# 下载代码和数据
import moxing as mox
mox.file.copy_parallel('obs://obs-aigallery-zc/clf/code/DeepSocial','DeepSocial')
# 引入依赖
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

%cd DeepSocial
!make

# import darknet functions to perform object detections
from darknet2 import *
# load in our YOLOv4 architecture network
network, class_names, class_colors = load_network("cfg/yolov4.cfg", "cfg/coco.data", "DeepSocial.weights")
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

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

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

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's path
Path_For_DTC = os.getcwd() + "/DeepSOCIAL DTC.mp4"
Path_For_SocialDistance = os.getcwd() + "/DeepSOCIAL Social Distancing.mp4"
Path_For_CrowdMap = os.getcwd() + "/DeepSOCIAL Crowd Map.mp4"
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 == 'person': 
 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

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("Video Reolution: ",(width, height))
if DTC: DTCVid = cv2.VideoWriter(Path_For_DTC, cv2.VideoWriter_fourcc(*'X264'), 30.0, (width, height))
if SocialDistance: SDimageVid = cv2.VideoWriter(Path_For_SocialDistance, cv2.VideoWriter_fourcc(*'X264'), 30.0, (width, height))
if CrowdMap: CrowdVid = cv2.VideoWriter(Path_For_CrowdMap, cv2.VideoWriter_fourcc(*'X264'), 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('-- Frame : {}'.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]['id']
 couplesBox = couples[coupled]['box']
            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('::: Analysis Completed')
cap.release()
if DTC: DTCVid.release(); print("::: Video Write Completed : ", Path_For_DTC)
if SocialDistance: SDimageVid.release() ; print("::: Video Write Completed : ", Path_For_SocialDistance)
if CrowdMap: CrowdVid.release() ; print("::: Video Write Completed : ", Path_For_CrowdMap)

from IPython.display import HTML
outpath = "DeepSOCIAL DTC.mp4"
mp4 = open(outpath,'rb').read()
data_url = "data:video/mp4;base64," + b64encode(mp4).decode()
HTML("""
<video width=400 controls>
 <source src="%s" type="video/mp4">
</video>
""" % data_url)