test_cv_preds = test_cv_preds.iloc[:, 3:].values.reshape(
len(test_cv_preds), -1, 4)
predictions = predictions.reshape(-1, 240, order='A')
predictions = predictions.reshape(-1, 4, 60)
predictions = utils.xywh_to_x1y1x2y2(predictions)
predictions = np.swapaxes(predictions, 1, 2)
predictions = np.around(predictions).astype(int)
predictions = test_cv_preds - predictions
gt_df = pd.read_csv('./outputs/ground_truth/test_' + detector +
'_fold_' + str(fold) + '.csv')
gt_boxes = gt_df.iloc[:, 3:].values.reshape(len(gt_df), -1, 4)
aiou = utils.calc_aiou(gt_boxes, predictions)
fiou = utils.calc_fiou(gt_boxes, predictions)
print('AIOU: ', round(aiou * 100, 1))
print('FIOU: ', round(fiou * 100, 1))
print('Saving predictions to ./outputs/sted/test_' + detector +
'_fold_' + str(fold) + '.csv')
for i in range(1, 61):
results_df['x1_' + str(i)] = predictions[:, i - 1, 0]
results_df['y1_' + str(i)] = predictions[:, i - 1, 1]
results_df['x2_' + str(i)] = predictions[:, i - 1, 2]
results_df['y2_' + str(i)] = predictions[:, i - 1, 3]
results_df.to_csv('./outputs/sted/test_' + detector + '_fold_' +
str(fold) + '.csv',
fious = []
ades = []
fdes = []
for fold in [1, 2, 3]:
gt_df = pd.read_csv(args.gt + '/test_' + detector + '_fold_' +
str(fold) + '.csv')
pred_df = pd.read_csv(args.pred + '/test_' + detector + '_fold_' +
str(fold) + '.csv')
gt_boxes = gt_df[box_names].values.reshape(len(gt_df), -1, 4)
pred_boxes = pred_df[box_names].values.reshape(len(pred_df), -1, 4)
ade = utils.calc_ade(gt_boxes, pred_boxes)
fde = utils.calc_fde(gt_boxes, pred_boxes)
aiou = utils.calc_aiou(gt_boxes, pred_boxes)
fiou = utils.calc_fiou(gt_boxes, pred_boxes)
aious.append(aiou)
fious.append(fiou)
ades.append(ade)
fdes.append(fde)
print()
print(detector + ' fold ' + str(fold))
print('AIOU: ', round(aiou * 100, 1))
print('FIOU: ', round(fiou * 100, 1))
print('ADE: ', round(ade, 1))
print('FDE: ', round(fde, 1))
print()