Python DPED机器学习之实现照片美化

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。 

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