server.py 8.2 KB

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  1. import os
  2. from utils import *
  3. from EmbedNet import *
  4. import torchvision.transforms as transforms
  5. from detectors import S3FD
  6. import argparse
  7. def createParser():
  8. # hello world 123
  9. parser = argparse.ArgumentParser(description = "FaceNet");
  10. parser.add_argument('--config', type=str, default=None, help='Config YAML file');
  11. ## Data loader
  12. parser.add_argument('--batch_size', type=int, default=200, help='Batch size, number of classes per batch');
  13. parser.add_argument('--max_img_per_cls', type=int, default=500, help='Maximum number of images per class per epoch');
  14. parser.add_argument('--nDataLoaderThread', type=int, default=5, help='Number of loader threads');
  15. ## Training details
  16. parser.add_argument('--test_interval', type=int, default=5, help='Test and save every [test_interval] epochs');
  17. parser.add_argument('--max_epoch', type=int, default=100, help='Maximum number of epochs');
  18. parser.add_argument('--trainfunc', type=str, default="softmax", help='Loss function');
  19. ## Optimizer
  20. parser.add_argument('--optimizer', type=str, default="adam", help='sgd or adam');
  21. parser.add_argument('--scheduler', type=str, default="steplr", help='Learning rate scheduler');
  22. parser.add_argument('--lr', type=float, default=0.001, help='Learning rate');
  23. parser.add_argument("--lr_decay", type=float, default=0.90, help='Learning rate decay every [test_interval] epochs');
  24. parser.add_argument('--weight_decay', type=float, default=0, help='Weight decay in the optimizer');
  25. ## Loss functions
  26. parser.add_argument("--hard_prob", type=float, default=0.5, help='Hard negative mining probability, otherwise random, only for some loss functions');
  27. parser.add_argument("--hard_rank", type=int, default=10, help='Hard negative mining rank in the batch, only for some loss functions');
  28. parser.add_argument('--margin', type=float, default=0.1, help='Loss margin, only for some loss functions');
  29. parser.add_argument('--scale', type=float, default=30, help='Loss scale, only for some loss functions');
  30. parser.add_argument('--nPerClass', type=int, default=1, help='Number of images per class per batch, only for metric learning based losses');
  31. parser.add_argument('--nClasses', type=int, default=8700, help='Number of classes in the softmax layer, only for softmax-based losses');
  32. ## Load and save
  33. parser.add_argument('--initial_model', type=str, default="./models/amsoft_model.model", help='Initial model weights');
  34. parser.add_argument('--save_path', type=str, default="exps/exp1", help='Path for model and logs');
  35. ## Training and test data
  36. parser.add_argument('--train_path', type=str, default="data/vggface2", help='Absolute path to the train set');
  37. parser.add_argument('--train_ext', type=str, default="jpg", help='Training files extension');
  38. parser.add_argument('--test_path', type=str, default="data/test", help='Absolute path to the test set');
  39. parser.add_argument('--test_list', type=str, default="data/test_list.csv", help='Evaluation list');
  40. ## Model definition
  41. parser.add_argument('--model', type=str, default="ResNet18", help='Name of model definition');
  42. parser.add_argument('--nOut', type=int, default=512, help='Embedding size in the last FC layer');
  43. ## For test only
  44. parser.add_argument('--eval', dest='eval', action='store_true', help='Eval only')
  45. ## For server
  46. parser.add_argument('--server', dest='server', action='store_true', help='Server mode')
  47. parser.add_argument('--feat_save_path', type=str, default='saved_feats', help='Absolute path to the feature')
  48. parser.add_argument('--port', type=int, default=10000, help='Port for the server')
  49. ## Distributed and mixed precision training
  50. parser.add_argument('--mixedprec', dest='mixedprec', action='store_true', help='Enable mixed precision training')
  51. args = parser.parse_args()
  52. return args
  53. def loadParameters(model, path):
  54. state = model.state_dict()
  55. loaded_state = torch.load(path)
  56. for name, param in loaded_state.items():
  57. origname = name;
  58. if name not in state:
  59. if name not in state:
  60. print("%s is not in the model."%origname);
  61. continue;
  62. if state[name].size() != loaded_state[origname].size():
  63. print("Wrong parameter length: %s, model: %s, loaded: %s"%(origname, state[name].size(), loaded_state[origname].size()));
  64. continue;
  65. state[name].copy_(param);
  66. DET = S3FD(device='cuda')
  67. app = Flask(__name__)
  68. args = createParser()
  69. UNKNOWN_THRESHOLD = 0.5
  70. s = EmbedNet(**vars(args)).cuda()
  71. transform = transforms.Compose(
  72. [transforms.ToTensor(),
  73. transforms.Resize(256),
  74. transforms.CenterCrop([224,224]),
  75. transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])
  76. # trainer = ModelTrainer(s, **vars(args))
  77. loadParameters(s, args.initial_model)
  78. s.eval()
  79. @app.route('/query', methods=['POST'])
  80. def query():
  81. # unpack the received data
  82. data = pickle.loads(request.get_data())
  83. image = data['img']
  84. image_np = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
  85. bboxes = DET.detect_faces(image_np, conf_th=0.9, scales=[0.5])
  86. if len(bboxes) != 1:
  87. return "fail"
  88. bsi = 100
  89. sx = int((bboxes[0][0]+bboxes[0][2])/2) + bsi
  90. sy = int((bboxes[0][1]+bboxes[0][3])/2) + bsi
  91. ss = int(max((bboxes[0][3]-bboxes[0][1]),(bboxes[0][2]-bboxes[0][0]))/2)
  92. image = numpy.pad(image,((bsi,bsi),(bsi,bsi),(0,0)), 'constant', constant_values=(110,110))
  93. face = image[int(sy-ss):int(sy+ss),int(sx-ss):int(sx+ss)]
  94. face = cv2.resize(face,(240,240))
  95. im1 = Image.fromarray(cv2.cvtColor(face, cv2.COLOR_BGR2RGB))
  96. inp1 = transform(im1).cuda()
  97. com_feat = s(inp1).detach().cpu()
  98. files = glob.glob(os.path.join(args.feat_save_path, '*.pt'))
  99. max_score = 0
  100. pname = 'none'
  101. for file in files:
  102. ref_feat = torch.load(file)
  103. score = F.cosine_similarity(ref_feat, com_feat)
  104. if(score>max_score) :
  105. max_score = score.item()
  106. pname = file.split('/')[1].split('.')[0]
  107. print('{} {:.2f}'.format(file,score.item()))
  108. if max_score < UNKNOWN_THRESHOLD:
  109. max_score = 0
  110. pname = "Unknown"
  111. return {
  112. "file":pname,
  113. "score":max_score,
  114. "x1":bboxes[0][0],
  115. "y1":bboxes[0][1],
  116. "x2":bboxes[0][2],
  117. "y2":bboxes[0][3]
  118. }
  119. @app.route('/enroll', methods=['POST'])
  120. def enroll():
  121. # unpack the received data
  122. data = pickle.loads(request.get_data())
  123. iname = data['name']
  124. image = data['img']
  125. image_np = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
  126. bboxes = DET.detect_faces(image_np, conf_th=0.9, scales=[0.5])
  127. bsi = 100
  128. sx = int((bboxes[0][0]+bboxes[0][2])/2) + bsi
  129. sy = int((bboxes[0][1]+bboxes[0][3])/2) + bsi
  130. ss = int(max((bboxes[0][3]-bboxes[0][1]),(bboxes[0][2]-bboxes[0][0]))/2)
  131. image = numpy.pad(image,((bsi,bsi),(bsi,bsi),(0,0)), 'constant', constant_values=(110,110))
  132. face = image[int(sy-ss):int(sy+ss),int(sx-ss):int(sx+ss)]
  133. face = cv2.resize(face,(240,240))
  134. # TO-DO / 2022-08-25
  135. # 0. Client 요구사항 : Enroll 시 종료 시까지 지속해서 사진 전송, 입력값(Name)은 중복없이 고유한 값이라고 가정
  136. # 1. 인물별 폴더에 이미지를 저장
  137. # 2. 이미지 저장 시 중복 방지 처리
  138. # 3. 인물별 폴더의 사진들을 centroid를 통해 feature 추출
  139. if not(os.path.exists(args.feat_save_path)):
  140. os.makedirs(args.feat_save_path)
  141. cv2.imwrite(os.path.join(args.feat_save_path, '{}.jpg'.format(iname)),face)
  142. im1 = Image.fromarray(cv2.cvtColor(face, cv2.COLOR_BGR2RGB))
  143. inp1 = transform(im1).cuda()
  144. ref_feat = s(inp1).detach().cpu()
  145. torch.save(ref_feat, os.path.join(args.feat_save_path,'{}.pt'.format(iname)))
  146. return "success"
  147. if __name__ == "__main__":
  148. app.run(host='0.0.0.0', debug=True, port=args.port, threaded=False)