Python人工智能之sg2im文字转图像

从github上下载源码

!git clone https://github.com/google/sg2im.git

Cloning into 'sg2im'...
remote: Enumerating objects: 85, done.[K
remote: Total 85 (delta 0), reused 0 (delta 0), pack-reused 85[K
Unpacking objects: 100% (85/85), done.

! cp -r sg2im/sg2im sg2im/scripts/

!pip install -r sg2im/requirements.txt

Collecting cloudpickle==0.5.3
Downloading https://files.pythonhosted.org/packages/e7/bf/60ae7ec1e8c6742d2abbb6819c39a48ee796793bcdb7e1d5e41a3e379ddd/cloudpickle-0.5.3-py2.py3-none-any.whl
Requirement already satisfied: cycler==0.10.0 in /usr/local/lib/python3.6/dist-packages (from -r sg2im/requirements.txt (line 2)) (0.10.0)
Collecting Cython==0.28.3
[?25l Downloading https://files.pythonhosted.org/packages/6f/79/d8e2cd00bea8156a995fb284ce7b6677c49eccd2d318f73e201a9ce560dc/Cython-0.28.3-cp36-cp36m-manylinux1_x86_64.whl (3.4MB)
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Collecting toolz==0.9.0
[?25l Downloading https://files.pythonhosted.org/packages/14/d0/a73c15bbeda3d2e7b381a36afb0d9cd770a9f4adc5d1532691013ba881db/toolz-0.9.0.tar.gz (45kB)
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[?25hCollecting torchvision==0.2.1
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[?25hRequirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from kiwisolver==1.0.1->-r sg2im/requirements.txt (line 8)) (47.1.1)
Building wheels for collected packages: networkx, toolz
Building wheel for networkx (setup.py) ... [?25l[?25hdone
Created wheel for networkx: filename=networkx-2.1-py2.py3-none-any.whl size=1447765 sha256=4e89cc8350ab7270295c4e879190531eee2b1205e4a7b0c073ed8fe950717a25
Stored in directory: /root/.cache/pip/wheels/44/c0/34/6f98693a554301bdb405f8d65d95bbcd3e50180cbfdd98a94e
Building wheel for toolz (setup.py) ... [?25l[?25hdone
Created wheel for toolz: filename=toolz-0.9.0-cp36-none-any.whl size=53240 sha256=eb0e9434019a90c774ffcbfb077542b8688b43df4895b0c5c57204702dadc064
Stored in directory: /root/.cache/pip/wheels/f4/0c/f6/ce6b2d1aa459ee97cc3c0f82236302bd62d89c86c700219463
Successfully built networkx toolz
[31mERROR: xarray 0.15.1 has requirement numpy>=1.15, but you'll have numpy 1.14.4 which is incompatible.[0m
[31mERROR: umap-learn 0.4.3 has requirement numpy>=1.17, but you'll have numpy 1.14.4 which is incompatible.[0m
[31mERROR: umap-learn 0.4.3 has requirement scipy>=1.3.1, but you'll have scipy 1.1.0 which is incompatible.[0m
[31mERROR: tifffile 2020.5.30 has requirement numpy>=1.15.1, but you'll have numpy 1.14.4 which is incompatible.[0m
[31mERROR: tensorflow 2.2.0 has requirement h5py<2.11.0,>=2.10.0, but you'll have h5py 2.8.0 which is incompatible.[0m
[31mERROR: tensorflow 2.2.0 has requirement numpy<2.0,>=1.16.0, but you'll have numpy 1.14.4 which is incompatible.[0m
[31mERROR: tensorflow 2.2.0 has requirement scipy==1.4.1; python_version >= "3", but you'll have scipy 1.1.0 which is incompatible.[0m
[31mERROR: tensorflow 2.2.0 has requirement six>=1.12.0, but you'll have six 1.11.0 which is incompatible.[0m
[31mERROR: tensorflow-probability 0.10.0 has requirement cloudpickle>=1.2.2, but you'll have cloudpickle 0.5.3 which is incompatible.[0m
[31mERROR: tensorflow-hub 0.8.0 has requirement six>=1.12.0, but you'll have six 1.11.0 which is incompatible.[0m
[31mERROR: spacy 2.2.4 has requirement numpy>=1.15.0, but you'll have numpy 1.14.4 which is incompatible.[0m
[31mERROR: plotnine 0.6.0 has requirement matplotlib>=3.1.1, but you'll have matplotlib 2.2.2 which is incompatible.[0m
[31mERROR: plotnine 0.6.0 has requirement numpy>=1.16.0, but you'll have numpy 1.14.4 which is incompatible.[0m
[31mERROR: plotnine 0.6.0 has requirement scipy>=1.2.0, but you'll have scipy 1.1.0 which is incompatible.[0m
[31mERROR: numba 0.48.0 has requirement numpy>=1.15, but you'll have numpy 1.14.4 which is incompatible.[0m
[31mERROR: mizani 0.6.0 has requirement matplotlib>=3.1.1, but you'll have matplotlib 2.2.2 which is incompatible.[0m
[31mERROR: imgaug 0.2.9 has requirement numpy>=1.15.0, but you'll have numpy 1.14.4 which is incompatible.[0m
[31mERROR: gym 0.17.2 has requirement cloudpickle<1.4.0,>=1.2.0, but you'll have cloudpickle 0.5.3 which is incompatible.[0m
[31mERROR: google-colab 1.0.0 has requirement six~=1.12.0, but you'll have six 1.11.0 which is incompatible.[0m
[31mERROR: featuretools 0.4.1 has requirement dask>=0.19.4, but you'll have dask 0.17.5 which is incompatible.[0m
[31mERROR: fbprophet 0.6 has requirement python-dateutil>=2.8.0, but you'll have python-dateutil 2.7.3 which is incompatible.[0m
[31mERROR: fastai 1.0.61 has requirement numpy>=1.15, but you'll have numpy 1.14.4 which is incompatible.[0m
[31mERROR: fastai 1.0.61 has requirement torch>=1.0.0, but you'll have torch 0.4.0 which is incompatible.[0m
[31mERROR: distributed 1.25.3 has requirement dask>=0.18.0, but you'll have dask 0.17.5 which is incompatible.[0m
[31mERROR: datascience 0.10.6 has requirement folium==0.2.1, but you'll have folium 0.8.3 which is incompatible.[0m
[31mERROR: cvxpy 1.0.31 has requirement numpy>=1.15, but you'll have numpy 1.14.4 which is incompatible.[0m
[31mERROR: blis 0.4.1 has requirement numpy>=1.15.0, but you'll have numpy 1.14.4 which is incompatible.[0m
[31mERROR: astropy 4.0.1.post1 has requirement numpy>=1.16, but you'll have numpy 1.14.4 which is incompatible.[0m
[31mERROR: albumentations 0.1.12 has requirement imgaug<0.2.7,>=0.2.5, but you'll have imgaug 0.2.9 which is incompatible.[0m
Installing collected packages: cloudpickle, Cython, dask, decorator, six, numpy, h5py, Pillow, imageio, kiwisolver, python-dateutil, pytz, pyparsing, matplotlib, networkx, PyWavelets, scipy, scikit-image, toolz, torch, torchvision
Found existing installation: cloudpickle 1.3.0
Uninstalling cloudpickle-1.3.0:
Successfully uninstalled cloudpickle-1.3.0
Found existing installation: Cython 0.29.19
Uninstalling Cython-0.29.19:
Successfully uninstalled Cython-0.29.19
Found existing installation: dask 2.12.0
Uninstalling dask-2.12.0:
Successfully uninstalled dask-2.12.0
Found existing installation: decorator 4.4.2
Uninstalling decorator-4.4.2:
Successfully uninstalled decorator-4.4.2
Found existing installation: six 1.12.0
Uninstalling six-1.12.0:
Successfully uninstalled six-1.12.0
Found existing installation: numpy 1.18.4
Uninstalling numpy-1.18.4:
Successfully uninstalled numpy-1.18.4
Found existing installation: h5py 2.10.0
Uninstalling h5py-2.10.0:
Successfully uninstalled h5py-2.10.0
Found existing installation: Pillow 7.0.0
Uninstalling Pillow-7.0.0:
Successfully uninstalled Pillow-7.0.0
Found existing installation: imageio 2.4.1
Uninstalling imageio-2.4.1:
Successfully uninstalled imageio-2.4.1
Found existing installation: kiwisolver 1.2.0
Uninstalling kiwisolver-1.2.0:
Successfully uninstalled kiwisolver-1.2.0
Found existing installation: python-dateutil 2.8.1
Uninstalling python-dateutil-2.8.1:
Successfully uninstalled python-dateutil-2.8.1
Found existing installation: pytz 2018.9
Uninstalling pytz-2018.9:
Successfully uninstalled pytz-2018.9
Found existing installation: pyparsing 2.4.7
Uninstalling pyparsing-2.4.7:
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Found existing installation: matplotlib 3.2.1
Uninstalling matplotlib-3.2.1:
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Found existing installation: networkx 2.4
Uninstalling networkx-2.4:
Successfully uninstalled networkx-2.4
Found existing installation: PyWavelets 1.1.1
Uninstalling PyWavelets-1.1.1:
Successfully uninstalled PyWavelets-1.1.1
Found existing installation: scipy 1.4.1
Uninstalling scipy-1.4.1:
Successfully uninstalled scipy-1.4.1
Found existing installation: scikit-image 0.16.2
Uninstalling scikit-image-0.16.2:
Successfully uninstalled scikit-image-0.16.2
Found existing installation: toolz 0.10.0
Uninstalling toolz-0.10.0:
Successfully uninstalled toolz-0.10.0
Found existing installation: torch 1.5.0+cu101
Uninstalling torch-1.5.0+cu101:
Successfully uninstalled torch-1.5.0+cu101
Found existing installation: torchvision 0.6.0+cu101
Uninstalling torchvision-0.6.0+cu101:
Successfully uninstalled torchvision-0.6.0+cu101
Successfully installed Cython-0.28.3 Pillow-5.1.0 PyWavelets-0.5.2 cloudpickle-0.5.3 dask-0.17.5 decorator-4.3.0 h5py-2.8.0 imageio-2.3.0 kiwisolver-1.0.1 matplotlib-2.2.2 networkx-2.1 numpy-1.14.4 pyparsing-2.2.0 python-dateutil-2.7.3 pytz-2018.4 scikit-image-0.14.0 scipy-1.1.0 six-1.11.0 toolz-0.9.0 torch-0.4.0 torchvision-0.2.1

!bash sg2im/scripts/download_models.sh

--2020-06-05 08:11:22-- https://storage.googleapis.com/sg2im-data/small/coco64.pt
Resolving storage.googleapis.com (storage.googleapis.com)... 173.194.79.128, 2a00:1450:4013:c05::80
Connecting to storage.googleapis.com (storage.googleapis.com)|173.194.79.128|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 119806264 (114M) [application/octet-stream]
Saving to: ‘sg2im-models/coco64.pt’

sg2im-models/coco64 100%[===================>] 114.26M 38.5MB/s in 3.0s

2020-06-05 08:11:25 (38.5 MB/s) - ‘sg2im-models/coco64.pt’ saved [119806264/119806264]

--2020-06-05 08:11:25-- https://storage.googleapis.com/sg2im-data/small/vg64.pt
Resolving storage.googleapis.com (storage.googleapis.com)... 108.177.119.128, 2a00:1450:4013:c00::80
Connecting to storage.googleapis.com (storage.googleapis.com)|108.177.119.128|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 119873465 (114M) [application/octet-stream]
Saving to: ‘sg2im-models/vg64.pt’

sg2im-models/vg64.p 100%[===================>] 114.32M 44.0MB/s in 2.6s

2020-06-05 08:11:29 (44.0 MB/s) - ‘sg2im-models/vg64.pt’ saved [119873465/119873465]

--2020-06-05 08:11:29-- https://storage.googleapis.com/sg2im-data/small/vg128.pt
Resolving storage.googleapis.com (storage.googleapis.com)... 74.125.128.128, 2a00:1450:4013:c02::80
Connecting to storage.googleapis.com (storage.googleapis.com)|74.125.128.128|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 129319241 (123M) [application/octet-stream]
Saving to: ‘sg2im-models/vg128.pt’

sg2im-models/vg128. 100%[===================>] 123.33M 54.2MB/s in 2.3s

2020-06-05 08:11:32 (54.2 MB/s) - ‘sg2im-models/vg128.pt’ saved [129319241/129319241]

训练与结果展示

!python3 sg2im/scripts/run_model.py --checkpoint sg2im-models/vg128.pt --scene_graphs sg2im/scene_graphs/figure_6_sheep.json --output_dir outputs

import matplotlib.pyplot as plt
import cv2
%matplotlib inline

img0 = cv2.imread("outputs/img000000.png")
img1 = cv2.imread("outputs/img000001.png")
img2 = cv2.imread("outputs/img000002.png")
img3 = cv2.imread("outputs/img000003.png")
img4 = cv2.imread("outputs/img000004.png")
img5 = cv2.imread("outputs/img000005.png")
img6 = cv2.imread("outputs/img000006.png")


plt.figure()
plt.subplot(3,3,1)
plt.imshow(img0)
plt.subplot(3,3,2)
plt.imshow(img1)
plt.subplot(3,3,3)
plt.imshow(img2)
plt.subplot(3,3,4)
plt.imshow(img3)
plt.subplot(3,3,5)
plt.imshow(img4)
plt.subplot(3,3,6)
plt.imshow(img5)
plt.subplot(3,3,7)
plt.imshow(img6)

<matplotlib.image.AxesImage at 0x7fa2bdfb36d8>

!python3 sg2im/scripts/run_model.py --checkpoint sg2im-models/vg128.pt --scene_graphs sg2im/scene_graphs/figure_6_street.json --output_dir outputs

import matplotlib.pyplot as plt
import cv2
%matplotlib inline

img0 = cv2.imread("outputs/img000000.png")
img1 = cv2.imread("outputs/img000001.png")
img2 = cv2.imread("outputs/img000002.png")
img3 = cv2.imread("outputs/img000003.png")
img4 = cv2.imread("outputs/img000004.png")
img5 = cv2.imread("outputs/img000005.png")
img6 = cv2.imread("outputs/img000006.png")


plt.figure()
plt.subplot(3,3,1)
plt.imshow(img0)
plt.subplot(3,3,2)
plt.imshow(img1)
plt.subplot(3,3,3)
plt.imshow(img2)
plt.subplot(3,3,4)
plt.imshow(img3)
plt.subplot(3,3,5)
plt.imshow(img4)
plt.subplot(3,3,6)
plt.imshow(img5)
plt.subplot(3,3,7)
plt.imshow(img6)

<matplotlib.image.AxesImage at 0x7fa2be14d1d0>

!python3 sg2im/scripts/run_model.py --checkpoint sg2im-models/vg128.pt --scene_graphs sg2im/scene_graphs/figure_5_vg.json --output_dir outputs

import matplotlib.pyplot as plt
import cv2
%matplotlib inline

img0 = cv2.imread("outputs/img000000.png")
img1 = cv2.imread("outputs/img000001.png")
img2 = cv2.imread("outputs/img000002.png")
img3 = cv2.imread("outputs/img000003.png")
img4 = cv2.imread("outputs/img000004.png")
img5 = cv2.imread("outputs/img000005.png")
img6 = cv2.imread("outputs/img000006.png")
img7 = cv2.imread("outputs/img000007.png")

plt.figure()
plt.subplot(3,3,1)
plt.imshow(img0)
plt.subplot(3,3,2)
plt.imshow(img1)
plt.subplot(3,3,3)
plt.imshow(img2)
plt.subplot(3,3,4)
plt.imshow(img3)
plt.subplot(3,3,5)
plt.imshow(img4)
plt.subplot(3,3,6)
plt.imshow(img5)
plt.subplot(3,3,7)
plt.imshow(img6)
plt.subplot(3,3,8)
plt.imshow(img7)

<matplotlib.image.AxesImage at 0x7fa2bdd710f0>

小结

到此这篇关于Python人工智能之sg2im文字转图像的文章就介绍到这了,更多相关Python 人工智能内容请搜索脚本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脚本之家！