传统cv算法python

# ```python #图像读取方式1 import matplotlib matplotlib.use('TkAgg') import matplotlib.pyplot as plt import cv2 img_cv=cv2.imread(r"1.jpg") print("imgh_cv:",img_cv.shape) # 第一种cv画的就是原图，不用转换 # 创建窗口，显示图像，窗口名称为src_image cv2.imshow("src_image",img_cv) # 等待任意按键按下，保持图片始终显示 cv2.waitKey(0) # 关闭所有的窗口 cv2.destroyAllWindows() # 显示可以使用cv也可以plt，plt要先转换成rgb才能画出原图 img = cv2.cvtColor(img_cv, cv2.COLOR_BGR2RGB) plt.imshow(img) plt.show() #图像读取方式2 # import matplotlib # matplotlib.use('TkAgg') # import matplotlib.pyplot as plt # import cv2 # from PIL import Image # import numpy as np # img_PIL=Image.open(r"1.jpg") # print("img_PIL",img_PIL) # print("img_PIL:",type(img_PIL)) # img_PIL=np.array(img_PIL) # print("img_PIL:",img_PIL.shape) # print("img_PIL:",type(img_PIL)) # plt.imshow(img_PIL) # plt.show() # # #图像读取方式3 # import matplotlib # matplotlib.use('TkAgg') # import matplotlib.pyplot as plt # # from keras.preproecssing.image import array_to_img , img_to_array # # load_imgimg_keras = load_img(r"1.jpg") # # import skimage.io as io #pytorch的框架 # img_io=io.imread(r"1.jpg") # print("img_io:",img_io.shape) # plt.imshow(img_io) # plt.show() # # #图像读取方式4 # import matplotlib # matplotlib.use('TkAgg') # import matplotlib.pyplot as plt # import matplotlib.image as mpig # img_mpig=mpig.imread(r"1.jpg") # print("img_mpig:",img_mpig.shape) # plt.imshow(img_mpig) # plt.show() # # #图像读取方式5 # import matplotlib # matplotlib.use('TkAgg') # import matplotlib.pyplot as plt # img_plt = plt.imread(r"1.jpg") # print("img_plt:",img_plt.shape) # plt.imshow(img_plt,cmap=plt.cm.binary) # plt.show() # # plt.imshow(img_plt[:,:,0], cmap=plt.cm.binary) # plt.show() # # # 图像读取方式6 # import matplotlib # matplotlib.use('TkAgg') # import matplotlib.pyplot as plt # digit = [[[130,26,33,12], # [14,27,43,140], # [120,124,134,205]], # # [[15,26, 33, 12], # [14, 27, 50, 190], # [120, 124, 134, 25]], # # [[135,26,33,12], # [14,27,43,10], # [20,12,134,205]], # ] # plt.imshow(digit,cmap=plt.cm.binary) # plt.show()