WIP docker file updates

Dockerfile

@@ -1,7 +1,7 @@
 # Python version can be changed, e.g.
 # FROM python:3.8
 # FROM docker.io/fnndsc/conda:python3.10.2-cuda11.6.0
-FROM docker.io/python:3.11.3-slim-bullseye
+FROM docker.io/python:3.11.0-slim-bullseye
 
 LABEL org.opencontainers.image.authors="FNNDSC <[email protected]>" \
       org.opencontainers.image.title="My ChRIS Plugin" \
@@ -15,8 +15,9 @@ RUN pip install -r requirements.txt
 
 COPY . .
 ARG extras_require=none
-RUN apt-get update && apt-get install ffmpeg libsm6 libxext6  -y
+
 RUN pip install ".[${extras_require}]" \
+    && apt update && apt -y install ffmpeg libsm6 libxext6 \
     && cd / && rm -rf ${SRCDIR}
 WORKDIR /
 

app.py

@@ -56,14 +56,11 @@ def main(options: Namespace, inputdir: Path, outputdir: Path):
     #
     # Refer to the documentation for more options, examples, and advanced uses e.g.
     # adding a progress bar and parallelism.
-    mapper = PathMapper.file_mapper(inputdir, outputdir, glob=options.pattern, suffix='.count.txt')
+    mapper = PathMapper.file_mapper(inputdir, outputdir, glob="dcm")
     for input_file, output_file in mapper:
         # The code block below is a small and easy example of how to use a ``PathMapper``.
         # It is recommended that you put your functionality in a helper function, so that
         # it is more legible and can be unit tested.
-        data = input_file.read_text()
-        frequency = data.count(options.word)
-        output_file.write_text(str(frequency))
         pipeline = keras_ocr.pipeline.Pipeline()
         final_image = inpaint_text(input_file, pipeline)
         img_rgb = cv2.cvtColor(final_image, cv2.COLOR_BGR2RGB)

requirements.txt

@@ -1,5 +1,6 @@
 chris_plugin==0.2.1
 matplotlib
-keras_ocr
-tensorflow
+keras-ocr
+tensorflow==2.12.0
+opencv-python