基于深度学习的视频检测（六） 行人计数，监控视频人员管理

搭建 darkflow与 sort/deep_sort 环境

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修改 darkflow/net/yolov2/predict.py

import numpy as np
import math
import cv2
import os
import json
import csv
#from scipy.special import expit
#from utils.box import BoundBox, box_iou, prob_compare
#from utils.box import prob_compare2, box_intersection
from ...utils.box import BoundBox
from ...cython_utils.cy_yolo2_findboxes import box_constructor

######################################### 
id_list = []
blist = []
clist = []

loc_dic={}
in_count = 0 #in 计数器
out_count = 0 #out 计数器
######################################### 

ds = True
try :
    from deep_sort.application_util import preprocessing as prep
    from deep_sort.application_util import visualization
    from deep_sort.deep_sort.detection import Detection
except :
    ds = False


def expit(x):
    return 1. / (1. + np.exp(-x))

def _softmax(x):
    e_x = np.exp(x - np.max(x))
    out = e_x / e_x.sum()
    return out

def findboxes(self, net_out):
    # meta
    meta = self.meta
    boxes = list()
    boxes=box_constructor(meta,net_out)
    return boxes


def extract_boxes(self,new_im):
    cont = []
    new_im=new_im.astype(np.uint8)
    ret, thresh=cv2.threshold(new_im, 127, 255, 0)
    p, contours, hierarchy=cv2.findContours(
        thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
    for i in range(0, len(contours)):
        cnt=contours[i]
        x, y, w, h=cv2.boundingRect(cnt)
        if w*h > 30**2 and ((w < new_im.shape[0] and h <= new_im.shape[1]) or (w <= new_im.shape[0] and h < new_im.shape[1])):
            if self.FLAGS.tracker == "sort":
                cont.append([x, y, x+w, y+h])
            else : cont.append([x, y, w, h])
    return cont




def postprocess(self,net_out, im,frame_id = 0,csv_file=None,csv=None,mask = None,encoder=None,tracker=None):
    """
    Takes net output, draw net_out, save to disk
    """
    boxes = self.findboxes(net_out)


    # meta
    meta = self.meta
    nms_max_overlap = 0.1
    threshold = meta['thresh']
    colors = meta['colors']
    labels = meta['labels']
    if type(im) is not np.ndarray:
        imgcv = cv2.imread(im)
    else: imgcv = im
    h, w, _ = imgcv.shape
    thick = int((h + w) // 300)
    resultsForJSON = []
######################################### 
    global id_list
    global blist
    global cliset
    global in_count
    global out_count
    cv2.line(imgcv, (int(w/2), int(0)),(int(w/2) ,int(h)), (255,255,255))

    cv2.putText(imgcv, "in number:" + str(int(in_count)),(10, 40), 0, 1e-3 * h, (255,0,0),2)
    cv2.putText(imgcv, "out number:" + str(int(out_count)),(10, 60),0, 1e-3 * h, (255,0,0),2)
######################################### 
    if not self.FLAGS.track :
        for b in boxes:
            boxResults = self.process_box(b, h, w, threshold)
            if boxResults is None:
                continue
            left, right, top, bot, mess, max_indx, confidence = boxResults
            if self.FLAGS.json:
                resultsForJSON.append({"label": mess, "confidence": float('%.2f' % confidence), "topleft": {"x": left, "y": top}, "bottomright": {"x": right, "y": bot}})
                continue
            if self.FLAGS.display or self.FLAGS.saveVideo:
                cv2.rectangle(imgcv,
                    (left, top), (right, bot),
                    colors[max_indx], thick)
                cv2.putText(imgcv, mess, (left, top - 12),
                    0, 1e-3 * h, colors[max_indx],thick//3)
    else :
        if not ds :
            print("ERROR : deep sort or sort submodules not found for tracking please run :")
            print("\tgit submodule update --init --recursive")
            print("ENDING")
            exit(1)
        detections = []
        scores = []
        for b in boxes:
            boxResults = self.process_box(b, h, w, threshold)
            if boxResults is None:
                continue
            left, right, top, bot, mess, max_indx, confidence = boxResults
            if mess not in self.FLAGS.trackObj :
                continue
            if self.FLAGS.tracker == "deep_sort":
                detections.append(np.array([left,top,right-left,bot-top]).astype(np.float64))
                scores.append(confidence)
            elif self.FLAGS.tracker == "sort":
                detections.append(np.array([left,top,right,bot]).astype(np.float64))
        if len(detections) < 3  and self.FLAGS.BK_MOG:
            detections = detections + extract_boxes(self,mask)

        detections = np.array(detections)
        if detections.shape[0] == 0 :
            return imgcv
        if self.FLAGS.tracker == "deep_sort":
            scores = np.array(scores)
            features = encoder(imgcv, detections.copy())
            detections = [
                        Detection(bbox, score, feature) for bbox,score, feature in
                        zip(detections,scores, features)]
            # Run non-maxima suppression.
            boxes = np.array([d.tlwh for d in detections])
            scores = np.array([d.confidence for d in detections])
            indices = prep.non_max_suppression(boxes, nms_max_overlap, scores)
            detections = [detections[i] for i in indices]
            tracker.predict()
            tracker.update(detections)
            trackers = tracker.tracks
        elif self.FLAGS.tracker == "sort":
            trackers = tracker.update(detections)
        for track in trackers:
            if self.FLAGS.tracker == "deep_sort":
                if not track.is_confirmed() or track.time_since_update > 1:
                    continue
                bbox = track.to_tlbr()
                id_num = str(track.track_id)
#########################################        
                id_list.append(id_num)
                blist = list(set(id_list))
                clist = sorted([int(i) for i in blist])             
#########################################                                              
            elif self.FLAGS.tracker == "sort":
                bbox = [int(track[0]),int(track[1]),int(track[2]),int(track[3])]
                id_num = str(int(track[4]))
            if self.FLAGS.csv:
                csv.writerow([frame_id,id_num,int(bbox[0]),int(bbox[1]),int(bbox[2])-int(bbox[0]),int(bbox[3])-int(bbox[1])])                    
                csv_file.flush()
#########################################
            if id_num in loc_dic:
                #判断上一帧运动轨迹
                #向右运动，且经过分界线
                #print("###########")
                if bbox[0] > loc_dic[id_num] and (bbox[0] > float(w/2) and loc_dic[id_num] < float(w/2)):
                    print("##################in one##################")
                    loc_dic[id_num] = bbox[0]
                    in_count = in_count + 1
                #向左移动，且经过分解线    
                elif bbox[0] < loc_dic[id_num] and (bbox[0] < float(w/2) and loc_dic[id_num] > float(w/2)):
                    print("##################out_one#################")
                    loc_dic[id_num] = bbox[0]
                    out_count = out_count + 1
                    if in_count > 0 :    
                        in_count = in_count - 1
            else:
                loc_dic[id_num] = bbox[0]
#########################################                                           
            if self.FLAGS.display or self.FLAGS.saveVideo:

                cv2.rectangle(imgcv, (int(bbox[0]), int(bbox[1])), (int(bbox[2]), int(bbox[3])),
                                (255,255,255), thick//3)
                cv2.putText(imgcv, id_num,(int(bbox[0]), int(bbox[1]) - 12),0, 1e-3 * h, (255,255,255),thick//6)
                cv2.putText(imgcv, 'current  number: '+ str(int(len(clist) - out_count)),(10, 20),0, 1e-3 * h, (255,0,0),2)
    return imgcv

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原理就是使用Opencv3画线，然后根据追踪物体 boundingbox 中心点坐标判断前后帧位置变化与这条线的关联，如果前后帧的中心点坐标在线的两边，说明有人员进出，效果图如下：