Convert an Image to Grayscale

How can I convert an RGB image into grayscale in Python?

How about doing it with Pillow:

from PIL import Image
img = Image.open('image.png').convert('L')
img.save('greyscale.png')

If an alpha (transparency) channel is present in the input image and should be preserved, use mode LA:

img = Image.open('image.png').convert('LA')

Using matplotlib and the formula

Y' = 0.2989 R + 0.5870 G + 0.1140 B

you could do:

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.image as mpimg

def rgb2gray(rgb):
    return np.dot(rgb[...,:3], [0.2989, 0.5870, 0.1140])

img = mpimg.imread('image.png')     
gray = rgb2gray(img)    
plt.imshow(gray, cmap=plt.get_cmap('gray'), vmin=0, vmax=1)
plt.show()

Convert Color Image to Grayscale in Python without OpenCV

You could write it, based on the conversion formula "Standard" RGB to Grayscale Conversion:

import numpy as np

def rgb2gray(rgb):

    r, g, b = rgb[:,:,0], rgb[:,:,1], rgb[:,:,2]
    gray = 0.2989 * r + 0.5870 * g + 0.1140 * b

    return gray

Converting image to grayscale

You've encountered a spot where Python's type system isn't protecting you in the way that C++ would.

cv2.IMREAD_GRAYSCALE and cv2.COLOR_BGR2GRAY are values from different enumerations. The former, whose numerical value is 0, applies to cv2.imread(). The latter, whose numerical value is 6, applies to cv2.cvtColor(). C++ would have told you that cv2.IMREAD_GRAYSCALE can't be passed to cv2.cvtColor(). Python quietly accepts the corresponding int value.

Thus, you think you're asking cv2 to convert a color image to gray, but by passing cv2.IMREAD_GRAYSCALE, cv2.cvtColor() sees the value 0, and thinks you're passing cv2.COLOR_BGR2BGRA. Instead of a grayscale image, you get the original image with an alpha channel added.

gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

is what you need instead.

The other issue you're seeing, assuming you're using a Jupyter notebook, is that cv2 layers color planes in BGR order instead of RGB. To display them properly, first do

image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

and then display the result.

Converting an image to grayscale using numpy

Here is a working code:

def grayConversion(image):
    grayValue = 0.07 * image[:,:,2] + 0.72 * image[:,:,1] + 0.21 * image[:,:,0]
    gray_img = grayValue.astype(np.uint8)
    return gray_img

orig = cv2.imread(r'C:\Users\Jackson\Desktop\drum.png', 1)
g = grayConversion(orig)

cv2.imshow("Original", orig)
cv2.imshow("GrayScale", g)
cv2.waitKey(0)
cv2.destroyAllWindows()

Convert Color image to grayscale image using Octave

According to Octave documentation on rgb2gray, the conversion is done as follows:

I = 0.298936*R + 0.587043*G + 0.114021*B

So converting a 3D RGB image matrix to a 2D gray-scale can be done by this code:

gray_img = (...
 0.298936 * resized_img1(:,:,1) +...
 0.587043 * resized_img1(:,:,2) +...
 0.114021 * resized_img1(:,:,3));

When you call imread than the pixels are integers of type uint8, in this case you can round the result for better accuracy by adding 0.5:

gray_img = (...
 0.298936 * resized_img1(:,:,1) +...
 0.587043 * resized_img1(:,:,2) +...
 0.114021 * resized_img1(:,:,3) + 0.5);

To get the pixels into a range between 0-1 use im2double