To convert to colormap, I do
import cv2
im = cv2.imread('test.jpg', cv2.IMREAD_GRAYSCALE)
im_color = cv2.applyColorMap(im, cv2.COLORMAP_JET)
cv2.imwrite('colormap.jpg', im_color)
Then,
cv2.imread('colormap.jpg')
# ??? What should I do here?
Obviously, reading it in grayscale (with , 0
) wouldn't magically give us the grayscale, so how do I do it?
Method 1: Using the cv2.Import the OpenCV and read the original image using imread() than convert to grayscale using cv2. cvtcolor() function.
Define a colormap : A colormap is a mapping from 0-255 values to 256 colors. In OpenCV, we need to create an 8-bit color image of size 256 x 1 to store the 256 color values. Map the colors using a lookup table : In OpenCV you can apply a colormap stored in a 256 x 1 color image to an image using a lookup table LUT.
Step 1: Import OpenCV. Step 2: Read the original image using imread(). Step 3: Convert to grayscale using cv2. cvtcolor() function.
The grayscale conversion option is very useful for captured images which do not need to match coloured detail.
You could create an inverse of the colormap, i.e., a lookup table from the colormap values to the associated gray values. If using a lookup table, exact values of the original colormap are needed. In that case, the false color images will most likely need to be saved in a lossless format to avoid colors being changed. There's probably a faster way to do map over the numpy array. If exact values cannot be preserved, then a nearest neighbor lookup in the inverse map would be needed.
import cv2
import numpy as np
# load a color image as grayscale, convert it to false color, and save false color version
im_gray = cv2.imread('test.jpg', cv2.IMREAD_GRAYSCALE)
cv2.imwrite('gray_image_original.png', im_gray)
im_color = cv2.applyColorMap(im_gray, cv2.COLORMAP_JET)
cv2.imwrite('colormap.png', im_color) # save in lossless format to avoid colors changing
# create an inverse from the colormap to gray values
gray_values = np.arange(256, dtype=np.uint8)
color_values = map(tuple, cv2.applyColorMap(gray_values, cv2.COLORMAP_JET).reshape(256, 3))
color_to_gray_map = dict(zip(color_values, gray_values))
# load false color and reserve space for grayscale image
false_color_image = cv2.imread('colormap.png')
# apply the inverse map to the false color image to reconstruct the grayscale image
gray_image = np.apply_along_axis(lambda bgr: color_to_gray_map[tuple(bgr)], 2, false_color_image)
# save reconstructed grayscale image
cv2.imwrite('gray_image_reconstructed.png', gray_image)
# compare reconstructed and original gray images for differences
print('Number of pixels different:', np.sum(np.abs(im_gray - gray_image) > 0))
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