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

墨理学AI2024-09-27 17:54

🥇 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 ❤️

文章目录

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

python 复制代码 
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'

python 复制代码 
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

python 复制代码 
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 复制代码 
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 for 3 epochs...

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
        1/3         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,  1.30
                   all          4         17      0.613      0.883      0.888      0.616

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
        2/3         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,  2.24
                   all          4         17      0.598      0.896      0.888      0.618

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
        3/3         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,  2.06
                   all          4         17       0.58      0.833      0.874      0.613

3 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, 6.5MB

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,  1.72
                   all          4         17      0.599      0.898      0.888      0.618
                person          3         10      0.647        0.5       0.52       0.29
                   dog          1          1      0.315          1      0.995      0.597
                 horse          1          2      0.689          1      0.995      0.598
              elephant          1          2      0.629      0.887      0.828      0.332
              umbrella          1          1      0.539          1      0.995      0.995
          potted 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%|██████████| 4/4
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.02
                   all          4         17      0.599      0.898      0.888      0.618
                person          3         10      0.647        0.5       0.52       0.29
                   dog          1          1      0.315          1      0.995      0.597
                 horse          1          2      0.689          1      0.995      0.598
              elephant          1          2      0.629      0.887      0.828      0.332
              umbrella          1          1      0.539          1      0.995      0.995
          potted 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/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' 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>=1.12.0
  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>=1.20 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-5.27.3

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

python 复制代码 
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
python 复制代码 
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：

调用 Mac 电脑摄像头

source = 0 即可

python 复制代码 
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

PersistingTracksLoop 持续目标跟踪

vim yolov8PersistingTracksLoop.py

python 复制代码 
                  
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 frames
while 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 pressed
        if cv2.waitKey(1) & 0xFF == ord("q"):
            break
    else:
        # Break the loop if the end of the video is reached
        break

# Release the video capture object and close the display window
cap.release()
cv2.destroyAllWindows()

python3 yolov8PersistingTracksLoop.py 运行效果如下

Plotting Tracks 画轨迹

vim yolov8PlottingTracks.py

python 复制代码 
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 frames
while 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 tracks
        for box, track_id in zip(boxes, track_ids):
            x, y, w, h = box
            track = track_history[track_id]
            track.append((float(x), float(y)))  # x, y center point
            if len(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 pressed
        if cv2.waitKey(1) & 0xFF == ord("q"):
            break
    else:
        # Break the loop if the end of the video is reached
        break

# 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

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
python 复制代码 
import threading

import cv2

from ultralytics import YOLO


def 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 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 file

    while True:
        ret, frame = video.read()  # Read the video frames

        # Exit the loop if no more frames in either video
        if not 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
🟨 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

  • 🎉 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
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