] for r in range(0, 4): for c in range(0, 5): img_t.append(image[:, 50*r:50*r+300, 50*c:50*c+400]) return img_t def summision_generate(model, batch_size, arccos=None, voting=True): result = {}
if len(qntfaces) == 1: #desenhando retangulo ao redor de cada face: for (x, y, w, h) in qntfaces: cv2.rectangle(image, (x, y), (x + w, y + h), (255, 255, 0), 2) face = image[y:y + w, x:x + h] #recortando imagem atraves do retangulo face = cv2.cvtColor(face, cv2.COLOR_BGR2GRAY) # Se nesse momento quisesse salvar seria assim mas n quero salvar # path_face = '/home/pi/Documents/iotprograms/preproc/'+ str(nome) +'/'+str(num_foto) +'.pgm' # cv2.imwrite(path_face,face) #salvando a imagem facepp = pp.preproc(face) cv2.imshow('face pre process', facepp) cv2.waitKey(0) cv2.destroyAllWindows() path_facepp = '/home/pi/Documents/iotprograms/preproc/' + str( nome) + '/' + str(num_foto) + '.pgm' cv2.imwrite(path_facepp, facepp) num_foto = num_foto + 1 ############## --------- PROGRAMA 2 ------------------- INICIO -------------------------------------########################################### # Para usar o programa 2 comente o programa 1 e descomente o programada 2 # total_fotos = 3
NomeImgRef = AllNames[IndThetaMin] NomeImg = AllNames[Ind] NewAllNames = AllNames Menor10 = False while AllNames[IndThetaMin] != AllNames[Ind]: # Imagem Referencia ------------------------------------------------------------------------------------------------------- PathImgRef = Path +NomeImgRef ImgRef = cv2.imread(PathImgRef,0) ImgRef = pp.preproc(ImgRef) print(('\n\nImagem Referencia: {} '.format(NomeImgRef))) # 2a Imagem --------------------------------------------------------------------------------------------------------------- PathImg = Path +NomeImg Img = cv2.imread(PathImg,0) Img = pp.preproc(Img) print(('2a Imagem : {} '.format(NomeImg))) # 2D ->>>>> 1D ------------------------------------------------------------------------------------------------------------
if len(qntfaces) == 1: #desenhando retangulo ao redor de cada face: for (x, y, w, h) in qntfaces: cv2.rectangle(image, (x, y), (x + w, y + h), (255, 255, 0), 2) face = image[y:y + w, x:x + h] #recortando imagem atraves do retangulo face = cv2.cvtColor(face, cv2.COLOR_BGR2GRAY) cv2.imwrite(path_facepp, face) facepp = cv2.imread(path_facepp, cv2.CV_LOAD_IMAGE_GRAYSCALE) img = pp.preproc(facepp) # Salvar imagem cv2.imwrite(path_facepp, img) # foto preta cv2.imshow('face pre process', img) cv2.waitKey(0) cv2.destroyAllWindows() print(img) # # Features # rows = 0 # # 2D image --->>> 1D image # for j in xrange(0, size_cols):