├── utils
    ├── __init__.py
    └── cvfpscalc.py
├── model
    ├── __init__.py
    └── keypoint_classifier
    │   ├── keypoint_classifier_label.csv
    │   ├── keypoint_classifier.tflite
    │   └── keypoint_classifier.py
├── README.md
├── .gitignore
├── _old
    └── app.py
├── LICENSE
└── app.py


/utils/__init__.py:
--------------------------------------------------------------------------------
1 | from utils.cvfpscalc import CvFpsCalc


--------------------------------------------------------------------------------
/model/__init__.py:
--------------------------------------------------------------------------------
1 | from model.keypoint_classifier.keypoint_classifier import KeyPointClassifier


--------------------------------------------------------------------------------
/model/keypoint_classifier/keypoint_classifier_label.csv:
--------------------------------------------------------------------------------
1 | Open
2 | Close
3 | Pointer
4 | 


--------------------------------------------------------------------------------
/model/keypoint_classifier/keypoint_classifier.tflite:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/Kazuhito00/simple-virtual-mouse-using-mediapipe/HEAD/model/keypoint_classifier/keypoint_classifier.tflite


--------------------------------------------------------------------------------
/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 | 


--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
 1 | # simple-virtual-mouse-using-mediapipe
 2 | MediaPipeを用いたハンドジェスチャーによる簡単なマウス操作を行うプログラムです。<br>
 3 | マウス移動：手の移動<br>
 4 | マウス左クリック：手をパーからグーに変える<br><br>
 5 | <img src="https://user-images.githubusercontent.com/37477845/111347926-070d7800-86c3-11eb-851f-22f1e87f67b5.gif" width="75%">
 6 | 
 7 | # Requirements
 8 | * mediapipe 0.8.1 or Later
 9 | * PyAutoGUI 0.9.52 or Later
10 | * OpenCV 3.4.2 or Later
11 | * Tensorflow 2.3.0 or Later
12 | 
13 | # Demo
14 | Webカメラを使ったデモの実行方法は以下です。
15 | ```bash
16 | python app.py
17 | ```
18 | 
19 | デモ実行時には、以下のオプションが指定可能です。
20 | * --device<br>カメラデバイス番号の指定 (デフォルト：0)
21 | * --width<br>カメラキャプチャ時の横幅 (デフォルト：960)
22 | * --height<br>カメラキャプチャ時の縦幅 (デフォルト：540)
23 | * --min_detection_confidence<br>
24 | 検出信頼値の閾値 (デフォルト：0.7)
25 | * --min_tracking_confidence<br>
26 | トラッキング信頼値の閾値 (デフォルト：0.5)
27 | * --margin_width<br>ハンドジェスチャーの操作範囲(横幅のマージン割合)(デフォルト：0.2)
28 | * --margin_height<br>ハンドジェスチャーの操作範囲(縦幅のマージン割合)(デフォルト：0.2)
29 | 
30 | # Reference
31 | * [MediaPipe：Hands](https://google.github.io/mediapipe/solutions/hands)
32 | 
33 | # Author
34 | 高橋かずひと(https://twitter.com/KzhtTkhs)
35 |  
36 | # License 
37 | simple-virtual-mouse-using-mediapipe under [Apache-2.0 License](LICENSE).
38 | 


--------------------------------------------------------------------------------
/model/keypoint_classifier/keypoint_classifier.py:
--------------------------------------------------------------------------------
 1 | #!/usr/bin/env python
 2 | # -*- coding: utf-8 -*-
 3 | import numpy as np
 4 | import tensorflow as tf
 5 | 
 6 | 
 7 | class KeyPointClassifier(object):
 8 |     def __init__(
 9 |         self,
10 |         model_path='model/keypoint_classifier/keypoint_classifier.tflite',
11 |         num_threads=1,
12 |     ):
13 |         self.interpreter = tf.lite.Interpreter(model_path=model_path,
14 |                                                num_threads=num_threads)
15 | 
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 |         landmark_list,
23 |     ):
24 |         input_details_tensor_index = self.input_details[0]['index']
25 |         self.interpreter.set_tensor(
26 |             input_details_tensor_index,
27 |             np.array([landmark_list], dtype=np.float32))
28 |         self.interpreter.invoke()
29 | 
30 |         output_details_tensor_index = self.output_details[0]['index']
31 | 
32 |         result = self.interpreter.get_tensor(output_details_tensor_index)
33 | 
34 |         result_index = np.argmax(np.squeeze(result))
35 | 
36 |         return result_index
37 | 


--------------------------------------------------------------------------------
/.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


--------------------------------------------------------------------------------
/_old/app.py:
--------------------------------------------------------------------------------
  1 | #!/usr/bin/env python
  2 | # -*- coding: utf-8 -*-
  3 | import copy
  4 | import time
  5 | import argparse
  6 | from collections import deque
  7 | 
  8 | import cv2 as cv
  9 | import numpy as np
 10 | import mediapipe as mp
 11 | 
 12 | import pyautogui
 13 | 
 14 | from utils import CvFpsCalc
 15 | 
 16 | 
 17 | def get_args():
 18 |     parser = argparse.ArgumentParser()
 19 | 
 20 |     parser.add_argument("--device", type=int, default=0)
 21 |     parser.add_argument("--width", help='cap width', type=int, default=960)
 22 |     parser.add_argument("--height", help='cap height', type=int, default=540)
 23 | 
 24 |     parser.add_argument("--min_detection_confidence",
 25 |                         help='face mesh min_detection_confidence',
 26 |                         type=float,
 27 |                         default=0.75)
 28 |     parser.add_argument("--min_tracking_confidence",
 29 |                         help='face mesh min_tracking_confidence',
 30 |                         type=int,
 31 |                         default=0.75)
 32 | 
 33 |     parser.add_argument('--use_left_hand', action='store_true')
 34 | 
 35 |     args = parser.parse_args()
 36 | 
 37 |     return args
 38 | 
 39 | 
 40 | def main():
 41 |     # 引数解析 #################################################################
 42 |     args = get_args()
 43 | 
 44 |     cap_device = args.device
 45 |     cap_width = args.width
 46 |     cap_height = args.height
 47 | 
 48 |     min_detection_confidence = args.min_detection_confidence
 49 |     min_tracking_confidence = args.min_tracking_confidence
 50 | 
 51 |     use_left_hand = args.use_left_hand
 52 | 
 53 |     # カメラ準備 ###############################################################
 54 |     cap = cv.VideoCapture(cap_device)
 55 |     cap.set(cv.CAP_PROP_FRAME_WIDTH, cap_width)
 56 |     cap.set(cv.CAP_PROP_FRAME_HEIGHT, cap_height)
 57 | 
 58 |     # モデルロード #############################################################
 59 |     mp_holistic = mp.solutions.holistic
 60 |     holistic = mp_holistic.Holistic(
 61 |         upper_body_only=False,
 62 |         min_detection_confidence=min_detection_confidence,
 63 |         min_tracking_confidence=min_tracking_confidence,
 64 |     )
 65 | 
 66 |     #  ########################################################################
 67 |     # 人差指のID
 68 |     ID_FINGER_TIP = 8
 69 | 
 70 |     history_length = 6
 71 |     point_x_history = deque(maxlen=history_length)
 72 |     point_y_history = deque(maxlen=history_length)
 73 |     point_z_history = deque(maxlen=history_length)
 74 | 
 75 |     display_size = pyautogui.size()
 76 | 
 77 |     # FPS計測モジュール ########################################################
 78 |     start_time = time.time()
 79 |     cvFpsCalc = CvFpsCalc(buffer_len=10)
 80 | 
 81 |     while True:
 82 |         display_fps = cvFpsCalc.get()
 83 | 
 84 |         # キー処理(ESC：終了) #################################################
 85 |         key = cv.waitKey(1)
 86 |         if key == 27:  # ESC
 87 |             break
 88 | 
 89 |         # カメラキャプチャ #####################################################
 90 |         ret, image = cap.read()
 91 |         if not ret:
 92 |             break
 93 |         image = cv.flip(image, 1)  # ミラー表示
 94 |         image_width, image_height = image.shape[1], image.shape[0]
 95 |         debug_image = copy.deepcopy(image)
 96 | 
 97 |         # 検出実施 #############################################################
 98 |         image = cv.cvtColor(image, cv.COLOR_BGR2RGB)
 99 |         image.flags.writeable = False
100 |         results = holistic.process(image)
101 |         image.flags.writeable = True
102 | 
103 |         # Hands ###############################################################
104 |         left_hand_landmarks = results.left_hand_landmarks
105 |         right_hand_landmarks = results.right_hand_landmarks
106 |         landmarks = None
107 |         # 右手
108 |         if (left_hand_landmarks is not None) and (not use_left_hand):
109 |             landmarks = calc_hands_landmarks(image, left_hand_landmarks)
110 |         # 左手
111 |         if (right_hand_landmarks is not None) and (use_left_hand):
112 |             landmarks = calc_hands_landmarks(image, right_hand_landmarks)
113 | 
114 |         if landmarks is not None:
115 |             point_x_history.append(landmarks[ID_FINGER_TIP][0])
116 |             point_y_history.append(landmarks[ID_FINGER_TIP][1])
117 |             point_z_history.append(landmarks[ID_FINGER_TIP][2])
118 | 
119 |             point_x = int(sum(point_x_history) / len(point_x_history))
120 |             point_y = int(sum(point_y_history) / len(point_y_history))
121 |             point_z = point_z_history[-1]
122 | 
123 |             # 描画
124 |             debug_image = draw_hands_landmarks(debug_image, point_x, point_y,
125 |                                                point_z, display_fps)
126 | 
127 |             mouse_x = int(display_size.width * (point_x / image_width))
128 |             mouse_y = int(display_size.height * (point_y / image_height))
129 | 
130 |             if (time.time() - start_time) > 0.3:
131 |                 start_time = time.time()
132 |                 pyautogui.moveTo(mouse_x, mouse_y)
133 | 
134 |             if len(point_z_history) >= history_length:
135 |                 diff_z = max(point_z_history) - min(point_z_history)
136 |             else:
137 |                 diff_z = 0
138 |             max_index_z = point_z_history.index(max(point_z_history))
139 |             min_index_z = point_z_history.index(min(point_z_history))
140 |             if diff_z > 0.10 and max_index_z < min_index_z:
141 |                 pyautogui.click()
142 |                 point_z_history.clear()
143 | 
144 |         else:
145 |             if len(point_z_history):
146 |                 point_z_history.popleft()
147 | 
148 |         # 画面反映 #############################################################
149 |         image_width, image_height = debug_image.shape[1], debug_image.shape[0]
150 |         debug_image = cv.resize(debug_image,
151 |                                 (int(image_width / 2), int(image_height / 2)))
152 |         cv.imshow('Simple Virtual Mouse Demo', debug_image)
153 | 
154 |     cap.release()
155 |     cv.destroyAllWindows()
156 | 
157 | 
158 | def calc_hands_landmarks(image, landmarks):
159 |     image_width, image_height = image.shape[1], image.shape[0]
160 | 
161 |     landmark_point = []
162 | 
163 |     for _, landmark in enumerate(landmarks.landmark):
164 |         landmark_x = min(int(landmark.x * image_width), image_width - 1)
165 |         landmark_y = min(int(landmark.y * image_height), image_height - 1)
166 |         landmark_z = landmark.z
167 | 
168 |         landmark_point.append((landmark_x, landmark_y, landmark_z))
169 | 
170 |     return landmark_point
171 | 
172 | 
173 | def draw_hands_landmarks(
174 |     image,
175 |     point_x,
176 |     point_y,
177 |     point_z,
178 |     display_fps,
179 | ):
180 |     cv.circle(image, (point_x, point_y), 12, (255, 255, 255), 8)
181 |     cv.circle(image, (point_x, point_y), 12, (0, 0, 0), 2)
182 | 
183 |     cv.putText(image, "z:" + str(round(point_z, 3)),
184 |                (point_x - 20, point_y - 20), cv.FONT_HERSHEY_SIMPLEX, 1,
185 |                (255, 255, 255), 6, cv.LINE_AA)
186 |     cv.putText(image, "z:" + str(round(point_z, 3)),
187 |                (point_x - 20, point_y - 20), cv.FONT_HERSHEY_SIMPLEX, 1,
188 |                (0, 0, 0), 2, cv.LINE_AA)
189 | 
190 |     cv.putText(image, "FPS:" + str(display_fps), (10, 50),
191 |                cv.FONT_HERSHEY_SIMPLEX, 1.5, (255, 255, 255), 8, cv.LINE_AA)
192 |     cv.putText(image, "FPS:" + str(display_fps), (10, 50),
193 |                cv.FONT_HERSHEY_SIMPLEX, 1.5, (0, 0, 0), 3, cv.LINE_AA)
194 | 
195 |     return image
196 | 
197 | 
198 | if __name__ == '__main__':
199 |     main()
200 | 


--------------------------------------------------------------------------------
/LICENSE:
--------------------------------------------------------------------------------
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--------------------------------------------------------------------------------
/app.py:
--------------------------------------------------------------------------------
  1 | #!/usr/bin/env python
  2 | # -*- coding: utf-8 -*-
  3 | import csv
  4 | import time
  5 | import copy
  6 | import argparse
  7 | import itertools
  8 | from collections import Counter
  9 | from collections import deque
 10 | 
 11 | import cv2 as cv
 12 | import numpy as np
 13 | import mediapipe as mp
 14 | 
 15 | import pyautogui
 16 | 
 17 | from utils import CvFpsCalc
 18 | from model import KeyPointClassifier
 19 | 
 20 | 
 21 | def get_args():
 22 |     parser = argparse.ArgumentParser()
 23 | 
 24 |     parser.add_argument("--device", type=int, default=0)
 25 |     parser.add_argument("--width", help='cap width', type=int, default=960)
 26 |     parser.add_argument("--height", help='cap height', type=int, default=540)
 27 | 
 28 |     parser.add_argument('--use_static_image_mode', action='store_true')
 29 |     parser.add_argument("--min_detection_confidence",
 30 |                         help='min_detection_confidence',
 31 |                         type=float,
 32 |                         default=0.7)
 33 |     parser.add_argument("--min_tracking_confidence",
 34 |                         help='min_tracking_confidence',
 35 |                         type=int,
 36 |                         default=0.5)
 37 | 
 38 |     parser.add_argument("--margin_width", type=float, default=0.2)
 39 |     parser.add_argument("--margin_height", type=float, default=0.2)
 40 | 
 41 |     args = parser.parse_args()
 42 | 
 43 |     return args
 44 | 
 45 | 
 46 | def main():
 47 |     # 引数解析 #################################################################
 48 |     args = get_args()
 49 | 
 50 |     cap_device = args.device
 51 |     cap_width = args.width
 52 |     cap_height = args.height
 53 | 
 54 |     use_static_image_mode = args.use_static_image_mode
 55 |     min_detection_confidence = args.min_detection_confidence
 56 |     min_tracking_confidence = args.min_tracking_confidence
 57 | 
 58 |     margin_width = args.margin_width
 59 |     margin_height = args.margin_height
 60 | 
 61 |     # カメラ準備 ###############################################################
 62 |     cap = cv.VideoCapture(cap_device)
 63 |     cap.set(cv.CAP_PROP_FRAME_WIDTH, cap_width)
 64 |     cap.set(cv.CAP_PROP_FRAME_HEIGHT, cap_height)
 65 | 
 66 |     # モデルロード #############################################################
 67 |     mp_hands = mp.solutions.hands
 68 |     hands = mp_hands.Hands(
 69 |         static_image_mode=use_static_image_mode,
 70 |         max_num_hands=1,
 71 |         min_detection_confidence=min_detection_confidence,
 72 |         min_tracking_confidence=min_tracking_confidence,
 73 |     )
 74 | 
 75 |     keypoint_classifier = KeyPointClassifier()
 76 | 
 77 |     # ラベル読み込み ###########################################################
 78 |     with open('model/keypoint_classifier/keypoint_classifier_label.csv',
 79 |               encoding='utf-8-sig') as f:
 80 |         keypoint_classifier_labels = csv.reader(f)
 81 |         keypoint_classifier_labels = [
 82 |             row[0] for row in keypoint_classifier_labels
 83 |         ]
 84 | 
 85 |     # FPS計測モジュール ########################################################
 86 |     start_time = time.time()
 87 |     cvFpsCalc = CvFpsCalc(buffer_len=10)
 88 | 
 89 |     #  ########################################################################
 90 |     display_size = pyautogui.size()
 91 |     prev_hand_sign_id = 0
 92 | 
 93 |     while True:
 94 |         fps = cvFpsCalc.get()
 95 | 
 96 |         # キー処理(ESC：終了) #################################################
 97 |         key = cv.waitKey(1)
 98 |         if key == 27:  # ESC
 99 |             break
100 | 
101 |         # カメラキャプチャ #####################################################
102 |         ret, image = cap.read()
103 |         if not ret:
104 |             break
105 |         image = cv.flip(image, 1)  # ミラー表示
106 |         image_width, image_height = image.shape[1], image.shape[0]
107 |         debug_image = copy.deepcopy(image)
108 | 
109 |         # 検出実施 #############################################################
110 |         image = cv.cvtColor(image, cv.COLOR_BGR2RGB)
111 | 
112 |         image.flags.writeable = False
113 |         results = hands.process(image)
114 |         image.flags.writeable = True
115 | 
116 |         #  ####################################################################
117 |         if results.multi_hand_landmarks is not None:
118 |             for hand_landmarks, handedness in zip(results.multi_hand_landmarks,
119 |                                                   results.multi_handedness):
120 |                 # 手の平重心計算
121 |                 cx, cy = calc_palm_moment(debug_image, hand_landmarks)
122 |                 # 外接矩形の計算
123 |                 brect = calc_bounding_rect(debug_image, hand_landmarks)
124 |                 # ランドマークの計算
125 |                 landmark_list = calc_landmark_list(debug_image, hand_landmarks)
126 | 
127 |                 # 相対座標・正規化座標への変換
128 |                 pre_processed_landmark_list = pre_process_landmark(
129 |                     landmark_list)
130 | 
131 |                 # ハンドサイン分類
132 |                 hand_sign_id = keypoint_classifier(pre_processed_landmark_list)
133 | 
134 |                 # マウス移動
135 |                 area_x1 = int(image_width * margin_width)
136 |                 area_y1 = int(image_height * margin_height)
137 |                 area_x2 = image_width - area_x1
138 |                 area_y2 = image_height - area_y1
139 |                 mouse_x = int(display_size.width * ((cx - area_x1) /
140 |                                                     (area_x2 - area_x1)))
141 |                 mouse_y = int(display_size.height * ((cy - area_y1) /
142 |                                                      (area_y2 - area_y1)))
143 |                 mouse_x = 0 if mouse_x < 0 else mouse_x
144 |                 mouse_y = 0 if mouse_y < 0 else mouse_y
145 |                 mouse_x = display_size.width if mouse_x > display_size.width else mouse_x
146 |                 mouse_y = display_size.height if mouse_y > display_size.height else mouse_y
147 |                 if (time.time() - start_time) > 0.2:
148 |                     start_time = time.time()
149 |                     pyautogui.moveTo(mouse_x, mouse_y)
150 | 
151 |                 # パー→グー：マウスクリック
152 |                 if hand_sign_id == 1 and prev_hand_sign_id == 0:
153 |                     pyautogui.click()
154 |                     prev_hand_sign_id = hand_sign_id
155 |                 elif prev_hand_sign_id == 1 and hand_sign_id == 0:
156 |                     prev_hand_sign_id = hand_sign_id
157 | 
158 |                 # 描画
159 |                 debug_image = draw_bounding_rect(True, debug_image, brect)
160 |                 debug_image = draw_landmarks(debug_image, landmark_list, cx,
161 |                                              cy)
162 |                 debug_image = draw_info_text(
163 |                     debug_image,
164 |                     brect,
165 |                     handedness,
166 |                     keypoint_classifier_labels[hand_sign_id],
167 |                 )
168 | 
169 |         debug_image = draw_info(debug_image, fps, margin_width, margin_height)
170 | 
171 |         # 画面反映 #############################################################
172 |         debug_image = cv.resize(debug_image,
173 |                                 (int(image_width / 2), int(image_height / 2)))
174 |         cv.imshow('Hand Gesture Recognition', debug_image)
175 | 
176 |     cap.release()
177 |     cv.destroyAllWindows()
178 | 
179 | 
180 | def calc_palm_moment(image, landmarks):
181 |     image_width, image_height = image.shape[1], image.shape[0]
182 | 
183 |     palm_array = np.empty((0, 2), int)
184 | 
185 |     for index, landmark in enumerate(landmarks.landmark):
186 |         landmark_x = min(int(landmark.x * image_width), image_width - 1)
187 |         landmark_y = min(int(landmark.y * image_height), image_height - 1)
188 | 
189 |         landmark_point = [np.array((landmark_x, landmark_y))]
190 | 
191 |         if index == 0:  # 手首1
192 |             palm_array = np.append(palm_array, landmark_point, axis=0)
193 |         if index == 1:  # 手首2
194 |             palm_array = np.append(palm_array, landmark_point, axis=0)
195 |         if index == 5:  # 人差指：付け根
196 |             palm_array = np.append(palm_array, landmark_point, axis=0)
197 |         if index == 9:  # 中指：付け根
198 |             palm_array = np.append(palm_array, landmark_point, axis=0)
199 |         if index == 13:  # 薬指：付け根
200 |             palm_array = np.append(palm_array, landmark_point, axis=0)
201 |         if index == 17:  # 小指：付け根
202 |             palm_array = np.append(palm_array, landmark_point, axis=0)
203 |     M = cv.moments(palm_array)
204 |     cx, cy = 0, 0
205 |     if M['m00'] != 0:
206 |         cx = int(M['m10'] / M['m00'])
207 |         cy = int(M['m01'] / M['m00'])
208 | 
209 |     return cx, cy
210 | 
211 | 
212 | def calc_bounding_rect(image, landmarks):
213 |     image_width, image_height = image.shape[1], image.shape[0]
214 | 
215 |     landmark_array = np.empty((0, 2), int)
216 | 
217 |     for _, landmark in enumerate(landmarks.landmark):
218 |         landmark_x = min(int(landmark.x * image_width), image_width - 1)
219 |         landmark_y = min(int(landmark.y * image_height), image_height - 1)
220 | 
221 |         landmark_point = [np.array((landmark_x, landmark_y))]
222 | 
223 |         landmark_array = np.append(landmark_array, landmark_point, axis=0)
224 | 
225 |     x, y, w, h = cv.boundingRect(landmark_array)
226 | 
227 |     return [x, y, x + w, y + h]
228 | 
229 | 
230 | def calc_landmark_list(image, landmarks):
231 |     image_width, image_height = image.shape[1], image.shape[0]
232 | 
233 |     landmark_point = []
234 | 
235 |     # キーポイント
236 |     for _, landmark in enumerate(landmarks.landmark):
237 |         landmark_x = min(int(landmark.x * image_width), image_width - 1)
238 |         landmark_y = min(int(landmark.y * image_height), image_height - 1)
239 |         # landmark_z = landmark.z
240 | 
241 |         landmark_point.append([landmark_x, landmark_y])
242 | 
243 |     return landmark_point
244 | 
245 | 
246 | def pre_process_landmark(landmark_list):
247 |     temp_landmark_list = copy.deepcopy(landmark_list)
248 | 
249 |     # 相対座標に変換
250 |     base_x, base_y = 0, 0
251 |     for index, landmark_point in enumerate(temp_landmark_list):
252 |         if index == 0:
253 |             base_x, base_y = landmark_point[0], landmark_point[1]
254 | 
255 |         temp_landmark_list[index][0] = temp_landmark_list[index][0] - base_x
256 |         temp_landmark_list[index][1] = temp_landmark_list[index][1] - base_y
257 | 
258 |     # 1次元リストに変換
259 |     temp_landmark_list = list(
260 |         itertools.chain.from_iterable(temp_landmark_list))
261 | 
262 |     # 正規化
263 |     max_value = max(list(map(abs, temp_landmark_list)))
264 | 
265 |     def normalize_(n):
266 |         return n / max_value
267 | 
268 |     temp_landmark_list = list(map(normalize_, temp_landmark_list))
269 | 
270 |     return temp_landmark_list
271 | 
272 | 
273 | def draw_landmarks(image, landmark_point, cx, cy):
274 |     # 接続線
275 |     if len(landmark_point) > 0:
276 |         # 親指
277 |         cv.line(image, tuple(landmark_point[2]), tuple(landmark_point[3]),
278 |                 (0, 0, 0), 6)
279 |         cv.line(image, tuple(landmark_point[2]), tuple(landmark_point[3]),
280 |                 (255, 255, 255), 2)
281 |         cv.line(image, tuple(landmark_point[3]), tuple(landmark_point[4]),
282 |                 (0, 0, 0), 6)
283 |         cv.line(image, tuple(landmark_point[3]), tuple(landmark_point[4]),
284 |                 (255, 255, 255), 2)
285 | 
286 |         # 人差指
287 |         cv.line(image, tuple(landmark_point[5]), tuple(landmark_point[6]),
288 |                 (0, 0, 0), 6)
289 |         cv.line(image, tuple(landmark_point[5]), tuple(landmark_point[6]),
290 |                 (255, 255, 255), 2)
291 |         cv.line(image, tuple(landmark_point[6]), tuple(landmark_point[7]),
292 |                 (0, 0, 0), 6)
293 |         cv.line(image, tuple(landmark_point[6]), tuple(landmark_point[7]),
294 |                 (255, 255, 255), 2)
295 |         cv.line(image, tuple(landmark_point[7]), tuple(landmark_point[8]),
296 |                 (0, 0, 0), 6)
297 |         cv.line(image, tuple(landmark_point[7]), tuple(landmark_point[8]),
298 |                 (255, 255, 255), 2)
299 | 
300 |         # 中指
301 |         cv.line(image, tuple(landmark_point[9]), tuple(landmark_point[10]),
302 |                 (0, 0, 0), 6)
303 |         cv.line(image, tuple(landmark_point[9]), tuple(landmark_point[10]),
304 |                 (255, 255, 255), 2)
305 |         cv.line(image, tuple(landmark_point[10]), tuple(landmark_point[11]),
306 |                 (0, 0, 0), 6)
307 |         cv.line(image, tuple(landmark_point[10]), tuple(landmark_point[11]),
308 |                 (255, 255, 255), 2)
309 |         cv.line(image, tuple(landmark_point[11]), tuple(landmark_point[12]),
310 |                 (0, 0, 0), 6)
311 |         cv.line(image, tuple(landmark_point[11]), tuple(landmark_point[12]),
312 |                 (255, 255, 255), 2)
313 | 
314 |         # 薬指
315 |         cv.line(image, tuple(landmark_point[13]), tuple(landmark_point[14]),
316 |                 (0, 0, 0), 6)
317 |         cv.line(image, tuple(landmark_point[13]), tuple(landmark_point[14]),
318 |                 (255, 255, 255), 2)
319 |         cv.line(image, tuple(landmark_point[14]), tuple(landmark_point[15]),
320 |                 (0, 0, 0), 6)
321 |         cv.line(image, tuple(landmark_point[14]), tuple(landmark_point[15]),
322 |                 (255, 255, 255), 2)
323 |         cv.line(image, tuple(landmark_point[15]), tuple(landmark_point[16]),
324 |                 (0, 0, 0), 6)
325 |         cv.line(image, tuple(landmark_point[15]), tuple(landmark_point[16]),
326 |                 (255, 255, 255), 2)
327 | 
328 |         # 小指
329 |         cv.line(image, tuple(landmark_point[17]), tuple(landmark_point[18]),
330 |                 (0, 0, 0), 6)
331 |         cv.line(image, tuple(landmark_point[17]), tuple(landmark_point[18]),
332 |                 (255, 255, 255), 2)
333 |         cv.line(image, tuple(landmark_point[18]), tuple(landmark_point[19]),
334 |                 (0, 0, 0), 6)
335 |         cv.line(image, tuple(landmark_point[18]), tuple(landmark_point[19]),
336 |                 (255, 255, 255), 2)
337 |         cv.line(image, tuple(landmark_point[19]), tuple(landmark_point[20]),
338 |                 (0, 0, 0), 6)
339 |         cv.line(image, tuple(landmark_point[19]), tuple(landmark_point[20]),
340 |                 (255, 255, 255), 2)
341 | 
342 |         # 手の平
343 |         cv.line(image, tuple(landmark_point[0]), tuple(landmark_point[1]),
344 |                 (0, 0, 0), 6)
345 |         cv.line(image, tuple(landmark_point[0]), tuple(landmark_point[1]),
346 |                 (255, 255, 255), 2)
347 |         cv.line(image, tuple(landmark_point[1]), tuple(landmark_point[2]),
348 |                 (0, 0, 0), 6)
349 |         cv.line(image, tuple(landmark_point[1]), tuple(landmark_point[2]),
350 |                 (255, 255, 255), 2)
351 |         cv.line(image, tuple(landmark_point[2]), tuple(landmark_point[5]),
352 |                 (0, 0, 0), 6)
353 |         cv.line(image, tuple(landmark_point[2]), tuple(landmark_point[5]),
354 |                 (255, 255, 255), 2)
355 |         cv.line(image, tuple(landmark_point[5]), tuple(landmark_point[9]),
356 |                 (0, 0, 0), 6)
357 |         cv.line(image, tuple(landmark_point[5]), tuple(landmark_point[9]),
358 |                 (255, 255, 255), 2)
359 |         cv.line(image, tuple(landmark_point[9]), tuple(landmark_point[13]),
360 |                 (0, 0, 0), 6)
361 |         cv.line(image, tuple(landmark_point[9]), tuple(landmark_point[13]),
362 |                 (255, 255, 255), 2)
363 |         cv.line(image, tuple(landmark_point[13]), tuple(landmark_point[17]),
364 |                 (0, 0, 0), 6)
365 |         cv.line(image, tuple(landmark_point[13]), tuple(landmark_point[17]),
366 |                 (255, 255, 255), 2)
367 |         cv.line(image, tuple(landmark_point[17]), tuple(landmark_point[0]),
368 |                 (0, 0, 0), 6)
369 |         cv.line(image, tuple(landmark_point[17]), tuple(landmark_point[0]),
370 |                 (255, 255, 255), 2)
371 | 
372 |     # キーポイント
373 |     for index, landmark in enumerate(landmark_point):
374 |         if index == 0:  # 手首1
375 |             cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255),
376 |                       -1)
377 |             cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1)
378 |         if index == 1:  # 手首2
379 |             cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255),
380 |                       -1)
381 |             cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1)
382 |         if index == 2:  # 親指：付け根
383 |             cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255),
384 |                       -1)
385 |             cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1)
386 |         if index == 3:  # 親指：第1関節
387 |             cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255),
388 |                       -1)
389 |             cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1)
390 |         if index == 4:  # 親指：指先
391 |             cv.circle(image, (landmark[0], landmark[1]), 8, (255, 255, 255),
392 |                       -1)
393 |             cv.circle(image, (landmark[0], landmark[1]), 8, (0, 0, 0), 1)
394 |         if index == 5:  # 人差指：付け根
395 |             cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255),
396 |                       -1)
397 |             cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1)
398 |         if index == 6:  # 人差指：第2関節
399 |             cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255),
400 |                       -1)
401 |             cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1)
402 |         if index == 7:  # 人差指：第1関節
403 |             cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255),
404 |                       -1)
405 |             cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1)
406 |         if index == 8:  # 人差指：指先
407 |             cv.circle(image, (landmark[0], landmark[1]), 8, (255, 255, 255),
408 |                       -1)
409 |             cv.circle(image, (landmark[0], landmark[1]), 8, (0, 0, 0), 1)
410 |         if index == 9:  # 中指：付け根
411 |             cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255),
412 |                       -1)
413 |             cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1)
414 |         if index == 10:  # 中指：第2関節
415 |             cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255),
416 |                       -1)
417 |             cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1)
418 |         if index == 11:  # 中指：第1関節
419 |             cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255),
420 |                       -1)
421 |             cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1)
422 |         if index == 12:  # 中指：指先
423 |             cv.circle(image, (landmark[0], landmark[1]), 8, (255, 255, 255),
424 |                       -1)
425 |             cv.circle(image, (landmark[0], landmark[1]), 8, (0, 0, 0), 1)
426 |         if index == 13:  # 薬指：付け根
427 |             cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255),
428 |                       -1)
429 |             cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1)
430 |         if index == 14:  # 薬指：第2関節
431 |             cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255),
432 |                       -1)
433 |             cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1)
434 |         if index == 15:  # 薬指：第1関節
435 |             cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255),
436 |                       -1)
437 |             cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1)
438 |         if index == 16:  # 薬指：指先
439 |             cv.circle(image, (landmark[0], landmark[1]), 8, (255, 255, 255),
440 |                       -1)
441 |             cv.circle(image, (landmark[0], landmark[1]), 8, (0, 0, 0), 1)
442 |         if index == 17:  # 小指：付け根
443 |             cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255),
444 |                       -1)
445 |             cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1)
446 |         if index == 18:  # 小指：第2関節
447 |             cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255),
448 |                       -1)
449 |             cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1)
450 |         if index == 19:  # 小指：第1関節
451 |             cv.circle(image, (landmark[0], landmark[1]), 5, (255, 255, 255),
452 |                       -1)
453 |             cv.circle(image, (landmark[0], landmark[1]), 5, (0, 0, 0), 1)
454 |         if index == 20:  # 小指：指先
455 |             cv.circle(image, (landmark[0], landmark[1]), 8, (255, 255, 255),
456 |                       -1)
457 |             cv.circle(image, (landmark[0], landmark[1]), 8, (0, 0, 0), 1)
458 | 
459 |     cv.circle(image, (cx, cy), 10, (255, 255, 255), -1)
460 |     cv.circle(image, (cx, cy), 8, (0, 0, 0), 1)
461 | 
462 |     return image
463 | 
464 | 
465 | def draw_bounding_rect(use_brect, image, brect):
466 |     if use_brect:
467 |         # 外接矩形
468 |         cv.rectangle(image, (brect[0], brect[1]), (brect[2], brect[3]),
469 |                      (0, 0, 0), 1)
470 | 
471 |     return image
472 | 
473 | 
474 | def draw_info_text(image, brect, handedness, hand_sign_text):
475 |     cv.rectangle(image, (brect[0], brect[1]), (brect[2], brect[1] - 22),
476 |                  (0, 0, 0), -1)
477 | 
478 |     info_text = handedness.classification[0].label[0:]
479 |     if hand_sign_text != "":
480 |         info_text = info_text + ':' + hand_sign_text
481 |     cv.putText(image, info_text, (brect[0] + 5, brect[1] - 4),
482 |                cv.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 255), 1, cv.LINE_AA)
483 | 
484 |     return image
485 | 
486 | 
487 | def draw_info(image, fps, margin_width, margin_height):
488 |     image_width, image_height = image.shape[1], image.shape[0]
489 | 
490 |     area_x1 = int(image_width * margin_width)
491 |     area_y1 = int(image_height * margin_height)
492 |     area_x2 = image_width - area_x1
493 |     area_y2 = image_height - area_y1
494 |     cv.rectangle(image, (area_x1, area_y1), (area_x2, area_y2),
495 |                  (255, 255, 255), 2)
496 | 
497 |     cv.putText(image, "FPS:" + str(fps), (10, 30), cv.FONT_HERSHEY_SIMPLEX,
498 |                1.0, (0, 0, 0), 4, cv.LINE_AA)
499 |     cv.putText(image, "FPS:" + str(fps), (10, 30), cv.FONT_HERSHEY_SIMPLEX,
500 |                1.0, (255, 255, 255), 2, cv.LINE_AA)
501 |     return image
502 | 
503 | 
504 | if __name__ == '__main__':
505 |     main()
506 | 


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