本文整理汇总了Python中scipy.histogram方法的典型用法代码示例。如果您正苦于以下问题:Python scipy.histogram方法的具体用法?Python scipy.histogram怎么用?Python scipy.histogram使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类scipy
的用法示例。
在下文中一共展示了scipy.histogram方法的10个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: __findKnuth
# 需要导入模块: import scipy [as 别名]
# 或者: from scipy import histogram [as 别名]
def __findKnuth(x, minv, maxv):
"""
Implement Knuth method for histogram bin selection
"""
N = ((x >= minv) & (x <= maxv)).sum()
def funcer(M):
hh, loc = scipy.histogram(x, bins=M, range=[minv, maxv])
return np.log(M) + 1. / N * (scipy.special.gammaln(M / 2.) -
M * scipy.special.gammaln(0.5) -
scipy.special.gammaln(N + M / 2.) +
scipy.special.gammaln(hh + 0.5).sum())
maxN = 1000
ns = np.arange(1, maxN + 1)
vals = ns * 0.
for i in range(len(ns)):
vals[i] = funcer(ns[i])
bestn = ns[np.argmax(vals)]
if bestn == maxN:
print('WARNING the best number of bins is > maxbin(%d)' % (maxn))
return bestn
示例2: _auto_color
# 需要导入模块: import scipy [as 别名]
# 或者: from scipy import histogram [as 别名]
def _auto_color(self, url:str, ranks):
phrases = ["Calculating colors..."] # in case I want more
#try:
await self.bot.say("**{}**".format(random.choice(phrases)))
clusters = 10
async with aiohttp.get(url) as r:
image = await r.content.read()
with open('data/leveler/temp_auto.png','wb') as f:
f.write(image)
im = Image.open('data/leveler/temp_auto.png').convert('RGBA')
im = im.resize((290, 290)) # resized to reduce time
ar = scipy.misc.fromimage(im)
shape = ar.shape
ar = ar.reshape(scipy.product(shape[:2]), shape[2])
codes, dist = scipy.cluster.vq.kmeans(ar.astype(float), clusters)
vecs, dist = scipy.cluster.vq.vq(ar, codes) # assign codes
counts, bins = scipy.histogram(vecs, len(codes)) # count occurrences
# sort counts
freq_index = []
index = 0
for count in counts:
freq_index.append((index, count))
index += 1
sorted_list = sorted(freq_index, key=operator.itemgetter(1), reverse=True)
colors = []
for rank in ranks:
color_index = min(rank, len(codes))
peak = codes[sorted_list[color_index][0]] # gets the original index
peak = peak.astype(int)
colors.append(''.join(format(c, '02x') for c in peak))
return colors # returns array
#except:
#await self.bot.say("```Error or no scipy. Install scipy doing 'pip3 install numpy' and 'pip3 install scipy' or read here: https://github.com/AznStevy/Maybe-Useful-Cogs/blob/master/README.md```")
# converts hex to rgb
示例3: window_func
# 需要导入模块: import scipy [as 别名]
# 或者: from scipy import histogram [as 别名]
def window_func(x, y, func, xmin=None, xmax=None, nbin=100, empty=False,
xlog=False):
"""This function does compute a user-supplied function
on the subsets of y grouped by values of x
E.g. imagine that you have x from 0 to 100 and you want
to know the mean values of y for 0<x<10, 10<x<2- etc..
In that case you need to do
xbin,funcy,nperbin=window_func(x,y,lambda x: x.mean(),0,100,nbin=10)
where xbin is the array with the centers of bins,
funcy is the func -- evaluated for y where x is within the appropriate bin
and nperbin is the number of points in the appropriate bin
empty keyword is needed if you want to retrieve the function evaluation
in empty bins too, otherwise they aren't returned at all
"""
if xmin is None:
xmin = x.min()
if xmax is None:
xmax = x.max()
if xlog:
xmin,xmax,x=[numpy.log10(tmp) for tmp in [xmin,xmax,x]]
if (len(x)!=len(y) and x.ndim==1) or (x.shape!=y.shape and x.ndim>1):
raise ValueError('Input arrays must have the same size')
#hh,loc=scipy.histogram(x,range=(xmin,xmax),bins=nbin)
inds = ((x-xmin)/float(xmax-xmin)*nbin).astype(int)
mask = numpy.zeros(nbin, bool)
retv = numpy.zeros(nbin)
hh = numpy.zeros(nbin,int)
for i in range(nbin):
cury=y[inds==i]
hh[i]=len(cury)
mask[i]=len(cury)>0
if len(cury)>0 or empty:
retv[i]=func(cury)
retx = xmin+(xmax-xmin)*1./nbin*(0.5+numpy.arange(nbin))
if xlog:
retx = 10**retx
if empty:
mask |= True
return retx[mask], retv[mask],hh[mask]
示例4: discrete_kldiv
# 需要导入模块: import scipy [as 别名]
# 或者: from scipy import histogram [as 别名]
def discrete_kldiv(X_baseline: np.array, X_sampled: np.array) -> float:
P, bins = histogram(X_baseline, bins=10, density=True)
P += 1e-10
Q, _ = histogram(X_sampled, bins=bins, density=True)
Q += 1e-10
return entropy(P, Q)
示例5: extract_dsift
# 需要导入模块: import scipy [as 别名]
# 或者: from scipy import histogram [as 别名]
def extract_dsift(cls, rawfilename, winsize, nbins):
"""read_ppm(rawfilename, filename)
Read in raw pixel data from rawfilename (.ppm).
Create a histogram around each pixel to become
the feature vector for that obsevation (pixel).
Pickle the result and save it to filename.
Note: does NOT update object fields.
Follow this with a call to readin().
"""
im = Image.open(rawfilename)
(width, height) = im.size
# To be removed in the future
# Pick up all windows, stepping by half of the window size
labels = []
halfwin = int(winsize/2)
for y in range(halfwin, height-halfwin, int(halfwin/2)):
for x in range(halfwin, width-halfwin, int(halfwin/2)):
labels += ['(%d,%d)' % (y,x)]
mlab.bb_dsift(N.array(im), winsize, 'temp.mat')
sift_features = scipy.io.loadmat('temp.mat')
sift_features = sift_features['d_']
return (sift_features, labels, width, height)
示例6: extract_hist_subimg
# 需要导入模块: import scipy [as 别名]
# 或者: from scipy import histogram [as 别名]
def extract_hist_subimg(sub_image):
hist_bins = range(0,260,1)
hist_features = N.histogram(sub_image.ravel(), hist_bins)[0]
return hist_features
示例7: get_dominant_image_colors
# 需要导入模块: import scipy [as 别名]
# 或者: from scipy import histogram [as 别名]
def get_dominant_image_colors(image, num_clusters=4):
"""
Returns the dominant image color that isn't pure white or black. Uses
kmeans on the colors. Returns the result as RGB hex strings in the format
['#rrggbb', '#rrggbb', ...].
:param image: PIL image or path
"""
if isinstance(image, basestring):
image = Image.open(image)
# downsample for speed
im = image.resize((512, 512), Image.ANTIALIAS)
# reshape
ar0 = scipy.misc.fromimage(im)
shape = ar0.shape
npixels = scipy.product(shape[:2])
ar0 = ar0.reshape(npixels, shape[2])
# keep only nontransparent elements
ar = ar0[ar0[:, 3] == 255][:, 0:3]
try:
# kmeans clustering
codes, dist = scipy.cluster.vq.kmeans(ar, num_clusters)
except:
# kmeans sometimes fails -- if that is the case, use the mean color and
# nothing else.
arf = ar.astype(float)
clamp = lambda p: max(0, min(255, int(p)))
return ['#' + ''.join(['%0.2x' % clamp(arf[:, i].sum() / float(arf.shape[1])) for i in (0, 1, 2)])]
vecs, dist = scipy.cluster.vq.vq(ar, codes) # assign codes
counts, bins = scipy.histogram(vecs, len(codes)) # count occurrences
# sort by count frequency
indices = [i[0] for i in
sorted(enumerate(counts), key=lambda x:x[1], reverse=True)]
# convert to hex strings
colors = [''.join(chr(c) for c in code).encode('hex') for code in codes]
results = []
for idx in indices:
color = colors[idx]
if color != 'ffffff' and color != '000000':
results.append('#' + color)
return results
示例8: extract_sift
# 需要导入模块: import scipy [as 别名]
# 或者: from scipy import histogram [as 别名]
def extract_sift(cls, rawfilename, winsize, nbins):
"""read_ppm(rawfilename, filename)
Read in raw pixel data from rawfilename (.ppm).
Create a histogram around each pixel to become
the feature vector for that obsevation (pixel).
Pickle the result and save it to filename.
Note: does NOT update object fields.
Follow this with a call to readin().
"""
if cls._VL_SIFT_:
# VLSIFT matlab
im = Image.open(rawfilename)
(width, height) = im.size
mlab.bb_sift(N.array(im), 'temp.mat')
sift_features = scipy.io.loadmat('temp.mat')
kp = sift_features['f_']
sift_features = sift_features['d_']
sift_features = scipy.concatenate((sift_features.transpose(), kp[2:4].transpose()), 1).transpose()
labels = [];
for ikp in kp.transpose():
(x,y) = ikp[0:2]
labels += ['(%d,%d)' % (y,x)]
else:
#Opencv SIFT
img = cv2.imread(rawfilename)
gray= cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
height, width = gray.shape
# Computing SIFT
sift = cv2.SIFT(edgeThreshold = 3)
kp, des = sift.detectAndCompute(gray,None)
labels = []
sift_features = N.transpose(des)
scale_angle = []
for ikp in kp:
(x,y) = ikp.pt
scale_angle.append([ikp.size/12, ikp.angle])
labels += ['(%d,%d)' % (y,x)]
scale_angle = N.array(scale_angle)
sift_features = scipy.concatenate((sift_features.transpose(), scale_angle), 1).transpose()
return (sift_features, labels, width, height)
示例9: extract_hist
# 需要导入模块: import scipy [as 别名]
# 或者: from scipy import histogram [as 别名]
def extract_hist(cls, rawfilename, winsize, nbins):
# This function extracts the histogram features from the image
im = Image.open(rawfilename)
(width, height) = im.size
npixels = width * height
pix = scipy.array(im)
# Generate one feature vector (histogram) per pixel
#winsize = 20 # for test.pgm
#winsize = 0 # for RGB
halfwin = int(winsize/2)
bins = scipy.linspace(0, 255, nbins)
# Only use windows that are fully populated
mywidth = width-winsize
myheight = height-winsize
#data = scipy.zeros((nbins-1, mywidth * myheight))
#data = scipy.zeros((3*winsize*winsize, mywidth * myheight))
data = []
labels = []
# Pick up all windows, stepping by half of the window size
for y in range(halfwin, height-halfwin, int(halfwin/2)):
for x in range(halfwin, width-halfwin, int(halfwin/2)):
# Read in data in row-major order
ind = (y-halfwin)*mywidth + (x-halfwin)
#data[:,ind] = \
# scipy.histogram(pix[y-halfwin:y+halfwin,
# x-halfwin:x+halfwin],
# bins)[0]
# Just RGB
#data[:,ind] = pix[y,x]
# RGB window
#data[:,ind] = pix[y-halfwin:y+halfwin,x-halfwin:x+halfwin].flat
hist_features = TCData.extract_hist_subimg(pix[y-halfwin:y+halfwin,x-halfwin:x+halfwin])
if data == []:
data = hist_features.reshape(-1,1)
else:
data = scipy.concatenate((data, hist_features.reshape(-1,1)),1)
labels += ['(%d,%d)' % (y,x)]
return (data, labels, width, height)
示例10: read_ppm
# 需要导入模块: import scipy [as 别名]
# 或者: from scipy import histogram [as 别名]
def read_ppm(self, rawfilename, filename):
# This function reads the ppm/jpg file and extracts the features if the
# features pkl file doesn't exist. It is also compatible for extension
# of the feauture vector and doesn't compute the already computed features
new_feature_string = []
updated_feature = 0
data = N.array([], dtype=int)
if os.path.exists(filename):
pkl_f = open(filename, 'r')
(data, labels, feature_string, width, height, winsize, nbins)= pickle.load(pkl_f)
self.winsize = winsize
self.nbins = nbins
new_feature_string = list(feature_string)
pkl_f.close()
if not new_feature_string.count('dsift'):
updated_feature = 1
(sift_features, labels, width, height) = self.extract_dsift(rawfilename, self.winsize, self.nbins)
if data.size:
data = scipy.concatenate((data.transpose(), sift_features.transpose()), 1).transpose()
else:
data = sift_features
new_feature_string.append('dsift')
if not new_feature_string.count('histogram'):
updated_feature = 1
(hist_features, labels, width, height) = self.extract_hist(rawfilename, self.winsize, self.nbins)
hist_features = hist_features/(self.winsize)
if data.size:
data = scipy.concatenate((data.transpose(), hist_features.transpose()), 1).transpose()
else:
data = hist_features
new_feature_string.append('histogram')
'''
if not new_feature_string.count('position'):
updated_feature = 1
position_features = []
for label in labels:
(y,x) = map(int, label.strip('()').split(','))
position_features.append([x,y])
position_features = N.array(position_features)
if data.size:
data = scipy.concatenate((data.transpose(), position_features), 1).transpose()
else:
data = position_features
new_feature_string.append('position')
'''
if updated_feature:
outf = open(filename, 'w')
pickle.dump((data, labels, new_feature_string, width, height, self.winsize, self.nbins),outf)
outf.close()
print 'Saved data to %s.' % filename
return (data, labels, new_feature_string, width, height, self.winsize, self.nbins)