- Modify some examples.

- Rename the folder `tests` to `examples` to avoid misleading.
- Update the `readme.md`

examples/metric_recorder.py

@@ -0,0 +1,106 @@
+# -*- coding: utf-8 -*-
+# @Time    : 2021/1/4
+# @Author  : Lart Pang
+# @GitHub  : https://github.com/lartpang
+
+import numpy as np
+
+from py_sod_metrics import Emeasure, Fmeasure, MAE, Smeasure, WeightedFmeasure
+
+
+def ndarray_to_basetype(data):
+    """
+    将单独的ndarray，或者tuple，list或者dict中的ndarray转化为基本数据类型，
+    即列表(.tolist())和python标量
+    """
+
+    def _to_list_or_scalar(item):
+        listed_item = item.tolist()
+        if isinstance(listed_item, list) and len(listed_item) == 1:
+            listed_item = listed_item[0]
+        return listed_item
+
+    if isinstance(data, (tuple, list)):
+        results = [_to_list_or_scalar(item) for item in data]
+    elif isinstance(data, dict):
+        results = {k: _to_list_or_scalar(item) for k, item in data.items()}
+    else:
+        assert isinstance(data, np.ndarray)
+        results = _to_list_or_scalar(data)
+    return results
+
+
+class CalTotalMetric(object):
+    def __init__(self):
+        """
+        用于统计各种指标的类
+        https://github.com/lartpang/Py-SOD-VOS-EvalToolkit/blob/81ce89da6813fdd3e22e3f20e3a09fe1e4a1a87c/utils/recorders/metric_recorder.py
+        """
+        self.mae = MAE()
+        self.fm = Fmeasure()
+        self.sm = Smeasure()
+        self.em = Emeasure()
+        self.wfm = WeightedFmeasure()
+
+    def step(self, pre: np.ndarray, gt: np.ndarray):
+        assert pre.shape == gt.shape
+        assert pre.dtype == np.uint8
+        assert gt.dtype == np.uint8
+
+        self.mae.step(pre, gt)
+        self.sm.step(pre, gt)
+        self.fm.step(pre, gt)
+        self.em.step(pre, gt)
+        self.wfm.step(pre, gt)
+
+    def get_results(self, num_bits: int = 3, return_ndarray: bool = False) -> dict:
+        """
+        返回指标计算结果：
+
+        - 曲线数据(sequential)： fm/em/p/r
+        - 数值指标(numerical)： SM/MAE/maxE/avgE/adpE/maxF/avgF/adpF/wFm
+        """
+        fm_info = self.fm.get_results()
+        fm = fm_info["fm"]
+        pr = fm_info["pr"]
+        wfm = self.wfm.get_results()["wfm"]
+        sm = self.sm.get_results()["sm"]
+        em = self.em.get_results()["em"]
+        mae = self.mae.get_results()["mae"]
+
+        sequential_results = {
+            "fm": np.flip(fm["curve"]),
+            "em": np.flip(em["curve"]),
+            "p": np.flip(pr["p"]),
+            "r": np.flip(pr["r"]),
+        }
+        numerical_results = {
+            "SM": sm,
+            "MAE": mae,
+            "maxE": em["curve"].max(),
+            "avgE": em["curve"].mean(),
+            "adpE": em["adp"],
+            "maxF": fm["curve"].max(),
+            "avgF": fm["curve"].mean(),
+            "adpF": fm["adp"],
+            "wFm": wfm,
+        }
+        if num_bits is not None and isinstance(num_bits, int):
+            numerical_results = {k: v.round(num_bits) for k, v in numerical_results.items()}
+        if not return_ndarray:
+            sequential_results = ndarray_to_basetype(sequential_results)
+            numerical_results = ndarray_to_basetype(numerical_results)
+        return {"sequential": sequential_results, "numerical": numerical_results}
+
+
+if __name__ == "__main__":
+    data_loader = ...
+    model = ...
+
+    cal_total_seg_metrics = CalTotalMetric()
+    for batch in data_loader:
+        seg_preds = model(batch)
+        for seg_pred in seg_preds:
+            mask_array = ...
+            cal_total_seg_metrics.step(seg_pred, mask_array)
+    fixed_seg_results = cal_total_seg_metrics.get_results()

examples/test_metrics.py

@@ -0,0 +1,53 @@
+# -*- coding: utf-8 -*-
+# @Time    : 2020/11/21
+# @Author  : Lart Pang
+# @GitHub  : https://github.com/lartpang
+
+import os
+
+import cv2
+from tqdm import tqdm
+
+# pip install pysodmetrics
+from py_sod_metrics import Emeasure, Fmeasure, MAE, Smeasure, WeightedFmeasure
+
+FM = Fmeasure()
+WFM = WeightedFmeasure()
+SM = Smeasure()
+EM = Emeasure()
+MAE = MAE()
+
+data_root = "./test_data"
+mask_root = os.path.join(data_root, "masks")
+pred_root = os.path.join(data_root, "preds")
+mask_name_list = sorted(os.listdir(mask_root))
+for mask_name in tqdm(mask_name_list, total=len(mask_name_list)):
+    mask_path = os.path.join(mask_root, mask_name)
+    pred_path = os.path.join(pred_root, mask_name)
+    mask = cv2.imread(mask_path, cv2.IMREAD_GRAYSCALE)
+    pred = cv2.imread(pred_path, cv2.IMREAD_GRAYSCALE)
+    FM.step(pred=pred, gt=mask)
+    WFM.step(pred=pred, gt=mask)
+    SM.step(pred=pred, gt=mask)
+    EM.step(pred=pred, gt=mask)
+    MAE.step(pred=pred, gt=mask)
+
+fm = FM.get_results()["fm"]
+wfm = WFM.get_results()["wfm"]
+sm = SM.get_results()["sm"]
+em = EM.get_results()["em"]
+mae = MAE.get_results()["mae"]
+
+results = {
+    "Smeasure": sm.round(3),
+    "wFmeasure": wfm.round(3),
+    "MAE": mae.round(3),
+    "adpEm": em["adp"].round(3),
+    "meanEm": em["curve"].mean().round(3),
+    "maxEm": em["curve"].max().round(3),
+    "adpFm": fm["adp"].round(3),
+    "meanFm": fm["curve"].mean().round(3),
+    "maxFm": fm["curve"].max().round(3),
+}
+
+print(results)

py_sod_metrics/__init__.py

@@ -0,0 +1,3 @@
+from py_sod_metrics.sod_metrics import *
+
+__version__ = "1.2.2"

py_sod_metrics/sod_metrics.py

@@ -1,8 +1,6 @@
 import numpy as np
 from scipy.ndimage import convolve, distance_transform_edt as bwdist
 
-__version__ = "1.2.1"
-
 _EPS = 1e-16
 _TYPE = np.float64
 
@@ -282,9 +280,9 @@ def cal_em_with_threshold(self, pred: np.ndarray, gt: np.ndarray, threshold: flo
             results_parts = []
             for i, (part_numel, combination) in enumerate(zip(parts_numel, combinations)):
                 align_matrix_value = (
-                    2
-                    * (combination[0] * combination[1])
-                    / (combination[0] ** 2 + combination[1] ** 2 + _EPS)
+                        2
+                        * (combination[0] * combination[1])
+                        / (combination[0] ** 2 + combination[1] ** 2 + _EPS)
                 )
                 enhanced_matrix_value = (align_matrix_value + 1) ** 2 / 4
                 results_parts.append(enhanced_matrix_value * part_numel)
@@ -324,9 +322,9 @@ def cal_em_with_cumsumhistogram(self, pred: np.ndarray, gt: np.ndarray) -> np.nd
             results_parts = np.empty(shape=(4, 256), dtype=np.float64)
             for i, (part_numel, combination) in enumerate(zip(parts_numel_w_thrs, combinations)):
                 align_matrix_value = (
-                    2
-                    * (combination[0] * combination[1])
-                    / (combination[0] ** 2 + combination[1] ** 2 + _EPS)
+                        2
+                        * (combination[0] * combination[1])
+                        / (combination[0] ** 2 + combination[1] ** 2 + _EPS)
                 )
                 enhanced_matrix_value = (align_matrix_value + 1) ** 2 / 4
                 results_parts[i] = enhanced_matrix_value * part_numel
@@ -336,7 +334,7 @@ def cal_em_with_cumsumhistogram(self, pred: np.ndarray, gt: np.ndarray) -> np.nd
         return em
 
     def generate_parts_numel_combinations(
-        self, fg_fg_numel, fg_bg_numel, pred_fg_numel, pred_bg_numel
+            self, fg_fg_numel, fg_bg_numel, pred_fg_numel, pred_bg_numel
     ):
         bg_fg_numel = self.gt_fg_numel - fg_fg_numel
         bg_bg_numel = pred_bg_numel - bg_fg_numel
@@ -428,7 +426,7 @@ def matlab_style_gauss2D(self, shape: tuple = (7, 7), sigma: int = 5) -> np.ndar
         fspecial('gaussian',[shape],[sigma])
         """
         m, n = [(ss - 1) / 2 for ss in shape]
-        y, x = np.ogrid[-m : m + 1, -n : n + 1]
+        y, x = np.ogrid[-m: m + 1, -n: n + 1]
         h = np.exp(-(x * x + y * y) / (2 * sigma * sigma))
         h[h < np.finfo(h.dtype).eps * h.max()] = 0
         sumh = h.sum()

readme_zh.md

@@ -50,8 +50,8 @@ pip install pysodmetrics
 
 ### 示例
 
-* <./tests/test_metrics.py>
-* <./tests/metric_recorder.py>
+* <examples/metric_recorder.py>
+* <examples/test_metrics.py>
 
 ## 感谢
 

setup.py

@@ -1,11 +1,17 @@
 from setuptools import setup, find_packages
+import py_sod_metrics as my_script
+
+with open("readme.md", "r") as fh:
+    long_description = fh.read()
 
 setup(
     name="pysodmetrics",
     packages=find_packages(),
-    version="1.2.1",
+    version=my_script.__version__,
     license="MIT",
     description="A simple and efficient implementation of SOD metrics.",
+    long_description=long_description,
+    long_description_content_type="text/markdown",
     author="lartpang",
     author_email="[email protected]",
     url="https://github.com/lartpang/PySODMetrics",