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

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文章目录

📙 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 yolopy39

pip3 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==4.6.0.66

cd Desktop

mkdir moli

cd moli

git 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

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 GFLOPs

Downloading 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/train

Dataset '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 for3 epochs...

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
        1/3         0G      1.4122.8151.75522640:100%|██████████|1/1[00:01<00:00,1.90s/it]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95):100%|██████████|1/1[00:00<00:00,1.30all4170.6130.8830.8880.616

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
        2/3         0G      1.2492.6211.44123640:100%|██████████|1/1[00:01<00:00,1.51s/it]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95):100%|██████████|1/1[00:00<00:00,2.24all4170.5980.8960.8880.618

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
        3/3         0G      1.1424.2211.49516640:100%|██████████|1/1[00:01<00:00,1.50s/it]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95):100%|██████████|1/1[00:00<00:00,2.06all4170.580.8330.8740.6133 epochs completed in0.002 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,6.5MB

Validating /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 GFLOPs
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95):100%|██████████|1/1[00:00<00:00,1.72all4170.5990.8980.8880.618
                person          3100.6470.50.520.29
                   dog          110.31510.9950.597
                 horse          120.68910.9950.598
              elephant          120.6290.8870.8280.332
              umbrella          110.53910.9950.995
          potted plant          110.77410.9950.895
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%|██████████|4/4
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95):100%|██████████|1/1[00:00<00:00,2.02all4170.5990.8980.8880.618
                person          3100.6470.50.520.29
                   dog          110.31510.9950.597
                 horse          120.68910.9950.598
              elephant          120.6290.8870.8280.332
              umbrella          110.53910.9950.995
          potted plant          110.77410.9950.895
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/train2

image 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'withinput 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>=1.12.0
  Downloading 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>=1.20in/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-5.27.3

requirements: AutoUpdate success ✅ 22.0s, installed 1 package:['onnx>=1.12.0']
requirements: ⚠️ Restart runtime or rerun command for updates to take effect

ONNX: 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 cv2

from 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 frameswhile cap.isOpened():# Read a frame from the video
    success, frame = cap.read()if success:# Run YOLOv8 tracking on the frame, persisting tracks between frames
        results = model.track(frame, persist=True)# Visualize the results on the frame
        annotated_frame = results[0].plot()# Display the annotated frame
        cv2.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 defaultdict

import cv2
import numpy as np

from 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 frameswhile cap.isOpened():# Read a frame from the video
    success, frame = cap.read()if success:# Run YOLOv8 tracking on the frame, persisting tracks between frames
        results = model.track(frame, persist=True)# Get the boxes and track IDs
        boxes = results[0].boxes.xywh.cpu()
        track_ids = results[0].boxes.id.int().cpu().tolist()# Visualize the results on the frame
        annotated_frame = results[0].plot()# Plot the tracksfor box, track_id inzip(boxes, track_ids):
            x, y, w, h = box
            track = track_history[track_id]
            track.append((float(x),float(y)))# x, y center pointiflen(track)>30:# retain 90 tracks for 90 frames
                track.pop(0)# Draw the tracking lines
            points = 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 frame
        cv2.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 threading

import cv2

from ultralytics import YOLO

defrun_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 object
    tracking. 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 filewhileTrue:
        ret, frame = video.read()# Read the video frames# Exit the loop if no more frames in either videoifnot ret:break# Track objects in frames if available
        results = 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 sources
    video.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】

🟦 Yolov3 系列

💙 yolov3（darknet ）训练 - 测试 - 模型转换❤️darknet 转 ncnn 之C++运行推理❤️【yolov3 实战一览】
💙 YOLOv3 ncnn 模型 yolov3-spp.cpp ❤️【YOLOv3之Ncnn推理实现———附代码】

🟨 YOLOX 系列

Ubuntu 22.04 搭建 yolov8 环境运行示例代码（轨迹跟踪、过线人数统计、目标热力图）

🟦 持续补充更新

❤️ 人生苦短，欢迎和墨理一起学AI

🎉 作为全网 AI 领域干货最多的博主之一，❤️ 不负光阴不负卿 ❤️

❤️ 如果文章对你有些许帮助、蟹蟹各位读者大大点赞、评论鼓励博主的每一分认真创作

9-9

标签： macos YOLO yolov8

本文转载自: https://blog.csdn.net/sinat_28442665/article/details/141199709
版权归原作者 墨理学AI 所有，如有侵权，请联系我们删除。

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

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

发表评论

“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”的评论:

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文章导航

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

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

发表评论

“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”的评论:

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