I wrote a python program that used scikit-image to return the histogram of an image:
def get_hist(image):
image = img_as_float(image)
hs, dis = exposure.histogram(image)
cdf = np.cumsum(hs) #cumulative distribution function
cdf = 255 * cdf / cdf[-1] #normalize
return hs, cdf
while True:
# Capture frame-by-frame
ret, frame = video_capture.read()
hs, cdf = get_hist(frame)
print ("Histogram: " + str(hs))
And it works fine:
Histogram: [15258 1224 1221 1040 924 973 1035 1080 1144 1201 1363 1357
1565 1721 1996 2118 2069 2385 2332 2730 2554 2380 2594 2523
2748 2667 2553 2716 3001 2962 3051 2921 2992 3213 3439 3424
3443 3400 3750 3774 3752 3502 3632 3648 3625 3665 4160 3718
3892 3578 3774 4224 4178 4400 4391 4420 4368 4293 3992 4329
4401 4315 4471 4267 4362 4373 4559 3812 3974 4015 3988 3887
3647 3616 3424 3657 3721 3660 3544 3581 3434 3382 3428 3420
3256 3204 3366 3324 3177 3221 3102 3258 3160 3195 3212 3242
3157 2974 3071 2960 2956 2925 3002 3092 2952 2961 2854 2909
3080 2942 3057 3038 2850 2912 3000 2824 2857 2877 2656 2860
2848 2838 2701 2799 2625 2646 2656 2691 2600 2696 2738 2649
2721 2563 2709 2663 2584 2546 2565 2547 2505 2641 2614 2759
2554 2746 2723 2727 2505 2599 2755 2627 2552 2603 2605 2484
2465 2393 2319 2090 2059 2028 1882 1979 1868 1940 1854 1853
1696 1781 1694 1667 1682 1643 1692 1602 1540 1488 1549 1489
1472 1411 1414 1392 1423 1302 1252 1355 1287 1268 1186 1254
1172 1155 1175 1169 1238 1164 1165 1102 1205 1135 1118 1132
1065 1036 969 1009 941 1015 946 979 964 957 997 960
906 894 948 936 882 870 860 911 926 854 870 858
813 855 850 816 793 866 805 788 815 819 801 736
824 808 806 791 805 827 826 805 828 853 845 882
836 867 862 90039]
My question is, how do I convert this array into a value of how bright/dark the image is? I've been looking up online, and the only tutorials I can find are on how to alter the actual image, when that's not what I want.
Well, I don't think you need a histogram for this. Also, "how bright" is not really a defined value. What you could do, is to simply look at the average pixel value.
image = img_as_float(image)
print(np.mean(image))
Does this solve your problem?
An example:
>>> from skimage import data
>>> import numpy as np
>>> from skimage.util import img_as_float
>>> img = img_as_float(data.moon())
>>> img
array([[ 0.45490196, 0.45490196, 0.47843137, ..., 0.36470588,
0.37647059, 0.37647059],
[ 0.45490196, 0.45490196, 0.47843137, ..., 0.36470588,
0.37647059, 0.37647059],
[ 0.45490196, 0.45490196, 0.47843137, ..., 0.36470588,
0.37647059, 0.37647059],
...,
[ 0.42745098, 0.42745098, 0.43921569, ..., 0.45882353,
0.45490196, 0.45490196],
[ 0.44705882, 0.44705882, 0.44313725, ..., 0.4627451 ,
0.4627451 , 0.4627451 ],
[ 0.44705882, 0.44705882, 0.44313725, ..., 0.4627451 ,
0.4627451 , 0.4627451 ]])
>>> np.mean(img)
0.43988067028569255