CNN网络中的小工具——绘制热力图(Heat_Map)

注意：本贴主要来自网络搜集，自己做了一些修改，原作者链接：https://blog.youkuaiyun.com/guduruyu/article/details/60868501

热力图(Heat_Map)示意图

热力图可以高亮显示所关注区域中的数据所占比重情况，红色是占比最高，蓝色是占比最低。热力图以其好看、易于理解的可视化格式在CNN网络中广泛被用来分析特征分布情况。

彩色映射

如上图所示，上图按照灰度的高低将灰度图映射成彩色图，这个过程称为伪彩色映射表，即colormap,或color bar。

1. 内嵌的colormaps
   python的matplotlib模块内嵌了很多常用的colormaps，将colormaps绘制的代码如下所示：

import numpy as np
import matplotlib.pyplot as plt
 
# Have colormaps separated into categories:
# http://matplotlib.org/examples/color/colormaps_reference.html
 
cmaps = [('Perceptually Uniform Sequential',
                            ['viridis', 'inferno', 'plasma', 'magma']),
         ('Sequential',     ['Blues', 'BuGn', 'BuPu',
                             'GnBu', 'Greens', 'Greys', 'Oranges', 'OrRd',
                             'PuBu', 'PuBuGn', 'PuRd', 'Purples', 'RdPu',
                             'Reds', 'YlGn', 'YlGnBu', 'YlOrBr', 'YlOrRd']),
         ('Sequential (2)', ['afmhot', 'autumn', 'bone', 'cool',
                             'copper', 'gist_heat', 'gray', 'hot',
                             'pink', 'spring', 'summer', 'winter']),
         ('Diverging',      ['BrBG', 'bwr', 'coolwarm', 'PiYG', 'PRGn', 'PuOr',
                             'RdBu', 'RdGy', 'RdYlBu', 'RdYlGn', 'Spectral',
                             'seismic']),
         ('Qualitative',    ['Accent', 'Dark2', 'Paired', 'Pastel1',
                             'Pastel2', 'Set1', 'Set2', 'Set3']),
         ('Miscellaneous',  ['gist_earth', 'terrain', 'ocean', 'gist_stern',
                             'brg', 'CMRmap', 'cubehelix',
                             'gnuplot', 'gnuplot2', 'gist_ncar',
                             'nipy_spectral', 'jet', 'rainbow',
                             'gist_rainbow', 'hsv', 'flag', 'prism'])]
 
 
nrows = max(len(cmap_list) for cmap_category, cmap_list in cmaps)
gradient = np.linspace(0, 1, 256)
gradient = np.vstack((gradient, gradient))
 
 
def plot_color_gradients(cmap_category, cmap_list):
    fig, axes = plt.subplots(nrows=nrows)
    fig.subplots_adjust(top=0.95, bottom=0.01, left=0.2, right=0.99)
    axes[0].set_title(cmap_category + ' colormaps', fontsize=14)
 
    for ax, name in zip(axes, cmap_list):
        ax.imshow(gradient, aspect='auto', cmap=plt.get_cmap(name))
        pos = list(ax.get_position().bounds)
        x_text = pos[0] - 0.01
        y_text = pos[1] + pos[3]/2.
        fig.text(x_text, y_text, name, va='center', ha='right', fontsize=10)
 
    # Turn off *all* ticks & spines, not just the ones with colormaps.
    for ax in axes:
        ax.set_axis_off()
 
for cmap_category, cmap_list in cmaps:
    plot_color_gradients(cmap_category, cmap_list)
 
plt.show()

输出结果如下所示：

２. 获取colormap

以获取最常用的jet映射表为例，我们可以使用如下代码分别获取整型和浮点型的jet map，并将其保存在txt文件中：

import numpy as np
from matplotlib import cm
 # 获取cmp彩色映射图
def get_jet():
 
    colormap_int = np.zeros((256, 3), np.uint8)
    colormap_float = np.zeros((256, 3), np.float)
 
    for i in range(0, 256, 1):
       colormap_float[i, 0] = cm.jet(i)[0]
       colormap_float[i, 1] = cm.jet(i)[1]
       colormap_float[i, 2] = cm.jet(i)[2]
 
       colormap_int[i, 0] = np.int_(np.round(cm.jet(i)[0] * 255.0))
       colormap_int[i, 1] = np.int_(np.round(cm.jet(i)[1] * 255.0))
       colormap_int[i, 2] = np.int_(np.round(cm.jet(i)[2] * 255.0))
 
    np.savetxt("jet_float.txt", colormap_float, fmt = "%f", delimiter = ' ', newline = '\n')
    np.savetxt("jet_int.txt", colormap_int, fmt = "%d", delimiter = ' ', newline = '\n')
 
    print(colormap_int)
 
    return
if __name__ == "__main__":
	get_jet()

获取其他种类的colormap与之类似：

def get_spectral():
 
    colormap_int = np.zeros((256, 3), np.uint8)
    colormap_float = np.zeros((256, 3), np.float)
 
    for i in range(0, 256, 1):
       colormap_float[i, 0] = cm.spectral(i)[0]
       colormap_float[i, 1] = cm.spectral(i)[1]
       colormap_float[i, 2] = cm.spectral(i)[2]
 
       colormap_int[i, 0] = np.int_(np.round(cm.spectral(i)[0] * 255.0))
       colormap_int[i, 1] = np.int_(np.round(cm.spectral(i)[1] * 255.0))
       colormap_int[i, 2] = np.int_(np.round(cm.spectral(i)[2] * 255.0))
 
    np.savetxt("spectral_float.txt", colormap_float, fmt = "%f", delimiter = ' ', newline = '\n')
    np.savetxt("spectral_int.txt", colormap_int, fmt = "%d", delimiter = ' ', newline = '\n')
 
    print(colormap_int)
 
    return

3. 伪彩映射示例

from matplotlib import cm
import matplotlib.pyplot as plt
import numpy as np
import cv2


# 将所绘制图映射到热力图
def gray2color(gray_array, color_map):
    '''

    :param gray_array: 必须为二维numpy_narray矩阵
    :param color_map: 保存的cmp彩色映射图
    :return: 返回映射后的热力图(heat_map)
    '''
    rows, cols = gray_array.shape
    color_array = np.zeros((rows, cols, 3), np.uint8)

    for i in range(0, rows):
        for j in range(0, cols):
            color_array[i, j] = color_map[gray_array[i, j]]

    # color_image = Image.fromarray(color_array)

    return color_array


# the path of jet_map
jet_map = np.loadtxt('/home/alfred/fromlinux/objectTracking/code/NoOfficialCode/SiamRPN-PyTorch_36_star/jet_int.txt', dtype=np.int)

# numpy narray
picture = cv2.imread('/home/alfred/fromlinux/objectTracking/code/NoOfficialCode/SiamRPN-PyTorch_36_star/0020.jpg')  # cv2.imread读入的图片已经是numpy narray形式
gray_picture = cv2.cvtColor(picture, cv2.COLOR_RGB2GRAY)  # 转换成灰度图

# get the heat_map
gray_to_color = gray2color(gray_picture, jet_map)

# draw picture
plt.figure()
plt.subplot(121)
plt.imshow(gray_picture, cmap='gray')

plt.subplot(122)
plt.imshow(gray_to_color)
plt.show()

示例结果：