Python |自动DataFrame生成

Question

我有两个文件夹来自城市天际线两个不同的白天（白天和黑夜）。我想读取相应文件夹中不同颜色空间的所有图像，然后我想计算所有颜色通道的统计数据。然后我想创建一个包含所有统计信息的pandas数据框。

为了防止不必要的重复代码，我试图使用字典。目前，我能够打印出所有颜色空间x通道x统计组合的所有统计数据。但是我在概念上没有把这些东西变成带有行（单独的图像）和列（文件名，color_space x channel x statistic）的pandas DataFrame。

我将不胜感激任何帮助。

import os

import numpy as np
import matplotlib.pyplot as plt
import cv2
import pandas as pd


dictionary_of_color_spaces = {
    'RGB': cv2.COLOR_BGR2RGB,  # Red, Green, Blue
    'HSV': cv2.COLOR_BGR2HSV,  # Hue, Saturation, Value
    'HLS': cv2.COLOR_BGR2HLS,  # Hue, Lightness, Saturation
    'YUV': cv2.COLOR_BGR2YUV,  # Y = Luminance , U, V = Chrominance color components   
}

dictionary_of_channels = {
    'channel_1': 0,
    'channel_2': 1,
    'channel_3': 2,
}

dictionary_of_statistics = {
    'min': np.min,
    'max': np.max,
    'mean': np.mean,
    'median': np.median,
    'std': np.std,
}

# get filenames inside training folders for day and night
path_training_day = './day_night_images/training/day/'
path_training_night = './day_night_images/training/night/'
filenames_training_day = [file for file in os.listdir(path_training_day)]
filenames_training_night = [file for file in os.listdir(path_training_night)]

for filename in filenames_training_day:
    image = cv2.imread(path_training_day + filename)
    for color_space in dictionary_of_color_spaces:
        image = cv2.cvtColor(image, dictionary_of_color_spaces[color_space])
        for channel in dictionary_of_channels:
            for statistic in dictionary_of_statistics:
                print(dictionary_of_statistics[statistic](image[:,:,dictionary_of_channels[channel]]))

Answer 1

在不改变代码大小的情况下，我能想到的最简单的事情是：

• 创建一个空的df，其列是统计x通道x color_space的所有组合（很容易用列表推导完成）;
• 对于每个图像，将所有统计信息附加到变量（row）：
• 将row转换为pd.Series对象，使用row作为值，数据帧的列作为索引，filename作为其名称;
• 将行附加到空df中。

最重要的细节是使df列名称正确，即与填充row变量的值的顺序相同。当我们在列名称中为列名创建组合时，重要的是我们从最里面的循环移动到最外面的循环，以便稍后当我们将row附加到df时匹配的值。

这应该工作：

import os

import numpy as np
import matplotlib.pyplot as plt
import cv2
import pandas as pd


dictionary_of_color_spaces = {
    'RGB': cv2.COLOR_BGR2RGB,  # Red, Green, Blue
    'HSV': cv2.COLOR_BGR2HSV,  # Hue, Saturation, Value
    'HLS': cv2.COLOR_BGR2HLS,  # Hue, Lightness, Saturation
    'YUV': cv2.COLOR_BGR2YUV,  # Y = Luminance , U, V = Chrominance color components   
}

dictionary_of_channels = {
    'channel_1': 0,
    'channel_2': 1,
    'channel_3': 2,
}

dictionary_of_statistics = {
    'min': np.min,
    'max': np.max,
    'mean': np.mean,
    'median': np.median,
    'std': np.std,
}

# creates column names in the same order as loops below
cols = [f'{s}_{c}_{cs}' for s in dictionary_of_statistics for c in dictionary_of_channels for cs in dictionary_of_color_spaces]
# creates empty df
df = pd.DataFrame(column=cols)


# get filenames inside training folders for day and night
path_training_day = './day_night_images/training/day/'
path_training_night = './day_night_images/training/night/'
filenames_training_day = [file for file in os.listdir(path_training_day)]
filenames_training_night = [file for file in os.listdir(path_training_night)]

for filename in filenames_training_day:
    row = []  # row for the current image - to be populated with stat values
    image = cv2.imread(path_training_day + filename)
    for color_space in dictionary_of_color_spaces:
        image = cv2.cvtColor(image, dictionary_of_color_spaces[color_space])
        for channel in dictionary_of_channels:
            for statistic in dictionary_of_statistics:
                row.append(dictionary_of_statistics[statistic](image[:,:,dictionary_of_channels[channel]]))
    row_series = pd.Series(row, index=cols, name=filename)
    df = df.append(row_series)

此代码将每个图像的文件名转换为最终df中每行的索引。如果您不想这样，请将索引转换为新列（df['filename'] = df.index）并在之后使用pandas.reset_index（pd = pd.reset_index(drop=True)。