Most Dominant Color In Rgb Image - Opencv / Numpy / Python
Solution 1:
Two approaches using np.unique
and np.bincount
to get the most dominant color could be suggested. Also, in the linked page, it talks about bincount
as a faster alternative, so that could be the way to go.
Approach #1
def unique_count_app(a):
colors, count = np.unique(a.reshape(-1,a.shape[-1]), axis=0, return_counts=True)
return colors[count.argmax()]
Approach #2
def bincount_app(a):
a2D = a.reshape(-1,a.shape[-1])
col_range = (256, 256, 256) # generically : a2D.max(0)+1
a1D = np.ravel_multi_index(a2D.T, col_range)
return np.unravel_index(np.bincount(a1D).argmax(), col_range)
Verification and timings on 1000 x 1000
color image in a dense range [0,9)
for reproducible results -
In [28]: np.random.seed(0)
...: a = np.random.randint(0,9,(1000,1000,3))
...:
...: print unique_count_app(a)
...: print bincount_app(a)
[472]
(4, 7, 2)
In [29]: %timeit unique_count_app(a)
1 loop, best of 3: 820 ms per loop
In [30]: %timeit bincount_app(a)
100 loops, best of 3: 11.7 ms per loop
Further boost
Further boost upon leveraging multi-core
with numexpr
module for large data -
import numexpr as ne
defbincount_numexpr_app(a):
a2D = a.reshape(-1,a.shape[-1])
col_range = (256, 256, 256) # generically : a2D.max(0)+1
eval_params = {'a0':a2D[:,0],'a1':a2D[:,1],'a2':a2D[:,2],
's0':col_range[0],'s1':col_range[1]}
a1D = ne.evaluate('a0*s0*s1+a1*s0+a2',eval_params)
return np.unravel_index(np.bincount(a1D).argmax(), col_range)
Timings -
In [90]: np.random.seed(0)
...: a = np.random.randint(0,9,(1000,1000,3))
In [91]: %timeit unique_count_app(a)
...: %timeit bincount_app(a)
...: %timeit bincount_numexpr_app(a)
1 loop, best of 3: 843 ms per loop
100 loops, best of 3: 12 ms per loop
100 loops, best of 3: 8.94 ms per loop
Solution 2:
@Divakar has given a great answer. But if you want to port your own code to OpenCV, then:
img = cv2.imread('image.jpg',cv2.IMREAD_UNCHANGED)
data = np.reshape(img, (-1,3))
print(data.shape)
data = np.float32(data)
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0)
flags = cv2.KMEANS_RANDOM_CENTERS
compactness,labels,centers = cv2.kmeans(data,1,None,criteria,10,flags)
print('Dominant color is: bgr({})'.format(centers[0].astype(np.int32)))
Result for your image:
Dominant color is: bgr([41 31 23])
Time it took: 0.10798478126525879 secs
Solution 3:
The equivalent code for cv2.calcHist()
is to replace:
(hist, _) = np.histogram(clt.labels_, bins=num_labels)
with
dmin, dmax, _, _ = cv2.minMaxLoc(clt.labels_)
if np.issubdtype(data.dtype, 'float'): dmax += np.finfo(data.dtype).eps
else: dmax += 1
hist = cv2.calcHist([clt.labels_], [0], None, [num_labels], [dmin, dmax]).flatten()
Note that cv2.calcHist
only accepts uint8
and float32
as element type.
Update
It seems like opencv's and numpy's binning differs from each other as the histograms differ if the number of bins doesn't map the value range:
import numpy as np
from matplotlib import pyplot as plt
import cv2
#data = np.random.normal(128, 1, (100, 100)).astype('float32')
data = np.random.randint(0, 256, (100, 100), 'uint8')
BINS = 20
np_hist, _ = np.histogram(data, bins=BINS)
dmin, dmax, _, _ = cv2.minMaxLoc(data)
if np.issubdtype(data.dtype, 'float'): dmax += np.finfo(data.dtype).eps
else: dmax += 1
cv_hist = cv2.calcHist([data], [0], None, [BINS], [dmin, dmax]).flatten()
plt.plot(np_hist, '-', label='numpy')
plt.plot(cv_hist, '-', label='opencv')
plt.gcf().set_size_inches(15, 7)
plt.legend()
plt.show()
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