1、点类型的转栅格：
PointToRaster example 1 (Pythonwindow)
Converts point features to a raster dataset.
import arcpy
from arcpy import env
env.workspace = "c:/data"
arcpy.PointToRaster_conversion("ca_ozone_pts.shp", "ELEVATION",
"c:/output/ca_elev", "MAXIMUM", "", 2000)
PointToRaster example 2
(stand-alone script)
Converts point features to a raster dataset.
# Name: PointToRaster_Ex_02.py
# Description: Converts point features to a raster dataset.
# Requirements: ArcInfo
# Import system modules
import arcpy
from arcpy import env
# Set environment settings
env.workspace = "C:/data"
# Set local variables
inFeatures = "ca_ozone_pts.shp"
valField = "ELEVATION"
outRaster = "c:/output/ca_elev02"
assignmentType = "MAXIMUM"
priorityField = ""
cellSize = 2000
# Execute PointToRaster
arcpy.PointToRaster_conversion(inFeatures, valField, outRaster,
assignmentType, priorityField, cellSize)
2、面类型的转栅格：
PolygonToRaster example 1(Python window)
Converts polygon features to a raster dataset.
import arcpy
from arcpy import env
env.workspace = "c:/data"
arcpy.PolygonToRaster_conversion("ca_counties.shp", "NAME",
"c:/output/ca_counties.img",
"MAXIMUM_AREA", "MALES", 0.25)
PolygonToRaster example 2
(stand-alone script)
Converts polygon features to a raster dataset.
# Name: PolygonToRaster_Ex_02.py
# Description: Converts polygon features to a raster dataset.
# Requirements: ArcInfo
# Import system modules
import arcpy
from arcpy import env
# Set environment settings
env.workspace = "C:/data"
# Set local variables
inFeatures = "ca_counties.shp"
valField = "NAME"
outRaster = "c:/output/ca_counties"
assignmentType = "MAXIMUM_AREA"
priorityField = "MALES"
cellSize = 0.5
# Execute PolygonToRaster
arcpy.PolygonToRaster_conversion(inFeatures, valField, outRaster,
assignmentType, priorityField, cellSize)
3、线类型转栅格
PolylineToRaster example 1(Python window)
Converts polyline features to a raster dataset.
import arcpy
from arcpy import env
env.workspace = "c:/data"
arcpy.PolylineToRaster_conversion("roads.shp", "CLASS", "c:/output/roads.img",
"MAXIMUM_COMBINED_LENGTH", "LENGTH", 30)
PolylineToRaster example 2
(stand-alone script)
Converts polyline features to a raster dataset.
# Name: PolylineToRaster_Ex_02.py
# Description: Converts polyline features to a raster dataset.
# Requirements: ArcInfo
# Import system modules
import arcpy
from arcpy import env
# Set environment settings
env.workspace = "C:/data"
# Set local variables
inFeatures = "roads.shp"
valField = "CLASS"
outRaster = "c:/output/roads.tif"
assignmentType = "MAXIMUM_COMBINED_LENGTH"
priorityField = "LENGTH"
cellSize = 30
# Execute PolylineToRaster
arcpy.PolylineToRaster_conversion(inFeatures, valField, outRaster,
assignmentType, priorityField, cellSize)
在 python 中除了用 opencv，也可以用 matplotlib 和 PIL 这两个库操作图片。本人偏爱 matpoltlib，因为它的语法更像 matlab。一、matplotlib
1. 显示图片
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import matplotlib.pyplot as plt # plt 用于显示图片
import matplotlib.image as mpimg # mpimg 用于读取图片
import numpy as np
lena = mpimg.imread('lena.png') # 读取和代码处于同一目录下的 lena.png
# 此时 lena 就已经是一个 np.array 了，可以对它进行任意处理
lena.shape #(512, 512, 3)
plt.imshow(lena) # 显示图片
plt.axis('off') # 不显示坐标轴
plt.show()
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2. 显示某个通道
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# 显示图片的第一个通道
lena_1 = lena[:,:,0]
plt.imshow('lena_1')
plt.show()
# 此时会发现显示的是热量图，不是我们预想的灰度图，可以添加 cmap 参数，有如下几种添加方法：
plt.imshow('lena_1', cmap='Greys_r')
plt.show()
img = plt.imshow('lena_1')
img.set_cmap('gray') # 'hot' 是热量图
plt.show()
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3. 将 RGB 转为灰度图
matplotlib 中没有合适的函数可以将 RGB 图转换为灰度图，可以根据公式自定义一个：
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def rgb2gray(rgb):
return np.dot(rgb[...,:3], [0.299, 0.587, 0.114])
gray = rgb2gray(lena)
# 也可以用 plt.imshow(gray, cmap = plt.get_cmap('gray'))
plt.imshow(gray, cmap='Greys_r')
plt.axis('off')
plt.show()
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4. 对图像进行放缩
这里要用到 scipy
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from scipy import misc
lena_new_sz = misc.imresize(lena, 0.5) # 第二个参数如果是整数，则为百分比，如果是tuple，则为输出图像的尺寸
plt.imshow(lena_new_sz)
plt.axis('off')
plt.show()
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5. 保存图像
5.1 保存 matplotlib 画出的图像
该方法适用于保存任何 matplotlib 画出的图像，相当于一个 screencapture。
plt.imshow(lena_new_sz)
plt.axis('off')
plt.savefig('lena_new_sz.png')
5.2 将 array 保存为图像
from scipy import misc
misc.imsave('lena_new_sz.png', lena_new_sz)
5.3 直接保存 array
读取之后还是可以按照前面显示数组的方法对图像进行显示，这种方法完全不会对图像质量造成损失
np.save('lena_new_sz', lena_new_sz) # 会在保存的名字后面自动加上.npy
img = np.load('lena_new_sz.npy') # 读取前面保存的数组
二、PIL
1. 显示图片
from PIL import Image
im = Image.open('lena.png')
im.show()
2. 将 PIL Image 图片转换为 numpy 数组
im_array = np.array(im)
# 也可以用 np.asarray(im) 区别是 np.array() 是深拷贝，np.asarray() 是浅拷贝
3. 保存 PIL 图片
直接调用 Image 类的 save 方法
from PIL import Image
I = Image.open('lena.png')
I.save('new_lena.png')
4. 将 numpy 数组转换为 PIL 图片
这里采用 matplotlib.image 读入图片数组，注意这里读入的数组是 float32 型的，范围是 0-1，而 PIL.Image 数据是 uinit8 型的，范围是0-255，所以要进行转换：
import matplotlib.image as mpimg
from PIL import Image
lena = mpimg.imread('lena.png') # 这里读入的数据是 float32 型的，范围是0-1
im = Image.fromarray(np.uinit8(lena*255))
im.show()
5. RGB 转换为灰度图
from PIL import Image
I = Image.open('lena.png')
I.show()
L = I.convert('L')
L.show()
