Merge pull request #229 from zhouzi180/add_sconet

Update Scoliosis1K dataset

README.md

@@ -34,7 +34,7 @@ Our team's latest checkpoints for projects such as DeepGaitv2, SkeletonGait, Ske
 - [Mar 2022] Dataset [GREW](https://www.grew-benchmark.org) is supported in [datasets/GREW](./datasets/GREW). -->
 
 ## Our Publications
-- [**MICCAI'24**] Gait Patterns as Biomarkers: A Video-Based Approach for Classifying Scoliosis, [*Paper*](https://zhouzi180.github.io/Scoliosis1K)(Coming soon), [*Dataset*](https://zhouzi180.github.io/Scoliosis1K)(Coming soon), and [*Code*](opengait/modeling/models/sconet.py).
+- [**MICCAI'24**] Gait Patterns as Biomarkers: A Video-Based Approach for Classifying Scoliosis, [*Paper*](https://arxiv.org/pdf/2407.05726), [*Dataset*](https://zhouzi180.github.io/Scoliosis1K), and [*Code*](opengait/modeling/models/sconet.py).
 - [**CVPR'24**] BigGait: Learning Gait Representation You Want by Large Vision Models. [*Paper*](https://arxiv.org/pdf/2402.19122.pdf), and [*Code*](opengait/modeling/models/BigGait.py). 
 - [**AAAI'24**] SkeletonGait++: Gait Recognition Using Skeleton Maps. [*Paper*](https://arxiv.org/pdf/2311.13444.pdf), and [*Code*](opengait/modeling/models/skeletongait%2B%2B.py).
 - [**AAAI'24**] Cross-Covariate Gait Recognition: A Benchmark. [*Paper*](https://arxiv.org/pdf/2312.14404.pdf), [*Dataset*](https://github.com/ShinanZou/CCGR), and [*Code*](https://github.com/ShiqiYu/OpenGait/blob/master/opengait/modeling/models/deepgaitv2.py).

opengait/evaluation/evaluator.py

@@ -425,7 +425,7 @@ def evaluate_scoliosis(data, dataset, metric='euc'):
     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])
+    class_id_int = np.array([1 if status == 'positive' else 2 if status == 'neutral' else 0 for status in class_id])
     print('class_id=', class_id_int)
 
     features = np.array(feature)

opengait/modeling/models/sconet.py

@@ -18,7 +18,7 @@ class ScoNet(BaseModel):
     def forward(self, inputs):
         ipts, labs, class_id, _, seqL = inputs
 
-        class_id_int = np.array([1 if status == 'positive' else 2 if status == 'critical' else 0 for status in class_id])
+        class_id_int = np.array([1 if status == 'positive' else 2 if status == 'neutral' else 0 for status in class_id])
         class_id = torch.tensor(class_id_int).cuda()
 
         sils = ipts[0]