基于ModelArts停止流感患者密接排查

天空很蓝袄 · 发表于 2023-2-5 06:24:39

择要：针对于疫情期间存留的排查及时性好、排查服从高、没法跟踪稀交者等成就，能够使用鉴于YOLOv4的止人检测、止人世隔估量、多目标追踪的计划截至处置。

原文分享自华为云社区《鉴于ModelArts截至流感患者稀交排查》，作家：HWCloudAI。
今朝流感病毒患者稀交易以排查，特别是正在人流质年夜的地区，截至排查需要消耗大批人力且需要等候。针对于疫情期间存留的排查及时性好、排查服从高、没法跟踪稀交者等成就，能够使用鉴于YOLOv4的止人检测、止人世隔估量、多目标追踪的计划截至处置。
1）使用止人沉识别手艺完毕流感病毒患者及稀交者识别功用；
2）分离Stereo-vision和YOLO算法完毕患者的实在密切打仗辨别；
3）使用SORT多目标追踪算法画造出患者及稀交者的举措轨迹；

鉴于ModelArts截至流感患者稀交排查-1.jpg

该体系能够有用进步防疫服从，减少经济取防疫压力，进步宁静性。
来日诰日将戴各人理解颠末华为云ModelArts的 DeepSocial-COVID-19社会距离监测案例完毕AI排查新冠稀交。
面打链交加入到AI Gallery的“DeepSocial-COVID-19社会距离监测”案例页里，面打Run in ModelArts，便可加入ModelArts Jupyter运行情况，此处需要采用GPU的规格。
注：如下步调所涉及的代码皆已经写佳，间接面打代码前面的箭头，让其主动运行便可。
步调一：从华为云工具保存效劳（OBS）拷贝案例所需代码。

# 下载代码战数据
import moxing as mox
mox.file.copy_parallel(&#39;obs://obs-aigallery-zc/clf/code/DeepSocial&#39;,&#39;DeepSocial&#39;)
# 引进依靠
from IPython.display import display, Javascript, Image
from base64 import b64decode, b64encode
import os
import cv2
import numpy as np
import PIL
import io
import html
import time
import matplotlib.pyplot as plt
%matplotlib inline

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步调两：正在当地编译YOLO。

需要按照运行情况改正Makefile 如可否有GPU等
假设编译报错：/bin/sh:nvcc not found
处置方法（参照）：
1）检察nvcc可施行文献的路子
which nvcc
2）改正Makefile文献中的NVCC=nvcc，把nvcc交流为上面盘问到的nvcc可施行文献的路子,如：/usr/local/cuda/bin/nvcc
NVCC=/usr/local/cuda/bin/nvcc

%cd DeepSocial
!make

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步调三：使用Darknet的python交心

# import darknet functions to perform object detections
from darknet2 import *
# load in our YOLOv4 architecture network
network, class_names, class_colors = load_network(&#34;cfg/yolov4.cfg&#34;, &#34;cfg/coco.data&#34;, &#34;DeepSocial.weights&#34;)
width = network_width(network)
height = network_height(network)
# darknet helper function to run detection on image
def darknet_helper(img, width, height):
darknet_image = make_image(width, height, 3)
img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img_resized = cv2.resize(img_rgb, (width, height),
interpolation=cv2.INTER_LINEAR)
# get image ratios to convert bounding boxes to proper size
img_height, img_width, _ = img.shape
width_ratio = img_width/width
height_ratio = img_height/height
# run model on darknet style image to get detections
copy_image_from_bytes(darknet_image, img_resized.tobytes())
detections = detect_image(network, class_names, darknet_image)
free_image(darknet_image)
return detections, width_ratio, height_ratio

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步调四：使用SORT去及时追踪目标

!pip install filterpy
from sort import *
mot_tracker = Sort(max_age=25, min_hits=4, iou_threshold=0.3)

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步调五：输出树立

Input = &#34;OxfordTownCentreDataset.avi&#34; # 需要检测的适配
ReductionFactor = 2 # 采样果子
calibration = [[180,162],[618,0],[552,540],[682,464]] # 相机标定的参数

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步调六：DeepSocial参数树立战函数引进

from deepsocial import *
######################## Frame number
StartFrom = 0
EndAt = 500 #-1 for the end of the video
######################## (0:OFF/ 1:ON) Outputs
CouplesDetection = 1 # Enable Couple Detection
DTC = 1 # Detection, Tracking and Couples
SocialDistance = 1
CrowdMap = 1
# MoveMap = 0
# ViolationMap = 0
# RiskMap = 0
######################## Units are Pixel
ViolationDistForIndivisuals = 28
ViolationDistForCouples = 31
####
CircleradiusForIndivsual = 14
CircleradiusForCouples = 17
########################
MembershipDistForCouples = (16 , 10) # (Forward, Behind) per Pixel
MembershipTimeForCouples = 35 # Time for considering as a couple (per Frame)
######################## (0:OFF/ 1:ON)
CorrectionShift = 1 # Ignore people in the margins of the video
HumanHeightLimit = 200 # Ignore people with unusual heights
########################
Transparency = 0.7
######################## Output Video&#39;s path
Path_For_DTC = os.getcwd() + &#34;/DeepSOCIAL DTC.mp4&#34;
Path_For_SocialDistance = os.getcwd() + &#34;/DeepSOCIAL Social Distancing.mp4&#34;
Path_For_CrowdMap = os.getcwd() + &#34;/DeepSOCIAL Crowd Map.mp4&#34;
def extract_humans(detections):
detetcted = []
if len(detections) > 0: # At least 1 detection in the image and check detection presence in a frame
idList = []
id = 0
for label, confidence, bbox in detections:
if label == &#39;person&#39;:
xmin, ymin, xmax, ymax = bbox2points(bbox)
id +=1
if id not in idList: idList.append(id)
detetcted.append([int(xmin), int(ymin), int(xmax), int(ymax), idList[-1]])
return np.array(detetcted)
def centroid(detections, image, calibration, _centroid_dict, CorrectionShift, HumanHeightLimit):
e = birds_eye(image.copy(), calibration)
centroid_dict = dict()
now_present = list()
if len(detections) > 0:
for d in detections:
p = int(d[4])
now_present.append(p)
xmin, ymin, xmax, ymax = d[0], d[1], d[2], d[3]
w = xmax - xmin
h = ymax - ymin
x = xmin + w/2
y = ymax - h/2
if h < HumanHeightLimit:
overley = e.image
bird_x, bird_y = e.projection_on_bird((x, ymax))
if CorrectionShift:
if checkupArea(overley, 1, 0.25, (x, ymin)):
continue
e.setImage(overley)
center_bird_x, center_bird_y = e.projection_on_bird((x, ymin))
centroid_dict[p] = (
int(bird_x), int(bird_y),
int(x), int(ymax),
int(xmin), int(ymin), int(xmax), int(ymax),
int(center_bird_x), int(center_bird_y))
_centroid_dict[p] = centroid_dict[p]
return _centroid_dict, centroid_dict, e.image
def ColorGenerator(seed=1, size=10):
np.random.seed = seed
color=dict()
for i in range(size):
h = int(np.random.uniform() *255)
color[i]= h
return color
def VisualiseResult(_Map, e):
Map = np.uint8(_Map)
histMap = e.convrt2Image(Map)
visualBird = cv2.applyColorMap(np.uint8(_Map), cv2.COLORMAP_JET)
visualMap = e.convrt2Image(visualBird)
visualShow = cv2.addWeighted(e.original, 0.7, visualMap, 1 - 0.7, 0)
return visualShow, visualBird, histMap

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步调七：拉理历程

cap = cv2.VideoCapture(Input)
frame_width = int(cap.get(3))
frame_height = int(cap.get(4))
height, width = frame_height // ReductionFactor, frame_width // ReductionFactor
print(&#34;Video Reolution: &#34;,(width, height))
if DTC: DTCVid = cv2.VideoWriter(Path_For_DTC, cv2.VideoWriter_fourcc(*&#39;X264&#39;), 30.0, (width, height))
if SocialDistance: SDimageVid = cv2.VideoWriter(Path_For_SocialDistance, cv2.VideoWriter_fourcc(*&#39;X264&#39;), 30.0, (width, height))
if CrowdMap: CrowdVid = cv2.VideoWriter(Path_For_CrowdMap, cv2.VideoWriter_fourcc(*&#39;X264&#39;), 30.0, (width, height))
colorPool = ColorGenerator(size = 3000)
_centroid_dict = dict()
_numberOFpeople = list()
_greenZone = list()
_redZone = list()
_yellowZone = list()
_final_redZone = list()
_relation = dict()
_couples = dict()
_trackMap = np.zeros((height, width, 3), dtype=np.uint8)
_crowdMap = np.zeros((height, width), dtype=np.int)
_allPeople = 0
_counter = 1
frame = 0
while True:
print(&#39;-- Frame : {}&#39;.format(frame))
prev_time = time.time()
ret, frame_read = cap.read()
if not ret: break
frame += 1
if frame <= StartFrom: continue
if frame != -1:
if frame > EndAt: break
frame_resized = cv2.resize(frame_read,(width, height), interpolation=cv2.INTER_LINEAR)
image = frame_resized
e = birds_eye(image, calibration)
detections, width_ratio, height_ratio = darknet_helper(image, width, height)
humans = extract_humans(detections)
track_bbs_ids = mot_tracker.update(humans) if len(humans) != 0 else humans
_centroid_dict, centroid_dict, partImage = centroid(track_bbs_ids, image, calibration, _centroid_dict, CorrectionShift, HumanHeightLimit)
redZone, greenZone = find_zone(centroid_dict, _greenZone, _redZone, criteria=ViolationDistForIndivisuals)
if CouplesDetection:
_relation, relation = find_relation(e, centroid_dict, MembershipDistForCouples, redZone, _couples, _relation)
_couples, couples, coupleZone = find_couples(image, _centroid_dict, relation, MembershipTimeForCouples, _couples)
yellowZone, final_redZone, redGroups = find_redGroups(image, centroid_dict, calibration, ViolationDistForCouples, redZone, coupleZone, couples , _yellowZone, _final_redZone)
else:
couples = []
coupleZone = []
yellowZone = []
redGroups = redZone
final_redZone = redZone
if DTC:
DTC_image = image.copy()
_trackMap = Apply_trackmap(centroid_dict, _trackMap, colorPool, 3)
DTC_image = cv2.add(e.convrt2Image(_trackMap), image)
DTCShow = DTC_image
for id, box in centroid_dict.items():
center_bird = box[0], box[1]
if not id in coupleZone:
cv2.rectangle(DTCShow,(box[4], box[5]),(box[6], box[7]),(0,255,0),2)
cv2.rectangle(DTCShow,(box[4], box[5]-13),(box[4]+len(str(id))*10, box[5]),(0,200,255),-1)
cv2.putText(DTCShow,str(id),(box[4]+2, box[5]-2),cv2.FONT_HERSHEY_SIMPLEX,.4,(0,0,0),1,cv2.LINE_AA)
for coupled in couples:
p1 , p2 = coupled
couplesID = couples[coupled][&#39;id&#39;]
couplesBox = couples[coupled][&#39;box&#39;]
cv2.rectangle(DTCShow, couplesBox[2:4], couplesBox[4:], (0,150,255), 4)
loc = couplesBox[0] , couplesBox[3]
offset = len(str(couplesID)*5)
captionBox = (loc[0] - offset, loc[1]-13), (loc[0] + offset, loc[1])
cv2.rectangle(DTCShow,captionBox[0],captionBox[1],(0,200,255),-1)
wc = captionBox[1][0] - captionBox[0][0]
hc = captionBox[1][1] - captionBox[0][1]
cx = captionBox[0][0] + wc // 2
cy = captionBox[0][1] + hc // 2
textLoc = (cx - offset, cy + 4)
cv2.putText(DTCShow, str(couplesID) ,(textLoc),cv2.FONT_HERSHEY_SIMPLEX,.4,(0,0,0),1,cv2.LINE_AA)
DTCVid.write(DTCShow)
if SocialDistance:
SDimage, birdSDimage = Apply_ellipticBound(centroid_dict, image, calibration, redZone, greenZone, yellowZone, final_redZone, coupleZone, couples, CircleradiusForIndivsual, CircleradiusForCouples)
SDimageVid.write(SDimage)
if CrowdMap:
_crowdMap, crowdMap = Apply_crowdMap(centroid_dict, image, _crowdMap)
crowd = (crowdMap - crowdMap.min()) / (crowdMap.max() - crowdMap.min())*255
crowd_visualShow, crowd_visualBird, crowd_histMap = VisualiseResult(crowd, e)
CrowdVid.write(crowd_visualShow)
cv2.waitKey(3)
print(&#39;::: Analysis Completed&#39;)
cap.release()
if DTC: DTCVid.release(); print(&#34;::: Video Write Completed : &#34;, Path_For_DTC)
if SocialDistance: SDimageVid.release() ; print(&#34;::: Video Write Completed : &#34;, Path_For_SocialDistance)
if CrowdMap: CrowdVid.release() ; print(&#34;::: Video Write Completed : &#34;, Path_For_CrowdMap)