File size: 2,585 Bytes
fb24bef | 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 70 71 72 | import os
import numpy as np
from .metrics import DIR_FAR
def evaluate(
all_features,
image_paths,
meta,
ranks=[1, 5, 20]
):
evaluator = TinyFaceTest(meta)
results = evaluator.test_identification(all_features, image_paths, ranks)
results = {k: v for k, v in zip(['rank-{}'.format(r) for r in ranks], results)}
results = {k: v * 100 for k, v in results.items()}
return results
class TinyFaceTest:
def __init__(self, meta):
self.meta = meta
def get_key(self, image_path):
return os.path.splitext(os.path.basename(image_path))[0]
def get_label(self, image_path):
return int(os.path.basename(image_path).split('_')[0])
def init_proto(self, image_paths, probe_paths, match_paths, distractor_paths):
index_dict = {}
for i, image_path in enumerate(image_paths):
index_dict[self.get_key(image_path)] = i
self.indices_probe = np.array([index_dict[self.get_key(img)] for img in probe_paths])
self.indices_match = np.array([index_dict[self.get_key(img)] for img in match_paths])
self.indices_distractor = np.array([index_dict[self.get_key(img)] for img in distractor_paths])
self.labels_probe = np.array([self.get_label(img) for img in probe_paths])
self.labels_match = np.array([self.get_label(img) for img in match_paths])
self.labels_distractor = np.array([-100 for img in distractor_paths])
self.indices_gallery = np.concatenate([self.indices_match, self.indices_distractor])
self.labels_gallery = np.concatenate([self.labels_match, self.labels_distractor])
def test_identification(self, features, image_paths, ranks=[1, 5, 20]):
assert len(image_paths) == len(features)
assert len(image_paths) == len(self.meta['image_paths'])
self.init_proto(image_paths,
self.meta['probe_paths'],
self.meta['gallery_paths'],
self.meta['distractor_paths'])
feat_probe = features[self.indices_probe]
feat_gallery = features[self.indices_gallery]
compare_func = inner_product
score_mat = compare_func(feat_probe, feat_gallery)
label_mat = self.labels_probe[:, None] == self.labels_gallery[None, :]
results, _, __ = DIR_FAR(score_mat, label_mat, ranks)
return results
def inner_product(x1, x2):
# normalize
x1 = x1 / np.linalg.norm(x1, axis=1, keepdims=True)
x2 = x2 / np.linalg.norm(x2, axis=1, keepdims=True)
return np.dot(x1, x2.T)
|