image op

opencv

• 使用 opencv 读取图片，并对生成的矩阵进行模式变换

  src_img = './test.jpg'
  array_bgr = cv2.imread(src_img)
  array_rgb = cv2.cvtColor(array_bgr, cv2.COLOR_BGR2RGB)
  array_gray = cv2.cvtColor(array_bgr, cv2.COLOR_BGR2GRAY)
  

• 将矩阵写到文件中

  cv2.imwrite('./new_img.jpg', array_gray)
  

• 显示opencv 中所有图片的模式转变

  mode_convert = [x for x in dir(cv2) if 'COLOR_' in x]
  

PIL

• 使用 Image 读取图片，并展示

from PIL import Image
img_rgb = Image.open(src_img)
img_gray = Image.open(src_img).convert('L')
img_rgb.show()

• Image 对象与 numpy 矩阵转换

  array_rgb = np.asarray(img_rgb, dtype=np.int32) # shape=(height, width, channel)
  array_gray = np.asarray(img_gray, dtype=np.int32)
  new_img_rgb = Image.fromarray(array_rgb.astype('uint8'), mode='RGB') # uint8 is necessary
  new_img_gray = Image.fromarray(array_gray.astype('uint8'), mode='L')
  

• 将 Image 对象写入文件中

img_rgb.save('./img_rgb.jpg')

maplotlib

• fig, axes = plt.subplots(nrows, ncols, figsize=(10, 5), **kwargs)

fig: Figure对象, 表示整张图, 可通过 fig.text(x, y, string) 在图上任意位置添加注释

axes: numpy array，其中每个元素表示不同子图的对象, axes.shape=(nrows, ncols), 通过 axes[0].get_position().bounds 返回坐标值 (x_left, y_lower, width, height)

kwargs: sharex, sharey, {True, ‘col’, ‘row’, ‘all’}

• 为图片添加 colorbar

• prepare image arrays

  src_face = './test.jpg'
  array_bgr = cv2.imread(src_face)
  array_rgb = cv2.cvtColor(array_bgr, cv2.COLOR_BGR2RGB)
  array_gray = cv2.cvtColor(array_bgr, cv2.COLOR_BGR2GRAY)
  

• plot

  fig, axes = plt.subplots(ncols=2, figsize=(8, 3))
  mappable_1 = axes[0].imshow(array_gray, cmap='Blues', interpolation='none')
  mappable_2 = axes[1].imshow(array_gray, cmap='Greens', interpolation='none')
  fig.colorbar(mappable_1, ax=axes[0])
  fig.colorbar(mappable_2, ax=axes[1])
  axes[0].axis('off')
  axes[1].axis('off')
  plt.show()
  

  如果图片矩阵若为 rgb 类型，则 colorbar 不再生效

• 以 iris data 为列
  • prepare data

    import pandas as pd
    import matplotlib.pyplot as plt
    iris_file = '/home/huizhu/bitbucket/test_data/iris/iris.csv'
    df = pd.read_csv(iris_file)
    x_1 = df['sepal length']
    x_2 = df['sepal width']
    x_3 = df['petal length']
    class_dict = {'Iris-setosa': 0, 'Iris-versicolor': 1, 'Iris-virginica': 2}
    Y = [class_dict[n] for n in df.values[:, 4].tolist()]
    

  • plot

    fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(8, 3), sharey=True)
    mappable_1 = ax1.scatter(x_1, x_2, c=Y, cmap='Blues')
    mappable_2 = ax2.scatter(x_3, x_2, c=Y, cmap='Blues')
    ax1.set_xlabel('sepal length')
    ax1.set_ylabel('sepal width')
    ax2.set_xlabel('petal length')
    fig.colorbar(mappable_1, ax=ax1)
    fig.colorbar(mappable_2, ax=ax2)
    plt.show()
    

    

• 可用的 cmaps

   # Have colormaps separated into categories:
   # http://matplotlib.org/examples/color/colormaps_reference.html
  cmaps = [('Perceptually Uniform Sequential', [
            'viridis', 'plasma', 'inferno', 'magma']),
         ('Sequential', [
            'Greys', 'Purples', 'Blues', 'Greens', 'Oranges', 'Reds',
            'YlOrBr', 'YlOrRd', 'OrRd', 'PuRd', 'RdPu', 'BuPu',
            'GnBu', 'PuBu', 'YlGnBu', 'PuBuGn', 'BuGn', 'YlGn']),
         ('Sequential (2)', [
            'binary', 'gist_yarg', 'gist_gray', 'gray', 'bone', 'pink',
            'spring', 'summer', 'autumn', 'winter', 'cool', 'Wistia',
            'hot', 'afmhot', 'gist_heat', 'copper']),
         ('Diverging', [
            'PiYG', 'PRGn', 'BrBG', 'PuOr', 'RdGy', 'RdBu',
            'RdYlBu', 'RdYlGn', 'Spectral', 'coolwarm', 'bwr', 'seismic']),
         ('Qualitative', [
            'Pastel1', 'Pastel2', 'Paired', 'Accent',
            'Dark2', 'Set1', 'Set2', 'Set3',
            'tab10', 'tab20', 'tab20b', 'tab20c']),
         ('Miscellaneous', [
            'flag', 'prism', 'ocean', 'gist_earth', 'terrain', 'gist_stern',
            'gnuplot', 'gnuplot2', 'CMRmap', 'cubehelix', 'brg', 'hsv',
            'gist_rainbow', 'rainbow', 'jet', 'nipy_spectral', 'gist_ncar'])]
  

import numpy as np
import pandas as pd
import cv2
import PIL
from PIL import Image
import matplotlib.pyplot as plt
import glob