import numpy as npimport sysdef conv_(img, conv_filter): filter_size = conv_filter.shape[1] result = np.zeros((img.shape)) # 循环遍历图像以应用卷积运算 for r in np.uint16(np.arange(filter_size/2.0, img.shape[0]-filter_size/2.0+1)): for c in np.uint16(np.arange(filter_size/2.0, img.shape[1]-filter_size/2.0+1)): # 卷积的区域 curr_region = img[r-np.uint16(np.floor(filter_size/2.0)):r+np.uint16(np.ceil(filter_size/2.0)), c-np.uint16(np.floor(filter_size/2.0)):c+np.uint16(np.ceil(filter_size/2.0))] # 卷积操作 curr_result = curr_region * conv_filter conv_sum = np.sum(curr_result) # 将求和保存到特征图中 result[r, c] = conv_sum # 裁剪结果矩阵的异常值 final_result = result[np.uint16(filter_size/2.0):result.shape[0]-np.uint16(filter_size/2.0), np.uint16(filter_size/2.0):result.shape[1]-np.uint16(filter_size/2.0)] return final_resultdef conv(img, conv_filter): # 检查图像通道的数量是否与过滤器深度匹配 if len(img.shape) > 2 or len(conv_filter.shape) > 3: if img.shape[-1] != conv_filter.shape[-1]: print("错误：图像和过滤器中的通道数必须匹配") sys.exit() # 检查过滤器是否是方阵 if conv_filter.shape[1] != conv_filter.shape[2]: print('错误：过滤器必须是方阵') sys.exit() # 检查过滤器大小是否是奇数 if conv_filter.shape[1] % 2 == 0: print('错误：过滤器大小必须是奇数') sys.exit() # 定义一个空的特征图，用于保存过滤器与图像的卷积输出 feature_maps = np.zeros((img.shape[0] - conv_filter.shape[1] + 1, img.shape[1] - conv_filter.shape[1] + 1, conv_filter.shape[0])) # 卷积操作 for filter_num in range(conv_filter.shape[0]): print("Filter ", filter_num + 1) curr_filter = conv_filter[filter_num, :] # 检查单个过滤器是否有多个通道。如果有，那么每个通道将对图像进行卷积。所有卷积的结果加起来得到一个特征图。 if len(curr_filter.shape) > 2: conv_map = conv_(img[:, :, 0], curr_filter[:, :, 0]) for ch_num in range(1, curr_filter.shape[-1]): conv_map = conv_map + conv_(img[:, :, ch_num], curr_filter[:, :, ch_num]) else: conv_map = conv_(img, curr_filter) feature_maps[:, :, filter_num] = conv_map return feature_mapsdef pooling(feature_map, size=2, stride=2): # 定义池化操作的输出 pool_out = np.zeros((np.uint16((feature_map.shape[0] - size + 1) / stride + 1), np.uint16((feature_map.shape[1] - size + 1) / stride + 1), feature_map.shape[-1])) for map_num in range(feature_map.shape[-1]): r2 = 0 for r in np.arange(0, feature_map.shape[0] - size + 1, stride): c2 = 0 for c in np.arange(0, feature_map.shape[1] - size + 1, stride): pool_out[r2, c2, map_num] = np.max([feature_map[r: r+size, c: c+size, map_num]]) c2 = c2 + 1 r2 = r2 + 1 return pool_outimport skimage.dataimport numpyimport matplotlibimport matplotlib.pyplot as pltimport NumPyCNN as numpycnn# 读取图像img = skimage.data.chelsea()# 转成灰度图像img = skimage.color.rgb2gray(img)# 初始化卷积核l1_filter = numpy.zeros((2, 3, 3))# 检测垂直边缘l1_filter[0, :, :] = numpy.array([[[-1, 0, 1], [-1, 0, 1], [-1, 0, 1]]])# 检测水平边缘l1_filter[1, :, :] = numpy.array([[[1, 1, 1], [0, 0, 0], [-1, -1, -1]]])"""第一个卷积层"""# 卷积操作l1_feature_map = numpycnn.conv(img, l1_filter)# ReLUl1_feature_map_relu = numpycnn.relu(l1_feature_map)# Poolingl1_feature_map_relu_pool = numpycnn.pooling(l1_feature_map_relu, 2, 2)"""第二个卷积层"""# 初始化卷积核l2_filter = numpy.random.rand(3, 5, 5, l1_feature_map_relu_pool.shape[-1])# 卷积操作l2_feature_map = numpycnn.conv(l1_feature_map_relu_pool, l2_filter)# ReLUl2_feature_map_relu = numpycnn.relu(l2_feature_map)# Poolingl2_feature_map_relu_pool = numpycnn.pooling(l2_feature_map_relu, 2, 2)"""第三个卷积层"""# 初始化卷积核l3_filter = numpy.random.rand(1, 7, 7, l2_feature_map_relu_pool.shape[-1])# 卷积操作l3_feature_map = numpycnn.conv(l2_feature_map_relu_pool, l3_filter)# ReLUl3_feature_map_relu = numpycnn.relu(l3_feature_map)# Poolingl3_feature_map_relu_pool = numpycnn.pooling(l3_feature_map_relu, 2, 2)"""结果可视化"""fig0, ax0 = plt.subplots(nrows=1, ncols=1)ax0.imshow(img).set_cmap("gray")ax0.set_title("Input Image")ax0.get_xaxis().set_ticks([])ax0.get_yaxis().set_ticks([])plt.savefig("in_img1.png", bbox_inches="tight")plt.close(fig0)# 第一层fig1, ax1 = plt.subplots(nrows=3, ncols=2)ax1[0, 0].imshow(l1_feature_map[:, :, 0]).set_cmap("gray")ax1[0, 0].get_xaxis().set_ticks([])ax1[0, 0].get_yaxis().set_ticks([])ax1[0, 0].set_title("L1-Map1")ax1[0, 1].imshow(l1_feature_map[:, :, 1]).set_cmap("gray")ax1[0, 1].get_xaxis().set_ticks([])ax1[0, 1].get_yaxis().set_ticks([])ax1[0, 1].set_title("L1-Map2")ax1[1, 0].imshow(l1_feature_map_relu[:, :, 0]).set_cmap("gray")ax1[1, 0].get_xaxis().set_ticks([])ax1[1, 0].get_yaxis().set_ticks([])ax1[1, 0].set_title("L1-Map1ReLU")ax1[1, 1].imshow(l1_feature_map_relu[:, :, 1]).set_cmap("gray")ax1[1, 1].get_xaxis().set_ticks([])ax1[1, 1].get_yaxis().set_ticks([])ax1[1, 1].set_title("L1-Map2ReLU")ax1[2, 0].imshow(l1_feature_map_relu_pool[:, :, 0]).set_cmap("gray")ax1[2, 0].get_xaxis().set_ticks([])ax1[2, 0].get_yaxis().set_ticks([])ax1[2, 0].set_title("L1-Map1ReLUPool")ax1[2, 1].imshow(l1_feature_map_relu_pool[:, :, 1]).set_cmap("gray")ax1[2, 0].get_xaxis().set_ticks([])ax1[2, 0].get_yaxis().set_ticks([])ax1[2, 1].set_title("L1-Map2ReLUPool")plt.savefig("L1.png", bbox_inches="tight")plt.close(fig1)# 第二层fig2, ax2 = plt.subplots(nrows=3, ncols=3)ax2[0, 0].imshow(l2_feature_map[:, :, 0]).set_cmap("gray")ax2[0, 0].get_xaxis().set_ticks([])ax2[0, 0].get_yaxis().set_ticks([])ax2[0, 0].set_title("L2-Map1")ax2[0, 1].imshow(l2_feature_map[:, :, 1]).set_cmap("gray")ax2[0, 1].get_xaxis().set_ticks([])ax2[0, 1].get_yaxis().set_ticks([])ax2[0, 1].set_title("L2-Map2")ax2[0, 2].imshow(l2_feature_map[:, :, 2]).set_cmap("gray")ax2[0, 2].get_xaxis().set_ticks([])ax2[0, 2].get_yaxis().set_ticks([])ax2[0, 2].set_title("L2-Map3")ax2[1, 0].imshow(l2_feature_map_relu[:, :, 0]).set_cmap("gray")ax2[1, 0].get_xaxis().set_ticks([])ax2[1, 0].get_yaxis().set_ticks([])ax2[1, 0].set_title("L2-Map1ReLU")ax2[1, 1].imshow(l2_feature_map_relu[:, :, 1]).set_cmap("gray")ax2[1, 1].get_xaxis().set_ticks([])ax2[1, 1].get_yaxis().set_ticks([])ax2[1, 1].set_title("L2-Map2ReLU")ax2[1, 2].imshow(l2_feature_map_relu[:, :, 2]).set_cmap("gray")ax2[1, 2].get_xaxis().set_ticks([])ax2[1, 2].get_yaxis().set_ticks([])ax2[1, 2].set_title("L2-Map3ReLU")ax2[2, 0].imshow(l2_feature_map_relu_pool[:, :, 0]).set_cmap("gray")ax2[2, 0].get_xaxis().set_ticks([])ax2[2, 0].get_yaxis().set_ticks([])ax2[2, 0].set_title("L2-Map1ReLUPool")ax2[2, 1].imshow(l2_feature_map_relu_pool[:, :, 1]).set_cmap("gray")ax2[2, 1].get_xaxis().set_ticks([])ax2[2, 1].get_yaxis().set_ticks([])ax2[2, 1].set_title("L2-Map2ReLUPool")ax2[2, 2].imshow(l2_feature_map_relu_pool[:, :, 2]).set_cmap("gray")ax2[2, 2].get_xaxis().set_ticks([])ax2[2, 2].get_yaxis().set_ticks([])ax2[2, 2].set_title("L2-Map3ReLUPool")plt.savefig("L2.png", bbox_inches="tight")plt.close(fig2)# 第三层fig3, ax3 = plt.subplots(nrows=1, ncols=3)ax3[0].imshow(l3_feature_map[:, :, 0]).set_cmap("gray")ax3[0].get_xaxis().set_ticks([])ax3[0].get_yaxis().set_ticks([])ax3[0].set_title("L3-Map1")ax3[1].imshow(l3_feature_map_relu[:, :, 0]).set_cmap("gray")ax3[1].get_xaxis().set_ticks([])ax3[1].get_yaxis().set_ticks([])ax3[1].set_title("L3-Map1ReLU")ax3[2].imshow(l3_feature_map_relu_pool[:, :, 0]).set_cmap("gray")ax3[2].get_xaxis().set_ticks([])ax3[2].get_yaxis().set_ticks([])ax3[2].set_title("L3-Map1ReLUPool")plt.savefig("L3.png", bbox_inches="tight")plt.close(fig3)

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