本文整理汇总了Python中pandac.PandaModules.PNMImage.gaussianFilterFrom方法的典型用法代码示例。如果您正苦于以下问题：Python PNMImage.gaussianFilterFrom方法的具体用法？Python PNMImage.gaussianFilterFrom怎么用？Python PNMImage.gaussianFilterFrom使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类pandac.PandaModules.PNMImage的用法示例。
在下文中一共展示了PNMImage.gaussianFilterFrom方法的4个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: setupHeightfield
# 需要导入模块: from pandac.PandaModules import PNMImage [as 别名]
# 或者: from pandac.PandaModules.PNMImage import gaussianFilterFrom [as 别名]
def setupHeightfield( self ):
# Prep terrain textures
#coverTextureFile = "data/textures/ground/green.jpg"
#self.mCoverTexture = loader.loadTexture(coverTextureFile)
# Setup heightfield
self.mHeightFieldTesselator = HeightfieldTesselator("Heightfield")
#fName = "data/textures/ground/heightfield.png"
#self.mHeightFieldTesselator.setPolyCount(10000)
#fileObj = Filename(fName)
self.mTerrainVScale = self.mTerrainUScale = 1.0/1024.0
myImage=PNMImage(256,256)
myImage.makeGrayscale()
p = Perlin(numberOfOctaves = 10, persistance = 0.65, smooth = False)
for y in range(0,256):
for x in range(0,256):
i = p.noise2D(float(x)/256.0,float(y)/256.0)
myImage.setGray(x, y, abs(i))
bigImage=PNMImage(1024, 1024)
bigImage.gaussianFilterFrom(1.0, myImage)
fileObj = Filename("data/textures/ground/myHeightfield.png")
bigImage.write(fileObj)
#myTexture = Texture()
#myTexture.load(bigImage)
self.mHeightFieldTesselator.setHeightfield(fileObj)
self.mTerrainHeight = MAPSIZE/10
self.mHeightFieldTesselator.setVerticalScale(self.mTerrainHeight)
self.mHorizontalScale = MAPSIZE/1024.0
self.mHeightFieldTesselator.setHorizontalScale(self.mHorizontalScale)
self.mHeightFieldNode = None
# self.tex0 = loader.loadTexture( 'models/textures/ground/schachbrett.png' )
self.tex0 = loader.loadTexture( 'data/textures/ground/mud-tile.png' )
#self.tex1 = loader.loadTexture( 'data/models/textures/ground/green.jpg' )
#self.tex2 = loader.loadTexture( 'data/models/textures/ground/grey-green-leaves.jpg' )
#self.ts0 = TextureStage( 'dirt' )
#self.ts1 = TextureStage( 'fungus' )
#self.ts2 = TextureStage( 'grass' )
self.updateHeightField()示例2: imgNoise2D
# 需要导入模块: from pandac.PandaModules import PNMImage [as 别名]
# 或者: from pandac.PandaModules.PNMImage import gaussianFilterFrom [as 别名]
def imgNoise2D(self, size = 512, autoOctave = False):
myImage=PNMImage(1,1)
myImage.makeGrayscale()
myImage.setMaxval( (2<<16)-1 )
myImage.fill(0.5)
octaves = self.numberOfOctaves
if autoOctave == True:
octaves = int(math.log(size,2))
self.pNoise = range(0,octaves)
random.seed(self.seed)
for i in range(1,octaves):
self.pNoise[i] = PerlinNoise2( 1, 1, 256, random.randint(1,10000))
for o in range(1,octaves):
freq = 2**o
oldImage = myImage
myImage = PNMImage(freq+1,freq+1)
myImage.makeGrayscale()
myImage.setMaxval( (2<<16)-1 )
myImage.gaussianFilterFrom(1.0, oldImage)
for x in range(0,freq):
for y in range(0,freq):
newNoise = (self.pNoise[o].noise( x, y)*(self.persistance**o)) / 2
myImage.setGray(x,y, myImage.getGray(x,y) + newNoise)#*32)
for i in range(0,freq+1):
myImage.setGray(i,freq, myImage.getGray(i%freq,0))
myImage.setGray(freq,i, myImage.getGray(0,i%freq))
oldImage = myImage
myImage = PNMImage(size,size)
myImage.makeGrayscale()
myImage.setMaxval( (2<<16)-1 )
myImage.gaussianFilterFrom(1.0, oldImage)
return myImage示例3: flattenArea
# 需要导入模块: from pandac.PandaModules import PNMImage [as 别名]
# 或者: from pandac.PandaModules.PNMImage import gaussianFilterFrom [as 别名]
def flattenArea( self ):
tilePos = (500,500)
tileSize = (4000,4000)
imgTilePos = self.world2MapPos(tilePos)
imgTileSize = self.world2MapPos( (tilePos[0] + tileSize[0], tilePos[1] + tileSize[1]) )
imgTileSize = (imgTileSize[0] - imgTilePos[0], imgTileSize[1] - imgTilePos[1])
tileSquare = PNMImage(Filename("tile.png"))
tileStamp = PNMImage(int(imgTileSize[0] * (5/3)),int(imgTileSize[1] * (5/3)))
tileStamp.makeGrayscale()
tileStamp.addAlpha()
tileStamp.gaussianFilterFrom(1, tileSquare)
count = 4
total = 0.0
selectXLow = int(imgTilePos[0] + imgTileSize[0] * 0.25)
selectXHigh = int(imgTilePos[0] + imgTileSize[0] * 0.75)
selectYLow = int(imgTilePos[1] + imgTileSize[1] * 0.25)
selectYHigh = int(imgTilePos[1] + imgTileSize[1] * 0.75)
total += self.myImage.getGray(selectXLow,selectYLow)
total += self.myImage.getGray(selectXLow,selectYLow)
total += self.myImage.getGray(selectXHigh,selectYHigh)
total += self.myImage.getGray(selectXHigh,selectYHigh)
average = total/count
tileStamp.fill(average)
edgeWidth = imgTilePos[0]*(1/3)
self.myImage.blendSubImage(tileStamp, int( imgTilePos[0]-edgeWidth),
int( imgTilePos[1]-edgeWidth),
0, 0, int(imgTileSize[0]*( 5/3 ) ),
int(imgTileSize[1]*( 5/3 ) ), 1)示例4: PerlinTest
# 需要导入模块: from pandac.PandaModules import PNMImage [as 别名]
# 或者: from pandac.PandaModules.PNMImage import gaussianFilterFrom [as 别名]
class PerlinTest(DirectObject):
def __init__(self):
self.size = 64
self.p = Perlin.Perlin(numberOfOctaves=10, persistance=0.75, smooth=False)
self.myImage = PNMImage(self.size, self.size)
self.myImage.makeGrayscale()
self.myTexture = Texture()
self.myImage.fill(0.5)
self.myTexture.load(self.myImage)
self.imageObject = OnscreenImage(image=self.myTexture, pos=(0, 0, 0))
self.myList = [None] * (self.size)
for a in range(self.size):
self.myList[a] = [0.5] * (self.size)
taskMgr.add(self.noiseTaskVerySmart, "perlinNoiseTask")
self.startTime = time()
self.noiseTaskVerySmart()
self.accept("arrow_up", self.run)
self.i = [None] * (self.size + 1)
for x in range(0, self.size + 1):
self.i[x] = [None] * (self.size + 1)
def run(self):
for a in range(self.size):
self.myList[a] = [0.5] * (self.size)
self.startTime = time()
taskMgr.add(self.noiseTaskVerySmart, "perlinNoiseTask")
def noiseTask(self, Task=None):
numLines = 8
if Task == None:
y = 0
else:
y = Task.frame * numLines
for yNum in range(0, numLines):
for x in range(0, self.size):
i = self.p.noise2D(float(x) / self.size, float(y + yNum) / self.size)
self.myImage.setGray(x, y + yNum, (i + 1.0) / 2)
self.myTexture.load(self.myImage)
if Task != None:
if self.size >= y + numLines:
return Task.cont
else:
self.myTexture.load(self.myImage)
print time() - self.startTime
return Task.done
def noiseTaskSmart(self, Task=None):
if Task == None:
o = 0
else:
o = Task.frame
p = Perlin.Perlin(numberOfOctaves=1, smooth=False, seed=0)
freq = 2 ** o
for x in range(0, freq + 1):
for y in range(0, freq + 1):
self.i[x][y] = p.intNoise2D(x * freq, y * freq)
for y in range(0, self.size):
for x in range(0, self.size):
intX = (x * freq) / self.size
fraX = (float(x) * freq) / self.size - intX
intY = (y * freq) / self.size
i1 = p.linearInterpolate(self.i[intX][intY], self.i[intX + 1][intY], fraX)
i2 = p.linearInterpolate(self.i[intX][intY + 1], self.i[intX + 1][intY + 1], fraX)
interNoise = p.linearInterpolate(i1, i2, (float(y) * freq) / self.size - intY)
self.myList[x][y] += interNoise * (0.75 ** o) / 2
self.myImage.setGray(x, y, self.myList[x][y])
self.myTexture.load(self.myImage)
if Task != None:
if freq < self.size:
return Task.cont
else:
print time() - self.startTime
return Task.done
def noiseTaskVerySmart(self, Task=None):
if Task == None:
o = 0
else:
o = Task.frame
p = Perlin.Perlin(numberOfOctaves=1, smooth=False, seed=0)
freq = 2 ** o
self.oldImage = self.myImage
self.myImage = PNMImage(freq + 1, freq + 1)
self.myImage.makeGrayscale()
self.myImage.gaussianFilterFrom(1.0, self.oldImage)
for x in range(0, freq + 1):
for y in range(0, freq + 1):
self.myImage.setGray(
x, y, self.myImage.getGray(x, y) + p.intNoise2D(x * freq, y * freq) * (0.75 ** o) / 2
)
self.myTexture.load(self.myImage)
if Task != None:
if freq < self.size:
return Task.cont
#.........这里部分代码省略.........