ScoNet_V1

opengait/evaluation/evaluator.py

@@ -5,7 +5,7 @@ from utils import get_msg_mgr, mkdir
 
 from .metric import mean_iou, cuda_dist, compute_ACC_mAP, evaluate_rank, evaluate_many
 from .re_rank import re_ranking
-
+from sklearn.metrics import confusion_matrix, accuracy_score
 
 def de_diag(acc, each_angle=False):
     # Exclude identical-view cases
@@ -415,3 +415,45 @@ def evaluate_CCPG(data, dataset, metric='euc'):
         msg_mgr.log_info('DN: {}'.format(de_diag(acc[2, :, :, i], True)))
         msg_mgr.log_info('BG: {}'.format(de_diag(acc[3, :, :, i], True)))
     return result_dict
+
+def evaluate_scoliosis(data, dataset, metric='euc'):
+    msg_mgr = get_msg_mgr()
+
+    feature, label, class_id, view = data['embeddings'], data['labels'], data['types'], data['views']
+
+    label = np.array(label)
+    class_id = np.array(class_id)
+
+    # Update class_id with integer labels based on status
+    class_id_int = np.array([1 if status == 'positive' else 2 if status == 'critical' else 0 for status in class_id])
+    print('class_id=', class_id_int)
+
+    features = np.array(feature)
+    c_id_int = np.argmax(features.mean(-1), axis=-1)
+    print('predicted_labels', c_id_int)
+
+    # Calculate sensitivity and specificity
+    cm = confusion_matrix(class_id_int, c_id_int, labels=[0, 1, 2])
+    FP = cm.sum(axis=0) - np.diag(cm)
+    FN = cm.sum(axis=1) - np.diag(cm)
+    TP = np.diag(cm)
+    TN = cm.sum() - (FP + FN + TP)
+
+    # Sensitivity, hit rate, recall, or true positive rate
+    TPR = TP / (TP + FN)
+    # Specificity or true negative rate
+    TNR = TN / (TN + FP)
+    accuracy = accuracy_score(class_id_int, c_id_int)
+
+    result_dict = {}
+    result_dict["scalar/test_accuracy/"] = accuracy
+    result_dict["scalar/test_sensitivity/"] = TPR
+    result_dict["scalar/test_specificity/"] = TNR
+
+    # Printing the sensitivity and specificity
+    for i, cls in enumerate(['Positive']):
+        print(f"{cls} Sensitivity (Recall): {TPR[i] * 100:.2f}%")
+        print(f"{cls} Specificity: {TNR[i] * 100:.2f}%")
+    print(f"Accuracy: {accuracy * 100:.2f}%")
+
+    return result_dict