网站建设内部下单流程图,深圳浪尖工业设计公司,应用商店wordpress,聊城做网站的公司行情大家好#xff0c;YOLO(You Only Look Once) 是由Joseph Redmon和Ali开发的一种对象检测和图像分割模型。
YOLO的第一个版本于2015年发布#xff0c;由于其高速度和准确性#xff0c;瞬间得到了广大AI爱好者的喜爱。 Ultralytics YOLOv8则是一款前沿、最先进(SOTA)的模型YOLO(You Only Look Once) 是由Joseph Redmon和Ali开发的一种对象检测和图像分割模型。
YOLO的第一个版本于2015年发布由于其高速度和准确性瞬间得到了广大AI爱好者的喜爱。 Ultralytics YOLOv8则是一款前沿、最先进(SOTA)的模型基于先前YOLO版本的成功引入了新功能和改进进一步提升性能和灵活性。
YOLOv8设计快速、准确且易于使用使其成为各种物体检测与跟踪、实例分割、图像分类和姿态估计任务的绝佳选择。 项目地址
https://github.com/ultralytics/ultralytics
其中官方提供了示例通过Python代码即可实现YOLOv8对象检测算法模型使用预训练模型来检测我们的目标。而且对电脑需求也不高CPU就能运行代码。
今天我就给大家介绍三个使用YOLOv8制作的计数器非常实用。使用到Python版本以及相关Python库。
Python 3.9.7
ultralytics 8.0.178
opencv-contrib-python 4.8.1.78
opencv-python 4.8.0.74
cvzone 1.5.6技术交流群
前沿技术资讯、算法交流、求职内推、算法竞赛、面试交流(校招、社招、实习)等、与 10000来自港科大、北大、清华、中科院、CMU、腾讯、百度等名校名企开发者互动交流~
建了技术交流群星球想要本文源码、进交流群的同学可以直接加微信号mlc2060。加的时候备注一下研究方向 学校/公司即可。然后就可以拉你进群了。 方式①、添加微信号mlc2060备注技术交流 方式②、微信搜索公众号机器学习社区后台回复技术交流 / 01 /
客流检测器
使用OpenCV检测顾客并且设定客人进出区域实现实时计算进出顾客的数量。 客流量统计对于零售行业来说是非常重要的。
统计每天的进出店人数、过店人数以及人均驻留时间等。
依据这些数据经营者可以对店铺的经营策略进行调整实现店铺的经营效益最大化。
接下来就来看一下客流计数器的检测代码吧
import cv2
import numpy as np
from tracker import \*
import cvzone
import time bg\_subtractor cv2.createBackgroundSubtractorMOG2(history200, varThreshold140) # 打开视频
video\_capture cv2.VideoCapture(rstore.mp4) def RGB(event, x, y, flags, param): if event cv2.EVENT\_MOUSEMOVE: point \[x, y\] print(point) cv2.namedWindow(RGB)
cv2.setMouseCallback(RGB, RGB)
tracker Tracker() area1 \[(213, 165), (200, 189), (693, 373), (697, 341)\]
area2 \[(195, 199), (186, 213), (683, 404), (689, 388)\]
er {}
counter1 \[\]
ex {}
counter2 \[\]
while True: ret, frame video\_capture.read() if not ret: break frame cv2.resize(frame, (1028, 500)) mask bg\_subtractor.apply(frame) \_, mask cv2.threshold(mask, 245, 255, cv2.THRESH\_BINARY) contours, \_ cv2.findContours(mask, cv2.RETR\_EXTERNAL, cv2.CHAIN\_APPROX\_SIMPLE) list \[\] for cnt in contours: area cv2.contourArea(cnt) if area 1500: # cv2.drawContours(frame, \[cnt\], -1, (0, 255, 0), 2) x, y, w, h cv2.boundingRect(cnt) list.append(\[x, y, w, h\]) bbox\_idx tracker.update(list) for bbox in bbox\_idx: x1, y1, x2, y2, id bbox cx int(x1 x1 x2) // 2 cy int(y1 y1 y2) // 2 result cv2.pointPolygonTest(np.array(area1, np.int32), ((cx, cy)), False) if result 0: er\[id\] (cx, cy) if id in er: result1 cv2.pointPolygonTest(np.array(area2, np.int32), ((cx, cy)), False) if result1 0: cv2.rectangle(frame, (x1, y1), (x2 x1, y2 y1), (0, 255, 0), 3) cvzone.putTextRect(frame, f{id}, (cx, cy), 2, 2) cv2.circle(frame, (cx, cy), 5, (0, 255, 0), \-1) if counter1.count(id) 0: counter1.append(id) result2 cv2.pointPolygonTest(np.array(area2, np.int32), ((cx, cy)), False) if result2 0: ex\[id\] (cx, cy) if id in ex: result3 cv2.pointPolygonTest(np.array(area1, np.int32), ((cx, cy)), False) if result3 0: cv2.rectangle(frame, (x1, y1), (x2 x1, y2 y1), (0, 0, 255), 3) cvzone.putTextRect(frame, f{id}, (cx, cy), 2, 2) cv2.circle(frame, (cx, cy), 5, (0, 255, 0), \-1) if counter2.count(id) 0: counter2.append(id) cv2.polylines(frame, \[np.array(area1, np.int32)\], True, (0, 0, 255), 2) cv2.polylines(frame, \[np.array(area2, np.int32)\], True, (0, 0, 255), 2) Enter len(counter1) Exit len(counter2) cvzone.putTextRect(frame, fENTER:-{Enter}, (50, 60), 2, 2) cvzone.putTextRect(frame, fEXIT:-{Exit}, (50, 130), 2, 2) cv2.imshow(RGB, frame) time.sleep(0.01) if cv2.waitKey(1) 0xFF 27: # Press Esc to exit break # 释放资源, 关闭窗口
video\_capture.release()
cv2.destroyAllWindows()结果如下。 / 02 /
鸡蛋计数器
使用OpenCV和YOLOv8检测鸡蛋个数。
能够高效、准确、安全可靠的完成鸡蛋个数的统计工作。 基于yolov8-seg实例分割的实时检测。
具体代码如下。
import cv2
from yolo\_segmentation import YOLOSEG
import cvzone
from tracker import \*
import numpy as np ys YOLOSEG(best.pt) my\_file open(coco1.txt, r)
data my\_file.read()
class\_list data.split(\\n) cap cv2.VideoCapture(egg.mp4)
count 0 def RGB(event, x, y, flags, param): if event cv2.EVENT\_MOUSEMOVE: point \[x, y\] print(point) cv2.namedWindow(RGB)
cv2.setMouseCallback(RGB, RGB)
tracker Tracker()
area \[(434, 39), (453, 629), (473, 634), (456, 36)\]
counter1 \[\]
while True: ret, frame cap.read() if not ret: break frame cv2.resize(frame, (1020, 700)) overlay frame.copy() alpha 0.5 bboxes, classes, segmentations, scores ys.detect(frame) bbox\_idx tracker.update(bboxes) for bbox, seg in zip(bbox\_idx, segmentations): x3, y3, x4, y4, id bbox cx int(x3 x4) // 2 cy int(y3 y4) // 2 result cv2.pointPolygonTest(np.array(area, np.int32), ((cx, cy)), False) if result 0: # cv2.rectangle(frame, (x3, y3), (x4, y4), (255, 0, 0), 2) # cv2.polylines(frame, \[seg\], True, (0, 0, 255), 4) cv2.circle(frame, (cx, cy), 4, (0, 255, 0), \-1) cv2.fillPoly(overlay, \[seg\], (0, 0, 255)) cv2.addWeighted(overlay, alpha, frame, 1 - alpha, 2, frame) cvzone.putTextRect(frame, f{id}, (x3, y3), 1, 1) if counter1.count(id) 0: counter1.append(id) cv2.polylines(frame, \[np.array(area, np.int32)\], True, (255, 0, 0), 2) ca1 len(counter1) cvzone.putTextRect(frame, fEgg: {ca1}, (50, 60), 2, 2) cv2.imshow(RGB, frame) if cv2.waitKey(1) 0xFF 27: break
cap.release()
cv2.destroyAllWindows()
运行代码结果如下。 / 03 /
停车位计数器
使用OpenCV和YOLOv8检测停车场剩余车位。
提醒车主停车场各个区域的剩余车位信息。
使停车场车位管理更加规范有序提高车位使用率。
import cv2
import pandas as pd
import numpy as np
from ultralytics import YOLO
import time model YOLO(yolov8s.pt) def RGB(event, x, y, flags, param): if event cv2.EVENT\_MOUSEMOVE: colorsBGR \[x, y\] print(colorsBGR) cv2.namedWindow(RGB)
cv2.setMouseCallback(RGB, RGB) cap cv2.VideoCapture(parking1.mp4) my\_file open(coco.txt, r)
data my\_file.read()
class\_list data.split(\\n) area1 \[(52, 364), (30, 417), (73, 412), (88, 369)\] area2 \[(105, 353), (86, 428), (137, 427), (146, 358)\] area3 \[(159, 354), (150, 427), (204, 425), (203, 353)\] area4 \[(217, 352), (219, 422), (273, 418), (261, 347)\] area5 \[(274, 345), (286, 417), (338, 415), (321, 345)\] area6 \[(336, 343), (357, 410), (409, 408), (382, 340)\] area7 \[(396, 338), (426, 404), (479, 399), (439, 334)\] area8 \[(458, 333), (494, 397), (543, 390), (495, 330)\] area9 \[(511, 327), (557, 388), (603, 383), (549, 324)\] area10 \[(564, 323), (615, 381), (654, 372), (596, 315)\] area11 \[(616, 316), (666, 369), (703, 363), (642, 312)\] area12 \[(674, 311), (730, 360), (764, 355), (707, 308)\] while True: ret, frame cap.read() if not ret: break time.sleep(1) frame cv2.resize(frame, (1020, 500)) results model.predict(frame) # print(results) a results\[0\].boxes.boxes px pd.DataFrame(a).astype(float) # print(px) list1 \[\] list2 \[\] list3 \[\] list4 \[\] list5 \[\] list6 \[\] list7 \[\] list8 \[\] list9 \[\] list10 \[\] list11 \[\] list12 \[\] for index, row in px.iterrows(): # print(row) x1 int(row\[0\]) y1 int(row\[1\]) x2 int(row\[2\]) y2 int(row\[3\]) d int(row\[5\]) c class\_list\[d\] if car in c: cx int(x1 x2) // 2 cy int(y1 y2) // 2 results1 cv2.pointPolygonTest(np.array(area1, np.int32), ((cx, cy)), False) if results1 0: cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2) cv2.circle(frame, (cx, cy), 3, (0, 0, 255), \-1) list1.append(c) cv2.putText(frame, str(c), (x1, y1), cv2.FONT\_HERSHEY\_COMPLEX, 0.5, (255, 255, 255), 1) results2 cv2.pointPolygonTest(np.array(area2, np.int32), ((cx, cy)), False) if results2 0: cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2) cv2.circle(frame, (cx, cy), 3, (0, 0, 255), \-1) list2.append(c) results3 cv2.pointPolygonTest(np.array(area3, np.int32), ((cx, cy)), False) if results3 0: cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2) cv2.circle(frame, (cx, cy), 3, (0, 0, 255), \-1) list3.append(c) results4 cv2.pointPolygonTest(np.array(area4, np.int32), ((cx, cy)), False) if results4 0: cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2) cv2.circle(frame, (cx, cy), 3, (0, 0, 255), \-1) list4.append(c) results5 cv2.pointPolygonTest(np.array(area5, np.int32), ((cx, cy)), False) if results5 0: cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2) cv2.circle(frame, (cx, cy), 3, (0, 0, 255), \-1) list5.append(c) results6 cv2.pointPolygonTest(np.array(area6, np.int32), ((cx, cy)), False) if results6 0: cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2) cv2.circle(frame, (cx, cy), 3, (0, 0, 255), \-1) list6.append(c) results7 cv2.pointPolygonTest(np.array(area7, np.int32), ((cx, cy)), False) if results7 0: cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2) cv2.circle(frame, (cx, cy), 3, (0, 0, 255), \-1) list7.append(c) results8 cv2.pointPolygonTest(np.array(area8, np.int32), ((cx, cy)), False) if results8 0: cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2) cv2.circle(frame, (cx, cy), 3, (0, 0, 255), \-1) list8.append(c) results9 cv2.pointPolygonTest(np.array(area9, np.int32), ((cx, cy)), False) if results9 0: cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2) cv2.circle(frame, (cx, cy), 3, (0, 0, 255), \-1) list9.append(c) results10 cv2.pointPolygonTest(np.array(area10, np.int32), ((cx, cy)), False) if results10 0: cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2) cv2.circle(frame, (cx, cy), 3, (0, 0, 255), \-1) list10.append(c) results11 cv2.pointPolygonTest(np.array(area11, np.int32), ((cx, cy)), False) if results11 0: cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2) cv2.circle(frame, (cx, cy), 3, (0, 0, 255), \-1) list11.append(c) results12 cv2.pointPolygonTest(np.array(area12, np.int32), ((cx, cy)), False) if results12 0: cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2) cv2.circle(frame, (cx, cy), 3, (0, 0, 255), \-1) list12.append(c) a1 (len(list1)) a2 (len(list2)) a3 (len(list3)) a4 (len(list4)) a5 (len(list5)) a6 (len(list6)) a7 (len(list7)) a8 (len(list8)) a9 (len(list9)) a10 (len(list10)) a11 (len(list11)) a12 (len(list12)) o (a1 a2 a3 a4 a5 a6 a7 a8 a9 a10 a11 a12) space (12 - o) print(space) if a1 1: cv2.polylines(frame, \[np.array(area1, np.int32)\], True, (0, 0, 255), 2) cv2.putText(frame, str(1), (50, 441), cv2.FONT\_HERSHEY\_COMPLEX, 0.5, (0, 0, 255), 1) else: cv2.polylines(frame, \[np.array(area1, np.int32)\], True, (0, 255, 0), 2) cv2.putText(frame, str(1), (50, 441), cv2.FONT\_HERSHEY\_COMPLEX, 0.5, (255, 255, 255), 1) if a2 1: cv2.polylines(frame, \[np.array(area2, np.int32)\], True, (0, 0, 255), 2) cv2.putText(frame, str(2), (106, 440), cv2.FONT\_HERSHEY\_COMPLEX, 0.5, (0, 0, 255), 1) else: cv2.polylines(frame, \[np.array(area2, np.int32)\], True, (0, 255, 0), 2) cv2.putText(frame, str(2), (106, 440), cv2.FONT\_HERSHEY\_COMPLEX, 0.5, (255, 255, 255), 1) if a3 1: cv2.polylines(frame, \[np.array(area3, np.int32)\], True, (0, 0, 255), 2) cv2.putText(frame, str(3), (175, 436), cv2.FONT\_HERSHEY\_COMPLEX, 0.5, (0, 0, 255), 1) else: cv2.polylines(frame, \[np.array(area3, np.int32)\], True, (0, 255, 0), 2) cv2.putText(frame, str(3), (175, 436), cv2.FONT\_HERSHEY\_COMPLEX, 0.5, (255, 255, 255), 1) if a4 1: cv2.polylines(frame, \[np.array(area4, np.int32)\], True, (0, 0, 255), 2) cv2.putText(frame, str(4), (250, 436), cv2.FONT\_HERSHEY\_COMPLEX, 0.5, (0, 0, 255), 1) else: cv2.polylines(frame, \[np.array(area4, np.int32)\], True, (0, 255, 0), 2) cv2.putText(frame, str(4), (250, 436), cv2.FONT\_HERSHEY\_COMPLEX, 0.5, (255, 255, 255), 1) if a5 1: cv2.polylines(frame, \[np.array(area5, np.int32)\], True, (0, 0, 255), 2) cv2.putText(frame, str(5), (315, 429), cv2.FONT\_HERSHEY\_COMPLEX, 0.5, (0, 0, 255), 1) else: cv2.polylines(frame, \[np.array(area5, np.int32)\], True, (0, 255, 0), 2) cv2.putText(frame, str(5), (315, 429), cv2.FONT\_HERSHEY\_COMPLEX, 0.5, (255, 255, 255), 1) if a6 1: cv2.polylines(frame, \[np.array(area6, np.int32)\], True, (0, 0, 255), 2) cv2.putText(frame, str(6), (386, 421), cv2.FONT\_HERSHEY\_COMPLEX, 0.5, (0, 0, 255), 1) else: cv2.polylines(frame, \[np.array(area6, np.int32)\], True, (0, 255, 0), 2) cv2.putText(frame, str(6), (386, 421), cv2.FONT\_HERSHEY\_COMPLEX, 0.5, (255, 255, 255), 1) if a7 1: cv2.polylines(frame, \[np.array(area7, np.int32)\], True, (0, 0, 255), 2) cv2.putText(frame, str(7), (456, 414), cv2.FONT\_HERSHEY\_COMPLEX, 0.5, (0, 0, 255), 1) else: cv2.polylines(frame, \[np.array(area7, np.int32)\], True, (0, 255, 0), 2) cv2.putText(frame, str(7), (456, 414), cv2.FONT\_HERSHEY\_COMPLEX, 0.5, (255, 255, 255), 1) if a8 1: cv2.polylines(frame, \[np.array(area8, np.int32)\], True, (0, 0, 255), 2) cv2.putText(frame, str(8), (527, 406), cv2.FONT\_HERSHEY\_COMPLEX, 0.5, (0, 0, 255), 1) else: cv2.polylines(frame, \[np.array(area8, np.int32)\], True, (0, 255, 0), 2) cv2.putText(frame, str(8), (527, 406), cv2.FONT\_HERSHEY\_COMPLEX, 0.5, (255, 255, 255), 1) if a9 1: cv2.polylines(frame, \[np.array(area9, np.int32)\], True, (0, 0, 255), 2) cv2.putText(frame, str(9), (591, 398), cv2.FONT\_HERSHEY\_COMPLEX, 0.5, (0, 0, 255), 1) else: cv2.polylines(frame, \[np.array(area9, np.int32)\], True, (0, 255, 0), 2) cv2.putText(frame, str(9), (591, 398), cv2.FONT\_HERSHEY\_COMPLEX, 0.5, (255, 255, 255), 1) if a10 1: cv2.polylines(frame, \[np.array(area10, np.int32)\], True, (0, 0, 255), 2) cv2.putText(frame, str(10), (649, 384), cv2.FONT\_HERSHEY\_COMPLEX, 0.5, (0, 0, 255), 1) else: cv2.polylines(frame, \[np.array(area10, np.int32)\], True, (0, 255, 0), 2) cv2.putText(frame, str(10), (649, 384), cv2.FONT\_HERSHEY\_COMPLEX, 0.5, (255, 255, 255), 1) if a11 1: cv2.polylines(frame, \[np.array(area11, np.int32)\], True, (0, 0, 255), 2) cv2.putText(frame, str(11), (697, 377), cv2.FONT\_HERSHEY\_COMPLEX, 0.5, (0, 0, 255), 1) else: cv2.polylines(frame, \[np.array(area11, np.int32)\], True, (0, 255, 0), 2) cv2.putText(frame, str(11), (697, 377), cv2.FONT\_HERSHEY\_COMPLEX, 0.5, (255, 255, 255), 1) if a12 1: cv2.polylines(frame, \[np.array(area12, np.int32)\], True, (0, 0, 255), 2) cv2.putText(frame, str(12), (752, 371), cv2.FONT\_HERSHEY\_COMPLEX, 0.5, (0, 0, 255), 1) else: cv2.polylines(frame, \[np.array(area12, np.int32)\], True, (0, 255, 0), 2) cv2.putText(frame, str(12), (752, 371), cv2.FONT\_HERSHEY\_COMPLEX, 0.5, (255, 255, 255), 1) cv2.putText(frame, parking lots: str(space), (23, 40), cv2.FONT\_HERSHEY\_PLAIN, 2, (255, 0, 255), 2) cv2.imshow(RGB, frame) if cv2.waitKey(1) 0xFF 27: break
cap.release()
cv2.destroyAllWindows()
# stream.stop()运行代码结果如下。 发现效果还不错~
/ 04 /
总结
以上操作就是三个使用YOLOv8实现的计数视觉项目。
当然我们还可以通过预训练模型实现其它功能。
如果预训练模型的检测效果在你要使用的场景不太好那就是需要加加数据了~
