isortize

2/1.py

@@ -1,6 +1,6 @@
-import numpy as np
 import cv2 as cv
 import matplotlib.pyplot as plt
+import numpy as np
 
 
 def global_linear_transmation(im, c=0, d=255):

2/2.py

@@ -1,6 +1,6 @@
-import numpy as np
 import cv2 as cv
 import matplotlib.pyplot as plt
+import numpy as np
 
 
 def gamma_trans(img, gamma=1.0):

2/4.py

@@ -1,7 +1,8 @@
 import random as rd
-import numpy as np
+
 import cv2 as cv
 import matplotlib.pyplot as plt
+import numpy as np
 
 
 def addSaltAndPepper(src, percentage):

2/5.py

@@ -1,6 +1,6 @@
 import cv2 as cv
-from matplotlib import pyplot as plt
 import numpy as np
+from matplotlib import pyplot as plt
 
 if __name__ == "__main__":
     img = cv.imread(r"img\iris.jpg", 0)

3/2.py

@@ -1,8 +1,9 @@
-import cv2 as cv
-import numpy as np
-from math import *  # type: ignore
 import random
+from math import *  # type: ignore
+
+import cv2 as cv
 import matplotlib.pyplot as plt
+import numpy as np
 
 plt.rcParams["font.sans-serif"] = ["SimSun"]
 plt.rcParams["axes.unicode_minus"] = False

3/3.py

@@ -1,8 +1,9 @@
-import cv2 as cv
-import numpy as np
-from math import *  # type: ignore
 import random
+from math import *  # type: ignore
+
+import cv2 as cv
 import matplotlib.pyplot as plt
+import numpy as np
 
 plt.rcParams["font.sans-serif"] = ["SimSun"]
 plt.rcParams["axes.unicode_minus"] = False

3/4.py

@@ -1,6 +1,6 @@
 import cv2 as cv
-import numpy as np
 import matplotlib.pyplot as plt
+import numpy as np
 
 img = cv.imread(r"img\peppers.bmp", 0)
 m, n = img.shape

7/1.py

@@ -1,6 +1,6 @@
 import cv2 as cv
-import numpy as np
 import matplotlib.pyplot as plt
+import numpy as np
 
 plt.rcParams["font.sans-serif"] = ["SimSun"]
 

7/2.py

@@ -1,6 +1,6 @@
 import cv2 as cv
-import numpy as np
 import matplotlib.pyplot as plt
+import numpy as np
 
 plt.rcParams["font.sans-serif"] = ["SimSun"]
 

8/1.py

@@ -1,9 +1,12 @@
-from sklearn import datasets
+import math
-from skimage.feature import hog
+import os
-from sklearn.svm import LinearSVC
+import struct
-import numpy as np
+
-import os, math, cv2, struct
 import matplotlib.pyplot as plt
+import numpy as np
+from skimage.feature import hog
+from sklearn import datasets
+from sklearn.svm import LinearSVC
 
 plt.rcParams["font.sans-serif"] = ["Times New Roman"]
 plt.rcParams["axes.unicode_minus"] = False
@@ -394,8 +397,8 @@ print(metrics.classification_report(testlabels, test_est))
 
 print(testlabels.shape)
 
-from sklearn.svm import LinearSVC
 from sklearn.ensemble import RandomForestClassifier
+from sklearn.svm import LinearSVC
 
 rf_model = RandomForestClassifier()
 rf_model.fit(hog_features, labels)

8/2/main.py

@@ -1,30 +1,31 @@
 # -*- coding: UTF-8 -*-
-from globalObject import *
+import os
+import time
 import tkinter
-from tkinter.simpledialog import askinteger, askfloat, askstring
 from tkinter.filedialog import (
+    askdirectory,
     askopenfilename,
     askopenfilenames,
     asksaveasfilename,
-    askdirectory,
 )
-from tkinter.messagebox import showinfo, showwarning, showerror, askyesno
+from tkinter.messagebox import askyesno, showerror, showinfo, showwarning
-from PIL import Image, ImageTk
+from tkinter.simpledialog import askfloat, askinteger, askstring
-import os
+
-import time
+import carLicense.predict as predict
-from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
+import description
-import PointProcessing
-import Histogram
-import SpatialFilter
 import Fourier
 import geometric
-import spatialRestore
+import Histogram
-import Restore
-import morphology
-import segmentation
-import description
-import carLicense.predict as predict
 import matplotlib.pyplot as plt
+import morphology
+import PointProcessing
+import Restore
+import segmentation
+import SpatialFilter
+import spatialRestore
+from globalObject import *
+from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
+from PIL import Image, ImageTk
 
 plt.rcParams["font.sans-serif"] = ["SimHei"]  # 用来正常显示中文标签
 plt.rcParams["axes.unicode_minus"] = False  # 用来正常显示负