Find the dominant image color

Question

I want to find the dominant color in the image. For this, I know that I have to use a histogram of the image. But I'm not sure about the image format. Which rgb, hsv or gray image should i use?

After calculating the histogram, I have to find the maximum value on the histogram. To do this, should I find below the maximum binVal value for the hsv image? Why is my image result only black?

float binVal = hist.at<float>(h, s);

EDIT:

I tried the code below. I am drawing an hs bar graph. And my resulting images are here. I did not find anything after the binary threshold. Perhaps I find the maximum value of the histogram.

 cvtColor(src, hsv, CV_BGR2HSV); // Quantize the hue to 30 levels // and the saturation to 32 levels int hbins = 20, sbins = 22; int histSize[] = {hbins, sbins}; // hue varies from 0 to 179, see cvtColor float hranges[] = { 0, 180 }; // saturation varies from 0 (black-gray-white) to // 255 (pure spectrum color) float sranges[] = { 0, 256 }; const float* ranges[] = { hranges, sranges }; MatND hist; // we compute the histogram from the 0-th and 1-st channels int channels[] = {0, 1}; calcHist( &hsv, 1, channels, Mat(), // do not use mask hist, 2, histSize, ranges, true, // the histogram is uniform false ); double maxVal=0; minMaxLoc(hist, 0, &maxVal, 0, 0); int scale = 10; Mat histImg = Mat::zeros(sbins*scale, hbins*10, CV_8UC3); int maxIntensity = -100; for( int h = 0; h < hbins; h++ ) { for( int s = 0; s < sbins; s++ ) { float binVal = hist.at<float>(h, s); int intensity = cvRound(binVal*255/maxVal); rectangle( histImg, Point(h*scale, s*scale), Point( (h+1)*scale - 1, (s+1)*scale - 1), Scalar::all(intensity), CV_FILLED ); if(intensity > maxIntensity) maxIntensity = intensity; } } std::cout << "max Intensity " << maxVal << std::endl; Mat dst; cv::threshold(src, dst, maxIntensity, 255, cv::THRESH_BINARY); namedWindow( "Dest", 1 ); imshow( "Dest", dst ); namedWindow( "Source", 1 ); imshow( "Source", src ); namedWindow( "HS Histogram", 1 ); imshow( "HS Histogram", histImg ); 

Answer 1

Decision

• Find HS Histogram
• Find the peak value of H (using the minmaxLoc function)
• Split image 3 channels (h, s, v)
• Apply to doorstep.
• Create image on merge 3 channel

Answer 2

Alternatively, you can try k-means . Calculate k clusters with k ~ 2..5 and take the center of gravity of the largest group as the dominant color.

The python OpenCv document has an illustrated example that pretty well dominates color (s):

Answer 3

Here are some tips to get you started.

• All 3 channels in RGB contribute to color, so you have to figure out somehow where the three different histograms are all at maximum. (Or their sum is maximum, or whatever.)
• HSV has all the color information (well, Hue) in one channel, so you only need to consider one histogram.
• Grayscale discards all color information, so it is pretty much useless for finding color.

Try converting to HSV, then calculate the histogram on channel H.

As you say, you want to find the maximum value in the histogram. But:

• You can consider a range of values ​​instead of one, say from 20-40 instead of just 30 . Try different range sizes.
• Remember that Hue is round in shape, so H=0 and H=360 same.
• Try building a histogram as follows:
  http://docs.opencv.org/doc/tutorials/imgproc/histograms/histogram_calculation/histogram_calculation.html
  to see if your results make sense.
• If you use a range of hues and you find the maximum range, you can either simply use the middle of this range as the dominant color, or you can find the average value of the colors in this range and use it.

Answer 4

Here's a Python approach that uses K-Means Clustering to determine the dominant colors in an image using sklearn.cluster.KMeans()

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Input image

results

With n_clusters=5 , the most dominant colors and percent distribution are presented here.

 [14.69488554 34.23074345 41.48107857] 13.67% [141.44980073 207.52576948 236.30722987] 15.69% [ 31.75790423 77.52713644 114.33328324] 18.77% [ 48.41205713 118.34814452 176.43411287] 25.19% [ 84.04820266 161.6848298 217.14045211] 26.69% 

Visualization of each color cluster

Similarities to n_clusters=10 ,

 [ 55.09073171 113.28271003 74.97528455] 3.25% [ 85.36889668 145.80759374 174.59846237] 5.24% [164.17201088 223.34258123 241.81929254] 6.60% [ 9.97315932 22.79468111 22.01822211] 7.16% [19.96940211 47.8375841 72.83728002] 9.27% [ 26.73510467 70.5847759 124.79314278] 10.52% [118.44741779 190.98204701 230.66728334] 13.55% [ 51.61750364 130.59930047 198.76335878] 13.82% [ 41.10232129 104.89923271 160.54431333] 14.53% [ 81.70930412 161.823664 221.10258949] 16.04% 

 import cv2, numpy as np from sklearn.cluster import KMeans def visualize_colors(cluster, centroids): # Get the number of different clusters, create histogram, and normalize labels = np.arange(0, len(np.unique(cluster.labels_)) + 1) (hist, _) = np.histogram(cluster.labels_, bins = labels) hist = hist.astype("float") hist /= hist.sum() # Create frequency rect and iterate through each cluster color and percentage rect = np.zeros((50, 300, 3), dtype=np.uint8) colors = sorted([(percent, color) for (percent, color) in zip(hist, centroids)]) start = 0 for (percent, color) in colors: print(color, "{:0.2f}%".format(percent * 100)) end = start + (percent * 300) cv2.rectangle(rect, (int(start), 0), (int(end), 50), \ color.astype("uint8").tolist(), -1) start = end return rect # Load image and convert to a list of pixels image = cv2.imread('1.jpg') image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) reshape = image.reshape((image.shape[0] * image.shape[1], 3)) # Find and display most dominant colors cluster = KMeans(n_clusters=5).fit(reshape) visualize = visualize_colors(cluster, cluster.cluster_centers_) visualize = cv2.cvtColor(visualize, cv2.COLOR_RGB2BGR) cv2.imshow('visualize', visualize) cv2.waitKey()