import sys, os, argparse
|
|
import numpy as np
|
import cv2
|
import matplotlib.pyplot as plt
|
|
import torch
|
import torch.nn as nn
|
from torch.autograd import Variable
|
from torch.utils.data import DataLoader
|
from torchvision import transforms
|
import torch.backends.cudnn as cudnn
|
import torchvision
|
import torch.nn.functional as F
|
|
import datasets, hopenet, utils
|
|
def parse_args():
|
"""Parse input arguments."""
|
parser = argparse.ArgumentParser(description='Head pose estimation using the Hopenet network.')
|
parser.add_argument('--gpu', dest='gpu_id', help='GPU device id to use [0]',
|
default=0, type=int)
|
parser.add_argument('--data_dir', dest='data_dir', help='Directory path for data.',
|
default='', type=str)
|
parser.add_argument('--filename_list', dest='filename_list', help='Path to text file containing relative paths for every example.',
|
default='', type=str)
|
parser.add_argument('--snapshot', dest='snapshot', help='Name of model snapshot.',
|
default='', type=str)
|
parser.add_argument('--batch_size', dest='batch_size', help='Batch size.',
|
default=1, type=int)
|
parser.add_argument('--save_viz', dest='save_viz', help='Save images with pose cube.',
|
default=False, type=bool)
|
parser.add_argument('--dataset', dest='dataset', help='Dataset type.', default='AFLW2000', type=str)
|
|
args = parser.parse_args()
|
|
return args
|
|
if __name__ == '__main__':
|
args = parse_args()
|
|
cudnn.enabled = True
|
gpu = args.gpu_id
|
snapshot_path = args.snapshot
|
|
# ResNet50 structure
|
model = hopenet.Hopenet(torchvision.models.resnet.Bottleneck, [3, 4, 6, 3], 66)
|
|
print 'Loading snapshot.'
|
# Load snapshot
|
saved_state_dict = torch.load(snapshot_path)
|
model.load_state_dict(saved_state_dict)
|
|
print 'Loading data.'
|
|
transformations = transforms.Compose([transforms.Scale(224),
|
transforms.CenterCrop(224), transforms.ToTensor(),
|
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])
|
|
if args.dataset == 'Pose_300W_LP':
|
pose_dataset = datasets.Pose_300W_LP(args.data_dir, args.filename_list, transformations)
|
elif args.dataset == 'Pose_300W_LP_random_ds':
|
pose_dataset = datasets.Pose_300W_LP_random_ds(args.data_dir, args.filename_list, transformations)
|
elif args.dataset == 'AFLW2000':
|
pose_dataset = datasets.AFLW2000(args.data_dir, args.filename_list, transformations)
|
elif args.dataset == 'BIWI':
|
pose_dataset = datasets.BIWI(args.data_dir, args.filename_list, transformations)
|
elif args.dataset == 'AFLW':
|
pose_dataset = datasets.AFLW(args.data_dir, args.filename_list, transformations)
|
elif args.dataset == 'AFLW_aug':
|
pose_dataset = datasets.AFLW_aug(args.data_dir, args.filename_list, transformations)
|
elif args.dataset == 'AFW':
|
pose_dataset = datasets.AFW(args.data_dir, args.filename_list, transformations)
|
else:
|
print 'Error: not a valid dataset name'
|
sys.exit()
|
test_loader = torch.utils.data.DataLoader(dataset=pose_dataset,
|
batch_size=args.batch_size,
|
num_workers=2)
|
|
model.cuda(gpu)
|
|
print 'Ready to test network.'
|
|
# Test the Model
|
model.eval() # Change model to 'eval' mode (BN uses moving mean/var).
|
total = 0
|
|
yaw_error = .0
|
pitch_error = .0
|
roll_error = .0
|
|
l1loss = torch.nn.L1Loss(size_average=False)
|
|
for i, (images, labels, cont_labels, name) in enumerate(test_loader):
|
images = Variable(images).cuda(gpu)
|
total += cont_labels.size(0)
|
|
label_yaw = cont_labels[:,0].float()
|
label_pitch = cont_labels[:,1].float()
|
label_roll = cont_labels[:,2].float()
|
|
yaw, pitch, roll, angles = model(images)
|
|
# Binned predictions
|
_, yaw_bpred = torch.max(yaw.data, 1)
|
_, pitch_bpred = torch.max(pitch.data, 1)
|
_, roll_bpred = torch.max(roll.data, 1)
|
|
# Continuous predictions
|
yaw_predicted = angles[:,0].data.cpu()
|
pitch_predicted = angles[:,1].data.cpu()
|
roll_predicted = angles[:,2].data.cpu()
|
|
# Mean absolute error
|
yaw_error += torch.sum(torch.abs(yaw_predicted - label_yaw))
|
pitch_error += torch.sum(torch.abs(pitch_predicted - label_pitch))
|
roll_error += torch.sum(torch.abs(roll_predicted - label_roll))
|
|
# Save first image in batch with pose cube or axis.
|
if args.save_viz:
|
name = name[0]
|
if args.dataset == 'BIWI':
|
cv2_img = cv2.imread(os.path.join(args.data_dir, name + '_rgb.png'))
|
else:
|
cv2_img = cv2.imread(os.path.join(args.data_dir, name + '.jpg'))
|
if args.batch_size == 1:
|
error_string = 'y %.2f, p %.2f, r %.2f' % (torch.sum(torch.abs(yaw_predicted - label_yaw)), torch.sum(torch.abs(pitch_predicted - label_pitch)), torch.sum(torch.abs(roll_predicted - label_roll)))
|
cv2.putText(cv2_img, error_string, (30, cv2_img.shape[0]- 30), fontFace=1, fontScale=1, color=(0,0,255), thickness=2)
|
# utils.plot_pose_cube(cv2_img, yaw_predicted[0], pitch_predicted[0], roll_predicted[0], size=100)
|
utils.draw_axis(cv2_img, yaw_predicted[0], pitch_predicted[0], roll_predicted[0], tdx = 200, tdy= 200, size=100)
|
cv2.imwrite(os.path.join('output/images', name + '.jpg'), cv2_img)
|
|
print('Test error in degrees of the model on the ' + str(total) +
|
' test images. Yaw: %.4f, Pitch: %.4f, Roll: %.4f' % (yaw_error / total,
|
pitch_error / total, roll_error / total))
|
