Datasets:
File size: 3,052 Bytes
e4d46d3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 | import os
import cv2
import numpy as np
from evaluate import comput_sad_loss, compute_mse_loss, compute_connectivity_error, compute_gradient_loss
import argparse
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--pred-dir', type=str, default='./predDIM/', help="pred alpha dir")
parser.add_argument('--label-dir', type=str, default='./Test_set/alpha_copy/', help="GT alpha dir")
parser.add_argument('--trimap-dir', type=str, default='./Test_set/trimaps/', help="trimap dir")
args = parser.parse_args()
mse_loss = []
sad_loss = []
### loss_unknown only consider the unknown regions, i.e. trimap==128, as trimap-based methods do
mse_loss_unknown = []
sad_loss_unknown = []
grad_loss = []
compute_connectivity_loss = []
grad_loss_unknown = []
compute_connectivity_loss_unknown = []
for img in os.listdir(args.pred_dir):
label = cv2.imread(os.path.join(args.label_dir, img), 0).astype(np.float32)
pred = cv2.imread(os.path.join(args.pred_dir, img), 0).astype(np.float32)
trimap = cv2.imread(os.path.join(args.trimap_dir, img), 0).astype(np.float32)
if pred.shape != label.shape:
pred = cv2.resize(pred, (label.shape[1], label.shape[0]))
mse_loss_unknown_ = compute_mse_loss(pred, label, trimap)
sad_loss_unknown_ = comput_sad_loss(pred, label, trimap)[0]
gradient_loss_unknown = compute_gradient_loss(pred, label, trimap)
connectivity_loss_unknown = compute_connectivity_error(pred, label, trimap, 0.1)
trimap[...] = 128
mse_loss_ = compute_mse_loss(pred, label, trimap)
sad_loss_ = comput_sad_loss(pred, label, trimap)[0]
gradient_loss = compute_gradient_loss(pred, label, trimap)
connectivity_loss = compute_connectivity_error(pred, label, trimap, 0.1)
print('Whole Image: MSE: ', mse_loss_, ' SAD:', sad_loss_, "GRAD:", gradient_loss, "Conn:", connectivity_loss)
print('Unknown Region: MSE:', mse_loss_unknown_, ' SAD:', sad_loss_unknown_, "GRAD:", gradient_loss_unknown, "Conn:", connectivity_loss_unknown)
mse_loss_unknown.append(mse_loss_unknown_)
sad_loss_unknown.append(sad_loss_unknown_)
mse_loss.append(mse_loss_)
sad_loss.append(sad_loss_)
grad_loss.append(gradient_loss)
compute_connectivity_loss.append(connectivity_loss)
grad_loss_unknown.append(gradient_loss_unknown)
compute_connectivity_loss_unknown.append(connectivity_loss_unknown)
print('Average:')
print('Whole Image: MSE:', np.array(mse_loss).mean(), ' SAD:', np.array(sad_loss).mean())
print('Unknown Region: MSE:', np.array(mse_loss_unknown).mean(), ' SAD:', np.array(sad_loss_unknown).mean())
print("whole Grad, Conn:", np.array(grad_loss).mean(), ' compute_connectivity_loss:', np.array(compute_connectivity_loss).mean())
print("Unknown GRAD, CONN:", np.array(grad_loss_unknown).mean(), ' CONN:', np.array(compute_connectivity_loss_unknown).mean())
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