diff --git a/examples/metric_recorder.py b/examples/metric_recorder.py
new file mode 100644
index 0000000..15ad3b5
--- /dev/null
+++ b/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()
diff --git a/tests/test_data/masks/0001.png b/examples/test_data/masks/0001.png
similarity index 100%
rename from tests/test_data/masks/0001.png
rename to examples/test_data/masks/0001.png
diff --git a/tests/test_data/masks/19.png b/examples/test_data/masks/19.png
similarity index 100%
rename from tests/test_data/masks/19.png
rename to examples/test_data/masks/19.png
diff --git a/tests/test_data/masks/aerial-1867541__340.png b/examples/test_data/masks/aerial-1867541__340.png
similarity index 100%
rename from tests/test_data/masks/aerial-1867541__340.png
rename to examples/test_data/masks/aerial-1867541__340.png
diff --git a/tests/test_data/preds/0001.png b/examples/test_data/preds/0001.png
similarity index 100%
rename from tests/test_data/preds/0001.png
rename to examples/test_data/preds/0001.png
diff --git a/tests/test_data/preds/19.png b/examples/test_data/preds/19.png
similarity index 100%
rename from tests/test_data/preds/19.png
rename to examples/test_data/preds/19.png
diff --git a/tests/test_data/preds/aerial-1867541__340.png b/examples/test_data/preds/aerial-1867541__340.png
similarity index 100%
rename from tests/test_data/preds/aerial-1867541__340.png
rename to examples/test_data/preds/aerial-1867541__340.png
diff --git a/tests/test_data/readme.md b/examples/test_data/readme.md
similarity index 100%
rename from tests/test_data/readme.md
rename to examples/test_data/readme.md
diff --git a/examples/test_metrics.py b/examples/test_metrics.py
new file mode 100644
index 0000000..0758502
--- /dev/null
+++ b/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)
diff --git a/py_sod_metrics/__init__.py b/py_sod_metrics/__init__.py
index e69de29..b6cec16 100755
--- a/py_sod_metrics/__init__.py
+++ b/py_sod_metrics/__init__.py
@@ -0,0 +1,3 @@
+from py_sod_metrics.sod_metrics import *
+
+__version__ = "1.2.2"
diff --git a/py_sod_metrics/sod_metrics.py b/py_sod_metrics/sod_metrics.py
index bd26421..28399e9 100644
--- a/py_sod_metrics/sod_metrics.py
+++ b/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()
diff --git a/readme_zh.md b/readme_zh.md
index 3dbc1ca..79930a2 100644
--- a/readme_zh.md
+++ b/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
 
diff --git a/setup.py b/setup.py
index 8e78b26..098adae 100644
--- a/setup.py
+++ b/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="lartpang@gmail.com",
     url="https://github.com/lartpang/PySODMetrics",
diff --git a/tests/metric_recorder.py b/tests/metric_recorder.py
deleted file mode 100644
index 619aaf5..0000000
--- a/tests/metric_recorder.py
+++ /dev/null
@@ -1,58 +0,0 @@
-# -*- coding: utf-8 -*-
-# @Time    : 2021/3/5
-# @Author  : Lart Pang
-# @GitHub  : https://github.com/lartpang
-
-
-import numpy as np
-
-from py_sod_metrics.sod_metrics import Emeasure, Fmeasure, MAE, Smeasure, WeightedFmeasure
-
-
-class CalTotalMetric(object):
-    def __init__(self):
-        self.cal_mae = MAE()
-        self.cal_fm = Fmeasure()
-        self.cal_sm = Smeasure()
-        self.cal_em = Emeasure()
-        self.cal_wfm = WeightedFmeasure()
-
-    def step(self, pred: np.ndarray, gt: np.ndarray, gt_path: str):
-        assert pred.ndim == gt.ndim and pred.shape == gt.shape, (pred.shape, gt.shape, gt_path)
-        assert pred.dtype == np.uint8, pred.dtype
-        assert gt.dtype == np.uint8, gt.dtype
-
-        self.cal_mae.step(pred, gt)
-        self.cal_fm.step(pred, gt)
-        self.cal_sm.step(pred, gt)
-        self.cal_em.step(pred, gt)
-        self.cal_wfm.step(pred, gt)
-
-    def get_results(self, bit_width: int = 3) -> dict:
-        fm = self.cal_fm.get_results()["fm"]
-        wfm = self.cal_wfm.get_results()["wfm"]
-        sm = self.cal_sm.get_results()["sm"]
-        em = self.cal_em.get_results()["em"]
-        mae = self.cal_mae.get_results()["mae"]
-        results = {
-            "Smeasure": sm,
-            "wFmeasure": wfm,
-            "MAE": mae,
-            "adpEm": em["adp"],
-            "meanEm": em["curve"].mean(),
-            "maxEm": em["curve"].max(),
-            "adpFm": fm["adp"],
-            "meanFm": fm["curve"].mean(),
-            "maxFm": fm["curve"].max(),
-        }
-        results = {name: metric.round(bit_width) for name, metric in results.items()}
-        return results
-
-
-if __name__ == '__main__':
-    cal_total_seg_metrics = CalTotalMetric()
-    for batch in data_loader:
-        seg_preds = model(batch)
-        for seg_pred in seg_preds:
-            cal_total_seg_metrics.step(seg_pred, mask_array, mask_path)
-    fixed_seg_results = cal_total_seg_metrics.get_results()
diff --git a/tests/test_metrics.py b/tests/test_metrics.py
deleted file mode 100644
index 4854d3f..0000000
--- a/tests/test_metrics.py
+++ /dev/null
@@ -1,54 +0,0 @@
-# -*- coding: utf-8 -*-
-# @Time    : 2020/11/21
-# @Author  : Lart Pang
-# @GitHub  : https://github.com/lartpang
-
-import os
-import sys
-
-import cv2
-from tqdm import tqdm
-
-sys.path.append('../')
-
-import py_sod_metrics.sod_metrics as M
-
-FM = M.Fmeasure()
-WFM = M.WeightedFmeasure()
-SM = M.Smeasure()
-EM = M.Emeasure()
-MAE = M.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']
-
-print(
-    'Smeasure:', sm.round(3), '; ',
-    'wFmeasure:', wfm.round(3), '; ',
-    'MAE:', mae.round(3), '; ',
-    'adpEm:', em['adp'].round(3), '; ',
-    'meanEm:', '-' if em['curve'] is None else em['curve'].mean().round(3), '; ',
-    'maxEm:', '-' if em['curve'] is None else em['curve'].max().round(3), '; ',
-    'adpFm:', fm['adp'].round(3), '; ',
-    'meanFm:', fm['curve'].mean().round(3), '; ',
-    'maxFm:', fm['curve'].max().round(3),
-    sep=''
-)
diff --git a/tests/test_speed_for_count_nonzero.py b/tests/test_speed_for_count_nonzero.py
deleted file mode 100644
index 92dc9d1..0000000
--- a/tests/test_speed_for_count_nonzero.py
+++ /dev/null
@@ -1,26 +0,0 @@
-# -*- coding: utf-8 -*-
-# @Time    : 2020/11/27
-# @Author  : Lart Pang
-# @GitHub  : https://github.com/lartpang
-import time
-
-import numpy as np
-
-
-# 快速统计numpy数组的非零值建议使用np.count_nonzero，一个简单的小实验
-def cal_nonzero(size):
-    a = np.random.randn(size, size)
-    a = a > 0
-    start = time.time()
-    print(np.count_nonzero(a), time.time() - start)
-    start = time.time()
-    print(np.sum(a), time.time() - start)
-    start = time.time()
-    print(len(np.nonzero(a)[0]), time.time() - start)
-
-
-if __name__ == '__main__':
-    cal_nonzero(1000)
-    # 499950 6.723403930664062e-05
-    # 499950 0.0006949901580810547
-    # 499950 0.007088184356689453
diff --git a/tests/test_speed_for_ops.py b/tests/test_speed_for_ops.py
deleted file mode 100644
index 6594e19..0000000
--- a/tests/test_speed_for_ops.py
+++ /dev/null
@@ -1,48 +0,0 @@
-# -*- coding: utf-8 -*-
-# @Time    : 2020/11/27
-# @Author  : Lart Pang
-# @GitHub  : https://github.com/lartpang
-import time
-
-import numpy as np
-
-
-# TPs = fg_w_thrs.copy()
-# Ps = (fg_w_thrs + bg_w_thrs).copy()
-# T = np.count_nonzero(gt)
-# Ps[Ps == 0] = 1
-# if T == 0:
-#     T = 1
-# 0.06196483850479126
-def ori_process_for_copyop(fg_w_thrs, bg_w_thrs, gt):
-    TPs = fg_w_thrs.copy()
-    Ps = (fg_w_thrs + bg_w_thrs).copy()
-    T = np.count_nonzero(gt)
-    Ps[Ps == 0] = 1
-    if T == 0:
-        T = 1
-
-
-# 0.060863380432128904
-def new_process_for_copyop(fg_w_thrs, bg_w_thrs, gt):
-    TPs = fg_w_thrs
-    Ps = fg_w_thrs + bg_w_thrs
-    T = max(np.count_nonzero(gt), 1)
-    Ps[Ps == 0] = 1
-
-
-if __name__ == '__main__':
-    size = 1000
-    fg_w_thrs = np.random.randn(size, size) > 0
-    bg_w_thrs = np.random.randn(size, size) < 0
-    gt = np.random.randn(size, size) > 0
-    start = time.time()
-    for _ in range(1000):
-        ori_process_for_copyop(fg_w_thrs=fg_w_thrs, bg_w_thrs=bg_w_thrs, gt=gt)
-    print((time.time() - start) / 100)
-    start = time.time()
-    for _ in range(1000):
-        new_process_for_copyop(fg_w_thrs=fg_w_thrs, bg_w_thrs=bg_w_thrs, gt=gt)
-    print((time.time() - start) / 100)
-    # 0.042382607460021975
-    # 0.040832839012145995