Python光学仿真学习处理高斯光束分布图像

>>> plt.imshow(img,cmap=plt.cm.gray)
<matplotlib.image.AxesImage object at 0x000002E84F5B4788>
>>> plt.show()

>>> plt.imshow(img)
<matplotlib.image.AxesImage object at 0x000002E857A21448>
>>> plt.ginput(2)
[(717.0757575757577, 299.8290043290042), (783.5692640692644, 233.33549783549768)]

>>> roi = img[233:299,717:783]
>>> plt.imshow(roi)
<matplotlib.image.AxesImage object at 0x000002E84F5B4948>
>>> plt.show()

>>> xNum,yNum = roi.shape						#获取roi的维度
>>> xAxis,yAxis = np.meshgrid(range(yNum),range(xNum))	#range创建长度为xNum的自然数列
>>> ax = plt.GCa(projection='3d')				#建立3D坐标轴
>>> ax.plot_surface(xAxis,yAxis,roi)			#创建面元图
<mpl_toolkits.mplot3d.art3d.Poly3DCollection object at 0x0000019EAFF19D48>
>>> plt.show()

>>> arr = np.max(roi,0)
>>> x = np.arange(len(arr))
>>> plt.plot(x,arr)
[<matplotlib.lines.Line2D object at 0x0000019EB469EB48>]
>>> plt.show()

>>> from scipy.optimize import curve_fit
>>> def gauss(x, a, b, c):
...     return a*np.exp(-(x-b)**2/c**2)
...
>>> abc,  para = curve_fit(gauss,x,arr)
>>> abc							#即上式中的a,b,c
array([89.72326971, 35.58522403, 20.86186403])	
>>> fitValue = gauss(x,abc[0],abc[1],abc[2])	#拟合值
>>> plt.scatter(x,arr)			#绘制原始数据的散点图
<matplotlib.collections.PathCollection object at 0x0000019EB5438D88>
>>> plt.plot(x,fitValue)		#绘制拟合数据的曲线图
[<matplotlib.lines.Line2D object at 0x0000019EB46D4048>]
>>> plt.show()

> pip install numpy
> pip install matplotlib
> pip install scipy