I wrote a program for analyzing pictures. The problem I’m having is very slow processing time for medium images like (800×800). And I think the root of the problem for this is the ‘for loop’ where i complete my numpy array with values. The main task for the program is to count how many times does *x* intensity show in each color channel and then plot them in histogram. For example in the end we will be able to see how many times do we get intensity 200 in red channel and so on.

My code is probably very hard to read but i tried to add comments to make things easier.

`from PIL import Image import numpy as np from IPython import get_ipython get_ipython().run_line_magic('matplotlib', 'inline') import matplotlib.pyplot as plt #Load an image fname = 'picture1.jpg' image_grey = Image.open(fname).convert("L") image_rgb = Image.open(fname).convert("RGB") arrey_grey = np.asarray(image_grey) arrey_rgb = np.asarray(image_rgb) #Get image size with Image.open('picture1.jpg') as img: width, height = img.size size = width * height #Plot the uploaded image both grey and rgb plt.imshow(arrey_grey, cmap='gray') plt.show() plt.imshow(arrey_rgb) plt.show() #Define numpy arrey for each color channel matrix_red = np.zeros((256, 4),dtype = object) matrix_red[:,1] = int(0) matrix_red[:,2:] = float(0) matrix_green = np.zeros((256, 4),dtype = object) matrix_green[:,1] = int(0) matrix_green[:,2:] = float(0) matrix_blue = np.zeros((256, 4),dtype = object) matrix_blue[:,1] = int(0) matrix_blue[:,2:] = float(0) # Completing first column with 0-255 for i in range(256): matrix_red[i][0] = i matrix_green[i][0] = i matrix_blue[i][0] = i # Counting intensity for each color channel for i in range(width): Matrix_Width = arrey_rgb[i] for i in range(height): Matrix_Height = Matrix_Width[i] Red_Value = Matrix_Height[0] Green_Value = Matrix_Height[1] Blue_Value = Matrix_Height[2] for i in range(256): if (matrix_red[i][0] == Red_Value): matrix_red[i][1] = matrix_red[i][1] + 1 if (matrix_green[i][0] == Green_Value): matrix_green[i][1] = matrix_green[i][1] + 1 if (matrix_blue[i][0] == Blue_Value): matrix_blue[i][1] = matrix_blue[i][1] + 1 # Data for task ahead Hx = 0 for i in range(256): matrix_red[i][2] = matrix_red[i][1] / size Hx = (matrix_red[i][2] + matrix_red[i][3]) + Hx matrix_red[i][3] = Hx #Plotting results Frequencie_Red = np.zeros((256, 1),dtype = object) Frequencie_Red[:,0] = int(0) Frequencie_Green = np.zeros((256, 1),dtype = object) Frequencie_Green[:,0] = int(0) Frequencie_Blue = np.zeros((256, 1),dtype = object) Frequencie_Blue[:,0] = int(0) Intensity = np.zeros((256, 1),dtype = object) Intensity[:,0] = int(0) for i in range(256): Frequencie_Red[i] = matrix_red[i][1] Frequencie_Green[i] = matrix_green[i][1] Frequencie_Blue[i] = matrix_blue[i][1] for i in range(256): Intensity[i] = i pos = Intensity width = 1.0 ax = plt.axes() ax.set_xticks(pos + (width / 2)) ax.set_xticklabels(Intensity) plt.bar(pos, Frequencie_Red, width, color='r') plt.show() plt.bar(pos, Frequencie_Green, width, color='g') plt.show() plt.bar(pos, Frequencie_Blue, width, color='b') plt.show() `

Any help will be greatly appreciated.