├── README.md ├── __pycache__ └── hand_tracker.cpython-36.pyc ├── data ├── anchors.csv └── test_img.jpg ├── detect_on_camera.py ├── gasture_utils ├── Finger.py ├── FingerCurled.py ├── FingerDataFormation.py ├── FingerPoseEstimate.py ├── FingerPosition.py ├── __pycache__ │ ├── Finger.cpython-36.pyc │ ├── FingerCurled.cpython-36.pyc │ ├── FingerDataFormation.cpython-36.pyc │ ├── FingerPoseEstimate.cpython-36.pyc │ ├── FingerPosition.cpython-36.pyc │ └── determine_gasture.cpython-36.pyc └── determine_gasture.py ├── hand_tracker.py ├── images └── show.png ├── models ├── mediapipe_hand_landmark.tflite └── palm_detection_without_custom_op.tflite └── requirements.txt /README.md: -------------------------------------------------------------------------------- 1 | # hand-keypoint-detect 2 | 3 | 直接在python中使用谷歌mediapipe的手关键点检测 4 | 5 | 直接调用电脑摄像头检测命令 6 | 7 | python detect_on_camera.py 8 | 9 | 10 | models文件夹中是检测用模型 11 | 12 | data文件夹中存放的是anchors配置文件 13 | 14 | gasture_utils是根据关键点设置一些规则进行手势分类，该方法不适用深度学习方式，可以在gasture_utils/determine_gastures.py自定义五根手指的指向弯曲程度设置新的手势 15 | 16 | 在电脑上检测效果没有mediapipe给的安卓apk效果好，应该是谷歌还用了一些别的方法进行优化，后面会持续优化更新,最终目标是做一个可靠的远距离手势检测来控制其他设备。 17 | 18 | 19 | 20 | 检测图像 21 | ![show detect image](https://github.com/ndkjing/hand-keypoint-detect/blob/master/images/show.png) 22 | 23 | 24 | 25 | ## 参考： 26 | 27 | [检测模型](https://github.com/google/mediapipe) 28 | -------------------------------------------------------------------------------- /__pycache__/hand_tracker.cpython-36.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ndkjing/hand-keypoint-detect/143a2d17557ab21bed1b6e2192b3645e882a3296/__pycache__/hand_tracker.cpython-36.pyc -------------------------------------------------------------------------------- /data/anchors.csv: -------------------------------------------------------------------------------- 1 | 0.015625,0.015625,1,1 2 | 0.015625,0.015625,1,1 3 | 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| 0.6875,0.8125,1,1 2881 | 0.6875,0.8125,1,1 2882 | 0.6875,0.8125,1,1 2883 | 0.6875,0.8125,1,1 2884 | 0.6875,0.8125,1,1 2885 | 0.8125,0.8125,1,1 2886 | 0.8125,0.8125,1,1 2887 | 0.8125,0.8125,1,1 2888 | 0.8125,0.8125,1,1 2889 | 0.8125,0.8125,1,1 2890 | 0.8125,0.8125,1,1 2891 | 0.9375,0.8125,1,1 2892 | 0.9375,0.8125,1,1 2893 | 0.9375,0.8125,1,1 2894 | 0.9375,0.8125,1,1 2895 | 0.9375,0.8125,1,1 2896 | 0.9375,0.8125,1,1 2897 | 0.0625,0.9375,1,1 2898 | 0.0625,0.9375,1,1 2899 | 0.0625,0.9375,1,1 2900 | 0.0625,0.9375,1,1 2901 | 0.0625,0.9375,1,1 2902 | 0.0625,0.9375,1,1 2903 | 0.1875,0.9375,1,1 2904 | 0.1875,0.9375,1,1 2905 | 0.1875,0.9375,1,1 2906 | 0.1875,0.9375,1,1 2907 | 0.1875,0.9375,1,1 2908 | 0.1875,0.9375,1,1 2909 | 0.3125,0.9375,1,1 2910 | 0.3125,0.9375,1,1 2911 | 0.3125,0.9375,1,1 2912 | 0.3125,0.9375,1,1 2913 | 0.3125,0.9375,1,1 2914 | 0.3125,0.9375,1,1 2915 | 0.4375,0.9375,1,1 2916 | 0.4375,0.9375,1,1 2917 | 0.4375,0.9375,1,1 2918 | 0.4375,0.9375,1,1 2919 | 0.4375,0.9375,1,1 2920 | 0.4375,0.9375,1,1 2921 | 0.5625,0.9375,1,1 2922 | 0.5625,0.9375,1,1 2923 | 0.5625,0.9375,1,1 2924 | 0.5625,0.9375,1,1 2925 | 0.5625,0.9375,1,1 2926 | 0.5625,0.9375,1,1 2927 | 0.6875,0.9375,1,1 2928 | 0.6875,0.9375,1,1 2929 | 0.6875,0.9375,1,1 2930 | 0.6875,0.9375,1,1 2931 | 0.6875,0.9375,1,1 2932 | 0.6875,0.9375,1,1 2933 | 0.8125,0.9375,1,1 2934 | 0.8125,0.9375,1,1 2935 | 0.8125,0.9375,1,1 2936 | 0.8125,0.9375,1,1 2937 | 0.8125,0.9375,1,1 2938 | 0.8125,0.9375,1,1 2939 | 0.9375,0.9375,1,1 2940 | 0.9375,0.9375,1,1 2941 | 0.9375,0.9375,1,1 2942 | 0.9375,0.9375,1,1 2943 | 0.9375,0.9375,1,1 2944 | 0.9375,0.9375,1,1 2945 | -------------------------------------------------------------------------------- /data/test_img.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ndkjing/hand-keypoint-detect/143a2d17557ab21bed1b6e2192b3645e882a3296/data/test_img.jpg -------------------------------------------------------------------------------- /detect_on_camera.py: -------------------------------------------------------------------------------- 1 | import platform 2 | sysstr = platform.system() 3 | if (sysstr == "Windows"): 4 | print("Call Windows tasks") 5 | elif (sysstr == "Linux"): # 树莓派上也是Linux 6 | print("Call Linux tasks") 7 | else: 8 | print("other System tasks") 9 | 10 | import copy 11 | import time 12 | import numpy as np 13 | import cv2 14 | from collections import deque 15 | 16 | from hand_tracker import HandTracker 17 | from gasture_utils.determine_gasture import create_known_finger_poses, determine_position, get_position_name_with_pose_id 18 | from gasture_utils.FingerPoseEstimate import FingerPoseEstimate 19 | 20 | """ 21 | mediapipe 模型 handdetect模型 22 | """ 23 | 24 | palm_model_path = "./models/palm_detection_without_custom_op.tflite" 25 | landmark_model_path = "./models/mediapipe_hand_landmark.tflite" 26 | anchors_path = "./data/anchors.csv" 27 | MIN_CONFIDENCE = 0.10 28 | # cap = cv2.VideoCapture(r'C:\PythonProject\jing_vision\detection\keras_tf\tflite\posenet_mb2_ssd\dance.flv') 29 | # cap = cv2.VideoCapture(r'hand.flv') # 使用本地视频 30 | cap = cv2.VideoCapture(0) # 调用webcamera 31 | cap.set(cv2.CAP_PROP_FPS, 30) 32 | cap.set(cv2.CAP_PROP_FRAME_WIDTH, 480) 33 | cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 480) 34 | detector = HandTracker(palm_model_path, landmark_model_path, anchors_path, 35 | box_shift=0.2, box_enlarge=1.3) 36 | 37 | 38 | Q = deque(maxlen=5) 39 | gasture_define = { 'Simple Thumbs Up':0, 40 | 'Thumbs Up Right':1, 41 | 'I love you':2, 42 | 'Victory':3 , 43 | 'Pointing Up':4, 44 | 'Okay':5 , 45 | 'Spock':6, 46 | 'One':7, 47 | 'Three':8 48 | } 49 | reverse_gasture_define = {v:k for k,v in gasture_define.items()} 50 | known_finger_poses = create_known_finger_poses() 51 | # 8 12 16 20 52 | # | | | | 53 | # 7 11 15 19 54 | # 4 | | | | 55 | # | 6 10 14 18 56 | # 3 | | | | 57 | # | 5---9---13--17 58 | # 2 \ / 59 | # \ \ / 60 | # 1 \ / 61 | # \ \ / 62 | # ------0- 63 | 64 | # Anatomy guide 65 | # http://blog.handcare.org/blog/2017/10/26/anatomy-101-finger-joints/ 66 | HAND_POINTS = [ 67 | "BASE", 68 | "T_STT", "T_BCMC", "T_MCP", "T_IP", # Thumb 69 | "I_CMC", "I_MCP", "I_PIP", "I_DIP", # Index 70 | "M_CMC", "M_MCP", "M_PIP", "M_DIP", # Middle 71 | "R_CMC", "R_MCP", "R_PIP", "R_DIP", # Ring 72 | "P_CMC", "P_MCP", "P_PIP", "P_DIP", # Pinky 73 | ] 74 | # 关节连接 75 | limbs = [[0, 1], 76 | [1, 2], 77 | [2, 3], 78 | [3, 4], 79 | [0, 5], 80 | [5, 6], 81 | [6, 7], 82 | [7, 8], 83 | [0, 9], 84 | [9, 10], 85 | [10, 11], 86 | [11, 12], 87 | [0, 13], 88 | [13, 14], 89 | [14, 15], 90 | [15, 16], 91 | [0, 17], 92 | [17, 18], 93 | [18, 19], 94 | [19, 20] 95 | ] 96 | fps_list =[0 for i in range(10)] 97 | joint_color_code = [[139, 53, 255], 98 | [0, 56, 255], 99 | [43, 140, 237], 100 | [37, 168, 36], 101 | [147, 147, 0], 102 | [70, 17, 145]] 103 | 104 | 105 | frame_count = 0 106 | return_track={} 107 | while True: 108 | ret, color_image=cap.read() 109 | show_image=copy.deepcopy(color_image) 110 | color_image=cv2.cvtColor(color_image, cv2.COLOR_BGR2RGB) # 将BGR转为RGB 111 | # print(ret, color_image.shape) 112 | if not ret: 113 | print('video over') 114 | break 115 | start_time=time.time() 116 | # print(color_image.shape) 117 | # 判断检测间隔 118 | if frame_count%7==0: 119 | kp, box,return_track=detector(color_image,None,True) 120 | elif return_track is None: 121 | pass 122 | else: 123 | kp, box, _ = detector(color_image,return_track,False) 124 | if kp is not None and box is not None: 125 | # print('kp is ',kp) 126 | pts=np.array(box, np.int32) 127 | kp=np.array(kp, np.int32) 128 | # 计算手势 129 | fingerPoseEstimate = FingerPoseEstimate(kp) 130 | fingerPoseEstimate.calculate_positions_of_fingers(print_finger_info=False) 131 | obtained_positions = determine_position(fingerPoseEstimate.finger_curled, 132 | fingerPoseEstimate.finger_position, known_finger_poses, 133 | 0.45 * 10) 134 | # print(obtained_positions) 135 | # 根据字典的值进行排序 136 | gasture_pre = sorted(obtained_positions.items(), key=lambda item: item[1], reverse=True) 137 | # 仅绘制最高概率与绘制所有可能概率 138 | if len(gasture_pre)>0: 139 | Q.append(gasture_define[gasture_pre[0][0]]) # 通过队列中最多的元素输出为类别 140 | counts = np.bincount(Q) 141 | # 返回众数 142 | max_index = np.argmax(counts) 143 | print(reverse_gasture_define[max_index]) 144 | cv2.putText(show_image, 'rank 1 pre %s probably %f'%(reverse_gasture_define[max_index],gasture_pre[0][1]), (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 155), 1, cv2.LINE_AA) 145 | # cv2.putText(show_image, 'rank 1 pre %s probably %f'%(gasture_pre[0][0],gasture_pre[0][1]), (30, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 155), 1, cv2.LINE_AA) 146 | # for i,pre in enumerate(gasture_pre): 147 | # cv2.putText(show_image, 'rank %d pre：%s probably%f'%(i+1,pre[0],pre[1]), (20, i*20+20), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 155), 1, cv2.LINE_AA) 148 | for i in kp: 149 | cv2.circle(show_image, center=(i[0], i[1]), radius=3, color=(0, 255, 0), thickness=-1) 150 | # cv2.rectangle(show_image, (int(box[0,0]), int(box[0,1])), (int(box[1,0]), int(box[1,1])), (0, 255,255), 2) 151 | # 绘制关键点链接线 152 | cv2.line(show_image, (kp[0][0], kp[0][1]), 153 | (kp[1][0], kp[1][1]), (255, 0, 0), 2) 154 | cv2.line(show_image, (kp[1][0], kp[1][1]), 155 | (kp[2][0], kp[2][1]), (255, 0, 0), 2) 156 | cv2.line(show_image, (kp[2][0], kp[2][1]), 157 | (kp[3][0], kp[3][1]), (255, 0, 0), 2) 158 | cv2.line(show_image, (kp[3][0], kp[3][1]), 159 | (kp[4][0], kp[4][1]), (255, 0, 0), 2) 160 | 161 | cv2.line(show_image, (kp[0][0], kp[0][1]), 162 | (kp[5][0], kp[5][1]), (255, 255, 0), 2) 163 | cv2.line(show_image, (kp[5][0], kp[5][1]), 164 | (kp[6][0], kp[6][1]), (255, 255, 0), 2) 165 | cv2.line(show_image, (kp[6][0], kp[6][1]), 166 | (kp[7][0], kp[7][1]), (255, 255, 0), 2) 167 | cv2.line(show_image, (kp[7][0], kp[7][1]), 168 | (kp[8][0], kp[8][1]), (255, 255, 0), 2) 169 | 170 | cv2.line(show_image, (kp[5][0], kp[5][1]), 171 | (kp[9][0], kp[9][1]), (0, 0, 255), 2) 172 | cv2.line(show_image, (kp[9][0], kp[9][1]), 173 | (kp[10][0], kp[10][1]), (0, 0, 255), 2) 174 | cv2.line(show_image, (kp[10][0], kp[10][1]), 175 | (kp[11][0], kp[11][1]), (0, 0, 255), 2) 176 | cv2.line(show_image, (kp[11][0], kp[11][1]), 177 | (kp[12][0], kp[12][1]), (0, 0, 255), 2) 178 | 179 | cv2.line(show_image, (kp[9][0], kp[9][1]), 180 | (kp[13][0], kp[13][1]), (255, 0, 255), 2) 181 | cv2.line(show_image, (kp[13][0], kp[13][1]), 182 | (kp[14][0], kp[14][1]), (255, 0, 255), 2) 183 | cv2.line(show_image, (kp[14][0], kp[14][1]), 184 | (kp[15][0], kp[15][1]), (255, 0, 255), 2) 185 | cv2.line(show_image, (kp[15][0], kp[15][1]), 186 | (kp[16][0], kp[16][1]), (255, 0, 255), 2) 187 | 188 | cv2.line(show_image, (kp[0][0], kp[0][1]), 189 | (kp[17][0], kp[17][1]), (0, 255, 0), 2) 190 | cv2.line(show_image, (kp[13][0], kp[13][1]), 191 | (kp[17][0], kp[17][1]), (0, 255, 0), 2) 192 | cv2.line(show_image, (kp[17][0], kp[17][1]), 193 | (kp[18][0], kp[18][1]), (0, 255, 0), 2) 194 | cv2.line(show_image, (kp[18][0], kp[18][1]), 195 | (kp[19][0], kp[19][1]), (0, 255, 0), 2) 196 | cv2.line(show_image, (kp[19][0], kp[19][1]), 197 | (kp[20][0], kp[20][1]), (0, 255, 0), 2) 198 | 199 | 200 | cv2.polylines(show_image, [pts], True, (0, 255, 255), 1) # 绘制多边形 201 | # print(box) 202 | fps = 1 / (time.time() - start_time) 203 | print('FPS:', fps) 204 | index = frame_count%10 205 | fps_list[index]=fps 206 | cv2.putText(show_image, 207 | 'FPS:%f' % ( np.mean(fps_list)), (30, 70), 208 | cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 155), 1, cv2.LINE_AA) 209 | if sysstr == "Windows": 210 | cv2.namedWindow("USB Camera", cv2.WINDOW_AUTOSIZE) 211 | cv2.imshow("USB Camera", show_image) 212 | if cv2.waitKey(1) & 0xFF == ord('q'): 213 | break 214 | frame_count +=1 215 | 216 | cap.release() 217 | cv2.destroyAllWindows() 218 | -------------------------------------------------------------------------------- /gasture_utils/Finger.py: -------------------------------------------------------------------------------- 1 | from enum import IntEnum 2 | 3 | 4 | class Finger(IntEnum): 5 | Thumb = 0 6 | Index = 1 7 | Middle = 2 8 | Ring = 3 9 | Little = 4 10 | 11 | @staticmethod 12 | def get_array_of_points(finger): 13 | finger_array = None 14 | if finger == Finger.Thumb: 15 | finger_array = [(0, 1), (1, 2), (2 ,3), (3, 4)] 16 | elif finger == Finger.Index: 17 | finger_array = [(0, 5), (5, 6), (6, 7), (7, 8)] 18 | elif finger == Finger.Middle: 19 | finger_array = [(0, 9), (9, 10), (10, 11), (11, 12)] 20 | elif finger == Finger.Ring: 21 | finger_array = [(0, 13), (13, 14), (14, 15), (15, 16)] 22 | else: 23 | finger_array = [(0, 17), (17, 18), (18, 19), (19, 20)] 24 | return finger_array 25 | 26 | @staticmethod 27 | def get_finger_name(finger): 28 | finger_name = '' 29 | if finger == Finger.Thumb: 30 | finger_name = 'Thumb' 31 | elif finger == Finger.Index: 32 | finger_name = 'Index' 33 | elif finger == Finger.Middle: 34 | finger_name = 'Middle' 35 | elif finger == Finger.Ring: 36 | finger_name = 'Ring' 37 | elif finger == Finger.Little: 38 | finger_name = 'Little' 39 | return finger_name -------------------------------------------------------------------------------- /gasture_utils/FingerCurled.py: -------------------------------------------------------------------------------- 1 | from enum import IntEnum 2 | 3 | 4 | class FingerCurled(IntEnum): 5 | """ 6 | 定义手指弯曲 7 | """ 8 | NoCurl = 0 # 无弯曲 9 | HalfCurl = 1 # 微弯曲 10 | FullCurl = 2 #全弯曲 11 | 12 | @staticmethod 13 | def get_finger_curled_name(finger_curled): 14 | finger_curled_name = '' 15 | if finger_curled == FingerCurled.NoCurl: 16 | finger_curled_name = 'No Curl' 17 | elif finger_curled == FingerCurled.HalfCurl: 18 | finger_curled_name = 'Half Curl' 19 | elif finger_curled == FingerCurled.FullCurl: 20 | finger_curled_name = 'Full Curl' 21 | return finger_curled_name -------------------------------------------------------------------------------- /gasture_utils/FingerDataFormation.py: -------------------------------------------------------------------------------- 1 | class FingerDataFormation: 2 | def __init__(self): 3 | self.curl_position = [] 4 | self.finger_position = [] 5 | self.curl_position_confidence = [] 6 | self.finger_position_confidence = [] 7 | self.position_name = '' 8 | self.position_id = -1 -------------------------------------------------------------------------------- /gasture_utils/FingerPoseEstimate.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import math 3 | 4 | from gasture_utils.Finger import Finger 5 | from gasture_utils.FingerCurled import FingerCurled 6 | from gasture_utils.FingerPosition import FingerPosition 7 | 8 | 9 | class FingerPoseEstimate: 10 | def __init__(self, coords_xy): 11 | self.coords_xy = np.squeeze(coords_xy) 12 | 13 | self.finger_position = [ 14 | FingerPosition.VerticalUp, 15 | FingerPosition.VerticalUp, 16 | FingerPosition.VerticalUp, 17 | FingerPosition.VerticalUp, 18 | FingerPosition.VerticalUp] 19 | self.finger_curled = [ 20 | FingerCurled.NoCurl, 21 | FingerCurled.NoCurl, 22 | FingerCurled.NoCurl, 23 | FingerCurled.NoCurl, 24 | FingerCurled.NoCurl, 25 | ] 26 | self.slopes_xy = [] 27 | self.slopes_yz = [] 28 | 29 | def get_slope(self, point1, point2): 30 | slope_xy = self._calculate_slope_procedure( 31 | point1[0], point1[1], point2[0], point2[1]) 32 | if len(point1) == 2: 33 | return slope_xy 34 | 35 | slope_yz = self._calculate_slope_procedure( 36 | point1[1], point1[2], point2[1], point2[2]) 37 | return slope_xy, slope_yz 38 | 39 | def calculate_slope_of_fingers(self): 40 | for finger in Finger: 41 | # print('Angle Finger is {}'.format(Finger.get_finger_name(finger))) 42 | points = Finger.get_array_of_points(finger) 43 | slope_at_xy = [] 44 | for point in points: 45 | 46 | point1 = self.coords_xy[point[0]] 47 | point2 = self.coords_xy[point[1]] 48 | slope_xy = self.get_slope(point1, point2) 49 | slope_at_xy.append(slope_xy) 50 | 51 | self.slopes_xy.append(slope_at_xy) 52 | 53 | 54 | def angle_orientation_at(self, angle, weightage_at=1.0): 55 | is_vertical, is_diagonal, is_horizontal = 0, 0, 0 56 | if angle >= 75.0 and angle <= 105.0: 57 | is_vertical = 1 * weightage_at 58 | elif angle >= 25.0 and angle <= 155.0: 59 | is_diagonal = 1 * weightage_at 60 | else: 61 | is_horizontal = 1 * weightage_at 62 | return (is_vertical, is_diagonal, is_horizontal) 63 | 64 | def is_finger_curled(self, start_point, mid_point, end_point): 65 | start_mid_x_dist = start_point[0] - mid_point[0] 66 | start_end_x_dist = start_point[0] - end_point[0] 67 | mid_end_x_dist = mid_point[0] - end_point[0] 68 | 69 | start_mid_y_dist = start_point[1] - mid_point[1] 70 | start_end_y_dist = start_point[1] - end_point[1] 71 | mid_end_y_dist = mid_point[1] - end_point[1] 72 | 73 | # start_mid_z_dist = start_point[2] - mid_point[2] 74 | # start_end_z_dist = start_point[2] - end_point[2] 75 | # mid_end_z_dist = mid_point[2] - end_point[2] 76 | 77 | start_mid_dist = math.sqrt( 78 | start_mid_x_dist ** 2 + 79 | start_mid_y_dist ** 2 ) 80 | start_end_dist = math.sqrt( 81 | start_end_x_dist ** 2 + 82 | start_end_y_dist ** 2 ) 83 | mid_end_dist = math.sqrt( 84 | mid_end_x_dist ** 2 + 85 | mid_end_y_dist ** 2 ) 86 | 87 | cos_in = (mid_end_dist ** 2 + start_mid_dist ** 2 - 88 | start_end_dist ** 2) / (2 * mid_end_dist * start_mid_dist+1e-10) 89 | if cos_in > 1.0: 90 | cos_in = 1.0 91 | elif cos_in < -1.0: 92 | cos_in = -1.0 93 | angle_of_curve = math.acos(cos_in) 94 | angle_of_curve = (57.2958 * angle_of_curve) % 180 95 | 96 | # print('Angle of curve = {}'.format(angle_of_curve)) 97 | HALF_CURL_START_LIMIT = 60.0 98 | NO_CURL_START_LIMIT = 130.0 99 | 100 | finger_curled = None 101 | if angle_of_curve > NO_CURL_START_LIMIT: 102 | finger_curled = FingerCurled.NoCurl 103 | elif angle_of_curve > HALF_CURL_START_LIMIT: 104 | finger_curled = FingerCurled.HalfCurl 105 | else: 106 | finger_curled = FingerCurled.FullCurl 107 | 108 | return finger_curled 109 | 110 | def estimate_horizontal_direction( 111 | self, 112 | start_end_x_dist, 113 | start_mid_x_dist, 114 | mid_end_x_dist, 115 | max_dist_x): 116 | reqd_direction = None 117 | if max_dist_x == abs(start_end_x_dist): 118 | if start_end_x_dist > 0: 119 | reqd_direction = FingerPosition.HorizontalLeft 120 | else: 121 | reqd_direction = FingerPosition.HorizontalRight 122 | elif max_dist_x == abs(start_mid_x_dist): 123 | if start_mid_x_dist > 0: 124 | reqd_direction = FingerPosition.HorizontalLeft 125 | else: 126 | reqd_direction = FingerPosition.HorizontalRight 127 | else: 128 | if mid_end_x_dist > 0: 129 | reqd_direction = FingerPosition.HorizontalLeft 130 | else: 131 | reqd_direction = FingerPosition.HorizontalRight 132 | return reqd_direction 133 | 134 | def estimate_vertical_direction( 135 | self, 136 | start_end_y_dist, 137 | start_mid_y_dist, 138 | mid_end_y_dist, 139 | max_dist_y): 140 | reqd_direction = None 141 | if max_dist_y == abs(start_end_y_dist): 142 | if start_end_y_dist < 0: 143 | reqd_direction = FingerPosition.VerticalDown 144 | else: 145 | reqd_direction = FingerPosition.VerticalUp 146 | elif max_dist_y == abs(start_mid_y_dist): 147 | if start_mid_y_dist < 0: 148 | reqd_direction = FingerPosition.VerticalDown 149 | else: 150 | reqd_direction = FingerPosition.VerticalUp 151 | else: 152 | if mid_end_y_dist < 0: 153 | reqd_direction = FingerPosition.VerticalDown 154 | else: 155 | reqd_direction = FingerPosition.VerticalUp 156 | return reqd_direction 157 | 158 | def estimate_diagonal_direction( 159 | self, 160 | start_end_y_dist, 161 | start_mid_y_dist, 162 | mid_end_y_dist, 163 | max_dist_y, 164 | start_end_x_dist, 165 | start_mid_x_dist, 166 | mid_end_x_dist, 167 | max_dist_x): 168 | reqd_direction = None 169 | reqd_vertical_direction = self.estimate_vertical_direction( 170 | start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y) 171 | reqd_horizontal_direction = self.estimate_horizontal_direction( 172 | start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) 173 | 174 | # print('Direction obtained v = {}, h = {}'.format(FingerPosition.get_finger_position_name(reqd_vertical_direction), 175 | # FingerPosition.get_finger_position_name(reqd_horizontal_direction))) 176 | if reqd_vertical_direction == FingerPosition.VerticalUp: 177 | if reqd_horizontal_direction == FingerPosition.HorizontalLeft: 178 | reqd_direction = FingerPosition.DiagonalUpLeft 179 | else: 180 | reqd_direction = FingerPosition.DiagonalUpRight 181 | else: 182 | if reqd_horizontal_direction == FingerPosition.HorizontalLeft: 183 | reqd_direction = FingerPosition.DiagonalDownLeft 184 | else: 185 | reqd_direction = FingerPosition.DiagonalDownRight 186 | return reqd_direction 187 | 188 | def calculate_direction_of_finger( 189 | self, 190 | start_point, 191 | mid_point, 192 | end_point, 193 | finger_slopes): 194 | start_mid_x_dist = start_point[0] - mid_point[0] 195 | start_end_x_dist = start_point[0] - end_point[0] 196 | mid_end_x_dist = mid_point[0] - end_point[0] 197 | 198 | start_mid_y_dist = start_point[1] - mid_point[1] 199 | start_end_y_dist = start_point[1] - end_point[1] 200 | mid_end_y_dist = mid_point[1] - end_point[1] 201 | 202 | max_dist_x = max( 203 | abs(start_mid_x_dist), 204 | abs(start_end_x_dist), 205 | abs(mid_end_x_dist)) 206 | max_dist_y = max( 207 | abs(start_mid_y_dist), 208 | abs(start_end_y_dist), 209 | abs(mid_end_y_dist)) 210 | 211 | DISTANCE_VOTE_POWER = 1.1 212 | SINGLE_ANGLE_VOTE_POWER = 0.9 213 | TOTAL_ANGLE_VOTE_POWER = 1.6 214 | 215 | vote_vertical, vote_diagonal, vote_horizontal = 0.0, 0.0, 0.0 216 | start_end_x_y_dist_ratio = max_dist_y / (max_dist_x + 0.00001) 217 | if start_end_x_y_dist_ratio > 1.5: 218 | vote_vertical += DISTANCE_VOTE_POWER 219 | elif start_end_x_y_dist_ratio > 0.66: 220 | vote_diagonal += DISTANCE_VOTE_POWER 221 | else: 222 | vote_horizontal += DISTANCE_VOTE_POWER 223 | # print('Iteration 1: Ratio = {:.2f}, ({}, {}, {})'.format(start_end_x_y_dist_ratio, vote_vertical, 224 | # vote_diagonal, vote_horizontal)) 225 | 226 | start_mid_dist = math.sqrt( 227 | start_mid_x_dist ** 2 + 228 | start_mid_y_dist ** 2) 229 | start_end_dist = math.sqrt( 230 | start_end_x_dist ** 2 + 231 | start_end_y_dist ** 2) 232 | mid_end_dist = math.sqrt(mid_end_x_dist ** 2 + mid_end_y_dist ** 2) 233 | 234 | max_dist = max(start_mid_dist, start_end_dist, mid_end_dist) 235 | calc_start_point_x, calc_start_point_y = start_point[0], start_point[1] 236 | calc_end_point_x, calc_end_point_y = end_point[0], end_point[1] 237 | if max_dist == start_mid_dist: 238 | calc_end_point_x, calc_end_point_y = end_point[0], end_point[1] 239 | elif max_dist == mid_end_dist: 240 | calc_start_point_x, calc_start_point_y = mid_point[0], mid_point[1] 241 | 242 | calc_start_point = (calc_start_point_x, calc_start_point_y) 243 | calc_end_point = (calc_end_point_x, calc_end_point_y) 244 | 245 | total_angle = self.get_slope(calc_start_point, calc_end_point) 246 | vote1, vote2, vote3 = self.angle_orientation_at( 247 | total_angle, weightage_at=TOTAL_ANGLE_VOTE_POWER) 248 | vote_vertical += vote1 249 | vote_diagonal += vote2 250 | vote_horizontal += vote3 251 | # print('Iteration 2: Total Angle = {:.3f}, ({}, {}, {})'.format(total_angle, vote1, vote2, vote3)) 252 | 253 | for finger_slope in finger_slopes: 254 | vote1, vote2, vote3 = self.angle_orientation_at( 255 | finger_slope, weightage_at=SINGLE_ANGLE_VOTE_POWER) 256 | vote_vertical += vote1 257 | vote_diagonal += vote2 258 | vote_horizontal += vote3 259 | # print('Iteration 3: Total Angle = {:.3f}, ({}, {}, {})'.format(finger_slope, vote1, vote2, vote3)) 260 | # print('Total weights: ({}, {}, {})'.format(vote_vertical, vote_diagonal, vote_horizontal)) 261 | 262 | # Incase of tie, highest preference goes to Vertical, followed by 263 | # horizontal and then diagonal 264 | reqd_direction = None 265 | if vote_vertical == max(vote_vertical, vote_diagonal, vote_horizontal): 266 | reqd_direction = self.estimate_vertical_direction( 267 | start_end_y_dist, start_mid_y_dist, mid_end_y_dist, max_dist_y) 268 | elif vote_horizontal == max(vote_diagonal, vote_horizontal): 269 | reqd_direction = self.estimate_horizontal_direction( 270 | start_end_x_dist, start_mid_x_dist, mid_end_x_dist, max_dist_x) 271 | else: 272 | reqd_direction = self.estimate_diagonal_direction( 273 | start_end_y_dist, 274 | start_mid_y_dist, 275 | mid_end_y_dist, 276 | max_dist_y, 277 | start_end_x_dist, 278 | start_mid_x_dist, 279 | mid_end_x_dist, 280 | max_dist_x) 281 | # print('Vote at {}, {}, {}'.format(vote_vertical, vote_diagonal, vote_horizontal)) 282 | return reqd_direction 283 | 284 | def calculate_orientation_of_fingers(self, print_finger_info): 285 | for finger in Finger: 286 | point_index_at = 0 287 | if finger == Finger.Thumb: 288 | point_index_at = 1 289 | # print(finger,self.slopes_xy[finger]) 290 | angle_at = self.slopes_xy[finger][point_index_at] 291 | 292 | finger_points_at = Finger.get_array_of_points(finger) 293 | start_point_at = self.coords_xy[finger_points_at[point_index_at][0]] 294 | mid_point_at = self.coords_xy[finger_points_at[point_index_at + 1][1]] 295 | end_point_at = self.coords_xy[finger_points_at[3][1]] 296 | 297 | finger_curled = self.is_finger_curled( 298 | start_point_at, mid_point_at, end_point_at) 299 | # print('Finger: {} = {}'.format(Finger.get_finger_name(finger), FingerCurled.get_finger_curled_name(finger_curled))) 300 | finger_position = self.calculate_direction_of_finger( 301 | start_point_at, mid_point_at, end_point_at, self.slopes_xy[finger][point_index_at:]) 302 | # print('Finger: {} = {}'.format(Finger.get_finger_name(finger), 303 | # FingerPosition.get_finger_position_name(finger_position))) 304 | 305 | self.finger_curled[finger] = finger_curled 306 | self.finger_position[finger] = finger_position 307 | 308 | if print_finger_info: 309 | for finger_index, curl, pos in zip( 310 | Finger, self.finger_curled, self.finger_position): 311 | print( 312 | 'Finger: {}, Curl: {}, Orientation: {}'.format( 313 | Finger.get_finger_name(finger_index), 314 | FingerCurled.get_finger_curled_name(curl), 315 | FingerPosition.get_finger_position_name(pos))) 316 | 317 | def calculate_positions_of_fingers(self, print_finger_info): 318 | """ 319 | 根据手指21点坐标计算各个手指姿势 320 | :param print_finger_info: 321 | :return: 322 | """ 323 | self.calculate_slope_of_fingers() # 计算手指角度 324 | self.calculate_orientation_of_fingers(print_finger_info) # 计算手指指向 325 | 326 | # Private methods 327 | def _calculate_slope_procedure( 328 | self, 329 | point1_x, 330 | point1_y, 331 | point2_x, 332 | point2_y): 333 | """ 334 | 利用反正切计算角度 335 | :param point1_x: 336 | :param point1_y: 337 | :param point2_x: 338 | :param point2_y: 339 | :return: 340 | """ 341 | value = (point1_y - point2_y) / (point1_x - point2_x) 342 | slope = math.degrees(math.atan(value)) 343 | if slope < 0 or slope == -0.0: 344 | slope = -slope 345 | elif slope > 0: 346 | slope = 180 - slope 347 | return slope 348 | -------------------------------------------------------------------------------- /gasture_utils/FingerPosition.py: -------------------------------------------------------------------------------- 1 | from enum import IntEnum 2 | 3 | 4 | class FingerPosition(IntEnum): 5 | """ 6 | 定义手指姿势 7 | """ 8 | VerticalUp = 0 # 垂直向上 9 | VerticalDown = 1 # 垂直向下 10 | HorizontalLeft = 2 # 水平向左 11 | HorizontalRight = 3 # 水平向右 12 | DiagonalUpRight = 4 # 右上 13 | DiagonalUpLeft = 5 # 左上 14 | DiagonalDownRight = 6 # 右下 15 | DiagonalDownLeft = 7 # 左下 16 | 17 | @staticmethod 18 | def get_finger_position_name(finger_position): 19 | if finger_position == FingerPosition.VerticalUp: 20 | finger_type = 'Vertical Up' 21 | elif finger_position == FingerPosition.VerticalDown: 22 | finger_type = 'Vertical Down' 23 | elif finger_position == FingerPosition.HorizontalLeft: 24 | finger_type = 'Horizontal Left' 25 | elif finger_position == FingerPosition.HorizontalRight: 26 | finger_type = 'Horizontal Right' 27 | elif finger_position == FingerPosition.DiagonalUpRight: 28 | finger_type = 'Diagonal Up Right' 29 | elif finger_position == FingerPosition.DiagonalUpLeft: 30 | finger_type = 'Diagonal Up Left' 31 | elif finger_position == FingerPosition.DiagonalDownRight: 32 | finger_type = 'Diagonal Down Right' 33 | elif finger_position == FingerPosition.DiagonalDownLeft: 34 | finger_type = 'Diagonal Down Left' 35 | return finger_type 36 | -------------------------------------------------------------------------------- /gasture_utils/__pycache__/Finger.cpython-36.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ndkjing/hand-keypoint-detect/143a2d17557ab21bed1b6e2192b3645e882a3296/gasture_utils/__pycache__/Finger.cpython-36.pyc -------------------------------------------------------------------------------- /gasture_utils/__pycache__/FingerCurled.cpython-36.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ndkjing/hand-keypoint-detect/143a2d17557ab21bed1b6e2192b3645e882a3296/gasture_utils/__pycache__/FingerCurled.cpython-36.pyc -------------------------------------------------------------------------------- /gasture_utils/__pycache__/FingerDataFormation.cpython-36.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ndkjing/hand-keypoint-detect/143a2d17557ab21bed1b6e2192b3645e882a3296/gasture_utils/__pycache__/FingerDataFormation.cpython-36.pyc -------------------------------------------------------------------------------- /gasture_utils/__pycache__/FingerPoseEstimate.cpython-36.pyc: -------------------------------------------------------------------------------- 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-------------------------------------------------------------------------------- /gasture_utils/determine_gasture.py: -------------------------------------------------------------------------------- 1 | from gasture_utils.FingerCurled import FingerCurled 2 | from gasture_utils.FingerPosition import FingerPosition 3 | from gasture_utils.FingerDataFormation import FingerDataFormation 4 | 5 | 6 | def determine_position(curled_positions, finger_positions, known_finger_poses, min_threshold): 7 | obtained_positions = {} 8 | 9 | for finger_pose in known_finger_poses: 10 | score_at = 0.0 11 | for known_curl, known_curl_confidence, given_curl in \ 12 | zip(finger_pose.curl_position, finger_pose.curl_position_confidence, curled_positions): 13 | if len(known_curl) == 0: 14 | if len(known_curl_confidence) == 1: 15 | score_at += known_curl_confidence[0] 16 | continue 17 | 18 | if given_curl in known_curl: 19 | confidence_at = known_curl.index(given_curl) 20 | score_at += known_curl_confidence[confidence_at] 21 | 22 | for known_position, known_position_confidence, given_position in \ 23 | zip(finger_pose.finger_position, finger_pose.finger_position_confidence, finger_positions): 24 | if len(known_position) == 0: 25 | if len(known_position_confidence) == 1: 26 | score_at += known_position_confidence[0] 27 | continue 28 | 29 | if given_position in known_position: 30 | confidence_at = known_position.index(given_position) 31 | score_at += known_position_confidence[confidence_at] 32 | 33 | if score_at >= min_threshold: 34 | obtained_positions[finger_pose.position_name] = score_at 35 | 36 | return obtained_positions 37 | 38 | 39 | def get_position_name_with_pose_id(pose_id, finger_poses): 40 | for finger_pose in finger_poses: 41 | if finger_pose.position_id == pose_id: 42 | return finger_pose.position_name 43 | return None 44 | 45 | 46 | def create_known_finger_poses(): 47 | """ 48 | 建立已知的手势的姿态信息 49 | :return: 50 | """ 51 | known_finger_poses = [] 52 | 53 | ####### 1 Simple Thumbs up 大拇指向上 54 | simple_thumbs_up = FingerDataFormation() 55 | simple_thumbs_up.position_name = 'Simple Thumbs Up' 56 | simple_thumbs_up.curl_position = [ 57 | [FingerCurled.NoCurl], # Thumb 58 | [FingerCurled.FullCurl], # Index 59 | [FingerCurled.FullCurl], # Middle 60 | [FingerCurled.FullCurl], # Ring 61 | [FingerCurled.FullCurl] # Little 62 | ] 63 | simple_thumbs_up.curl_position_confidence = [ 64 | [1.0], # Thumb 65 | [1.0], # Index 66 | [1.0], # Middle 67 | [1.0], # Ring 68 | [1.0] # Little 69 | ] 70 | simple_thumbs_up.finger_position = [ 71 | [FingerPosition.VerticalUp, FingerPosition.DiagonalUpLeft, FingerPosition.DiagonalUpRight], # Thumb 72 | [FingerPosition.HorizontalLeft, FingerPosition.HorizontalRight], # Index 73 | [FingerPosition.HorizontalLeft, FingerPosition.HorizontalRight], # Middle 74 | [FingerPosition.HorizontalLeft, FingerPosition.HorizontalRight], # Ring 75 | [FingerPosition.HorizontalLeft, FingerPosition.HorizontalRight] # Little 76 | ] 77 | simple_thumbs_up.finger_position_confidence = [ 78 | [1.0, 0.25, 0.25], # Thumb 79 | [1.0, 1.0], # Index 80 | [1.0, 1.0], # Middle 81 | [1.0, 1.0], # Ring 82 | [1.0, 1.0] # Little 83 | ] 84 | simple_thumbs_up.position_id = 0 85 | known_finger_poses.append(simple_thumbs_up) 86 | 87 | ####### 2 Thumbs up right 88 | thumbs_up_right = FingerDataFormation() 89 | thumbs_up_right.position_name = 'Thumbs Up Right' 90 | thumbs_up_right.curl_position = [ 91 | [FingerCurled.NoCurl], # Thumb 92 | [FingerCurled.FullCurl], # Index 93 | [FingerCurled.FullCurl], # Middle 94 | [FingerCurled.FullCurl], # Ring 95 | [FingerCurled.FullCurl] # Little 96 | ] 97 | thumbs_up_right.curl_position_confidence = [ 98 | [1.0], # Thumb 99 | [1.0], # Index 100 | [1.0], # Middle 101 | [1.0], # Ring 102 | [1.0] # Little 103 | ] 104 | thumbs_up_right.finger_position = [ 105 | [FingerPosition.HorizontalLeft, FingerPosition.DiagonalUpLeft, FingerPosition.DiagonalDownLeft], # Thumb 106 | [FingerPosition.VerticalUp, FingerPosition.DiagonalUpRight, FingerPosition.DiagonalUpLeft], # Index 107 | [FingerPosition.VerticalUp, FingerPosition.DiagonalUpRight, FingerPosition.DiagonalUpLeft], # Middle 108 | [FingerPosition.VerticalUp, FingerPosition.DiagonalUpRight, FingerPosition.DiagonalUpLeft], # Ring 109 | [FingerPosition.VerticalUp, FingerPosition.DiagonalUpRight, FingerPosition.DiagonalUpLeft] # Little 110 | ] 111 | thumbs_up_right.finger_position_confidence = [ 112 | [1.0, 0.25, 0.25], # Thumb 113 | [1.0, 0.25, 0.25], # Index 114 | [1.0, 0.25, 0.25], # Middle 115 | [1.0, 0.25, 0.25], # Ring 116 | [1.0, 0.25, 0.25] # Little 117 | ] 118 | thumbs_up_right.position_id = 1 119 | known_finger_poses.append(thumbs_up_right) 120 | 121 | ####### 3 Spock 122 | spock = FingerDataFormation() 123 | spock.position_name = 'Spock' 124 | spock.curl_position = [ 125 | [FingerCurled.NoCurl], # Thumb 126 | [FingerCurled.NoCurl], # Index 127 | [FingerCurled.NoCurl], # Middle 128 | [FingerCurled.NoCurl], # Ring 129 | [FingerCurled.NoCurl] # Little 130 | ] 131 | spock.curl_position_confidence = [ 132 | [1.0], # Thumb 133 | [1.0], # Index 134 | [1.0], # Middle 135 | [1.0], # Ring 136 | [1.0] # Little 137 | ] 138 | spock.finger_position = [ 139 | [FingerPosition.DiagonalUpLeft, FingerPosition.HorizontalLeft], # Thumb 140 | [FingerPosition.DiagonalUpLeft], # Index 141 | [FingerPosition.DiagonalUpLeft], # Middle 142 | [FingerPosition.DiagonalUpRight], # Ring 143 | [FingerPosition.DiagonalUpRight] # Little 144 | ] 145 | spock.finger_position_confidence = [ 146 | [1.0, 0.5], # Thumb 147 | [1.0], # Index 148 | [1.0], # Middle 149 | [1.0], # Ring 150 | [1.0] # Little 151 | ] 152 | spock.position_id = 2 153 | known_finger_poses.append(spock) 154 | 155 | ####### 4 I Love you 156 | i_love_you = FingerDataFormation() 157 | i_love_you.position_name = 'I love you' 158 | i_love_you.curl_position = [ 159 | [FingerCurled.NoCurl], # Thumb 160 | [FingerCurled.NoCurl], # Index 161 | [FingerCurled.FullCurl], # Middle 162 | [FingerCurled.FullCurl], # Ring 163 | [FingerCurled.NoCurl] # Little 164 | ] 165 | i_love_you.curl_position_confidence = [ 166 | [1.0], # Thumb 167 | [1.0], # Index 168 | [1.0], # Middle 169 | [1.0], # Ring 170 | [1.0] # Little 171 | ] 172 | i_love_you.finger_position = [ 173 | [FingerPosition.DiagonalUpLeft], # Thumb 174 | [FingerPosition.DiagonalUpLeft, FingerPosition.VerticalUp], # Index 175 | [FingerPosition.DiagonalUpRight], # Middle 176 | [FingerPosition.DiagonalUpRight], # Ring 177 | [FingerPosition.DiagonalUpRight, FingerPosition.VerticalUp] # Little 178 | ] 179 | i_love_you.finger_position_confidence = [ 180 | [1.0], # Thumb 181 | [1.0, 0.25], # Index 182 | [1.0], # Middle 183 | [1.0], # Ring 184 | [1.0, 0.25] # Little 185 | ] 186 | i_love_you.position_id = 3 187 | known_finger_poses.append(i_love_you) 188 | 189 | ####### 5 Pointer 大拇指和食指向上 190 | pointer = FingerDataFormation() 191 | pointer.position_name = 'Pointing Up' 192 | pointer.curl_position = [ 193 | [FingerCurled.NoCurl], # Thumb 194 | [FingerCurled.NoCurl], # Index 195 | [FingerCurled.FullCurl], # Middle 196 | [FingerCurled.FullCurl], # Ring 197 | [FingerCurled.FullCurl] # Little 198 | ] 199 | pointer.curl_position_confidence = [ 200 | [1.0], # Thumb 201 | [1.0], # Index 202 | [1.0], # Middle 203 | [1.0], # Ring 204 | [1.0] # Little 205 | ] 206 | pointer.finger_position = [ 207 | [FingerPosition.VerticalUp, FingerPosition.DiagonalUpLeft], # Thumb 208 | [FingerPosition.VerticalUp, FingerPosition.DiagonalUpLeft], # Index 209 | [FingerPosition.VerticalUp], # Middle 210 | [FingerPosition.VerticalUp], # Ring 211 | [FingerPosition.VerticalUp] # Little 212 | ] 213 | pointer.finger_position_confidence = [ 214 | [1.0, 0.5], # Thumb 215 | [1.0, 0.3], # Index 216 | [1.0], # Middle 217 | [1.0], # Ring 218 | [1.0] # Little 219 | ] 220 | pointer.position_id = 4 221 | known_finger_poses.append(pointer) 222 | 223 | ####### 6 Ok 224 | ok_pos = FingerDataFormation() 225 | ok_pos.position_name = 'Okay' 226 | ok_pos.curl_position = [ 227 | [FingerCurled.HalfCurl], # Thumb 228 | [FingerCurled.HalfCurl], # Index 229 | [FingerCurled.NoCurl], # Middle 230 | [FingerCurled.NoCurl], # Ring 231 | [FingerCurled.NoCurl] # Little 232 | ] 233 | ok_pos.curl_position_confidence = [ 234 | [1.0], # Thumb 235 | [1.0], # Index 236 | [1.0], # Middle 237 | [1.0], # Ring 238 | [1.0] # Little 239 | ] 240 | ok_pos.finger_position = [ 241 | [FingerPosition.DiagonalUpLeft, FingerPosition.HorizontalLeft], # Thumb 242 | [FingerPosition.DiagonalUpLeft, FingerPosition.HorizontalLeft], # Index 243 | [FingerPosition.VerticalUp], # Middle 244 | [FingerPosition.VerticalUp], # Ring 245 | [FingerPosition.VerticalUp] # Little 246 | ] 247 | ok_pos.finger_position_confidence = [ 248 | [1.0, 0.5], # Thumb 249 | [1.0, 0.2], # Index 250 | [1.0], # Middle 251 | [1.0], # Ring 252 | [1.0] # Little 253 | ] 254 | ok_pos.position_id = 5 255 | known_finger_poses.append(ok_pos) 256 | 257 | ####### 7 Victory V 手势 258 | victory = FingerDataFormation() 259 | victory.position_name = 'Victory' 260 | victory.curl_position = [ 261 | [FingerCurled.FullCurl, FingerCurled.NoCurl], # Thumb 262 | [FingerCurled.NoCurl], # Index 263 | [FingerCurled.NoCurl], # Middle 264 | [FingerCurled.FullCurl], # Ring 265 | [FingerCurled.FullCurl] # Little 266 | ] 267 | victory.curl_position_confidence = [ 268 | [1.0, 1.0], # Thumb 269 | [1.0], # Index 270 | [1.0], # Middle 271 | [1.0], # Ring 272 | [1.0] # Little 273 | ] 274 | victory.finger_position = [ 275 | [FingerPosition.VerticalUp, FingerPosition.HorizontalLeft], # Thumb 276 | [FingerPosition.DiagonalUpLeft, FingerPosition.VerticalUp], # Index 277 | [FingerPosition.DiagonalUpRight], # Middle 278 | [FingerPosition.DiagonalUpRight], # Ring 279 | [FingerPosition.DiagonalUpRight] # Little 280 | ] 281 | victory.finger_position_confidence = [ 282 | [0.5, 1.0], # Thumb 283 | [1.0, 0.4], # Index 284 | [1.0], # Middle 285 | [1.0], # Ring 286 | [1.0] # Little 287 | ] 288 | victory.position_id = 6 289 | known_finger_poses.append(victory) 290 | 291 | 292 | ####### 8 1 食指向上 293 | one = FingerDataFormation() 294 | one.position_name = 'One' 295 | one.curl_position = [ 296 | [FingerCurled.FullCurl], # Thumb 297 | [FingerCurled.NoCurl], # Index 298 | [FingerCurled.FullCurl], # Middle 299 | [FingerCurled.FullCurl], # Ring 300 | [FingerCurled.FullCurl] # Little 301 | ] 302 | one.curl_position_confidence = [ 303 | [1.0], # Thumb 304 | [1.0], # Index 305 | [1.0], # Middle 306 | [1.0], # Ring 307 | [1.0] # Little 308 | ] 309 | one.finger_position = [ 310 | [FingerPosition.VerticalUp, FingerPosition.DiagonalUpLeft], # Thumb 311 | [FingerPosition.VerticalUp, FingerPosition.DiagonalUpLeft], # Index 312 | [FingerPosition.VerticalUp], # Middle 313 | [FingerPosition.VerticalUp], # Ring 314 | [FingerPosition.VerticalUp] # Little 315 | ] 316 | one.finger_position_confidence = [ 317 | [1.0, 0.5], # Thumb 318 | [1.0, 0.3], # Index 319 | [1.0], # Middle 320 | [1.0], # Ring 321 | [1.0] # Little 322 | ] 323 | one.position_id = 7 324 | known_finger_poses.append(one) 325 | 326 | ####### 9 3 3 手势 327 | three = FingerDataFormation() 328 | three.position_name = 'Three' 329 | three.curl_position = [ 330 | [FingerCurled.FullCurl, FingerCurled.NoCurl], # Thumb 331 | [FingerCurled.NoCurl], # Index 332 | [FingerCurled.NoCurl], # Middle 333 | [FingerCurled.NoCurl], # Ring 334 | [FingerCurled.FullCurl] # Little 335 | ] 336 | three.curl_position_confidence = [ 337 | [1.0, 1.0], # Thumb 338 | [1.0], # Index 339 | [1.0], # Middle 340 | [1.0], # Ring 341 | [1.0] # Little 342 | ] 343 | three.finger_position = [ 344 | [FingerPosition.VerticalUp, FingerPosition.HorizontalLeft], # Thumb 345 | [FingerPosition.DiagonalUpLeft, FingerPosition.VerticalUp], # Index 346 | [FingerPosition.DiagonalUpLeft, FingerPosition.VerticalUp], # Middle 347 | [FingerPosition.DiagonalUpLeft, FingerPosition.VerticalUp], # Ring 348 | [FingerPosition.DiagonalUpRight] # Little 349 | ] 350 | three.finger_position_confidence = [ 351 | [0.5, 1.0], # Thumb 352 | [1.0, 0.4], # Index 353 | [1.0,1.0], # Middle 354 | [1.0,1.0], # Ring 355 | [1.0] # Little 356 | ] 357 | three.position_id = 8 358 | known_finger_poses.append(three) 359 | 360 | return known_finger_poses -------------------------------------------------------------------------------- /hand_tracker.py: -------------------------------------------------------------------------------- 1 | import csv 2 | import time 3 | import cv2 4 | import numpy as np 5 | try: 6 | import tflite_runtime.interpreter as tflite 7 | except: 8 | import tensorflow as tf 9 | import tensorflow.lite as tflite 10 | 11 | print(tf.__version__) 12 | print(cv2.__version__) 13 | print(csv.__version__) 14 | print(np.__version__) 15 | class HandTracker(): 16 | r""" 17 | Class to use Google's Mediapipe HandTracking pipeline from Python. 18 | So far only detection of a single hand is supported. 19 | Any any image size and aspect ratio supported. 20 | 21 | Args: 22 | palm_model: path to the palm_detection.tflite 23 | joint_model: path to the hand_landmark.tflite 24 | anchors_path: path to the csv containing SSD anchors 25 | Ourput: 26 | (21,2) array of hand joints. 27 | Examples:: 28 | >>> det = HandTracker(path1, path2, path3) 29 | >>> input_img = np.random.randint(0,255, 256*256*3).reshape(256,256,3) 30 | >>> keypoints, bbox = det(input_img) 31 | """ 32 | 33 | def __init__(self, palm_model, joint_model, anchors_path, 34 | box_enlarge=1.5, box_shift=0.2): 35 | self.box_shift = box_shift 36 | self.box_enlarge = box_enlarge 37 | 38 | self.interp_palm = tflite.Interpreter(palm_model) 39 | self.interp_palm.allocate_tensors() 40 | self.interp_joint = tflite.Interpreter(joint_model) 41 | self.interp_joint.allocate_tensors() 42 | 43 | # reading the SSD anchors 44 | with open(anchors_path, "r") as csv_f: 45 | self.anchors = np.r_[ 46 | [x for x in csv.reader(csv_f, quoting=csv.QUOTE_NONNUMERIC)] 47 | ] 48 | # reading tflite model paramteres 49 | output_details = self.interp_palm.get_output_details() 50 | input_details = self.interp_palm.get_input_details() 51 | 52 | self.in_idx = input_details[0]['index'] 53 | self.out_reg_idx = output_details[0]['index'] 54 | self.out_clf_idx = output_details[1]['index'] 55 | 56 | self.in_idx_joint = self.interp_joint.get_input_details()[0]['index'] 57 | self.out_idx_joint = self.interp_joint.get_output_details()[0]['index'] 58 | 59 | # 90° rotation matrix used to create the alignment trianlge 60 | self.R90 = np.r_[[[0,1],[-1,0]]] 61 | 62 | # trianlge target coordinates used to move the detected hand 63 | # into the right position 64 | self._target_triangle = np.float32([ 65 | [128, 128], 66 | [128, 0], 67 | [ 0, 128] 68 | ]) 69 | self._target_box = np.float32([ 70 | [ 0, 0, 1], 71 | [256, 0, 1], 72 | [256, 256, 1], 73 | [ 0, 256, 1], 74 | ]) 75 | 76 | def _get_triangle(self, kp0, kp2, dist=1): 77 | """get a triangle used to calculate Affine transformation matrix""" 78 | 79 | dir_v = kp2 - kp0 80 | dir_v /= np.linalg.norm(dir_v) 81 | 82 | dir_v_r = dir_v @ self.R90.T 83 | return np.float32([kp2, kp2+dir_v*dist, kp2 + dir_v_r*dist]) 84 | 85 | @staticmethod 86 | def _triangle_to_bbox(source): 87 | # plain old vector arithmetics 88 | bbox = np.c_[ 89 | [source[2] - source[0] + source[1]], 90 | [source[1] + source[0] - source[2]], 91 | [3 * source[0] - source[1] - source[2]], 92 | [source[2] - source[1] + source[0]], 93 | ].reshape(-1,2) 94 | return bbox 95 | 96 | @staticmethod 97 | def _im_normalize(img): 98 | return np.ascontiguousarray( 99 | 2 * ((img / 255) - 0.5 100 | ).astype('float32')) 101 | 102 | @staticmethod 103 | def _sigm(x): 104 | return 1 / (1 + np.exp(-x) ) 105 | 106 | @staticmethod 107 | def _pad1(x): 108 | return np.pad(x, ((0,0),(0,1)), constant_values=1, mode='constant') 109 | 110 | 111 | def predict_joints(self, img_norm): 112 | self.interp_joint.set_tensor( 113 | self.in_idx_joint, img_norm.reshape(1,256,256,3)) 114 | self.interp_joint.invoke() 115 | 116 | joints = self.interp_joint.get_tensor(self.out_idx_joint) 117 | return joints.reshape(-1,2) 118 | 119 | def detect_hand(self, img_norm): 120 | assert -1 <= img_norm.min() and img_norm.max() <= 1,\ 121 | "img_norm should be in range [-1, 1]" 122 | assert img_norm.shape == (256, 256, 3),\ 123 | "img_norm shape must be (256, 256, 3)" 124 | 125 | # predict hand location and 7 initial landmarks 126 | self.interp_palm.set_tensor(self.in_idx, img_norm[None]) 127 | self.interp_palm.invoke() 128 | 129 | out_reg = self.interp_palm.get_tensor(self.out_reg_idx)[0] 130 | out_clf = self.interp_palm.get_tensor(self.out_clf_idx)[0,:,0] 131 | 132 | # finding the best prediction 133 | # TODO: replace it with non-max suppression 134 | detecion_mask = self._sigm(out_clf) > 0.7 135 | candidate_detect = out_reg[detecion_mask] 136 | candidate_anchors = self.anchors[detecion_mask] 137 | 138 | if candidate_detect.shape[0] == 0: 139 | print("No hands found") 140 | return None, None 141 | # picking the widest suggestion while NMS is not implemented 142 | max_idx = np.argmax(candidate_detect[:, 3]) 143 | 144 | # bounding box offsets, width and height 145 | dx,dy,w,h = candidate_detect[max_idx, :4] 146 | center_wo_offst = candidate_anchors[max_idx,:2] * 256 147 | 148 | # 7 initial keypoints 149 | keypoints = center_wo_offst + candidate_detect[max_idx,4:].reshape(-1,2) 150 | side = max(w,h) * self.box_enlarge 151 | 152 | # now we need to move and rotate the detected hand for it to occupy a 153 | # 256x256 square 154 | # line from wrist keypoint to middle finger keypoint 155 | # should point straight up 156 | # TODO: replace triangle with the bbox directly 157 | source = self._get_triangle(keypoints[0], keypoints[2], side) 158 | source -= (keypoints[0] - keypoints[2]) * self.box_shift 159 | return source, keypoints 160 | 161 | def preprocess_img(self, img): 162 | # fit the image into a 256x256 square 163 | shape = np.r_[img.shape] 164 | pad = (shape.max() - shape[:2]).astype('uint32') // 2 165 | img_pad = np.pad( 166 | img, 167 | ((pad[0],pad[0]), (pad[1],pad[1]), (0,0)), 168 | mode='constant') 169 | img_small = cv2.resize(img_pad, (256, 256)) 170 | img_small = np.ascontiguousarray(img_small) 171 | 172 | img_norm = self._im_normalize(img_small) 173 | return img_pad, img_norm, pad 174 | 175 | 176 | def __call__(self, img,return_track,detect_hand=True,detect_keypoints=True): 177 | img_pad, img_norm, pad = self.preprocess_img(img) 178 | 179 | if detect_hand: 180 | return_track = {} 181 | hand_detect_start_time = time.time() 182 | source, keypoints = self.detect_hand(img_norm) 183 | # print('hand detect time spend',time.time()-hand_detect_start_time) 184 | return_track['source'] = source 185 | return_track['keypoints'] = keypoints 186 | else: 187 | keypoints = return_track['keypoints'] 188 | source = return_track['source'] 189 | 190 | if source is None: # 未检测到 191 | return None, None,None 192 | 193 | # calculating transformation from img_pad coords 194 | # to img_landmark coords (cropped hand image) 195 | scale = max(img.shape) / 256 196 | Mtr = cv2.getAffineTransform( 197 | source * scale, 198 | self._target_triangle 199 | ) 200 | 201 | img_landmark = cv2.warpAffine( 202 | self._im_normalize(img_pad), Mtr, (256,256) 203 | ) 204 | 205 | joints = self.predict_joints(img_landmark) 206 | 207 | # adding the [0,0,1] row to make the matrix square 208 | Mtr = self._pad1(Mtr.T).T 209 | Mtr[2,:2] = 0 210 | 211 | Minv = np.linalg.inv(Mtr) 212 | 213 | # projecting keypoints back into original image coordinate space 214 | kp_orig = (self._pad1(joints) @ Minv.T)[:,:2] 215 | box_orig = (self._target_box @ Minv.T)[:,:2] 216 | kp_orig -= pad[::-1] 217 | box_orig -= pad[::-1] 218 | 219 | return kp_orig, box_orig,return_track -------------------------------------------------------------------------------- /images/show.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ndkjing/hand-keypoint-detect/143a2d17557ab21bed1b6e2192b3645e882a3296/images/show.png -------------------------------------------------------------------------------- /models/mediapipe_hand_landmark.tflite: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ndkjing/hand-keypoint-detect/143a2d17557ab21bed1b6e2192b3645e882a3296/models/mediapipe_hand_landmark.tflite -------------------------------------------------------------------------------- /models/palm_detection_without_custom_op.tflite: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ndkjing/hand-keypoint-detect/143a2d17557ab21bed1b6e2192b3645e882a3296/models/palm_detection_without_custom_op.tflite -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | opencv-python 2 | tensorflow 3 | numpy --------------------------------------------------------------------------------