get image size size = image.shape[0] * image.shape[1] # calculate probability of each level prob = hist / size # calculate cumulative probability cumprob = np.cumsum(prob)含义

这段代码是用来获取图像的尺寸的。其中 image.shape 是一个包含图像的高、宽和通道数的元组。shape[0] 表示图像的高，shape[1] 表示图像的宽。最后，通过将 shape[0] 乘以 shape[1] 得到图像的总像素数，并将结果赋值给 size 变量。

相关问题

def imgResize(self,image, height, inter = cv2.INTER_AREA): # initialize the dimensions of the image to be resized and grab the image size dim = None (h, w) = image.shape[:2] # calculate the ratio of the height and construct the dimensions r = height / float(h) dim = (int(w * r), height) # resize the image resized = cv2.resize(image, dim, interpolation = inter) # return the resized image return resized

这是一个Python函数，用于将图像按照指定的高度进行缩放。它使用OpenCV库来处理图像。具体来说，它首先获取图像的原始尺寸和指定的高度，然后计算缩放比例，并根据比例计算新的图像尺寸。最后，它使用OpenCV的resize()函数来执行缩放操作，并返回缩放后的图像。其中，参数inter表示缩放的插值方法，默认为cv2.INTER_AREA。

请帮我看看这个代码，在python 3.7版本中显示operands could not be broadcast together with shapes (11,) (1000,) 请帮我改成可以运行的格式 import numpy as np import matplotlib.pyplot as plt from scipy.constants import c, pi # Input parameters wavelength = 380e-9 # m grating_constant = 1600e3 # m angle_range = np.linspace(-10, 10, 1000) # degrees # Convert angle to radians theta = np.deg2rad(angle_range) # Calculate grating period and wavenumber grating_period = 1 / grating_constant k = 2 * pi / wavelength # Calculate diffraction orders m = np.arange(-5, 6) # diffraction orders order_wavenumbers = m * k order_angles = np.rad2deg(np.arcsin(order_wavenumbers / grating_constant)) # Calculate diffraction efficiency diff_efficiency = np.sin(m * pi * grating_period * np.sin(theta))**2 / (m * pi * grating_period * np.sin(theta))**2 # Plot diffraction efficiency vs angle plt.plot(angle_range, diff_efficiency) plt.xlabel('Angle (degrees)') plt.ylabel('Diffraction efficiency') plt.title('Diffraction pattern for a grating with a constant of {} mm^-1'.format(grating_constant/1000)) plt.show()

这个错误通常是由于数组的形状不匹配导致的。在这个代码中，出现这个错误的原因可能是`diff_efficiency`和`angle_range`的形状不一致。

你可以尝试使用`np.newaxis`来解决这个问题，比如将`diff_efficiency`的形状从`(11,)`变为`(11, 1)`，这样就可以和`angle_range`的形状`(1000,)`匹配了。

以下是修改后的代码：

import numpy as np
import matplotlib.pyplot as plt
from scipy.constants import c, pi

# Input parameters
wavelength = 380e-9  # m
grating_constant = 1600e3  # m
angle_range = np.linspace(-10, 10, 1000)  # degrees

# Convert angle to radians
theta = np.deg2rad(angle_range)

# Calculate grating period and wavenumber
grating_period = 1 / grating_constant
k = 2 * pi / wavelength

# Calculate diffraction orders
m = np.arange(-5, 6)
order_wavenumbers = m * k
order_angles = np.rad2deg(np.arcsin(order_wavenumbers / grating_constant))

# Calculate diffraction efficiency
diff_efficiency = np.sin(m * pi * grating_period * np.sin(theta))**2 / (m * pi * grating_period * np.sin(theta))**2
diff_efficiency = diff_efficiency[:, np.newaxis]  # Add a new axis to match the shape of angle_range

# Plot diffraction efficiency vs angle
plt.plot(angle_range, diff_efficiency)
plt.xlabel('Angle (degrees)')
plt.ylabel('Diffraction efficiency')
plt.title('Diffraction pattern for a grating with a constant of {} mm^-1'.format(grating_constant/1000))
plt.show()

希望这可以帮助你解决问题。