做为一个程序员，整天在讨论什么算法，什么数据，有时候也应该为自己打造自己的数据，可视化自己的数据。这次我们用的是python，当然还有CartoDB，我们要生成的是一个在线的地图数据

最后结果见

dream|Phodal

EXIF

可交换图像文件常被简称为EXIF（Exchangeable image file format），是专门为数码相机的照片设定的，可以记录数码照片的属性信息和拍摄数据。

EXIF信息以0xFFE1作为开头标记，后两个字节表示EXIF信息的长度。所以EXIF信息最大为64 kB，而内部采用TIFF格式。

ExifRead

来自官方的简述

Python library to extract EXIF data from tiff and jpeg files.

ExifRead安装

    pip install exifread

ExifRead Exif.py

     EXIF.py images.jpg

CartoDB

简介

打造自己的大数据照片地图

python+cartodb+ExifRead python打造自己的小数据照片地图

主要步骤如下

1. 需要遍历自己的全部图片文件，
2. 解析照片信息
3. 生成地理信息文件
4. 上传到cartodb

python 遍历文件

import os, fnmatch


def all_files(root, patterns='*', single_level=False, yield_folders=False):
    patterns = patterns.split(';')
    for path, subdirs, files in os.walk(root):
        if yield_folders:
            files.extend(subdirs)
        files.sort()
        for name in files:
            for pattern in patterns:
                if fnmatch.fnmatch(name, pattern):
                    yield os.path.join(path, name)
                    break
                if single_level:
                    break

python 解析照片信息

[34, 12, 51513/1000]

N 34� 13' 12.718

34.2143091667

def parse_gps(titude):
    first_number = titude.split(',')[0]
    second_number = titude.split(',')[1]
    third_number = titude.split(',')[2]
    third_number_parent = third_number.split('/')[0]
    third_number_child = third_number.split('/')[1]
    third_number_result = float(third_number_parent) / float(third_number_child)
    return float(first_number) + float(second_number)/60 + third_number_result/3600

python 提取照片信息生成文件

#!/usr/bin/env python
import json
import exifread
import os, fnmatch
from exifread.tags import DEFAULT_STOP_TAG, FIELD_TYPES
from exifread import process_file, __version__

def all_files(root, patterns='*', single_level=False, yield_folders=False):
    patterns = patterns.split(';')
    for path, subdirs, files in os.walk(root):
        if yield_folders:
            files.extend(subdirs)
        files.sort()
        for name in files:
            for pattern in patterns:
                if fnmatch.fnmatch(name, pattern):
                    yield os.path.join(path, name)
                    break
                if single_level:
                    break

def parse_gps(titude):
    first_number = titude.split(',')[0]
    second_number = titude.split(',')[1]
    third_number = titude.split(',')[2]
    third_number_parent = third_number.split('/')[0]
    third_number_child = third_number.split('/')[1]
    third_number_result = float(third_number_parent) / float(third_number_child)
    return float(first_number) + float(second_number)/60 + third_number_result/3600

jsonFile = open("gps.geojson", "w")
jsonFile.writelines('{\n"type": "FeatureCollection","features": [\n')

def write_data(paths):
    index = 1
    for path in all_files('./' + paths, '*.jpg'):
        f = open(path[2:], 'rb')
        tags = exifread.process_file(f)
        # jsonFile.writelines('"type": "Feature","properties": {"cartodb_id":"'+str(index)+'"},"geometry": {"type": "Point","coordinates": [')
        latitude = tags['GPS GPSLatitude'].printable[1:-1]
        longitude = tags['GPS GPSLongitude'].printable[1:-1]
        print latitude
        print parse_gps(latitude)
        # print tags['GPS GPSLongitudeRef']
        # print tags['GPS GPSLatitudeRef']
        jsonFile.writelines('{"type": "Feature","properties": {"cartodb_id":"' + str(index) + '"')
        jsonFile.writelines(',"OS":"' + str(tags['Image Software']) + '","Model":"' + str(tags['Image Model']) + '","Picture":"'+str(path[7:])+'"')
        jsonFile.writelines('},"geometry": {"type": "Point","coordinates": [' + str(parse_gps(longitude)) + ',' + str(
            parse_gps(latitude)) + ']}},\n')
        index += 1

write_data('imgs')

jsonFile.writelines(']}\n')
jsonFile.close()

上传到cartodb