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from __future__ import absolute_import |
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from __future__ import division |
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from __future__ import print_function |
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import numpy as np |
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from core.inference import get_max_preds |
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def calc_dists(preds, target, normalize): |
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preds = preds.astype(np.float32) |
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target = target.astype(np.float32) |
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dists = np.zeros((preds.shape[1], preds.shape[0])) |
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for n in range(preds.shape[0]): |
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for c in range(preds.shape[1]): |
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if target[n, c, 0] > 1 and target[n, c, 1] > 1: |
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normed_preds = preds[n, c, :] / normalize[n] |
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normed_targets = target[n, c, :] / normalize[n] |
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dists[c, n] = np.linalg.norm(normed_preds - normed_targets) |
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else: |
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dists[c, n] = -1 |
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return dists |
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def dist_acc(dists, thr=0.5): |
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''' Return percentage below threshold while ignoring values with a -1 ''' |
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dist_cal = np.not_equal(dists, -1) |
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num_dist_cal = dist_cal.sum() |
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if num_dist_cal > 0: |
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return np.less(dists[dist_cal], thr).sum() * 1.0 / num_dist_cal |
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else: |
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return -1 |
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def accuracy(output, target, hm_type='gaussian', thr=0.5): |
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''' |
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Calculate accuracy according to PCK, |
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but uses ground truth heatmap rather than x,y locations |
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First value to be returned is average accuracy across 'idxs', |
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followed by individual accuracies |
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''' |
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idx = list(range(output.shape[1])) |
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norm = 1.0 |
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if hm_type == 'gaussian': |
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pred, _ = get_max_preds(output) |
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target, _ = get_max_preds(target) |
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h = output.shape[2] |
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w = output.shape[3] |
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norm = np.ones((pred.shape[0], 2)) * np.array([h, w]) / 10 |
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dists = calc_dists(pred, target, norm) |
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acc = np.zeros((len(idx) + 1)) |
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avg_acc = 0 |
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cnt = 0 |
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for i in range(len(idx)): |
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acc[i + 1] = dist_acc(dists[idx[i]]) |
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if acc[i + 1] >= 0: |
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avg_acc = avg_acc + acc[i + 1] |
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cnt += 1 |
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avg_acc = avg_acc / cnt if cnt != 0 else 0 |
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if cnt != 0: |
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acc[0] = avg_acc |
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return acc, avg_acc, cnt, pred |
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