v1

SandSnapModel.dockerfile.notes

@@ -0,0 +1,12 @@
+
+FROM docker.io/library/python:3.8.9@sha256:49d05fff9cb3b185b15ffd92d8e6bd61c20aa916133dca2e3dbe0215270faf53
+RUN sed -i 's http://deb.debian.org http://cdn-aws.deb.debian.org g' /etc/apt/sources.list && sed -i 's http://archive.ubuntu.com http://us-east-1.ec2.archive.ubuntu.com g' /etc/apt/sources.list && sed -i '/security/d' /etc/apt/sources.list && apt-get update && apt-get install -y 	git 	git-lfs 	ffmpeg 	libsm6 	libxext6 	cmake 	libgl1-mesa-glx 	&& rm -rf /var/lib/apt/lists/* 	&& git lfs install
+RUN pip install --no-cache-dir pip==22.3.1 &&     pip install --no-cache-dir         datasets         "huggingface-hub>=0.12.1" "protobuf<4" "click<8.1"
+RUN --mount=target=/root/packages.txt,source=packages.txt 	sed -i 's http://deb.debian.org http://cdn-aws.deb.debian.org g' /etc/apt/sources.list && sed -i 's http://archive.ubuntu.com http://us-east-1.ec2.archive.ubuntu.com g' /etc/apt/sources.list && sed -i '/security/d' /etc/apt/sources.list && apt-get update &&     xargs -r -a /root/packages.txt apt-get install -y     && rm -rf /var/lib/apt/lists/*
+WORKDIR /home/user/app
+RUN useradd -m -u 1000 user
+RUN --mount=target=pre-requirements.txt,source=pre-requirements.txt 	pip install --no-cache-dir -r pre-requirements.txt
+RUN --mount=target=requirements.txt,source=requirements.txt 	pip install --no-cache-dir -r requirements.txt
+RUN pip install --no-cache-dir         gradio==3.28.3
+COPY --link --chown=1000 --from=lfs /app /home/user/app
+COPY --link --chown=1000 ./ /home/user/app

app.py

@@ -4,6 +4,8 @@
 import gradio as gr
 import numpy as np
 import sys, json, os
+import pandas as pd
+
 sys.path.insert(1, 'app_files'+os.sep+'src')
 from sedinet_eval import *
 
@@ -83,16 +85,18 @@ def grainsize(input_img, dims=(1024, 1024)):
 
     result = estimate_siso_simo_1image(vars, input_img, greyscale,
                        dropout, weights_path)
-    
+    result_str = [str(i)[:5] for i in result]
+
     result = np.array(result)
     print(result)
-    
+    plt.clf()    
     plt.plot(np.hstack((result[:3], result[4:])),[10,16,25,50,65,75,84,90],  'k-o')
     plt.xlabel('Grain size (pixels)')
     plt.ylabel('Percent finer')
     plt.savefig("psd.png", dpi=300, bbox_inches="tight")
 
-    return 'mean grain size = %f pixels' % (result[4]), '90th percentile grain size = %f pixels' % (result[-1]), plt
+
+    return 'mean grain size = %f pixels' % (result[4]), '90th percentile grain size = %f pixels' % (result[-1]), plt, pd.DataFrame(data=result_str, index=['10','16','25','50','mean','65','75','84','90']).transpose(),
 
 title = "SandSnap/SediNet Model Demo- Measure grain size from image of sand!" 
 description = "Allows upload of imagery and download of grain size statistics. Statistics are unscaled (i.e. in pixels)" 
@@ -104,9 +108,10 @@ examples = [
 ] 
 
 inp = gr.Image()
-out2 = gr.Plot(type='matplotlib')
+out1 = gr.Plot(type='matplotlib')
+out2 = gr.Dataframe(label='Summary statistics', headers=['10','16','25','50','mean','65','75','84','90'], type='pandas')
 
-Segapp = gr.Interface(grainsize, inp, ["text", "text", out2], title = title, description = description, examples=examples)
+Segapp = gr.Interface(grainsize, inp, ["text", "text", out1, out2], title = title, description = description, examples=examples)
 #, allow_flagging='manual', flagging_options=["bad", "ok", "good", "perfect"], flagging_dir="flagged")
 
 Segapp.launch(enable_queue=True)