如何在较小的图像中增长标记的特征

import cv2
import numpy as np

# 读取图像
image = cv2.imread('small_image.jpg')

# 使用双三次插值放大图像
scale_factor = 2
height, width = image.shape[:2]
new_height, new_width = int(height * scale_factor), int(width * scale_factor)
resized_image = cv2.resize(image, (new_width, new_height), interpolation=cv2.INTER_CUBIC)

# 显示结果
cv2.imshow('Resized Image', resized_image)
cv2.waitKey(0)
cv2.destroyAllWindows()

import tensorflow as tf
from tensorflow.keras.applications.imagenet_utils import preprocess_input
from tensorflow.keras.preprocessing.image import load_img, img_to_array
import numpy as np

# 加载预训练的超分辨率模型
model = tf.keras.models.load_model('espcn_model.h5')

# 读取图像
image = load_img('small_image.jpg', target_size=(64, 64))
image = img_to_array(image)
image = preprocess_input(image)
image = np.expand_dims(image, axis=0)

# 预测放大后的图像
output = model.predict(image)
output = np.squeeze(output, axis=0)
output = (output + 1) * 127.5  # 反归一化

# 显示结果
output = np.clip(output, 0, 255).astype(np.uint8)
output_image = tf.keras.preprocessing.image.array_to_img(output)
output_image.show()