Python DPED機器學習之實現照片美化
前言
最近發現瞭一個可以把照片美化的項目,自己玩瞭玩,挺有意思的,分享一下。
Github地址:DPED項目地址
下面來看看項目怎麼玩?先放一些項目給出的效果圖。可以看出照片更明亮好看瞭。
環境部署
項目結構
下面是項目的原始結構:
tensorflow安裝
按照項目的說明,我們需要安裝tensorflow以及一些必要的庫。
如果安裝gpu版本的tensorflow需要對照一下
tensorflow官方對照地址:TensorFlow官方CUDA版本對照
我的cuda是11.1的版本,按照tensorflow後還是缺少部分dll,如果有相同問題的,可以用我提供的資源包 提取碼:TUAN。
缺少哪個dll,直接復制到你的NVIDIA GPU Computing Toolkit目錄對應cuda的bin目錄下。
按照自己的版本來,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
其他依賴安裝
Pillow, scipy, numpy, imageio安裝
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下載
因為模型文件太大,github的項目中無法上傳這麼大的文件,作者讓我們自己下。
我把DPED的資源包統一打包瞭,也可以從我的雲盤下載, 放到項目的vgg_pretrained目錄下。下圖是資源包的目錄
資源包地址 提取碼:TUAN。
項目運行
項目需要的環境我們都裝好瞭,我們跳過訓練的部分,測試model的方法官方給出瞭命令。
準備圖片素材
我準備瞭幾張圖,就不全展示瞭,展示其中的一張。
按照項目的要求,需要放在對應的目錄下。
測試效果
執行命令
python test_model.py model=iphone_orig test_subset=full resolution=orig use_gpu=true
執行過程
(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.
項目會生成前後對比圖以及最終結果圖。
前後效果圖,左邊為原始圖,右邊為對比圖。
結果圖如下
可以明顯的看出,新圖已經明亮瞭許多,色彩也變的比較鮮明瞭,效果還是很不錯的。
到此這篇關於Python DPED機器學習之實現照片美化的文章就介紹到這瞭,更多相關照片美化內容請搜索WalkonNet以前的文章或繼續瀏覽下面的相關文章希望大傢以後多多支持WalkonNet!
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