前言
这篇博客针对《Python+Yolov8+onnx-deepsort方法物体人流量识别统计》编写代码，代码整洁，规则，易读。 学习与应用推荐首选。
运行结果
文章目录
一、所需工具软件
二、使用步骤
1. 主要代码
2. 运行结果
三、在线协助
一、所需工具软件
1. Python
2. Pycharm
二、使用步骤
代码如下（示例）：
import cv2
from yolov8 import YOLOv8
from deep_sort_pytorch.deep_sort import DeepSort
from deep_sort_pytorch.utils.parser import get_config
from collections import deque
def init_tracker():
print("init_tracker")
global deepsort
cfg_deep = get_config()
cfg_deep.merge_from_file("deep_sort_pytorch/configs/deep_sort.yaml")
deepsort= DeepSort(cfg_deep.DEEPSORT.REID_CKPT,
max_dist=cfg_deep.DEEPSORT.MAX_DIST, min_confidence=cfg_deep.DEEPSORT.MIN_CONFIDENCE,
nms_max_overlap=cfg_deep.DEEPSORT.NMS_MAX_OVERLAP, max_iou_distance=cfg_deep.DEEPSORT.MAX_IOU_DISTANCE,
max_age=cfg_deep.DEEPSORT.MAX_AGE, n_init=cfg_deep.DEEPSORT.N_INIT, nn_budget=cfg_deep.DEEPSORT.NN_BUDGET,
use_cuda=True)
##########################################################################################
def xyxy_to_xywh(*xyxy):
"""" Calculates the relative bounding box from absolute pixel values. """
bbox_left = min([xyxy[0].item(), xyxy[2].item()])
bbox_top = min([xyxy[1].item(), xyxy[3].item()])
bbox_w = abs(xyxy[0].item() - xyxy[2].item())
bbox_h = abs(xyxy[1].item() - xyxy[3].item())
x_c = (bbox_left + bbox_w / 2)
y_c = (bbox_top + bbox_h / 2)
w = bbox_w
h = bbox_h
return x_c, y_c, w, h
def xyxy_to_tlwh(bbox_xyxy):
tlwh_bboxs = []
for i, box in enumerate(bbox_xyxy):
x1, y1, x2, y2 = [int(i) for i in box]
top = x1
left = y1
w = int(x2 - x1)
h = int(y2 - y1)
tlwh_obj = [top, left, w, h]
tlwh_bboxs.append(tlwh_obj)
return tlwh_bboxs
def compute_color_for_labels(label):
"""
Simple function that adds fixed color depending on the class
"""
if label == 0: #person
color = (85,45,255)
elif label == 2: # Car
color = (222,82,175)
elif label == 3: # Motobike
color = (0, 204, 255)
elif label == 5: # Bus
color = (0, 149, 255)
else:
color = [int((p * (label ** 2 - label + 1)) % 255) for p in palette]
return tuple(color)
def draw_border(img, pt1, pt2, color, thickness, r, d):
x1,y1 = pt1
x2,y2 = pt2
# Top left
cv2.line(img, (x1 + r, y1), (x1 + r + d, y1), color, thickness)
cv2.line(img, (x1, y1 + r), (x1, y1 + r + d), color, thickness)
cv2.ellipse(img, (x1 + r, y1 + r), (r, r), 180, 0, 90, color, thickness)
# Top right
cv2.line(img, (x2 - r, y1), (x2 - r - d, y1), color, thickness)
cv2.line(img, (x2, y1 + r), (x2, y1 + r + d), color, thickness)
cv2.ellipse(img, (x2 - r, y1 + r), (r, r), 270, 0, 90, color, thickness)
# Bottom left
cv2.line(img, (x1 + r, y2), (x1 + r + d, y2), color, thickness)
cv2.line(img, (x1, y2 - r), (x1, y2 - r - d), color, thickness)
cv2.ellipse(img, (x1 + r, y2 - r), (r, r), 90, 0, 90, color, thickness)
# Bottom right
cv2.line(img, (x2 - r, y2), (x2 - r - d, y2), color, thickness)
cv2.line(img, (x2, y2 - r), (x2, y2 - r - d), color, thickness)
cv2.ellipse(img, (x2 - r, y2 - r), (r, r), 0, 0, 90, color, thickness)
cv2.rectangle(img, (x1 + r, y1), (x2 - r, y2), color, -1, cv2.LINE_AA)
cv2.rectangle(img, (x1, y1 + r), (x2, y2 - r - d), color, -1, cv2.LINE_AA)
cv2.circle(img, (x1 +r, y1+r), 2, color, 12)
cv2.circle(img, (x2 -r, y1+r), 2, color, 12)
cv2.circle(img, (x1 +r, y2-r), 2, color, 12)
cv2.circle(img, (x2 -r, y2-r), 2, color, 12)
return img
def UI_box(x, img, color=None, label=None, line_thickness=None):
# Plots one bounding box on image img
tl = line_thickness or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1 # line/font thickness
color = color or [random.randint(0, 255) for _ in range(3)]
c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3]))
def intersect(A,B,C,D):
return ccw(A,C,D) != ccw(B,C,D) and ccw(A,B,C) != ccw(A,B,D)
def ccw(A,B,C):
return (C[1]-A[1]) * (B[0]-A[0]) > (B[1]-A[1]) * (C[0]-A[0])
def get_direction(point1, point2):
direction_str = ""
def draw_boxes(img, bbox, names,object_id, identities=None, offset=(0, 0)):
cv2.line(img, line[0], line[1], (0,0,255), 3)
height, width, _ = img.shape
# remove tracked point from buffer if object is lost
for key in list(data_deque):
if key not in identities:
data_deque.pop(key)
for i, box in enumerate(bbox):
x1, y1, x2, y2 = [int(i) for i in box]
x1 += offset[0]
x2 += offset[0]
y1 += offset[1]
y2 += offset[1]
# code to find center of bottom edge
center = (int((x2+x1)/ 2), int((y2+y2)/2))
# get ID of object
id = int(identities[i]) if identities is not None else 0
# create new buffer for new object
if id not in data_deque:
data_deque[id] = deque(maxlen= 64)
color = compute_color_for_labels(object_id[i])
obj_name = names[object_id[i]]
label = '{}{:d}'.format("", id) + ":"+ '%s' % (obj_name)
# add center to buffer
data_deque[id].appendleft(center)
if len(data_deque[id]) >= 2:
direction = get_direction(data_deque[id][0], data_deque[id][1])
if intersect(data_deque[id][0], data_deque[id][1], line[0], line[1]):
cv2.line(img, line[0], line[1], (255, 255, 255), 3)
if "South" in direction:
if obj_name not in object_counter:
object_counter[obj_name] = 1
else:
object_counter[obj_name] += 1
if "North" in direction:
if obj_name not in object_counter1:
object_counter1[obj_name] = 1
else:
object_counter1[obj_name] += 1
UI_box(box, img, label=label, color=color,
运行结果
三、在线协助：
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