YOLO_NAS_SORT
SORT (People)
Step0. 필요 라이브러리 다운로드
#-- requirments.txt
# base -------------------------------------------------------------------------
torch>=1.7.0
torchvision>=0.8.1
numpy==1.23.1 # otherwise issues with track eval
loguru>=0.7.0
opencv-python>=4.6.0
PyYAML>=5.3.1 # read tracker configs
pandas>=1.1.4 # export matrix
gdown>=4.7.1 # google drive model download
GitPython>=3.1.0 # track eval cloning
# tracker-specific packages ----------------------------------------------------
filterpy>=1.4.5 # OCSORT & DeepOCSORT
# Export ----------------------------------------------------------------------
# onnx>=1.12.0 # ONNX export
# onnxsim>=0.4.1 # ONNX simplifier
# nvidia-pyindex # TensorRT export
# nvidia-tensorrt # TensorRT export
# openvino-dev>=2022.3 # OpenVINO export
# onnx2tf>=1.10.0 # TFLite export
# Hyperparam search -----------------------------------------------------------
# optuna # genetic algo
# plotly # hyper param importance and pareto front plots
# kaleido
# joblib
Step1. sort.py 파일 복사 후 같은 작업폴더에 붙여넣기
#-- sort.py
"""
SORT: A Simple, Online and Realtime Tracker
Copyright (C) 2016-2020 Alex Bewley alex@bewley.ai
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
"""
from __future__ import print_function
import os
import numpy as np
import matplotlib
#matplotlib.use('TkAgg')
import matplotlib.pyplot as plt
import matplotlib.patches as patches
from skimage import io
import glob
import time
import argparse
from filterpy.kalman import KalmanFilter
np.random.seed(0)
def linear_assignment(cost_matrix):
try:
import lap
_, x, y = lap.lapjv(cost_matrix, extend_cost=True)
return np.array([[y[i], i] for i in x if i >= 0]) #
except ImportError:
from scipy.optimize import linear_sum_assignment
x, y = linear_sum_assignment(cost_matrix)
return np.array(list(zip(x, y)))
def iou_batch(bb_test, bb_gt):
"""
From SORT: Computes IOU between two bboxes in the form [x1,y1,x2,y2]
"""
bb_gt = np.expand_dims(bb_gt, 0)
bb_test = np.expand_dims(bb_test, 1)
xx1 = np.maximum(bb_test[..., 0], bb_gt[..., 0])
yy1 = np.maximum(bb_test[..., 1], bb_gt[..., 1])
xx2 = np.minimum(bb_test[..., 2], bb_gt[..., 2])
yy2 = np.minimum(bb_test[..., 3], bb_gt[..., 3])
w = np.maximum(0., xx2 - xx1)
h = np.maximum(0., yy2 - yy1)
wh = w * h
o = wh / ((bb_test[..., 2] - bb_test[..., 0]) * (bb_test[..., 3] - bb_test[..., 1])
+ (bb_gt[..., 2] - bb_gt[..., 0]) * (bb_gt[..., 3] - bb_gt[..., 1]) - wh)
return (o)
def convert_bbox_to_z(bbox):
"""
Takes a bounding box in the form [x1,y1,x2,y2] and returns z in the form
[x,y,s,r] where x,y is the centre of the box and s is the scale/area and r is
the aspect ratio
"""
w = bbox[2] - bbox[0]
h = bbox[3] - bbox[1]
x = bbox[0] + w / 2.
y = bbox[1] + h / 2.
s = w * h # scale is just area
r = w / float(h)
return np.array([x, y, s, r]).reshape((4, 1))
def convert_x_to_bbox(x, score=None):
"""
Takes a bounding box in the centre form [x,y,s,r] and returns it in the form
[x1,y1,x2,y2] where x1,y1 is the top left and x2,y2 is the bottom right
"""
w = np.sqrt(x[2] * x[3])
h = x[2] / w
if (score == None):
return np.array([x[0] - w / 2., x[1] - h / 2., x[0] + w / 2., x[1] + h / 2.]).reshape((1, 4))
else:
return np.array([x[0] - w / 2., x[1] - h / 2., x[0] + w / 2., x[1] + h / 2., score]).reshape((1, 5))
class KalmanBoxTracker(object):
"""
This class represents the internal state of individual tracked objects observed as bbox.
"""
count = 0
def __init__(self, bbox):
"""
Initialises a tracker using initial bounding box.
"""
# define constant velocity model
self.kf = KalmanFilter(dim_x=7, dim_z=4)
self.kf.F = np.array(
[[1, 0, 0, 0, 1, 0, 0], [0, 1, 0, 0, 0, 1, 0], [0, 0, 1, 0, 0, 0, 1], [0, 0, 0, 1, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0, 1]])
self.kf.H = np.array(
[[1, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0]])
self.kf.R[2:, 2:] *= 10.
self.kf.P[4:, 4:] *= 1000. # give high uncertainty to the unobservable initial velocities
self.kf.P *= 10.
self.kf.Q[-1, -1] *= 0.01
self.kf.Q[4:, 4:] *= 0.01
self.kf.x[:4] = convert_bbox_to_z(bbox)
self.time_since_update = 0
self.id = KalmanBoxTracker.count
KalmanBoxTracker.count += 1
self.history = []
self.hits = 0
self.hit_streak = 0
self.age = 0
def update(self, bbox):
"""
Updates the state vector with observed bbox.
"""
self.time_since_update = 0
self.history = []
self.hits += 1
self.hit_streak += 1
self.kf.update(convert_bbox_to_z(bbox))
def predict(self):
"""
Advances the state vector and returns the predicted bounding box estimate.
"""
if ((self.kf.x[6] + self.kf.x[2]) <= 0):
self.kf.x[6] *= 0.0
self.kf.predict()
self.age += 1
if (self.time_since_update > 0):
self.hit_streak = 0
self.time_since_update += 1
self.history.append(convert_x_to_bbox(self.kf.x))
return self.history[-1]
def get_state(self):
"""
Returns the current bounding box estimate.
"""
return convert_x_to_bbox(self.kf.x)
def associate_detections_to_trackers(detections, trackers, iou_threshold=0.3):
"""
Assigns detections to tracked object (both represented as bounding boxes)
Returns 3 lists of matches, unmatched_detections and unmatched_trackers
"""
if (len(trackers) == 0):
return np.empty((0, 2), dtype=int), np.arange(len(detections)), np.empty((0, 5), dtype=int)
iou_matrix = iou_batch(detections, trackers)
if min(iou_matrix.shape) > 0:
a = (iou_matrix > iou_threshold).astype(np.int32)
if a.sum(1).max() == 1 and a.sum(0).max() == 1:
matched_indices = np.stack(np.where(a), axis=1)
else:
matched_indices = linear_assignment(-iou_matrix)
else:
matched_indices = np.empty(shape=(0, 2))
unmatched_detections = []
for d, det in enumerate(detections):
if (d not in matched_indices[:, 0]):
unmatched_detections.append(d)
unmatched_trackers = []
for t, trk in enumerate(trackers):
if (t not in matched_indices[:, 1]):
unmatched_trackers.append(t)
# filter out matched with low IOU
matches = []
for m in matched_indices:
if (iou_matrix[m[0], m[1]] < iou_threshold):
unmatched_detections.append(m[0])
unmatched_trackers.append(m[1])
else:
matches.append(m.reshape(1, 2))
if (len(matches) == 0):
matches = np.empty((0, 2), dtype=int)
else:
matches = np.concatenate(matches, axis=0)
return matches, np.array(unmatched_detections), np.array(unmatched_trackers)
class Sort(object):
def __init__(self, max_age=1, min_hits=3, iou_threshold=0.3):
"""
Sets key parameters for SORT
"""
self.max_age = max_age
self.min_hits = min_hits
self.iou_threshold = iou_threshold
self.trackers = []
self.frame_count = 0
def update(self, dets=np.empty((0, 5))):
"""
Params:
dets - a numpy array of detections in the format [[x1,y1,x2,y2,score],[x1,y1,x2,y2,score],...]
Requires: this method must be called once for each frame even with empty detections (use np.empty((0, 5)) for frames without detections).
Returns the a similar array, where the last column is the object ID.
NOTE: The number of objects returned may differ from the number of detections provided.
"""
self.frame_count += 1
# get predicted locations from existing trackers.
trks = np.zeros((len(self.trackers), 5))
to_del = []
ret = []
for t, trk in enumerate(trks):
pos = self.trackers[t].predict()[0]
trk[:] = [pos[0], pos[1], pos[2], pos[3], 0]
if np.any(np.isnan(pos)):
to_del.append(t)
trks = np.ma.compress_rows(np.ma.masked_invalid(trks))
for t in reversed(to_del):
self.trackers.pop(t)
matched, unmatched_dets, unmatched_trks = associate_detections_to_trackers(dets, trks, self.iou_threshold)
# update matched trackers with assigned detections
for m in matched:
self.trackers[m[1]].update(dets[m[0], :])
# create and initialise new trackers for unmatched detections
for i in unmatched_dets:
trk = KalmanBoxTracker(dets[i, :])
self.trackers.append(trk)
i = len(self.trackers)
for trk in reversed(self.trackers):
d = trk.get_state()[0]
if (trk.time_since_update < 1) and (trk.hit_streak >= self.min_hits or self.frame_count <= self.min_hits):
ret.append(np.concatenate((d, [trk.id + 1])).reshape(1, -1)) # +1 as MOT benchmark requires positive
i -= 1
# remove dead tracklet
if (trk.time_since_update > self.max_age):
self.trackers.pop(i)
if (len(ret) > 0):
return np.concatenate(ret)
return np.empty((0, 5))
def parse_args():
"""Parse input arguments."""
parser = argparse.ArgumentParser(description='SORT demo')
parser.add_argument('--display', dest='display', help='Display online tracker output (slow) [False]',
action='store_true')
parser.add_argument("--seq_path", help="Path to detections.", type=str, default='data')
parser.add_argument("--phase", help="Subdirectory in seq_path.", type=str, default='train')
parser.add_argument("--max_age",
help="Maximum number of frames to keep alive a track without associated detections.",
type=int, default=1)
parser.add_argument("--min_hits",
help="Minimum number of associated detections before track is initialised.",
type=int, default=3)
parser.add_argument("--iou_threshold", help="Minimum IOU for match.", type=float, default=0.3)
args = parser.parse_args()
return args
if __name__ == '__main__':
# all train
args = parse_args()
display = args.display
phase = args.phase
total_time = 0.0
total_frames = 0
colours = np.random.rand(32, 3) # used only for display
if (display):
if not os.path.exists('mot_benchmark'):
print(
'\n\tERROR: mot_benchmark link not found!\n\n Create a symbolic link to the MOT benchmark\n (https://motchallenge.net/data/2D_MOT_2015/#download). E.g.:\n\n $ ln -s /path/to/MOT2015_challenge/2DMOT2015 mot_benchmark\n\n')
exit()
plt.ion()
fig = plt.figure()
ax1 = fig.add_subplot(111, aspect='equal')
if not os.path.exists('output'):
os.makedirs('output')
pattern = os.path.join(args.seq_path, phase, '*', 'det', 'det.txt')
for seq_dets_fn in glob.glob(pattern):
mot_tracker = Sort(max_age=args.max_age,
min_hits=args.min_hits,
iou_threshold=args.iou_threshold) # create instance of the SORT tracker
seq_dets = np.loadtxt(seq_dets_fn, delimiter=',')
seq = seq_dets_fn[pattern.find('*'):].split(os.path.sep)[0]
with open(os.path.join('output', '%s.txt' % (seq)), 'w') as out_file:
print("Processing %s." % (seq))
for frame in range(int(seq_dets[:, 0].max())):
frame += 1 # detection and frame numbers begin at 1
dets = seq_dets[seq_dets[:, 0] == frame, 2:7]
dets[:, 2:4] += dets[:, 0:2] # convert to [x1,y1,w,h] to [x1,y1,x2,y2]
total_frames += 1
if (display):
fn = os.path.join('mot_benchmark', phase, seq, 'img1', '%06d.jpg' % (frame))
im = io.imread(fn)
ax1.imshow(im)
plt.title(seq + ' Tracked Targets')
start_time = time.time()
trackers = mot_tracker.update(dets)
cycle_time = time.time() - start_time
total_time += cycle_time
for d in trackers:
print('%d,%d,%.2f,%.2f,%.2f,%.2f,1,-1,-1,-1' % (frame, d[4], d[0], d[1], d[2] - d[0], d[3] - d[1]),
file=out_file)
if (display):
d = d.astype(np.int32)
ax1.add_patch(patches.Rectangle((d[0], d[1]), d[2] - d[0], d[3] - d[1], fill=False, lw=3,
ec=colours[d[4] % 32, :]))
if (display):
fig.canvas.flush_events()
plt.draw()
ax1.cla()
print("Total Tracking took: %.3f seconds for %d frames or %.1f FPS" % (
total_time, total_frames, total_frames / total_time))
if (display):
print("Note: to get real runtime results run without the option: --display")
Step3. people_counting.py 파일 작성
-
필요 라이브러리 import
#-- 필요 라이브러리 import import cv2 import torch from super_gradients.training import models import numpy as np import math from sort import * -
카메라 설정 및 GPU 설정
#-- 카메라 설정 및 GPU 설정 cap = cv2.VideoCapture("video/people.mp4") frame_width = int(cap.get(3)) frame_height = int(cap.get(4)) device = torch.device("cuda:0") if torch.cuda.is_available() else torch.device("cpu") -
모델 가져오기
#-- yolo_nas_small model get model = models.get('yolo_nas_s', pretrained_weights="coco").to(device) count = 0 classNames = ["person", "bicycle", "car", "motorbike", "aeroplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "sofa", "pottedplant", "bed", "diningtable", "toilet", "tvmonitor", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush" ] out = cv2.VideoWriter('Output.avi', cv2.VideoWriter_fourcc('M', 'J', 'P', 'G'), 10, (frame_width, frame_height)) tracker = Sort(max_age=20, min_hits=3, iou_threshold=0.3) totalCountUp = [] totalCountDown = [] limitup = [103, 161, 296, 161] limitdown = [527, 489, 735, 489] -
반복문을 돌면서 동영상에서 people counting
while True: ret, frame = cap.read() # 비디오 프레임 읽기 count += 1 # 프레임 카운트 증가 if ret: detections = np.empty((0, 5)) # 모델을 사용하여 객체 검출 및 추적 수행 result = list(model.predict(frame, conf=0.35))[0] bbox_xyxys = result.prediction.bboxes_xyxy.tolist() # 객체의 경계상자 좌표 confidences = result.prediction.confidence # 객체의 신뢰도 labels = result.prediction.labels.tolist() # 객체의 레이블 for (bbox_xyxy, confidence, cls) in zip(bbox_xyxys, confidences, labels): bbox = np.array(bbox_xyxy) x1, y1, x2, y2 = bbox[0], bbox[1], bbox[2], bbox[3] x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2) classname = int(cls) class_name = classNames[classname] conf = math.ceil((confidence*100))/100 if class_name == "person" and conf > 0.3: currentarray = np.array([x1, y1, x2, y2, conf]) detections = np.vstack((detections, currentarray)) resultsTracker = tracker.update(detections) # 객체 추적 업데이트 # 경계선 그리기 cv2.line(frame, (limitup[0], limitup[1]), (limitup[2], limitup[3]), (255,0,0), 3) # 상한선 cv2.line(frame, (limitdown[0], limitdown[1]), (limitdown[2], limitdown[3]), (255,0,0), 3) # 하한선 for result in resultsTracker: x1, y1, x2, y2, id = result x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2) # 객체를 사각형으로 표시 cv2.rectangle(frame, (x1, y1), (x2, y2), (255, 144, 30), 3) cx, cy = int((x1+x2)/2), int((y1+y2)/2) cv2.circle(frame, (cx, cy), 5, (255, 0, 255), cv2.FILLED) label = f'{int(id)}' t_size = cv2.getTextSize(label, 0, fontScale=1, thickness=2)[0] c2 = x1 + t_size[0], y1 - t_size[1] - 3 # 객체 ID와 함께 사각형 위에 텍스트 표시 cv2.rectangle(frame, (x1, y1), c2, [255, 0, 255], -1, cv2.LINE_AA) cv2.putText(frame, label, (x1, y1-2), 0, 1, [255, 255, 255], thickness=1, lineType=cv2.LINE_AA) # 상한선과 하한선을 통과한 객체 수 계산 및 표시 if limitup[0] < cx < limitup[2] and limitup[1] - 15 < cy < limitup[3] + 15: if totalCountUp.count(id) == 0: totalCountUp.append(id) cv2.line(frame, (limitup[0], limitup[1]), (limitup[2], limitup[3]), (0, 255, 0), 3) if limitdown[0] < cx < limitdown[2] and limitdown[1] - 15 < cy < limitdown[3] + 15: if totalCountDown.count(id) == 0: totalCountDown.append(id) cv2.line(frame, (limitdown[0], limitdown[1]), (limitdown[2], limitdown[3]), (0, 255, 0), 3) # 상단 영역에 인원 수 표시 cv2.rectangle(frame, (100, 65), (441, 97), [255, 0, 255], -1, cv2.LINE_AA) cv2.putText(frame, str("Person Entering") + ":" + str(len(totalCountUp)), (141, 91), 0, 1, [255, 255, 255], thickness=2, lineType=cv2.LINE_AA) # 하단 영역에 인원 수 표시 cv2.rectangle(frame, (710, 65), (1100, 97), [255, 0, 255], -1, cv2.LINE_AA) cv2.putText(frame, str("Person Leaving") + ":" + str(len(totalCountDown)), (741, 91), 0, 1, [255, 255, 255], thickness=2, lineType=cv2.LINE_AA) resize_frame = cv2.resize(frame, (0, 0), fx=0.5, fy=0.5, interpolation=cv2.INTER_AREA) out.write(frame) cv2.imshow("Frame", frame) if cv2.waitKey(1) & 0xFF == ord('1'): # '1' 키를 누르면 반복문 종료 break else: break -
자원 반납
out.release() cap.release() cv2.destroyAllWindows() -
Output 비디오 확인