AOD-NET去雾网络与ACE自适应对比度增强结合代码实现

以下是一个示例的AOD-NET去雾网络与ACE自适应对比度增强结合的代码：\n\n首先，导入所需的库和模块：\n\npython\nimport cv2\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torchvision.transforms as transforms\nfrom PIL import Image\n\n\n定义AOD-NET去雾网络的模型：\n\npython\nclass AODNet(nn.Module):\n def __init__(self):\n super(AODNet, self).__init__()\n\n self.conv1 = nn.Conv2d(3, 3, kernel_size=3, stride=1, padding=1)\n self.conv2 = nn.Conv2d(6, 3, kernel_size=3, stride=1, padding=1)\n self.conv3 = nn.Conv2d(9, 3, kernel_size=3, stride=1, padding=1)\n self.conv4 = nn.Conv2d(12, 3, kernel_size=3, stride=1, padding=1)\n self.conv5 = nn.Conv2d(15, 3, kernel_size=3, stride=1, padding=1)\n self.conv6 = nn.Conv2d(18, 3, kernel_size=3, stride=1, padding=1)\n self.conv7 = nn.Conv2d(21, 3, kernel_size=3, stride=1, padding=1)\n self.conv8 = nn.Conv2d(24, 3, kernel_size=3, stride=1, padding=1)\n self.conv9 = nn.Conv2d(27, 3, kernel_size=3, stride=1, padding=1)\n self.conv10 = nn.Conv2d(30, 3, kernel_size=3, stride=1, padding=1)\n self.conv11 = nn.Conv2d(33, 3, kernel_size=3, stride=1, padding=1)\n self.conv12 = nn.Conv2d(36, 3, kernel_size=3, stride=1, padding=1)\n self.conv13 = nn.Conv2d(39, 3, kernel_size=3, stride=1, padding=1)\n self.conv14 = nn.Conv2d(42, 3, kernel_size=3, stride=1, padding=1)\n self.conv15 = nn.Conv2d(45, 3, kernel_size=3, stride=1, padding=1)\n self.conv16 = nn.Conv2d(48, 3, kernel_size=3, stride=1, padding=1)\n self.conv17 = nn.Conv2d(51, 3, kernel_size=3, stride=1, padding=1)\n self.conv18 = nn.Conv2d(54, 3, kernel_size=3, stride=1, padding=1)\n self.conv19 = nn.Conv2d(57, 3, kernel_size=3, stride=1, padding=1)\n self.conv20 = nn.Conv2d(60, 3, kernel_size=3, stride=1, padding=1)\n self.conv21 = nn.Conv2d(63, 3, kernel_size=3, stride=1, padding=1)\n self.conv22 = nn.Conv2d(66, 3, kernel_size=3, stride=1, padding=1)\n self.conv23 = nn.Conv2d(69, 3, kernel_size=3, stride=1, padding=1)\n self.conv24 = nn.Conv2d(72, 3, kernel_size=3, stride=1, padding=1)\n self.conv25 = nn.Conv2d(75, 3, kernel_size=3, stride=1, padding=1)\n self.conv26 = nn.Conv2d(78, 3, kernel_size=3, stride=1, padding=1)\n self.conv27 = nn.Conv2d(81, 3, kernel_size=3, stride=1, padding=1)\n self.conv28 = nn.Conv2d(84, 3, kernel_size=3, stride=1, padding=1)\n self.conv29 = nn.Conv2d(87, 3, kernel_size=3, stride=1, padding=1)\n self.conv30 = nn.Conv2d(90, 3, kernel_size=3, stride=1, padding=1)\n\n self.relu = nn.ReLU()\n\n def forward(self, x):\n out1 = self.relu(self.conv1(x))\n out2 = self.relu(self.conv2(torch.cat((x, out1), dim=1)))\n out3 = self.relu(self.conv3(torch.cat((x, out1, out2), dim=1)))\n out4 = self.relu(self.conv4(torch.cat((x, out1, out2, out3), dim=1)))\n out5 = self.relu(self.conv5(torch.cat((x, out1, out2, out3, out4), dim=1)))\n out6 = self.relu(self.conv6(torch.cat((x, out1, out2, out3, out4, out5), dim=1)))\n out7 = self.relu(self.conv7(torch.cat((x, out1, out2, out3, out4, out5, out6), dim=1)))\n out8 = self.relu(self.conv8(torch.cat((x, out1, out2, out3, out4, out5, out6, out7), dim=1)))\n out9 = self.relu(self.conv9(torch.cat((x, out1, out2, out3, out4, out5, out6, out7, out8), dim=1)))\n out10 = self.relu(self.conv10(torch.cat((x, out1, out2, out3, out4, out5, out6, out7, out8, out9), dim=1)))\n out11 = self.relu(self.conv11(torch.cat((x, out1, out2, out3, out4, out5, out6, out7, out8, out9, out10), dim=1)))\n out12 = self.relu(self.conv12(torch.cat((x, out1, out2, out3, out4, out5, out6, out7, out8, out9, out10, out11), dim=1)))\n out13 = self.relu(self.conv13(torch.cat((x, out1, out2, out3, out4, out5, out6, out7, out8, out9, out10, out11, out12), dim=1)))\n out14 = self.relu(self.conv14(torch.cat((x, out1, out2, out3, out4, out5, out6, out7, out8, out9, out10, out11, out12, out13), dim=1)))\n out15 = self.relu(self.conv15(torch.cat((x, out1, out2, out3, out4, out5, out6, out7, out8, out9, out10, out11, out12, out13, out14), dim=1)))\n out16 = self.relu(self.conv16(torch.cat((x, out1, out2, out3, out4, out5, out6, out7, out8, out9, out10, out11, out12, out13, out14, out15), dim=1)))\n out17 = self.relu(self.conv17(torch.cat((x, out1, out2, out3, out4, out5, out6, out7, out8, out9, out10, out11, out12, out13, out14, out15, out16), dim=1)))\n out18 = self.relu(self.conv18(torch.cat((x, out1, out2, out3, out4, out5, out6, out7, out8, out9, out10, out11, out12, out13, out14, out15, out16, out17), dim=1)))\n out19 = self.relu(self.conv19(torch.cat((x, out1, out2, out3, out4, out5, out6, out7, out8, out9, out10, out11, out12, out13, out14, out15, out16, out17, out18), dim=1)))\n out20 = self.relu(self.conv20(torch.cat((x, out1, out2, out3, out4, out5, out6, out7, out8, out9, out10, out11, out12, out13, out14, out15, out16, out17, out18, out19), dim=1)))\n out21 = self.relu(self.conv21(torch.cat((x, out1, out2, out3, out4, out5, out6, out7, out8, out9, out10, out11, out12, out13, out14, out15, out16, out17, out18, out19, out20), dim=1)))\n out22 = self.relu(self.conv22(torch.cat((x, out1, out2, out3, out4, out5, out6, out7, out8, out9, out10, out11, out12, out13, out14, out15, out16, out17, out18, out19, out20, out21), dim=1)))\n out23 = self.relu(self.conv23(torch.cat((x, out1, out2, out3, out4, out5, out6, out7, out8, out9, out10, out11, out12, out13, out14, out15, out16, out17, out18, out19, out20, out21, out22), dim=1)))\n out24 = self.relu(self.conv24(torch.cat((x, out1, out2, out3, out4, out5, out6, out7, out8, out9, out10, out11, out12, out13, out14, out15, out16, out17, out18, out19, out20, out21, out22, out23), dim=1)))\n out25 = self.relu(self.conv25(torch.cat((x, out1, out2, out3, out4, out5, out6, out7, out8, out9, out10, out11, out12, out13, out14, out15, out16, out17, out18, out19, out20, out21, out22, out23, out24), dim=1)))\n out26 = self.relu(self.conv26(torch.cat((x, out1, out2, out3, out4, out5, out6, out7, out8, out9, out10, out11, out12, out13, out14, out15, out16, out17, out18, out19, out20, out21, out22, out23, out24, out25), dim=1)))\n out27 = self.relu(self.conv27(torch.cat((x, out1, out2, out3, out4, out5, out6, out7, out8, out9, out10, out11, out12, out13, out14, out15, out16, out17, out18, out19, out20, out21, out22, out23, out24, out25, out26), dim=1)))\n out28 = self.relu(self.conv28(torch.cat((x, out1, out2, out3, out4, out5, out6, out7, out8, out9, out10, out11, out12, out13, out14, out15, out16, out17, out18, out19, out20, out21, out22, out23, out24, out25, out26, out27), dim=1)))\n out29 = self.relu(self.conv29(torch.cat((x, out1, out2, out3, out4, out5, out6, out7, out8, out9, out10, out11, out12, out13, out14, out15, out16, out17, out18, out19, out20, out21, out22, out23, out24, out25, out26, out27, out28), dim=1)))\n out30 = self.relu(self.conv30(torch.cat((x, out1, out2, out3, out4, out5, out6, out7, out8, out9, out10, out11, out12, out13, out14, out15, out16, out17, out18, out19, out20, out21, out22, out23, out24, out25, out26, out27, out28, out29), dim=1)))\n\n return out30\n\n\n定义ACE自适应对比度增强的函数：\n\npython\ndef ace_contrast_enhancement(image):\n # Convert image to LAB color space\n lab = cv2.cvtColor(image, cv2.COLOR_RGB2LAB)\n\n # Split LAB image into L, A, and B channels\n l, a, b = cv2.split(lab)\n\n # Apply adaptive contrast enhancement to L channel\n clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))\n enhanced_l = clahe.apply(l)\n\n # Merge enhanced L channel with A and B channels\n enhanced_lab = cv2.merge((enhanced_l, a, b))\n\n # Convert enhanced LAB image back to RGB color space\n enhanced_rgb = cv2.cvtColor(enhanced_lab, cv2.COLOR_LAB2RGB)\n\n return enhanced_rgb\n\n\n加载AOD-NET模型：\n\npython\nmodel = AODNet()\nmodel.load_state_dict(torch.load('aod_net.pth'))\nmodel.eval()\n\n\n定义对输入图像进行去雾处理的函数：\n\npython\ndef dehaze(image):\n transform = transforms.Compose([\n transforms.ToTensor()\n ])\n\n image_tensor = transform(image).unsqueeze(0)\n\n with torch.no_grad():\n haze_free = model(image_tensor)\n\n haze_free = haze_free.squeeze(0).permute(1, 2, 0).numpy()\n\n return haze_free\n\n\n加载输入图像：\n\npython\ninput_image = Image.open('input_image.jpg')\n\n\n对输入图像进行去雾处理：\n\npython\ndehazed_image = dehaze(input_image)\n\n\n对去雾图像进行ACE自适应对比度增强：\n\npython\nenhanced_image = ace_contrast_enhancement(dehazed_image)\n\n\n显示原始图像、去雾图像和增强图像：\n\npython\ncv2.imshow('Original Image', cv2.cvtColor(np.array(input_image), cv2.COLOR_RGB2BGR))\ncv2.imshow('Dehazed Image', cv2.cvtColor(dehazed_image, cv2.COLOR_RGB2BGR))\ncv2.imshow('Enhanced Image', cv2.cvtColor(enhanced_image, cv2.COLOR_RGB2BGR))\ncv2.waitKey(0)\ncv2.destroyAllWindows()\n\n\n请注意，以上代码仅为示例，实际应用中可能需要根据具体情况进行适当修改。