prediction

options.py

+import argparse
+import os
+
+saved_model = '/media/banglv/DATA/CORE_Quality_Preprocess_GUI_backup/data_files/svm_models/occlusion_detection'
+BASED_PATH = '/media/banglv/DATA/CORE_Quality_Preprocess_GUI_backup/data_files'
+BASED_PATH_YOLO = os.path.join(BASED_PATH, 'face_detection_yolo_weight')
+BASED_PATH_INFERENCE = os.path.join(BASED_PATH, 'face_insight_pretrained')
+
+def get_yolo_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--model', type=str,
+                        default=os.path.join(BASED_PATH_YOLO, 'model-weights/model-weights/YOLO_Face.h5'),
+                        help='path to model weights file')
+    parser.add_argument('--anchors', type=str,
+                        default=os.path.join(BASED_PATH_YOLO, 'cfg/yolo_anchors.txt'),
+                        help='path to anchor definitions')
+    parser.add_argument('--classes', type=str,
+                        default=os.path.join(BASED_PATH_YOLO, 'cfg/face_classes.txt'),
+                        help='path to class definitions')
+    parser.add_argument('--score', type=float, default=0.35,
+                        help='the score threshold')
+    parser.add_argument('--iou', type=float, default=0.45,
+                        help='the iou threshold')
+    parser.add_argument('--img-size', type=list, action='store',
+                        default=(416, 416), help='input image size')
+    parser.add_argument('--image', default=True, action="store_true",
+                        help='image detection mode')
+    parser.add_argument('--video', type=str, default='samples/subway.mp4',
+                        help='path to the video')
+    parser.add_argument('--output', type=str, default='outputs/',
+                        help='image/video output path')
+    args = parser.parse_args()
+    return args
+
+def get_YOLO_tiny_args():
+    parser = argparse.ArgumentParser(description='YOLO tiny parameters')
+    parser.add_argument('--saved_weight', type=str, default=os.path.join(BASED_PATH_YOLO, 'model-weights/model-weights/yolov2_tiny-face.h5'))
+    parser.add_argument('--image_size', type=int, default=416)
+    parser.add_argument('--obj_threshold', type=float, default=0.30)
+    parser.add_argument('--nms_threshold', type=float, default=0.30)
+    parser.add_argument('--anchors', type=list, default=[0.57273, 0.677385, 1.87446, 2.06253, 3.33843, 5.47434, 7.88282, 3.52778, 9.77052, 9.16828])
+    parser.add_argument('--nb_box', type=int, default=5)
+    parser.add_argument('--nb_class', type=int, default=1)
+    parser.add_argument('--crop_ratio', type=float, default=1.0)
+    args = parser.parse_args()
+    return args
+
+def get_landmark_args():
+    parser = argparse.ArgumentParser(description='Landmark detection options')
+    parser.add_argument('--landmark_model', type=str, default=os.path.join(BASED_PATH, 'landmark_models/facial_landmark_SqueezeNet.pb'))
+    parser.add_argument('--CNN_size', type=int, default=64)
+    args = parser.parse_args()
+    return args
+
+def get_system_args():
+    parser = argparse.ArgumentParser(description='VHT Face recognition system option parameters')
+    parser.add_argument('--dlib_predictor', type=str,
+                        default=os.path.join(BASED_PATH, 'dlib_predictor/shape_predictor_68_face_landmarks.dat'))
+    parser.add_argument('--is_url', default=True, help='Use url or not')
+    parser.add_argument('--url_link', type=str, default='/home/banglv/Videos/ex4.mp4',
+                        help='url link to open stream video')
+    # rtsp://admin:123456a@@192.168.0.205:554/cam/realmonitor?channel=1&subtype=0
+    # /home/banglv/Videos/ex4.mp4
+    # #rtsp://root:123456a@@192.168.0.204/axis-media/media.amp
+    parser.add_argument('--detect_method', type=str, default='yolo',
+                        help='Face detection method: YOLO/SSD/MTCNN')
+    parser.add_argument('--detect_mode', type=str, default='center')
+
+    parser.add_argument('--detect_weight_ssd', type=str,
+                        default=os.path.join(BASED_PATH,'face_detection_ssd_weight/graph_mobilenet_v2_all_180627.pb'))
+    parser.add_argument('--detect_haar_xml', type=str,
+                        default=os.path.join(BASED_PATH, 'haar_xml_set/haarcascade_frontalface_default.xml'))
+    parser.add_argument('--detect_select_mode', type=str, default='tiny', help='Choice face detection method: yolo/ssd')
+
+    parser.add_argument('--used_feature_main', type=str, default='arcface', help='Choice image feature type')
+    parser.add_argument('--image_feature', type=str, default='arcface', help='Feature type for face classification')
+    parser.add_argument('--path_to_training_feature', type=str, default='',
+                        help='Path to classifiers training feature set')
+    parser.add_argument('--thr_location_jitter', type=int, default=20)
+    parser.add_argument('--thr_size_jitter', type=int, default=20)
+    parser.add_argument('--thr_bright', type=float, default=0.15, help='IQA: brightness threshold')
+    parser.add_argument('--thr_contrast', type=float, default=30, help='IQA: contrast threshold')
+    parser.add_argument('--thr_sharpness', type=float, default=5, help='IQA: sharpness threshold')
+    parser.add_argument('--thr_noise', type=float, default=10, help='IQA: noise threshold')
+    parser.add_argument('--thr_roll', type=float, default=20, help='IQA: roll angle threshold')
+    parser.add_argument('--thr_pitch', type=float, default=20, help='IQA: pitch angle threshold')
+    parser.add_argument('--thr_yaw', type=float, default=20, help='IQA: yaw angle threshold')
+    parser.add_argument('--thr_eye_distance', type=float, default=10, help='Minimal eye to eye distance (pixels)')
+    parser.add_argument('--thr_minimal_size', type=float, default=20, help='Minimal passed cropped face image size')
+    parser.add_argument('--thr_decision_detect', type=float, default=.75, help='Threshold for detector')
+    parser.add_argument('--thr_decision_classify', type=float, default=.5, help='Threshold for classifier')
+    parser.add_argument('--is_remove_occlusion', default=True, help='Is remove the occluded sample or not')
+    parser.add_argument('--path_to_occlusion_scaler', type=str, default=saved_model + '/occlusion_detect_scaler.sav',
+                        help='Occluded face detection (SVM) model')
+    parser.add_argument('--path_to_occlusion_model', type=str, default=saved_model + '/occlusion_detect_svm.sav')
+    parser.add_argument('--is_align', default=True, help='Face alignment')
+    parser.add_argument('--align_method', type=str, default='', help='Choice face alignment method')
+
+    parser.add_argument('--is_brighten', default=True)
+    parser.add_argument('--is_contrast', default=False)
+    parser.add_argument('--is_sharpen', default=False)
+    parser.add_argument('--is_denoise', default=True)
+
+    parser.add_argument('--is_apply_shape', default=False, help='Use landmark features or not')
+    parser.add_argument('--is_assist', default=True, help='Use the second feature or not')
+    parser.add_argument('--used_feature_assist', type=str, default='lbp', help='The second feature to face classification')
+
+    parser.add_argument('--is_multi_classifiers', default=False)
+    parser.add_argument('--path_to_svm_model', type=str, default='/media/banglv/DATA/CORE_Quality_Preprocess_GUI_backup/data_files/svm_models/vht_llq_svm_model/backbone_res101_model_minmax.sav')
+    parser.add_argument('--path_to_svm_scaler', type=str, default='/media/banglv/DATA/CORE_Quality_Preprocess_GUI_backup/data_files/svm_models/vht_llq_svm_model/backbone_res101_scaler_minmax.sav')
+    parser.add_argument('--data_sample_names', type=str, default='/media/banglv/DATA/CORE_Quality_Preprocess_GUI_backup/data_files/features/VHT_LLQ/backbone_res101/res101_names.pkl')
+    parser.add_argument('--path_to_rf_model', type=str, default='')
+    parser.add_argument('--path_to_rf_scaler', type=str, default='')
+
+    sys_args = parser.parse_args()
+    return sys_args
+
+def get_reference_args():
+    parser = argparse.ArgumentParser(description='Arcface parameters')
+    parser.add_argument('--face_describer_input_tensor_names', type=list, default=['img_inputs:0', 'dropout_rate:0'])
+    parser.add_argument('--face_describer_output_tensor_names', type=list, default=['resnet_v1_50/E_BN2/Identity:0'])
+    parser.add_argument('--face_describer_device', type=str, default='/gpu:0')
+    parser.add_argument('--face_describer_model_fp', type=str, default='{}/insightface_res50.pb'.format(BASED_PATH_INFERENCE))
+    parser.add_argument('--face_describer_tensor_shape', default=(112, 112))
+    parser.add_argument('--face_describer_drop_out_rate',type=float, default=0.1)
+    parser.add_argument('--face_similarity_threshold', type=int, default=800)
+    inference_opts = parser.parse_args()
+    return inference_opts
+
+def get_dual_network_args():
+    parser = argparse.ArgumentParser(description='Dual network training parameters')
+    des = 'dual_kid_inV3'
+    parser.add_argument('--path_to_save', type=str, default=os.path.join(BASED_PATH, 'classification_weights', des))
+    parser.add_argument('--path_to_dataset', type=str, default='/media/banglv/DATA/Data/child_alignn')
+    parser.add_argument('--path_to_load', type=str, default=BASED_PATH)
+    parser.add_argument('--batch_size', type=int, default=64)
+    parser.add_argument('--model_name', type=str, default='dual_inception_celebA_l2.h5')
+    dual_opts = parser.parse_args()
+    return dual_opts
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prediction_dual.py

+import numpy as np
+from keras import Input
+
+from DualTraining.dual_network import DualNetwork
+from keras.models import Model
+from PIL import Image
+import os
+from FaceRecognitionVHT.options import get_dual_network_args
+import matplotlib.pyplot as plt
+from keras.models import model_from_json
+
+opts = get_dual_network_args()
+class FacePredict():
+    def __init__(self, base_net='resnet101'):
+        path_to_weights = opts.path_to_save
+        load_target_h5 = os.path.join(path_to_weights, os.path.split(path_to_weights)[1] + '_celebA.h5')
+        load_target_json = os.path.join(path_to_weights, os.path.split(path_to_weights)[1] + '_celebA.json')
+        json_file = open(load_target_json, 'r')
+        loaded_model_json = json_file.read()
+        json_file.close()
+        self.model = model_from_json(loaded_model_json)
+        self.model.load_weights(load_target_h5)
+
+        # todo: load the trained weights
+        # self.dual_network.model.load_weights(load_target)
+        # input_tensor = Input(shape=(224, 224, 3))
+        core = self.model.get_layer(name='model_1')
+        #
+        self.extractor = Model(core.get_input_at(0), core.get_layer(name='dense5').output)
+
+    def image_loader(self, path):
+        image = Image.open(path)
+        image = image.resize((112, 112))
+        image = np.asarray(image).astype(np.float64)
+        if np.min(image) <= 0:
+            image = image - np.min(image)
+
+        image /= 127.5
+        image -= 1.
+        # image = image / 255
+        return image
+
+    def images_similarity(self, image1, image2):
+        pairs = [np.zeros((1, 112, 112, 3)) for i in range(2)]
+        pairs[0][0, :, :, 0] = image1
+        pairs[1][0, :, :, 0] = image2
+        sim = self.model.model.predict(pairs)
+        return sim
+
+    def feature_extraction(self, path):
+        image = self.image_loader(path)
+        image = image.astype(np.float)
+        image = image - np.mean(image)
+        expanded = np.zeros((1, 224, 224, 3))
+        expanded[0, :, :, :] = image
+        f = self.extractor.predict(expanded)[0]
+        # f = f / np .linalg.norm(f)
+        return f
+
+# if __name__ == '__main__':
+#     path = '/media/banglv/DATA/Data/celebA_sorted/images_training/1004/cropped_173474.jpg'
+#     extractor = FacePredict()
+#     f = extractor.feature_extraction(path)
+#     plt.figure()
+#     plt.plot(f)
+#     plt.show()
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