├── .env ├── .gitignore ├── testt.jpeg ├── testtt.jpeg ├── try.py ├── checker.py ├── main.py ├── test.py └── followings_data1.json /.env: -------------------------------------------------------------------------------- 1 | 2 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | /test.jpg -------------------------------------------------------------------------------- /testt.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Adityaadpandey/people_find/HEAD/testt.jpeg -------------------------------------------------------------------------------- /testtt.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Adityaadpandey/people_find/HEAD/testtt.jpeg -------------------------------------------------------------------------------- /try.py: -------------------------------------------------------------------------------- 1 | import requests 2 | import json 3 | 4 | # Access token and Instagram user ID (replace with your values) 5 | access_token = 'YOUR_ACCESS_TOKEN' 6 | user_id = 'YOUR_USER_ID' 7 | 8 | # Base URL for Instagram Graph API 9 | base_url = 'https://graph.instagram.com/' 10 | 11 | # Function to fetch user profile 12 | def fetch_user_profile(user_id, access_token): 13 | url = f'{base_url}{user_id}?fields=id,username,account_type,media_count&access_token={access_token}' 14 | response = requests.get(url) 15 | return response.json() 16 | 17 | # Function to fetch user media 18 | def fetch_user_media(user_id, access_token): 19 | url = f'{base_url}{user_id}/media?fields=id,caption,media_type,media_url,permalink,thumbnail_url,timestamp&access_token={access_token}' 20 | response = requests.get(url) 21 | return response.json() 22 | 23 | # Fetch and print user profile 24 | user_profile = fetch_user_profile(user_id, access_token) 25 | print(json.dumps(user_profile, indent=4)) 26 | 27 | # Fetch and print user media 28 | user_media = fetch_user_media(user_id, access_token) 29 | print(json.dumps(user_media, indent=4)) 30 | -------------------------------------------------------------------------------- /checker.py: -------------------------------------------------------------------------------- 1 | import json 2 | import face_recognition 3 | import numpy as np 4 | 5 | # Load the JSON data with embeddings 6 | with open('followings_data.json', 'r') as f: 7 | followings_data = json.load(f) 8 | 9 | # Function to calculate Euclidean distance between two face embeddings 10 | def calculate_distance(embedding1, embedding2): 11 | return np.linalg.norm(np.array(embedding1) - np.array(embedding2)) 12 | 13 | # Function to find all matches sorted by their distance 14 | def find_all_matches(image_path, followings_data): 15 | # Load the input image and calculate its face embedding 16 | input_image = face_recognition.load_image_file(image_path) 17 | input_embedding = face_recognition.face_encodings(input_image) 18 | 19 | if not input_embedding: 20 | print("No face found in the input image.") 21 | return [] 22 | 23 | input_embedding = input_embedding[0] 24 | 25 | # Initialize a list to store matches and their distances 26 | matches = [] 27 | 28 | # Iterate through followings' embeddings to find all matches 29 | for following in followings_data: 30 | if following['face_embedding'] is not None: 31 | distance = calculate_distance(input_embedding, following['face_embedding']) 32 | matches.append((following, distance)) 33 | 34 | # Sort the matches by distance 35 | matches.sort(key=lambda x: x[1]) 36 | 37 | return matches 38 | 39 | # Main function to take an input image and find the top N closest matches 40 | def main(image_path, top_n=5, max_distance=0.6): 41 | all_matches = find_all_matches(image_path, followings_data) 42 | if all_matches: 43 | closest_matches = [match for match in all_matches if match[1] <= max_distance][:top_n] 44 | if closest_matches: 45 | print(f"Top {top_n} closest matches within distance {max_distance}:") 46 | for match, distance in closest_matches: 47 | print(f"Username: {match['username']}, Distance: {distance}") 48 | else: 49 | print(f"No matches found within the distance of {max_distance}.") 50 | else: 51 | print("No matches found.") 52 | 53 | # Example usage 54 | if __name__ == '__main__': 55 | input_image_path = 'testt.jpeg' 56 | main(input_image_path, top_n=8, max_distance=0.6) 57 | -------------------------------------------------------------------------------- /main.py: -------------------------------------------------------------------------------- 1 | import instaloader 2 | import os 3 | import json 4 | import requests 5 | from datetime import datetime 6 | import face_recognition 7 | 8 | 9 | # Initialize Instaloader 10 | L = instaloader.Instaloader() 11 | 12 | # Log in to Instagram 13 | USERNAME = 'aditya_isfit' 14 | PASSWORD = 'jjyespapa@123' 15 | 16 | # Try to load session cookies 17 | try: 18 | L.load_session_from_file(USERNAME) 19 | except FileNotFoundError: 20 | # If session file doesn't exist, log in and save session 21 | L.login(USERNAME, PASSWORD) 22 | L.save_session_to_file() 23 | 24 | # Load profile of your account 25 | profile = instaloader.Profile.from_username(L.context, USERNAME) 26 | 27 | # Create a directory to save the data 28 | directory = 'instagram_followings' 29 | if not os.path.exists(directory): 30 | os.makedirs(directory) 31 | 32 | # Function to download profile picture 33 | def download_profile_pic(path, url): 34 | try: 35 | # Ensure the directory exists 36 | os.makedirs(os.path.dirname(path), exist_ok=True) 37 | 38 | # Download the picture 39 | response = requests.get(url, stream=True) 40 | if response.status_code == 200: 41 | with open(path, 'wb') as out_file: 42 | for chunk in response.iter_content(1024): 43 | out_file.write(chunk) 44 | os.utime(path, (datetime.now().timestamp(), datetime.now().timestamp())) 45 | print(f"Downloaded: {path}") 46 | else: 47 | print(f"Failed to download {url}: {response.status_code}") 48 | except Exception as e: 49 | print(f"Failed to download {url}: {e}") 50 | 51 | # Function to check if an image contains a face and generate embeddings 52 | def get_face_embedding(image_path): 53 | try: 54 | # Load the image file into a numpy array 55 | image = face_recognition.load_image_file(image_path) 56 | # Find all the faces and face encodings in the image 57 | face_encodings = face_recognition.face_encodings(image) 58 | if face_encodings: 59 | return face_encodings[0] # Return the first face encoding found 60 | else: 61 | return None 62 | except Exception as e: 63 | print(f"Failed to process {image_path}: {e}") 64 | return None 65 | 66 | # Fetch and save data of followings 67 | followings_data = [] 68 | for following in profile.get_followees(): 69 | username = following.username 70 | full_name = following.full_name 71 | profile_pic_url = following.profile_pic_url 72 | 73 | # Download profile picture 74 | profile_pic_path = f"{directory}/{username}.jpg" 75 | download_profile_pic(profile_pic_path, profile_pic_url) 76 | 77 | # Check if the profile picture contains a face and get the embedding 78 | face_embedding = get_face_embedding(profile_pic_path) 79 | 80 | # Append to followings data list 81 | followings_data.append({ 82 | 'username': username, 83 | 'full_name': full_name, 84 | 'profile_pic_path': profile_pic_path, 85 | 'face_embedding': face_embedding.tolist() if face_embedding is not None else None 86 | }) 87 | 88 | # Save followings data to a JSON file 89 | with open(f'{directory}/followings_data.json', 'w') as f: 90 | json.dump(followings_data, f, indent=4) 91 | 92 | print("Data fetched and saved successfully.") -------------------------------------------------------------------------------- /test.py: -------------------------------------------------------------------------------- 1 | import instaloader 2 | import os 3 | import json 4 | import requests 5 | from datetime import datetime 6 | import face_recognition 7 | from dotenv import load_dotenv 8 | from multiprocessing import Pool, cpu_count 9 | 10 | # Load environment variables from .env file 11 | load_dotenv() 12 | 13 | # Initialize Instaloader 14 | L = instaloader.Instaloader() 15 | 16 | # Function to download profile picture 17 | def download_profile_pic(username): 18 | try: 19 | profile = instaloader.Profile.from_username(L.context, username) 20 | profile_pic_url = profile.profile_pic_url 21 | profile_pic_path = f"instagram_followings/{username}.jpg" 22 | response = requests.get(profile_pic_url, stream=True) 23 | if response.status_code == 200: 24 | with open(profile_pic_path, 'wb') as out_file: 25 | out_file.write(response.content) 26 | os.utime(profile_pic_path, (datetime.now().timestamp(), datetime.now().timestamp())) 27 | return username, profile_pic_path 28 | else: 29 | print(f"Failed to download {profile_pic_url}: {response.status_code}") 30 | return None, None 31 | except Exception as e: 32 | print(f"Failed to download profile picture for {username}: {e}") 33 | return None, None 34 | 35 | # Function to check if an image contains a face and generate embeddings 36 | def get_face_embedding(profile_pic_path): 37 | try: 38 | image = face_recognition.load_image_file(profile_pic_path) 39 | face_encodings = face_recognition.face_encodings(image) 40 | if face_encodings: 41 | return face_encodings[0].tolist() 42 | else: 43 | return None 44 | except Exception as e: 45 | print(f"Failed to process {profile_pic_path}: {e}") 46 | return None 47 | 48 | # Get Instagram username and password from environment variables 49 | instagram_username = os.getenv("INSTAGRAM_USERNAME") 50 | instagram_password = os.getenv("INSTAGRAM_PASSWORD") 51 | 52 | # Check if Instagram username and password are provided 53 | if not instagram_username or not instagram_password: 54 | raise ValueError("Instagram username or password not found in environment variables.") 55 | 56 | # Try to load session cookies 57 | try: 58 | L.load_session_from_file(instagram_username) 59 | except FileNotFoundError: 60 | L.login(instagram_username, instagram_password) 61 | L.save_session_to_file() 62 | 63 | # Load profile of your account 64 | profile = instaloader.Profile.from_username(L.context, instagram_username) 65 | 66 | # Create a directory to save the data 67 | directory = 'instagram_followings' 68 | if not os.path.exists(directory): 69 | os.makedirs(directory) 70 | 71 | # Function to process each user 72 | def process_user(username): 73 | profile_pic_username, profile_pic_path = download_profile_pic(username) 74 | if profile_pic_username and profile_pic_path: 75 | face_embedding = get_face_embedding(profile_pic_path) 76 | return { 77 | 'username': profile_pic_username, 78 | 'profile_pic_path': profile_pic_path, 79 | 'face_embedding': face_embedding 80 | } 81 | else: 82 | return None 83 | 84 | # Fetch and save data of followings 85 | followings_data = [] 86 | followings = [following.username for following in profile.get_followees()] 87 | 88 | # Parallel processing for followings 89 | with Pool(cpu_count()) as pool: 90 | followings_data = pool.map(process_user, followings) 91 | 92 | # Filter out None results 93 | followings_data = [data for data in followings_data if data is not None] 94 | 95 | # Fetch and save data of followings' followings 96 | for following in followings: 97 | try: 98 | following_profile = instaloader.Profile.from_username(L.context, following) 99 | followings_followings = [ff.username for ff in following_profile.get_followees()] 100 | 101 | with Pool(cpu_count()) as pool: 102 | followings_followings_data = pool.map(process_user, followings_followings) 103 | 104 | followings_followings_data = [data for data in followings_followings_data if data is not None] 105 | followings_data.extend(followings_followings_data) 106 | except Exception as e: 107 | print(f"Failed to fetch followings for {following}: {e}") 108 | 109 | # Save followings data to a JSON file 110 | with open(f'{directory}/followings_data.json', 'w') as f: 111 | json.dump(followings_data, f, indent=4) 112 | 113 | print("Data fetched and saved successfully.") 114 | -------------------------------------------------------------------------------- /followings_data1.json: -------------------------------------------------------------------------------- 1 | [ 2 | { 3 | "username": "sks__brothers", 4 | "full_name": "SKS", 5 | "profile_pic_path": "instagram_followings/sks__brothers.jpg", 6 | "face_embedding": [ 7 | -0.13743872940540314, 8 | 0.025923337787389755, 9 | 0.017525913193821907, 10 | -0.057825956493616104, 11 | -0.00697892252355814, 12 | -0.04339059442281723, 13 | 0.03412013500928879, 14 | -0.14806407690048218, 15 | 0.20350104570388794, 16 | -0.10201858729124069, 17 | 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"username": "my_self_shiva579", 274 | "full_name": "Shiva Vishwakarma", 275 | "profile_pic_path": "instagram_followings/my_self_shiva579.jpg", 276 | "face_embedding": null 277 | }, 278 | { 279 | "username": "shivamdubey2021", 280 | "full_name": "Shivam Dubey", 281 | "profile_pic_path": "instagram_followings/shivamdubey2021.jpg", 282 | "face_embedding": null 283 | }, 284 | { 285 | "username": "ananyamish_", 286 | "full_name": "Ananya \u2764\ufe0f\ud83e\udd27", 287 | "profile_pic_path": "instagram_followings/ananyamish_.jpg", 288 | "face_embedding": null 289 | }, 290 | { 291 | "username": "crexmy_mxxn", 292 | "full_name": "SHREYA", 293 | "profile_pic_path": "instagram_followings/crexmy_mxxn.jpg", 294 | "face_embedding": null 295 | }, 296 | { 297 | "username": "shivamdwivedi_626", 298 | "full_name": "Shivam Dubey", 299 | "profile_pic_path": "instagram_followings/shivamdwivedi_626.jpg", 300 | "face_embedding": [ 301 | -0.18630778789520264, 302 | 0.05449550971388817, 303 | 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