如何使用Python对图片进行图像分类

pip install opencv-python
pip install keras

python -m keras.applications.resnet50

import cv2

def load_image(image_path):
    img = cv2.imread(image_path)
    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)   # 转换为RGB格式
    return img

image = load_image('image.jpg')

import cv2
import numpy as np

def preprocess_image(image):
    image = cv2.resize(image, (224, 224))   # 缩放图像为224x224像素
    image = image.astype("float32")   # 将图像数据类型转换为float32
    image /= 255   # 归一化图像数据
    return image

preprocessed_image = preprocess_image(image)

from keras.applications.resnet50 import ResNet50
from keras.preprocessing import image
from keras.applications.resnet50 import preprocess_input, decode_predictions
import numpy as np

model = ResNet50(weights='imagenet')

def classify_image(image):
    x = image[np.newaxis, ...]   # 将图像数据加一维以适应模型输入
    predictions = model.predict(x)   # 进行图像分类
    decoded_predictions = decode_predictions(predictions, top=3)[0]   # 解码预测结果
    return decoded_predictions

predictions = classify_image(preprocessed_image)
print(predictions)

def explain_predictions(predictions):
    for pred in predictions:
        print(f"类别：{pred[1]}，概率：{pred[2]*100}%")

explain_predictions(predictions)