Python numpy.save() Examples
The following are 30 code examples for showing how to use numpy.save(). These examples are extracted from open source projects. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example.
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Example 1
Project: pytorch-mri-segmentation-3D Author: Achilleas File: PP.py License: MIT License | 10 votes |
def generateImgSlicesFolder(data_folder = '../Data/MS2017a/scans/'): scan_folders = glob.glob(data_folder + '*') for sf in scan_folders: slice_dir_path = os.path.join(sf, 'slices/') if not os.path.exists(slice_dir_path): print('Creating directory at:' , slice_dir_path) os.makedirs(slice_dir_path) img = nib.load(os.path.join(sf, 'pre/FLAIR.nii.gz')) img_np = img.get_data() img_affine = img.affine print(sf) print('The img shape', img_np.shape[2]) for i in range(img_np.shape[2]): slice_img_np = img_np[:,:,i] nft_img = nib.Nifti1Image(slice_img_np, img_affine) nib.save(nft_img, slice_dir_path + 'FLAIR_' + str(i) + '.nii.gz') if os.path.basename(sf) == '0': slice_img = nib.load(slice_dir_path + 'FLAIR_' + str(i) + '.nii.gz').get_data() / 5 print('DID I GET HERE?') print('Writing to', str(i) + '.jpg')
Example 2
Project: face-attendance-machine Author: matiji66 File: encoding_images.py License: Apache License 2.0 | 7 votes |
def encoding_images(path): """ 对path路径下的子文件夹中的图片进行编码, TODO: 对人脸数据进行历史库中的人脸向量进行欧式距离的比较,当距离小于某个阈值的时候提醒: 如果相似的是本人,则跳过该条记录,并提醒已经存在,否则警告人脸过度相似问题, :param path: :return: """ with open(name_and_encoding, 'w') as f: subdirs = [os.path.join(path, x) for x in os.listdir(path) if os.path.isdir(os.path.join(path, x))] for subdir in subdirs: print('process image name :', subdir) person_image_encoding = [] for y in os.listdir(subdir): print("image name is ", y) _image = face_recognition.load_image_file(os.path.join(subdir, y)) face_encodings = face_recognition.face_encodings(_image) name = os.path.split(subdir)[-1] if face_encodings and len(face_encodings) == 1: if len(person_image_encoding) == 0: person_image_encoding.append(face_encodings[0]) known_face_names.append(name) continue for i in range(len(person_image_encoding)): distances = face_recognition.compare_faces(person_image_encoding, face_encodings[0], tolerance=image_thread) if False in distances: person_image_encoding.append(face_encodings[0]) known_face_names.append(name) print(name, " new feature") f.write(name + ":" + str(face_encodings[0]) + "\n") break # face_encoding = face_recognition.face_encodings(_image)[0] # face_recognition.compare_faces() known_face_encodings.extend(person_image_encoding) bb = np.array(known_face_encodings) print("--------") np.save(KNOWN_FACE_ENCODINGS, known_face_encodings) np.save(KNOWN_FACE_NANE, known_face_names)
Example 3
Project: pytorch-mri-segmentation-3D Author: Achilleas File: PP.py License: MIT License | 7 votes |
def generateGTSlicesFolder(data_folder = '../Data/MS2017a/scans/'): scan_folders = glob.glob(data_folder + '*') for sf in scan_folders: slice_dir_path = os.path.join(sf, 'gt_slices/') if not os.path.exists(slice_dir_path): print('Creating directory at:' , slice_dir_path) os.makedirs(slice_dir_path) img = nib.load(os.path.join(sf, 'wmh.nii.gz')) img_np = img.get_data() img_affine = img.affine print(sf) print('The img shape', img_np.shape[2]) for i in range(img_np.shape[2]): slice_img_np = img_np[:,:,i] nft_img = nib.Nifti1Image(slice_img_np, img_affine) nib.save(nft_img, slice_dir_path + 'wmh_' + str(i) + '.nii.gz') if os.path.basename(sf) == '0': slice_img = nib.load(slice_dir_path + 'wmh_' + str(i) + '.nii.gz').get_data() * 256 #cv2.imwrite('temp/' + str(i) + '.jpg', slice_img)
Example 4
Project: vergeml Author: mme File: cache.py License: MIT License | 6 votes |
def _serialize_data(self, data): # Default to raw bytes type_ = _BYTES if isinstance(data, np.ndarray): # When the data is a numpy array, use the more compact native # numpy format. buf = io.BytesIO() np.save(buf, data) data = buf.getvalue() type_ = _NUMPY elif not isinstance(data, (bytearray, bytes)): # Everything else except byte data is serialized in pickle format. data = pickle.dumps(data) type_ = _PICKLE if self.compress: # Optional compression data = lz4.frame.compress(data) return type_, data
Example 5
Project: gated-graph-transformer-network Author: hexahedria File: ggtnn_train.py License: MIT License | 6 votes |
def visualize(m, story_buckets, wordlist, answerlist, output_format, outputdir, batch_size=1, seq_len=5, debugmode=False, snap=False): cur_bucket = random.choice(story_buckets) sampled_batch = sample_batch(cur_bucket, batch_size, len(answerlist), output_format) part_sampled_batch = sampled_batch[:3] with open(os.path.join(outputdir,'stories.txt'),'w') as f: ggtnn_graph_parse.print_batch(part_sampled_batch, wordlist, answerlist, file=f) with open(os.path.join(outputdir,'answer_list.txt'),'w') as f: f.write('\n'.join(answerlist) + '\n') if debugmode: args = sampled_batch fn = m.debug_test_fn else: args = part_sampled_batch[:2] + ((seq_len,) if output_format == model.ModelOutputFormat.sequence else ()) fn = m.snap_test_fn if snap else m.fuzzy_test_fn results = fn(*args) for i,result in enumerate(results): np.save(os.path.join(outputdir,'result_{}.npy'.format(i)), result)
Example 6
Project: dustmaps Author: gregreen File: json_serializers.py License: GNU General Public License v2.0 | 6 votes |
def serialize_ndarray_npy(o): """ Serializes a :obj:`numpy.ndarray` using numpy's built-in :obj:`save` function. This produces totally unreadable (and very un-JSON-like) results (in "npy" format), but it's basically guaranteed to work in 100% of cases. Args: o (:obj:`numpy.ndarray`): :obj:`ndarray` to be serialized. Returns: A dictionary that can be passed to :obj:`json.dumps`. """ with io.BytesIO() as f: np.save(f, o) f.seek(0) serialized = json.dumps(f.read().decode('latin-1')) return dict( _type='np.ndarray', npy=serialized)
Example 7
Project: dustmaps Author: gregreen File: json_serializers.py License: GNU General Public License v2.0 | 6 votes |
def deserialize_ndarray_npy(d): """ Deserializes a JSONified :obj:`numpy.ndarray` that was created using numpy's :obj:`save` function. Args: d (:obj:`dict`): A dictionary representation of an :obj:`ndarray` object, created using :obj:`numpy.save`. Returns: An :obj:`ndarray` object. """ with io.BytesIO() as f: f.write(json.loads(d['npy']).encode('latin-1')) f.seek(0) return np.load(f)
Example 8
Project: DOTA_models Author: ringringyi File: input.py License: Apache License 2.0 | 6 votes |
def extract_mnist_data(filename, num_images, image_size, pixel_depth): """ Extract the images into a 4D tensor [image index, y, x, channels]. Values are rescaled from [0, 255] down to [-0.5, 0.5]. """ # if not os.path.exists(file): if not tf.gfile.Exists(filename+".npy"): with gzip.open(filename) as bytestream: bytestream.read(16) buf = bytestream.read(image_size * image_size * num_images) data = np.frombuffer(buf, dtype=np.uint8).astype(np.float32) data = (data - (pixel_depth / 2.0)) / pixel_depth data = data.reshape(num_images, image_size, image_size, 1) np.save(filename, data) return data else: with tf.gfile.Open(filename+".npy", mode='r') as file_obj: return np.load(file_obj)
Example 9
Project: pytorch-mri-segmentation-3D Author: Achilleas File: PP.py License: MIT License | 6 votes |
def extractMeanDataStats(size = [200, 200, 100], postfix = '_200x200x100orig', main_folder_path = '../../Data/MS2017b/', ): scan_folders = glob.glob(main_folder_path + 'scans/*') img_path = 'pre/FLAIR' + postfix + '.nii.gz' segm_path = 'wmh' + postfix + '.nii.gz' shape_ = [len(scan_folders), size[0], size[1], size[2]] arr = np.zeros(shape_) for i, sf in enumerate(scan_folders): arr[i, :,:,:] = numpyFromScan(os.path.join(sf,img_path)).squeeze() arr /= len(scan_folders) means = np.mean(arr) stds = np.std(arr, axis = 0) np.save(main_folder_path + 'extra_data/std' + postfix, stds) np.save(main_folder_path + 'extra_data/mean' + postfix, means)
Example 10
Project: pytorch-mri-segmentation-3D Author: Achilleas File: PP.py License: MIT License | 6 votes |
def generateTrainValFile_Slices(train_fraction, main_folder = '../Data/MS2017a/'): train_folders, val_folders = splitTrainVal_Slices(0.8) train_folder_names = [train_folders[i].split(main_folder)[1] for i in range(len(train_folders))] val_folder_names = [val_folders[i].split(main_folder)[1] for i in range(len(val_folders))] f_train = open(main_folder + 'train_slices.txt', 'w+') f_val = open(main_folder + 'val_slices.txt', 'w+') for fn in train_folder_names: f_train.write(fn + '\n') for fn in val_folder_names: f_val.write(fn + '\n') f_train.close() f_val.close() #Use this to save the images quickly (for testing purposes)
Example 11
Project: UROP-Adversarial-Feature-Matching-for-Text-Generation Author: Jeff-HOU File: data.py License: GNU Affero General Public License v3.0 | 6 votes |
def next_batch(self, whichSet='train'): if whichSet == 'train': self.trainBatchCnt += 1 assert self.trainBatchCnt < self.trainMaxBatch return self.train[self.trainBatchCnt * self.batch_size: (self.trainBatchCnt + 1) * self.batch_size] elif whichSet == 'validation': self.validationBatchCnt += 1 assert self.validationBatchCnt < self.validationMaxBatch return self.validation[self.validationBatchCnt * self.batch_size: (self.validationBatchCnt + 1) * self.batch_size] elif whichSet == 'test': self.testBatchCnt += 1 assert self.testBatchCnt < self.testMaxBatch return self.test[self.testBatchCnt * self.batch_size: (self.testBatchCnt + 1) * self.batch_size] else: msg = 'Wrong set name!\n'+ \ 'Should be train / validation / test.' raise Exception(msg) # Following code copied here: # https://stackoverflow.com/questions/17219481/save-to-file-and-load-an-instance-of-a-python-class-with-its-attributes
Example 12
Project: argus-freesound Author: lRomul File: predict_folds.py License: MIT License | 6 votes |
def pred_test_fold(predictor, fold, test_data): fold_prediction_dir = PREDICTION_DIR / f'fold_{fold}' / 'test' fold_prediction_dir.mkdir(parents=True, exist_ok=True) fname_lst, images_lst = test_data pred_lst = [] for fname, image in zip(fname_lst, images_lst): pred = predictor.predict(image) pred_path = fold_prediction_dir / f'{fname}.npy' np.save(pred_path, pred) pred = pred.mean(axis=0) pred_lst.append(pred) preds = np.stack(pred_lst, axis=0) subm_df = pd.DataFrame(data=preds, index=fname_lst, columns=config.classes) subm_df.index.name = 'fname' subm_df.to_csv(fold_prediction_dir / 'probs.csv')
Example 13
Project: overhaul-distillation Author: clovaai File: calculate_weights.py License: MIT License | 6 votes |
def calculate_weigths_labels(dataset, dataloader, num_classes): # Create an instance from the data loader z = np.zeros((num_classes,)) # Initialize tqdm tqdm_batch = tqdm(dataloader) print('Calculating classes weights') for sample in tqdm_batch: y = sample['label'] y = y.detach().cpu().numpy() mask = (y >= 0) & (y < num_classes) labels = y[mask].astype(np.uint8) count_l = np.bincount(labels, minlength=num_classes) z += count_l tqdm_batch.close() total_frequency = np.sum(z) class_weights = [] for frequency in z: class_weight = 1 / (np.log(1.02 + (frequency / total_frequency))) class_weights.append(class_weight) ret = np.array(class_weights) classes_weights_path = os.path.join(Path.db_root_dir(dataset), dataset+'_classes_weights.npy') np.save(classes_weights_path, ret) return ret
Example 14
Project: pyscf Author: pyscf File: wmme.py License: Apache License 2.0 | 6 votes |
def MakeBaseIntegrals(self, Smh=True, MakeS=False): """Invoke bfint to calculate CoreEnergy (scalar), CoreH (nOrb x nOrb), Int2e_Frs (nFit x nOrb x nOrb), and overlap matrix (nOrb x nOrb)""" # assemble arguments to integral generation program Args = [] if Smh: Args.append("--orb-trafo=Smh") # ^- calculate integrals in symmetrically orthogonalized AO basis Outputs = [] Outputs.append(("--save-coreh", "INT1E")) Outputs.append(("--save-fint2e", "INT2E")) Outputs.append(("--save-overlap", "OVERLAP")) CoreH, Int2e, Overlap = self._InvokeBfint(Args, Outputs) nOrb = CoreH.shape[0] Int2e = Int2e.reshape((Int2e.shape[0], nOrb, nOrb)) CoreEnergy = self.Atoms.fCoreRepulsion() if MakeS: return CoreEnergy, CoreH, Int2e, Overlap else: return CoreEnergy, CoreH, Int2e
Example 15
Project: pyscf Author: pyscf File: wmme.py License: Apache License 2.0 | 6 votes |
def MakeOverlap(self, OrbBasis2=None): """calculate overlap within main orbital basis, and, optionally, between main orbital basis and a second basis, as described in OrbBasis2. Returns <1|1>, <1|2>, and <2|2> matrices.""" Args = [] Outputs = [] Outputs.append(("--save-overlap", "OVERLAP_1")) if OrbBasis2 is not None: MoreBases = {'--basis-orb-2': OrbBasis2} Outputs.append(("--save-overlap-12", "OVERLAP_12")) Outputs.append(("--save-overlap-2", "OVERLAP_2")) return self._InvokeBfint(Args, Outputs, MoreBases=MoreBases) else: MoreBases = None Overlap, = self._InvokeBfint(Args, Outputs, MoreBases=MoreBases) return Overlap
Example 16
Project: pyscf Author: pyscf File: wmme.py License: Apache License 2.0 | 6 votes |
def MakeRaw2eIntegrals(self, Smh=True, Kernel2e="coulomb"): """compute Int2e_Frs (nFit x nOrb x nOrb) and fitting metric Int2e_FG (nFit x nFit), where the fitting metric is *not* absorbed into the 2e integrals.""" # assemble arguments to integral generation program Args = [] if Smh: Args.append("--orb-trafo=Smh") # ^- calculate integrals in symmetrically orthogonalized AO basis Args.append("--kernel2e='%s'" % Kernel2e) Args.append("--solve-fitting-eq=false") Outputs = [] Outputs.append(("--save-fint2e", "INT2E_3IX")) Outputs.append(("--save-fitting-metric", "INT2E_METRIC")) Int2e_Frs, Int2e_FG = self._InvokeBfint(Args, Outputs) nOrb = int(Int2e_Frs.shape[1]**.5 + .5) assert(nOrb**2 == Int2e_Frs.shape[1]) Int2e_Frs = Int2e_Frs.reshape((Int2e_Frs.shape[0], nOrb, nOrb)) assert(Int2e_Frs.shape[0] == Int2e_FG.shape[0]) assert(Int2e_FG.shape[0] == Int2e_FG.shape[1]) return Int2e_FG, Int2e_Frs
Example 17
Project: SEDST Author: AuCson File: unsup_model.py License: MIT License | 6 votes |
def load_glove_embedding(self, freeze=False): initial_arr = self.m.u_encoder.embedding.weight.data.cpu().numpy() mat = get_glove_matrix(self.reader.vocab, initial_arr) # np.save('./data/embedding.npy',mat) # mat = np.load('./data/embedding.npy') embedding_arr = torch.from_numpy(mat) self.m.u_encoder.embedding.weight.data.copy_(embedding_arr) self.m.p_encoder.embedding.weight.data.copy_(embedding_arr) self.m.m_decoder.emb.weight.data.copy_(embedding_arr) self.m.p_decoder.emb.weight.data.copy_(embedding_arr) self.m.qz_decoder.mu.weight.data.copy_(embedding_arr.transpose(1, 0)) self.m.pz_decoder.mu.weight.data.copy_(embedding_arr.transpose(1, 0)) if freeze: self.freeze_module(self.m.u_encoder.embedding) self.freeze_module(self.m.m_e.embedding) self.freeze_module(self.m.m_decoder.emb)
Example 18
Project: PathCon Author: hwwang55 File: data_loader.py License: MIT License | 6 votes |
def read_relations(file_name): bow = [] count_vec = CountVectorizer() d = {} file = open(file_name) for line in file: index, name = line.strip().split('\t') d[name] = int(index) if args.feature_type == 'bow' and not os.path.exists('../data/' + args.dataset + '/bow.npy'): tokens = re.findall('[a-z]{2,}', name) bow.append(' '.join(tokens)) file.close() if args.feature_type == 'bow' and not os.path.exists('../data/' + args.dataset + '/bow.npy'): bow = count_vec.fit_transform(bow) np.save('../data/' + args.dataset + '/bow.npy', bow.toarray()) return d
Example 19
Project: ConvolutionalEmotion Author: Zebreu File: emotionclassification.py License: MIT License | 6 votes |
def getPeakFeatures(): net = DecafNet() features = numpy.zeros((number_sequences,feature_length)) labels = numpy.zeros((number_sequences,1)) counter = 0 # Maybe sort them for participant in os.listdir(os.path.join(data_dir,image_dir)): for sequence in os.listdir(os.path.join(data_dir,image_dir, participant)): if sequence != ".DS_Store": image_files = sorted(os.listdir(os.path.join(data_dir,image_dir, participant,sequence))) image_file = image_files[-1] print counter, image_file imarray = cv2.imread(os.path.join(data_dir,image_dir, participant,sequence,image_file)) imarray = cv2.cvtColor(imarray,cv2.COLOR_BGR2GRAY) scores = net.classify(imarray, center_only=True) features[counter] = net.feature(feature_level)#.flatten() label_file = open(os.path.join(data_dir,label_dir, participant,sequence,image_file[:-4]+"_emotion.txt")) labels[counter] = eval(label_file.read()) label_file.close() counter += 1 numpy.save("featuresPeak5",features) numpy.save("labelsPeak5",labels)
Example 20
Project: Keras-GAN Author: eriklindernoren File: data_loader.py License: MIT License | 6 votes |
def setup_mnist(self, img_res): print ("Setting up MNIST...") if not os.path.exists('datasets/mnist_x.npy'): # Load the dataset (mnist_X, mnist_y), (_, _) = mnist.load_data() # Normalize and rescale images mnist_X = self.normalize(mnist_X) mnist_X = np.array([imresize(x, img_res) for x in mnist_X]) mnist_X = np.expand_dims(mnist_X, axis=-1) mnist_X = np.repeat(mnist_X, 3, axis=-1) self.mnist_X, self.mnist_y = mnist_X, mnist_y # Save formatted images np.save('datasets/mnist_x.npy', self.mnist_X) np.save('datasets/mnist_y.npy', self.mnist_y) else: self.mnist_X = np.load('datasets/mnist_x.npy') self.mnist_y = np.load('datasets/mnist_y.npy') print ("+ Done.")
Example 21
Project: sudoku Author: Kyubyong File: generate_sudoku.py License: GNU General Public License v3.0 | 6 votes |
def main(num): ''' Generates `num` games of Sudoku. ''' quizzes = np.zeros((num, 9, 9), np.int32) solutions = np.zeros((num, 9, 9), np.int32) for i in range(num): all_results, solution = run(n=23, iter=10) quiz = best(all_results) quizzes[i] = quiz solutions[i] = solution if (i+1) % 1000 == 0: print i+1 np.save('data/sudoku.npz', quizzes=quizzes, solutions=solutions)
Example 22
Project: insightface Author: deepinsight File: IJB_11.py License: MIT License | 6 votes |
def verification(template_norm_feats = None, unique_templates = None, p1 = None, p2 = None): # ========================================================== # Compute set-to-set Similarity Score. # ========================================================== template2id = np.zeros((max(unique_templates)+1,1),dtype=int) for count_template, uqt in enumerate(unique_templates): template2id[uqt] = count_template score = np.zeros((len(p1),)) # save cosine distance between pairs total_pairs = np.array(range(len(p1))) batchsize = 100000 # small batchsize instead of all pairs in one batch due to the memory limiation sublists = [total_pairs[i:i + batchsize] for i in range(0, len(p1), batchsize)] total_sublists = len(sublists) for c, s in enumerate(sublists): feat1 = template_norm_feats[template2id[p1[s]]] feat2 = template_norm_feats[template2id[p2[s]]] similarity_score = np.sum(feat1 * feat2, -1) score[s] = similarity_score.flatten() if c % 10 == 0: print('Finish {}/{} pairs.'.format(c, total_sublists)) return score # In[ ]:
Example 23
Project: EDeN Author: fabriziocosta File: util.py License: MIT License | 5 votes |
def store_matrix(matrix='', output_dir_path='', out_file_name='', output_format=''): """store_matrix.""" if not os.path.exists(output_dir_path): os.mkdir(output_dir_path) full_out_file_name = os.path.join(output_dir_path, out_file_name) if output_format == "MatrixMarket": if len(matrix.shape) == 1: raise Exception( "'MatrixMarket' format supports only 2D dimensional array\ and not vectors") else: io.mmwrite(full_out_file_name, matrix, precision=None) elif output_format == "numpy": np.save(full_out_file_name, matrix) elif output_format == "joblib": joblib.dump(matrix, full_out_file_name) elif output_format == "text": with open(full_out_file_name, "w") as f: if len(matrix.shape) == 1: for x in matrix: f.write("%s\n" % (x)) else: raise Exception( "'text' format supports only mono dimensional array\ and not matrices") logger.info("Written file: %s" % full_out_file_name)
Example 24
Project: DDPAE-video-prediction Author: jthsieh File: metrics.py License: MIT License | 5 votes |
def get_scores(self): # Save positions if self.save_path != '': positions = np.array([np.concatenate(self.pred_positions, axis=0), np.concatenate(self.gt_positions, axis=0)]) np.save(os.path.join(self.save_path), positions) masks = np.concatenate(self.masks, axis=0) cosine = np.concatenate(self.cosine_similarities, axis=0) rel_error = np.concatenate(self.relative_errors, axis=0) numel = np.sum(masks == 1, axis=(0,2)) rel_error = np.sum(rel_error * masks, axis=(0,2)) / numel cosine = np.sum(cosine * masks, axis=(0,2)) / numel return {'relative_errors': rel_error, 'cosine_similarities': cosine}
Example 25
Project: gated-graph-transformer-network Author: hexahedria File: convert_story.py License: MIT License | 5 votes |
def main(storyfile, outputdir): with gzip.open(storyfile,'rb') as f: story, sents, query, ans = pickle.load(f) with open(os.path.join(outputdir,'story.txt'),'w') as f: f.write("{}\n{}\n{}".format("\n".join(" ".join(s) for s in sents), " ".join(query), " ".join(ans))) results = convert(story) if not os.path.exists(outputdir): os.makedirs(outputdir) for i,res in enumerate(results): np.save(os.path.join(outputdir,'result_{}.npy'.format(i)), res)
Example 26
Project: disentangling_conditional_gans Author: zalandoresearch File: dataset_tool.py License: MIT License | 5 votes |
def add_labels(self, labels): if self.print_progress: print('%-40s\r' % 'Saving labels...', end='', flush=True) assert labels.shape[0] == self.cur_images with open(self.tfr_prefix + '-rxx.labels', 'wb') as f: np.save(f, labels.astype(np.float32))
Example 27
Project: disentangling_conditional_gans Author: zalandoresearch File: dataset_tool.py License: MIT License | 5 votes |
def extract(tfrecord_dir, output_dir): print('Loading dataset "%s"' % tfrecord_dir) tfutil.init_tf({'gpu_options.allow_growth': True}) dset = dataset.TFRecordDataset(tfrecord_dir, max_label_size=0, repeat=False, shuffle_mb=0) tfutil.init_uninited_vars() print('Extracting images to "%s"' % output_dir) if not os.path.isdir(output_dir): os.makedirs(output_dir) idx = 0 while True: if idx % 10 == 0: print('%d\r' % idx, end='', flush=True) try: images, labels = dset.get_minibatch_np(1) except tf.errors.OutOfRangeError: break if images.shape[1] == 1: img = PIL.Image.fromarray(images[0][0], 'L') else: img = PIL.Image.fromarray(images[0].transpose(1, 2, 0), 'RGB') img.save(os.path.join(output_dir, 'img%08d.png' % idx)) idx += 1 print('Extracted %d images.' % idx) #----------------------------------------------------------------------------
Example 28
Project: Deep_VoiceChanger Author: pstuvwx File: dataset.py License: MIT License | 5 votes |
def save(path, bps, data): if data.dtype != np.int16: data = data.astype(np.int16) data = np.reshape(data, -1) wav.write(path, bps, data)
Example 29
Project: Deep_VoiceChanger Author: pstuvwx File: dataset.py License: MIT License | 5 votes |
def pre_encode(): import tqdm path = input('enter wave path...') ds = WaveDataset(path, -1, True) num = ds.max // dif imgs = [ds.get_example(i) for i in tqdm.tqdm(range(num))] dst = np.concatenate(imgs, axis=1) print(dst.shape) np.save(path[:-3]+'npy', dst) print('encoded file saved at', path[:-3]+'npy')
Example 30
Project: deep-learning-note Author: wdxtub File: preprocessing.py License: MIT License | 5 votes |
def main(): start = datetime.datetime.now() print("start", start) with tf.Session() as sess: processed_data = create_image_lists(sess, TEST_PERCENTAGE, VALIDATION_PERCENTAGE) # 通过 numpy 格式保存处理后的数据 np.save(OUTPUT_FILE, processed_data) end = datetime.datetime.now() print("done", end)