Python list与NumPy array 区分详解
1.数据类型type()
#!/usr/bin/envpython #-*-coding:utf-8-*- #YongqiangCheng from__future__importabsolute_import from__future__importprint_function from__future__importdivision importos importsys sys.path.append(os.path.dirname(os.path.abspath(__file__))+'/..') current_directory=os.path.dirname(os.path.abspath(__file__)) importnumpyasnp #importtensorflowastf importcv2 importtime print(16*"++--") print("current_directory:",current_directory) PIXEL_MEAN=[123.68,116.779,103.939]#R,G,B.InTensorFlow,channelisRGB.InOpenCV,channelisBGR. print("Pythonlist") print("PIXEL_MEAN:",PIXEL_MEAN) print("type(PIXEL_MEAN):",type(PIXEL_MEAN)) print("type(PIXEL_MEAN[0]):",type(PIXEL_MEAN[0]),"\n") PIXEL_MEAN_array=np.array(PIXEL_MEAN) print("NumPyarray") print("PIXEL_MEAN_array:",PIXEL_MEAN_array) print("type(PIXEL_MEAN_array):",type(PIXEL_MEAN_array)) print("type(PIXEL_MEAN_array[0]):",type(PIXEL_MEAN_array[0])) print("PIXEL_MEAN_array.dtype:",PIXEL_MEAN_array.dtype)
/usr/bin/python2.7/home/strong/tensorflow_work/R2CNN_Faster-RCNN_Tensorflow/yongqiang.py--gpu=0 ++--++--++--++--++--++--++--++--++--++--++--++--++--++--++--++-- current_directory:/home/strong/tensorflow_work/R2CNN_Faster-RCNN_Tensorflow Pythonlist PIXEL_MEAN:[123.68,116.779,103.939] type(PIXEL_MEAN):type(PIXEL_MEAN[0]): NumPyarray PIXEL_MEAN_array:[123.68116.779103.939] type(PIXEL_MEAN_array): type(PIXEL_MEAN_array[0]): PIXEL_MEAN_array.dtype:float64 Processfinishedwithexitcode0
2.数据融合(datafusion)
#!/usr/bin/envpython #-*-coding:utf-8-*- #YongqiangCheng from__future__importabsolute_import from__future__importprint_function from__future__importdivision importos importsys sys.path.append(os.path.dirname(os.path.abspath(__file__))+'/..') current_directory=os.path.dirname(os.path.abspath(__file__)) importnumpyasnp #importtensorflowastf importcv2 importtime print(16*"++--") print("current_directory:",current_directory) PIXEL_MEAN=[123.68,116.779,103.939]#R,G,B.InTensorFlow,channelisRGB.InOpenCV,channelisBGR. print("Pythonlist") print("PIXEL_MEAN:",PIXEL_MEAN) print("type(PIXEL_MEAN):",type(PIXEL_MEAN)) print("type(PIXEL_MEAN[0]):",type(PIXEL_MEAN[0]),"\n") PIXEL_MEAN_array=np.array(PIXEL_MEAN) print("NumPyarray") print("PIXEL_MEAN_array:",PIXEL_MEAN_array) print("type(PIXEL_MEAN_array):",type(PIXEL_MEAN_array)) print("type(PIXEL_MEAN_array[0]):",type(PIXEL_MEAN_array[0])) print("PIXEL_MEAN_array.dtype:",PIXEL_MEAN_array.dtype,"\n") image_array=np.array( [[[1,2,3],[4,5,6],[7,8,9],[10,11,12]],[[21,22,23],[24,25,26],[27,28,29],[30,31,32]]]) print("image_array:",image_array) print("type(image_array):",type(image_array)) print("type(image_array[0]):",type(image_array[0])) print("image_array.dtype:",image_array.dtype,"\n") image_array_fusion=image_array+np.array(PIXEL_MEAN) print("image_array_fusion:",image_array_fusion) print("type(image_array_fusion):",type(image_array_fusion)) print("type(image_array_fusion[0]):",type(image_array_fusion[0])) print("image_array_fusion.dtype:",image_array_fusion.dtype)
/usr/bin/python2.7/home/strong/tensorflow_work/R2CNN_Faster-RCNN_Tensorflow/yongqiang.py--gpu=0 ++--++--++--++--++--++--++--++--++--++--++--++--++--++--++--++-- current_directory:/home/strong/tensorflow_work/R2CNN_Faster-RCNN_Tensorflow Pythonlist PIXEL_MEAN:[123.68,116.779,103.939] type(PIXEL_MEAN):type(PIXEL_MEAN[0]): NumPyarray PIXEL_MEAN_array:[123.68116.779103.939] type(PIXEL_MEAN_array): type(PIXEL_MEAN_array[0]): PIXEL_MEAN_array.dtype:float64 image_array:[[[123] [456] [789] [101112]] [[212223] [242526] [272829] [303132]]] type(image_array): type(image_array[0]): image_array.dtype:int64 image_array_fusion:[[[124.68118.779106.939] [127.68121.779109.939] [130.68124.779112.939] [133.68127.779115.939]] [[144.68138.779126.939] [147.68141.779129.939] [150.68144.779132.939] [153.68147.779135.939]]] type(image_array_fusion): type(image_array_fusion[0]): image_array_fusion.dtype:float64 Processfinishedwithexitcode0
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