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Track_count.py
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Track_count.py
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import sys
sys.path.insert(0, './YOLOX')
import torch
import cv2
from yolox.utils import vis
import time
from yolox.exp import get_exp
import numpy as np
from collections import deque
from collections import Counter
# importing Detector
from yolox.data.datasets.coco_classes import COCO_CLASSES
from detector import Predictor
# Importing Deepsort
from deep_sort.utils.parser import get_config
from deep_sort.deep_sort import DeepSort
# Importing Visuals
from visuals import *
from intersect_ import *
# A Dictionary to keep data of tracking
data_deque = {}
class_names = COCO_CLASSES
lines = [
{'Title' : 'Line1', 'Cords' : [(580, 500), (100, 500)]},
{'Title' : 'Line2', 'Cords' : [(680, 500), (1070, 500)]}
]
object_counter = {
'Line1' : Counter(),
'Line2' : Counter()
}
#Draw the Lines
def draw_lines(lines, img):
for line in lines:
img = cv2.line(img, line['Cords'][0], line['Cords'][1], (255,255,255), 3)
return img
# Update the Counter
def update_counter(centerpoints, obj_name):
for line in lines:
p1 = Point(*centerpoints[0])
q1 = Point(*centerpoints[1])
p2 = Point(*line['Cords'][0])
q2 = Point(*line['Cords'][1])
if doIntersect(p1, q1, p2, q2):
object_counter[line['Title']].update([obj_name])
return True
return False
# Draw the Final Results
def draw_results(img):
x = 100
y = 100
offset = 50
for line_name, line_counter in object_counter.items():
Text = line_name + " : " + ' '.join([f"{label}={count}" for label, count in line_counter.items()])
cv2.putText(img, Text, (x,y), 6, 1, (104, 52, 235), 3, cv2.LINE_AA)
y = y+offset
return img
# Function to calculate delta time for FPS when using cuda
def time_synchronized():
torch.cuda.synchronize() if torch.cuda.is_available() else None
return time.time()
# Draw the boxes having tracking indentities
def draw_boxes(img, bbox, object_id, identities=None, offset=(0, 0)):
height, width, _ = img.shape
# Cleaning any previous Enteries
[data_deque.pop(key) for key in set(data_deque) if key not in identities]
for i, box in enumerate(bbox):
x1, y1, x2, y2 = [int(i) +offset[0] for i in box]
box_height = (y2-y1)
center = (int((x2+x1)/ 2), int((y2+y2)/2))
id = int(identities[i]) if identities is not None else 0
if id not in set(data_deque):
data_deque[id] = deque(maxlen= 100)
color = compute_color_for_labels(object_id[i])
obj_name = class_names[object_id[i]]
label = '%s' % (obj_name)
data_deque[id].appendleft(center) #appending left to speed up the check we will check the latest map
UI_box(box, img, label=label + str(id), color=color, line_thickness=3, boundingbox=True)
if len(data_deque[id]) >=2:
update_counter(centerpoints = data_deque[id], obj_name = obj_name)
return img
# Tracking class to integrate Deepsort tracking with our detector
class Tracker():
def __init__(self, filter_classes=None, model='yolox-s', ckpt='wieghts/yolox_s.pth'):
self.detector = Predictor(model, ckpt)
cfg = get_config()
cfg.merge_from_file("deep_sort/configs/deep_sort.yaml")
self.deepsort = DeepSort(cfg.DEEPSORT.REID_CKPT,
max_dist=cfg.DEEPSORT.MAX_DIST, min_confidence=cfg.DEEPSORT.MIN_CONFIDENCE,
nms_max_overlap=cfg.DEEPSORT.NMS_MAX_OVERLAP, max_iou_distance=cfg.DEEPSORT.MAX_IOU_DISTANCE,
max_age=cfg.DEEPSORT.MAX_AGE, n_init=cfg.DEEPSORT.N_INIT, nn_budget=cfg.DEEPSORT.NN_BUDGET,
use_cuda=True)
self.filter_classes = filter_classes
def update(self, image, visual = True, logger_=True):
height, width, _ = image.shape
_,info = self.detector.inference(image, visual=False, logger_=logger_)
outputs = []
if info['box_nums']>0:
bbox_xywh = []
scores = []
objectids = []
for [x1, y1, x2, y2], class_id, score in zip(info['boxes'],info['class_ids'],info['scores']):
if self.filter_classes:
if class_names[class_id] not in set(filter_classes):
continue
bbox_xywh.append([int((x1+x2)/2), int((y1+y2)/2), x2-x1, y2-y1])
objectids.append(info['class_ids'])
scores.append(score)
bbox_xywh = torch.Tensor(bbox_xywh)
outputs = self.deepsort.update(bbox_xywh, scores, info['class_ids'],image)
data = []
if len(outputs) > 0:
if visual:
if len(outputs) > 0:
bbox_xyxy =outputs[:, :4]
identities =outputs[:, -2]
object_id =outputs[:, -1]
image = draw_boxes(image, bbox_xyxy, object_id,identities)
return image, outputs
if __name__=='__main__':
tracker = Tracker(filter_classes=None, model='yolox-s', ckpt='weights/yolox_s.pth') # instantiate Tracker
cap = cv2.VideoCapture(sys.argv[1])
width = cap.get(cv2.CAP_PROP_FRAME_WIDTH) # float
height = cap.get(cv2.CAP_PROP_FRAME_HEIGHT) # float
fps = cap.get(cv2.CAP_PROP_FPS)
property_id = int(cv2.CAP_PROP_FRAME_COUNT)
length = int(cv2.VideoCapture.get(cap, property_id))
vid_writer = cv2.VideoWriter(
f'count_demo_{sys.argv[1]}', cv2.VideoWriter_fourcc(*"mp4v"), fps, (int(width), int(height))
) # open one video
frame_count = 0
fps = 0.0
while True:
ret_val, frame = cap.read() # read frame from video
t1 = time_synchronized()
if ret_val:
frame, bbox = tracker.update(frame, visual=True, logger_=False) # feed one frame and get result
frame = draw_lines(lines, img = frame)
frame = draw_results(img= frame)
vid_writer.write(frame)
ch = cv2.waitKey(1)
if ch == 27 or ch == ord("q") or ch == ord("Q"):
break
fps = ( fps + (1./(time_synchronized()-t1)) ) / 2
else:
break
cap.release()
vid_writer.release()
cv2.destroyAllWindows()