def main():
weights_path = '/home/lab/Desktop/wzndeep/posenet-build--eular/contrast_experiment/weights/'
model_lst = [x for x in sorted(
os.listdir(weights_path)) if x.endswith('.pkl')]
if len(model_lst) == 0:
print('No previous model found, please train first!')
exit()
else:
print('Using previous model %s' % model_lst[-1])
base_model = mobilenet.mobilenet_v2(pretrained=True) # 加载模型并设置为预训练模式
# model = Model(features=base_model).cuda()
# base_model = MobileNetV3_Large()
# state_dict = model_dict()
# base_model.load_state_dict(state_dict)
model = Model(features=base_model).cuda()
checkpoint = torch.load(weights_path + '/%s' % model_lst[-1])
model.load_state_dict(checkpoint['model_state_dict'])
model.eval()
# defaults to /eval
eval_path = '/media/lab/TOSHIBAEXT/BuildingData/testing'
dataset = Dataset(eval_path) # 自定义的数据集
all_images = dataset.all_objects()
trans_errors_norm = []
trans_errors_single = []
rot_errors_arccos = []
rot_errors_single = []
adds = []
for key in sorted(all_images.keys()):
start_time = time.time()
data = all_images[key]
truth_img = data['Image']
img = np.copy(truth_img)
objects = data['Objects']
cam_to_img = data['Calib']
for detectedObject in objects:
label = detectedObject.label
theta_ray = detectedObject.theta_ray
input_img = detectedObject.img
input_tensor = torch.zeros([1, 3, 224, 224]).cuda()
input_tensor[0, :, :, :] = input_img
input_tensor.cuda()
rotation = model(input_tensor)
rotation = rotation.cpu().data.numpy()
print(rotation)
patch_sin = rotation[0][0]
patch_cos = rotation[0][1]
yaw_sin = rotation[0][2]
yaw_cos = rotation[0][3]
patch = np.arctan2(patch_sin, patch_cos)
yaw = np.arctan2(yaw_sin, yaw_cos)
if yaw >= 1.57:
yaw -= 1.57
roll = 0.
r_x = label['Patch']
r_y = label['Yaw']
r_z = label['Roll']
gt_rot = np.array([r_x, r_y, r_z])
est_rot = np.array([patch, yaw, roll])
rot_errors = rot_error(gt_rot, est_rot)
rot_errors_arccos.append(rot_errors[0])
rot_errors_single.append(rot_errors[1])
print('Estimated patch|Truth patch: {:.3f}/{:.3f}'.format(
patch, r_x))
print(
'Estimated yaw|Truth yaw: {:.3f}/{:.3f}'.format(yaw, r_y))
mean_rot_error_arccos = np.mean(rot_errors_arccos)
mean_rot_error_single = np.mean(rot_errors_single, axis=0)
# mean_add = np.mean(adds)
print('=' * 50)
print('Got %s poses in %.3f seconds\n' %
(len(objects), time.time() - start_time))
print("\tMean Rotation Error Norm: {:.3f}".format(mean_rot_error_arccos))
print("\tMean Rotation Errors: patch: {:.3f}, yaw: {:.3f}, roll: {:.3f}".format(
np.rad2deg(mean_rot_error_single[0]), np.rad2deg(mean_rot_error_single[1]),
np.rad2deg(mean_rot_error_single[2])))
def main():
weights_path = '/home/lab/Desktop/wzndeep/posenet-build--eular/weights/'
model_lst = [x for x in sorted(
os.listdir(weights_path)) if x.endswith('.pkl')]
if len(model_lst) == 0:
print('No previous model found, please train first!')
exit()
else:
print('Using previous model %s' % model_lst[-1])
base_model = mobilenet.mobilenet_v2(pretrained=True) # 加载模型并设置为预训练模式
# model = Model(features=base_model).cuda()
# base_model = MobileNetV3_Large()
# state_dict = model_dict()
# base_model.load_state_dict(state_dict)
model = Model(features=base_model).cuda()
checkpoint = torch.load(weights_path + '/%s' % model_lst[-1])
model.load_state_dict(checkpoint['model_state_dict'])
model.eval()
# defaults to /eval
eval_path = '/media/lab/TOSHIBAEXT/BuildingData/testing'
dataset = Dataset(eval_path) # 自定义的数据集
all_images = dataset.all_objects()
trans_errors_norm = []
trans_errors_single = []
rot_errors_arccos = []
rot_errors_single = []
adds = []
for key in sorted(all_images.keys()):
start_time = time.time()
data = all_images[key]
truth_img = data['Image']
image = Image.open(truth_img)
objects = data['Objects']
cam_to_img = data['Calib']
for detectedObject in objects:
label = detectedObject.label
theta_ray = detectedObject.theta_ray
input_img = detectedObject.img
input_tensor = torch.zeros([1, 3, 224, 224]).cuda()
input_tensor[0, :, :, :] = input_img
input_tensor.cuda()
[orient_patch, conf_patch, orient_yaw,
conf_yaw, est_trans] = model(input_tensor)
orient_patch = orient_patch.cpu().data.numpy()[0, :, :]
conf_patch = conf_patch.cpu().data.numpy()[0, :]
orient_yaw = orient_yaw.cpu().data.numpy()[0, :, :]
conf_yaw = conf_yaw.cpu().data.numpy()[0, :]
est_trans = est_trans.cpu().data.numpy()
argmax_patch = np.argmax(conf_patch)
orient_patch = orient_patch[argmax_patch, :]
cos = orient_patch[0]
sin = orient_patch[1]
patch = np.arctan2(sin, cos)
patch += dataset.angle_bins[argmax_patch]
argmax_yaw = np.argmax(conf_yaw)
orient_yaw = orient_yaw[argmax_yaw, :]
cos_yaw = orient_yaw[0]
sin_yaw = orient_yaw[1]
yaw = np.arctan2(sin_yaw, cos_yaw)
yaw += dataset.angle_bins[argmax_yaw]
if (yaw > (2 * np.pi)):
yaw -= (2 * np.pi)
# yaw -= np.pi
roll = 0.
r_x = label['Patch']
r_y = label['Yaw']
r_z = label['Roll']
gt_trans = label['Location']
gt_rot = np.array([r_x, r_y, r_z])
est_rot = np.array([patch, yaw, roll])
trans_errors = trans_error(gt_trans, est_trans)
trans_errors_norm.append(trans_errors[0])
trans_errors_single.append(trans_errors[1])
rot_errors = rot_error(gt_rot, est_rot)
rot_errors_arccos.append(rot_errors[0])
rot_errors_single.append(rot_errors[1])
dim = label['Dimensions']
adds.append(add_err(dim, gt_trans, est_trans, gt_rot, est_rot))
# 画图
# dim = label['Dimensions']
# bbox = label['Box_2D']
# draw(image, bbox, cam_to_img, dim, gt_trans, est_trans, r_x, r_y, r_z, patch, yaw, roll)
print('Estimated patch|Truth patch: {:.3f}/{:.3f}'.format(
patch, r_x))
print(
'Estimated loction', est_trans)
print(
'Truth loction', gt_trans)
print('----------')
# plt.show()
# plt.savefig(
# '/home/lab/Desktop/wzndeep/posenet-build--eular/model_26/{}_proj'.format(key))
# plt.close()
mean_rot_error_arccos = np.mean(rot_errors_arccos)
mean_rot_error_single = np.mean(rot_errors_single, axis=0)
mean_trans_error_norm = np.mean(trans_errors_norm)
mean_trans_error_single = np.mean(trans_errors_single, axis=0)
mean_add = np.mean(adds)
print('=' * 50)
print('Got %s poses in %.3f seconds\n' %
(len(objects), time.time() - start_time))
print("\tMean Rotation Error Norm: {:.3f}".format(mean_rot_error_arccos))
print("\tMean Rotation Errors: patch: {:.3f}, yaw: {:.3f}, roll: {:.3f}".format(
np.rad2deg(mean_rot_error_single[0]), np.rad2deg(mean_rot_error_single[1]),
np.rad2deg(mean_rot_error_single[2])))
print("\tMean Trans Error Norm: {:.3f}".format(mean_trans_error_norm))
print("\tMean Trans Errors: X: {:.3f}, Y: {:.3f}, Z: {:.3f}".format(
mean_trans_error_single[0][0], mean_trans_error_single[0][1],
mean_trans_error_single[0][2]))
print("\tMean ADD: {:.3f}".format(mean_add))