def k(screen):
Z = screen.reshape((-1,3))
# convert to np.float32
Z = np.float32(Z)
# define criteria, number of clusters(K) and apply kmeans()
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0)
K = 2
ret,label,center=cv2.kmeans(Z,K,None,criteria,10,cv2.KMEANS_RANDOM_CENTERS)
# Now convert back into uint8, and make original image
center = np.uint8(center)
res = center[label.flatten()]
res2 = res.reshape((screen.shape))
return res2
python类KMEANS_RANDOM_CENTERS的实例源码
def color_quant(input,K,output):
img = cv2.imread(input)
Z = img.reshape((-1,3))
# convert to np.float32
Z = np.float32(Z)
# define criteria, number of clusters(K) and apply kmeans()
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 15, 1.0)
ret,label,center=cv2.kmeans(Z,K,None,criteria,10,cv2.KMEANS_RANDOM_CENTERS)
# Now convert back into uint8, and make original image
center = np.uint8(center)
res = center[label.flatten()]
res2 = res.reshape((img.shape))
cv2.imshow('res2',res2)
cv2.waitKey(0)
cv2.imwrite(output, res2)
cv2.destroyAllWindows()
def k_means(self, a_frame, K=2):
"""
:param a_frame:
:param K:
:return: np.ndarray draw the frame use K color's centers
"""
i = 0
Z = a_frame.reshape((-1, 1))
Z = np.float32(Z)
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0)
ret, label, center = cv2.kmeans(Z, K, criteria, 10, cv2.KMEANS_RANDOM_CENTERS)
center = np.uint8(center)
res = center[label.flatten()]
res2 = res.reshape((a_frame.shape))
return res2
def cluster(frame_matrix):
new_frame_matrix = []
i = 0
for frame in frame_matrix:
print "reader {} frame".format(i)
i += 1
Z = frame.reshape((-1, 1))
Z = np.float32(Z)
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0)
K = 2
ret, label, center = cv2.kmeans(Z, K, criteria, 10, cv2.KMEANS_RANDOM_CENTERS)
center = np.uint8(center)
res = center[label.flatten()]
res2 = res.reshape((frame.shape))
new_frame_matrix.append(res2)
cv2.imshow('res2', res2)
cv2.waitKey(1)
cv2.destroyAllWindows()
def gen_codebook(dataset, descriptors, k = 64):
"""
Generate a k codebook for the dataset.
Args:
dataset (Dataset object): An object that stores information about the dataset.
descriptors (list of integer arrays): The descriptors for every class.
k (integer): The number of clusters that are going to be calculated.
Returns:
list of integer arrays: The k codewords for the dataset.
"""
iterations = 10
epsilon = 1.0
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, iterations, epsilon)
compactness, labels, centers = cv2.kmeans(descriptors, k , criteria, iterations, cv2.KMEANS_RANDOM_CENTERS)
return centers
def is_grid(self, grid, image):
"""
Checks the "gridness" by analyzing the results of a hough transform.
:param grid: binary image
:return: wheter the object in the image might be a grid or not
"""
# - Distance resolution = 1 pixel
# - Angle resolution = 1° degree for high line density
# - Threshold = 144 hough intersections
# 8px digit + 3*2px white + 2*1px border = 16px per cell
# => 144x144 grid
# 144 - minimum number of points on the same line
# (but due to imperfections in the binarized image it's highly
# improbable to detect a 144x144 grid)
lines = cv2.HoughLines(grid, 1, np.pi / 180, 144)
if lines is not None and np.size(lines) >= 20:
lines = lines.reshape((lines.size / 2), 2)
# theta in [0, pi] (theta > pi => rho < 0)
# normalise theta in [-pi, pi] and negatives rho
lines[lines[:, 0] < 0, 1] -= np.pi
lines[lines[:, 0] < 0, 0] *= -1
criteria = (cv2.TERM_CRITERIA_EPS, 0, 0.01)
# split lines into 2 groups to check whether they're perpendicular
if cv2.__version__[0] == '2':
density, clmap, centers = cv2.kmeans(
lines[:, 1], 2, criteria, 5, cv2.KMEANS_RANDOM_CENTERS)
else:
density, clmap, centers = cv2.kmeans(
lines[:, 1], 2, None, criteria,
5, cv2.KMEANS_RANDOM_CENTERS)
if self.debug:
self.save_hough(lines, clmap)
# Overall variance from respective centers
var = density / np.size(clmap)
sin = abs(np.sin(centers[0] - centers[1]))
# It is probably a grid only if:
# - centroids difference is almost a 90° angle (+-15° limit)
# - variance is less than 5° (keeping in mind surface distortions)
return sin > 0.99 and var <= (5*np.pi / 180) ** 2
else:
return False
def is_grid(self, grid, image):
"""
Checks the "gridness" by analyzing the results of a hough transform.
:param grid: binary image
:return: wheter the object in the image might be a grid or not
"""
# - Distance resolution = 1 pixel
# - Angle resolution = 1° degree for high line density
# - Threshold = 144 hough intersections
# 8px digit + 3*2px white + 2*1px border = 16px per cell
# => 144x144 grid
# 144 - minimum number of points on the same line
# (but due to imperfections in the binarized image it's highly
# improbable to detect a 144x144 grid)
lines = cv2.HoughLines(grid, 1, np.pi / 180, 144)
if lines is not None and np.size(lines) >= 20:
lines = lines.reshape((lines.size/2), 2)
# theta in [0, pi] (theta > pi => rho < 0)
# normalise theta in [-pi, pi] and negatives rho
lines[lines[:, 0] < 0, 1] -= np.pi
lines[lines[:, 0] < 0, 0] *= -1
criteria = (cv2.TERM_CRITERIA_EPS, 0, 0.01)
# split lines into 2 groups to check whether they're perpendicular
if cv2.__version__[0] == '2':
density, clmap, centers = cv2.kmeans(
lines[:, 1], 2, criteria,
5, cv2.KMEANS_RANDOM_CENTERS)
else:
density, clmap, centers = cv2.kmeans(
lines[:, 1], 2, None, criteria,
5, cv2.KMEANS_RANDOM_CENTERS)
# Overall variance from respective centers
var = density / np.size(clmap)
sin = abs(np.sin(centers[0] - centers[1]))
# It is probably a grid only if:
# - centroids difference is almost a 90° angle (+-15° limit)
# - variance is less than 5° (keeping in mind surface distortions)
return sin > 0.99 and var <= (5*np.pi / 180) ** 2
else:
return False
def test_kmeans(img):
## K????
z = img.reshape((-1, 3))
z = np.float32(z)
criteria = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0)
ret, label, center = cv2.kmeans(z, 20, criteria, 10, cv2.KMEANS_RANDOM_CENTERS)
center = np.uint8(center)
res = center[label.flatten()]
res2 = res.reshape((img.shape))
cv2.imshow('preview', res2)
cv2.waitKey()
def test_kmeans(img):
## K????
z = img.reshape((-1, 3))
z = np.float32(z)
criteria = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0)
ret, label, center = cv2.kmeans(z, 20, criteria, 10, cv2.KMEANS_RANDOM_CENTERS)
center = np.uint8(center)
res = center[label.flatten()]
res2 = res.reshape((img.shape))
cv2.imshow('preview', res2)
cv2.waitKey()
def find_label_clusters(kitti_base, kittiLabels, shape, num_clusters, descriptors=None):
if descriptors is None:
progressbar = ProgressBar('Computing descriptors', max=len(kittiLabels))
descriptors = []
for label in kittiLabels:
progressbar.next()
img = getCroppedSampleFromLabel(kitti_base, label)
# img = cv2.resize(img, (shape[1], shape[0]), interpolation=cv2.INTER_AREA)
img = resizeSample(img, shape, label)
hist = get_hog(img)
descriptors.append(hist)
progressbar.finish()
else:
print 'find_label_clusters,', 'Using supplied descriptors.'
print len(kittiLabels), len(descriptors)
assert(len(kittiLabels) == len(descriptors))
# X = np.random.randint(25,50,(25,2))
# Y = np.random.randint(60,85,(25,2))
# Z = np.vstack((X,Y))
# convert to np.float32
Z = np.float32(descriptors)
# define criteria and apply kmeans()
K = num_clusters
print 'find_label_clusters,', 'kmeans:', K
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0)
attempts = 10
ret,label,center=cv2.kmeans(Z,K,None,criteria,attempts,cv2.KMEANS_RANDOM_CENTERS)
# ret,label,center=cv2.kmeans(Z,2,criteria,attempts,cv2.KMEANS_PP_CENTERS)
print 'ret:', ret
# print 'label:', label
# print 'center:', center
# # Now separate the data, Note the flatten()
# A = Z[label.ravel()==0]
# B = Z[label.ravel()==1]
clusters = partition(kittiLabels, label)
return clusters
# # Plot the data
# from matplotlib import pyplot as plt
# plt.scatter(A[:,0],A[:,1])
# plt.scatter(B[:,0],B[:,1],c = 'r')
# plt.scatter(center[:,0],center[:,1],s = 80,c = 'y', marker = 's')
# plt.xlabel('Height'),plt.ylabel('Weight')
# plt.show()
def find_sample_clusters(pos_reg_generator, window_dims, hog, num_clusters):
regions = list(pos_reg_generator)
descriptors = trainhog.compute_hog_descriptors(hog, regions, window_dims, 1)
# convert to np.float32
descriptors = [rd.descriptor for rd in descriptors]
Z = np.float32(descriptors)
# define criteria and apply kmeans()
K = num_clusters
print 'find_label_clusters,', 'kmeans:', K
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0)
attempts = 10
ret,label,center=cv2.kmeans(Z,K,None,criteria,attempts,cv2.KMEANS_RANDOM_CENTERS)
# ret,label,center=cv2.kmeans(Z,2,criteria,attempts,cv2.KMEANS_PP_CENTERS)
print 'ret:', ret
# print 'label:', label
# print 'center:', center
# # Now separate the data, Note the flatten()
# A = Z[label.ravel()==0]
# B = Z[label.ravel()==1]
clusters = partition(regions, label)
return clusters
def createTrainingInstances(self, images):
instances = []
img_descriptors = []
master_descriptors = []
cv2.ocl.setUseOpenCL(False)
orb = cv2.ORB_create()
for img, label in images:
print img
img = read_color_image(img)
keypoints = orb.detect(img, None)
keypoints, descriptors = orb.compute(img, keypoints)
if descriptors is None:
descriptors = []
img_descriptors.append(descriptors)
for i in descriptors:
master_descriptors.append(i)
master_descriptors = np.float32(master_descriptors)
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0)
ret, labels, centers = cv2.kmeans(master_descriptors, self.center_num, None, criteria, 10, cv2.KMEANS_RANDOM_CENTERS)
labels = labels.ravel()
count = 0
img_num = 0
for img, label in images:
histogram = np.zeros(self.center_num)
feature_vector = img_descriptors[img_num]
for f in xrange(len(feature_vector)):
index = count + f
histogram.itemset(labels[index], 1 + histogram.item(labels[index]))
count += len(feature_vector)
pairing = Instance(histogram, label)
instances.append(pairing)
self.training_instances = instances
self.centers = centers
def createTrainingInstances(self, images):
instances = []
img_descriptors = []
master_descriptors = []
cv2.ocl.setUseOpenCL(False)
orb = cv2.ORB_create()
for img, label in images:
print img
img = read_color_image(img)
keypoints = orb.detect(img, None)
keypoints, descriptors = orb.compute(img, keypoints)
if descriptors is None:
descriptors = []
img_descriptors.append(descriptors)
for i in descriptors:
master_descriptors.append(i)
master_descriptors = np.float32(master_descriptors)
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0)
ret, labels, centers = cv2.kmeans(master_descriptors, self.center_num, None, criteria, 10, cv2.KMEANS_RANDOM_CENTERS)
labels = labels.ravel()
count = 0
img_num = 0
for img, label in images:
histogram = np.zeros(self.center_num)
feature_vector = img_descriptors[img_num]
for f in xrange(len(feature_vector)):
index = count + f
histogram.itemset(labels[index], 1 + histogram.item(labels[index]))
count += len(feature_vector)
pairing = Instance(histogram, label)
instances.append(pairing)
self.training_instances = instances
self.centers = centers
def local_bow_train(image):
instances = []
img_descriptors = []
master_descriptors = []
cv2.ocl.setUseOpenCL(False)
orb = cv2.ORB_create()
for img, label in images:
print img
img = read_color_image(img)
keypoints = orb.detect(img, None)
keypoints, descriptors = orb.compute(img, keypoints)
if descriptors is None:
descriptors = []
img_descriptors.append(descriptors)
for i in descriptors:
master_descriptors.append(i)
master_descriptors = np.float32(master_descriptors)
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0)
ret, labels, centers = cv2.kmeans(master_descriptors, self.center_num, None, criteria, 10, cv2.KMEANS_RANDOM_CENTERS)
labels = labels.ravel()
count = 0
img_num = 0
for img, label in images:
histogram = np.zeros(self.center_num)
feature_vector = img_descriptors[img_num]
for f in xrange(len(feature_vector)):
index = count + f
histogram.itemset(labels[index], 1 + histogram.item(labels[index]))
count += len(feature_vector)
pairing = Instance(histogram, label)
instances.append(pairing)
self.training_instances = instances
self.centers = centers
