Python DPED机器学习怎么实现照片美化

这篇文章主要介绍“python DPED机器学习怎么实现照片美化”，在日常操作中，相信很多人在Python DPED机器学习怎么实现照片美化问题上存在疑惑，小编查阅了各式资料，整理出简单好用的操作方法，希望对大家解答”Python DPED机器学习怎么实现照片美化”的疑惑有所帮助！接下来，请跟着小编一起来学习吧！

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/simplepip 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/simplepip install scipy -i https://pypi.douban.com/simplepip install numpy -i https://pypi.douban.com/simplepip 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=true2021-11-27 23:42:57.922965: I tensorflow/stream_executor/platfORM/default/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll2021-11-27 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) touse the following CPU instructions in performance-critical operations:  AVX2To 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.dll2021-11-27 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: 6.1coreClock: 1.759GHz coreCount: 15 deviceMemorySize: 8.00GiB deviceMemoryBandwidth: 238.66GiB/s2021-11-27 23:43:00.560121: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll2021-11-27 23:43:00.577706: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublas64_11.dll2021-11-27 23:43:00.577812: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublasLt64_11.dll2021-11-27 23:43:00.588560: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cufft64_10.dll2021-11-27 23:43:00.591950: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library curand64_10.dll2021-11-27 23:43:00.614412: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusolver64_10.dll2021-11-27 23:43:00.624267: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusparse64_11.dll2021-11-27 23:43:00.626309: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudnn64_8.dll2021-11-27 23:43:00.626481: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 02021-11-27 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]      02021-11-27 23:43:01.113098: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1280] 0:   N2021-11-27 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 setWARNING:tensorflow:From C:\Users\yi\AppData\Roaming\Python\python36\site-packages\tensorflow\python\compat\v2_compat.py:96: disable_resource_variables (from tensorflow.python.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 term2021-11-27 23:43:01.478512: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set2021-11-27 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: 6.1coreClock: 1.759GHz coreCount: 15 deviceMemorySize: 8.00GiB deviceMemoryBandwidth: 238.66GiB/s2021-11-27 23:43:01.479747: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll2021-11-27 23:43:01.480519: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublas64_11.dll2021-11-27 23:43:01.480927: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublasLt64_11.dll2021-11-27 23:43:01.481155: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cufft64_10.dll2021-11-27 23:43:01.481568: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library curand64_10.dll2021-11-27 23:43:01.481823: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusolver64_10.dll2021-11-27 23:43:01.482188: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusparse64_11.dll2021-11-27 23:43:01.482416: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudnn64_8.dll2021-11-27 23:43:01.482638: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 02021-11-27 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: 6.1coreClock: 1.759GHz coreCount: 15 deviceMemorySize: 8.00GiB deviceMemoryBandwidth: 238.66GiB/s2021-11-27 23:43:01.483077: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll2021-11-27 23:43:01.483254: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublas64_11.dll2021-11-27 23:43:01.483426: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublasLt64_11.dll2021-11-27 23:43:01.483638: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cufft64_10.dll2021-11-27 23:43:01.483817: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library curand64_10.dll2021-11-27 23:43:01.484052: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusolver64_10.dll2021-11-27 23:43:01.484250: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusparse64_11.dll2021-11-27 23:43:01.484433: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudnn64_8.dll2021-11-27 23:43:01.484662: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 02021-11-27 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]      02021-11-27 23:43:01.485152: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1280] 0:   N2021-11-27 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 set2021-11-27 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.jpg2021-11-27 23:43:01.863678: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudnn64_8.dll2021-11-27 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.dll2021-11-27 23:43:03.509052: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublasLt64_11.dllLossy 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.jpgLossy 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.jpgLossy 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.jpgLossy 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。 

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