{"text_format":"2","workbook":"xlsx","n_sheets":7,"sheets":["Info_stylegan2","操作_tensorflow","操作_pytorch","操作_圖片標籤","測試清單","Info_CartoonGAN","Demo By conda env"],"not_rendered":{"charts":0,"images":3,"macros":0,"external_links":0,"defined_names":0,"comments":0,"pivot_tables":0},"unopened":[],"bounds":{"document_bytes":16777216,"sheet_cells":100000,"run_cost":"one","rows_first":2000,"rows_last":500,"rows_between":2000,"halvings":0,"cell_chars":16384,"cell_head_chars":4096},"cells_cut":0,"cells_raw":0,"read":"whole","date_system":"1900"}

{"sheet":"Info_stylegan2","index":0,"n_rows":37,"n_cols":2,"rows_kept":37,"cut":false,"hidden":false,"merged":[]}
["official 版本"]
["github","https://github.com/NVlabs/stylegan2"]
["requirements"]
[null,"Both Linux and Windows are supported. Linux is recommended for performance and compatibility reasons."]
[null,"64-bit Python 3.6 installation. We recommend Anaconda3 with numpy 1.14.3 or newer."]
[null,"We recommend TensorFlow 1.14, which we used for all experiments in the paper, but TensorFlow 1.15 is also supported on Linux. TensorFlow 2.x is not supported."]
[null,"On Windows you need to use TensorFlow 1.14, as the standard 1.15 installation does not include necessary C++ headers."]
[null,"One or more high-end NVIDIA GPUs, NVIDIA drivers, CUDA 10.0 toolkit and cuDNN 7.5. To reproduce the results reported in the paper, you need an NVIDIA GPU with at least 16 GB of DRAM."]
[null,"GPU Compute Capability >=3.5 for tensorflow-gpu"]
[null,"Check your GPU compute capability：https://developer.nvidia.com/cuda-gpus"]
["model"]
[null,"https://drive.google.com/drive/folders/1yanUI9m4b4PWzR0eurKNq6JR1Bbfbh6L"]
[null,"https://github.com/justinpinkney/awesome-pretrained-stylegan2"]
[]
["What GAN?"]
[null,"https://ithelp.ithome.com.tw/articles/10196257"]
{"$rows_empty":15}
["pytorch版本(可透過設定走cpu，不用修改內碼)"]
["github","https://github.com/Tetratrio/stylegan2_pytorch"]
["model","透過stylegan2 產出的tensorflow pkl檔轉換"]
[]
["人像圖片標籤"]
["github","https://github.com/KichangKim/DeepDanbooru"]

{"sheet":"操作_tensorflow","index":1,"n_rows":20,"n_cols":2,"rows_kept":20,"cut":false,"hidden":false,"merged":[]}
["Demo"]
[null,"https://gist.github.com/tobeywang/72434c9167336c159e54ede1e3978c97"]
[]
["取得臉部"]
["python -W ignore align_images.py realimage/ realimage/images/"]
[]
["取得臉部轉tfrecords"]
["python dataset_tool.py create_from_images ./datasets/mypictures realimage/images/"]
[]
["取得生成人臉"]
["python -W ignore run_generator.py generate-images --network networks/stylegan2-ffhq-config-f.pkl --seeds 6600-6625 --truncation-psi 0.5"]
[]
[]
["參數說明"]
["seed","種子的概念，給一個隨機數進行方式程的運算，達到動態生成人臉"]
[null,"可以是單一數也可以是一個範圍，單一數就只產生一張，一個範圍就產出二數相差+1的人臉數"]
[null,"依據官方說明值落在0~4,294,967,295 （Seed must be between 0 and 2**32 – 1）"]
["truncation-psi "]
[null,"生成人物與現實人物的差距值，值越大，就差越大"]
[null,"值的範圍在：0.0-1.0"]

{"sheet":"操作_pytorch","index":2,"n_rows":11,"n_cols":18,"rows_kept":11,"cut":false,"hidden":false,"merged":[]}
["pkl to gs.pth",{"$nulls":16},"紅色為目前專案產出失敗"]
["python -W ignore run_convert_from_tf.py --input=networks/stylegan2-ffhq-config-f.pkl --output checkpoint"]
[]
["取得生成人臉"]
["python -W ignore run_generator.py generate_images --network=checkpoint/Gs.pth --seeds=6600-6625 --truncation_psi=0.5"]
[]
["假圖投射到StyleGAN2 dlatents(維度)空間並重建圖像",{"$nulls":5},"注意：這個在cpu上要跑很久"]
["python -W ignore run_projector.py project_generated_images --network=checkpoint_star/Gs.pth --seed=6600"]
[]
["真實圖投射到StyleGAN2 dlatents(維度)空間並重建圖像",{"$nulls":5},"注意：這個在cpu上要跑很久"]
["python -W ignore run_projector.py project_real_images --network=checkpoint_star/Gs.pth --data_dir=dataset/image"]

{"sheet":"操作_圖片標籤","index":3,"n_rows":2,"n_cols":1,"rows_kept":2,"cut":false,"hidden":false,"merged":[]}
["取得圖片標籤(人像的部分)"]
["python program.py evaluate \"danbooru-resnet_custom_v2-p4\" \"image0000-target.png\""]

{"sheet":"測試清單","index":4,"n_rows":4,"n_cols":13,"rows_kept":4,"cut":false,"hidden":false,"merged":[[6,0,6,11],[1,0,1,11],[0,0,0,11],[2,0,2,11],[3,0,3,11],[4,0,4,11],[5,0,5,11]]}
["目的/目標",{"$nulls":11},"執行結果"]
["在colab下可執行GPU版本，透過不同的目標pkl模型，同一個vgg16.pkl架構\n1.產出多張不存在的臉\n2.一張真實圖片+假圖 合成為接近真實圖片的假圖",{"$nulls":11},"done"]
["pytorch 版本在cpu下執行，透過不同的目標pkl模型，pytorch版本lpip架構\n1.產出多張不存在的臉",{"$nulls":11},"done"]
["承上\n真實圖片+假圖合成失敗(產出全黑或不完全圖像)"]

{"sheet":"Info_CartoonGAN","index":5,"n_rows":9,"n_cols":2,"rows_kept":9,"cut":false,"hidden":false,"merged":[]}
["Gan延申應用-CartoonGan"]
["github"]
[null,"https://github.com/mnicnc404/CartoonGan-tensorflow"]
[null,"https://github.com/keras-team/keras-contrib"]
["requirements"]
[null,"CPU and GPU are supported"]
[null,"need keras-contrib"]
["What CartoonGan?"]
[null,"https://openaccess.thecvf.com/content_cvpr_2018/papers/Chen_CartoonGAN_Generative_Adversarial_CVPR_2018_paper.pdf"]

{"sheet":"Demo By conda env","index":6,"n_rows":33,"n_cols":2,"rows_kept":33,"cut":false,"hidden":false,"merged":[]}
["requirements"]
[null,"pip install -r  requirements_cpu.txt"]
["conda"]
[null,"git clone https://github.com/mnicnc404/CartoonGan-tensorflow.git"]
[null,"cd CartoonGan-tensorflow"]
[null,"git clone https://www.github.com/keras-team/keras-contrib.git"]
[null,"cd keras-contrib"]
[null,"python convert_to_tf_keras.py "]
[null,"python setup.py install"]
[null,"cd .."]
["圖片原檔預設路徑"]
[null,"CartoonGan-tensorflow-master\\input_images"]
["conda"]
[null,"python -W ignore cartoonize.py"]
{"$rows_empty":16}
["圖片輸出預設路徑"]
[null,"CartoonGan-tensorflow-master\\output_images"]
["新海城風格："]