166 lines
6.7 KiB
Python
166 lines
6.7 KiB
Python
import sys
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from pathlib import Path
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import math
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import cv2
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import numpy as np
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import torch
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import math
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import time
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FILE = Path(__file__).absolute()
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#sys.path.append(FILE.parents[0].as_posix()) # add yolov5/ to path
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def calculate_distance(point1, point2):
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"""计算两个点之间的欧氏距离"""
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point= center_coordinate(point1)
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point=np.array(point)
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other_point = center_coordinate(point2)
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other_point = np.array(other_point)
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return np.linalg.norm(point - other_point)
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def find_clusters(preds, min_distance):
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"""按照最小距离将点分成簇"""
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points=preds
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points=np.array(points)
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clusters = []
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used_points = set()
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for i, point in enumerate(points):
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if i not in used_points: # 如果该点未被使用过
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cluster = [point]
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used_points.add(i)
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# 寻找与该点距离小于等于min_distance的其他点
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for j, other_point in enumerate(points):
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if j not in used_points:
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if all(calculate_distance(point, other_point) <= min_distance
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for point in cluster):
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cluster.append(other_point)
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used_points.add(j)
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clusters.append(cluster)
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return clusters
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def center_coordinate(boundbxs):
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'''
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根据检测矩形框，得到其矩形长度和宽度
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输入：两个对角坐标xyxy
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输出：矩形框重点坐标xy
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'''
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boundbxs_x1 = boundbxs[0]
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boundbxs_y1 = boundbxs[1]
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boundbxs_x2 = boundbxs[2]
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boundbxs_y2 = boundbxs[3]
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center_x = 0.5 * (boundbxs_x1 + boundbxs_x2)
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center_y = 0.5 * (boundbxs_y1 + boundbxs_y2)
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return [center_x, center_y]
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def get_bounding_rectangle(rectangles):
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'''
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通过输入多个矩形的对角坐标，得到这几个矩形的外包矩形对角坐标
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输入：点簇列表 （嵌套列表）
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输出：多个矩形的外包矩形对角坐标 （列表）
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'''
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min_x, max_x, min_y, max_y = float('inf'), float('-inf'), float('inf'), float('-inf')
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for rect in rectangles:
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x1, y1, x2, y2,c1,t1 = rect
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min_x = min(min_x, min(x1, x2))
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max_x = max(max_x, max(x1, x2))
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min_y = min(min_y, min(y1, y2))
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max_y = max(max_y, max(y1, y2))
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return [min_x, min_y, max_x, max_y]
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def calculate_score(input_value):
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'''
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计算人群聚集置信度，检测出3-10人内，按照0.85-1的上升趋势取值；
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当检测超过10人，直接判断分数为1.
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'''
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if input_value == 3:
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output_value=0.85
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elif input_value == 4:
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output_value=0.9
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elif 5<= input_value <=10:
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output_value = 0.9+(input_value-4)*0.015
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else:
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output_value=1
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return output_value
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def gather_post_process(predsList, pars):
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'''
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后处理程序，针对检测出的pedestrian，进行人员聚集的算法检测，按照类别'crowd_people'增加predsList
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①原类别：
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['ForestSpot', 'PestTree', 'pedestrian', 'fire', 'smog','cloud']=[0,1,2,3,4,5]
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②处理后的类别汇总：
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['ForestSpot', 'PestTree', 'pedestrian', 'fire', 'smog','cloud','crowd_people']=[0,1,2,3,4,5,6]
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输入：
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preds 一张图像的检测结果，为嵌套列表，tensor，包括x_y_x_y_conf_class
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imgwidth,imgheight 图像的原始宽度及长度
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输出：检测结果（将其中未悬挂国旗的显示）
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'''
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t0=time.time()
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predsList = predsList[0]
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predsList = [x for x in predsList if int(x[5]) !=5 ]##把类别“云朵”去除
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# 1、过滤掉类别2以外的目标，只保留行人
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preds = [ x for x in predsList if int(x[5]) ==pars['pedestrianId'] ]
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if len(preds)< pars['crowdThreshold']:
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return predsList,'gaher postTime:No gathering'
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preds = np.array(preds)
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longs = np.mean(np.max(preds[:,2:4]-preds[:,0:2]))
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distanceThreshold = pars['distancePersonScale']*longs
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# 2、查找点簇
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clusters = find_clusters(preds, distanceThreshold)
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clusters_crowd = []
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# 3、输出点簇信息，点簇中数量超过阈值，判断人员聚集
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for i, cluster in enumerate(clusters):
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if len(cluster) >= pars['crowdThreshold']: # 超过一定人数，即为人员聚集
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#print(f"Cluster {i + 1}: {len(cluster)} points")
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clusters_crowd.append(cluster)
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#print(clusters_crowd)
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# 4、根据得到的人员聚集点簇，合并其他类别检测结果
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for i in range(len(clusters_crowd)):
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xyxy = get_bounding_rectangle(clusters_crowd[i]) # 人群聚集包围框
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#score = calculate_score(len(clusters_crowd[i])) # 人群聚集置信度
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score = len(clusters_crowd[i])
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xyxy.append(score) # 人群聚集置信度
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xyxy.append(pars['gatherId']) # 人群聚集类别
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predsList.append(xyxy)
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# 5、输出最终类别，共7类，用于绘图显示
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output_predslist = predsList
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#print('craoGaher line131:',output_predslist)
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t1=time.time()
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return output_predslist,'gaher postTime:%.1f ms'%( (t1-t0)*1000 )
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if __name__ == "__main__":
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t1 = time.time()
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# 对应vendor1_20240529_99.jpg检测结果
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preds=[[224.19933, 148.30751, 278.19156, 199.87828, 0.87625, 2.00000],
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[362.67139, 161.25760, 417.72357, 211.51706, 0.86919, 2.00000],
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[437.00131, 256.19083, 487.88870, 307.72897, 0.85786, 2.00000],
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[442.64606, 335.78168, 493.75720, 371.41418, 0.85245, 2.00000],
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[324.58362, 256.18488, 357.72626, 294.08929, 0.84512, 2.00000],
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[343.59781, 301.06506, 371.04105, 350.01086, 0.84207, 2.00000],
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[301.35858, 210.64088, 332.64862, 250.78883, 0.84063, 2.00000],
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[406.02994, 216.91214, 439.44455, 249.26077, 0.83698, 2.00000],
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[321.53494, 99.68467, 354.67477, 135.53226, 0.82515, 2.00000],
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[253.97131, 202.65234, 302.06055, 233.30634, 0.81498, 2.00000],
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[365.62521, 66.42108, 442.02292, 127.37558, 0.79556, 1.00000]]
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#preds=torch.tensor(preds) #返回的预测结果
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imgwidth=1920
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imgheight=1680
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pars={'imgSize':(imgwidth,imgheight),'pedestrianId':2,'crowdThreshold':4,'gatherId':6,'distancePersonScale':2.0}
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'''
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pedestrianId 为行人识别的类别；
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crowdThreshold为设置的判断人员聚集的人数阈值，默认4人为聚集
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distanceThreshold为设置的判断人员聚集的距离阈值，为了测试默认300像素内为聚集(可自行设置)
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'''
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yyy=gather_post_process(preds,pars) #送入后处理函数
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t2 = time.time()
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ttt = t2 - t1
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print('时间', ttt * 1000)
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