├── README.md ├── facescrub.py ├── imdb_crawl.py ├── run_experiment.py ├── gen_megaface.py ├── facescrub_noise_list.txt └── distractor_noise_list.txt /README.md: -------------------------------------------------------------------------------- 1 | # Face Recognition dataset 2 | 3 | ## IMDb-Face dataset 4 | 5 | * [IMDb](http://openaccess.thecvf.com/content_ECCV_2018/papers/Liren_Chen_The_Devil_of_ECCV_2018_paper.pdf) 6 | + Available at https://github.com/fwang91/IMDb-Face (IMDb-Face.csv) 7 | 8 | * For fast processing, multi processing is supported with concurrent.futures module. 9 | 10 | ### Running instruction 11 | * Download 'IMDb-Face.csv' file from https://drive.google.com/open?id=134kOnRcJgHZ2eREu8QRi99qj996Ap_ML 12 | * `python imdb_crawl.py` 13 | * Arguments 14 | ``` 15 | -c: whether you crop the image with bounding box 16 | -d: delete existing data directory be 17 | ``` 18 | * If you save non-cropped image, corresponding bounding box will also be recorded in bb.txt file for each direcory. 19 | * Make sure 'IMDb-Face.csv' and 'imdb_crawl.py' are located in same directory. 20 | 21 | ## Megaface dataset 22 | To run megaface test including identification(1m distractors), verification(@1e-6), 23 | 1. Download distractors and probe dataset 24 | 2. Preprocess dataset 25 | 3. Generate bin files with a trained face recognition model 26 | 4. Run megaface devkit 27 | 28 | ### Download dataset 29 | * Distractors
30 | `wget -c --user 'id' --password 'pwd' http://megaface.cs.washington.edu/dataset/download/content/MegaFace_dataset.tar.gz` 31 | * Facescrub
32 | - `wget -c --user 'id' --password 'pwd' http://megaface.cs.washington.edu/dataset/download/content/downloaded.tgz` 33 | - `python facescrub.py`
34 | But, many urls can not be accessed. 35 | * Both datasets can be accessed at **http://megaface.cs.washington.edu/participate/challenge.html** 36 | * Dataset structure 37 | 
 38 |    MEGAFACE -- distractors -- parent id -- ids -- images
 39 |             |                                  |- json file for each image 
 40 |             |
 41 |             |- facescrub -- ids -- images, bb.txt
 42 |                                 |- bb.txt
 43 |
44 | * Facescrub bounding box file 45 | - facescrub.py 46 | - Need `facescrub bounding box actor.txt`, `facescrub bounding box actress.txt` files at [megaface](http://megaface.cs.washington.edu/participate/challenge.html) 47 | - Arguments 48 | 
 49 |     - txt_files: [facescrub_actor.txt, facescrub_actress.txt]
 50 |     - timeout: timeout seconds for accessing url (need for downloading image)
 51 |
52 | - This script file can also download image from text files, but many rows have missing urls. 53 | 54 | ### Preprocess 55 | Preprocess with your face detection/alignment model. 56 | 57 | ### Generate bin files 58 | * gen_megaface.py (Need your face recognition model) 59 | - Make bin files of megaface distractors/facescrub images from trained face recognition model. 60 | - Arguments 61 | 
 62 |     - megaface_path: path of pre-processed distractor images
 63 |     - facescrub_path: path of pre-processed facescrub images
 64 |     - megaface_noise: noise list of distractors
 65 |     - facescrub_noise: noise list of facescrub
 66 |     - megaface_bin_path: distractor bins save directory
 67 |     - facescrub_bin_path: facescrub bins save directory
 68 |     - ckpt: trained face recognition model
 69 |     - file_ending: file ending name, ex) _baseline.bin: aaa.jpg -> aaa_baseline.bin    
 70 |
71 | - Resulting bin files of gen_megaface.py (ex: file_ending: \_baseline.bin_) 72 | 73 | 
 74 |     _baseline -- facescrub_bin -- ids -- bin files (***_baseline.bin)
 75 |                              | 
 76 |                              |- megaface_bin -- parend id -- ids -- bin files (***_baseline.bin)
 77 |
78 | 79 | 80 | ### Run megaface devkit 81 | * On terminal,
82 | ``` python run_experiment.py --file_ending _baseline.bin --out_root baseline_results -d``` 83 | - Need at least 32G memory 84 | - Need devkit from http://megaface.cs.washington.edu/participate/challenge.html 85 | - bin/FuseResults, Identification 86 | - models/jb_identity.bin, jb_LBP.bin 87 | - templatelists 88 | 89 | * run_experiment.py 90 | - Executes identification and verification binary files (**bin/Identification**, **bin/FuseResults**). 91 | - Arguments 92 | 
 93 |     - distractor_feature_path: distractor bin files path (megaface_bin_path of gen_megaface.py)
 94 |     - probe_feature_path: facescrub bin files path (facescrub_bin_path of gen_megaface.py)
 95 |     - file_ending: file ending format (file_ending of gen_megaface.py)
 96 |     - sizes: number of distractors, set as [1000000] 
 97 |
98 | * Caution 99 | - binary files (**bin/Identification**, **bin/FuseResults**) can be only executed on opencv2.4. 100 | 101 | ## Reference 102 | * Megaface devkit 103 | - Can download at http://megaface.cs.washington.edu/participate/challenge.html 104 | * distractor_noise_list, facescrub_noise_list 105 | - Megaface dataset has some noisy dataset (some images in facescrub are included distractors) 106 | - [arcface](https://arxiv.org/pdf/1801.07698.pdf) 107 | - [arcface github](https://github.com/deepinsight/insightface/tree/master/src/megaface) 108 | 109 | -------------------------------------------------------------------------------- /facescrub.py: -------------------------------------------------------------------------------- 1 | import os 2 | import urllib 3 | import requests 4 | import argparse 5 | from PIL import Image 6 | import io 7 | import logging 8 | import multiprocessing as mp 9 | import subprocess as sp 10 | import concurrent.futures 11 | import shutil 12 | 13 | DATA_ROOT = 'facescrub' 14 | 15 | def downloads(line, timeout, downloaded): 16 | # Name/image_id/face_id/url/bbox/sha256 17 | # Line parse 18 | contents = line.split('\t') 19 | act_name = contents[0].strip().replace(' ', '_') 20 | face_id = contents[2].strip() 21 | url = contents[3].strip() 22 | bbox = contents[4].strip() 23 | 24 | download_dir = os.path.join(DATA_ROOT, act_name) 25 | 26 | if not os.path.exists(download_dir): 27 | os.makedirs(download_dir) 28 | logging.info('%s directory created' % act_name) 29 | 30 | try: 31 | if not downloaded: 32 | r = requests.get(url, stream=True, timeout=timeout) 33 | status = r.status_code 34 | content_size = int(r.headers.get('content-length')) 35 | logging.info('Status {} for {}, size of {}'.format(status, save_path, content_size)) 36 | 37 | if content_size == 0: 38 | return None 39 | 40 | I = Image.open(io.BytesIO(r.content)) 41 | I.save(save_path) 42 | logging.info('%s saved\n' % save_path) 43 | 44 | # Write bounding box 45 | with open(os.path.join(download_dir, 'bb.txt'), 'a') as bbf: 46 | bbf.write(('_').join((act_name, face_id)) + ',' + bbox + '\n') 47 | bbf.close() 48 | 49 | except Exception as e: 50 | logging.error(', '.join((url, str(e))) + '\n') 51 | 52 | def clean_dir_file_name(): 53 | directories = os.listdir(DATA_ROOT) 54 | 55 | for d in directories: 56 | d_changed = d.strip().replace(' ', '_') 57 | if d != d_changed: 58 | sp.call(['mv', os.path.join(DATA_ROOT, d), os.path.join(DATA_ROOT, d_changed)]) 59 | logging.info('From {} to {}'.format(d, d_changed)) 60 | files = os.listdir(os.path.join(DATA_ROOT, d_changed)) 61 | for f in files: 62 | f_changed = f.split('_')[0].strip().replace(' ', '_') 63 | filename = f.split('_')[-1] 64 | filename = '_'.join((f_changed, filename)) 65 | logging.info('Filename %s' % filename) 66 | dir_path = os.path.join(DATA_ROOT, d_changed) 67 | if f != f_changed: 68 | sp.call(['mv', os.path.join(dir_path, f), os.path.join(dir_path, filename)]) 69 | logging.info('From {} to {}'.format(os.path.join(dir_path, f), os.path.join(dir_path, filename))) 70 | 71 | 72 | 73 | if __name__ == "__main__": 74 | parser = argparse.ArgumentParser(description='Script for download face scrub dataset') 75 | # ['facescrub_actors.txt', 'facescrub_actresses.txt'] 76 | parser.add_argument('--txt_files', nargs='+') 77 | parser.add_argument('--timeout', type=int) 78 | parser.add_argument('-d', '--delete', action='store_true') 79 | parser.add_argument('--downloaded', action='store_true', help='Downloaded full tgz file or not') 80 | parser.add_argument('--datapath', type=str) 81 | parser.set_defaults(txt_files=['facescrub_actors.txt', 'facescrub_actresses.txt'], timeout=10, datapath='facescrub') 82 | 83 | args = parser.parse_args() 84 | logging.basicConfig(level=logging.INFO, format='[%(levelname)s][%(filename)s:%(lineno)d] %(message)s') 85 | 86 | if args.delete: 87 | logging.warning('Delete existing images directory') 88 | try: 89 | shutil.rmtree(DATA_ROOT) 90 | logging.warning('Delete %s' % DATA_ROOT) 91 | except OSError as e: 92 | logging.error('%s-%s' % (e.filename, e.strerror)) 93 | 94 | else: 95 | for (p, d, files) in os.walk(DATA_ROOT): 96 | for f in files: 97 | ext = os.path.splitext(f)[-1] 98 | if ext == '.txt': 99 | os.remove(os.path.join(p, f)) 100 | logging.warning('Delete %s' % os.path.join(p, f)) 101 | 102 | 103 | if not os.path.exists(DATA_ROOT): 104 | os.makedirs(DATA_ROOT) 105 | 106 | if args.downloaded: 107 | clean_dir_file_name() 108 | num_threads = 1 109 | 110 | else: 111 | num_threads = mp.cpu_count() 112 | 113 | logging.info('Facescrub text files: %s' % args.txt_files) 114 | 115 | num_threads = 1 116 | logging.info('%d cpus' % num_threads) 117 | 118 | for i in range(len(args.txt_files)): 119 | logging.info('Open %s' % args.txt_files[i]) 120 | with open(args.txt_files[i]) as f, concurrent.futures.ProcessPoolExecutor(max_workers=num_threads) as executor: 121 | lines = f.readlines() 122 | 123 | try: 124 | futures = [executor.submit(downloads, line, args.timeout, args.downloaded) for line in lines[1:]] 125 | for future in concurrent.futures.as_completed(futures): 126 | try: 127 | data = future.result() 128 | except Exception as exc: 129 | logging.warning(exc) 130 | # else: 131 | # logging.info('%s bytes done' % data) 132 | 133 | except Exception as e: 134 | logging.warning(e.strerror) 135 | 136 | 137 | f.close() 138 | executor.shutdown(wait=True) 139 | -------------------------------------------------------------------------------- /imdb_crawl.py: -------------------------------------------------------------------------------- 1 | import os 2 | import shutil 3 | import logging 4 | import argparse 5 | import urllib 6 | import requests 7 | import concurrent.futures 8 | import csv 9 | from PIL import Image 10 | import io 11 | import multiprocessing as mp 12 | import pickle 13 | 14 | def create_logger(logger_name): 15 | logger = logging.getLogger(logger_name) 16 | 17 | # Set log level 18 | logger.setLevel(logging.DEBUG) 19 | 20 | # Log formatter 21 | formatter = logging.Formatter('[%(levelname)s][%(lineno)d] %(message)s') 22 | 23 | # Logfile handler 24 | logfile_name = logger_name+'.log' 25 | if os.path.exists(logfile_name): 26 | os.remove(logfile_name) 27 | 28 | file_handler = logging.FileHandler(logfile_name) 29 | file_handler.setLevel(logging.INFO) 30 | file_handler.setFormatter(formatter) 31 | logger.addHandler(file_handler) 32 | 33 | # Print log to console 34 | console = logging.StreamHandler() 35 | console.setLevel(logging.DEBUG) 36 | console.setFormatter(formatter) 37 | logger.addHandler(console) 38 | 39 | return logger 40 | 41 | 42 | def down_save(save_dir, row, logname, crop, counter): 43 | logger = logging.getLogger(logname) 44 | 45 | # Make download directory 46 | dir_path = os.path.join(save_dir, row['index']) 47 | if not os.path.exists(dir_path): 48 | logger.info('Make directory of {name}\n'.format(name=row['index'])) 49 | os.makedirs(dir_path, exist_ok=True) 50 | # Download image 51 | response = download_image(row['url'], logger, counter) 52 | if response: 53 | # response.content: bytes type 54 | save_image(response.content, crop, dir_path, row['image'], row['rect'], row['height width']) 55 | 56 | 57 | def save_image(content, crop, dir_path, image_name, bb, image_size): 58 | assert os.path.exists(dir_path) 59 | 60 | save_path = os.path.join(dir_path, image_name) 61 | 62 | # Get size 63 | h, w = list(map(int, image_size.split(' '))) 64 | logger.info('Image size >> height: %d, width: %d' % (h, w)) 65 | 66 | # Get bounding box 67 | bbox = list(map(int, bb.split(' '))) 68 | logger.info('Bounding box >> x0: {x0}, y0: {y0}, x1: {x1}, y1: {y1}'.format(x0=bbox[0], y0=bbox[1], x1=bbox[2], y1=bbox[3])) 69 | 70 | if crop: 71 | try: 72 | I = Image.open(io.BytesIO(content)) 73 | I.resize((w,h), Image.ANTIALIAS).crop(bbox).save(save_path) 74 | logger.info('Image saved as %s\n' % save_path) 75 | 76 | except IOError as e: 77 | logger.error('{error}: {image}\n'.format(error=e, image=save_path)) 78 | pass 79 | else: 80 | try: 81 | I = Image.open(io.BytesIO(content)) 82 | I.resize((w,h), Image.ANTIALIAS).save(save_path) 83 | logger.info('Image saved as %s\n' % save_path) 84 | # Write bounding box 85 | with open(os.path.join(dir_path, 'bb.txt'), 'a') as f: 86 | f.write(save_path + ',' + ','.join(str(bb) for bb in bbox) + '\n') 87 | f.close() 88 | 89 | except IOError as e: 90 | logger.error('{error}: {image}\n'.format(error=e, image=save_path)) 91 | pass 92 | 93 | 94 | def download_image(url, logger, counter): 95 | ''' 96 | Download image from url 97 | Returns response object if successful else None 98 | ''' 99 | try: 100 | r = requests.get(url, stream=True) 101 | # 200: OK 102 | status = r.status_code 103 | 104 | if status != requests.codes.OK: 105 | logger.warning('Line {line}: Can not access to {url}'.format(line, counter, url=url)) 106 | r.raise_for_status() 107 | return None 108 | 109 | total_size = int(r.headers.get('content-length')) 110 | logger.info('Line {line}: Get image size of {size} from {url} with status {status}'.format(url=url, size=total_size, status=status, line=counter)) 111 | 112 | return r 113 | 114 | except IOError as e: 115 | logger.error('{error}: {url}\n'.format(error=e, url=url)) 116 | 117 | 118 | if __name__ == "__main__": 119 | parser = argparse.ArgumentParser(description='Script for download IMDb dataset for face recognition') 120 | parser.add_argument('--csv_file', type=str, default='IMDb-Face.csv') 121 | parser.add_argument('--logger', type=str, default='imdb') 122 | parser.add_argument('--save_dir', type=str, default='imdb_data') 123 | parser.add_argument('-c', '--crop', action="store_true") 124 | parser.add_argument('-d', '--delete', action="store_true") 125 | args = parser.parse_args() 126 | 127 | logger = create_logger(args.logger) 128 | 129 | if args.delete: 130 | logger.warning('Delete existing files') 131 | files = os.listdir() 132 | for i in range(len(files)): 133 | if os.path.isdir(files[i]): 134 | try: 135 | shutil.rmtree(files[i]) 136 | logger.warning('Delete %s' % files[i]) 137 | except OSError as e: 138 | logger.error('%s-%s' % (e.filename, e.strerror)) 139 | 140 | elif os.path.splitext(files[i])[-1] == '.log': 141 | os.remove(files[i]) 142 | logger.warning('Delete logfile %s' % files[i]) 143 | else: 144 | continue 145 | else: 146 | # Delete bounding box text file 147 | for (p,d,f) in os.walk('./'): 148 | for filename in f: 149 | ext = os.path.splitext(filename)[-1] 150 | if ext == '.txt': 151 | os.remove(os.path.join(p, filename)) 152 | logger.warning('Delete %s/%s' % (p, filename)) 153 | 154 | num_threads = mp.cpu_count() 155 | 156 | # Check if csv file exists 157 | if not os.path.exists(os.path.expanduser(args.csv_file)): 158 | logger.error('%s does not exist' % args.csv_file) 159 | raise Exception 160 | 161 | # Make save directory 162 | if not os.path.exists(args.save_dir): 163 | os.makedirs(args.save_dir, exist_ok=True) 164 | 165 | 166 | # Multiprocess must be called from __main__ 167 | with open(args.csv_file) as imdb, concurrent.futures.ProcessPoolExecutor(max_workers=num_threads) as executor: 168 | csv_reader = csv.DictReader(imdb) 169 | 170 | headers = csv_reader.fieldnames 171 | # Name, index, image, rect, size, url 172 | name = headers[0] 173 | index = headers[1] 174 | image_name = headers[2] 175 | rect = headers[3] 176 | size = headers[4] 177 | url = headers[5] 178 | logger.info('Headers: {name}, {index}, {image_index}, {rect}, {size}, {url}\n'.format(name=name, index=index, image_index=image_name, rect=rect, size=size, url=url)) 179 | 180 | # logger is a lock object incurs pickle thread lock error 181 | #pickle.dumps(logger) 182 | 183 | try: 184 | futures = [executor.submit(down_save, args.save_dir, row, args.logger, args.crop, counter) for counter, row in enumerate(csv_reader)] 185 | for future in concurrent.futures.as_completed(futures): 186 | #print(i.result()) 187 | pass 188 | except Exception as e: 189 | logger.warning(e) 190 | -------------------------------------------------------------------------------- /run_experiment.py: -------------------------------------------------------------------------------- 1 | # Created by MegaFace Team 2 | # Please cite the our paper if you use our code, results, or dataset in a publication 3 | # http://megaface.cs.washington.edu/ 4 | 5 | import argparse 6 | import os 7 | import sys 8 | import json 9 | import random 10 | import subprocess 11 | import nsml 12 | 13 | MODEL = os.path.join('models', 'jb_identity.bin') 14 | IDENTIFICATION_EXE = os.path.join('bin', 'Identification') 15 | FUSE_RESULTS_EXE = os.path.join('bin', 'FuseResults') 16 | MEGAFACE_LIST_BASENAME = os.path.join('templatelists','megaface_features_list.json') 17 | PROBE_LIST_BASENAME = os.path.join('templatelists','facescrub_features_list.json') 18 | 19 | def main(): 20 | # print(os.environ['LD_LIBRARY_PATH']) 21 | parser = argparse.ArgumentParser(description= 22 | 'Runs the MegaFace challenge experiment with the provided feature files') 23 | parser.add_argument('--distractor_feature_path', help='Path to MegaFace Features') 24 | parser.add_argument('--probe_feature_path', help='Path to FaceScrub Features') 25 | parser.add_argument('--file_ending',help='Ending appended to original photo files. i.e. 11084833664_0.jpg_LBP_100x100.bin => _LBP_100x100.bin') 26 | parser.add_argument( 27 | '--out_root', help='File output directory, outputs results files, score matrix files, and feature lists used') 28 | parser.add_argument('-s', '--sizes', type=int, nargs='+', 29 | help='(optional) Size(s) of feature list(s) to create. Default: 10 100 1000 10000 100000 1000000') 30 | parser.add_argument('-m', '--model', type=str, 31 | help='(optional) Scoring model to use. Default: ../models/jb_identity.bin') 32 | parser.add_argument('-ns','--num_sets', help='Set to change number of sets to run on. Default: 1') 33 | parser.add_argument('-d','--delete_matrices', dest='delete_matrices', action='store_true', help='Deletes matrices used while computing results. Reduces space needed to run test.') 34 | parser.add_argument('-p','--probe_list', help='Set to use different probe list. Default: ../templatelists/facescrub_features_list.json') 35 | parser.add_argument('-dlp','--distractor_list_path', help='Set to change path used for distractor lists') 36 | parser.set_defaults(model=MODEL, num_sets=1, file_endint='.bin', sizes=[1000000], probe_list=PROBE_LIST_BASENAME, distractor_list_path=os.path.dirname(MEGAFACE_LIST_BASENAME)) 37 | args = parser.parse_args() 38 | 39 | distractor_feature_path = args.distractor_feature_path 40 | out_root = args.out_root 41 | probe_feature_path = args.probe_feature_path 42 | file_ending = args.file_ending 43 | 44 | # feature_path 45 | distractor_feature_path = os.path.join(file_ending.split('.')[0], 'megaface_bin') 46 | print(distractor_feature_path) 47 | 48 | probe_feature_path = os.path.join(file_ending.split('.')[0], 'facescrub_bin') 49 | print(probe_feature_path) 50 | 51 | model = args.model 52 | num_sets = args.num_sets 53 | sizes = args.sizes 54 | alg_name = file_ending.split('.')[0].strip('_') 55 | delete_matrices = args.delete_matrices 56 | probe_list_basename = args.probe_list 57 | megaface_list_basename = os.path.join(args.distractor_list_path,os.path.basename(MEGAFACE_LIST_BASENAME)) 58 | set_indices = range(1,int(num_sets) + 1) 59 | 60 | os.chmod(IDENTIFICATION_EXE, 0o755) 61 | os.chmod(FUSE_RESULTS_EXE, 0o755) 62 | 63 | assert os.path.exists(distractor_feature_path) 64 | assert os.path.exists(probe_feature_path) 65 | if not os.path.exists(out_root): 66 | os.makedirs(out_root) 67 | if(not os.path.exists(os.path.join(out_root, "otherFiles"))): 68 | os.makedirs(os.path.join(out_root, "otherFiles")) 69 | other_out_root = os.path.join(out_root, "otherFiles") 70 | 71 | probe_name = os.path.basename(probe_list_basename).split('_')[0] 72 | distractor_name = os.path.basename(megaface_list_basename).split('_')[0] 73 | 74 | #Create feature lists for megaface for all sets and sizes and verifies all features exist 75 | missing = False 76 | for index in set_indices: 77 | for size in sizes: 78 | print('Creating feature list of {} photos for set {}'.format(size,str(index))) 79 | cur_list_name = megaface_list_basename + "_{}_{}".format(str(size), str(index)) 80 | print(cur_list_name) 81 | with open(cur_list_name) as fp: 82 | featureFile = json.load(fp) 83 | path_list = featureFile["path"] 84 | for i in range(len(path_list)): 85 | path_list[i] = os.path.join(distractor_feature_path,os.path.splitext(path_list[i])[0] + file_ending) 86 | if(not os.path.isfile(path_list[i])): 87 | print(path_list[i] + " is missing") 88 | missing = True 89 | if (i % 10000 == 0 and i > 0): 90 | print(str(i) + " / " + str(len(path_list))) 91 | featureFile["path"] = path_list 92 | json.dump(featureFile, open(os.path.join( 93 | other_out_root, '{}_features_{}_{}_{}'.format(distractor_name,alg_name,size,index)), 'w'), sort_keys=True, indent=4) 94 | if(missing): 95 | sys.exit("Features are missing...") 96 | 97 | #Create feature list for probe set 98 | with open(probe_list_basename) as fp: 99 | featureFile = json.load(fp) 100 | path_list = featureFile["path"] 101 | for i in range(len(path_list)): 102 | path_list[i] = os.path.join(probe_feature_path,os.path.splitext(path_list[i])[0] + file_ending) 103 | if(not os.path.isfile(path_list[i])): 104 | print(path_list[i] + " is missing") 105 | missing = True 106 | featureFile["path"] = path_list 107 | json.dump(featureFile, open(os.path.join( 108 | other_out_root, '{}_features_{}'.format(probe_name,alg_name)), 'w'), sort_keys=True, indent=4) 109 | probe_feature_list = os.path.join(other_out_root, '{}_features_{}'.format(probe_name,alg_name)) 110 | if(missing): 111 | sys.exit("Features are missing...") 112 | 113 | print('Running probe to probe comparison') 114 | probe_score_filename = os.path.join( 115 | other_out_root, '{}_{}_{}.bin'.format(probe_name, probe_name, alg_name)) 116 | proc = subprocess.Popen( 117 | [IDENTIFICATION_EXE, model, "path", probe_feature_list, probe_feature_list, probe_score_filename]) 118 | proc.communicate() 119 | 120 | for index in set_indices: 121 | for size in sizes: 122 | print('Running test with size {} images for set {}'.format( 123 | str(size), str(index))) 124 | args = [IDENTIFICATION_EXE, model, "path", os.path.join(other_out_root, '{}_features_{}_{}_{}'.format(distractor_name,alg_name,size,index) 125 | ), probe_feature_list, os.path.join(other_out_root, '{}_{}_{}_{}_{}.bin'.format(probe_name, distractor_name, alg_name, str(size),str(index)))] 126 | proc = subprocess.Popen(args) 127 | proc.communicate() 128 | 129 | print('Computing test results with {} images for set {}'.format( 130 | str(size), str(index))) 131 | args = [FUSE_RESULTS_EXE] 132 | args += [os.path.join(other_out_root, '{}_{}_{}_{}_{}.bin'.format( 133 | probe_name, distractor_name, alg_name, str(size), str(index)))] 134 | args += [os.path.join(other_out_root, '{}_{}_{}.bin'.format( 135 | probe_name, probe_name, alg_name)), probe_feature_list, str(size)] 136 | args += [os.path.join(out_root, "cmc_{}_{}_{}_{}_{}.json".format( 137 | probe_name, distractor_name, alg_name, str(size), str(index)))] 138 | args += [os.path.join(out_root, "matches_{}_{}_{}_{}_{}.json".format( 139 | probe_name, distractor_name, alg_name, str(size), str(index)))] 140 | proc = subprocess.Popen(args) 141 | proc.communicate() 142 | print('Finish test with size {} images for set {}'.format(str(size), str(index))) 143 | 144 | if(delete_matrices): 145 | os.remove(os.path.join(other_out_root, '{}_{}_{}_{}_{}.bin'.format( 146 | probe_name, distractor_name, alg_name, str(size), str(index)))) 147 | 148 | if __name__ == '__main__': 149 | main() 150 | -------------------------------------------------------------------------------- /gen_megaface.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import struct 3 | import logging 4 | import numpy as np 5 | import os 6 | import cv2 7 | from glob import glob 8 | from PIL import Image 9 | from face_recognition import FaceRecognition 10 | 11 | class GEN_MEGAFACE(object): 12 | def __init__(self, args): 13 | self.args = args 14 | 15 | self.fr = FaceRecognition(model_file=self.args.ckpt, embedding_size=self.args.feature_dim) 16 | logging.info('Face recognition loaded') 17 | 18 | self.parent_path = './MEGAFACE' 19 | self.fname2center = dict() 20 | self.facescrub_noise = list() 21 | self.distractor_noise = list() 22 | 23 | def generate_filelist(self, path, noise_file): 24 | path = os.path.join(self.parent_path, path) 25 | logging.info('%s' % path) 26 | 27 | noise_list = list() 28 | with open(noise_file, 'r') as noise_f: 29 | while True: 30 | line = noise_f.readline() 31 | if not line: 32 | break 33 | target = line.split('.')[0].strip() 34 | noise_list.append(target) 35 | logging.info('Check %s' % noise_list) 36 | 37 | img_files = list() 38 | 39 | for (p,d,files) in os.walk(path): 40 | if len(files) == 0: 41 | continue 42 | 43 | for f in files: 44 | filename, file_ext = os.path.splitext(f) 45 | file_ext_lower = file_ext[1:].lower() 46 | 47 | if file_ext_lower in self.args.img_ext: 48 | logging.info('%s added' % filename) 49 | img_files.append(os.path.join(p, f)) 50 | 51 | return img_files, noise_list 52 | 53 | def generate_bin(self, img_path, bin_path, save_path, noise_list, megaface=True): 54 | logging.info('For %s' % img_path) 55 | path, filename = os.path.split(img_path) 56 | path, identity = os.path.split(path) 57 | path, parent_id = os.path.split(path) 58 | filename_head = os.path.splitext(filename)[0].strip() 59 | logging.debug('Filename: %s\nIdentity: %s\nPath: %s' % (filename_head, identity, path)) 60 | 61 | if megaface: 62 | # 001~082, 100~999 63 | if parent_id == 'aligned_distractors': 64 | output_bin_parent_dir = os.path.join(save_path, bin_path) 65 | else: 66 | output_bin_parent_dir = os.path.join(save_path, bin_path, parent_id) 67 | else: 68 | output_bin_parent_dir = os.path.join(save_path, bin_path) 69 | output_bin_dir = os.path.join(output_bin_parent_dir, identity) 70 | 71 | if not os.path.exists(output_bin_dir): 72 | os.makedirs(output_bin_dir, exist_ok=True) 73 | logging.debug('Output directory: %s' % output_bin_dir) 74 | 75 | output_bin_path = os.path.join(output_bin_dir, filename_head + self.args.file_ending) 76 | 77 | img_ext = os.path.splitext(img_path)[-1] 78 | if img_ext == '.gif': 79 | gif = cv2.VideoCapture(img_path) 80 | _, img = gif.read() 81 | else: 82 | img = cv2.imread(img_path) 83 | 84 | if img is None: 85 | raise Exception('%s not valid' % img_path) 86 | 87 | self.fr.forward([Image.fromarray(img[:,:,::-1])]) 88 | feature = self.fr.numpy() 89 | feature = np.squeeze(feature, 1) 90 | 91 | if megaface: 92 | self.write_bin(output_bin_path, feature) 93 | else: 94 | self.facescrub_write_bin(output_bin_path, noise_list, filename_head, feature, identity) 95 | 96 | 97 | def megaface_write_bin(self, output_bin_path, noise_list, filename, feature, identity, parent_id): 98 | logging.debug('Filename: %s' % filename) 99 | noise_path = os.path.join(parent_id, identity, filename + '.jpg') 100 | if not noise_path in noise_list: 101 | feature_ = np.full((self.args.feature_dim+self.args.feature_ext), 0, dtype=np.float32) 102 | feature_[0:self.args.feature_dim] = feature 103 | self.write_bin(output_bin_path, feature) 104 | else: 105 | feature_ = np.full((self.args.feature_dim+self.args.feature_ext), 100, dtype=np.float32) 106 | feature_[0:self.args.feature_dim] = feature 107 | self.write_bin(output_bin_path, feature) 108 | self.distractor_noise.append(output_bin_path) 109 | 110 | def write_bin(self, path, feature): 111 | features = list(feature) 112 | 113 | with open(path, 'wb') as f: 114 | f.write(struct.pack('4i', len(features),1,4,5)) 115 | f.write(struct.pack('f'*len(features), *features)) 116 | 117 | logging.info('Save done in %s' % path) 118 | 119 | def facescrub_write_bin(self, output_bin_path, noise_list, filename, feature, identity): 120 | logging.debug('Filename: %s' % filename) 121 | if not filename in noise_list: 122 | feature_ = np.full((self.args.feature_dim+self.args.feature_ext), 0, dtype=np.float32) 123 | feature_[0:self.args.feature_dim] = feature 124 | self.write_bin(output_bin_path, feature) 125 | if not identity in self.fname2center: 126 | self.fname2center[identity] = np.zeros((self.args.feature_dim+self.args.feature_ext), dtype=np.float32) 127 | self.fname2center[identity] += feature_ 128 | else: 129 | self.facescrub_noise.append((identity, filename, output_bin_path)) 130 | logging.debug('%s added to facescrub_noise' % filename) 131 | 132 | def facescrub_noise_write_bin(self): 133 | logging.info('Facescrub noise length: %d' % len(self.facescrub_noise)) 134 | for k in self.facescrub_noise: 135 | identity, filename, output_bin_path = k 136 | assert identity in self.fname2center 137 | center = self.fname2center[identity] 138 | g = np.zeros((self.args.feature_dim+self.args.feature_ext), dtype=np.float32) 139 | g2 = np.random.uniform(-0.001, 0.001, (self.args.feature_dim)) 140 | g[0:self.args.feature_dim] = g2 141 | f = center + g 142 | _norm = np.linalg.norm(f) 143 | f /= _norm 144 | self.write_bin(output_bin_path, f) 145 | 146 | 147 | if __name__ == "__main__": 148 | p = argparse.ArgumentParser() 149 | p.add_argument('--megaface_path', type=str) 150 | p.add_argument('--facescrub_path', type=str) 151 | p.add_argument('--megaface_noise', type=str) 152 | p.add_argument('--facescrub_noise', type=str) 153 | p.add_argument('--megascrub_bin_path', type=str) 154 | p.add_argument('--facescrub_bin_path', type=str) 155 | p.add_argument('--ckpt', type=str) 156 | p.add_argument('--img_ext', nargs='+') 157 | p.add_argument('--noise', action='store_true') 158 | p.add_argument('--file_ending', type=str) 159 | p.add_argument('--feature_ext', type=int) 160 | p.add_argument('--feature_dim', type=int) 161 | 162 | p.set_defaults(megaface_path='aligned_distractors', 163 | facescrub_path='aligned_facescrub', 164 | megaface_noise='./distractor_noise_list.txt', 165 | facescrub_noise='./facescrub_noise_list.txt', 166 | megaface_bin_path='megaface_bin', 167 | facescrub_bin_path='facescrub_bin', 168 | ckpt='_weights/net_epoch_0455_acc0.9614_thd0.165000.pth', 169 | img_ext=['jpg', 'png', 'jpeg', 'gif'], 170 | file_ending='_baseline.bin', 171 | feature_ext=1, 172 | feature_dim=512) 173 | 174 | args = p.parse_args() 175 | 176 | logging.basicConfig(level=logging.DEBUG, format='[%(levelname)s][%(filename)s:%(lineno)d] %(message)s') 177 | 178 | if not os.path.exists(args.megaface_bin_path): 179 | os.makedirs(args.megaface_bin_path, exist_ok=True) 180 | if not os.path.exists(args.facescrub_bin_path): 181 | os.makedirs(args.facescrub_bin_path, exist_ok=True) 182 | 183 | mg = GEN_MEGAFACE(args) 184 | 185 | try: 186 | megaface_files, megaface_noise = mg.generate_filelist(args.megaface_path, args.megaface_noise) 187 | logging.info('#files: %d' % len(megaface_files)) 188 | facescrub_files, facescrub_noise = mg.generate_filelist(args.facescrub_path, args.facescrub_noise) 189 | logging.info('#files: %d' % len(facescrub_files)) 190 | except Exception as e: 191 | logging.error(e) 192 | raise 193 | 194 | success_counter = 0 195 | error_counter = 0 196 | save_path = args.file_ending.split('.')[0] 197 | 198 | for f in megaface_files: 199 | try: 200 | mg.generate_bin(f, args.megaface_bin_path, save_path, megaface_noise, megaface=True) 201 | success_counter += 1 202 | 203 | except Exception as e: 204 | logging.warn(e) 205 | error_counter += 1 206 | raise 207 | 208 | finally: 209 | logging.info('#success: %d, #fail: %d \n' % (success_counter, error_counter)) 210 | 211 | logging.info('#Distractor noise: %d' % len(mg.distractor_noise)) 212 | 213 | success_counter = 0 214 | error_counter = 0 215 | 216 | for f in facescrub_files: 217 | try: 218 | mg.generate_bin(f, args.facescrub_bin_path, save_path, facescrub_noise, megaface=False) 219 | success_counter += 1 220 | 221 | except Exception as e: 222 | logging.warn(e) 223 | error_counter += 1 224 | raise 225 | 226 | finally: 227 | logging.info('#success: %d, #fail: %d\n' % (success_counter, error_counter)) 228 | 229 | try: 230 | logging.info('Write noise files') 231 | mg.facescrub_noise_write_bin() 232 | logging.info('Finish facescrub bin files') 233 | except Exception as e: 234 | logging.error(e) 235 | raise 236 | 237 | 238 | -------------------------------------------------------------------------------- /facescrub_noise_list.txt: -------------------------------------------------------------------------------- 1 | Aaron_Eckhart_136.png 2 | Aaron_Eckhart_221.png 3 | Adam_McKay_468.png 4 | Adam_McKay_478.png 5 | Adam_Sandler_558.png 6 | Adrienne_Barbeau_4259.png 7 | Adrienne_Frantz_27683.png 8 | Adrienne_Frantz_27693.png 9 | Adrienne_Frantz_27699.png 10 | Adrienne_Frantz_27701.png 11 | Adrienne_Frantz_27829.png 12 | Aisha_Hinds_35768.png 13 | Alec_Baldwin_2121.png 14 | Alice_Krige_40717.png 15 | Alley_Mills_52029.png 16 | Alley_Mills_52039.png 17 | Alley_Mills_52044.png 18 | 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