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import numpy as np
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import lqmtest_x3_2_load_data
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class ModelObj: # 网络对象
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def __init__(self, ObjID, ObjType, ObjLable, ParaString, ObjX, ObjY):
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self.ObjID = ObjID # 图元号
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self.ObjType = ObjType # 图元类别
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self.ObjLable = ObjLable # 对象标签
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self.ParaString = ParaString # 参数字符串
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self.ObjX = ObjX # 对象位置x坐标
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self.ObjY = ObjY # 对象位置y坐标
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class Conv_Class(ModelObj): # 卷积对象
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def __init__(self, ObjID, ObjType, ObjLable, ParaString, ObjX, ObjY):
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super().__init__(ObjID, ObjType, ObjLable, ParaString, ObjX, ObjY)
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self.ConvProc = self.conv_proc # 基本操作函数
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self.SetConvPara = self.setconv_para # 参数设置函数
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# 定义卷积函数ConvProc()
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def conv_proc(self, image, ConvPara):
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# 获取输入数据的大小，这里假设是单通道的图片
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c, image_h, image_w = image.shape
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kernel_h = ConvPara["kernel_h"]
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kernel_w = ConvPara["kernel_w"]
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kernel = ConvPara["kernel"]
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out_h = (image_h - kernel_h) // ConvPara["stride"] + 1
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out_w = (image_w - kernel_w) // ConvPara["stride"] + 1
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output = np.zeros((c, out_h, out_w)) # 初始化输出数据为零矩阵
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for k in range(c): # 遍历每个通道
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for i in range(out_h): # 遍历每个输出位置
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for j in range(out_w):
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stride = ConvPara["stride"] # 获得步长
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output[k, i, j] = np.sum(
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image[k, i * stride:i * stride + 3, j *
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stride:j * stride + 3] * kernel)#计算卷积
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return output # 返回卷积计算后的数组（特征向量）
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def setconv_para(self): # 定义设置卷积参数的函数SetConvPara()
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kernel_h = int(input("请输入卷积核的高度: ")) # 用户输入卷积核的高度 3
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kernel_w = int(input("请输入卷积核的宽度: ")) # 用户输入卷积核的宽度 3
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# 用户输入卷积核的值
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kernel = []
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# [[1.289202, -1.471377, -0.238452],
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# [-0.562343, -0.019988, -0.441446],
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# [1.627381, 1.390266, 0.812486]]
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print("请输入卷积核的值（一行一行输入，各值之间用空格分开）：")
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for i in range(kernel_h):
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row = [float(val) for val in input().split()]
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kernel.append(row)
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stride = int(input("请输入步长: ")) # 用户输入步长 1
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padding = int(input("请输入填充: ")) # 用户输入填充 0
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# 返回ConvPara参数，这里用一个字典来存储
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ConvPara = {"kernel": kernel, "kernel_h": kernel_h,
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"kernel_w": kernel_w, "stride": stride,
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"padding": padding}
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return ConvPara
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if __name__ == '__main__':
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DataSet = lqmtest_x3_2_load_data.Data_Class("DataSet1", 1, "数据集1", [], 120, 330)
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# setload_data()函数，获取加载数据集的参数
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DataPara = DataSet.SetDataPara()
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train_images, test_images = DataSet.LoadData(DataPara)
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Conv = Conv_Class("Conv1", 2, "卷积1", [], 250, 330)
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ConvPara = Conv.SetConvPara()
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for i in range(len(train_images) // 32):
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images = train_images[i * 32:(i + 1) * 32]
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conv_images = []# 存储卷积处理后的图片的列表
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for image in images:# 获取训练集的图片数据
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dim = len(image.shape)# 获取矩阵的维度
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if dim == 2: # 如果是二维矩阵,则转化为三维矩阵
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image_h, image_w = image.shape
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image = np.reshape(image, (1,image_h,image_w))
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# 调用ConvProc()函数，根据ConvPara参数完成卷积计算
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output = Conv.ConvProc(image, ConvPara)
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conv_images.append(output)# 将卷积结果存储到列表
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elif dim == 3: # 若为三维矩阵，则保持不变直接卷积处理
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output = Conv.ConvProc(image, ConvPara)
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conv_images.append(output)
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# 将卷积处理后的图片列表转换为数组形式，方便后续处理
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conv_images = np.array(conv_images)
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