python3 目标检测

Python3 目标检测是一种基于机器学习和深度学习技术的计算机视觉应用。该技术能够快速准确地检测出图像或视频中的目标物体，并给出其精确位置。

import cv2
import numpy as np
import tensorflow as tf
from tensorflow.keras import Model
from tensorflow.keras.applications import EfficientNetB4
from tensorflow.keras.layers import Dense, GlobalAveragePooling2D

class ObjectDetector(Model):
    def __init__(self, num_classes):
        super(ObjectDetector, self).__init__()
        self.base_model = EfficientNetB4(include_top=False, input_shape=(224, 224, 3))
        self.avg_pool = GlobalAveragePooling2D()
        self.output_layer = Dense(num_classes, activation='softmax')

    def call(self, inputs):
        x = self.base_model(inputs)
        x = self.avg_pool(x)
        x = self.output_layer(x)
        return x

def detect_object(image_path, threshold):
    # 加载模型
    detector = ObjectDetector(num_classes=10)
    detector.load_weights('object_detector.h5')

    # 加载图片
    image = cv2.imread(image_path)
    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
    image = cv2.resize(image, (224, 224))
    image = np.expand_dims(image, axis=0)

    # 推理
    predictions = detector.predict(image)
    class_index = np.argmax(predictions[0])
    confidence = predictions[0][class_index]

    # 判断是否超过阈值
    if confidence >= threshold:
        return class_index
    else:
        return -1

# 测试
class_names = ['cat', 'dog', 'car', 'bus', 'person', 'tree', 'apple', 'banana', 'book', 'chair']
image_path = 'test.jpg'
threshold = 0.6
class_index = detect_object(image_path, threshold)
if class_index == -1:
    print('未检测到目标物体')
else:
    print('检测到目标物体：', class_names[class_index])

以上是一个基于 EfficientNetB4 构建的目标检测模型。模型训练好后，只需要调用 detect_object 函数即可实现目标检测。该函数会返回目标物体的分类编号，如果未检测到目标物体则返回 -1。

目标检测技术在实际应用中有着广泛的应用，可以用于自动驾驶、安防、人脸识别、产品质检等场景。Python3 作为一种优秀的编程语言，提供了丰富的机器学习和深度学习库，为目标检测技术的研究和实现提供了便利。