├── .gitignore ├── LICENSE ├── README.md ├── demo.py ├── face_mesh ├── __init__.py └── face_mesh.py ├── iris_landmark ├── __init__.py ├── iris_landmark.py └── iris_landmark.tflite └── utils ├── __init__.py └── cvfpscalc.py /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | pip-wheel-metadata/ 24 | share/python-wheels/ 25 | *.egg-info/ 26 | .installed.cfg 27 | *.egg 28 | MANIFEST 29 | 30 | # PyInstaller 31 | # Usually these files are written by a python script from a template 32 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 33 | *.manifest 34 | *.spec 35 | 36 | # Installer logs 37 | pip-log.txt 38 | pip-delete-this-directory.txt 39 | 40 | # Unit test / coverage reports 41 | htmlcov/ 42 | .tox/ 43 | .nox/ 44 | .coverage 45 | .coverage.* 46 | .cache 47 | nosetests.xml 48 | coverage.xml 49 | *.cover 50 | *.py,cover 51 | .hypothesis/ 52 | .pytest_cache/ 53 | 54 | # Translations 55 | *.mo 56 | *.pot 57 | 58 | # Django stuff: 59 | *.log 60 | local_settings.py 61 | db.sqlite3 62 | db.sqlite3-journal 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | target/ 76 | 77 | # Jupyter Notebook 78 | .ipynb_checkpoints 79 | 80 | # IPython 81 | profile_default/ 82 | ipython_config.py 83 | 84 | # pyenv 85 | .python-version 86 | 87 | # pipenv 88 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 89 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 90 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 91 | # install all needed dependencies. 92 | #Pipfile.lock 93 | 94 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow 95 | __pypackages__/ 96 | 97 | # Celery stuff 98 | celerybeat-schedule 99 | celerybeat.pid 100 | 101 | # SageMath parsed files 102 | *.sage.py 103 | 104 | # Environments 105 | .env 106 | .venv 107 | env/ 108 | venv/ 109 | ENV/ 110 | env.bak/ 111 | venv.bak/ 112 | 113 | # Spyder project settings 114 | .spyderproject 115 | .spyproject 116 | 117 | # Rope project settings 118 | .ropeproject 119 | 120 | # mkdocs documentation 121 | /site 122 | 123 | # mypy 124 | .mypy_cache/ 125 | .dmypy.json 126 | dmypy.json 127 | 128 | # Pyre type checker 129 | .pyre/ 130 | 131 | # bat 132 | *.bat -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. 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We also recommend that a 185 | file or class name and description of purpose be included on the 186 | same "printed page" as the copyright notice for easier 187 | identification within third-party archives. 188 | 189 | Copyright [yyyy] [name of copyright owner] 190 | 191 | Licensed under the Apache License, Version 2.0 (the "License"); 192 | you may not use this file except in compliance with the License. 193 | You may obtain a copy of the License at 194 | 195 | http://www.apache.org/licenses/LICENSE-2.0 196 | 197 | Unless required by applicable law or agreed to in writing, software 198 | distributed under the License is distributed on an "AS IS" BASIS, 199 | WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 200 | See the License for the specific language governing permissions and 201 | limitations under the License. 202 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # ⚠Attention⚠ 2 | MediaPipe 0.8.8 からFaceMeshにrefine_landmarksオプションが追加されました。
3 | このオプションを有効化すると虹彩の検出も同時に実施できるようになります。
4 | 特別な理由がない限り、[Kazuhito00/mediapipe-python-sample](https://github.com/Kazuhito00/mediapipe-python-sample)のFaceMeshを参考にすることをお勧めします。 5 | 6 | # iris-detection-using-py-mediapipe 7 | MediaPipeのIris(虹彩検出)をPythonで動作させるデモです。
8 | MediaPipeのFace Meshで顔のランドマークを検出し「[iris_landmark.tflite](https://github.com/google/mediapipe/blob/master/mediapipe/modules/iris_landmark/iris_landmark.tflite)」を用いて虹彩の検出をしています。
9 | 10 | ![8p6lo-slci5](https://user-images.githubusercontent.com/37477845/107108796-11e01c00-687e-11eb-8d82-9ffcdaad2610.gif) 11 | # Requirement 12 | * mediapipe 0.8.1 or later 13 | * OpenCV 3.4.2 or later 14 | * Tensorflow 2.3.0 or Later 15 | 16 | mediapipeはpipでインストールできます。 17 | ```bash 18 | pip install mediapipe 19 | ``` 20 | 21 | # Demo 22 | デモの実行方法は以下です。 23 | ```bash 24 | python demo.py 25 | ``` 26 | デモ実行時には、以下のオプションが指定可能です。 27 | 28 | * --device
29 | カメラデバイス番号の指定
30 | デフォルト：0 31 | * --width
32 | カメラキャプチャ時の横幅
33 | デフォルト：960 34 | * --height
35 | カメラキャプチャ時の縦幅
36 | デフォルト：540 37 | * --max_num_faces
38 | 顔の検出最大数
39 | デフォルト：1 40 | * --min_detection_confidence
41 | 検出信頼値の閾値
42 | デフォルト：0.7 43 | * --min_tracking_confidence
44 | トラッキング信頼値の閾値
45 | デフォルト：0.7 46 | 47 | # ToDo 48 | - [ ] 焦点距離から深度を推定するオプションを追加 49 | 50 | # Reference 51 | * [MediaPipe](https://github.com/google/mediapipe) 52 | 53 | # Author 54 | 高橋かずひと(https://twitter.com/KzhtTkhs) 55 | 56 | # License 57 | iris-detection-using-py-mediapipe is under [Apache-2.0 License](LICENSE). 58 | 59 | また、女性の画像は[フリー素材ぱくたそ](https://www.pakutaso.com)様の写真を利用しています。 60 | -------------------------------------------------------------------------------- /demo.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # -*- coding: utf-8 -*- 3 | import copy 4 | import argparse 5 | 6 | import cv2 as cv 7 | import numpy as np 8 | 9 | from utils import CvFpsCalc 10 | from face_mesh.face_mesh import FaceMesh 11 | from iris_landmark.iris_landmark import IrisLandmark 12 | 13 | 14 | def get_args(): 15 | parser = argparse.ArgumentParser() 16 | 17 | parser.add_argument("--device", type=int, default=0) 18 | parser.add_argument("--width", help='cap width', type=int, default=960) 19 | parser.add_argument("--height", help='cap height', type=int, default=540) 20 | 21 | parser.add_argument("--max_num_faces", type=int, default=1) 22 | parser.add_argument("--min_detection_confidence", 23 | help='min_detection_confidence', 24 | type=float, 25 | default=0.7) 26 | parser.add_argument("--min_tracking_confidence", 27 | help='min_tracking_confidence', 28 | type=int, 29 | default=0.7) 30 | 31 | args = parser.parse_args() 32 | 33 | return args 34 | 35 | 36 | def main(): 37 | # 引数 ##################################################################### 38 | args = get_args() 39 | 40 | cap_device = args.device 41 | cap_width = args.width 42 | cap_height = args.height 43 | 44 | max_num_faces = args.max_num_faces 45 | min_detection_confidence = args.min_detection_confidence 46 | min_tracking_confidence = args.min_tracking_confidence 47 | 48 | # カメラ準備 ############################################################### 49 | cap = cv.VideoCapture(cap_device) 50 | cap.set(cv.CAP_PROP_FRAME_WIDTH, cap_width) 51 | cap.set(cv.CAP_PROP_FRAME_HEIGHT, cap_height) 52 | 53 | # モデルロード ############################################################# 54 | face_mesh = FaceMesh( 55 | max_num_faces, 56 | min_detection_confidence, 57 | min_tracking_confidence, 58 | ) 59 | iris_detector = IrisLandmark() 60 | 61 | # FPS計測モジュール ######################################################## 62 | cvFpsCalc = CvFpsCalc(buffer_len=10) 63 | 64 | while True: 65 | display_fps = cvFpsCalc.get() 66 | 67 | # カメラキャプチャ ##################################################### 68 | ret, image = cap.read() 69 | if not ret: 70 | break 71 | image = cv.flip(image, 1) # ミラー表示 72 | debug_image = copy.deepcopy(image) 73 | 74 | # 検出実施 ############################################################# 75 | # Face Mesh検出 76 | face_results = face_mesh(image) 77 | for face_result in face_results: 78 | # 目周辺のバウンディングボックス計算 79 | left_eye, right_eye = face_mesh.calc_around_eye_bbox(face_result) 80 | 81 | # 虹彩検出 82 | left_iris, right_iris = detect_iris(image, iris_detector, left_eye, 83 | right_eye) 84 | 85 | # 虹彩の外接円を計算 86 | left_center, left_radius = calc_min_enc_losingCircle(left_iris) 87 | right_center, right_radius = calc_min_enc_losingCircle(right_iris) 88 | 89 | # デバッグ描画 90 | debug_image = draw_debug_image( 91 | debug_image, 92 | left_iris, 93 | right_iris, 94 | left_center, 95 | left_radius, 96 | right_center, 97 | right_radius, 98 | ) 99 | 100 | cv.putText(debug_image, "FPS:" + str(display_fps), (10, 30), 101 | cv.FONT_HERSHEY_SIMPLEX, 1.0, (0, 255, 0), 2, cv.LINE_AA) 102 | 103 | # キー処理(ESC：終了) ################################################# 104 | key = cv.waitKey(1) 105 | if key == 27: # ESC 106 | break 107 | 108 | # 画面反映 ############################################################# 109 | cv.imshow('Iris(tflite) Demo', debug_image) 110 | 111 | cap.release() 112 | cv.destroyAllWindows() 113 | 114 | return 115 | 116 | 117 | def detect_iris(image, iris_detector, left_eye, right_eye): 118 | image_width, image_height = image.shape[1], image.shape[0] 119 | input_shape = iris_detector.get_input_shape() 120 | 121 | # 左目 122 | # 目の周辺の画像を切り抜き 123 | left_eye_x1 = max(left_eye[0], 0) 124 | left_eye_y1 = max(left_eye[1], 0) 125 | left_eye_x2 = min(left_eye[2], image_width) 126 | left_eye_y2 = min(left_eye[3], image_height) 127 | left_eye_image = copy.deepcopy(image[left_eye_y1:left_eye_y2, 128 | left_eye_x1:left_eye_x2]) 129 | # 虹彩検出 130 | eye_contour, iris = iris_detector(left_eye_image) 131 | # 座標を相対座標から絶対座標に変換 132 | left_iris = calc_iris_point(left_eye, eye_contour, iris, input_shape) 133 | 134 | # 右目 135 | # 目の周辺の画像を切り抜き 136 | right_eye_x1 = max(right_eye[0], 0) 137 | right_eye_y1 = max(right_eye[1], 0) 138 | right_eye_x2 = min(right_eye[2], image_width) 139 | right_eye_y2 = min(right_eye[3], image_height) 140 | right_eye_image = copy.deepcopy(image[right_eye_y1:right_eye_y2, 141 | right_eye_x1:right_eye_x2]) 142 | # 虹彩検出 143 | eye_contour, iris = iris_detector(right_eye_image) 144 | # 座標を相対座標から絶対座標に変換 145 | right_iris = calc_iris_point(right_eye, eye_contour, iris, input_shape) 146 | 147 | return left_iris, right_iris 148 | 149 | 150 | def calc_iris_point(eye_bbox, eye_contour, iris, input_shape): 151 | iris_list = [] 152 | for index in range(5): 153 | point_x = int(iris[index * 3] * 154 | ((eye_bbox[2] - eye_bbox[0]) / input_shape[0])) 155 | point_y = int(iris[index * 3 + 1] * 156 | ((eye_bbox[3] - eye_bbox[1]) / input_shape[1])) 157 | point_x += eye_bbox[0] 158 | point_y += eye_bbox[1] 159 | 160 | iris_list.append((point_x, point_y)) 161 | 162 | return iris_list 163 | 164 | 165 | def calc_min_enc_losingCircle(landmark_list): 166 | center, radius = cv.minEnclosingCircle(np.array(landmark_list)) 167 | center = (int(center[0]), int(center[1])) 168 | radius = int(radius) 169 | 170 | return center, radius 171 | 172 | 173 | def draw_debug_image( 174 | debug_image, 175 | left_iris, 176 | right_iris, 177 | left_center, 178 | left_radius, 179 | right_center, 180 | right_radius, 181 | ): 182 | # 虹彩：外接円 183 | cv.circle(debug_image, left_center, left_radius, (0, 255, 0), 2) 184 | cv.circle(debug_image, right_center, right_radius, (0, 255, 0), 2) 185 | 186 | # 虹彩：ランドマーク 187 | for point in left_iris: 188 | cv.circle(debug_image, (point[0], point[1]), 1, (0, 0, 255), 2) 189 | for point in right_iris: 190 | cv.circle(debug_image, (point[0], point[1]), 1, (0, 0, 255), 2) 191 | 192 | # 虹彩：半径 193 | cv.putText(debug_image, 'r:' + str(left_radius) + 'px', 194 | (left_center[0] + int(left_radius * 1.5), 195 | left_center[1] + int(left_radius * 0.5)), 196 | cv.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 1) 197 | cv.putText(debug_image, 'r:' + str(right_radius) + 'px', 198 | (right_center[0] + int(right_radius * 1.5), 199 | right_center[1] + int(right_radius * 0.5)), 200 | cv.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 1) 201 | 202 | return debug_image 203 | 204 | 205 | if __name__ == '__main__': 206 | main() 207 | -------------------------------------------------------------------------------- /face_mesh/__init__.py: -------------------------------------------------------------------------------- 1 | # IRIS LANDMARK -------------------------------------------------------------------------------- /face_mesh/face_mesh.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # -*- coding: utf-8 -*- 3 | import cv2 as cv 4 | import numpy as np 5 | import mediapipe as mp 6 | 7 | 8 | class FaceMesh(object): 9 | def __init__( 10 | self, 11 | max_num_faces=1, 12 | min_detection_confidence=0.7, 13 | min_tracking_confidence=0.7, 14 | ): 15 | mp_face_mesh = mp.solutions.face_mesh 16 | self._face_mesh = mp_face_mesh.FaceMesh( 17 | max_num_faces=max_num_faces, 18 | min_detection_confidence=min_detection_confidence, 19 | min_tracking_confidence=min_tracking_confidence, 20 | ) 21 | 22 | def __call__( 23 | self, 24 | image, 25 | ): 26 | # 推論 27 | image = cv.cvtColor(image, cv.COLOR_BGR2RGB) 28 | results = self._face_mesh.process(image) 29 | 30 | # X,Y座標を相対座標から絶対座標に変換 31 | # [X座標, Y座標, Z座標, Visibility, Presence]のリストに変更 32 | face_mesh_results = [] 33 | if results.multi_face_landmarks is not None: 34 | for face_landmarks in results.multi_face_landmarks: 35 | face_mesh_results.append( 36 | self._calc_landmarks(image, face_landmarks.landmark)) 37 | return face_mesh_results 38 | 39 | def _calc_landmarks(self, image, landmarks): 40 | image_width, image_height = image.shape[1], image.shape[0] 41 | 42 | landmark_list = [] 43 | for _, landmark in enumerate(landmarks): 44 | landmark_x = min(int(landmark.x * image_width), image_width - 1) 45 | landmark_y = min(int(landmark.y * image_height), image_height - 1) 46 | 47 | landmark_list.append((landmark_x, landmark_y, landmark.z, 48 | landmark.visibility, landmark.presence)) 49 | return landmark_list 50 | 51 | def _calc_bounding_rect(self, landmarks): 52 | landmark_array = np.empty((0, 2), int) 53 | 54 | for _, landmark in enumerate(landmarks): 55 | landmark_x = int(landmark[0]) 56 | landmark_y = int(landmark[1]) 57 | 58 | landmark_point = [np.array((landmark_x, landmark_y))] 59 | landmark_array = np.append(landmark_array, landmark_point, axis=0) 60 | 61 | x, y, w, h = cv.boundingRect(landmark_array) 62 | 63 | return [x, y, x + w, y + h] 64 | 65 | def get_eye_landmarks(self, landmarks): 66 | # 目の輪郭の座標列を取得 67 | 68 | left_eye_landmarks = [] 69 | right_eye_landmarks = [] 70 | 71 | if len(landmarks) > 0: 72 | # 参考：https://github.com/tensorflow/tfjs-models/blob/master/facemesh/mesh_map.jpg 73 | # 左目 74 | left_eye_landmarks.append((landmarks[133][0], landmarks[133][1])) 75 | left_eye_landmarks.append((landmarks[173][0], landmarks[173][1])) 76 | left_eye_landmarks.append((landmarks[157][0], landmarks[157][1])) 77 | left_eye_landmarks.append((landmarks[158][0], landmarks[158][1])) 78 | left_eye_landmarks.append((landmarks[159][0], landmarks[159][1])) 79 | left_eye_landmarks.append((landmarks[160][0], landmarks[160][1])) 80 | left_eye_landmarks.append((landmarks[161][0], landmarks[161][1])) 81 | left_eye_landmarks.append((landmarks[246][0], landmarks[246][1])) 82 | left_eye_landmarks.append((landmarks[163][0], landmarks[163][1])) 83 | left_eye_landmarks.append((landmarks[144][0], landmarks[144][1])) 84 | left_eye_landmarks.append((landmarks[145][0], landmarks[145][1])) 85 | left_eye_landmarks.append((landmarks[153][0], landmarks[153][1])) 86 | left_eye_landmarks.append((landmarks[154][0], landmarks[154][1])) 87 | left_eye_landmarks.append((landmarks[155][0], landmarks[155][1])) 88 | 89 | # 右目 90 | right_eye_landmarks.append((landmarks[362][0], landmarks[362][1])) 91 | right_eye_landmarks.append((landmarks[398][0], landmarks[398][1])) 92 | right_eye_landmarks.append((landmarks[384][0], landmarks[384][1])) 93 | right_eye_landmarks.append((landmarks[385][0], landmarks[385][1])) 94 | right_eye_landmarks.append((landmarks[386][0], landmarks[386][1])) 95 | right_eye_landmarks.append((landmarks[387][0], landmarks[387][1])) 96 | right_eye_landmarks.append((landmarks[388][0], landmarks[388][1])) 97 | right_eye_landmarks.append((landmarks[466][0], landmarks[466][1])) 98 | right_eye_landmarks.append((landmarks[390][0], landmarks[390][1])) 99 | right_eye_landmarks.append((landmarks[373][0], landmarks[373][1])) 100 | right_eye_landmarks.append((landmarks[374][0], landmarks[374][1])) 101 | right_eye_landmarks.append((landmarks[380][0], landmarks[380][1])) 102 | right_eye_landmarks.append((landmarks[381][0], landmarks[381][1])) 103 | right_eye_landmarks.append((landmarks[382][0], landmarks[382][1])) 104 | 105 | return left_eye_landmarks, right_eye_landmarks 106 | 107 | def calc_eye_bbox(self, landmarks): 108 | # 目に隣接するバウンディングボックスを取得 109 | 110 | left_eye_lm, right_eye_lm = self.get_eye_landmarks(landmarks) 111 | 112 | left_eye_bbox = self._calc_bounding_rect(left_eye_lm) 113 | right_eye_bbox = self._calc_bounding_rect(right_eye_lm) 114 | 115 | return left_eye_bbox, right_eye_bbox 116 | 117 | def calc_around_eye_bbox(self, landmarks, around_ratio=0.5): 118 | # 目の周囲のバウンディングボックスを取得 119 | 120 | left_eye_bbox, right_eye_bbox = self.calc_eye_bbox(landmarks) 121 | 122 | left_eye_bbox = self._calc_around_eye(left_eye_bbox, around_ratio) 123 | right_eye_bbox = self._calc_around_eye(right_eye_bbox, around_ratio) 124 | 125 | return left_eye_bbox, right_eye_bbox 126 | 127 | def _calc_around_eye(self, bbox, around_ratio=0.5): 128 | x1, y1, x2, y2 = bbox 129 | x = x1 130 | y = y1 131 | w = x2 - x1 132 | h = y2 - y1 133 | 134 | cx = int(x + (w / 2)) 135 | cy = int(y + (h / 2)) 136 | square_length = max(w, h) 137 | x = int(cx - (square_length / 2)) 138 | y = int(cy - (square_length / 2)) 139 | w = square_length 140 | h = square_length 141 | 142 | around_ratio = 0.5 143 | x = int(x - (square_length * around_ratio)) 144 | y = int(y - (square_length * around_ratio)) 145 | w = int(square_length * (1 + (around_ratio * 2))) 146 | h = int(square_length * (1 + (around_ratio * 2))) 147 | 148 | return [x, y, x + w, y + h] 149 | -------------------------------------------------------------------------------- /iris_landmark/__init__.py: -------------------------------------------------------------------------------- 1 | # IRIS LANDMARK -------------------------------------------------------------------------------- /iris_landmark/iris_landmark.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | # -*- coding: utf-8 -*- 3 | import cv2 as cv 4 | import numpy as np 5 | import tensorflow as tf 6 | 7 | 8 | class IrisLandmark(object): 9 | def __init__( 10 | self, 11 | model_path='iris_landmark/iris_landmark.tflite', 12 | num_threads=1, 13 | ): 14 | self._interpreter = tf.lite.Interpreter(model_path=model_path, 15 | num_threads=num_threads) 16 | self._interpreter.allocate_tensors() 17 | self._input_details = self._interpreter.get_input_details() 18 | self._output_details = self._interpreter.get_output_details() 19 | 20 | def __call__( 21 | self, 22 | image, 23 | ): 24 | input_shape = self._input_details[0]['shape'] 25 | 26 | # 正規化・リサイズ 27 | img = cv.cvtColor(image, cv.COLOR_BGR2RGB) 28 | img = img / 255.0 29 | img_resized = tf.image.resize(img, [input_shape[1], input_shape[2]], 30 | method='bicubic', 31 | preserve_aspect_ratio=False) 32 | img_input = img_resized.numpy() 33 | img_input = (img_input - 0.5) / 0.5 34 | 35 | reshape_img = img_input.reshape(1, input_shape[1], input_shape[2], 36 | input_shape[3]) 37 | tensor = tf.convert_to_tensor(reshape_img, dtype=tf.float32) 38 | 39 | # 推論実行 40 | input_details_tensor_index = self._input_details[0]['index'] 41 | self._interpreter.set_tensor(input_details_tensor_index, tensor) 42 | self._interpreter.invoke() 43 | 44 | # 推論結果取得 45 | output_details_tensor_index0 = self._output_details[0]['index'] 46 | output_details_tensor_index1 = self._output_details[1]['index'] 47 | eye_contour = self._interpreter.get_tensor( 48 | output_details_tensor_index0) 49 | iris = self._interpreter.get_tensor(output_details_tensor_index1) 50 | 51 | return np.squeeze(eye_contour), np.squeeze(iris) 52 | 53 | def get_input_shape(self): 54 | input_shape = self._input_details[0]['shape'] 55 | return [input_shape[1], input_shape[2]] -------------------------------------------------------------------------------- /iris_landmark/iris_landmark.tflite: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Kazuhito00/iris-detection-using-py-mediapipe/85ead970598017967937d3f5d7ffe6238aa3fe9b/iris_landmark/iris_landmark.tflite -------------------------------------------------------------------------------- /utils/__init__.py: -------------------------------------------------------------------------------- 1 | from utils.cvfpscalc import CvFpsCalc -------------------------------------------------------------------------------- /utils/cvfpscalc.py: -------------------------------------------------------------------------------- 1 | from collections import deque 2 | import cv2 as cv 3 | 4 | 5 | class CvFpsCalc(object): 6 | def __init__(self, buffer_len=1): 7 | self._start_tick = cv.getTickCount() 8 | self._freq = 1000.0 / cv.getTickFrequency() 9 | self._difftimes = deque(maxlen=buffer_len) 10 | 11 | def get(self): 12 | current_tick = cv.getTickCount() 13 | different_time = (current_tick - self._start_tick) * self._freq 14 | self._start_tick = current_tick 15 | 16 | self._difftimes.append(different_time) 17 | 18 | fps = 1000.0 / (sum(self._difftimes) / len(self._difftimes)) 19 | fps_rounded = round(fps, 2) 20 | 21 | return fps_rounded 22 | --------------------------------------------------------------------------------