Merge pull request #2 from loretoparisi/main

Added Dockerfile

Dockerfile

@@ -0,0 +1,36 @@
+#
+# Lama Cleaner Dockerfile
+# @author Loreto Parisi (loretoparisi at gmail dot com)
+#
+
+FROM python:3.7.4-slim-buster
+
+LABEL maintainer Loreto Parisi loretoparisi@gmail.com
+
+WORKDIR app
+
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    software-properties-common \
+    libsm6 libxext6 ffmpeg libfontconfig1 libxrender1 libgl1-mesa-glx \
+    curl \
+    npm
+
+# python requirements
+COPY . .
+COPY requirements.txt /etc/tmp/requirements.txt
+RUN pip install -r /etc/tmp/requirements.txt
+
+# nodejs
+RUN npm install n -g && \
+    n lts
+# yarn
+RUN npm install -g yarn
+
+# webapp
+RUN cd lama_cleaner/app/ && \
+    yarn && \
+    yarn build
+
+EXPOSE 8080
+
+CMD ["bash"]

README.md

@@ -17,3 +17,24 @@ You can experience their great online services [here](https://cleanup.pictures/)
 - Install dependencies:`cd lama_cleaner/app/ && yarn`
 - Start development server: `yarn dev`
 - Build: `yarn build`
+
+## Docker
+Run within a Docker container. Set the `cache_dir` to models location path. 
+Optionally add a `-d` option to the `docker run` command below to run as a daemon.
+
+### Build Docker image
+```
+docker build -f Dockerfile -t lamacleaner .
+```
+
+### Run Docker (cpu)
+```
+docker run -p 8080:8080 -e cache_dir=/app/models -v  $(pwd)/models:/app/models -v $(pwd):/app --rm lamacleaner python3 main.py --device=cpu --port=8080
+```
+
+### Run Docker (gpu)
+```
+docker run --gpus all -p 8080:8080 -e cache_dir=/app/models -v $(pwd)/models:/app/models -v $(pwd):/app --rm lamacleaner python3 main.py --device=cuda --port=8080
+```
+
+Then open [http://localhost:8080](http://localhost:8080)

lama_cleaner/helper.py

@@ -17,8 +17,9 @@ def download_model(url=LAMA_MODEL_URL):
     parts = urlparse(url)
     hub_dir = get_dir()
     model_dir = os.path.join(hub_dir, "checkpoints")
+    if not os.path.isdir(model_dir):
+        os.makedirs(os.path.join(model_dir, "hub", "checkpoints"))
     filename = os.path.basename(parts.path)
-
     cached_file = os.path.join(model_dir, filename)
     if not os.path.exists(cached_file):
         sys.stderr.write('Downloading: "{}" to {}\n'.format(url, cached_file))

main.py

@@ -17,12 +17,17 @@ from lama_cleaner.helper import (
     pad_img_to_modulo,
 )
 
-NUM_THREADS = "4"
+import multiprocessing
+
+NUM_THREADS = str(multiprocessing.cpu_count())
+
 os.environ["OMP_NUM_THREADS"] = NUM_THREADS
 os.environ["OPENBLAS_NUM_THREADS"] = NUM_THREADS
 os.environ["MKL_NUM_THREADS"] = NUM_THREADS
 os.environ["VECLIB_MAXIMUM_THREADS"] = NUM_THREADS
 os.environ["NUMEXPR_NUM_THREADS"] = NUM_THREADS
+if os.environ["cache_dir"]:
+    os.environ["TORCH_HOME"] = os.environ["cache_dir"]
 
 BUILD_DIR = os.environ.get("LAMA_CLEANER_BUILD_DIR", "./lama_cleaner/app/build")