def TestDataset(): [x_test, y_test] = DataProcessor.ProcessTestData( "/Users/usi/PycharmProjects/data/beboptest.pickle", 60, 108, True) test_set = Dataset(x_test, y_test) frame = cv2.imread("13.jpg", 0) frame = np.reshape(frame, (60, 108, 1)) frame = np.swapaxes(frame, 0, 2) frame = np.swapaxes(frame, 1, 2) for i in range(20): newframe = torch.from_numpy(frame).float() newframe = test_set.augmentNoise(newframe) newframe = test_set.toNumpy(newframe) cv2.imshow("frame", newframe) cv2.waitKey()
def main(): logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s", datefmt="%Y-%m-%d %H:%M:%S", filename="log.txt", filemode='w') console = logging.StreamHandler() console.setLevel(logging.INFO) formatter = logging.Formatter('%(message)s') console.setFormatter(formatter) logging.getLogger('').addHandler(console) model = Dronet(PreActBlock, [1, 1, 1], True) ModelManager.Read('../PyTorch/Models/DronetGray.pt', model) DATA_PATH = "/Users/usi/PycharmProjects/data/" picklename = "HimaxDynamic_12_03_20.pickle" [x_test, y_test, z_test] = DataProcessor.ProcessTestData(DATA_PATH + picklename, True) t_test = DataProcessor.GetTimeStampsFromTestData(DATA_PATH + picklename) if picklename.find(".pickle"): picklename = picklename.replace(".pickle", '') x_test2 = [] y_test2 = [] z_test2 = [] # for i in range(len(x_test)): # gt = y_test[i] # if ((gt[0] > 1.0) and (gt[0] < 2.0)): # x_test2.append(x_test[i]) # y_test2.append(y_test[i]) # z_test2.append(z_test[i]) # # x_test = np.asarray(x_test2) # y_test = np.asarray(y_test2) # z_test = np.asarray(z_test2) test_set = Dataset(x_test, y_test) params = {'batch_size': 1, 'shuffle': False, 'num_workers': 1} test_generator = data.DataLoader(test_set, **params) trainer = ModelTrainer(model) MSE, MAE, r2_score, outputs, gt_labels = trainer.Test(test_generator) # utils.SaveResultsToCSV(gt_labels, outputs, t_test, "wow.csv") model2 = Dronet(PreActBlock, [1, 1, 1], True) ModelManager.Read('../PyTorch/Models/DronetGrayAug120.pt', model2) trainer2 = ModelTrainer(model2) MSE, MAE, r2_score, outputs2, gt_labels2 = trainer2.Test(test_generator) utils.SaveResultsToCSVWithCamPoses(gt_labels, outputs2, t_test, z_test, picklename + ".csv") outputs2 = np.reshape(outputs2, (-1, 4)) h = x_test.shape[2] w = x_test.shape[3] x_test = np.reshape(x_test, (-1, h, w)) VizWorldTopView(x_test, y_test, z_test, outputs, outputs2, True, picklename)