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Build-Deploy-Actions Details

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jianjiang 2023-04-26 19:28:24 +08:00
commit 31a6aa58e4
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*.7z filter=lfs diff=lfs merge=lfs -text
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name: Build
run-name: ${{ github.actor }} is upgrade release 🚀
on: [push]
env:
REPOSITORY: ${{ github.repository }}
COMMIT_ID: ${{ github.sha }}
jobs:
Build-Deploy-Actions:
runs-on: ubuntu-latest
steps:
- run: echo "🎉 The job was automatically triggered by a ${{ github.event_name }} event."
- run: echo "🐧 This job is now running on a ${{ runner.os }} server hosted by Gitea!"
- run: echo "🔎 The name of your branch is ${{ github.ref }} and your repository is ${{ github.repository }}."
- name: Check out repository code
uses: actions/checkout@v3
-
name: Setup Git LFS
run: |
git lfs install
git lfs fetch
git lfs checkout
- name: List files in the repository
run: |
ls ${{ github.workspace }}
-
name: Docker Image Info
id: image-info
run: |
echo "::set-output name=image_name::$(echo $REPOSITORY | tr '[:upper:]' '[:lower:]')"
echo "::set-output name=image_tag::${COMMIT_ID:0:10}"
-
name: Login to Docker Hub
uses: docker/login-action@v2
with:
registry: artifacts.iflytek.com
username: ${{ secrets.DOCKERHUB_USERNAME }}
password: ${{ secrets.DOCKERHUB_TOKEN }}
- name: Set up Docker Buildx
uses: docker/setup-buildx-action@v2
-
name: Build and push
run: |
docker version
docker buildx build -t artifacts.iflytek.com/docker-private/atp/${{ steps.image-info.outputs.image_name }}:${{ steps.image-info.outputs.image_tag }} . --file ${{ github.workspace }}/Dockerfile --load
docker push artifacts.iflytek.com/docker-private/atp/${{ steps.image-info.outputs.image_name }}:${{ steps.image-info.outputs.image_tag }}
docker rmi artifacts.iflytek.com/docker-private/atp/${{ steps.image-info.outputs.image_name }}:${{ steps.image-info.outputs.image_tag }}
- run: echo "🍏 This job's status is ${{ job.status }}."

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#FROM python:3.8.13
FROM artifacts.iflytek.com/docker-private/atp/base_image_for_ailab:0.0.1
WORKDIR /app
COPY requirements.txt /app
RUN pip config set global.index-url https://pypi.mirrors.ustc.edu.cn/simple
RUN pip install -r requirements.txt
RUN sed -i 's/deb.debian.org/mirrors.ustc.edu.cn/g' /etc/apt/sources.list
RUN apt-get update && apt-get install ffmpeg libsm6 libxext6 -y
COPY . /app
CMD ["python", "app.py"]

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---
title: Image Upscaling, Restoration and Colorization
emoji: 🖼️
colorFrom: blue
colorTo: gray
sdk: gradio
sdk_version: 3.12.0
app_file: app.py
pinned: false
license: apache-2.0
duplicated_from: nightfury/Image_Face_Upscale_Restoration-GFPGAN
---
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

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import os, pathlib
import cv2
import gradio as gr
import torch
from basicsr.archs.srvgg_arch import SRVGGNetCompact
from gfpgan.utils import GFPGANer
from realesrgan.utils import RealESRGANer
os.system("hub install deoldify==1.0.1")
import paddlehub as hub
os.system("pip freeze")
# download weights
if not os.path.exists('GFPGANv1.4.pth'):
os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth -P .")
model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
model_path = 'realesr-general-x4v3.pth'
half = True if torch.cuda.is_available() else False
upsampler = RealESRGANer(scale=4, model_path=model_path, model=model, tile=0, tile_pad=10, pre_pad=0, half=half)
os.makedirs('output', exist_ok=True)
colorizer = hub.Module(name='deoldify')
render_factor=5
def colorize_image(image):
color_image = colorizer.predict(image)
return color_image
def inference(img, scale):
print(img, scale)
try:
extension = os.path.splitext(os.path.basename(str(img)))[1]
img = cv2.imread(img, cv2.IMREAD_UNCHANGED)
if len(img.shape) == 3 and img.shape[2] == 4:
img_mode = 'RGBA'
elif len(img.shape) == 2: # for gray inputs
img_mode = None
img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
else:
img_mode = None
h, w = img.shape[0:2]
if h < 300:
img = cv2.resize(img, (w * 2, h * 2), interpolation=cv2.INTER_LANCZOS4)
face_enhancer = GFPGANer(model_path='GFPGANv1.4.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
try:
# _, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True, weight=weight)
_, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True)
except RuntimeError as error:
print('Error', error)
try:
if scale != 2:
interpolation = cv2.INTER_AREA if scale < 2 else cv2.INTER_LANCZOS4
h, w = img.shape[0:2]
output = cv2.resize(output, (int(w * scale / 2), int(h * scale / 2)), interpolation=interpolation)
except Exception as error:
print('wrong scale input.', error)
if img_mode == 'RGBA': # RGBA images should be saved in png format
extension = 'png'
else:
extension = 'jpg'
save_path = f'output/out.{extension}'
output = cv2.cvtColor(output, cv2.COLOR_BGR2RGB)
cv2.imwrite(save_path, output)
print('upsampled image!')
output_img = colorize_image(save_path)
outputim = output_img[1]
print(type(outputim))
print('colorized image!')
return pathlib.Path(outputim)
except Exception as error:
print('global exception', error)
return None, None
with gr.Blocks(css = "footer {visibility: hidden}" ) as demo:
with gr.Row():
with gr.Column():
in_image = gr.inputs.Image(type="filepath", label="Input")
factor = gr.Slider(2, 10, value=2, step = 1, label="Rescaling factor")
#gr.inputs.Number(, default=2)
btn = gr.Button("着色")
with gr.Column():
gallery = gr.Image()
#out_image = gr.outputs.Image(type="numpy", label="Output (The whole image)")
#dld = gr.outputs.File(label="Download the output image")
btn.click(fn=inference, inputs=[in_image,factor], outputs=gallery)
demo.launch(server_name="0.0.0.0", share=True)

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ffmpeg
libsm6
libxext6

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torch>=1.7
basicsr>=1.4.2
facexlib>=0.2.5
gfpgan>=1.3.7
realesrgan>=0.2.5
numpy
opencv-python
torchvision
scipy
tqdm
lmdb
pyyaml
yapf
paddlepaddle
paddlehub
fastapi[all]==0.85.*
uvicorn[standard]==0.18.*