本例主要讲解使用Tensorflow 2.16.1, CPU版本。还有Pretrained model做对象检测的一般步骤，因为YoloV8模型比较大，而且做Object Detection的模型训练需要自己做图像标注，这里直接使用预定义的模型来做简单的Object Detection。
详细的步骤可以参考keras.io的Object Detection Guide。
并非只有本例的模型可用，Tensorflow2.0所有的Keras模型都可以以本例的步骤来使用，三步曲，

1. load pretrained model
2. model.predict
3. visualization

步骤如下，

# 下载tensorflow CPU 2.16.1版本镜像，注意只有这个版本docs才work，
docker pull tensorflow/tensorflow:2.16.1-jupyter

# 在镜像中安装opencv-python
 pip install opencv-python

# 在镜像中安装GL相关库，opencv-python需要使用
 apt-get install libgl1-mesa-glx

完整的jupyter notebook 链接地址,
https://gitlab.com/zhuge20100104/cpp_practice/-/blob/master/simple_learn/deep_learning/14_tensorflow_object_detection/14.%20Tensorflow%20Object%20Detection.ipynb?ref_type=heads

notebook中的代码如下，

# Object detection demo
# Welcome to the object detection inference walkthrough! This notebook will walk you step by step through
# the process of using pre-trained model to detect objects in an image

# Install necessary dependencies
!python3 -m pip install --upgrade pip
!pip install --upgrade keras-cv
!pip install --upgrade keras  # Upgrade to Keras 3.

import os 
os.environ['KERAS_BACKEND'] = 'jax'

import tensorflow as tf

from tensorflow import data as tf_data
import tensorflow_datasets as tfds
import tensorflow.keras
import keras_cv
import keras
import numpy as np
from keras_cv import bounding_box
import os
from keras_cv import visualization
import tqdm

# 详细细节可参考: https://keras.io/guides/keras_cv/object_detection_keras_cv/

# Let's get started by constructing a YOLOV8Detector pretrained on the pascalvoc dataset.
pretrained_model = keras_cv.models.YOLOV8Detector.from_preset(
    "yolo_v8_m_pascalvoc", bounding_box_format="xywh"
)


file_path = './images/dog_pic.jpeg'
image = keras.utils.load_img(file_path)
image = np.array(image)
visualization.plot_image_gallery(
    np.array([image]),
    value_range=(0, 255),
    rows=1,
    cols=1,
    scale=5,);

# Resize the image to the model compat input size
inference_resizing = keras_cv.layers.Resizing(
    640, 640, pad_to_aspect_ratio=True, bounding_box_format='xywh'
)

# This can be used as our inference preprocessing pipeline:
image_batch = inference_resizing([image])

# keras_cv.visualization.plot_bounding_box_gallery() supports a class_mapping parameter to
# highlight what class each box was assigned to. Let's assemble a class mapping now.

class_ids = [
    "Aeroplane",
    "Bicycle",
    "Bird",
    "Boat",
    "Bottle",
    "Bus",
    "Car",
    "Cat",
    "Chair",
    "Cow",
    "Dining Table",
    "Dog",
    "Horse",
    "Motorbike",
    "Person",
    "Potted Plant",
    "Sheep",
    "Sofa",
    "Train",
    "Tvmonitor",
    "Total",
]

class_mapping = dict(zip(range(len(class_ids)), class_ids))

# Just like any other keras.Model you can predict bounding boxes using the model.predict() API.
y_pred = pretrained_model.predict(image_batch)

visualization.plot_bounding_box_gallery(image_batch,
    value_range=(0, 255),
    rows=1,
    cols=1,
    y_pred=y_pred,
    scale=5,
    font_scale=0.7,
    bounding_box_format='xywh',
    class_mapping=class_mapping
);

# 扩展: 上面已经完成prediction了，play with NonMaxSupression参数
# The following NonMaxSuppression layer is equivalent to disabling the operation
# 为啥是disable? 因为iou设置为1，表示只有当两者完全重合才去抑制他
# confidence设置成0，表示不管置信度多低，都不抑制他，都相信他
# 这样一弄就会出来一大坨
prediction_decoder = keras_cv.layers.NonMaxSuppression(
    bounding_box_format='xywh',
    from_logits=True,
    iou_threshold=1,
    confidence_threshold=0.0,
)

pretrained_model = keras_cv.models.YOLOV8Detector.from_preset(
    "yolo_v8_m_pascalvoc",
    bounding_box_format='xywh',
    prediction_decoder=prediction_decoder,
)

y_pred = pretrained_model.predict(image_batch)

visualization.plot_bounding_box_gallery(
    image_batch,
    value_range=(0, 255),
    rows=1,
    cols=1,
    y_pred=y_pred,
    scale=5,
    font_scale=0.7,
    bounding_box_format="xywh",
    class_mapping=class_mapping,
);


# Next, let's re-configure keras_cv.layers.NonMaxSuppression for our use case!
# In this case, we will tune the iou_threshold to 0.2, and the confidence_threshold to 0.7.

prediction_decoder = keras_cv.layers.NonMaxSuppression(
    bounding_box_format='xywh',
    from_logits=True,
    iou_threshold=0.2,
    confidence_threshold=0.7,
)

pretrained_model = keras_cv.models.YOLOV8Detector.from_preset(
    "yolo_v8_m_pascalvoc",
    bounding_box_format='xywh',
    prediction_decoder=prediction_decoder,
)

y_pred = pretrained_model.predict(image_batch)

visualization.plot_bounding_box_gallery(
    image_batch,
    value_range=(0, 255),
    rows=1,
    cols=1,
    y_pred=y_pred,
    scale=5,
    font_scale=0.7,
    bounding_box_format="xywh",
    class_mapping=class_mapping,
);

程序输出的Object Detection的结果如下

image.png