Convert Rgb Image to 1 Channel Image (Black/White)

Convert RGB image to 1 channel image (black/white)

If I understand your question, you want to apply a black and white thresholding to the image based on a pixel's luminance. For a fast way of doing this, you could use my open source GPUImage project (supporting back to iOS 4.x) and a couple of the image processing operations it provides.

In particular, the GPUImageLuminanceThresholdFilter and GPUImageAdaptiveThresholdFilter might be what you're looking for here. The former turns a pixel to black or white based on a luminance threshold you set (the default is 50%). The latter takes the local average luminance into account when applying this threshold, which can produce better results for text on pages of a book.

Usage of these filters on a UIImage is fairly simple:

UIImage *inputImage = [UIImage imageNamed:@"book.jpg"];
GPUImageLuminanceThresholdFilter *thresholdFilter = [[GPUImageLuminanceThresholdFilter alloc] init];
UIImage *quickFilteredImage = [thresholdFilter imageByFilteringImage:inputImage];

These can be applied to a live camera feed and photos taken by the camera, as well.

Convert RGB image to black and white

You don't need this:

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

because your image is already grayscale, which means R == G == B, so you may take GREEN channel (or any other if you like) and use it.

And yeah, specify the colormap for matplotlib:

plt.imshow(im[:,:,1], cmap='gray')

How to Convert RGB images dataset to single channel grayscale?

you directly read images as grayscale with:

im_gray = cv2.imread('gray_image.png', cv2.IMREAD_GRAYSCALE)

or you can convert an rgb image to grayscale with:

img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

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')

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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')

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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()

Directly convert black and white image: 3-channel to 1-channel?

Depending on the application, it may be enough to simply feed one of the channels to threshold(). You can split the 3-channel image using split(), it saves some time over cvtColor() as it does not have to do 3 multiplications per pixel.

How fast in Python change 3 channel rgb color image to 1 channel gray?

I had this Problem before.This is the Best way:
Your code is correct but needs some more changes to be suitable for a grayscaled image. Here is the Code:

ii = cv2.imread("0.png")
gray_image = cv2.cvtColor(ii, cv2.COLOR_BGR2GRAY)
print(gray_image)
plt.imshow(gray_image,cmap='Greys')
plt.show()

and this is the result:

[[196 196 197 195 195 194 195 197 196 195 194 194 196 194 196 189 188
195 195 196 197 198 195 194 194 195 193 191]
.
.
.
[194 194 193 193
191 189 193 193 192 193 191 194 193 192 192 191 192 192 193 196 199
198 200 200 200 201 200 199]]

.

OpenCV - I need to insert color image into black and white image and

A colour image needs 3 channels to represent red, green and blue components.

A greyscale image has only a single channel - grey.

You cannot put a 3-channel image in a 1-channel image any more than you can put a 3-pin plug in a single hole socket.

You need to promote your grey image to 3 identical channels first:

background = cv2.cvtColor(background, cv2.COLOR_GRAY2BGR)

Example:

# Make grey background, i.e. 1 channel
background = np.full((200,300), 127, dtype=np.uint8)

# Make small colour overlay, i.e. 3 channels
color = np.random.randint(0, 256, (100,100,3), dtype=np.uint8)

# Upgrade background from grey to BGR, i.e. 1 channel to 3 channels
background = cv2.cvtColor(background, cv2.COLOR_GRAY2BGR)

# Check its new shape
print(background.shape)       # prints (200, 300, 3)

# Paste in small coloured image
background[10:110, 180:280] = color

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