当前位置：首页 > news >正文

Mac 电脑配置yolov8运行环境实现目标追踪、计数、画出轨迹、多线程

news 来源：原创 2024/9/27 4:38:01

🥇 版权: 本文由【墨理学AI】原创首发、各位读者大大、敬请查阅、感谢三连
🎉 声明: 作为全网 AI 领域干货最多的博主之一，❤️ 不负光阴不负卿 ❤️

0-9

文章目录

- 📙 Mac 电脑配置 yolov8 环境
- 📙 代码运行
- - - 推理测试
    - 模型训练 - 转 onnx
    - 视频-目标检测
    - 调用 Mac 电脑摄像头
    - PersistingTracksLoop 持续目标跟踪
    - Plotting Tracks 画轨迹
    - Multithreaded Tracking - 多线程运行示例
- 📙 YOLO 系列实战博文汇总如下
- - - 🟦 YOLO 理论讲解学习篇
    - 🟧 Yolov5 系列
    - 🟨 YOLOX 系列
    - 🟦 Yolov3 系列
    - 🟨 YOLOX 系列
    - 🟦 持续补充更新
- ❤️ 人生苦短，欢迎和墨理一起学AI

📙 Mac 电脑配置 yolov8 环境

YOLO 推理测试、小数据集训练，基础版 Mac 即可满足
博主这里代码运行的 Mac 版本为 M1 Pro

conda 环境搭建步骤如下


conda create -n yolopy39 python=3.9
conda activate yolopy39pip3 install torch torchvision torchaudio# ultralytics 对 opencv-python 的版本需求如下
pip3 install opencv-python>=4.6.0
# 因此我选择安装的版本如下
pip3 install opencv-python==4.6.0.66cd Desktopmkdir molicd moligit clone https://github.com/ultralytics/ultralytics.git
pip install -e .pwd             
/Users/moli/Desktop/moli/ultralytics

📙 代码运行

代码运行主要参考如下两个官方教程

https://github.com/ultralytics/ultralytics
https://docs.ultralytics.com/modes/track/#persisting-tracks-loop

推理测试

yolo predict model=yolov8n.pt source='https://ultralytics.com/images/bus.jpg'

yolo predict model=yolov8n.pt source='https://ultralytics.com/images/bus.jpg'
# 输出如下
Matplotlib is building the font cache; this may take a moment.
Downloading https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n.pt to 'yolov8n.pt'...
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 6.25M/6.25M [01:34<00:00, 69.6kB/s]
Ultralytics YOLOv8.2.77 🚀 Python-3.9.19 torch-2.2.2 CPU (Apple M1 Pro)
[W NNPACK.cpp:64] Could not initialize NNPACK! Reason: Unsupported hardware.
YOLOv8n summary (fused): 168 layers, 3,151,904 parameters, 0 gradients, 8.7 GFLOPsDownloading https://ultralytics.com/images/bus.jpg to 'bus.jpg'...
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 134k/134k [00:00<00:00, 470kB/s]
image 1/1 /Users/moli/Desktop/moli/ultralytics/bus.jpg: 640x480 4 persons, 1 bus, 1 stop sign, 221.3ms
Speed: 5.8ms preprocess, 221.3ms inference, 4.0ms postprocess per image at shape (1, 3, 640, 480)
Results saved to /Users/moli/Desktop/moli/ultralytics/runs/detect/predict

模型训练 - 转 onnx

vim train_test.py

from ultralytics import YOLO# Load a model
model = YOLO("yolov8n.yaml")  # build a new model from scratch
model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)# Use the model
model.train(data="coco8.yaml", epochs=3)  # train the model
metrics = model.val()  # evaluate model performance on the validation set
results = model("https://ultralytics.com/images/bus.jpg")  # predict on an image# 转换 onnx 也是封装好的模块，这里调用传参即可
path = model.export(format="onnx")  # export the model to ONNX format

运行输出如下

python train_test.py [W NNPACK.cpp:64] Could not initialize NNPACK! Reason: Unsupported hardware.
Ultralytics YOLOv8.2.77 🚀 Python-3.9.19 torch-2.2.2 CPU (Apple M1 Pro)
engine/trainer: task=detect, mode=train, model=yolov8n.pt, data=coco8.yaml, epochs=3, time=None, patience=100, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=train, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, freeze=None, multi_scale=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=None, workspace=4, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, label_smoothing=0.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, bgr=0.0, mosaic=1.0, mixup=0.0, copy_paste=0.0, auto_augment=randaugment, erasing=0.4, crop_fraction=1.0, cfg=None, tracker=botsort.yaml, save_dir=/Users/moli/Desktop/moli/ultralytics/runs/detect/trainDataset 'coco8.yaml' images not found ⚠️, missing path '/Users/moli/Desktop/moli/datasets/coco8/images/val'
Downloading https://ultralytics.com/assets/coco8.zip to '/Users/moli/Desktop/moli/datasets/coco8.zip'...
100%|███████████████████████████████████████████████████████████████████████████████████████████| 433k/433k [00:03<00:00, 135kB/s]
Unzipping /Users/moli/Desktop/moli/datasets/coco8.zip to /Users/moli/Desktop/moli/datasets/coco8...: 100%|██████████| 25/25 [00:00
Dataset download success ✅ (5.4s), saved to /Users/moli/Desktop/moli/datasets...
...Logging results to /Users/moli/Desktop/moli/ultralytics/runs/detect/train
Starting training for 3 epochs...Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size1/3         0G      1.412      2.815      1.755         22        640: 100%|██████████| 1/1 [00:01<00:00,  1.90s/it]Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.30all          4         17      0.613      0.883      0.888      0.616Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size2/3         0G      1.249      2.621      1.441         23        640: 100%|██████████| 1/1 [00:01<00:00,  1.51s/it]Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.24all          4         17      0.598      0.896      0.888      0.618Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size3/3         0G      1.142      4.221      1.495         16        640: 100%|██████████| 1/1 [00:01<00:00,  1.50s/it]Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.06all          4         17       0.58      0.833      0.874      0.6133 epochs completed in 0.002 hours.
Optimizer stripped from /Users/moli/Desktop/moli/ultralytics/runs/detect/train/weights/last.pt, 6.5MB
Optimizer stripped from /Users/moli/Desktop/moli/ultralytics/runs/detect/train/weights/best.pt, 6.5MBValidating /Users/moli/Desktop/moli/ultralytics/runs/detect/train/weights/best.pt...
Ultralytics YOLOv8.2.77 🚀 Python-3.9.19 torch-2.2.2 CPU (Apple M1 Pro)
Model summary (fused): 168 layers, 3,151,904 parameters, 0 gradients, 8.7 GFLOPsClass     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.72all          4         17      0.599      0.898      0.888      0.618person          3         10      0.647        0.5       0.52       0.29dog          1          1      0.315          1      0.995      0.597horse          1          2      0.689          1      0.995      0.598elephant          1          2      0.629      0.887      0.828      0.332umbrella          1          1      0.539          1      0.995      0.995potted plant          1          1      0.774          1      0.995      0.895
Speed: 4.2ms preprocess, 134.0ms inference, 0.0ms loss, 0.8ms postprocess per image
Results saved to /Users/moli/Desktop/moli/ultralytics/runs/detect/train
Ultralytics YOLOv8.2.77 🚀 Python-3.9.19 torch-2.2.2 CPU (Apple M1 Pro)
Model summary (fused): 168 layers, 3,151,904 parameters, 0 gradients, 8.7 GFLOPs
val: Scanning /Users/moli/Desktop/moli/datasets/coco8/labels/val.cache... 4 images, 0 backgrounds, 0 corrupt: 100%|██████████| 4/4Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.02all          4         17      0.599      0.898      0.888      0.618person          3         10      0.647        0.5       0.52       0.29dog          1          1      0.315          1      0.995      0.597horse          1          2      0.689          1      0.995      0.598elephant          1          2      0.629      0.887      0.828      0.332umbrella          1          1      0.539          1      0.995      0.995potted plant          1          1      0.774          1      0.995      0.895
Speed: 4.1ms preprocess, 113.0ms inference, 0.0ms loss, 0.7ms postprocess per image
Results saved to /Users/moli/Desktop/moli/ultralytics/runs/detect/train2image 1/1 /Users/moli/Desktop/moli/ultralytics/ultralytics/assets/bus.jpg: 640x480 4 persons, 1 bus, 188.4ms
Speed: 3.9ms preprocess, 188.4ms inference, 1.0ms postprocess per image at shape (1, 3, 640, 480)
Ultralytics YOLOv8.2.77 🚀 Python-3.9.19 torch-2.2.2 CPU (Apple M1 Pro)# 开始模型转换PyTorch: starting from '/Users/moli/Desktop/moli/ultralytics/runs/detect/train/weights/best.pt' with input shape (1, 3, 640, 640) BCHW and output shape(s) (1, 84, 8400) (6.2 MB)
requirements: Ultralytics requirement ['onnx>=1.12.0'] not found, attempting AutoUpdate...Looking in indexes: http://pypi.douban.com/simple, http://mirrors.aliyun.com/pypi/simple/, https://pypi.tuna.tsinghua.edu.cn/simple/, http://pypi.mirrors.ustc.edu.cn/simple/
Collecting onnx>=1.12.0Downloading http://mirrors.ustc.edu.cn/pypi/packages/4e/35/abbf2fa3dbb96b430f6e810e3fb7bc042ed150f371cb1aedb47052c40f8e/onnx-1.16.2-cp39-cp39-macosx_11_0_universal2.whl (16.5 MB)━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 16.5/16.5 MB 11.4 MB/s eta 0:00:00
Requirement already satisfied: numpy>=1.20 in /Users/moli/opt/anaconda3/envs/yolopy39/lib/python3.9/site-packages (from onnx>=1.12.0) (1.26.4)
Collecting protobuf>=3.20.2 (from onnx>=1.12.0)Downloading http://mirrors.ustc.edu.cn/pypi/packages/ca/bc/bceb11aa96dd0b2ae7002d2f46870fbdef7649a0c28420f0abb831ee3294/protobuf-5.27.3-cp38-abi3-macosx_10_9_universal2.whl (412 kB)
Installing collected packages: protobuf, onnx
Successfully installed onnx-1.16.2 protobuf-5.27.3requirements: AutoUpdate success ✅ 22.0s, installed 1 package: ['onnx>=1.12.0']
requirements: ⚠️ Restart runtime or rerun command for updates to take effectONNX: starting export with onnx 1.16.2 opset 17...
ONNX: export success ✅ 24.4s, saved as '/Users/moli/Desktop/moli/ultralytics/runs/detect/train/weights/best.onnx' (12.2 MB)Export complete (26.1s)
Results saved to /Users/moli/Desktop/moli/ultralytics/runs/detect/train/weights
Predict:         yolo predict task=detect model=/Users/moli/Desktop/moli/ultralytics/runs/detect/train/weights/best.onnx imgsz=640  
Validate:        yolo val task=detect model=/Users/moli/Desktop/moli/ultralytics/runs/detect/train/weights/best.onnx imgsz=640 data=/Users/moli/Desktop/moli/ultralytics/ultralytics/cfg/datasets/coco8.yaml  
Visualize:       https://netron.app

可以看到运行成功、训练、转换 onnx 如下

ls runs/detect/train/       F1_curve.png			R_curve.png			confusion_matrix_normalized.png	results.csv			train_batch1.jpg		val_batch0_pred.jpg
PR_curve.png			args.yaml			labels.jpg			results.png			train_batch2.jpg		weights
P_curve.png			confusion_matrix.png		labels_correlogram.jpg		train_batch0.jpg		val_batch0_labels.jpg(yolopy39) moli@molideMacBook-Pro ultralytics % ls runs/detect/train/weights 
best.onnx	best.pt		last.pt

视频-目标检测

cat yolov8_1.py 
from ultralytics import YOLO# Load an official or custom model
model = YOLO("yolov8n.pt")  # Load an official Detect model
#model = YOLO("yolov8n-seg.pt")  # Load an official Segment model
#model = YOLO("yolov8n-pose.pt")  # Load an official Pose model
#model = YOLO("path/to/best.pt")  # Load a custom trained model# Perform tracking with the model
source = 'video/people.mp4'
results = model.track(source, show=True)  # Tracking with default tracker

代码运行效果如下：

1-001

调用 Mac 电脑摄像头

source = 0 即可

from ultralytics import YOLO# Load an official or custom model
model = YOLO("yolov8n.pt")  # Load an official Detect model#source = 'video/people.mp4'
source = 0
results = model.track(source, show=True)  # Tracking with default tracker# results = model.track(source, show=True, tracker="bytetrack.yaml")  # with ByteTrack

效果示例如下

1-0001

PersistingTracksLoop 持续目标跟踪

https://docs.ultralytics.com/modes/track/#tracker-selection

vim yolov8PersistingTracksLoop.py

                  
import cv2from ultralytics import YOLO# Load the YOLOv8 model
model = YOLO("yolov8n.pt")# Open the video file
video_path = "./video/test_people.mp4"
cap = cv2.VideoCapture(video_path)# Loop through the video frames
while cap.isOpened():# Read a frame from the videosuccess, frame = cap.read()if success:# Run YOLOv8 tracking on the frame, persisting tracks between framesresults = model.track(frame, persist=True)# Visualize the results on the frameannotated_frame = results[0].plot()# Display the annotated framecv2.imshow("YOLOv8 Tracking", annotated_frame)# Break the loop if 'q' is pressedif cv2.waitKey(1) & 0xFF == ord("q"):breakelse:# Break the loop if the end of the video is reachedbreak# Release the video capture object and close the display window
cap.release()
cv2.destroyAllWindows()

python3 yolov8PersistingTracksLoop.py 运行效果如下

2-0003

Plotting Tracks 画轨迹

vim yolov8PlottingTracks.py


from collections import defaultdictimport cv2
import numpy as npfrom ultralytics import YOLO# Load the YOLOv8 model
model = YOLO("yolov8n.pt")# Open the video file
video_path = "./video/test_people.mp4"
cap = cv2.VideoCapture(video_path)# Store the track history
track_history = defaultdict(lambda: [])# Loop through the video frames
while cap.isOpened():# Read a frame from the videosuccess, frame = cap.read()if success:# Run YOLOv8 tracking on the frame, persisting tracks between framesresults = model.track(frame, persist=True)# Get the boxes and track IDsboxes = results[0].boxes.xywh.cpu()track_ids = results[0].boxes.id.int().cpu().tolist()# Visualize the results on the frameannotated_frame = results[0].plot()# Plot the tracksfor box, track_id in zip(boxes, track_ids):x, y, w, h = boxtrack = track_history[track_id]track.append((float(x), float(y)))  # x, y center pointif len(track) > 30:  # retain 90 tracks for 90 framestrack.pop(0)# Draw the tracking linespoints = np.hstack(track).astype(np.int32).reshape((-1, 1, 2))cv2.polylines(annotated_frame, [points], isClosed=False, color=(230, 230, 230), thickness=10)# Display the annotated framecv2.imshow("YOLOv8 Tracking", annotated_frame)# Break the loop if 'q' is pressedif cv2.waitKey(1) & 0xFF == ord("q"):breakelse:# Break the loop if the end of the video is reachedbreak# Release the video capture object and close the display window
cap.release()
cv2.destroyAllWindows()

python3 yolov8PlottingTracks.py 运行效果如下，可以看看行人后有轨迹

2-0005

Multithreaded Tracking - 多线程运行示例

vim yolov8MultithreadedTracking.py

这里加载两个模型，运行两个线程，出现线程拥挤、导致无法弹窗，代码需要进一步修改

import threadingimport cv2from ultralytics import YOLOdef run_tracker_in_thread(filename, model, file_index):"""Runs a video file or webcam stream concurrently with the YOLOv8 model using threading.This function captures video frames from a given file or camera source and utilizes the YOLOv8 model for objecttracking. The function runs in its own thread for concurrent processing.Args:filename (str): The path to the video file or the identifier for the webcam/external camera source.model (obj): The YOLOv8 model object.file_index (int): An index to uniquely identify the file being processed, used for display purposes.Note:Press 'q' to quit the video display window."""video = cv2.VideoCapture(filename)  # Read the video filewhile True:ret, frame = video.read()  # Read the video frames# Exit the loop if no more frames in either videoif not ret:break# Track objects in frames if availableresults = model.track(frame, persist=True)res_plotted = results[0].plot()cv2.imshow(f"Tracking_Stream_{file_index}", res_plotted)key = cv2.waitKey(1)if key == ord("q"):break# Release video sourcesvideo.release()# Load the models
model1 = YOLO("yolov8n.pt")
model2 = YOLO("yolov8n-seg.pt")# Define the video files for the trackers
video_file1 = "video/test_people.mp4"  # Path to video file, 0 for webcam
#video_file2 = 'video/test_traffic.mp4'  # Path to video file, 0 for webcam, 1 for external camera
video_file2 = 0
# Create the tracker threads
tracker_thread1 = threading.Thread(target=run_tracker_in_thread, args=(video_file1, model1, 1), daemon=True)
tracker_thread2 = threading.Thread(target=run_tracker_in_thread, args=(video_file2, model2, 2), daemon=True)# Start the tracker threads
tracker_thread1.start()
tracker_thread2.start()# Wait for the tracker threads to finish
tracker_thread1.join()
tracker_thread2.join()# Clean up and close windows
cv2.destroyAllWindows()

📙 YOLO 系列实战博文汇总如下

🟦 YOLO 理论讲解学习篇

🟧 Yolov5 系列

💜 YOLOv5 环境搭建 | coco128 训练示例 |❤️ 详细记录❤️ |【YOLOv5】
💜 YOLOv5 COCO数据集训练 | 【YOLOv5 训练】

🟨 YOLOX 系列

💛 YOLOX 环境搭建 | 测试 | COCO训练复现【YOLOX 实战】
💛 YOLOX （pytorch）模型 ONNX export | 运行推理【YOLOX 实战二】
💛 YOLOX （pytorch）模型转 ONNX 转 ncnn 之运行推理【YOLOX 实战三】
💛 YOLOX （pytorch）模型转 tensorRT 之运行推理【YOLOX 实战四】