support CCPG

opengait/evaluation/metric.py

@@ -156,3 +156,66 @@ def evaluate_rank(distmat, p_lbls, g_lbls, max_rank=50):
     all_cmc = all_cmc.sum(0) / num_valid_p
 
     return all_cmc, all_AP, all_INP
+
+
+def evaluate_many(distmat, q_pids, g_pids, q_camids, g_camids, max_rank=50):
+    num_q, num_g = distmat.shape
+    if num_g < max_rank:
+        max_rank = num_g
+        print("Note: number of gallery samples is quite small, got {}".format(num_g))
+    indices = np.argsort(distmat, axis=1)   # 对应位置变成从小到大的序号
+    matches = (g_pids[indices] == q_pids[:, np.newaxis]).astype(
+        np.int32)  # 根据indices调整顺序 g_pids[indices]
+    # print(matches)
+
+    # compute cmc curve for each query
+    all_cmc = []
+    all_AP = []
+    all_INP = []
+    num_valid_q = 0.
+    for q_idx in range(num_q):
+        # get query pid and camid
+        q_pid = q_pids[q_idx]
+        q_camid = q_camids[q_idx]
+
+        # remove gallery samples that have the same pid and camid with query
+        order = indices[q_idx]
+        remove = (g_pids[order] == q_pid) & (g_camids[order] == q_camid)
+        keep = np.invert(remove)
+
+        # compute cmc curve
+        # binary vector, positions with value 1 are correct matches
+        orig_cmc = matches[q_idx][keep]
+        if not np.any(orig_cmc):
+            # this condition is true when query identity does not appear in gallery
+            continue
+
+        cmc = orig_cmc.cumsum()
+
+        pos_idx = np.where(orig_cmc == 1)
+        max_pos_idx = np.max(pos_idx)
+        inp = cmc[max_pos_idx] / (max_pos_idx + 1.0)
+        all_INP.append(inp)
+
+        cmc[cmc > 1] = 1
+
+        all_cmc.append(cmc[:max_rank])
+        num_valid_q += 1.
+
+        # compute average precision
+        # reference: https://en.wikipedia.org/wiki/Evaluation_measures_(information_retrieval)#Average_precision
+        num_rel = orig_cmc.sum()
+        tmp_cmc = orig_cmc.cumsum()
+        tmp_cmc = [x / (i+1.) for i, x in enumerate(tmp_cmc)]
+        tmp_cmc = np.asarray(tmp_cmc) * orig_cmc
+        AP = tmp_cmc.sum() / num_rel
+        all_AP.append(AP)
+
+    assert num_valid_q > 0, "Error: all query identities do not appear in gallery"
+
+    all_cmc = np.asarray(all_cmc).astype(np.float32)
+    all_cmc = all_cmc.sum(0) / num_valid_q
+    mAP = np.mean(all_AP)
+    mINP = np.mean(all_INP)
+
+    return all_cmc, mAP, mINP