我已经完成了一个非常快速的编码，并且从头开始（没有研究关于边缘检测的已建立或最先进的算法）。不出所料，结果很差。我在下面粘贴的代码仅适用于 RGB（即仅适用于三个通道，不适用于 CMYK、灰度或 RGBA 或其他任何图像）。我还测试了一个非常简单的图像。在现实生活中，图像很复杂。我认为那里还不会很公平。它需要做很多工作。但是，由于@Gia Tri 的要求，我犹豫着分享它。
这就是我所做的。对于每一列，我计算了平均强度和标准差强度。我希望在边缘，强度会从平均 +- 标准差（乘以一个因子）发生变化。如果我标记列中的第一个和最后一个，我将为每一列都有边缘，并且一旦我缝合它，它就会形成和边缘。代码和附件图片供您查看，我的表现如何。
from scipy import ndimage
import numpy as np
import matplotlib.pyplot as plt
UppperStdBoundaryMultiplier = 1.0
LowerStdBoundaryMultiplier = 1.0
NegativeSelection = False
def SumSquareRGBintensityOfPixel(Pixel):
return np.sum(np.power(Pixel,2),axis=0)
def GetTheContinousStretchForAcolumn(Column):
global UppperStdBoundaryMultiplier
global LowerStdBoundaryMultiplier
global NegativeSelection
SumSquaresIntensityOfColumn = np.apply_along_axis(SumSquareRGBintensityOfPixel,1,Column)
Mean = np.mean(SumSquaresIntensityOfColumn)
StdDev = np.std(SumSquaresIntensityOfColumn)
LowerThreshold = Mean - LowerStdBoundaryMultiplier*StdDev
UpperThreshold = Mean + UppperStdBoundaryMultiplier*StdDev
if NegativeSelection:
Index = np.where(SumSquaresIntensityOfColumn < LowerThreshold)
Column[Index,:] = np.array([255,255,255])
else:
Index = np.where(SumSquaresIntensityOfColumn >= LowerThreshold)
LeastIndex = Index[Index==True][0]
LastIndex = Index[Index==True][-1]
Column[[LeastIndex,LastIndex],:] = np.array([255,0,0])
return Column
def DoEdgeDetection(ImageFilePath):
FileHandle = ndimage.imread(ImageFilePath)
for Column in range(FileHandle.shape[1]):
FileHandle[:,Column,:] = GetTheContinousStretchForAcolumn(FileHandle[:,Column,:])
plt.imshow(FileHandle)
plt.show()
DoEdgeDetection("/PathToImage/Image_1.jpg")
下面是结果。左侧是必须检测边缘的查询图像，右侧是边缘检测图像。边缘点用红点标记。正如你所看到的，它表现不佳，但投入了一些时间和思考，它可能会做得更好......或者可能不会。也许这是一个好的开始，但远未结束..请你做法官！
***** 澄清 GiaTri 的要求后编辑 ***************
所以我确实设法改变了程序，想法保持不变。但是，这一次问题被过度简化为您只想检测蓝色火焰的情况。实际上，我继续让它适用于所有三个颜色通道。但是我怀疑，它对您在蓝色通道之外有用。
**如何使用下面的程序**
如果你的火焰是垂直的，那么在班级分配中选择 edges = "horizontal"。如果您的边缘是水平的，则选择边缘 =“垂直”。这可能有点令人困惑，但暂时请使用它。以后要么你改，要么我改。
首先让我说服您，边缘检测的效果比昨天好得多。请参阅下面的两张图片。我从互联网上拍摄了这两个火焰图像。与之前一样，需要检测边缘的图像在左侧，右侧是边缘检测图像。边缘是红点。
第一个horizontal flame。
然后是a vertical flame。
.
这方面还有很多工作要做。但是，如果您比昨天更有信心，那么下面是代码。
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.image import imread
class DetectEdges():
def __init__(self, ImagePath, Channel = ["blue"], edges="vertical"):
self.Channel = Channel
self.edges = edges
self.Image_ = imread(ImagePath)
self.Image = np.copy(self.Image_)
self.Dimensions_X, self.Dimensions_Y, self.Channels = self.Image.shape
self.BackGroundSamplingPercentage = 0.5
def ShowTheImage(self):
plt.imshow(self.Image)
plt.show()
def GetTheBackGroundPixels(self):
NumberOfPoints = int(self.BackGroundSamplingPercentage*min(self.Dimensions_X, self.Dimensions_Y))
Random_X = np.random.choice(self.Dimensions_X, size=NumberOfPoints, replace=False)
Random_Y = np.random.choice(self.Dimensions_Y, size=NumberOfPoints, replace=False)
Random_Pixels = np.array(list(zip(Random_X,Random_Y)))
return Random_Pixels
def GetTheChannelEdge(self):
BackGroundPixels = self.GetTheBackGroundPixels()
if self.edges == "vertical":
if self.Channel == ["blue"]:
MeanBackGroundInensity = np.mean(self.Image[BackGroundPixels[:,0],BackGroundPixels[:,1],2])
for column in range(self.Dimensions_Y):
PixelsAboveBackGround = np.where(self.Image[:,column,2]>MeanBackGroundInensity)
if PixelsAboveBackGround[PixelsAboveBackGround==True].shape[0] > 0:
TopPixel = PixelsAboveBackGround[PixelsAboveBackGround==True][0]
BottomPixel = PixelsAboveBackGround[PixelsAboveBackGround==True][-1]
self.Image[[TopPixel,BottomPixel],column,:] = [255,0,0]
if self.Channel == ["red"]:
MeanBackGroundInensity = np.mean(self.Image[BackGroundPixels[:,0],BackGroundPixels[:,1],0])
for column in range(self.Dimensions_Y):
PixelsAboveBackGround = np.where(self.Image[:,column,0]>MeanBackGroundInensity)
if PixelsAboveBackGround[PixelsAboveBackGround==True].shape[0] > 0:
TopPixel = PixelsAboveBackGround[PixelsAboveBackGround==True][0]
BottomPixel = PixelsAboveBackGround[PixelsAboveBackGround==True][-1]
self.Image[[TopPixel,BottomPixel],column,:] = [0,255,0]
if self.Channel == ["green"]:
MeanBackGroundInensity = np.mean(self.Image[BackGroundPixels[:,0],BackGroundPixels[:,1],1])
for column in range(self.Dimensions_Y):
PixelsAboveBackGround = np.where(self.Image[:,column,1]>MeanBackGroundInensity)
if PixelsAboveBackGround[PixelsAboveBackGround==True].shape[0] > 0:
TopPixel = PixelsAboveBackGround[PixelsAboveBackGround==True][0]
BottomPixel = PixelsAboveBackGround[PixelsAboveBackGround==True][-1]
self.Image[[TopPixel,BottomPixel],column,:] = [255,0,0]
elif self.edges=="horizontal":
if self.Channel == ["blue"]:
MeanBackGroundInensity = np.mean(self.Image[BackGroundPixels[:,0],BackGroundPixels[:,1],2])
for row in range(self.Dimensions_X):
PixelsAboveBackGround = np.where(self.Image[row,:,2]>MeanBackGroundInensity)
if PixelsAboveBackGround[PixelsAboveBackGround==True].shape[0] > 0:
LeftPixel = PixelsAboveBackGround[PixelsAboveBackGround==True][0]
RightPixel = PixelsAboveBackGround[PixelsAboveBackGround==True][-1]
self.Image[row,[LeftPixel,RightPixel],:] = [255,0,0]
if self.Channel == ["red"]:
MeanBackGroundInensity = np.mean(self.Image[BackGroundPixels[:,0],BackGroundPixels[:,1],0])
for row in range(self.Dimensions_X):
PixelsAboveBackGround = np.where(self.Image[row,:,0]>MeanBackGroundInensity)
if PixelsAboveBackGround[PixelsAboveBackGround==True].shape[0] > 0:
LeftPixel = PixelsAboveBackGround[PixelsAboveBackGround==True][0]
RightPixel = PixelsAboveBackGround[PixelsAboveBackGround==True][-1]
self.Image[row,[LeftPixel,RightPixel],:] = [0,255,0]
if self.Channel == ["green"]:
MeanBackGroundInensity = np.mean(self.Image[BackGroundPixels[:,0],BackGroundPixels[:,1],1])
for row in range(self.Dimensions_X):
PixelsAboveBackGround = np.where(self.Image[row,:,1]>MeanBackGroundInensity)
if PixelsAboveBackGround[PixelsAboveBackGround==True].shape[0] > 0:
LeftPixel = PixelsAboveBackGround[PixelsAboveBackGround==True][0]
RightPixel = PixelsAboveBackGround[PixelsAboveBackGround==True][-1]
self.Image[row,[LeftPixel,RightPixel],:] = [255,0,0]
Test = DetectEdges("FlameImagePath",Channel = ["blue"],edges="vertical")
Test.GetTheChannelEdge()
Test.ShowTheImage()
请让我知道这是否有任何“更多”帮助或我错过了一些重要的要求。
祝你好运，