用python找出那些被“标记”的照片
源码传送门 环境准备 下面的两个第三方模块都可以直接通过pip快速安装,这里使用py36作为运行环境。
思路
基础知识 下面是现今相片中会存在与GPS相关的关键字,大牛亦可一比带过~ [参考] { "GPSVersionID": "GPS版本","GPSLatitudeRef": "南北纬","GPSLatitude": "纬度","GPSLongitudeRef": "东西经","GPSLongitude": "经度","GPSAltitudeRef": "海拔参照值","GPSAltitude": "海拔","GPSTimeStamp": "GPS时间戳","GPSSatellites": "测量的卫星","GPSStatus": "接收器状态","GPSMeasureMode": "测量模式","GPSDOP": "测量精度","GPSSpeedRef": "速度单位","GPSSpeed": "GPS接收器速度","GPSTrackRef": "移动方位参照","GPSTrack": "移动方位","GPSImgDirectionRef": "图像方位参照","GPSImgDirection": "图像方位","GPSMapDatum": "地理测量资料","GPSDestLatitudeRef": "目标纬度参照","GPSDestLatitude": "目标纬度","GPSDestLongitudeRef": "目标经度参照","GPSDestLongitude": "目标经度","GPSDestBearingRef": "目标方位参照","GPSDestBearing": "目标方位","GPSDestDistanceRef": "目标距离参照","GPSDestDistance": "目标距离","GPSProcessingMethod": "GPS处理方法名","GPSAreaInformation": "GPS区功能变数名","GPSDateStamp": "GPS日期","GPSDifferential": "GPS修正" } 初始化 考虑到exifread的模块中有大量的logging输出,这里将它的level级别调到最高。 然后下边的KEY是某站在高德地图API的时候遗留下来的 我也很尴尬。。就当福利了 import os import time import json import random import logging import requests import exifread logging.basicConfig(level=logging.CRITICAL) KEY = "169d2dd7829fe45690fabec812d05bc3" 主逻辑函数 def main(): # 预设后缀列表 types = ["bmp","jpg","tiff","gif","png"] #结果数据集合 picex = [] # 文件存储路径 saves = "$" + input("| SavePath: ").strip() # 文件搜索路径 并遍历所有文件返回文件路径列表 pools = jpgwalk(input("| FindPath: "),types) #存储目录 savep = "%s/%s" % (os.getcwd().replace("","/"),saves) if savep in pools: pools.remove(savep) # 遍历数据集并获取exif信息 for path in pools: res = getEXIF(path) if res: picex.append(res) # 结果报告 print("| Result %s" % len(picex)) # 如果存在结果 保存结果到json并讲相关图片复制到该目录下 if picex: #创建目录 if not os.path.exists(saves): os.mkdir(saves) #生成一个4格缩进的json文件 with open("%s/%s.json" % (saves,saves),"wb") as f: f.write(json.dumps(picex,ensure_ascii=False,indent=4).encode("utf8")) #copy图像到该目录 for item in picex: source_path = item["Filename"] with open("%s/%s" % (saves,source_path.split("/")[-1]),"wb") as f_in: with open(source_path,"rb") as f_out: f_in.write(f_out.read()) 遍历方法 遍历指定及其所有下级目录,并返回全部的图片的路径集合,这里要注意的是每次扫描后的拷贝行为都会生成缓存,所以通过指定 $ 来避开。 # 获取指导目录全部的图片路径 def jpgwalk(path,types): _start = time.time() _pools = [] # 遍历该目录 并判断files后缀 如符合规则则拼接路径 for _root,_dirs,_files in os.walk(path): _pools.extend([_root.replace("","/") + "/" + _item for _item in _files if _item.split(".")[-1].lower() in types and "$" not in _root]) #报告消耗时间 print("| Find %s n| Time %.3fs" % (len(_pools),time.time() - _start)) return _pools 经纬度格式化 度分秒转浮点,方便api调用查询,因为存在一些诡异的数据比如 1/0,所以默认返回0 def cg(i): try: _ii = [float(eval(x)) for x in i[1:][:-1].split(',')] _res = _ii[0] + _ii[1] / 60 + _ii[2] / 3600 return _res except ZeroDivisionError: return 0 EXIF信息整理 考虑到大部分的设备还未开始支持朝向、速度、测量依据等关键字,这里暂时只使用比较常见的,如有需要的朋友可以自行添加。毕竟得到的信息越多对社工有更大的帮助。 def getEXIF(filepath): #基础关键字 _showlist = [ 'GPS GPSDOP','GPS GPSMeasureMode','GPS GPSAltitudeRef','GPS GPSAltitude','Image Software','Image Model','Image Make' ] #GPS关键字 _XYlist = ["GPS GPSLatitude","GPS GPSLongitude"] #时间关键字 _TimeList = ["EXIF DateTimeOrigina","Image DateTime","GPS GPSDate"] #初始化结果字典 _infos = { 'Filename': filepath } with open(filepath,"rb") as _files: _tags = None # 尝试去的EXIF信息 try: _tags = exifread.process_file(_files) except KeyError: return # 判断是否存在地理位置信息 _tagkeys = _tags.keys() if _tags and len(set(_tagkeys) & set(_XYlist)) == 2 and cg(str(_tags["GPS GPSLongitude"])) != 0.0: for _item in sorted(_tagkeys): if _item in _showlist: _infos[_item.split()[-1]] = str(_tags[_item]).strip() # 经纬度取值 _infos["GPS"] = (cg(str(_tags["GPS GPSLatitude"])) * float(1.0 if str(_tags.get("GPS GPSLatitudeRef","N")) == "N" else -1.0),cg(str(_tags["GPS GPSLongitude"])) * float(1.0 if str(_tags.get("GPS GPSLongitudeRef","E")) == "E" else -1.0)) # 获取实体地址 _infos["address"] = address(_infos["GPS"]) # 获取照片海拔高度 if "GPS GPSAltitudeRef" in _tagkeys: try: _infos["GPSAltitude"] = eval(_infos["GPSAltitude"]) except ZeroDivisionError: _infos["GPSAltitude"] = 0 _infos["GPSAltitude"] = "距%s%.2f米" % ("地面" if int( _infos["GPSAltitudeRef"]) == 1 else "海平面",_infos["GPSAltitude"]) del _infos["GPSAltitudeRef"] # 获取可用时间 _timeitem = list(set(_TimeList) & set(_tagkeys)) if _timeitem: _infos["Dates"] = str(_tags[_timeitem[0]]) return _infos 地址转换 一个简单的爬虫,调用高德地图api进行坐标转换,考虑到原本是跨域,这里添加基础的反防爬代码。这里有个小细节,海外的一律都取不到(包括台湾),可以通过更换googlemap的api来实现全球查询。 def address(gps): global KEY try: # 随机UA _ulist = [ "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/535.1 (KHTML,like Gecko) Chrome/14.0.835.163 Safari/535.1","Mozilla/5.0 (Windows NT 6.1; WOW64; rv:6.0) Gecko/20100101 Firefox/6.0","Mozilla/4.0 (compatible; MSIE 7.0; Windows NT 5.1; Trident/4.0; InfoPath.2; .NET4.0C; .NET4.0E; .NET CLR 2.0.50727; 360SE)","Mozilla/5.0 (Macintosh; Intel Mac OS X 10_7_0) AppleWebKit/535.11 (KHTML,like Gecko) Chrome/17.0.963.56 Safari/535.11","Mozilla/5.0 (Macintosh; U; Intel Mac OS X 10_6_8; en-us) AppleWebKit/534.50 (KHTML,like Gecko) Version/5.1 Safari/534.50","Mozilla/5.0 (compatible; MSIE 9.0; Windows NT 6.1; Win64; x64; Trident/5.0; .NET CLR 2.0.50727; SLCC2; .NET CLR 3.5.30729; .NET CLR 3.0.30729; Media Center PC 6.0; InfoPath.3; .NET4.0C; Tablet PC 2.0; .NET4.0E)","Mozilla/5.0 (compatible; MSIE 9.0; Windows NT 6.1; WOW64; Trident/5.0)","Mozilla/5.0 (X11; U; Linux i686; rv:1.7.3) Gecko/20040913 Firefox/0.10","Opera/9.80 (Macintosh; Intel Mac OS X 10.6.8; U; ja) Presto/2.10.289 Version/12.00","Mozilla/5.0 (Windows NT 6.2; WOW64) AppleWebKit/537.36 (KHTML,like Gecko) Chrome/45.0.2454.93 Safari/537.36" ] # 伪造header _header = { "User-Agent": random.choice(_ulist),"Accept": "text/javascript,application/javascript,application/ecmascript,application/x-ecmascript,*/*; q=0.01","Accept-Encoding": "gzip,deflate,sdch","Accept-Language": "zh-CN,zh;q=0.8","Referer": "http://www.gpsspg.com",} _res = requests.get( "http://restapi.amap.com/v3/geocode/regeo?key={2}&s=rsv3&location={1},{0}&platform=JS&logversion=2.0&sdkversion=1.3&appname=http%3A%2F%2Fwww.gpsspg.com%2Fiframe%2Fmaps%2Famap_161128.htm%3Fmapi%3D3&csid=945C5A2C-E67F-4362-B881-9608D9BC9913".format(gps[0],gps[1],KEY),headers=_header,timeout=(5,5)) _json = _res.json() # 判断是否取得数据 if _json and _json["status"] == "1" and _json["info"] == "OK": # 返回对应地址 return _json.get("regeocode").get("formatted_address") except Exception as e: pass 实例 运行该代码 然后输入保存文件夹名和扫描位置即可 这边可以看到8019张中有396张存在有效的地理位置,打码的地方就不解释了,各位老司机~后期打算加入图像识别,和相似度识别。 下面给大家分享小编收集整理的python专题知识: python基本语法 python多线程学习教程 python排序算法大全 以上所述是小编给大家介绍的用python找出那些被“标记”的照片,希望对大家有所帮助,如果大家有任何疑问请给我留言,小编会及时回复大家的。在此也非常感谢大家对编程小技巧网站的支持! 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