Python人工智能之sg2im文字转图像

【人工智能项目】sg2im文字转图像

本次主要对github上的sg2im源码进行执行训练，得到结果。

1.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
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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
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[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
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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]

2.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

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