前言
最近发现了一个可以把照片美化的项目，自己玩了玩，挺有意思的，分享一下。
Github地址：DPED项目地址
下面来看看项目怎么玩？先放一些项目给出的效果图。可以看出照片更明亮好看了。
环境部署
项目结构
下面是项目的原始结构：
tensorflow安装
按照项目的说明，我们需要安装tensorflow以及一些必要的库。
如果安装gpu版本的tensorflow需要对照一下
tensorflow官方对照地址：TensorFlow官方CUDA版本对照
我的cuda是11.1的版本，按照tensorflow后还是缺少部分dll，如果有相同问题的，可以用我提供的资源包 提取码：TUAN。
缺少哪个dll，直接复制到你的NVIDIA GPU Computing Toolkit目录对应cuda的bin目录下。
按照自己的版本来，我的tensorflow命令如下：
pip install tensorflow-gpu==2.4.2 -i https://pypi.douban.com/simple pip install tf-nightly -i https://pypi.douban.com/simple
其他依赖安装
Pillow, scipy, numpy, imageio安装
pip install Pillow -i https://pypi.douban.com/simple pip install scipy -i https://pypi.douban.com/simple pip install numpy -i https://pypi.douban.com/simple pip install imageio -i https://pypi.douban.com/simple
VGG-19下载
因为模型文件太大，github的项目中无法上传这么大的文件，作者让我们自己下。
我把DPED的资源包统一打包了，也可以从我的云盘下载， 放到项目的vgg_pretrained目录下。下图是资源包的目录
资源包地址 提取码：TUAN。
项目运行
项目需要的环境我们都装好了，我们跳过训练的部分，测试model的方法官方给出了命令。
准备图片素材
我准备了几张图，就不全展示了，展示其中的一张。
按照项目的要求，需要放在对应的目录下。
测试效果
执行命令
python test_model.py model=iphone_orig test_subset=full resolution=orig use_gpu=true
执行过程
(tensorflow) C:\Users\yi\PycharmProjects\DPED>python test_model.py model=iphone_orig test_subset=full resolution=orig use_gpu=true 2021-11-27 23:42:57.922965: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll 2021-11-27 23:43:00.532645: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags. 2021-11-27 23:43:00.535946: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library nvcuda.dll 2021-11-27 23:43:00.559967: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties: pciBusID: 0000:01:00.0 name: GeForce GTX 1070 computeCapability: 6.1 coreClock: 1.759GHz coreCount: 15 deviceMemorySize: 8.00GiB deviceMemoryBandwidth: 238.66GiB/s 2021-11-27 23:43:00.560121: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll 2021-11-27 23:43:00.577706: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublas64_11.dll 2021-11-27 23:43:00.577812: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublasLt64_11.dll 2021-11-27 23:43:00.588560: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cufft64_10.dll 2021-11-27 23:43:00.591950: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library curand64_10.dll 2021-11-27 23:43:00.614412: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusolver64_10.dll 2021-11-27 23:43:00.624267: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusparse64_11.dll 2021-11-27 23:43:00.626309: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudnn64_8.dll 2021-11-27 23:43:00.626481: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0 2021-11-27 23:43:01.112598: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1261] Device interconnect StreamExecutor with strength 1 edge matrix: 2021-11-27 23:43:01.112756: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1267] 0 2021-11-27 23:43:01.113098: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1280] 0: N 2021-11-27 23:43:01.113463: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1406] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 6720 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1070, pci bus id: 0000:01:00.0, compute capability: 6.1) 2021-11-27 23:43:01.114296: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set WARNING:tensorflow:From C:\Users\yi\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\compat\v2_compat.py:96: disable_resource_variables (from tensorflow.p ython.ops.variable_scope) is deprecated and will be removed in a future version. Instructions for updating: non-resource variables are not supported in the long term 2021-11-27 23:43:01.478512: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set 2021-11-27 23:43:01.479339: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties: pciBusID: 0000:01:00.0 name: GeForce GTX 1070 computeCapability: 6.1 coreClock: 1.759GHz coreCount: 15 deviceMemorySize: 8.00GiB deviceMemoryBandwidth: 238.66GiB/s 2021-11-27 23:43:01.479747: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll 2021-11-27 23:43:01.480519: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublas64_11.dll 2021-11-27 23:43:01.480927: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublasLt64_11.dll 2021-11-27 23:43:01.481155: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cufft64_10.dll 2021-11-27 23:43:01.481568: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library curand64_10.dll 2021-11-27 23:43:01.481823: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusolver64_10.dll 2021-11-27 23:43:01.482188: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusparse64_11.dll 2021-11-27 23:43:01.482416: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudnn64_8.dll 2021-11-27 23:43:01.482638: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0 2021-11-27 23:43:01.482959: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties: pciBusID: 0000:01:00.0 name: GeForce GTX 1070 computeCapability: 6.1 coreClock: 1.759GHz coreCount: 15 deviceMemorySize: 8.00GiB deviceMemoryBandwidth: 238.66GiB/s 2021-11-27 23:43:01.483077: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll 2021-11-27 23:43:01.483254: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublas64_11.dll 2021-11-27 23:43:01.483426: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublasLt64_11.dll 2021-11-27 23:43:01.483638: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cufft64_10.dll 2021-11-27 23:43:01.483817: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library curand64_10.dll 2021-11-27 23:43:01.484052: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusolver64_10.dll 2021-11-27 23:43:01.484250: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusparse64_11.dll 2021-11-27 23:43:01.484433: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudnn64_8.dll 2021-11-27 23:43:01.484662: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0 2021-11-27 23:43:01.484841: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1261] Device interconnect StreamExecutor with strength 1 edge matrix: 2021-11-27 23:43:01.484984: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1267] 0 2021-11-27 23:43:01.485152: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1280] 0: N 2021-11-27 23:43:01.485395: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1406] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 6720 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1070, pci bus id: 0000:01:00.0, compute capability: 6.1) 2021-11-27 23:43:01.485565: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set 2021-11-27 23:43:01.518135: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:196] None of the MLIR optimization passes are enabled (registered 0 passes) Testing original iphone model, processing image 3.jpg 2021-11-27 23:43:01.863678: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudnn64_8.dll 2021-11-27 23:43:02.517063: I tensorflow/core/platform/windows/subprocess.cc:308] SubProcess ended with return code: 0 2021-11-27 23:43:02.632790: I tensorflow/core/platform/windows/subprocess.cc:308] SubProcess ended with return code: 0 2021-11-27 23:43:03.210892: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublas64_11.dll 2021-11-27 23:43:03.509052: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublasLt64_11.dll Lossy conversion from float32 to uint8. Range [-0.06221151351928711, 1.0705437660217285]. Convert image to uint8 prior to saving to suppress this warning. Lossy conversion from float32 to uint8. Range [-0.06221151351928711, 1.0705437660217285]. Convert image to uint8 prior to saving to suppress this warning. Testing original iphone model, processing image 4.jpg Lossy conversion from float32 to uint8. Range [-0.05176264047622681, 1.0500218868255615]. Convert image to uint8 prior to saving to suppress this warning. Lossy conversion from float32 to uint8. Range [-0.05176264047622681, 1.0500218868255615]. Convert image to uint8 prior to saving to suppress this warning. Testing original iphone model, processing image 5.jpg Lossy conversion from float32 to uint8. Range [-0.03344374895095825, 1.0417983531951904]. Convert image to uint8 prior to saving to suppress this warning. Lossy conversion from float32 to uint8. Range [-0.03344374895095825, 1.0417983531951904]. Convert image to uint8 prior to saving to suppress this warning. Testing original iphone model, processing image 6.jpg Lossy conversion from float32 to uint8. Range [-0.03614246845245361, 1.063475251197815]. Convert image to uint8 prior to saving to suppress this warning. Lossy conversion from float32 to uint8. Range [-0.03614246845245361, 1.063475251197815]. Convert image to uint8 prior to saving to suppress this warning.
项目会生成前后对比图以及最终结果图。
前后效果图，左边为原始图，右边为对比图。
结果图如下
可以明显的看出，新图已经明亮了许多，色彩也变的比较鲜明了，效果还是很不错的。
到此这篇关于Python DPED机器学习之实现照片美化的文章就介绍到这了,更多相关照片美化内容请搜索服务器之家以前的文章或继续浏览下面的相关文章希望大家以后多多支持服务器之家！
原文链接：https://blog.csdn.net/zhiweihongyan1/article/details/121579505
