本文整理汇总了Python中matplotlib.cm.binary方法的典型用法代码示例。如果您正苦于以下问题：Python cm.binary方法的具体用法？Python cm.binary怎么用？Python cm.binary使用的例子？那么恭喜您, 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matplotlib.cm的用法示例。
在下文中一共展示了cm.binary方法的3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: pplot
# 需要导入模块: from matplotlib import cm [as 别名]
# 或者: from matplotlib.cm import binary [as 别名]
def pplot(As, titles):
# setup
try: vmin = min([A.min() for A, t in zip(As[:-1], titles) if "missing" not in t]) # for pixel color reference
except: vmin = As[0].min()
try: vmax = max([A.max() for A, t in zip(As[:-1], titles) if "missing" not in t])
except: vmax = As[0].max()
my_dpi = 96
plt.figure(figsize=(1.4*(250*len(As))/my_dpi, 250/my_dpi), dpi = my_dpi)
for i, (A, title) in enumerate(zip(As, titles)):
plt.subplot(1, len(As), i+1)
if i == len(As)-1: vmin, vmax = A.min(), A.max()
if "missing" in title:
missing = A
masked_data = ones(As[i-1].shape)
for j,k in missing: masked_data[j,k] = 0
masked_data = masked_where(masked_data > 0.5, masked_data)
plt.imshow(As[i-1], interpolation = 'nearest', vmin = vmin, vmax = vmax)
plt.colorbar()
plt.imshow(masked_data, cmap = cm.binary, interpolation = "nearest")
else:
plt.imshow(A, interpolation = 'nearest', vmin = vmin, vmax = vmax)
plt.colorbar()
plt.title(title)
plt.axis("off")
plt.show()
#
# def unroll_missing(missing, ns):
# missing_unrolled = []
# for i, (MM, n) in enumerate(zip(missing, ns)):
# for m in MM:
# n2 = m[1] + sum([ns[j] for j in range(i)])
# missing_unrolled.append((m[0], n2))
# return missing_unrolled
# 示例2: plot_activation
# 需要导入模块: from matplotlib import cm [as 别名]
# 或者: from matplotlib.cm import binary [as 别名]
def plot_activation(model, input_image, layer_name):
"""Plots a mosaic of feature activation.
# Arguments
model: Keras model
input_image: Test image which is feed into the network
layer_name: Layer name of feature map
"""
from keras import backend as K
from IPython.display import display
f = K.function(model.inputs, [model.get_layer(layer_name).output])
output = f([[input_image]])
output = np.moveaxis(output[0][0], [0,1,2], [1,2,0])
print('%-20s input_shape: %-16s output_shape: %-16s' % (layer_name, str(input_image.shape), str(output.shape)))
num_y = num_x = int(np.ceil(np.sqrt(output.shape[0])))
data = mosaic(output, (num_x, num_y), '5%')
#plt.figure(figsize=(12, 12))
ax = plt.gca()
divider = make_axes_locatable(ax)
cax = divider.append_axes('right', size=0.1, pad=0.05)
im = ax.imshow(data, vmin=data.min(), vmax=data.max(), interpolation='nearest', cmap=cm.binary)
plt.colorbar(im, cax=cax)
display(plt.gcf())
plt.close() 示例3: sorting_value_of_zone
# 需要导入模块: from matplotlib import cm [as 别名]
# 或者: from matplotlib.cm import binary [as 别名]
def sorting_value_of_zone(zone):
group_value=[[[]],[[]]] # liste(liste_value, liste_nb_of_this-value)
for i in range(len(zone)):
if zone[i] not in group_value[0][0]:
group_value[0][0].append(zone[i])
group_value[1][0].append(1)
else:
index = group_value[0][0].index(zone[i])
group_value[1][0][index] += 1
return group_value
# #
# #
# # plt.imshow(arr_bin, cmap=cm.binary)
# # plt.show()
# plt.imshow(arr_bin, cmap=cm.binary)
# plt.show()
# from scipy.ndimage import gaussian_filter, median_filter
#
# #kernel = np.ones((5,5),np.float32)/25
# #img_smooth_1 = gaussian_filter(img, sigma=(20, 20), order=0)
# img_smooth_2 = median_filter(image, size=(30,30))
# img_smooth_2.astype(dtype='uint8')
#
# im = Image.fromarray(img_smooth_2)
# #im_1 = Image.fromarray(img_1)
# if im.mode != 'RGB':
# im2 = im.convert('RGB')
# im2.save('gm_white_inv_smooth.png')
#
# plt.subplot(2,1,1)
# plt.imshow(image, cmap=cm.binary)
# # plt.subplot(2,2,2)
# # plt.imshow(img_smooth_1, cmap=cm.binary)
# plt.subplot(2,1,2)
# plt.imshow(img_smooth_2, cmap=cm.binary)
# plt.show()
#=======================================================================================================================
# Start program
#=======================================================================================================================