Opencv: Jetmap or colormap to grayscale, reverse applyColorMap()
Question:
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?
Answers:
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))
The other answer works if you have exact color values.
If your colors have been compressed lossily (JPEG), you need a different approach.
Here’s an approach using FLANN. It finds the nearest color and tells you the difference too, so you can handle implausible values.
complete notebook: https://gist.github.com/crackwitz/ccd54145bec1297ccdd4a0c8f4971deb
Highlights:
norm = cv.NORM_L2
FLANN_INDEX_KDTREE = 1
index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5)
search_params = dict(checks=50)
fm = cv.FlannBasedMatcher(index_params, search_params)
# JET, BGR order, excluding special palette values (>= 256)
fm.add(255 * np.float32([jet._lut[:256, (2,1,0)]])) # jet
fm.train()
# look up all pixels
query = im.reshape((-1, 3)).astype(np.float32)
matches = fm.match(query)
# statistics: `result` is palette indices ("grayscale image")
output = np.uint16([m.trainIdx for m in matches]).reshape(height, width)
result = np.where(output < 256, output, 0).astype(np.uint8)
dist = np.uint8([m.distance for m in matches]).reshape(height, width)
Source of colormapped picture: Separating Object Contours OpenCV
Above is brilliant answer from Christoph Rackwitz! But this is a bit confusing due Python Notebook specifics. Here is a full code for conversion.
from matplotlib import colormaps # colormaps['jet'], colormaps['turbo']
from matplotlib.colors import LinearSegmentedColormap
from matplotlib._cm import _jet_data
def convert_jet_to_grey(img):
(height, width) = img.shape[:2]
cm = LinearSegmentedColormap("jet", _jet_data, N=2 ** 8)
# cm = colormaps['turbo'] swap with jet if you use turbo colormap instead
cm._init() # Must be called first. cm._lut data field created here
FLANN_INDEX_KDTREE = 1
index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5)
search_params = dict(checks=50)
fm = cv2.FlannBasedMatcher(index_params, search_params)
# JET, BGR order, excluding special palette values (>= 256)
fm.add(255 * np.float32([cm._lut[:256, (2, 1, 0)]])) # jet
fm.train()
# look up all pixels
query = img.reshape((-1, 3)).astype(np.float32)
matches = fm.match(query)
# statistics: `result` is palette indices ("grayscale image")
output = np.uint16([m.trainIdx for m in matches]).reshape(height, width)
result = np.where(output < 256, output, 0).astype(np.uint8)
# dist = np.uint8([m.distance for m in matches]).reshape(height, width)
return result # , dist uncomment if you wish accuracy image
Have been facing a similar problem while working with a served JPEG compressed image. Since I am on c++, resorting to matplotlib is not an option.
The alternative is to fetch one of the lookup tables (lut) corresponding to the desired colormap, e.g. "jet", available in the imgproc/src/colormap.cpp source file. Unfortunately, what could be easily retrieved using cv::colormap::Jet(n) (where ‘n’ would even allow to interpolate more points) is not accessible through OpenCV’s API.
That said, here is my solution based on @Christoph Rackwitz‘s answer:
// GNU Octave colormap "jet" as in cv::colormap::Jet()._lut
const float r[] = { 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.00588235294117645f,0.02156862745098032f,0.03725490196078418f,0.05294117647058827f,0.06862745098039214f,0.084313725490196f,0.1000000000000001f,0.115686274509804f,0.1313725490196078f,0.1470588235294117f,0.1627450980392156f,0.1784313725490196f,0.1941176470588235f,0.2098039215686274f,0.2254901960784315f,0.2411764705882353f,0.2568627450980392f,0.2725490196078431f,0.2882352941176469f,0.303921568627451f,0.3196078431372549f,0.3352941176470587f,0.3509803921568628f,0.3666666666666667f,0.3823529411764706f,0.3980392156862744f,0.4137254901960783f,0.4294117647058824f,0.4450980392156862f,0.4607843137254901f,0.4764705882352942f,0.4921568627450981f,0.5078431372549019f,0.5235294117647058f,0.5392156862745097f,0.5549019607843135f,0.5705882352941174f,0.5862745098039217f,0.6019607843137256f,0.6176470588235294f,0.6333333333333333f,0.6490196078431372f,0.664705882352941f,0.6803921568627449f,0.6960784313725492f,0.7117647058823531f,0.7274509803921569f,0.7431372549019608f,0.7588235294117647f,0.7745098039215685f,0.7901960784313724f,0.8058823529411763f,0.8215686274509801f,0.8372549019607844f,0.8529411764705883f,0.8686274509803922f,0.884313725490196f,0.8999999999999999f,0.9156862745098038f,0.9313725490196076f,0.947058823529412f,0.9627450980392158f,0.9784313725490197f,0.9941176470588236f,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.9862745098039216f,0.9705882352941178f,0.9549019607843139f,0.93921568627451f,0.9235294117647062f,0.9078431372549018f,0.892156862745098f,0.8764705882352941f,0.8607843137254902f,0.8450980392156864f,0.8294117647058825f,0.8137254901960786f,0.7980392156862743f,0.7823529411764705f,0.7666666666666666f,0.7509803921568627f,0.7352941176470589f,0.719607843137255f,0.7039215686274511f,0.6882352941176473f,0.6725490196078434f,0.6568627450980391f,0.6411764705882352f,0.6254901960784314f,0.6098039215686275f,0.5941176470588236f,0.5784313725490198f,0.5627450980392159f,0.5470588235294116f,0.5313725490196077f,0.5156862745098039f,0.5f };
const float g[] = { 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.001960784313725483f,0.01764705882352935f,0.03333333333333333f,0.0490196078431373f,0.06470588235294117f,0.08039215686274503f,0.09607843137254901f,0.111764705882353f,0.1274509803921569f,0.1431372549019607f,0.1588235294117647f,0.1745098039215687f,0.1901960784313725f,0.2058823529411764f,0.2215686274509804f,0.2372549019607844f,0.2529411764705882f,0.2686274509803921f,0.2843137254901961f,0.3f,0.3156862745098039f,0.3313725490196078f,0.3470588235294118f,0.3627450980392157f,0.3784313725490196f,0.3941176470588235f,0.4098039215686274f,0.4254901960784314f,0.4411764705882353f,0.4568627450980391f,0.4725490196078431f,0.4882352941176471f,0.503921568627451f,0.5196078431372548f,0.5352941176470587f,0.5509803921568628f,0.5666666666666667f,0.5823529411764705f,0.5980392156862746f,0.6137254901960785f,0.6294117647058823f,0.6450980392156862f,0.6607843137254901f,0.6764705882352942f,0.692156862745098f,0.7078431372549019f,0.723529411764706f,0.7392156862745098f,0.7549019607843137f,0.7705882352941176f,0.7862745098039214f,0.8019607843137255f,0.8176470588235294f,0.8333333333333333f,0.8490196078431373f,0.8647058823529412f,0.8803921568627451f,0.8960784313725489f,0.9117647058823528f,0.9274509803921569f,0.9431372549019608f,0.9588235294117646f,0.9745098039215687f,0.9901960784313726f,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.9901960784313726f,0.9745098039215687f,0.9588235294117649f,0.943137254901961f,0.9274509803921571f,0.9117647058823528f,0.8960784313725489f,0.8803921568627451f,0.8647058823529412f,0.8490196078431373f,0.8333333333333335f,0.8176470588235296f,0.8019607843137253f,0.7862745098039214f,0.7705882352941176f,0.7549019607843137f,0.7392156862745098f,0.723529411764706f,0.7078431372549021f,0.6921568627450982f,0.6764705882352944f,0.6607843137254901f,0.6450980392156862f,0.6294117647058823f,0.6137254901960785f,0.5980392156862746f,0.5823529411764707f,0.5666666666666669f,0.5509803921568626f,0.5352941176470587f,0.5196078431372548f,0.503921568627451f,0.4882352941176471f,0.4725490196078432f,0.4568627450980394f,0.4411764705882355f,0.4254901960784316f,0.4098039215686273f,0.3941176470588235f,0.3784313725490196f,0.3627450980392157f,0.3470588235294119f,0.331372549019608f,0.3156862745098041f,0.2999999999999998f,0.284313725490196f,0.2686274509803921f,0.2529411764705882f,0.2372549019607844f,0.2215686274509805f,0.2058823529411766f,0.1901960784313728f,0.1745098039215689f,0.1588235294117646f,0.1431372549019607f,0.1274509803921569f,0.111764705882353f,0.09607843137254912f,0.08039215686274526f,0.06470588235294139f,0.04901960784313708f,0.03333333333333321f,0.01764705882352935f,0.001960784313725483f,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 };
const float b[] = { 0.5f,0.5156862745098039f,0.5313725490196078f,0.5470588235294118f,0.5627450980392157f,0.5784313725490196f,0.5941176470588235f,0.6098039215686275f,0.6254901960784314f,0.6411764705882352f,0.6568627450980392f,0.6725490196078432f,0.6882352941176471f,0.7039215686274509f,0.7196078431372549f,0.7352941176470589f,0.7509803921568627f,0.7666666666666666f,0.7823529411764706f,0.7980392156862746f,0.8137254901960784f,0.8294117647058823f,0.8450980392156863f,0.8607843137254902f,0.8764705882352941f,0.892156862745098f,0.907843137254902f,0.9235294117647059f,0.9392156862745098f,0.9549019607843137f,0.9705882352941176f,0.9862745098039216f,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.9941176470588236f,0.9784313725490197f,0.9627450980392158f,0.9470588235294117f,0.9313725490196079f,0.915686274509804f,0.8999999999999999f,0.884313725490196f,0.8686274509803922f,0.8529411764705883f,0.8372549019607844f,0.8215686274509804f,0.8058823529411765f,0.7901960784313726f,0.7745098039215685f,0.7588235294117647f,0.7431372549019608f,0.7274509803921569f,0.7117647058823531f,0.696078431372549f,0.6803921568627451f,0.6647058823529413f,0.6490196078431372f,0.6333333333333333f,0.6176470588235294f,0.6019607843137256f,0.5862745098039217f,0.5705882352941176f,0.5549019607843138f,0.5392156862745099f,0.5235294117647058f,0.5078431372549019f,0.4921568627450981f,0.4764705882352942f,0.4607843137254903f,0.4450980392156865f,0.4294117647058826f,0.4137254901960783f,0.3980392156862744f,0.3823529411764706f,0.3666666666666667f,0.3509803921568628f,0.335294117647059f,0.3196078431372551f,0.3039215686274508f,0.2882352941176469f,0.2725490196078431f,0.2568627450980392f,0.2411764705882353f,0.2254901960784315f,0.2098039215686276f,0.1941176470588237f,0.1784313725490199f,0.1627450980392156f,0.1470588235294117f,0.1313725490196078f,0.115686274509804f,0.1000000000000001f,0.08431372549019622f,0.06862745098039236f,0.05294117647058805f,0.03725490196078418f,0.02156862745098032f,0.00588235294117645f,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 };
// Declare the lut (please note it has to be a 3xN Mat of CV_32F)
int N(sizeof(r)/sizeof(float));
cv::Mat lut(cv::Mat(cv::Size(3,N), CV_32F, cv::Scalar(0.0)));
for (int i(0); i < N; ++i) {
lut.at<float>(i, 0, 0) = 255.0 * b[i];
lut.at<float>(i, 1, 0) = 255.0 * g[i];
lut.at<float>(i, 2, 0) = 255.0 * r[i];
}
// Initialize the FlannBasedMatcher
auto index_params = new cv::flann::KDTreeIndexParams(5);
auto search_params = new cv::flann::SearchParams(50);
cv::FlannBasedMatcher matcher(index_params, search_params);
matcher.add(lut);
matcher.train();
// Convert the image pixels to perform the query (3xH*W Mat of CV_32F)
int QLEN(im.rows*im.cols);
cv::Mat query(cv::Mat(cv::Size(3, QLEN), CV_32F, cv::Scalar(0.0)));
int i(0);
for (int y(0); y < im.rows; ++y) {
for (int x(0); x < im.cols; ++x) {
query.at<float>(i, 0) = float(im.at<cv::Vec3b>(y, x)[0]);
query.at<float>(i, 1) = float(im.at<cv::Vec3b>(y, x)[1]);
query.at<float>(i, 2) = float(im.at<cv::Vec3b>(y, x)[2]);
++i;
}
}
// Lookup all image pixels
std::vector<cv::DMatch> matches;
matcher.match(query, matches);
// Reconstruct the greyscale image
cv::Mat im_grey(cv::Mat(cv::Size(1, QLEN), CV_32F, cv::Scalar(0.0)));
for (int i(0); i < QLEN; ++i) {
im_grey.at<float>(i, 0) = matches[i].trainIdx / 255.0;
}
im_grey = im_grey.reshape(0, {im.rows,im.cols});
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?
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))
The other answer works if you have exact color values.
If your colors have been compressed lossily (JPEG), you need a different approach.
Here’s an approach using FLANN. It finds the nearest color and tells you the difference too, so you can handle implausible values.
complete notebook: https://gist.github.com/crackwitz/ccd54145bec1297ccdd4a0c8f4971deb
Highlights:
norm = cv.NORM_L2
FLANN_INDEX_KDTREE = 1
index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5)
search_params = dict(checks=50)
fm = cv.FlannBasedMatcher(index_params, search_params)
# JET, BGR order, excluding special palette values (>= 256)
fm.add(255 * np.float32([jet._lut[:256, (2,1,0)]])) # jet
fm.train()
# look up all pixels
query = im.reshape((-1, 3)).astype(np.float32)
matches = fm.match(query)
# statistics: `result` is palette indices ("grayscale image")
output = np.uint16([m.trainIdx for m in matches]).reshape(height, width)
result = np.where(output < 256, output, 0).astype(np.uint8)
dist = np.uint8([m.distance for m in matches]).reshape(height, width)
Source of colormapped picture: Separating Object Contours OpenCV
Above is brilliant answer from Christoph Rackwitz! But this is a bit confusing due Python Notebook specifics. Here is a full code for conversion.
from matplotlib import colormaps # colormaps['jet'], colormaps['turbo']
from matplotlib.colors import LinearSegmentedColormap
from matplotlib._cm import _jet_data
def convert_jet_to_grey(img):
(height, width) = img.shape[:2]
cm = LinearSegmentedColormap("jet", _jet_data, N=2 ** 8)
# cm = colormaps['turbo'] swap with jet if you use turbo colormap instead
cm._init() # Must be called first. cm._lut data field created here
FLANN_INDEX_KDTREE = 1
index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5)
search_params = dict(checks=50)
fm = cv2.FlannBasedMatcher(index_params, search_params)
# JET, BGR order, excluding special palette values (>= 256)
fm.add(255 * np.float32([cm._lut[:256, (2, 1, 0)]])) # jet
fm.train()
# look up all pixels
query = img.reshape((-1, 3)).astype(np.float32)
matches = fm.match(query)
# statistics: `result` is palette indices ("grayscale image")
output = np.uint16([m.trainIdx for m in matches]).reshape(height, width)
result = np.where(output < 256, output, 0).astype(np.uint8)
# dist = np.uint8([m.distance for m in matches]).reshape(height, width)
return result # , dist uncomment if you wish accuracy image
Have been facing a similar problem while working with a served JPEG compressed image. Since I am on c++, resorting to matplotlib is not an option.
The alternative is to fetch one of the lookup tables (lut) corresponding to the desired colormap, e.g. "jet", available in the imgproc/src/colormap.cpp source file. Unfortunately, what could be easily retrieved using cv::colormap::Jet(n) (where ‘n’ would even allow to interpolate more points) is not accessible through OpenCV’s API.
That said, here is my solution based on @Christoph Rackwitz‘s answer:
// GNU Octave colormap "jet" as in cv::colormap::Jet()._lut
const float r[] = { 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.00588235294117645f,0.02156862745098032f,0.03725490196078418f,0.05294117647058827f,0.06862745098039214f,0.084313725490196f,0.1000000000000001f,0.115686274509804f,0.1313725490196078f,0.1470588235294117f,0.1627450980392156f,0.1784313725490196f,0.1941176470588235f,0.2098039215686274f,0.2254901960784315f,0.2411764705882353f,0.2568627450980392f,0.2725490196078431f,0.2882352941176469f,0.303921568627451f,0.3196078431372549f,0.3352941176470587f,0.3509803921568628f,0.3666666666666667f,0.3823529411764706f,0.3980392156862744f,0.4137254901960783f,0.4294117647058824f,0.4450980392156862f,0.4607843137254901f,0.4764705882352942f,0.4921568627450981f,0.5078431372549019f,0.5235294117647058f,0.5392156862745097f,0.5549019607843135f,0.5705882352941174f,0.5862745098039217f,0.6019607843137256f,0.6176470588235294f,0.6333333333333333f,0.6490196078431372f,0.664705882352941f,0.6803921568627449f,0.6960784313725492f,0.7117647058823531f,0.7274509803921569f,0.7431372549019608f,0.7588235294117647f,0.7745098039215685f,0.7901960784313724f,0.8058823529411763f,0.8215686274509801f,0.8372549019607844f,0.8529411764705883f,0.8686274509803922f,0.884313725490196f,0.8999999999999999f,0.9156862745098038f,0.9313725490196076f,0.947058823529412f,0.9627450980392158f,0.9784313725490197f,0.9941176470588236f,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.9862745098039216f,0.9705882352941178f,0.9549019607843139f,0.93921568627451f,0.9235294117647062f,0.9078431372549018f,0.892156862745098f,0.8764705882352941f,0.8607843137254902f,0.8450980392156864f,0.8294117647058825f,0.8137254901960786f,0.7980392156862743f,0.7823529411764705f,0.7666666666666666f,0.7509803921568627f,0.7352941176470589f,0.719607843137255f,0.7039215686274511f,0.6882352941176473f,0.6725490196078434f,0.6568627450980391f,0.6411764705882352f,0.6254901960784314f,0.6098039215686275f,0.5941176470588236f,0.5784313725490198f,0.5627450980392159f,0.5470588235294116f,0.5313725490196077f,0.5156862745098039f,0.5f };
const float g[] = { 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.001960784313725483f,0.01764705882352935f,0.03333333333333333f,0.0490196078431373f,0.06470588235294117f,0.08039215686274503f,0.09607843137254901f,0.111764705882353f,0.1274509803921569f,0.1431372549019607f,0.1588235294117647f,0.1745098039215687f,0.1901960784313725f,0.2058823529411764f,0.2215686274509804f,0.2372549019607844f,0.2529411764705882f,0.2686274509803921f,0.2843137254901961f,0.3f,0.3156862745098039f,0.3313725490196078f,0.3470588235294118f,0.3627450980392157f,0.3784313725490196f,0.3941176470588235f,0.4098039215686274f,0.4254901960784314f,0.4411764705882353f,0.4568627450980391f,0.4725490196078431f,0.4882352941176471f,0.503921568627451f,0.5196078431372548f,0.5352941176470587f,0.5509803921568628f,0.5666666666666667f,0.5823529411764705f,0.5980392156862746f,0.6137254901960785f,0.6294117647058823f,0.6450980392156862f,0.6607843137254901f,0.6764705882352942f,0.692156862745098f,0.7078431372549019f,0.723529411764706f,0.7392156862745098f,0.7549019607843137f,0.7705882352941176f,0.7862745098039214f,0.8019607843137255f,0.8176470588235294f,0.8333333333333333f,0.8490196078431373f,0.8647058823529412f,0.8803921568627451f,0.8960784313725489f,0.9117647058823528f,0.9274509803921569f,0.9431372549019608f,0.9588235294117646f,0.9745098039215687f,0.9901960784313726f,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.9901960784313726f,0.9745098039215687f,0.9588235294117649f,0.943137254901961f,0.9274509803921571f,0.9117647058823528f,0.8960784313725489f,0.8803921568627451f,0.8647058823529412f,0.8490196078431373f,0.8333333333333335f,0.8176470588235296f,0.8019607843137253f,0.7862745098039214f,0.7705882352941176f,0.7549019607843137f,0.7392156862745098f,0.723529411764706f,0.7078431372549021f,0.6921568627450982f,0.6764705882352944f,0.6607843137254901f,0.6450980392156862f,0.6294117647058823f,0.6137254901960785f,0.5980392156862746f,0.5823529411764707f,0.5666666666666669f,0.5509803921568626f,0.5352941176470587f,0.5196078431372548f,0.503921568627451f,0.4882352941176471f,0.4725490196078432f,0.4568627450980394f,0.4411764705882355f,0.4254901960784316f,0.4098039215686273f,0.3941176470588235f,0.3784313725490196f,0.3627450980392157f,0.3470588235294119f,0.331372549019608f,0.3156862745098041f,0.2999999999999998f,0.284313725490196f,0.2686274509803921f,0.2529411764705882f,0.2372549019607844f,0.2215686274509805f,0.2058823529411766f,0.1901960784313728f,0.1745098039215689f,0.1588235294117646f,0.1431372549019607f,0.1274509803921569f,0.111764705882353f,0.09607843137254912f,0.08039215686274526f,0.06470588235294139f,0.04901960784313708f,0.03333333333333321f,0.01764705882352935f,0.001960784313725483f,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 };
const float b[] = { 0.5f,0.5156862745098039f,0.5313725490196078f,0.5470588235294118f,0.5627450980392157f,0.5784313725490196f,0.5941176470588235f,0.6098039215686275f,0.6254901960784314f,0.6411764705882352f,0.6568627450980392f,0.6725490196078432f,0.6882352941176471f,0.7039215686274509f,0.7196078431372549f,0.7352941176470589f,0.7509803921568627f,0.7666666666666666f,0.7823529411764706f,0.7980392156862746f,0.8137254901960784f,0.8294117647058823f,0.8450980392156863f,0.8607843137254902f,0.8764705882352941f,0.892156862745098f,0.907843137254902f,0.9235294117647059f,0.9392156862745098f,0.9549019607843137f,0.9705882352941176f,0.9862745098039216f,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.9941176470588236f,0.9784313725490197f,0.9627450980392158f,0.9470588235294117f,0.9313725490196079f,0.915686274509804f,0.8999999999999999f,0.884313725490196f,0.8686274509803922f,0.8529411764705883f,0.8372549019607844f,0.8215686274509804f,0.8058823529411765f,0.7901960784313726f,0.7745098039215685f,0.7588235294117647f,0.7431372549019608f,0.7274509803921569f,0.7117647058823531f,0.696078431372549f,0.6803921568627451f,0.6647058823529413f,0.6490196078431372f,0.6333333333333333f,0.6176470588235294f,0.6019607843137256f,0.5862745098039217f,0.5705882352941176f,0.5549019607843138f,0.5392156862745099f,0.5235294117647058f,0.5078431372549019f,0.4921568627450981f,0.4764705882352942f,0.4607843137254903f,0.4450980392156865f,0.4294117647058826f,0.4137254901960783f,0.3980392156862744f,0.3823529411764706f,0.3666666666666667f,0.3509803921568628f,0.335294117647059f,0.3196078431372551f,0.3039215686274508f,0.2882352941176469f,0.2725490196078431f,0.2568627450980392f,0.2411764705882353f,0.2254901960784315f,0.2098039215686276f,0.1941176470588237f,0.1784313725490199f,0.1627450980392156f,0.1470588235294117f,0.1313725490196078f,0.115686274509804f,0.1000000000000001f,0.08431372549019622f,0.06862745098039236f,0.05294117647058805f,0.03725490196078418f,0.02156862745098032f,0.00588235294117645f,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 };
// Declare the lut (please note it has to be a 3xN Mat of CV_32F)
int N(sizeof(r)/sizeof(float));
cv::Mat lut(cv::Mat(cv::Size(3,N), CV_32F, cv::Scalar(0.0)));
for (int i(0); i < N; ++i) {
lut.at<float>(i, 0, 0) = 255.0 * b[i];
lut.at<float>(i, 1, 0) = 255.0 * g[i];
lut.at<float>(i, 2, 0) = 255.0 * r[i];
}
// Initialize the FlannBasedMatcher
auto index_params = new cv::flann::KDTreeIndexParams(5);
auto search_params = new cv::flann::SearchParams(50);
cv::FlannBasedMatcher matcher(index_params, search_params);
matcher.add(lut);
matcher.train();
// Convert the image pixels to perform the query (3xH*W Mat of CV_32F)
int QLEN(im.rows*im.cols);
cv::Mat query(cv::Mat(cv::Size(3, QLEN), CV_32F, cv::Scalar(0.0)));
int i(0);
for (int y(0); y < im.rows; ++y) {
for (int x(0); x < im.cols; ++x) {
query.at<float>(i, 0) = float(im.at<cv::Vec3b>(y, x)[0]);
query.at<float>(i, 1) = float(im.at<cv::Vec3b>(y, x)[1]);
query.at<float>(i, 2) = float(im.at<cv::Vec3b>(y, x)[2]);
++i;
}
}
// Lookup all image pixels
std::vector<cv::DMatch> matches;
matcher.match(query, matches);
// Reconstruct the greyscale image
cv::Mat im_grey(cv::Mat(cv::Size(1, QLEN), CV_32F, cv::Scalar(0.0)));
for (int i(0); i < QLEN; ++i) {
im_grey.at<float>(i, 0) = matches[i].trainIdx / 255.0;
}
im_grey = im_grey.reshape(0, {im.rows,im.cols});