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python Batch Series Code-Raster Data Processed by Mask 2019-05-09
import arcpy from arcpy import env from import * import os import os.path import sys #arcpy.env.workspace = "D:\\TIF\\IMERGD\\Y" rootdir = 'D:\\TIF\\IMERGD\\Y' filenames = os.listdir(rootdir) for filename in filenames: if os.path.splitext(filename)[1] == '.tif': inRaster =rootdir + os.sep + filename outname = filename.replace('.tif','_cjmm') inMaskData = "D:\\TIF\\HB_PROVINCE.shp" arcpy.CheckOutExtension("Spatial") outExtractByMask = ExtractByMask(inRaster, inMaskData) print "ok""D:\\test\\"+outname+'.tif') print outname

python Batch Series Code-Raster Transition Point 2019-05-09
import arcpy from arcpy import env from import * import os import os.path import sys,string #arcpy.env.workspace = "D:\\TIF\\IMERGD\\Y" dir = "D:\\TIF\\IMERGD\\Y" filenames = os.listdir(dir) for filename in filenames: if os.path.splitext(filename)[1] == '.tif': inRaster = dir + os.sep + filename Output_Workspace = "D:\\test" basename = os.path.splitext(filename)[0] + '_bdmm'; outPoint = Output_Workspace + os.sep + basename + '.shp'; field = "VALUE" arcpy.RasterToPoint_conversion(inRaster,outPoint,field) print "ok"

sword refers to offer: jump step (python) 2019-05-09
A frog can jump up one step or two at a time.Find out how many jumping methods the frog can use to jump up an n-step. class Solution: def jumpFloor(self, number): # write code here if number==1 :return 1 if number==2 :return 2 a,b=1,2 for i in range(3,number+1): c=a+b a=b b=c return c

yum command under linux appears loaded plugins: fasttestmirrors determining fasttestmirrors 2019-05-09
There was a problem with yum install today. It took half a day to find a feasible solution. fastestmirror is an acceleration plug-in of yum. Here is the plug-in hint that the plug-in is not available. Don't use it if it can't be used. Disable it and let yum talk about it first. 1. Modify the configuration file of the plug-in # vi /etc/yum/pluginconf.d/fastestmirror.conf enabled = 1//changed from 1 to 0 to disable the plug-in

23-C++- Arithmetic Operator 2019-05-08
3.4 C+++Arithmetic Operator You may still remember the arithmetic exercises done by inside in school, and you can have the same fun on the computer.C++ uses arithmetic operators to operate.It provides several operators to complete the basic arithmetic operations in 5: addition, subtraction, multiplication, division and modulo.Each operator uses two values (operands) to calculate the result.Operators and their operands form expressions.For example, in the following statement: int wheels = 4

Android Face Recognition II (Call Open Source Face Recognition Algorithm seetaface Detection) 2019-05-08
Android Face Recognition II (Call Open Source Face Recognition Algorithm seetaface Detection) (official address of Seetaface)If necessary, you can study this algorithm yourself. I got it from the Internet by encapsulating open source c++ into so files through NDK and calling through JNI. Detection speed: Each picture is about 1700 milliseconds (MI note as testing machine)Several Points to Pay Attention to There is only so under armeabi-v7a, so it

C# Calls Dll of C++, Opencv 2019-05-08
C# Code calling C++, Opencv can be managed or unmanaged The form of non-dragging pipe is the form of [DllImport], which can only call C++ functions. Managed form is ref and methods in C++ classes can be called The following unmanaged form is preferred: 1, unmanaged form without parameter passing (1). that "win32 project" establish in C++is an application program in dll format (2). new cpp program add.cpp

Face Detection 2019-05-08
1、MTCNN_face_detection_alignmentSource: Self-recommendation ProjectAddress: Project Description: MTCNN from, a 2016 ECCV paperPaper/spl.pdf has been successfully applied in industrial applications at present. Many company detection modules use MTCNN or its accelerated version. MTCNN trains face detection and key point detection by using a model as a MultiTasks method. When inference, it can get both face frame information and key point information recommendation reasons: face detection is a very classic work in the field.

Google_FaceDetetor CameraHal Implementation 2019-05-08
Google_FaceDetetor CameraHal Implementation Based on RK3288 Platform. 安卓SDK提供人脸检测这个类,用法非常的简单,下面是需要分析一下这个功能的实现。 使用方法 首先,大致看一下从应用层调入到HAL

LibFaceDetection Open Source Library Introduction and Use 2019-05-08
unsigned char * result _ buffer//buffer, which must be 0x20000 bytes in size.buffer memory for storing face detection results, !!its size must be 0x20000 Bytes!!Unsignedchar * gray _ image _ data//single channel gray image (y in YUV data)Intwidth//width of single-channel gray imageIntheight//height of single-channel gray imageIntstep//the step parameter of the single-channel gray image is the same as the width of the single-channel gray image, inputimage, itmustbegray (single-channel) image!Float scale//The scale