想把计算一个人的相似度展成18个进程肯定不行。

标签：kpt 度展 18 self Pose keypoints np 进程 id

代码只进行了18次循环处理俩个人18个关键点的距离。单进程耗时0.001秒，多进程耗时6.34秒。

import cv2 import numpy as np from modules.keypoints import BODY_PARTS_KPT_IDS, BODY_PARTS_PAF_IDS from modules.one_euro_filter import OneEuroFilter import pandas as pd class Pose:     num_kpts = 18     kpt_names = ['nose', 'neck',                  'r_sho', 'r_elb', 'r_wri', 'l_sho', 'l_elb', 'l_wri',                  'r_hip', 'r_knee', 'r_ank', 'l_hip', 'l_knee', 'l_ank',                  'r_eye', 'l_eye',                  'r_ear', 'l_ear']     sigmas = np.array([.26, .79, .79, .72, .62, .79, .72, .62, 1.07, .87, .89, 1.07, .87, .89, .25, .25, .35, .35],                       dtype=np.float32) / 10.0     vars = (sigmas * 2) ** 2     last_id = -1     color = [0, 224, 255]
    def __init__(self, keypoints, confidence):         super().__init__()         self.all_save_image=0         self.keypoints = keypoints         self.confidence = confidence         self.bbox = Pose.get_bbox(self.keypoints)         self.id = None         self.filters = [[OneEuroFilter(), OneEuroFilter()] for _ in range(Pose.num_kpts)]
    @staticmethod     def get_bbox(keypoints):         found_keypoints = np.zeros((np.count_nonzero(keypoints[:, 0] != -1), 2), dtype=np.int32)         found_kpt_id = 0         for kpt_id in range(Pose.num_kpts):             if keypoints[kpt_id, 0] == -1:                 continue             found_keypoints[found_kpt_id] = keypoints[kpt_id]             found_kpt_id += 1         bbox = cv2.boundingRect(found_keypoints)
        return bbox
    def update_id(self, id=None):         self.id = id         if self.id is None:             self.id = Pose.last_id + 1             Pose.last_id += 1                 def update_all(self, id=None):         self.all_save_image = id         if self.all_save_image is None:             self.all_save_image = 0
                def draw(self, img):         assert self.keypoints.shape == (Pose.num_kpts, 2)
        for part_id in range(len(BODY_PARTS_PAF_IDS) - 2):             kpt_a_id = BODY_PARTS_KPT_IDS[part_id][0]             global_kpt_a_id = self.keypoints[kpt_a_id, 0]             if global_kpt_a_id != -1:                 x_a, y_a = self.keypoints[kpt_a_id]                 cv2.circle(img, (int(x_a), int(y_a)), 3, Pose.color, -1)             kpt_b_id = BODY_PARTS_KPT_IDS[part_id][1]             global_kpt_b_id = self.keypoints[kpt_b_id, 0]             if global_kpt_b_id != -1:                 x_b, y_b = self.keypoints[kpt_b_id]                 cv2.circle(img, (int(x_b), int(y_b)), 3, Pose.color, -1)             if global_kpt_a_id != -1 and global_kpt_b_id != -1:                 cv2.line(img, (int(x_a), int(y_a)), (int(x_b), int(y_b)), Pose.color, 2)

def get_similarity(a, b, threshold=0.5):     num_similar_kpt = 0     for kpt_id in range(Pose.num_kpts):         if a.keypoints[kpt_id, 0] != -1 and b.keypoints[kpt_id, 0] != -1:             distance = np.sum((a.keypoints[kpt_id] - b.keypoints[kpt_id]) ** 2)             area = max(a.bbox[2] * a.bbox[3], b.bbox[2] * b.bbox[3])             similarity = np.exp(-distance / (2 * (area + np.spacing(1)) * Pose.vars[kpt_id]))             if similarity > threshold:                 num_similar_kpt += 1     return num_similar_kpt
import time import numpy as np from concurrent.futures import ProcessPoolExecutor

def s(kpt_id,threshold=0.5):     if a.keypoints[kpt_id, 0] != -1 and b.keypoints[kpt_id, 0] != -1:         distance = np.sum((a.keypoints[kpt_id] - b.keypoints[kpt_id]) ** 2)         area = max(a.bbox[2] * a.bbox[3], b.bbox[2] * b.bbox[3])         similarity = np.exp(-distance / (2 * (area + np.spacing(1)) * Pose.vars[kpt_id]))         if similarity > threshold:             return 1 if __name__=='__main__':     keypoint=np.random.random((18,2))     pose=Pose(keypoint,1)     a=pose     b=pose     start=time.time()     get_similarity(pose,pose)     end=time.time()     print(end-start)
    ind=18     num_similar_kpt=0     start=time.time()                         with ProcessPoolExecutor() as pool:         results=pool.map(s,[i for i in range(ind)])     end=time.time()     sum1=end-start     print(sum1)

 

标签：kpt,度展,18,self,Pose,keypoints,np,进程,id
From： https://www.cnblogs.com/hahaah/p/17369235.html