shoulder - 腾讯云开发者社区

>>> e=copy.copy(a) >>> a.boyNextDoor() >>> a.banana() ['oh', 'my shoulder', 'my shoulder'] >>> e.banana...() ['oh', 'my shoulder', 'my shoulder'] >>> e.boyNextDoor() >>> e.banana() ['oh', 'my shoulder', 'my...shoulder', 'my shoulder'] >>> a.banana() ['oh', 'my shoulder', 'my shoulder', 'my shoulder'] >>> f=...copy.deepcopy(a) >>> a.boyNextDoor() >>> a.banana() ['oh', 'my shoulder', 'my shoulder', 'my shoulder...', 'my shoulder'] >>> f.banana() ['oh', 'my shoulder', 'my shoulder', 'my shoulder'] 所以,对于一个类的话,copy只是生成一个引用

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十三、自定义一个运动分析器，实现计时计数02

rules: [{ name: '左手臂垂直', calc: 'vertical', upperKey: 'left_shoulder...: 20 }, { name: '右手臂垂直', calc: 'vertical', upperKey: 'right_shoulder...rules: [{ name: '左手臂与腰齐垂直', calc: 'match-angle', angleKey: 'left_shoulder...}, { name: '右手臂与腰齐垂直', calc: 'match-angle', angleKey: 'right_shoulder...', calc: 'match-angle', angleKey: 'right_elbow', secondKey: 'right_shoulder

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如何用 Python 和循环神经网络预测严重交通拥堵？

{1: 'road closed due to construction', 2: 'traffic jam', 3: 'stopped car on the shoulder', 4: 'road...', 22: 'animal on the shoulder', 23: 'animal struck', 24: 'large traffic jam', 25: 'hazard on the...shoulder', 26: 'hazard on road', 27: 'ice on roadway', 28: 'weather hazard', 29: 'flooding', 30...accident', 'stopped car on the shoulder', 'heavy traffic', 'stopped car on the shoulder', 'heavy...'stopped car on the shoulder'] 读一遍，你就会发现，在超级拥堵发生之前，确实还是有一些先兆的。

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利用COCO数据集对人体上半身进行检测

= 5*3 right_shoulder = 6*3 left_hip = 11*3 right_hip = 12*3...if num_keypoints < 4: continue flag1 = keypoints[left_shoulder+2] flag2...= keypoints[right_shoulder+2] flag3 = keypoints[left_hip+2] flag4 = keypoints[right_hip...] y1 = keypoints[left_shoulder+1] x2 = keypoints[right_shoulder] y2 = keypoints...[right_shoulder+1] x3 = keypoints[left_hip] y3 = keypoints[left_hip+1] x4 = keypoints

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机械版CG 实验3 变换参考实例

#include #include static int shoulder = 0, elbow = 0;//shoulder：肩部角度，elbow... glTranslatef(-1.0f, 0.0f, 0.0f);//用平移矩阵乘当前矩阵，格式为：glTranslatef(x,y,z) glRotatef((GLfloat) shoulder...y) { switch (key) { case 'a'://处理四个按键，改变旋转角度，转动手臂 shoulder...= (shoulder + 5) % 360; glutPostRedisplay();//重画 break; ...case 'd': shoulder = (shoulder - 5) % 360; glutPostRedisplay

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八、利用body-calc进行姿态识别

',calc: '$or',rules: [{name: '左手180度伸直',calc: 'match-angle',angleKey: 'left_elbow',secondKey: 'left_shoulder...',thirdKey: 'right_wrist',angle: 180,offset: 25}]};const shoulder = {name: '腋下夹角90度检测',calc: '$or',rules...: [{name: '左腋90度',calc: 'match-angle',angleKey: 'left_shoulder',secondKey: 'left_elbow',thirdKey: 'left_hip...',angle: 90,offset: 25}, {name: '右腋90度',calc: 'match-angle',angleKey: 'right_shoulder',secondKey: 'right_elbow...;const rule = {name: '马步蹲姿势检查',calc: '$and',rules: [{name: '全身进入图像范围内检查',calc: 'whole'}, foot, arm, shoulder

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Webots中Spot或SpotMini案例-焦虑腾空-

WbDeviceTag motors[NUMBER_OF_JOINTS]; static const char *motor_names[NUMBER_OF_JOINTS] = { "front left shoulder...abduction motor", "front left shoulder rotation motor", "front left elbow motor", "front right shoulder...abduction motor", "front right shoulder rotation motor", "front right elbow motor", "rear left shoulder...abduction motor", "rear left shoulder rotation motor", "rear left elbow motor", "rear right shoulder...abduction motor", "rear right shoulder rotation motor", "rear right elbow motor"}; static WbDeviceTag

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glPushMatrix和glPopMatrix的作用

shoulderSubtract (void); void CALLBACK display(void); void CALLBACK myReshape(GLsizei w, GLsizei h); static int shoulder...elbowSubtract (void) { elbow = (elbow – 5) % 360; } void CALLBACK shoulderAdd (void) { shoulder...= (shoulder + 5) % 360; } void CALLBACK shoulderSubtract (void) { shoulder = (shoulder – 5) %...glTranslatef (-0.5, 0.0, 0.0); // 将旋转后的Wirebox向左移动0.5个单位 glRotatef ((GLfloat) shoulder, 0.0, 0.0,...1.0); //看到shoulder是相对于0的绝对角度，不是基于上一次位置的相对角度。

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牛！Python 也能实现图像姿态识别溺水行为了！

img, (int(self.dic['neck']['x']), int(self.dic['neck']['y'])), (int(self.dic['left_shoulder...']['x']), int(self.dic['left_shoulder']['y'])), (0, 255, 0), 2) # neck --> right_shoulder...']['x']), int(self.dic['right_shoulder']['y'])), (0, 255, 0), 2) # left_shoulder --> left_elbow...cv2.line(img, (int(self.dic['left_shoulder']['x']), int(self.dic['left_shoulder']['y'])),...--> right_elbow cv2.line(img, (int(self.dic['right_shoulder']['x']), int(self.dic['right_shoulder

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Caffe2 - (二十五) Detectron 之 utils 函数(3)

kp_mask = np.copy(img) # Draw mid shoulder / mid hip first for better visualization....mid_shoulder = ( kps[:2, dataset_keypoints.index('right_shoulder')] + kps[:2, dataset_keypoints.index...('left_shoulder')]) / 2.0 sc_mid_shoulder = np.minimum( kps[2, dataset_keypoints.index('right_shoulder...('right_shoulder')] + kps[:2, dataset_keypoints.index('left_shoulder')]) / 2.0...sc_mid_shoulder = np.minimum( kps[2, dataset_keypoints.index('right_shoulder')],

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Caffe2 - (二十五) Detectron 之 utils 函数(3)

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论文实践学习 - Multi-Context Attention for Human Pose Estimation

'pelvis': 6, 'thorax': 7, 'neck': 8, 'head': 9, 'r_wrist': 10, 'r_elbow': 11, 'r_shoulder...': 12, 'l_shoulder': 13, 'l_elbow': 14, 'l_wrist': 15} JointPairs = [['head', 'neck'],...['neck', 'thorax'], ['thorax', 'r_shoulder'], ['thorax', 'l_shoulder'], \...['r_shoulder', 'r_elbow'], ['r_elbow', 'r_wrist'], ['l_shoulder', 'l_elbow'], ['l_elbow

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目标检测 - 基于 SSD: Single Shot MultiBox Detector 的人体上下半身检测

943,226], "L_KNEE": [948,290], "L_ANKLE": [881,349], "R_WRIST": [772,294], "R_ELBOW": [754,247], "R_SHOULDER...": [792,147], "L_SHOULDER": [923,123], "L_ELBOW": [995,163], "L_WRIST": [961,223]} mpii/002058449.jpg...979,288], "L_KNEE": [1222,453], "L_ANKLE": [974,399], "R_WRIST": [441,490], "R_ELBOW": [446,434], "R_SHOULDER...": [599,270], "L_SHOULDER": [877,247], "L_ELBOW": [1112,384], "L_WRIST": [1012,489]} mpii/029122914.jpg...定义上下半身关节点: upper = ['HEAD', 'NECK', 'L_SHOULDER', 'L_ELBOW', 'L_WRIST', 'R_WRIST', 'R_ELBOW', 'R_SHOULDER

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十、姿态动作相似度比较

0.6806640625,name:"right_ear"}, {y:149.86859452983884,x:271.3040866650822,score:0.7246093153953552,name:"left_shoulder..."}, {y:162.78905492065545,x:158.09624324078422,score:0.82666015625,name:"right_shoulder"}, {y:236.41516213602512...0.4919433891773224,name:"right_ear"}, {y:201.4062690861481,x:205.9375190621646,score:0.51416015625,name:"left_shoulder..."}, {y:202.03125001265758,x:227.96874998853102,score:0.66259765625,name:"right_shoulder"}, {y:281.25001908561427

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跟着播客学英语-Why I use vim ? part one.

with one rep of each exercise, so the two exercises are going to do for this workout is a pushup and a shoulder...If you don't check out the show notes, or just do a Google image search, shoulder press you may or may...One shoulder press, then two pushups, two shoulder presses, then three pushups, then three shoulder presses...By the time you finished this workout, you will have done 100 pushups and 100 shoulder presses....rep pyramid or a Five Rep Pyramid, but with brakes, I know that everybody can do 100 pushups in 100 shoulder

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ES6——扩展运算符使用

let parts = ['shoulder', 'knees']; let Tshirts = ['Lee', 'Nike']; let lyrics = ['head', ...parts,...'and', 'toes']; // ["head", "shoulder", "knees", "and", "toes"] let lyrics = ['head', ...parts, '...and', 'toes', ...Tshirts]; // ["head", "shoulder", "knees", "and", "toes", "Lee", "Nike"] 就像扩展参数列表一样

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ROS-moveit！探索(2)universal_robot例程

184.510000000]: Unable to identify any set of controllers that can actuate the specified joints: [ elbow_joint shoulder_lift_joint...shoulder_pan_joint wrist_1_joint wrist_2_joint wrist_3_joint ] [ERROR] [1612319525.858243530, 184.510000000

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ROS1云课→16机器人模型从urdf到xacro

-- PR2 Arm --> </xacro:pr2_shoulder_v0

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HBO Max 介绍

目录 HBO Max的诞生 HBO Max体系的挑战需求转移可扩展性电影入场 Shoulder Content 国际化扩展 Q&A环节 HBO Max的诞生 HBO Max在2020年5月正式发布...Shoulder Content “Shoulder Content”是一个媒体行业名词，一般指除主要视频内容本身之外的延申内容。...如对于体育比赛直播而言，其shoulder content可能包含比赛集锦，赛后采访等内容。

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五、帧图像人体识别

"y": 162.78484030178413, "x": 311.0556351088547, "score": 0.919921875, "name": "left_shoulder...: 161.2381417518693, "x": 203.38841526521654, "score": 0.77197265625, "name": "right_shoulder

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数据类型(二)、copy模块

十三、自定义一个运动分析器，实现计时计数02

如何用 Python 和循环神经网络预测严重交通拥堵？

利用COCO数据集对人体上半身进行检测

机械版CG 实验3 变换参考实例

八、利用body-calc进行姿态识别

Webots中Spot或SpotMini案例-焦虑腾空-

glPushMatrix和glPopMatrix的作用

牛！Python 也能实现图像姿态识别溺水行为了！

Caffe2 - (二十五) Detectron 之 utils 函数(3)

Caffe2 - (二十五) Detectron 之 utils 函数(3)

论文实践学习 - Multi-Context Attention for Human Pose Estimation

目标检测 - 基于 SSD: Single Shot MultiBox Detector 的人体上下半身检测

十、姿态动作相似度比较

跟着播客学英语-Why I use vim ? part one.

ES6——扩展运算符使用

ROS-moveit！探索(2)universal_robot例程

ROS1云课→16机器人模型从urdf到xacro

HBO Max 介绍

五、帧图像人体识别

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