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+局部降噪B组
+
+conda env create --file environment.yml
+
+conda install --file requirements.txt -y
+
+pip install -e .
+
+测试方法：
+pytest --cov-report=html --cov=temporal_denoising --ignore=temporal_denoising.py test_denoising.py
+根据输出的html可知覆盖率达到100%
+不过依旧是个很简单的实现
+还有待进一步优化
+有一些备用图像可以测试看看
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diff --git a/spatial-temporal-denoising-B/temporal_denoising.py b/spatial-temporal-denoising-B/temporal_denoising.py
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+#coding:utf-8  
+#测试用库
+import unittest
+
+import sys,os  
+from PIL import Image,ImageDraw  
+
+#使用二值判断方法,如果确认是噪声,用该点周围灰度的均值进行替换  
+def getPixel(image,x,y,G,N):  
+    L = image.getpixel((x,y))  
+    if L > G:  
+        L = True  
+    else:  
+        L = False  
+    
+    nearDots = 0  
+    if L == (image.getpixel((x - 1,y - 1)) > G):  
+        nearDots += 1  
+    if L == (image.getpixel((x - 1,y)) > G):  
+        nearDots += 1  
+    if L == (image.getpixel((x - 1,y + 1)) > G):  
+        nearDots += 1  
+    if L == (image.getpixel((x,y - 1)) > G):  
+        nearDots += 1  
+    if L == (image.getpixel((x,y + 1)) > G):  
+        nearDots += 1  
+    if L == (image.getpixel((x + 1,y - 1)) > G):  
+        nearDots += 1  
+    if L == (image.getpixel((x + 1,y)) > G):  
+        nearDots += 1  
+    if L == (image.getpixel((x + 1,y + 1)) > G):  
+        nearDots += 1  
+    if nearDots < N:  
+        a = (image.getpixel((x - 1,y - 1)) +
+        image.getpixel((x - 1,y)) +
+        image.getpixel((x - 1,y + 1)) +
+        image.getpixel((x,y - 1)) +
+        image.getpixel((x,y + 1)) +
+        image.getpixel((x + 1,y - 1)) +
+        image.getpixel((x + 1,y)) +
+        image.getpixel((x + 1,y + 1)))/8
+        return int(a)
+#        return image.getpixel((x,y-1))
+    else:  
+        return None  
+  
+# 根据一个点A的灰度值，与周围的8个点的灰度值比较，设定一个值N（0 <N <8），当A的灰度值与周围8个点的灰度相等数小于N时，此点为噪点   
+# G:图像二值化阀值   
+# N:降噪率 0 <N <8   
+# Z:降噪次数   
+
+def deNoise(image,G,N,Z):  
+    draw = ImageDraw.Draw(image)  
+    for i in range(0,Z):  
+        for x in range(1,image.size[0] - 1):  
+            for y in range(1,image.size[1] - 1):  
+                color = getPixel(image,x,y,G,N)  
+                if color != None:  
+                    draw.point((x,y),color)  
+
+# 测试段1
+def main():  
+    
+    #打开图片  
+    image = Image.open("./source.jpg")  
+
+    #将图片转换成灰度图片  
+    image1 = image.convert("L")  
+    
+    #去噪,G = 50,N = 4,Z = 4
+    deNoise(image1,50,4,4)
+
+    #保存图片  
+    image1.save("./result.jpg")  
+    print(image.size[0])
+    print(image.size[1])
+    print(image1.size[0])
+    print(image1.size[1])
+    print("done")
+    
diff --git a/spatial-temporal-denoising-B/test.jpg b/spatial-temporal-denoising-B/test.jpg
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diff --git a/spatial-temporal-denoising-B/test_denoising.py b/spatial-temporal-denoising-B/test_denoising.py
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+#测试用库
+import unittest
+
+import temporal_denoising as td
+import sys,os  
+from PIL import Image,ImageDraw  
+
+#测试代码
+class Test_denoising(unittest.TestCase):
+    def test_denoising(self):
+        #先按照源代码自带的main函数生成一个结果图像result.jpg
+        td.main()
+        #测试段2：再次生成一个图像test.jpg以供比较
+        image_source = Image.open("./source.jpg")
+        image_source = image_source.convert("L")
+        image_result = Image.open("./result.jpg")
+        image_result = image_result.convert("L")
+        td.deNoise(image_source,50,4,4)
+        image_source.save("./test.jpg")
+        image_test = Image.open("./test.jpg")
+        for x in range(1,image_source.size[0] - 1):  
+            for y in range(1,image_source.size[1] - 1):  
+                self.assertEqual(image_test.getpixel((x,y)),image_result.getpixel((x,y)))
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