将数据输入KNN分类器进行预测

distances, indices = knn.kneighbors([[angle, angle1, angle_dl, angle_dr, angle_tr, angle_tr, angle_tl,angle_lka,angle_hls,angle_rka,angle_hrs,angle_nwr]]) if distances[0][0] > distance_threshold: label = '' confidence = 0 else: label = knn.predict([[angle, angle1, angle_dl, angle_dr, angle_tr, angle_tr, angle_tl,angle_lka,angle_hls,angle_rka,angle_hrs,angle_nwr]]) confidence = 1 - distances[0][0]/distance_threshold

print([angle, angle1, angle_dl, angle_dr, angle_tr, angle_tr, angle_tl,angle_lka,angle_hls,angle_rka,angle_hrs,angle_nwr]) poses_b = poses

if label == 'warm-up': i = t poses = 'warm-up' elif label == 'hit': i = t if (angle > -20 * a and angle <= 5 * a) and ((angle_dr > 130.5 / a and angle_dr <= 155 * a) or ( angle_dr > 205 / a and angle_dr <= 212 * a)) and ( (angle_tr > 239 / a and angle_tr <= 260 * a) or (angle_tr > 147 / a and angle_tr <= 170 * a)): poses = "shi zhan poses" else: poses = 'hit' elif label == 'SHIZHAN POSE': i = t poses = 'shi zhan poses' elif label == 'respect': i = t if (angle > -20 * a and angle <= 5 * a) and ((angle_dr > 130.5 / a and angle_dr <= 155 * a) or ( angle_dr > 205 / a and angle_dr <= 212 * a)) and ( (angle_tr > 239 / a and angle_tr <= 260 * a) or (angle_tr > 147 / a and angle_tr <= 170 * a)): poses = "shi zhan poses" else: poses = 'respect' elif label == 'gongbu': poses = 'gongbu' i = t else: if i!=0: peses = poses i = i-1 else: poses = 'N/A' i=t

print("Predicted pose: ", poses, " with confidence: ", confidence