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照片EXIF数据统计与可视化

news 2024/11/16 6:36:44

拍的照片越来越多，想要了解一下日常拍摄的习惯，便于后面换镜头、调整参数等操作，所以写了这个脚本来统计照片的EXIF数据。该脚本用于统计指定文件夹下所有JPG图片的EXIF数据，包括快门速度、ISO、焦距、光圈和拍摄时间，并生成相应的分布图。在使用时，需要将文中的代码段都粘贴到同一个文件中，然后修改folder_path变量为你要处理的文件夹路径，运行脚本即可。以下是脚本的详细说明。

依赖

Python 3.x
exifread库：用于读取图片的EXIF数据
matplotlib库：用于绘制分布图
numpy库：用于处理数值数据
pathlib库：用于处理文件路径
collections.Counter：用于统计频率

安装依赖

使用以下命令安装所需的Python库：

pip install exifread matplotlib numpy

使用方法

将脚本保存为photo_statistic.py。
修改脚本中的folder_path变量，设置为你要处理的文件夹路径。
运行脚本：

python photo_statistic.py

代码说明

导入依赖

from pathlib import Path
import exifread
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import numpy as np
from collections import Counter

获取EXIF数据

get_exif_data函数用于读取图片的EXIF数据，并提取快门速度、ISO、焦距、光圈和拍摄时间。

def get_exif_data(image_path):with open(image_path, 'rb') as f:tags = exifread.process_file(f)shutter_speed = tags.get('EXIF ExposureTime') or tags.get('EXIF ShutterSpeedValue')iso = tags.get('EXIF ISOSpeedRatings')focal_length = tags.get('EXIF FocalLength')aperture = tags.get('EXIF FNumber') or tags.get('EXIF ApertureValue')datetime = tags.get('EXIF DateTimeOriginal') or tags.get('Image DateTime')return {'file': image_path,'shutter_speed': shutter_speed,'iso': iso,'focal_length': focal_length,'aperture': aperture,'datetime': datetime}

处理文件夹中的图片

process_images_in_folder函数遍历指定文件夹下的所有JPG图片，并调用get_exif_data函数获取每张图片的EXIF数据。

def process_images_in_folder(folder_path):results = []folder = Path(folder_path)for image_path in folder.rglob('*.jpg'):results.append(get_exif_data(image_path))return results

绘制统计图

plot_statistics函数用于绘制快门速度、ISO、焦距和光圈的分布图，并统计指定焦距的时间分布。

def plot_statistics(results):shutter_speeds = [float(result['shutter_speed'].values[0]) for result in results if result['shutter_speed']]isos = [int(result['iso'].values[0]) for result in results if result['iso']]focal_lengths = [float(result['focal_length'].values[0].num) / float(result['focal_length'].values[0].den) for result in results if result['focal_length']]apertures = [float(result['aperture'].values[0].num) / float(result['aperture'].values[0].den) for result in results if result['aperture']]dates = [result['datetime'].values.split(' ')[0].replace(':', '.') for result in results if result['datetime']]# 删除快门速度大于阈值的数据shutter_speeds_thres = 0.1shutter_speeds = [speed for speed in shutter_speeds if speed <= shutter_speeds_thres]fig, axs = plt.subplots(2, 1)# 将快门速度转换为分数格式def format_shutter_speed(speed):return (f"1/{(1/speed):.0f}" if speed != 0 else "0") if speed < 1 else str(speed)# 绘制快门速度分布图并将x轴刻度标签设置为分数格式axs[0].hist(shutter_speeds, bins=100, color='blue', edgecolor='black')axs[0].set_title('Shutter Speed Distribution' + f' (Threshold: {shutter_speeds_thres}s)')axs[0].set_xlabel('Shutter Speed (s)')# 使用分数表示快门速度axs[0].xaxis.set_major_formatter(ticker.FuncFormatter(lambda x, _: format_shutter_speed(x)))axs[0].set_ylabel('Frequency')axs[1].hist(focal_lengths, bins=100, color='red', edgecolor='black')axs[1].set_title('Focal Length Distribution')axs[1].set_xlabel('Focal Length (mm)')axs[1].set_ylabel('Frequency')plt.tight_layout()# 标出频率最高的N个焦距N = 6focal_length_counts = np.bincount(focal_lengths)most_common_focal_lengths = np.argsort(focal_length_counts)[-N:][::-1]for focal_length in most_common_focal_lengths:axs[1].text(focal_length, focal_length_counts[focal_length], str(focal_length), color='black')# 统计所有时间的频率dates_freq = Counter(dates)# 统计指定焦距的时间分布def get_focal_length_time_distribution(results, focal_length):date_of_focal_length = []for result in results:if result['focal_length'] and result['focal_length'].values[0].num / result['focal_length'].values[0].den == focal_length:date_of_focal_length.append(result['datetime'].values)date_of_focal_length.sort()# 以空格分隔时间字符串，只保留日期部分date_of_focal_length = [time.split(' ')[0].replace(':', '.') for time in date_of_focal_length]# 统计相同时间的频率return Counter(date_of_focal_length)prefix_focal_length = 50focal_length_200mm_freq = get_focal_length_time_distribution(results, prefix_focal_length)fig, ax = plt.subplots()# dates_freq排序dates_freq = dict(sorted(dates_freq.items()))ax.barh(list(dates_freq.keys()), list(dates_freq.values()), alpha=0.5, label='All')ax.barh(list(focal_length_200mm_freq.keys()), list(focal_length_200mm_freq.values()), label='Focal Length ' + str(prefix_focal_length) + 'mm')ax.legend()ax.set_xlabel('Frequency')ax.set_ylabel('Date')plt.show()

主函数

main函数设置文件夹路径，并调用process_images_in_folder和plot_statistics函数。

def main():folder_path = 'c:/Users/25503/Desktop/照片合集'results = process_images_in_folder(folder_path)plot_statistics(results)if __name__ == "__main__":main()