Python numpy.save() Examples
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Example #1
Source File: PP.py From pytorch-mri-segmentation-3D with 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
Source File: encoding_images.py From face-attendance-machine with Apache License 2.0 | 8 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
Source File: PP.py From pytorch-mri-segmentation-3D with 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
Source File: cache.py From vergeml with 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
Source File: predict_folds.py From argus-freesound with 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 #6
Source File: calculate_weights.py From overhaul-distillation with 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 #7
Source File: input.py From DOTA_models with 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 #8
Source File: wmme.py From pyscf with 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 #9
Source File: wmme.py From pyscf with 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 #10
Source File: data.py From UROP-Adversarial-Feature-Matching-for-Text-Generation with 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 #11
Source File: PP.py From pytorch-mri-segmentation-3D with 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 #12
Source File: wmme.py From pyscf with 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 #13
Source File: unsup_model.py From SEDST with 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 #14
Source File: data_loader.py From PathCon with 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 #15
Source File: json_serializers.py From dustmaps with 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 #16
Source File: json_serializers.py From dustmaps with 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 #17
Source File: emotionclassification.py From ConvolutionalEmotion with 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 #18
Source File: data_loader.py From Keras-GAN with 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 #19
Source File: PP.py From pytorch-mri-segmentation-3D with 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 #20
Source File: ggtnn_train.py From gated-graph-transformer-network with 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 #21
Source File: generate_sudoku.py From sudoku with 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
Source File: IJB_11.py From insightface with 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
Source File: pack_dataset.py From medicaldetectiontoolkit with Apache License 2.0 | 5 votes |
def convert_to_npy(npz_file): if not os.path.isfile(npz_file[:-3] + "npy"): a = np.load(npz_file)['data'] np.save(npz_file[:-3] + "npy", a)
Example #24
Source File: generate_toys.py From medicaldetectiontoolkit with Apache License 2.0 | 5 votes |
def multi_processing_create_image(inputs): out_dir, six, foreground_margin, class_diameters, mode = inputs print('processing {} {}'.format(out_dir, six)) img = np.random.rand(320, 320) seg = np.zeros((320, 320)).astype('uint8') center_x = np.random.randint(foreground_margin, img.shape[0] - foreground_margin) center_y = np.random.randint(foreground_margin, img.shape[1] - foreground_margin) class_id = np.random.randint(0, 2) for y in range(img.shape[0]): for x in range(img.shape[0]): if ((x - center_x) ** 2 + (y - center_y) ** 2 - class_diameters[class_id] ** 2) < 0: img[y][x] += 0.2 seg[y][x] = 1 if 'donuts' in mode: whole_diameter = 4 if class_id == 1: for y in range(img.shape[0]): for x in range(img.shape[0]): if ((x - center_x) ** 2 + (y - center_y) ** 2 - whole_diameter ** 2) < 0: img[y][x] -= 0.2 if mode == 'donuts_shape': seg[y][x] = 0 out = np.concatenate((img[None], seg[None])) out_path = os.path.join(out_dir, '{}.npy'.format(six)) np.save(out_path, out) with open(os.path.join(out_dir, 'meta_info_{}.pickle'.format(six)), 'wb') as handle: pickle.dump([out_path, class_id, str(six)], handle)
Example #25
Source File: data.py From UROP-Adversarial-Feature-Matching-for-Text-Generation with GNU Affero General Public License v3.0 | 5 votes |
def save(self, fileName): assert fileName is not None with open(self.paramSavePath + fileName, 'wb') as f: pickle.dump(self, f, pickle.HIGHEST_PROTOCOL)
Example #26
Source File: util.py From EDeN with 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 #27
Source File: demo.py From RingNet with MIT License | 5 votes |
def main(config, template_mesh): sess = tf.Session() model = RingNet_inference(config, sess=sess) input_img, proc_param, img = preprocess_image(config.img_path) vertices, flame_parameters = model.predict(np.expand_dims(input_img, axis=0), get_parameters=True) cams = flame_parameters[0][:3] visualize(img, proc_param, vertices[0], cams, img_name=config.out_folder + '/images/' + config.img_path.split('/')[-1][:-4]) if config.save_obj_file: if not os.path.exists(config.out_folder + '/mesh'): os.mkdir(config.out_folder + '/mesh') mesh = Mesh(v=vertices[0], f=template_mesh.f) mesh.write_obj(config.out_folder + '/mesh/' + config.img_path.split('/')[-1][:-4] + '.obj') if config.save_flame_parameters: if not os.path.exists(config.out_folder + '/params'): os.mkdir(config.out_folder + '/params') flame_parameters_ = {'cam': flame_parameters[0][:3], 'pose': flame_parameters[0][3:3+config.pose_params], 'shape': flame_parameters[0][3+config.pose_params:3+config.pose_params+config.shape_params], 'expression': flame_parameters[0][3+config.pose_params+config.shape_params:]} np.save(config.out_folder + '/params/' + config.img_path.split('/')[-1][:-4] + '.npy', flame_parameters_) if config.neutralize_expression: from util.using_flame_parameters import make_prdicted_mesh_neutral if not os.path.exists(config.out_folder + '/neutral_mesh'): os.mkdir(config.out_folder + '/neutral_mesh') neutral_mesh = make_prdicted_mesh_neutral(config.out_folder + '/params/' + config.img_path.split('/')[-1][:-4] + '.npy', config.flame_model_path) neutral_mesh.write_obj(config.out_folder + '/neutral_mesh/' + config.img_path.split('/')[-1][:-4] + '.obj')
Example #28
Source File: nodclsgbt.py From DeepLung with GNU General Public License v3.0 | 5 votes |
def gbtfunc(dep): m = gbt(max_depth=dep, random_state=0) m.fit(traindata, trainlabel) predtrain = m.predict(traindata) predtest = m.predict_proba(testdata) # print predtest.shape, predtest[1,:] return np.sum(predtrain == trainlabel) / float(traindata.shape[0]), \ np.mean((predtest[:,1]>0.5).astype(int) == testlabel), predtest # / float(testdata.shape[0]), # trainacc, testacc, predtest = gbtfunc(3) # print trainacc, testacc # np.save('pixradiustest.npy', predtest[:,1])
Example #29
Source File: dataloader_utils.py From medicaldetectiontoolkit with Apache License 2.0 | 5 votes |
def convert_to_npy(npz_file): identifier = os.path.split(npz_file)[1][:-4] if not os.path.isfile(npz_file[:-4] + ".npy"): a = np.load(npz_file)[identifier] np.save(npz_file[:-4] + ".npy", a)
Example #30
Source File: emotionclassification.py From ConvolutionalEmotion with MIT License | 5 votes |
def getPeakFaceFeatures(): net = DecafNet() cascade = cv2.CascadeClassifier('haarcascade_frontalface_alt2.xml') 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) rects = cascade.detectMultiScale(imarray, 1.3, 3, cv2.cv.CV_HAAR_SCALE_IMAGE, (150,150)) if len(rects) > 0: facerect=rects[0] imarray = imarray[facerect[1]:facerect[1]+facerect[3], facerect[0]:facerect[0]+facerect[2]] 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("featuresPeakFace5",features) numpy.save("labelsPeakFace5",labels)