在相同颜色的图像上识别视觉效果不佳的数字

import cv2
import numpy as np
from tensorflow.keras.models import load_model

# 加载预训练的数字识别模型
model = load_model('path_to_your_model.h5')

def preprocess_image(image_path):
    img = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)
    img = cv2.GaussianBlur(img, (5, 5), 0)  # 高斯模糊去噪
    _, img = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY_INV)  # 二值化
    return img

def recognize_digits(image):
    # 假设图像已经被分割成单个数字的小图像
    digits = []  # 存储识别到的数字
    for digit_img in image:
        resized_digit = cv2.resize(digit_img, (28, 28))  # 调整大小以匹配模型输入
        input_digit = np.expand_dims(resized_digit, axis=-1)  # 增加通道维度
        input_digit = np.expand_dims(input_digit, axis=0)  # 增加批次维度
        prediction = model.predict(input_digit)
        digit = np.argmax(prediction, axis=1)[0]
        digits.append(digit)
    return digits

# 示例使用
image_path = 'path_to_your_image.jpg'
processed_image = preprocess_image(image_path)
recognized_digits = recognize_digits([processed_image])  # 这里假设已经分割好单个数字
print("Recognized Digits:", recognized_digits)