通过时间戳算法将照片分组到游戏中时光倒流 [英] Grouping Photo's to Timelapse by Time Stamp Algorithm

问题描述

我正在尝试编写一个程序，将缩时照片与时间戳进行分组.

• 输入文件是我从中读取时间戳的CR2 RAW文件
• 将时间戳放入文件名排序的字典中，并将时间戳作为值

我正在尝试提出一种将照片分类为延时拍摄的算法.

I'm trying to come up with an algorithm that categorises the photos into timelapses.

假设:

• 游戏中时光倒流由至少5张以上的照片依次组成

• A timelapse consists out of a minimum of 5+ photo's in sequence

延时摄影的时间间隔可能会有所不同，有时会有5分钟的间隔，其他时间是4秒.

The timelapse photo interval can vary, sometimes there is a 5 minute interval other times 4 seconds.

在延时之间拍摄了一些随机照片

Some random photo's are shot in between the timelapses

只要在大多数情况下对延时进行分组，就可以略微容错.

A slight error tolerance is okay, as long as the timelapses are grouped for the most part.

每天拍摄的CR2原始时间戳可以在以下位置找到:

Full Dataset: https://pastebin.com/aYmPhYTx

Example:
2018-12-08 11:06:41
2018-12-08 11:09:20
2018-12-08 11:12:29
2018-12-08 11:12:42
2018-12-08 11:12:54
2018-12-08 11:13:07
2018-12-08 11:13:19
2018-12-08 11:13:31
2018-12-08 11:13:44
2018-12-08 11:13:56
2018-12-08 11:14:09
2018-12-08 11:14:21
2018-12-08 11:14:33
2018-12-08 11:14:46
2018-12-08 11:14:58
2018-12-08 11:15:11

以及我到目前为止的代码摘录.当前，它仅检查与前一张照片的时差是否为5分钟以上，以查看是否开始了新的间隔拍摄.可以，但是可以改善.

And an excerpt of the code I have so far. Currently it only checks if the time difference with the previous photo is 5+ minutes to see if a new timelapse started. This works okay but could be improved.

import exifread
import time
import glob
import collections  # for ordered dictionary
import datetime
from itertools import groupby

def cat_algo(folder):
    # Get a list with all the CR2 files in the folder we are processing
    file_list = folder_to_file_list(folder)

    # Extract the timestamp out of the CR2 file into a sorted dictionary
    cr2_timestamp = collections.OrderedDict()

    for file in file_list:
        cr2_timestamp[file] = return_date_from_raw(file)
        print str(file) + " - METADATA TIMESTAMP: " + \
            str(return_date_from_raw(file))

    cr2_category = collections.OrderedDict()
    item_count = 1
    group_count = 0
    sequence_count = 0
    index = 0
    photo_difference_with_previous = collections.OrderedDict()

    # Loop over the dictionary to compare the timestamps and create a new dictionary
    # with a suspected group number per shot.

    # get item and the next item out of the sorted dictionary
    for item, nextitem in zip(cr2_timestamp.items(), cr2_timestamp.items()[1::]):
        # if not the first CR2 file
        if item_count >= 2:
            # get the datestamp of the current and the next photo in the dict
            current_date_stamp = item[1]
            next_date_stamp = nextitem[1]

            delta_previous = current_date_stamp - previous_date_stamp
            delta_next = next_date_stamp - current_date_stamp
            previous_difference_score = 0

            if delta_previous > datetime.timedelta(minutes=5):
                # if difference_score < 20:
                print item[0] + " - hit - " + str(delta_previous)
                group_count += 1
                cr2_category[item[0]] = group_count
            else:
                cr2_category[item[0]] = group_count

            # Calculations done, make the current date stamp the previous datestamp for the next iteration
            previous_date_stamp = current_date_stamp

            # If time difference with previous over X make a dict with name:number, in the end everything which has the
            # same number 5+ times in a row can be assumed as a timelapse.

        else:
            # If it is the first date stamp, assign it the current one to be used in the next loop
            previous_date_stamp = item[1]

        # To help make sure this is not the first image in the sequence.
        item_count += 1

    print cr2_category

我认为要走的路是利用照片之间的百分比差异和知识，即间隔拍摄是5张以上照片的序列，并且时间差异最小.

The way to go I think is to use the percentage difference between photos and the knowledge that a timelapse is a sequence of 5+ photo's with minimal time variance difference.

输出

Day 2/IMG_1995.CR2 - hit - 0:52:14
Day 2/IMG_2040.CR2 - hit - 0:07:01
Day 2/IMG_2117.CR2 - hit - 0:07:15
Day 2/IMG_2186.CR2 - hit - 1:02:05
Day 2/IMG_2206.CR2 - hit - 0:09:38
Day 2/IMG_2220.CR2 - hit - 0:08:45
Day 2/IMG_2372.CR2 - hit - 0:13:01

OrderedDict([('Day 2/IMG_1712.CR2', 0), ('Day 2/IMG_1713.CR2', 0), ('Day 2/IMG_1714.CR2', 0), ('Day 2/IMG_1715.CR2', 0), ('Day 2/IMG_1716.CR2', 0), ('Day 2/IMG_1717.CR2', 0), ('Day 2/IMG_1718.CR2', 0), ('Day 2/IMG_1719.CR2', 0), ('Day 2/IMG_1720.CR2', 0), ('Day 2/IMG_1721.CR2', 0), ('Day 2/IMG_1722.CR2', 0), ('Day 2/IMG_1723.CR2', 0), ('Day 2/IMG_1724.CR2', 0), ('Day 2/IMG_1725.CR2', 0), ('Day 2/IMG_1726.CR2', 0), ('Day 2/IMG_1727.CR2', 0), ('Day 2/IMG_1728.CR2',0), ('Day 2/IMG_1729.CR2', 0), ('Day 2/IMG_1730.CR2', 0), ('Day 2/IMG_1731.CR2', 0), ('Day 2/IMG_1732.CR2', 0), ('Day 2/IMG_1733.CR2', 0), ('Day 2/IMG_1734.CR2', 0), ('Day 2/IMG_1735.CR2', 0), ('Day 2/IMG_1736.CR2', 0), ('Day 2/IMG_1737.CR2', 0), ('Day 2/IMG_1738.CR2', 0), ('Day 2/IMG_1739.CR2', 0), ('Day 2/IMG_1740.CR2', 0), ('Day 2/IMG_1741.CR2', 0), ('Day 2/IMG_1742.CR2', 0), ('Day 2/IMG_1743.CR2', 0), ('Day 2/IMG_1744.CR2', 0), ('Day 2/IMG_1745.CR2', 0), ('Day 2/IMG_1746.CR2', 0), ('Day 2/IMG_1747.CR2', 0), ('Day 2/IMG_1748.CR2', 0), ('Day 2/IMG_1749.CR2', 0), ('Day 2/IMG_1750.CR2', 0), ('Day 2/IMG_1751.CR2', 0), ('Day 2/IMG_1752.CR2'.......

但是经过实验，我还没有弄清楚.有什么聪明的方法可以做到这一点吗?我在寻找一个简单的解决方案吗?谢谢！

推荐答案

这里是实现此目的的另一种方法:

Here is another way to achieve that:

import datetime

def diff(time1, time2):
    t1 = datetime.datetime.strptime(time1, '%Y-%m-%d %H:%M:%S')
    t2 = datetime.datetime.strptime(time2, '%Y-%m-%d %H:%M:%S')
    return t2-t1

with open('file.txt', 'r') as f:
        data = f.read()
        dates = [x for x in data.split('\n')]

r = []
result = []
for i in range(len(dates)-1):
    if diff(dates[i], dates[i+1]).total_seconds() > 300:
        result.append(r)
        r = []
    r.append(dates[i])
result.append(r)

for i, r in enumerate(result):
    print('Timelapse: {}. Photos: {}. Period: {} - {}. Duration: {}'.format(i+1, len(r), r[0], r[-1], diff(r[0], r[-1])))

按时间间隔分割的日期:

Dates splitted by timelapses:

[['2018-12-08 11:06:41', '2018-12-08 11:09:20', '2018-12-08 11:12:29', '2018-12-08 11:12:42', '2018-12-08 11:12:54', '2018-12-08 11:13:07', '2018-12-08 11:13:19', '2018-12-08 11:13:31', '2018-12-08 11:13:44', '2018-12-08 11:13:56', '2018-12-08 11:14:09', '2018-12-08 11:14:21', '2018-12-08 11:14:33', '2018-12-08 11:14:46', '2018-12-08 11:14:58', '2018-12-08 11:15:11', '2018-12-08 11:15:23', '2018-12-08 11:15:35', '2018-12-08 11:15:48', '2018-12-08 11:16:00', '2018-12-08 11:16:13', '2018-12-08 11:16:25', '2018-12-08 11:16:37', '2018-12-08 11:16:50', '2018-12-08 11:17:02', '2018-12-08 11:17:15', '2018-12-08 11:17:27', '2018-12-08 11:17:40', '2018-12-08 11:17:52', '2018-12-08 11:18:04', '2018-12-08 11:18:17', '2018-12-08 11:18:29', '2018-12-08 11:18:42', '2018-12-08 11:18:54', '2018-12-08 11:19:06', '2018-12-08 11:19:19', '2018-12-08 11:19:31', '2018-12-08 11:19:44', '2018-12-08 11:19:56', '2018-12-08 11:20:08', '2018-12-08 11:20:21', '2018-12-08 11:20:33', '2018-12-08 11:20:46', '2018-12-08 11:20:58', '2018-12-08 11:21:10', '2018-12-08 11:21:23', '2018-12-08 11:21:35', '2018-12-08 11:21:48', '2018-12-08 11:22:00', '2018-12-08 11:22:13', '2018-12-08 11:22:25', '2018-12-08 11:22:37', '2018-12-08 11:22:50', '2018-12-08 11:23:02', '2018-12-08 11:23:15', '2018-12-08 11:23:27', '2018-12-08 11:23:39', '2018-12-08 11:23:52', '2018-12-08 11:24:04', '2018-12-08 11:24:17', '2018-12-08 11:24:29', '2018-12-08 11:24:41', '2018-12-08 11:24:54', '2018-12-08 11:25:06', '2018-12-08 11:25:19', '2018-12-08 11:25:31', '2018-12-08 11:25:44', '2018-12-08 11:25:56', '2018-12-08 11:26:08', '2018-12-08 11:26:21', '2018-12-08 11:26:33', '2018-12-08 11:26:46', '2018-12-08 11:26:58', '2018-12-08 11:27:10', '2018-12-08 11:27:23', '2018-12-08 11:27:35', '2018-12-08 11:27:48', '2018-12-08 11:28:00', '2018-12-08 11:28:12', '2018-12-08 11:28:25', '2018-12-08 11:28:37', '2018-12-08 11:28:50', '2018-12-08 11:29:02', '2018-12-08 11:29:14', '2018-12-08 11:29:27', '2018-12-08 11:29:39', '2018-12-08 11:29:52', '2018-12-08 11:30:04', '2018-12-08 11:30:17', '2018-12-08 11:30:29', '2018-12-08 11:30:41', '2018-12-08 11:30:54', '2018-12-08 11:31:06', '2018-12-08 11:31:19', '2018-12-08 11:31:31', '2018-12-08 11:31:43', '2018-12-08 11:31:56', '2018-12-08 11:32:08', '2018-12-08 11:32:21', '2018-12-08 11:32:33', '2018-12-08 11:32:45', '2018-12-08 11:32:58', '2018-12-08 11:33:10', '2018-12-08 11:33:23', '2018-12-08 11:33:35', '2018-12-08 11:33:47', '2018-12-08 11:34:00', '2018-12-08 11:34:12', '2018-12-08 11:34:25', '2018-12-08 11:34:37', '2018-12-08 11:34:50', '2018-12-08 11:35:02', '2018-12-08 11:35:14', '2018-12-08 11:35:27', '2018-12-08 11:35:39', '2018-12-08 11:35:52', '2018-12-08 11:36:04', '2018-12-08 11:36:16', '2018-12-08 11:36:29', '2018-12-08 11:36:41', '2018-12-08 11:36:54', '2018-12-08 11:37:06', '2018-12-08 11:37:18', '2018-12-08 11:37:31', '2018-12-08 11:37:43', '2018-12-08 11:37:56', '2018-12-08 11:38:08', '2018-12-08 11:38:20', '2018-12-08 11:38:58', '2018-12-08 11:39:01', '2018-12-08 11:39:04', '2018-12-08 11:39:10', '2018-12-08 11:39:23', '2018-12-08 11:39:35', '2018-12-08 11:39:47', '2018-12-08 11:40:00', '2018-12-08 11:40:12', '2018-12-08 11:40:25', '2018-12-08 11:40:37', '2018-12-08 11:40:49', '2018-12-08 11:41:02', '2018-12-08 11:41:14', '2018-12-08 11:41:27', '2018-12-08 11:41:39', '2018-12-08 11:41:51', '2018-12-08 11:42:04', '2018-12-08 11:42:16', '2018-12-08 11:42:29', '2018-12-08 11:42:41', '2018-12-08 11:42:53', '2018-12-08 11:43:06', '2018-12-08 11:43:18', '2018-12-08 11:43:31', '2018-12-08 11:43:43', '2018-12-08 11:43:56', '2018-12-08 11:44:08', '2018-12-08 11:44:20', '2018-12-08 11:44:33', '2018-12-08 11:44:45', '2018-12-08 11:44:58', '2018-12-08 11:45:10', '2018-12-08 11:45:22', '2018-12-08 11:45:35', '2018-12-08 11:45:47', '2018-12-08 11:46:00', '2018-12-08 11:46:12', '2018-12-08 11:46:24', '2018-12-08 11:46:37', '2018-12-08 11:46:49', '2018-12-08 11:47:02', '2018-12-08 11:47:14', '2018-12-08 11:47:26', '2018-12-08 11:47:39', '2018-12-08 11:47:51', '2018-12-08 11:48:04', '2018-12-08 11:48:16', '2018-12-08 11:48:29', '2018-12-08 11:48:41', '2018-12-08 11:48:53', '2018-12-08 11:49:06', '2018-12-08 11:49:18', '2018-12-08 11:49:31', '2018-12-08 11:49:43', '2018-12-08 11:49:55', '2018-12-08 11:50:08', '2018-12-08 11:50:20', '2018-12-08 11:50:33', '2018-12-08 11:50:45', '2018-12-08 11:50:57', '2018-12-08 11:51:10', '2018-12-08 11:51:22', '2018-12-08 11:51:35', '2018-12-08 11:51:47', '2018-12-08 11:51:59', '2018-12-08 11:52:12', '2018-12-08 11:52:24', '2018-12-08 11:52:37', '2018-12-08 11:52:49', '2018-12-08 11:53:02', '2018-12-08 11:53:14', '2018-12-08 11:53:26', '2018-12-08 11:53:39', '2018-12-08 11:53:51', '2018-12-08 11:54:04', '2018-12-08 11:54:16', '2018-12-08 11:54:28', '2018-12-08 11:54:41', '2018-12-08 11:54:53', '2018-12-08 11:55:06', '2018-12-08 11:55:18', '2018-12-08 11:55:30', '2018-12-08 11:55:43', '2018-12-08 11:55:55', '2018-12-08 11:56:08', '2018-12-08 11:56:20', '2018-12-08 11:56:33', '2018-12-08 11:56:45', '2018-12-08 11:56:57', '2018-12-08 11:57:10', '2018-12-08 11:57:22', '2018-12-08 11:57:35', '2018-12-08 11:57:47', '2018-12-08 11:57:59', '2018-12-08 11:58:12', '2018-12-08 11:58:24', '2018-12-08 11:58:37', '2018-12-08 11:58:49', '2018-12-08 11:59:01', '2018-12-08 11:59:14', '2018-12-08 11:59:26', '2018-12-08 11:59:39', '2018-12-08 11:59:51', '2018-12-08 12:00:03', '2018-12-08 12:00:16', '2018-12-08 12:00:28', '2018-12-08 12:00:41', '2018-12-08 12:00:53', '2018-12-08 12:01:06', '2018-12-08 12:01:18', '2018-12-08 12:01:30', '2018-12-08 12:01:43', '2018-12-08 12:01:55', '2018-12-08 12:02:08', '2018-12-08 12:02:20', '2018-12-08 12:02:32', '2018-12-08 12:02:45', '2018-12-08 12:02:57', '2018-12-08 12:03:10', '2018-12-08 12:03:22', '2018-12-08 12:03:34', '2018-12-08 12:03:47', '2018-12-08 12:03:59', '2018-12-08 12:04:12', '2018-12-08 12:04:24', '2018-12-08 12:04:36', '2018-12-08 12:04:49', '2018-12-08 12:05:01', '2018-12-08 12:05:14', '2018-12-08 12:05:26', '2018-12-08 12:05:39', '2018-12-08 12:05:51', '2018-12-08 12:06:03', '2018-12-08 12:06:16', '2018-12-08 12:06:28', '2018-12-08 12:06:41', '2018-12-08 12:06:53', '2018-12-08 12:07:05', '2018-12-08 12:07:18', '2018-12-08 12:07:30', '2018-12-08 12:07:43', '2018-12-08 12:07:55', '2018-12-08 12:08:07', '2018-12-08 12:08:20', '2018-12-08 12:08:32', '2018-12-08 12:08:45', '2018-12-08 12:08:57', '2018-12-08 12:09:09', '2018-12-08 12:09:22', '2018-12-08 12:09:34', '2018-12-08 12:09:47', '2018-12-08 12:09:59'], ['2018-12-08 12:10:12', '2018-12-08 13:02:26', '2018-12-08 13:02:49', '2018-12-08 13:03:08', '2018-12-08 13:03:29', '2018-12-08 13:03:56', '2018-12-08 13:04:22', '2018-12-08 13:04:45', '2018-12-08 13:04:59', '2018-12-08 13:05:25', '2018-12-08 13:05:42', '2018-12-08 13:05:56', '2018-12-08 13:06:16', '2018-12-08 13:06:38', '2018-12-08 13:06:54', '2018-12-08 13:07:13', '2018-12-08 13:07:31', '2018-12-08 13:07:48', '2018-12-08 13:08:12', '2018-12-08 13:08:35', '2018-12-08 13:08:54', '2018-12-08 13:09:27', '2018-12-08 13:09:52', '2018-12-08 13:10:14', '2018-12-08 13:10:31', '2018-12-08 13:10:47', '2018-12-08 13:11:05', '2018-12-08 13:11:38', '2018-12-08 13:12:00', '2018-12-08 13:12:20', '2018-12-08 13:12:35', '2018-12-08 13:13:07', '2018-12-08 13:13:29', '2018-12-08 13:14:07', '2018-12-08 13:14:37', '2018-12-08 13:15:06', '2018-12-08 13:15:32', '2018-12-08 13:15:53', '2018-12-08 13:16:05', '2018-12-08 13:16:26', '2018-12-08 13:17:26', '2018-12-08 13:17:45', '2018-12-08 13:18:06'], ['2018-12-08 13:19:03', '2018-12-08 13:26:04', '2018-12-08 13:26:12', '2018-12-08 13:27:48', '2018-12-08 13:28:11', '2018-12-08 13:28:15', '2018-12-08 13:28:33', '2018-12-08 13:28:57', '2018-12-08 13:29:42', '2018-12-08 13:30:12', '2018-12-08 13:30:37', '2018-12-08 13:30:51', '2018-12-08 13:31:27', '2018-12-08 13:31:43', '2018-12-08 13:32:05', '2018-12-08 13:32:26', '2018-12-08 13:32:44', '2018-12-08 13:33:11', '2018-12-08 13:33:36', '2018-12-08 13:33:56', '2018-12-08 13:34:13', '2018-12-08 13:34:32', '2018-12-08 13:34:52', '2018-12-08 13:35:11', '2018-12-08 13:35:34', '2018-12-08 13:35:51', '2018-12-08 13:36:17', '2018-12-08 13:36:40', '2018-12-08 13:37:00', '2018-12-08 13:37:14', '2018-12-08 13:37:29', '2018-12-08 13:37:45', '2018-12-08 13:38:06', '2018-12-08 13:38:28', '2018-12-08 13:38:52', '2018-12-08 13:39:15', '2018-12-08 13:39:39', '2018-12-08 13:40:04', '2018-12-08 13:40:26', '2018-12-08 13:40:42', '2018-12-08 13:40:59', '2018-12-08 13:41:17', '2018-12-08 13:41:39', '2018-12-08 13:42:13', '2018-12-08 13:42:30', '2018-12-08 13:42:45', '2018-12-08 13:43:01', '2018-12-08 13:43:21', '2018-12-08 13:43:45', '2018-12-08 13:44:10', '2018-12-08 13:44:35', '2018-12-08 13:44:53', '2018-12-08 13:45:22', '2018-12-08 13:45:41', '2018-12-08 13:45:55', '2018-12-08 13:46:06', '2018-12-08 13:46:19', '2018-12-08 13:46:43', '2018-12-08 13:47:02', '2018-12-08 13:47:19', '2018-12-08 13:47:38', '2018-12-08 13:47:55', '2018-12-08 13:48:10', '2018-12-08 13:48:29', '2018-12-08 13:48:53', '2018-12-08 13:49:08', 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17:22:13', '2018-12-08 17:22:33', '2018-12-08 17:23:00', '2018-12-08 17:23:21', '2018-12-08 17:23:46', '2018-12-08 17:24:10', '2018-12-08 17:24:37', '2018-12-08 17:24:58', '2018-12-08 17:25:19', '2018-12-08 17:25:57', '2018-12-08 17:26:19', '2018-12-08 17:26:37', '2018-12-08 17:26:58', '2018-12-08 17:27:19', '2018-12-08 17:27:39', '2018-12-08 17:28:04', '2018-12-08 17:28:30', '2018-12-08 17:28:53', '2018-12-08 17:29:18']]

统计排序:

Timelapse: 1. Photos: 281. Period: 2018-12-08 11:06:41 - 2018-12-08 12:09:59. Duration: 1:03:18
Timelapse: 2. Photos: 43. Period: 2018-12-08 12:10:12 - 2018-12-08 13:18:06. Duration: 1:07:54
Timelapse: 3. Photos: 76. Period: 2018-12-08 13:19:03 - 2018-12-08 13:52:34. Duration: 0:33:31
Timelapse: 4. Photos: 69. Period: 2018-12-08 13:52:50 - 2018-12-08 14:27:33. Duration: 0:34:43
Timelapse: 5. Photos: 20. Period: 2018-12-08 14:27:51 - 2018-12-08 15:35:39. Duration: 1:07:48
Timelapse: 6. Photos: 14. Period: 2018-12-08 15:35:40 - 2018-12-08 15:46:01. Duration: 0:10:21
Timelapse: 7. Photos: 152. Period: 2018-12-08 15:46:02 - 2018-12-08 16:09:03. Duration: 0:23:01
Timelapse: 8. Photos: 143. Period: 2018-12-08 16:09:11 - 2018-12-08 17:29:18. Duration: 1:20:07

这篇关于通过时间戳算法将照片分组到游戏中时光倒流的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！