├── LICENSE ├── README.md ├── carbon.png ├── config.py ├── config └── credentials.ini ├── isearch ├── __init__.py ├── api.py ├── client.py └── face_recognition.py ├── isearch_osint.py └── requirements.txt /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Isearch (OSINT) 🔎 2 | 3 | Face recognition reverse image search on Instagram profile feed photos. 4 | 5 | carbon(1) 6 | 7 | Disclaimer: **FOR EDUCATIONAL PURPOSE ONLY! ** 8 | 9 | > You might encounter (false positive / false negative) results. This because Face recognition uses hog as a model which is fast but low on accuracy, the other model can be 'cnn' which is high on accuracy but very slow (on CPU && fast on GPU) 10 | 11 | 12 | 13 | ## Installation 14 | 15 | **Dlib installation**: (full guide: https://www.pyimagesearch.com/2017/03/27/how-to-install-dlib/) 16 | 17 | ```bash 18 | # Install prerequisites 19 | $ sudo apt-get install build-essential cmake 20 | $ sudo apt-get install libgtk-3-dev 21 | $ sudo apt-get install libboost-all-dev 22 | ``` 23 | 24 | **Note:** dlib installation will take few minutes 25 | 26 | ```bash 27 | # Install dlib python 28 | $ python3 -m pip install dlib 29 | ``` 30 | 31 | ```bash 32 | # Other requirements 33 | $ python3 -m pip install -r requirements.txt 34 | ``` 35 | 36 | 37 | 38 | ## Usage 39 | 40 | Set your Instagram username, password in config/credentials.ini, Also you might want to consider using a burner account. 41 | 42 | ```bash 43 | # Face search in all target feed photos 44 | $ python3 isearch-cli 45 | 46 | # Face search only 5 photos from target feed 47 | $ python3 isearch-cli --limit 5 48 | ``` 49 | 50 | **Options:** 51 | 52 | ``` 53 | --limit INTEGER Number of photos to Download and process from target_profile 54 | ``` 55 | -------------------------------------------------------------------------------- /carbon.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/malek10xdev/igsearch-osint/f32c38c4787fbb04a420018e0a9e818f4a6ee3f6/carbon.png -------------------------------------------------------------------------------- /config.py: -------------------------------------------------------------------------------- 1 | 2 | import configparser 3 | import sys 4 | 5 | 6 | try: 7 | config = configparser.ConfigParser(interpolation=None) 8 | config.read("config/credentials.ini") 9 | except (FileNotFoundError, BaseException) as e: 10 | print("Credentials.ini Not found" if type(e) == BaseException else e) 11 | sys.exit(1) 12 | 13 | 14 | def username(): 15 | try: 16 | user = config["Credentials"]["username"] 17 | if user == '': 18 | sys.exit(0) 19 | return user 20 | except KeyError: 21 | sys.exit(0) 22 | 23 | 24 | def password(): 25 | try: 26 | passwd = config["Credentials"]["password"] 27 | if passwd == '': 28 | sys.exit(0) 29 | return passwd 30 | except KeyError: 31 | sys.exit(0) 32 | -------------------------------------------------------------------------------- /config/credentials.ini: -------------------------------------------------------------------------------- 1 | [Credentials] 2 | username = 3 | password = -------------------------------------------------------------------------------- /isearch/__init__.py: -------------------------------------------------------------------------------- 1 | """ 2 | Isearch Module 3 | """ 4 | from .client import InstagramClient 5 | from .face_recognition import FaceRecognition 6 | -------------------------------------------------------------------------------- /isearch/api.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | 3 | # This code is taken from the face_recognition project. 4 | # 5 | # Author: Adam Geitgey 6 | # Email: ageitgey@gmail.com 7 | # Original project link: 8 | # https://github.com/ageitgey/face_recognition/blob/master/face_recognition/ 9 | 10 | 11 | import PIL.Image 12 | import dlib 13 | import numpy as np 14 | from PIL import ImageFile 15 | import face_recognition_models 16 | 17 | 18 | ImageFile.LOAD_TRUNCATED_IMAGES = True 19 | 20 | face_detector = dlib.get_frontal_face_detector() 21 | 22 | predictor_68_point_model = face_recognition_models.pose_predictor_model_location() 23 | pose_predictor_68_point = dlib.shape_predictor(predictor_68_point_model) 24 | 25 | predictor_5_point_model = face_recognition_models.pose_predictor_five_point_model_location() 26 | pose_predictor_5_point = dlib.shape_predictor(predictor_5_point_model) 27 | 28 | cnn_face_detection_model = face_recognition_models.cnn_face_detector_model_location() 29 | cnn_face_detector = dlib.cnn_face_detection_model_v1(cnn_face_detection_model) 30 | 31 | face_recognition_model = face_recognition_models.face_recognition_model_location() 32 | face_encoder = dlib.face_recognition_model_v1(face_recognition_model) 33 | 34 | 35 | def _rect_to_css(rect): 36 | """ 37 | Convert a dlib 'rect' object to a plain tuple in (top, right, bottom, left) order 38 | :param rect: a dlib 'rect' object 39 | :return: a plain tuple representation of the rect in (top, right, bottom, left) order 40 | """ 41 | return rect.top(), rect.right(), rect.bottom(), rect.left() 42 | 43 | 44 | def _css_to_rect(css): 45 | """ 46 | Convert a tuple in (top, right, bottom, left) order to a dlib `rect` object 47 | :param css: plain tuple representation of the rect in (top, right, bottom, left) order 48 | :return: a dlib `rect` object 49 | """ 50 | return dlib.rectangle(css[3], css[0], css[1], css[2]) 51 | 52 | 53 | def _trim_css_to_bounds(css, image_shape): 54 | """ 55 | Make sure a tuple in (top, right, bottom, left) order is within the bounds of the image. 56 | :param css: plain tuple representation of the rect in (top, right, bottom, left) order 57 | :param image_shape: numpy shape of the image array 58 | :return: a trimmed plain tuple representation of the rect in (top, right, bottom, left) order 59 | """ 60 | return max(css[0], 0), min(css[1], image_shape[1]), min(css[2], image_shape[0]), max(css[3], 0) 61 | 62 | 63 | def face_distance(face_encodings, face_to_compare): 64 | """ 65 | Given a list of face encodings, compare them to a known face encoding and get a euclidean distance 66 | for each comparison face. The distance tells you how similar the faces are. 67 | :param face_encodings: List of face encodings to compare 68 | :param face_to_compare: A face encoding to compare against 69 | :return: A numpy ndarray with the distance for each face in the same order as the 'faces' array 70 | """ 71 | if len(face_encodings) == 0: 72 | return np.empty((0)) 73 | 74 | return np.linalg.norm(face_encodings - face_to_compare, axis=1) 75 | 76 | 77 | def load_image_file(file, mode='RGB'): 78 | """ 79 | Loads an image file (.jpg, .png, etc) into a numpy array 80 | :param file: image file name or file object to load 81 | :param mode: format to convert the image to. Only 'RGB' (8-bit RGB, 3 channels) and 'L' (black and white) are supported. 82 | :return: image contents as numpy array 83 | """ 84 | im = PIL.Image.open(file) 85 | if mode: 86 | im = im.convert(mode) 87 | return np.array(im) 88 | 89 | 90 | def _raw_face_locations(img, number_of_times_to_upsample=1, model="hog"): 91 | """ 92 | Returns an array of bounding boxes of human faces in a image 93 | :param img: An image (as a numpy array) 94 | :param number_of_times_to_upsample: How many times to upsample the image looking for faces. Higher numbers find smaller faces. 95 | :param model: Which face detection model to use. "hog" is less accurate but faster on CPUs. "cnn" is a more accurate 96 | deep-learning model which is GPU/CUDA accelerated (if available). The default is "hog". 97 | :return: A list of dlib 'rect' objects of found face locations 98 | """ 99 | if model == "cnn": 100 | return cnn_face_detector(img, number_of_times_to_upsample) 101 | else: 102 | return face_detector(img, number_of_times_to_upsample) 103 | 104 | 105 | def face_locations(img, number_of_times_to_upsample=1, model="hog"): 106 | """ 107 | Returns an array of bounding boxes of human faces in a image 108 | :param img: An image (as a numpy array) 109 | :param number_of_times_to_upsample: How many times to upsample the image looking for faces. Higher numbers find smaller faces. 110 | :param model: Which face detection model to use. "hog" is less accurate but faster on CPUs. "cnn" is a more accurate 111 | deep-learning model which is GPU/CUDA accelerated (if available). The default is "hog". 112 | :return: A list of tuples of found face locations in css (top, right, bottom, left) order 113 | """ 114 | if model == "cnn": 115 | return [_trim_css_to_bounds(_rect_to_css(face.rect), img.shape) for face in _raw_face_locations(img, number_of_times_to_upsample, "cnn")] 116 | else: 117 | return [_trim_css_to_bounds(_rect_to_css(face), img.shape) for face in _raw_face_locations(img, number_of_times_to_upsample, model)] 118 | 119 | 120 | def _raw_face_locations_batched(images, number_of_times_to_upsample=1, batch_size=128): 121 | """ 122 | Returns an 2d array of dlib rects of human faces in a image using the cnn face detector 123 | :param images: A list of images (each as a numpy array) 124 | :param number_of_times_to_upsample: How many times to upsample the image looking for faces. Higher numbers find smaller faces. 125 | :return: A list of dlib 'rect' objects of found face locations 126 | """ 127 | return cnn_face_detector(images, number_of_times_to_upsample, batch_size=batch_size) 128 | 129 | 130 | def batch_face_locations(images, number_of_times_to_upsample=1, batch_size=128): 131 | """ 132 | Returns an 2d array of bounding boxes of human faces in a image using the cnn face detector 133 | If you are using a GPU, this can give you much faster results since the GPU 134 | can process batches of images at once. If you aren't using a GPU, you don't need this function. 135 | :param images: A list of images (each as a numpy array) 136 | :param number_of_times_to_upsample: How many times to upsample the image looking for faces. Higher numbers find smaller faces. 137 | :param batch_size: How many images to include in each GPU processing batch. 138 | :return: A list of tuples of found face locations in css (top, right, bottom, left) order 139 | """ 140 | def convert_cnn_detections_to_css(detections): 141 | return [_trim_css_to_bounds(_rect_to_css(face.rect), images[0].shape) for face in detections] 142 | 143 | raw_detections_batched = _raw_face_locations_batched(images, number_of_times_to_upsample, batch_size) 144 | 145 | return list(map(convert_cnn_detections_to_css, raw_detections_batched)) 146 | 147 | 148 | def _raw_face_landmarks(face_image, face_locations=None, model="large"): 149 | if face_locations is None: 150 | face_locations = _raw_face_locations(face_image) 151 | else: 152 | face_locations = [_css_to_rect(face_location) for face_location in face_locations] 153 | 154 | pose_predictor = pose_predictor_68_point 155 | 156 | if model == "small": 157 | pose_predictor = pose_predictor_5_point 158 | 159 | return [pose_predictor(face_image, face_location) for face_location in face_locations] 160 | 161 | 162 | def face_landmarks(face_image, face_locations=None, model="large"): 163 | """ 164 | Given an image, returns a dict of face feature locations (eyes, nose, etc) for each face in the image 165 | :param face_image: image to search 166 | :param face_locations: Optionally provide a list of face locations to check. 167 | :param model: Optional - which model to use. "large" (default) or "small" which only returns 5 points but is faster. 168 | :return: A list of dicts of face feature locations (eyes, nose, etc) 169 | """ 170 | landmarks = _raw_face_landmarks(face_image, face_locations, model) 171 | landmarks_as_tuples = [[(p.x, p.y) for p in landmark.parts()] for landmark in landmarks] 172 | 173 | # For a definition of each point index, see https://cdn-images-1.medium.com/max/1600/1*AbEg31EgkbXSQehuNJBlWg.png 174 | if model == 'large': 175 | return [{ 176 | "chin": points[0:17], 177 | "left_eyebrow": points[17:22], 178 | "right_eyebrow": points[22:27], 179 | "nose_bridge": points[27:31], 180 | "nose_tip": points[31:36], 181 | "left_eye": points[36:42], 182 | "right_eye": points[42:48], 183 | "top_lip": points[48:55] + [points[64]] + [points[63]] + [points[62]] + [points[61]] + [points[60]], 184 | "bottom_lip": points[54:60] + [points[48]] + [points[60]] + [points[67]] + [points[66]] + [points[65]] + [points[64]] 185 | } for points in landmarks_as_tuples] 186 | elif model == 'small': 187 | return [{ 188 | "nose_tip": [points[4]], 189 | "left_eye": points[2:4], 190 | "right_eye": points[0:2], 191 | } for points in landmarks_as_tuples] 192 | else: 193 | raise ValueError("Invalid landmarks model type. Supported models are ['small', 'large'].") 194 | 195 | 196 | def face_encodings(face_image, known_face_locations=None, num_jitters=1, model="small"): 197 | """ 198 | Given an image, return the 128-dimension face encoding for each face in the image. 199 | :param face_image: The image that contains one or more faces 200 | :param known_face_locations: Optional - the bounding boxes of each face if you already know them. 201 | :param num_jitters: How many times to re-sample the face when calculating encoding. Higher is more accurate, but slower (i.e. 100 is 100x slower) 202 | :param model: Optional - which model to use. "large" or "small" (default) which only returns 5 points but is faster. 203 | :return: A list of 128-dimensional face encodings (one for each face in the image) 204 | """ 205 | raw_landmarks = _raw_face_landmarks(face_image, known_face_locations, model) 206 | return [np.array(face_encoder.compute_face_descriptor(face_image, raw_landmark_set, num_jitters)) for raw_landmark_set in raw_landmarks] 207 | 208 | 209 | def compare_faces(known_face_encodings, face_encoding_to_check, tolerance=0.6): 210 | """ 211 | Compare a list of face encodings against a candidate encoding to see if they match. 212 | :param known_face_encodings: A list of known face encodings 213 | :param face_encoding_to_check: A single face encoding to compare against the list 214 | :param tolerance: How much distance between faces to consider it a match. Lower is more strict. 0.6 is typical best performance. 215 | :return: A list of True/False values indicating which known_face_encodings match the face encoding to check 216 | """ 217 | return list(face_distance(known_face_encodings, face_encoding_to_check) <= tolerance) -------------------------------------------------------------------------------- /isearch/client.py: -------------------------------------------------------------------------------- 1 | """ 2 | Instagram Client 3 | """ 4 | 5 | import codecs 6 | import concurrent.futures 7 | import json 8 | import os 9 | import sys 10 | import urllib 11 | from functools import wraps 12 | 13 | from instagram_private_api import Client as AppClient 14 | from instagram_private_api import ClientCookieExpiredError, ClientLoginRequiredError, ClientError 15 | 16 | import config 17 | 18 | 19 | def catch_exceptions(f): 20 | """Catch errors. 21 | """ 22 | 23 | @wraps(f) 24 | def catch(*args, **kwargs): 25 | try: 26 | f(*args, **kwargs) 27 | 28 | # OS file not found error 29 | except FileNotFoundError: 30 | print("Settings.json don't exist.\n") 31 | exit() 32 | 33 | # Instagram client error 34 | except ClientError as e: 35 | 36 | # Print error message 37 | error = json.loads(e.error_response) 38 | print(error['message']) 39 | 40 | if 'challenge' in error: # If instagram redirects to challenge page 41 | print("Please follow this link to complete the challenge: " + error['challenge']['url']) 42 | 43 | if 'message' in error: 44 | print(error['message']) 45 | 46 | if 'error_title' in error: 47 | print(error['error_title']) 48 | exit(1) 49 | 50 | return catch 51 | 52 | 53 | class InstagramClient: 54 | target = "" # target username 55 | user = "" # Target account object 56 | target_id = "" # Target account id 57 | is_private = "" # Is target account private ? 58 | following = "" # Am i following target ? 59 | 60 | BASE_OUTPUT_DIR = "" # Where can i save photos ? 61 | OUTPUT_DIR = "" # Folder will be created for target 62 | CONFIG_DIR = "" # Where can i find config files ? 63 | SETTING = "" # Where can i (read / write) "settings.json" ? 64 | 65 | def __init__(self, output_dir="output", config_dir="config"): 66 | """ 67 | :param output_dir: Directory name, where to save photos 68 | :param config_dir: Directory name, Where to find config files 69 | """ 70 | 71 | # Check for output directory, create it if not exists 72 | if not os.path.isdir(output_dir): 73 | os.mkdir(output_dir) 74 | 75 | # Get username, password from credentials.ini 76 | u = config.username() 77 | p = config.password() 78 | 79 | # Set class attributes 80 | self.BASE_OUTPUT_DIR = output_dir 81 | self.CONFIG_DIR = config_dir 82 | self.SETTING = self.CONFIG_DIR + '/settings.json' 83 | self.api = None 84 | 85 | # Authenticate to instagram 86 | # Note: login will assign to self.api 87 | self.__login__(u, p) 88 | 89 | def set_target(self, target): 90 | """ 91 | Fetch and set needed information about target, for later work 92 | :param target: An instagram username 93 | """ 94 | # Set class attributes 95 | self.target = target 96 | 97 | # Create target dir path 98 | self.OUTPUT_DIR = self.BASE_OUTPUT_DIR + '/' + target 99 | 100 | # Check if exists, then create 101 | if not os.path.isdir(self.OUTPUT_DIR): 102 | os.mkdir(self.OUTPUT_DIR) 103 | 104 | # Target account 105 | self.user = self.__target_profile__() 106 | 107 | # Target account id 108 | self.target_id = self.user['id'] 109 | 110 | # Is target account private ? 111 | self.is_private = self.user['is_private'] 112 | 113 | # Am i following target ? 114 | self.following = self.__ami_following_target__() 115 | 116 | @catch_exceptions 117 | def __login__(self, u, p): 118 | """ 119 | Login to instagram using private API. 120 | :param u: Instagram username 121 | :param p: Instagram Password 122 | """ 123 | try: # To handle client errors, like (ClientCookieExpiredError, ...) 124 | 125 | # Read from settings.json file 126 | settings_file = self.SETTING 127 | 128 | if not os.path.isfile(settings_file): # Check if file exists, if not, then we will make a new login 129 | 130 | # Settings file does not exist 131 | print(f"\n WARNING: settings.json not found! Don't worry i'll create one.") 132 | 133 | # Make new login and save cookies to settings.json 134 | self.api = AppClient(auto_patch=True, authenticate=True, username=u, password=p, 135 | on_login=lambda x: self.__onlogin_callback__(x, settings_file)) 136 | 137 | else: # If file exists, then load cookies and login 138 | 139 | with open(settings_file) as file_data: 140 | cached_settings = json.load(file_data, object_hook=self.__from_json__) 141 | 142 | # Authenticate with cached settings 143 | self.api = AppClient( 144 | username=u, password=p, 145 | settings=cached_settings, 146 | on_login=lambda x: self.__onlogin_callback__(x, settings_file)) 147 | 148 | # Catch if cookies expired or corrupted, then make new login if so 149 | except (ClientCookieExpiredError, ClientLoginRequiredError): 150 | 151 | # Login expired 152 | self.api = AppClient(auto_patch=True, authenticate=True, username=u, password=p, 153 | on_login=lambda x: self.__onlogin_callback__(x, settings_file)) 154 | 155 | @catch_exceptions 156 | def __onlogin_callback__(self, api, new_settings_file): 157 | """Save login settings to a json file.""" 158 | 159 | # Login settings 160 | cache_settings = api.settings 161 | 162 | # Open file and dumb json there 163 | with open(new_settings_file, 'w') as outfile: 164 | json.dump(cache_settings, outfile, default=self.__to_json__) 165 | 166 | @staticmethod 167 | def __to_json__(python_object): 168 | """ 169 | From object to json format. 170 | :param python_object Object to be transformed into json 171 | """ 172 | 173 | if isinstance(python_object, bytes): 174 | 175 | return { 176 | '__class__': 'bytes', 177 | '__value__': codecs.encode(python_object, 'base64').decode()} 178 | 179 | raise TypeError(repr(python_object) + ' is not JSON serializable') 180 | 181 | @staticmethod 182 | def __from_json__(json_object): 183 | """ 184 | From json format to object. 185 | :param json_object Json to be transformed into object 186 | """ 187 | 188 | if '__class__' in json_object and json_object['__class__'] == 'bytes': 189 | 190 | return codecs.decode(json_object['__value__'].encode(), 'base64') 191 | 192 | return json_object 193 | 194 | @staticmethod 195 | def __process_to_download__(output_dir, download_url, code): 196 | """ 197 | Given an image url, handles the downloads process of an image 198 | :param output_dir: Place where to save an image 199 | :param download_url: Download URL to image 200 | :param code: Short_code that represents an instagram post 201 | """ 202 | 203 | # Base url that represents an instagram posts 204 | base_post_url = "https:##www.instagram.com#p#*#" 205 | 206 | # Since we need to reference the actual IG post later, we format the post url to be 207 | # acceptable as an image name. image name format: __IG post url.jpeg 208 | end = f"{output_dir}/__{base_post_url.replace('*', code)}.jpeg" 209 | 210 | # Download image 211 | urllib.request.urlretrieve(download_url, end) 212 | 213 | def download_target_photo(self, limit=-1): 214 | """ 215 | Download Target's instagram photos. 216 | :param limit: Max images to download 217 | """ 218 | 219 | counter = 0 220 | 221 | # Get a list of photos 222 | # target_photos returns a list of tuples (url, short_code) 223 | image_urls = self.target_photos() 224 | image_urls = image_urls[: -1 if limit > len(image_urls) else limit] 225 | 226 | for item in image_urls: 227 | url, post_code = item[0], item[1] 228 | 229 | # Download the image, using multithreading 230 | with concurrent.futures.ThreadPoolExecutor() as executor: 231 | executor.submit(self.__process_to_download__(self.OUTPUT_DIR, url, post_code)) 232 | 233 | # Write progress message to stdout 234 | sys.stdout.write("\r+ Downloading instagram photo %i/%i" % (counter, len(image_urls))) 235 | sys.stdout.flush() 236 | counter += 1 237 | 238 | print(f"\n+ Downloaded {counter} photos. Saved in {self.OUTPUT_DIR}\n") 239 | 240 | return self.OUTPUT_DIR 241 | 242 | def target_photos(self): 243 | """ 244 | Get a list of all Target's instagram photos. 245 | :return: List of Urls 246 | """ 247 | 248 | if not self.__ami_allowed__(): # Check if target photos are not private 249 | 250 | # Image data from api 251 | data = [] 252 | 253 | # Get user feed photos and add them to data 254 | result = self.api.user_feed(str(self.target_id)) 255 | data.extend(result.get('items', [])) 256 | 257 | # Pagination 258 | next_max_id = result.get('next_max_id') 259 | 260 | while next_max_id: # Get all user feed photos 261 | 262 | results = self.api.user_feed(str(self.target_id), max_id=next_max_id) 263 | 264 | data.extend(results.get('items', [])) 265 | 266 | next_max_id = results.get('next_max_id') 267 | 268 | # Extract image urls 269 | urls = [] 270 | 271 | try: # To handle Attribute, Key errors 272 | 273 | for item in data: 274 | 275 | if "image_versions2" in item: 276 | 277 | urls.append((item["image_versions2"]["candidates"][0]["url"], item['code'])) 278 | 279 | else: # Handle Multiple images in one post (Carousel) 280 | 281 | carousel = item["carousel_media"] 282 | 283 | for i in carousel: # Loop through images 284 | 285 | urls.append((i["image_versions2"]["candidates"][0]["url"], item['code'])) 286 | 287 | except AttributeError: # Catch and pass (ignore) 288 | pass 289 | 290 | except KeyError: # Catch and pass (ignore) 291 | pass 292 | 293 | return urls 294 | return 295 | 296 | def __ami_following_target__(self): 297 | """ 298 | Check if i'm currently following the target. 299 | :return True OR False 300 | """ 301 | if str(self.target_id) == self.api.authenticated_user_id: 302 | return True 303 | 304 | # Retrieve target IG account information, in order to check for following status 305 | endpoint = 'users/{user_id!s}/full_detail_info/'.format(**{'user_id': self.target_id}) 306 | 307 | return self.api._call_api(endpoint)['user_detail']['user']['friendship_status']['following'] 308 | 309 | def __ami_allowed__(self): 310 | """ 311 | Check if account is private or not. 312 | :return True OR False 313 | """ 314 | return self.is_private and not self.following 315 | 316 | def __target_profile__(self): 317 | """ 318 | Get target profile information, user_id and is_private account status 319 | :return: Dict {id! , is_private: } 320 | """ 321 | 322 | # Get target username info 323 | content = self.api.username_info(self.target) 324 | user = dict() 325 | 326 | # Only extract id & is_private 327 | user['id'] = content['user']['pk'] 328 | user['is_private'] = content['user']['is_private'] 329 | 330 | return user 331 | 332 | def cleanup(self): 333 | """Cleanup output directory.""" 334 | import shutil 335 | shutil.rmtree(self.OUTPUT_DIR) 336 | -------------------------------------------------------------------------------- /isearch/face_recognition.py: -------------------------------------------------------------------------------- 1 | from isearch.api import (load_image_file, 2 | face_locations, 3 | face_encodings, 4 | compare_faces, ) 5 | import PIL 6 | import numpy as np 7 | 8 | 9 | class FaceRecognition: 10 | MODEL = "hog" # Fast but low on accuracy, other can be 'cnn', high accuracy and low speed (on CPU && fast on GPU) 11 | TOLERANCE = 0.6 12 | 13 | @staticmethod 14 | def train_known_faces(known_faces): 15 | """ 16 | Given an image, locate faces and get a 128-dimension face encodings 17 | :param known_faces: List of images to be processed. 18 | """ 19 | # List of encodings 20 | encodings = [] 21 | 22 | # We go through the list of known-faces images, then load each image 23 | # get encodings and append to encodings 24 | for img in known_faces: 25 | # Loads an image file (.jpg, .png, etc) into a numpy array 26 | image = load_image_file(img) 27 | 28 | # returns a list of found faces, for this purpose we take first face only (assuming one face per image) 29 | encodings.append(face_encodings(image)[0]) 30 | 31 | # return list of encodings 32 | return encodings 33 | 34 | @staticmethod 35 | def process_unknown_faces(unknown_faces, model): 36 | """ 37 | Given an image, locate faces and get a 128-dimension face encodings 38 | :param unknown_faces: List of images to be processed. 39 | :param model: Which face detection model to use. "hog" is less accurate but faster on CPUs. "cnn" is a more 40 | accurate deep-learning model which is GPU/CUDA accelerated (if available). The default is "hog". 41 | :return: Generator with encodings. 42 | """ 43 | 44 | # List of 128-dimension encodings 45 | all_encodings = [] 46 | 47 | # We go through the list of unknown-faces images, then load each image 48 | # get encodings, face_locations 49 | for image in unknown_faces: 50 | 51 | # Loads an image file (.jpg, .png, etc) into a numpy array, then 52 | np_image = load_image_file(image) 53 | 54 | # Scale down image to gain some speed. 55 | if max(np_image.shape) > 1600: 56 | pil_img = PIL.Image.fromarray(np_image) 57 | pil_img.thumbnail((1600, 1600), PIL.Image.LANCZOS) 58 | np_image = np.array(pil_img) 59 | 60 | # Locate all faces in image 61 | locations = face_locations(np_image, model=model) 62 | 63 | # Since we know face locations, we can pass them to face_encodings as second argument 64 | # Without that it will search for faces once again 65 | encodings = face_encodings(np_image, locations) 66 | 67 | # If face found yield a tuple with face encodings, original image 68 | if encodings: 69 | yield encodings, image 70 | 71 | def find_matches(self, known_faces, unknown_faces): 72 | """ 73 | Compare a list of face encodings against a candidate encoding to see if they match. 74 | :param known_faces: List of images of a person 75 | :param unknown_faces: List of images to compare 76 | :return: Generator 77 | """ 78 | 79 | # Contains list if a 128-dimension face encodings for all found faces in known_faces. 80 | known_faces_encodings = self.train_known_faces(known_faces) 81 | 82 | # Compare unknown_faces to the list of known_face encodings to check for a match 83 | for fc_encodings, original_image in self.process_unknown_faces(unknown_faces, self.MODEL): 84 | 85 | # A list of True/False values indicating which known_face_encodings is a positive match 86 | for fc_enc in fc_encodings: 87 | 88 | # List of tur false values 89 | result_array = compare_faces(known_faces_encodings, fc_enc, tolerance=self.TOLERANCE) 90 | 91 | if any(result_array): # If there is a match, yield 92 | 93 | yield original_image 94 | -------------------------------------------------------------------------------- /isearch_osint.py: -------------------------------------------------------------------------------- 1 | from isearch import InstagramClient, FaceRecognition 2 | import os 3 | import click 4 | 5 | 6 | # Config paths 7 | BASE_DIR = os.getcwd() 8 | CONFIG_DIR = BASE_DIR + '/config' 9 | 10 | 11 | def load_files(dir_path): 12 | """Load files form a dir path.""" 13 | assert os.path.exists(dir_path) 14 | return [f"{dir_path}/{file}" for file in os.listdir(dir_path)] 15 | 16 | 17 | def build_image_url(image_name): 18 | """Build url from image name.""" 19 | return image_name.split('__')[-1][:-5].replace('#', '/') 20 | 21 | 22 | @click.command() 23 | @click.argument('known_people_folder') 24 | @click.argument('target_username') 25 | @click.option('--limit', default=-1, help='Number of photos to Download and process from target_profile') 26 | def main(known_people_folder, target_username, limit): 27 | 28 | target_face, ig_profile = known_people_folder, target_username 29 | 30 | # Authenticate to instagram 31 | client = InstagramClient(config_dir=CONFIG_DIR) 32 | 33 | # Set target to ig_profile 34 | client.set_target(ig_profile) 35 | 36 | print() # for better stdout display 37 | 38 | # returns path to the downloaded photos 39 | output = client.download_target_photo(limit=limit) 40 | 41 | recon = FaceRecognition() 42 | 43 | # generator yield match if found. 44 | for match in recon.find_matches(load_files(target_face), load_files(output)): 45 | click.echo(" Possible match [{}]".format(build_image_url(match))) 46 | 47 | client.cleanup() # Delete downloaded photos 48 | 49 | 50 | if __name__ == '__main__': 51 | main() 52 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | face_recognition_models 2 | instagram_private_api 3 | numpy>=1.20.3 4 | Pillow==8.2.0 5 | click>=8.0.1 6 | opencv-python>=4.5.2.54 --------------------------------------------------------------------------------