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)
