update metric

ShinkaiZ · Aug 23, 2022 · 6f99ac7 · 6f99ac7
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diff --git a/docs/AWESOME_VPS.md b/docs/AWESOME_VPS.md
@@ -47,6 +47,10 @@ In addition, we provide links to each paper and its repository whenever possible
 
 **Yr.** | **Pub.** | **Title** | **Links** 
 :-: | :-: | :-  | :-:
+2022 | **Gastroenterology Insights** | UPolySeg: A U-Net-Based Polyp Segmentation Network Using Colonoscopy Images | [Paper](https://www.mdpi.com/2036-7422/13/3/27/pdf?version=1660117945)/Code
+2022 | **Electronics** | A Segmentation Algorithm of Colonoscopy Images Based on Multi-Scale Feature Fusion | [Paper](https://www.mdpi.com/2079-9292/11/16/2501/pdf?version=1660199760)/Code
+2022 | **TCSVT** | Polyp-Mixer: An Efficient Context-Aware MLP-based Paradigm for Polyp Segmentation | [Paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9852486)/[Code](https://github.com/shijinghuihub/Polyp-Mixer)
+2022 | **TETCI** | Adaptive Context Exploration Network for Polyp Segmentation in Colonoscopy Images | [Paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9852746)/Code
 2022 | **IJCAI** | ICGNet: Integration Context-Based Reverse-Contour Guidance Network for Polyp Segmentation | [Paper](https://www.ijcai.org/proceedings/2022/0123.pdf)/Code
 2022 | **IJCAI** | TCCNet: Temporally Consistent Context-Free Network for Semi-supervised Video Polyp Segmentation | [Paper](https://www.ijcai.org/proceedings/2022/0155.pdf)/[Code](https://github.com/wener-yung/TCCNet)
 2022 | **AIM** | An end-to-end tracking method for polyp detectors in colonoscopy videos | [Paper](https://reader.elsevier.com/reader/sd/pii/S0933365722001270?token=85406D788AF1B59597BD0BCA3456A70DD2ECE3040EBCB1C3D669584B712F58FA656224415F80070007005E3D36B81F8D&originRegion=us-east-1&originCreation=20220801152429)/Code
@@ -178,6 +182,7 @@ In addition, we provide links to each paper and its repository whenever possible
 
 **Yr.** | **Pub.** | **Title** | **Links** 
 :-: | :-: | :-  | :-: 
+2022 | **IJCARS** | Positive-gradient-weighted object activation mapping: visual explanation of object detector towards precise colorectal-polyp localisation | [Paper](https://link.springer.com/content/pdf/10.1007/s11548-022-02696-y.pdf)/Code
 2022 | **arXiv** | Colonoscopy polyp detection with massive endoscopic images | [Paper](https://arxiv.org/pdf/2202.08730.pdf)/Code
 2021 | **arXiv** | Detecting, Localising and Classifying Polyps from Colonoscopy Videos using Deep Learning | [Paper](https://arxiv.org/pdf/2101.03285.pdf)/Code 
 2020 | **IEEE ISBI** | Reduce false-positive rate by active learning for automatic polyp detection in colonoscopy videos | [Paper](https://www.researchgate.net/profile/Zhe-Guo-12/publication/322563091_Automatic_polyp_recognition_from_colonoscopy_images_based_on_bag_of_visual_words/links/5f9b60a7299bf1b53e512f47/Automatic-polyp-recognition-from-colonoscopy-images-based-on-bag-of-visual-words.pdf)/Code 

diff --git a/eval/metrics.py b/eval/metrics.py
@@ -154,16 +154,21 @@ def region(self, pred: np.ndarray, gt: np.ndarray) -> float:
         return w1 * score1 + w2 * score2 + w3 * score3 + w4 * score4
 
     def centroid(self, matrix: np.ndarray) -> tuple:
+        """
+        To ensure consistency with the matlab code, one is added to the centroid coordinate,
+        so there is no need to use the redundant addition operation when dividing the region later,
+        because the sequence generated by ``1:X`` in matlab will contain ``X``.
+        :param matrix: a bool data array
+        :return: the centroid coordinate
+        """
         h, w = matrix.shape
-        if matrix.sum() == 0:
+        area_object = np.count_nonzero(matrix)
+        if area_object == 0:
             x = np.round(w / 2)
             y = np.round(h / 2)
         else:
-            area_object = np.sum(matrix)
-            row_ids = np.arange(h)
-            col_ids = np.arange(w)
-            x = np.round(np.sum(np.sum(matrix, axis=0) * col_ids) / area_object)
-            y = np.round(np.sum(np.sum(matrix, axis=1) * row_ids) / area_object)
+            # More details can be found at: https://www.yuque.com/lart/blog/gpbigm
+            y, x = np.argwhere(matrix).mean(axis=0).round()
         return int(x) + 1, int(y) + 1
 
     def divide_with_xy(self, pred: np.ndarray, gt: np.ndarray, x, y) -> dict: