def lab_reduce_colors(colors, weights, threshold=30, return_rgb=True):
"""
Reduce colors by L*a*b* space.
"""
_colors = np.array([colors], dtype=np.uint8) # cv2.cvtColor only accept 2d array
lab_colors = cv2.cvtColor(_colors, cv2.COLOR_RGB2LAB)[0].astype(np.double)
inds = []
for i, color in enumerate(lab_colors):
dist = np.linalg.norm(hsv_colors_xyz[inds] - color, axis=1)
if not dist.size or dist.min() > threshold:
inds.append(i)
if return_rgb:
ret = colors[inds]
else:
ret = lab_colors[inds]
return ret
python类COLOR_RGB2LAB的实例源码
def Ra_space(img, Ra_ratio, a_threshold):
'''
Extract the Ra features by converting RGB to LAB space.
The higher is a value, the "redder" is the pixel.
'''
imgLab = cv2.cvtColor(img, cv2.COLOR_RGB2LAB);
w = img.shape[0]
h = img.shape[1]
Ra = np.zeros((w*h, 2))
for i in range(w):
for j in range(h):
R = math.sqrt((w/2-i)*(w/2-i) + (h/2-j)*(h/2-j))
Ra[i*h+j, 0] = R
Ra[i*h+j, 1] = min(imgLab[i][j][1], a_threshold)
Ra[:,0] /= max(Ra[:,0])
Ra[:,0] *= Ra_ratio
Ra[:,1] /= max(Ra[:,1])
return Ra
def Ra_space(img, Ra_ratio, a_threshold):
'''
Extract the Ra features by converting RGB to LAB space.
The higher is a value, the "redder" is the pixel.
'''
imgLab = cv2.cvtColor(img, cv2.COLOR_RGB2LAB);
w = img.shape[0]
h = img.shape[1]
Ra = np.zeros((w*h, 2))
for i in range(w):
for j in range(h):
R = math.sqrt((w/2-i)*(w/2-i) + (h/2-j)*(h/2-j))
Ra[i*h+j, 0] = R
Ra[i*h+j, 1] = min(imgLab[i][j][1], a_threshold)
Ra[:,0] /= max(Ra[:,0])
Ra[:,0] *= Ra_ratio
Ra[:,1] /= max(Ra[:,1])
return Ra
def color_reduction(image, n_colors, method='kmeans', palette=None):
"""Reduce the number of colors in image to n_colors using method"""
method = method.lower()
if method not in ('kmeans', 'linear', 'max', 'median', 'octree'):
method = 'kmeans'
if n_colors < 2:
n_colors = 2
elif n_colors > 128:
n_colors = 128
if method == 'kmeans':
n_clusters = n_colors
h, w = image.shape[:2]
img = cv2.cvtColor(image, cv2.COLOR_BGR2LAB)
img = img.reshape((-1, 3)) # -1 -> img.shape[0] * img.shape[1]
centers, labels = kmeans(img, n_clusters)
if palette is not None:
# palette comes in RGB
centers = cv2.cvtColor(np.array([palette]), cv2.COLOR_RGB2LAB)[0]
quant = centers[labels].reshape((h, w, 3))
output = cv2.cvtColor(quant, cv2.COLOR_LAB2BGR)
else:
img = PIL.Image.fromarray(image[:, :, ::-1], mode='RGB')
quant = img.quantize(colors=n_colors,
method=get_quantize_method(method))
if palette is not None:
palette = np.array(palette, dtype=np.uint8)
quant.putpalette(palette.flatten())
output = np.array(quant.convert('RGB'), dtype=np.uint8)[:, :, ::-1]
return output
def __MR_readimg(self,img):
if isinstance(img,str): # a image path
img = cv2.imread(img,cv2.CV_LOAD_IMAGE_COLOR)
# img = cv2.cvtColor(img,cv2.COLOR_BGR2RGB).astype(float)/255
# img = cv2.cvtColor(img,cv2.COLOR_BGR2LAB).astype(float)/255
img = cv2.cvtColor(img,cv2.COLOR_RGB2LAB).astype(float)/255
# h = 100
# w = int(float(h)/float(img.shape[0])*float(img.shape[1]))
return img #cv2.resize(img,(w,h))
