From d7879dee3045876889339ad39cfb6efec34a792e Mon Sep 17 00:00:00 2001 From: Zackory Date: Fri, 3 Apr 2020 15:30:28 -0400 Subject: [PATCH] Code and data release --- .gitignore | 5 + 3d_models/scio_camera_holder.stl | Bin 0 -> 228484 bytes README.md | 40 ++++- dataset/__init__.py | 0 images/spectrovision.jpg | Bin 0 -> 300687 bytes main.py | 280 +++++++++++++++++++++++++++++++ requirements.txt | 15 ++ util.py | 180 ++++++++++++++++++++ 8 files changed, 519 insertions(+), 1 deletion(-) create mode 100644 3d_models/scio_camera_holder.stl create mode 100644 dataset/__init__.py create mode 100644 images/spectrovision.jpg create mode 100644 main.py create mode 100644 requirements.txt create mode 100644 util.py diff --git a/.gitignore b/.gitignore index b6e4761..fe16317 100644 --- a/.gitignore +++ b/.gitignore @@ -3,6 +3,11 @@ __pycache__/ *.py[cod] *$py.class +*.DS_Store +*.h5 +*.csv +*.zip + # C extensions *.so diff --git a/3d_models/scio_camera_holder.stl b/3d_models/scio_camera_holder.stl 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+torch.cuda.manual_seed(seed_value) +torch.cuda.manual_seed_all(seed_value) +np.random.seed(seed_value) +random.seed(seed_value) +torch.manual_seed(seed_value) +torch.backends.cudnn.benchmark = False +torch.backends.cudnn.deterministic = True +torch.set_num_threads(1) + +import sys, pickle, time, gc +from poutyne.framework import Model +import multiprocessing +from functools import partial +from multiprocessing.pool import Pool +import util + +from sklearn.utils import shuffle +from sklearn import preprocessing +from sklearn.model_selection import StratifiedKFold +from sklearn.metrics import confusion_matrix + +def first_deriv(x, wavelengths): + # First derivative of measurements with respect to wavelength + x = np.copy(x) + for i, xx in enumerate(x): + dx = np.zeros(xx.shape, np.float) + dx[0:-1] = np.diff(xx)/np.diff(wavelengths) + dx[-1] = (xx[-1] - xx[-2])/(wavelengths[-1] - wavelengths[-2]) + x[i] = dx + return x + +def prepare_data(x_spectral_train, x_image_train, y_train, x_spectral_test, x_image_test, y_test, wavelengths, deriv=True, data_type='spectral_image', image_preprocess='resnet', objs=None): + if deriv: + # Finite difference (Numerical differentiation) + x_spectral_train = np.concatenate([x_spectral_train, first_deriv(x_spectral_train, wavelengths)], axis=-1) + x_spectral_test = np.concatenate([x_spectral_test, first_deriv(x_spectral_test, wavelengths)], axis=-1) + + if data_type == 'spectral': + # Zero mean unit variance + scaler_spectral = preprocessing.StandardScaler() + x_spectral_train = scaler_spectral.fit_transform(x_spectral_train) + x_spectral_test = scaler_spectral.transform(x_spectral_test) + elif data_type == 'image': + if 'none' in image_preprocess: + scaler_image = preprocessing.StandardScaler() + x_image_train = np.reshape(scaler_image.fit_transform(np.reshape(x_image_train, (len(x_image_train), -1))), np.shape(x_image_train)) + x_image_test = np.reshape(scaler_image.transform(np.reshape(x_image_test, (len(x_image_test), -1))), np.shape(x_image_test)) + else: + scaler_image = preprocessing.StandardScaler() + x_image_train = scaler_image.fit_transform(x_image_train) + x_image_test = scaler_image.transform(x_image_test) + elif data_type == 'spectral_image': + scaler_image = preprocessing.StandardScaler() + x = scaler_image.fit_transform(np.concatenate([x_spectral_train, x_image_train], axis=-1)) + x_spectral_train = x[:, :np.shape(x_spectral_train)[-1]] + x_image_train = x[:, np.shape(x_spectral_train)[-1]:] + x = scaler_image.transform(np.concatenate([x_spectral_test, x_image_test], axis=-1)) + x_spectral_test = x[:, :np.shape(x_spectral_test)[-1]] + x_image_test = x[:, np.shape(x_spectral_test)[-1]:] + + return x_spectral_train, x_image_train, y_train, x_spectral_test, x_image_test, y_test + +def nn(input_size, layers, dropout, material_count=None, batchnorm=True): + modules = [] + for i in range(len(layers)-1): + modules.append(torch.nn.Linear(input_size if i == 0 else layers[i-1], layers[i])) + if batchnorm: + modules.append(torch.nn.BatchNorm1d(layers[i])) + modules.append(torch.nn.LeakyReLU()) + if dropout > 0: + modules.append(torch.nn.Dropout(dropout)) + modules.append(torch.nn.Linear(layers[-2], layers[-1]) if len(layers) > 1 else torch.nn.Linear(input_size, layers[-1])) + if batchnorm: + modules.append(torch.nn.BatchNorm1d(layers[-1])) + modules.append(torch.nn.LeakyReLU()) + if material_count is not None: + modules.append(torch.nn.Linear(layers[-1], material_count)) + return torch.nn.Sequential(*modules) + +def learn(o, X_spectral, X_image, y, objs, wavelengths, data_type='spectral_image', image_preprocess='resnet', epochs=50, batch_size=128, material_count=8, layers=[64]*2, dropout=0.0, lr=0.0005, seed=1000, test='looo', verbose=False): + torch.manual_seed(seed) + np.random.seed(seed) + random.seed(seed) + torch.set_num_threads(1) + + if 'test' in test: + train_idx, test_idx = o + X_spectral_train = X_spectral[train_idx] + X_image_train = X_image[train_idx] + y_train = y[train_idx] + X_spectral_test = X_spectral[test_idx] + X_image_test = X_image[test_idx] + y_test = y[test_idx] + objs_train = objs[train_idx] + elif 'looo' in test: + _, obj = o + # Set up leave-one-object-out training and test sets + X_spectral_train = X_spectral[objs != obj] + X_image_train = X_image[objs != obj] + y_train = y[objs != obj] + X_spectral_test = X_spectral[objs == obj] + X_image_test = X_image[objs == obj] + y_test = y[objs == obj] + objs_train = objs[objs != obj] + + X_spectral_train, X_image_train, y_train, X_spectral_test, X_image_test, y_test = prepare_data(X_spectral_train, X_image_train, y_train, X_spectral_test, X_image_test, y_test, wavelengths, deriv=True, data_type=data_type, image_preprocess=image_preprocess, objs=objs) + + if data_type == 'spectral': + X_spectral_train, y_train = shuffle(X_spectral_train, y_train) + elif data_type == 'image': + X_image_train, y_train = shuffle(X_image_train, y_train) + elif data_type == 'spectral_image': + X_spectral_train, X_image_train, y_train = shuffle(X_spectral_train, X_image_train, y_train) + + y_train = torch.tensor(y_train, dtype=torch.long) + y_test = torch.tensor(y_test, dtype=torch.long) + + # Image layers + if data_type == 'spectral_image': + spectral_layers = [64, 64, 32, 32] + spectral_dropout = 0.25 + spectral_epochs = 50 + image_layers = [128, 64, 32] + image_dropout = 0.1 + image_epochs = 50 + + class ConcatPretrainedNetwork(torch.nn.Module): + def __init__(self): + # global spectral_accuracy, image_accuracy + super().__init__() + spectral_net = nn(np.shape(X_spectral_train)[-1], spectral_layers, spectral_dropout, material_count) + opt = torch.optim.Adam(spectral_net.parameters(), lr=lr) + spectral_model = Model(spectral_net, opt, 'cross_entropy', batch_metrics=['accuracy']) + spectral_model.fit(X_spectral_train, y_train, epochs=spectral_epochs, batch_size=batch_size, verbose=False) + + image_net = nn(np.shape(X_image_train)[-1], image_layers, image_dropout, material_count) + opt = torch.optim.Adam(image_net.parameters(), lr=lr) + image_model = Model(image_net, opt, 'cross_entropy', batch_metrics=['accuracy']) + image_model.fit(X_image_train, y_train, epochs=image_epochs, batch_size=batch_size, verbose=False) + + # Disable dropout, remove last layer and freeze network + self.trained_spectral_model = torch.nn.Sequential(*(list(spectral_net.children())[:-1])) + for p in self.trained_spectral_model.parameters(): + p.requires_grad = False + self.trained_image_model = torch.nn.Sequential(*(list(image_net.children())[:-1])) + for p in self.trained_image_model.parameters(): + p.requires_grad = False + + self.concat_net = nn(spectral_layers[-1] + image_layers[-1], layers, dropout, material_count, batchnorm=False) + def forward(self, x_spectral, x_image): + y1 = self.trained_spectral_model(x_spectral) + y2 = self.trained_image_model(x_image) + concat = torch.cat((y1, y2), -1) + return self.concat_net(concat) + net = ConcatPretrainedNetwork() + X_train = [X_spectral_train, X_image_train] + X_test = [X_spectral_test, X_image_test] + elif data_type == 'image': + net = nn(np.shape(X_image_train)[-1], layers, dropout, material_count) + X_train = X_image_train + X_test = X_image_test + elif data_type == 'spectral': + net = nn(np.shape(X_spectral_train)[-1], layers, dropout, material_count) + X_train = X_spectral_train + X_test = X_spectral_test + + opt = torch.optim.Adam(net.parameters(), lr=lr) + model = Model(net, opt, 'cross_entropy', batch_metrics=['accuracy']) + + model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=epochs, batch_size=batch_size, verbose=False) + cm = confusion_matrix(y_test, model.predict(X_test).argmax(axis=-1), labels=range(material_count)) + # Return accuracy and confusion matrices + if 'backprop' in test: + return {'accuracy': model.evaluate(X_test, y_test)[-1], 'cm': cm, 'obj_cm': np.copy(cm[y_test[0]]), 'model': model, 'net': net, 'X_test': X_test, 'y_test': y_test} + else: + if verbose: + print(obj, model.evaluate(X_test, y_test)[-1]) + return {'accuracy': model.evaluate(X_test, y_test)[-1], 'cm': cm, 'obj_cm': np.copy(cm[y_test[0]])} + +# NOTE: Leave-one-object-out cross-validation. + +def looo_cv(X_spectral, X_image, y, objs, wavelengths, data_type, image_preprocess, layers, dropout, epochs, batch_size, lr, seeds, material_count, jobs, test='looo', verbose=False): + objs_set = [] + for o in objs: + if o not in objs_set: + objs_set.append(o) + accuracies = [] + confusion_mat = None + object_confusion_matrix = [] + for s in seeds: + pool = Pool(processes=jobs) + results = pool.imap(partial(learn, X_spectral=X_spectral, X_image=X_image, y=y, objs=objs, wavelengths=wavelengths, data_type=data_type, image_preprocess=image_preprocess, epochs=epochs, batch_size=batch_size, material_count=material_count, layers=layers, dropout=dropout, lr=lr, seed=s, test=test, verbose=verbose), list(enumerate(objs_set))) + pool.close() + pool.join() + + seed_accuracy = [] + for result in list(results): + accuracies.append(result['accuracy']) + seed_accuracy.append(result['accuracy']) + if confusion_mat is None: + confusion_mat = result['cm'] + else: + confusion_mat += result['cm'] + object_confusion_matrix.append(result['obj_cm']) + print('Accuracy with seed', s, ':', np.mean(seed_accuracy)) + print('Results for:', data_type, '5 materials' if five_mats else '8 materials', image_preprocess, '- Accuracy:', np.mean(accuracies)) + # print(confusion_mat.astype('float') / confusion_mat.sum(axis=1)[:, np.newaxis], '\n') + sys.stdout.flush() + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description='Training and Evaluation for SpectroVision') + parser.add_argument('-s', '--seed', default=-1, help='Seed used for random number generators. (default -1) for averaging of 10 seeds', type=int) + parser.add_argument('-v', '--verbose', help='Verbose', action='store_true') + args = parser.parse_args() + + verbose = args.verbose + jobs = multiprocessing.cpu_count() + layers = {'spectral': [64, 64, 32, 32], 'image': [128, 64, 32], 'spectral_image': [32]} + dropout = {'spectral': 0.25, 'image': 0.1, 'spectral_image': 0.0} + epochs = {'spectral': 50, 'image': 50, 'spectral_image': 10} + lr = 0.0005 + batch_size = 128 + seeds = range(8000, 8010) if args.seed == -1 else [args.seed] + parent_directory = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'dataset') + + # Table I, Leave-one-object-out + print('Table I: leave-one-object-out') + for five_mats in [True, False]: + for data_type in ['spectral', 'image', 'spectral_image']: + X_spectral, X_image, y, objs, wavelengths = util.load_data(parent_directory, data_type, 'densenet201_240_320', (240, 320), five_mats) + # print(np.shape(X_spectral), np.shape(X_image)) + print('Computing results for:', data_type, '5 materials' if five_mats else '8 materials') + looo_cv(X_spectral, X_image, y, objs, wavelengths, data_type, 'densenet201_240_320', layers[data_type], dropout[data_type], epochs[data_type], batch_size, lr, seeds, 5 if five_mats else 8, jobs, 'looo', verbose) + + # Table II, Test set + print('Table II: test set') + for five_mats in [True, False]: + for data_type in ['spectral', 'image', 'spectral_image']: + X_spectral, X_image, y, objs, wavelengths = util.load_data(parent_directory, data_type, 'densenet201_240_320', (240, 320), five_mats) + X_spectral_test, X_image_test, y_test, objs_test, wavelengths_test = util.load_data(parent_directory, data_type, 'densenet201_240_320', (240, 320), five_mats, test_set=True) + accuracies = [] + confusion_mat = None + for seed in seeds: + results = learn([list(range(len(y))), list(range(len(y), len(y)+len(y_test)))], np.concatenate([X_spectral, X_spectral_test], axis=0), np.concatenate([X_image, X_image_test], axis=0), np.concatenate([y, y_test], axis=0), np.concatenate([objs, objs_test], axis=0), wavelengths, data_type, 'densenet201_240_320', epochs[data_type], batch_size, 5 if five_mats else 8, layers[data_type], dropout[data_type], lr, seed, 'test', verbose) + accuracies.append(results['accuracy']) + if confusion_mat is None: + confusion_mat = results['cm'] + else: + confusion_mat += results['cm'] + print(data_type, '5 materials' if five_mats else '8 materials', '- Accuracy:', np.mean(accuracies)) + # print(confusion_mat.astype('float') / confusion_mat.sum(axis=1)[:, np.newaxis], '\n') + sys.stdout.flush() + + # Table IV, Resizing and cropping images + print('Table IV: resizing and cropping images') + for image_shape, crop in [((240, 320), False), ((240, 320), True), ((480, 640), False), ((480, 640), True), ((960, 1280), False)]: + five_mats = False + data_type = 'image' + image_preprocess = 'densenet201_%d_%d%s' % (image_shape[0], image_shape[1], '_crop' if crop else '') + X_spectral, X_image, y, objs, wavelengths = util.load_data(parent_directory, data_type, image_preprocess, (240, 320), five_mats) + looo_cv(X_spectral, X_image, y, objs, wavelengths, data_type, image_preprocess, layers[data_type], dropout[data_type], epochs[data_type], batch_size, lr, seeds, 5 if five_mats else 8, jobs, 'looo', verbose) + + # Table V, ImageNet models + print('Table V: ImageNet models') + for model in ['vgg19', 'resnet50', 'resnet101', 'resnet152', 'densenet201', 'resnext101', 'efficientnet-b5']: + five_mats = False + data_type = 'image' + image_preprocess = ('%s_240_320' % model) if 'efficient' not in model else ('%s_456_608' % model) + X_spectral, X_image, y, objs, wavelengths = util.load_data(parent_directory, data_type, image_preprocess, (240, 320), five_mats) + looo_cv(X_spectral, X_image, y, objs, wavelengths, data_type, image_preprocess, layers[data_type], dropout[data_type], epochs[data_type], batch_size, lr, seeds, 5 if five_mats else 8, jobs, 'looo', verbose) + diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..295ddad --- /dev/null +++ b/requirements.txt @@ -0,0 +1,15 @@ +cycler==0.10.0 +h5py==2.10.0 +joblib==0.14.1 +kiwisolver==1.2.0 +matplotlib==3.2.1 +numpy==1.18.2 +Pillow==7.1.1 +Poutyne==0.7.2 +pyparsing==2.4.6 +python-dateutil==2.8.1 +scikit-learn==0.22.2.post1 +scipy==1.4.1 +six==1.14.0 +torch==1.4.0 +torchvision==0.5.0 diff --git a/util.py b/util.py new file mode 100644 index 0000000..4981aab --- /dev/null +++ b/util.py @@ -0,0 +1,180 @@ +import os, sys, csv, h5py +import numpy as np +# os.environ['KERAS_BACKEND'] = 'plaidml.keras.backend' + +def load_data(parent_directory, data_type, image_preprocess, image_shape, five_mats, crop=False, scio_extended=False, test_set=False): + materials = ['metal', 'plastic', 'wood', 'paper', 'fabric', 'glass', 'ceramic', 'foam'] + measurements_per_object = 100 + materials_objects = [] + materials_objects.extend([['metal', obj] for obj in ['alumfoil', 'bowl', 'canlarge', 'canrockstar', 'cupwithhandle', 'cyltall', 'muggt', 'panbot', 'pot', 'potlarge', 'spraycan', 'traytall', 'waterbottle']]) + materials_objects.extend([['plastic', obj] for obj in ['bag', 'blackcontainer', 'bottlesip', 'clorox', 'coffeemate', 'cupblack', 'cupblue', 'cups', 'folder', 'plate', 'poncho', 'redcokebottle', 'smartwater']]) + materials_objects.extend([['wood', obj] for obj in ['blocksides', 'bowlcylash', 'bowlwhiteshell', 'boxflagus', 'crate', 'cutboard', 'plank', 'platter', 'pothepta', 'pyramid', 'smallblocks', 'soapblock', 'tray']]) + materials_objects.extend([['paper', obj] for obj in ['bagamazon', 'bagstarbucks', 'cups', 'eggcarton', 'folder', 'napkins', 'newspaper', 'notebookgraph', 'packaging', 'printer', 'saltcontainer', 'tissuebox', 'towelbox']]) + materials_objects.extend([['fabric', obj] for obj in ['beanie', 'cardiganred', 'clothblue', 'collarcheckered', 'collargray', 'jeans', 'shirtgray', 'shirtgt', 'shirtwhite', 'socks', 'sweaterknit', 'towelwhite', 'waistcoat']]) + materials_objects.extend([['glass', obj] for obj in ['bowl', 'cupcurved', 'cuptall', 'cuptallpatterned', 'cuptumbler', 'halfliter', 'jartall', 'mason', 'measuringcup', 'pitcher', 'plate', 'tray', 'waterbottle']]) + materials_objects.extend([['ceramic', obj] for obj in ['bowl', 'bowlgreen', 'bowllarge', 'bowlscratched', 'mugblue', 'mugtan', 'mugwhite', 'plate', 'platecorelle', 'platepatterned', 'potorange', 'trayblack', 'traytall']]) + materials_objects.extend([['foam', obj] for obj in ['blacktwo', 'blockstyro', 'cup', 'drygreen', 'piecewhite', 'plate', 'plushies', 'sandal', 'soundpointy', 'spongepatterned', 'styrohead', 'tubes', 'yogamat']]) + + if test_set: + materials_objects = [] + materials_objects.extend([['metal', obj] for obj in ['bowllarge', 'cancampbell', 'mugbubba', 'trayshort', 'traywithhandles']]) + materials_objects.extend([['plastic', obj] for obj in ['bagtarget', 'bottlechew', 'bottlegt', 'coffeered', 'vaseline']]) + materials_objects.extend([['wood', obj] for obj in ['bowllarge', 'boxsoap', 'ladle', 'pot', 'servingbowl']]) + materials_objects.extend([['paper', obj] for obj in ['book', 'googlecardboard', 'napkinsbrown', 'plate', 'starbuckscup']]) + materials_objects.extend([['fabric', obj] for obj in ['hospitalgown', 'pantsblue', 'pillowcase', 'shirtirim', 'shortscargo']]) + materials_objects.extend([['glass', obj] for obj in ['bottlebeer', 'jarfrosted', 'jarrect', 'platepatterned', 'stagioni']]) + materials_objects.extend([['ceramic', obj] for obj in ['muggt', 'potgray', 'traycurved', 'trayshort', 'trayterracota']]) + materials_objects.extend([['foam', obj] for obj in ['evasheet', 'sheet', 'sheetyellow', 'sponge', 'yogablock']]) + + data_filename = os.path.join(parent_directory, 'traintest_spectral_data.h5' if data_type == 'spectral' else 'traintest_spectral_image_%s_data.h5' % image_preprocess) + if test_set: + data_filename = os.path.join(parent_directory, 'testset_spectral_data.h5' if data_type == 'spectral' else 'testset_spectral_image_%s_data.h5' % image_preprocess) + + if os.path.isfile(data_filename): + hf = h5py.File(data_filename, 'r') + X_spectral = np.array(hf.get('X_scio')) + X_image = np.array(hf.get('X_image')) + y = np.array(hf.get('y')) + objs = np.array([n.decode('ascii') for n in hf.get('objs')]) + wavelengths = np.array(hf.get('wavelengths')) + hf.close() + # If only five_mats, then delete the glass, ceramic, and foam objects + if five_mats: + indices = [i for i, key in enumerate(objs) if ('glass' not in key and 'ceramic' not in key and 'foam' not in key)] + X_spectral = X_spectral[indices] + if len(X_image) > 0: + X_image = X_image[indices] + y = y[indices] + objs = objs[indices] + X_spectral = np.array(X_spectral, dtype=np.float32) + if 'image' in data_type: + X_image = np.array(X_image, dtype=np.float32) + else: + X_image = np.array(X_spectral, dtype=np.float32) + return X_spectral, X_image, y, objs, wavelengths + + # Needed for downloading Keras resnet and densenet pretrained models + import ssl + ssl._create_default_https_context = ssl._create_unverified_context + + import keras, cv2 + from keras.layers import Flatten, GlobalAveragePooling2D, AveragePooling2D + from keras_applications import resnet50, densenet, resnext, inception_resnet_v2, nasnet, vgg19, xception, resnet_v2, resnet + + # Load spectral data + filename = os.path.join(parent_directory, 'spectraldata.csv') + spectral_data = dict() + wavelength_count = 331 + wavelengths = None + with open(filename, 'r') as f: + reader = csv.reader(f) + for i, row in enumerate(reader): + if i < 10 or i == 11: + continue + if i == 10: + # Header row + wavelengths = [float(r.strip().split('_')[-1].split()[0]) + 740.0 for r in row[10:wavelength_count+10]] + if scio_extended: + wavelengths.extend([float(r.strip().split('_')[-1].split()[0]) + 740.0 for r in row[672:wavelength_count+672]]) + continue + obj = row[3].strip() + material = row[4].strip() + key = material + '_' + obj + if key not in spectral_data: + spectral_data[key] = [] + spectral_data[key].append([float(v) for v in row[10:wavelength_count+10]]) + if scio_extended: + spectral_data[key][-1].extend([float(v) for v in row[672:wavelength_count+672]]) + + # Load spectral data and images + X_spectral = [] + X_image = [] + y = [] + objs = [] + + if 'image' in data_type and 'none' not in image_preprocess: + if 'resnet50' in image_preprocess: + preprocess_model = resnet.ResNet50(weights='imagenet', include_top=False, input_shape=(image_shape[0], image_shape[1], 3), pooling='avg', backend=keras.backend, layers=keras.layers, models=keras.models, utils=keras.utils) + elif 'vgg19' in image_preprocess: + preprocess_model = vgg19.VGG19(weights='imagenet', include_top=False, input_shape=(image_shape[0], image_shape[1], 3), pooling='avg', backend=keras.backend, layers=keras.layers, models=keras.models, utils=keras.utils) + elif 'densenet201' in image_preprocess: + preprocess_model = densenet.DenseNet201(weights='imagenet', include_top=False, input_shape=(image_shape[0], image_shape[1], 3), pooling='avg', backend=keras.backend, layers=keras.layers, models=keras.models, utils=keras.utils) + elif 'resnext101' in image_preprocess: + preprocess_model = resnext.ResNeXt101(weights='imagenet', include_top=False, input_shape=(image_shape[0], image_shape[1], 3), pooling='avg', backend=keras.backend, layers=keras.layers, models=keras.models, utils=keras.utils) + elif 'resnet152' in image_preprocess: + preprocess_model = resnet.ResNet152(weights='imagenet', include_top=False, input_shape=(image_shape[0], image_shape[1], 3), pooling='avg', backend=keras.backend, layers=keras.layers, models=keras.models, utils=keras.utils) + + for material, obj in materials_objects: + key = material + '_' + obj + X_image_raw = [] + for iteration in range(measurements_per_object): + if 'image' in data_type: + filename = os.path.join(parent_directory, 'data_sets_ABCD', material, obj, 'spectrovis_%s_%s_%04d.png' % (material, obj, iteration)) + if not os.path.isfile(filename): + print('Image file not found!', filename) + exit() + image = cv2.imread(filename, cv2.IMREAD_COLOR) + if crop: + h, w, _ = np.shape(image) + X_image_raw.append(np.copy(image[h//2-image_shape[0]//2:h//2+image_shape[0]//2, w//2-image_shape[1]//2:w//2+image_shape[1]//2])) + else: + X_image_raw.append(cv2.resize(image, (image_shape[1], image_shape[0]))) + # cv2.imshow('Image', X_image[-1]) + # cv2.waitKey(0) + # if len(X_image) % 50 == 0: + # print(len(X_image)) + # sys.stdout.flush() + X_spectral.append(spectral_data[key][iteration]) + y.append(materials.index(material)) + objs.append(key) + if len(X_image_raw) >= 100: + if 'image' in data_type and 'none' not in image_preprocess: + print('X_image_raw:', len(X_image_raw), np.shape(X_image_raw)) + if 'resnet50' in image_preprocess: + X_image_raw = resnet.preprocess_input(np.array(X_image_raw), backend=keras.backend, layers=keras.layers, models=keras.models, utils=keras.utils) + elif 'vgg19' in image_preprocess: + X_image_raw = vgg19.preprocess_input(np.array(X_image_raw), backend=keras.backend, layers=keras.layers, models=keras.models, utils=keras.utils) + elif 'densenet201' in image_preprocess: + X_image_raw = densenet.preprocess_input(np.array(X_image_raw), backend=keras.backend, layers=keras.layers, models=keras.models, utils=keras.utils) + elif 'resnext101' in image_preprocess: + X_image_raw = resnext.preprocess_input(np.array(X_image_raw), backend=keras.backend, layers=keras.layers, models=keras.models, utils=keras.utils) + elif 'resnet152' in image_preprocess: + X_image_raw = resnet.preprocess_input(np.array(X_image_raw), backend=keras.backend, layers=keras.layers, models=keras.models, utils=keras.utils) + X_image.extend(preprocess_model.predict(X_image_raw)) + print('X_image:', len(X_image), np.shape(X_image)) + sys.stdout.flush() + X_image_raw = [] + + X_spectral = np.array(X_spectral) + X_image = np.array(X_image) + y = np.array(y) + objs = np.array(objs) + wavelengths = np.array(wavelengths) + print('Data loaded. Preprocessing images') + print(np.shape(X_image)) + sys.stdout.flush() + + hf = h5py.File(data_filename, 'w') + hf.create_dataset('X_scio', data=X_spectral) + hf.create_dataset('X_image', data=X_image) + hf.create_dataset('y', data=y) + hf.create_dataset('objs', data=[n.encode('ascii') for n in objs]) + hf.create_dataset('wavelengths', data=wavelengths) + hf.close() + + # If only five_mats, then delete the glass, ceramic, and foam objects + if five_mats: + indices = [i for i, key in enumerate(objs) if ('glass' not in key and 'ceramic' not in key and 'foam' not in key)] + X_spectral = X_spectral[indices] + if len(X_image) > 0: + X_image = X_image[indices] + y = y[indices] + objs = objs[indices] + + X_spectral = np.array(X_spectral, dtype=np.float32) + if 'image' in data_type: + X_image = np.array(X_image, dtype=np.float32) + else: + X_image = np.array(X_spectral, dtype=np.float32) + return X_spectral, X_image, y, objs, wavelengths +