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demo.py
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demo.py
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#! /usr/bin/env python
# -*- coding: utf-8 -*-
from __future__ import division, print_function, absolute_import
import os
import datetime
from timeit import time
import warnings
import cv2
import numpy as np
import argparse
from PIL import Image
from yolo import YOLO
from deep_sort import preprocessing
from deep_sort import nn_matching
from deep_sort.detection import Detection
from deep_sort.tracker import Tracker
from tools import generate_detections as gdet
from deep_sort.detection import Detection as ddet
from collections import deque
from keras import backend
backend.clear_session()
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--input",help="path to input video", default = "./test_video/test.avi")
ap.add_argument("-c", "--class",help="name of class", default = "person")
args = vars(ap.parse_args())
pts = [deque(maxlen=30) for _ in range(9999)]
warnings.filterwarnings('ignore')
# initialize a list of colors to represent each possible class label
np.random.seed(100)
COLORS = np.random.randint(0, 255, size=(200, 3),
dtype="uint8")
def main(yolo):
start = time.time()
#Definition of the parameters
max_cosine_distance = 0.5 #余弦距离的控制阈值
nn_budget = None
nms_max_overlap = 0.3 #非极大抑制的阈值
counter = []
#deep_sort
model_filename = 'model_data/market1501.pb'
encoder = gdet.create_box_encoder(model_filename,batch_size=1)
metric = nn_matching.NearestNeighborDistanceMetric("cosine", max_cosine_distance, nn_budget)
tracker = Tracker(metric)
writeVideo_flag = True
#video_path = "./output/output.avi"
video_capture = cv2.VideoCapture(args["input"])
if writeVideo_flag:
# Define the codec and create VideoWriter object
w = int(video_capture.get(3))
h = int(video_capture.get(4))
fourcc = cv2.VideoWriter_fourcc(*'MJPG')
out = cv2.VideoWriter('./output/'+args["input"][43:57]+ "_" + args["class"] + '_output.avi', fourcc, 15, (w, h))
list_file = open('detection.txt', 'w')
frame_index = -1
fps = 0.0
while True:
ret, frame = video_capture.read() # frame shape 640*480*3
if ret != True:
break
t1 = time.time()
# image = Image.fromarray(frame)
image = Image.fromarray(frame[...,::-1]) #bgr to rgb
boxs,class_names = yolo.detect_image(image)
features = encoder(frame,boxs)
# score to 1.0 here).
detections = [Detection(bbox, 1.0, feature) for bbox, feature in zip(boxs, features)]
# Run non-maxima suppression.
boxes = np.array([d.tlwh for d in detections])
scores = np.array([d.confidence for d in detections])
indices = preprocessing.non_max_suppression(boxes, nms_max_overlap, scores)
detections = [detections[i] for i in indices]
# Call the tracker
tracker.predict()
tracker.update(detections)
i = int(0)
indexIDs = []
c = []
boxes = []
for det in detections:
bbox = det.to_tlbr()
cv2.rectangle(frame,(int(bbox[0]), int(bbox[1])), (int(bbox[2]), int(bbox[3])),(255,255,255), 2)
for track in tracker.tracks:
if not track.is_confirmed() or track.time_since_update > 1:
continue
#boxes.append([track[0], track[1], track[2], track[3]])
indexIDs.append(int(track.track_id))
counter.append(int(track.track_id))
bbox = track.to_tlbr()
color = [int(c) for c in COLORS[indexIDs[i] % len(COLORS)]]
cv2.rectangle(frame, (int(bbox[0]), int(bbox[1])), (int(bbox[2]), int(bbox[3])),(color), 3)
cv2.putText(frame,str(track.track_id),(int(bbox[0]), int(bbox[1] -50)),0, 5e-3 * 150, (color),2)
if len(class_names) > 0:
class_name = class_names[0]
cv2.putText(frame, str(class_names[0]),(int(bbox[0]), int(bbox[1] -20)),0, 5e-3 * 150, (color),2)
i += 1
#bbox_center_point(x,y)
center = (int(((bbox[0])+(bbox[2]))/2),int(((bbox[1])+(bbox[3]))/2))
#track_id[center]
pts[track.track_id].append(center)
thickness = 5
#center point
cv2.circle(frame, (center), 1, color, thickness)
#draw motion path
for j in range(1, len(pts[track.track_id])):
if pts[track.track_id][j - 1] is None or pts[track.track_id][j] is None:
continue
thickness = int(np.sqrt(64 / float(j + 1)) * 2)
cv2.line(frame,(pts[track.track_id][j-1]), (pts[track.track_id][j]),(color),thickness)
#cv2.putText(frame, str(class_names[j]),(int(bbox[0]), int(bbox[1] -20)),0, 5e-3 * 150, (255,255,255),2)
count = len(set(counter))
cv2.putText(frame, "Total Object Counter: "+str(count),(int(20), int(120)),0, 5e-3 * 200, (0,255,0),2)
cv2.putText(frame, "Current Object Counter: "+str(i),(int(20), int(80)),0, 5e-3 * 200, (0,255,0),2)
cv2.putText(frame, "FPS: %f"%(fps),(int(20), int(40)),0, 5e-3 * 200, (0,255,0),3)
cv2.namedWindow("YOLO3_Deep_SORT", 0);
cv2.resizeWindow('YOLO3_Deep_SORT', 1024, 768);
cv2.imshow('YOLO3_Deep_SORT', frame)
if writeVideo_flag:
#save a frame
out.write(frame)
frame_index = frame_index + 1
list_file.write(str(frame_index)+' ')
if len(boxs) != 0:
for i in range(0,len(boxs)):
list_file.write(str(boxs[i][0]) + ' '+str(boxs[i][1]) + ' '+str(boxs[i][2]) + ' '+str(boxs[i][3]) + ' ')
list_file.write('\n')
fps = ( fps + (1./(time.time()-t1)) ) / 2
#print(set(counter))
# Press Q to stop!
if cv2.waitKey(1) & 0xFF == ord('q'):
break
print(" ")
print("[Finish]")
end = time.time()
if len(pts[track.track_id]) != None:
print(args["input"][43:57]+": "+ str(count) + " " + str(class_name) +' Found')
else:
print("[No Found]")
video_capture.release()
if writeVideo_flag:
out.release()
list_file.close()
cv2.destroyAllWindows()
if __name__ == '__main__':
main(YOLO())