python图像批量添加数字水印

0; i < inputSize; ++i) {
int weightIndex = i + j * inputSize;
inputGradient[i] += gradient * weights[weightIndex];
weightGradient[weightIndex] += gradient * input[i];
}
}
}

private你可以使用Python的Pillow库来批量添加数字水印。以下是一个简单的示例代码：

:
    int inputSize;
    int outputSize;
    vector<double> weights;
    vector<double> biases;
};

4. 定from PIL import Image, ImageDraw, ImageFont
import os

# 要添加水印的文件夹路径
folder_path = "/path义神经网络的类

class NeuralNetwork {
public:
    NeuralNetwork() {
        // 构建卷积层和/to/folder"

# 水印文字
watermark_text = "123"

# 水印字体
font = ImageFont.truetype("池化层
        convolution1 = new ConvolutionLayer(kImageSize, kImageSize, 1, 5, 16);
arial.ttf", 36)

# 循环处理文件夹中的每张图片
for filename in os.listdir(folder_path):
    if filename        pooling1 = new PoolingLayer(convolution1->outputWidth, convolution1->outputHeight, convolution1->outputDepth, 2.endswith(".jpg") or filename.endswith(".png"):
        # 打开图片
        image_path = os.path.join(folder_path, filename)
       );
        convolution2 = new ConvolutionLayer(pooling1->outputWidth, pooling1->outputHeight, pooling1->outputDepth, 5, 32);
        pooling2 = new PoolingLayer(convolution2->outputWidth, convolution2->outputHeight, convolution2-> image = Image.open(image_path)

        # 添加水印
        draw = ImageDraw.Draw(image)
        draw.text((10, 10outputDepth, 2);

        // 计算全连接层的输入大小
        int fullyConnectedInputSize = pooling2->output), watermark_text, fill=(255, 255, 255), font=font)

        # 保存图片
        new_file_path =Width * pooling2->outputHeight * convolution2->outputDepth;

        // 构建全连接层
        fullyConnected1 = new os.path.join(folder_path, "watermarked_" + filename)
        image.save(new_file_path)

这个示例代码会将 FullyConnectedLayer(fullyConnectedInputSize, 128);
fullyConnected2 = new FullyConnectedLayer(128, kNumClasses);
指定文件夹中的所有jpg和png图片添加数字水印，并保存为新的文件名。你可以根据需要修改水印内容、字体、位置等参数。