first commit

README.md

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+---
+title: Membrane Segmentor
+emoji: 🏃
+colorFrom: purple
+colorTo: indigo
+sdk: streamlit
+sdk_version: 1.17.0
+app_file: app.py
+pinned: false
+---
+
+# devolearn-web
+devolearn models deployed on a webapp

app.py

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+import functions
+import streamlit as st
+import numpy as np
+import pandas as pd
+from PIL import Image
+from pathlib import Path
+import joblib
+
+import numpy as np
+import cv2
+import onnxruntime as ort
+import imutils
+# import matplotlib.pyplot as plt
+import pandas as pd
+import plotly.express as px
+
+
+
+functions.cell_membrane_segmentation()

functions.py

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+import streamlit as st
+import numpy as np
+import pandas as pd
+from PIL import Image
+from pathlib import Path
+import joblib
+
+import numpy as np
+import cv2
+import onnxruntime as ort
+import imutils
+# import matplotlib.pyplot as plt
+import pandas as pd
+import plotly.express as px
+
+
+
+def onnx_segment_membrane(input_image, threshold):
+    ort_session = ort.InferenceSession('onnx_models/membrane_segmentor.onnx')
+    img = Image.fromarray(np.uint8(input_image))
+    resized = img.resize((256, 256), Image.NEAREST)
+    img_unsqueeze = expand_dims_twice(resized)
+    onnx_outputs = ort_session.run(None, {'input': img_unsqueeze.astype('float32')}) 
+    binarized = 1.0 * (onnx_outputs[0][0][0] > threshold)
+
+    resized_ret = Image.fromarray(binarized.astype(np.uint8) ).resize((356, 256), Image.NEAREST)#.convert("L")
+    centroid_img = generate_centroid_image(np.array(onnx_outputs[0][0][0])) *255
+    resized_centroid_img = Image.fromarray(centroid_img.astype(np.uint8)).resize((356, 256), Image.NEAREST)
+    return(resized_ret, resized_centroid_img)
+
+
+def generate_centroid_image(thresh):
+    thresh = cv2.blur(thresh, (5,5))
+    thresh = thresh.astype(np.uint8)
+    centroid_image = np.zeros(thresh.shape)
+    cnts = cv2.findContours(thresh, cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
+    cnts = imutils.grab_contours(cnts)
+    centroids = []
+    for c in cnts:
+        try:
+            # compute the center of the contour
+            M = cv2.moments(c)
+            cX = int(M["m10"] / M["m00"])
+            cY = int(M["m01"] / M["m00"])
+            # draw the contour and center of the shape on the image
+            # cv2.drawContours(centroid_image, [c], -1, (255, 255, 255), 2)
+            cv2.circle(centroid_image, (cX, cY), 2, (1, 1, 1), -1)
+            centroids.append((cX, cY))
+        except:
+            pass
+    return(centroid_image)
+
+
+
+def expand_dims_twice(arr):
+    norm=(arr-np.min(arr))/(np.max(arr)-np.min(arr))
+    ret = np.expand_dims(np.expand_dims(norm, axis=0), axis=0)
+    return(ret)
+
+
+
+def cell_membrane_segmentation():
+    selected_box2 = st.sidebar.selectbox(
+    'Choose Example Input',
+    ('Example_1.png','Example_2.png')
+    )
+
+    st.title('Cell Membrane Segmentation')
+    instructions = """
+        Segment Cell Membrane from C. elegans embryo imaging data \n
+        Either upload your own image or select from the sidebar to get a preconfigured image. 
+        The image you select or upload will be fed through the Deep Neural Network in real-time 
+        and the output will be displayed to the screen.
+        """
+    st.text(instructions)
+    file = st.file_uploader('Upload an image or choose an example')
+    example_image = Image.open('./images/cell_membrane_segmentation_examples/'+selected_box2)
+    threshold = st.sidebar.slider("Select Threshold (Applied on model output)", 0.0, 1.0, 0.1)
+    col1, col2, col3 = st.beta_columns(3)
+
+    if file:
+        input = Image.open(file)
+        fig1 = px.imshow(input, binary_string=True, labels=dict(x="Input Image"))
+        fig1.update(layout_coloraxis_showscale=False)
+        fig1.update_layout(margin=dict(l=0, r=0, b=0, t=0))
+        col1.plotly_chart(fig1, use_container_width=True)
+
+    else:
+        input = example_image
+        fig1 = px.imshow(input, binary_string=True, labels=dict(x="Input Image"))
+        fig1.update(layout_coloraxis_showscale=False)
+        fig1.update_layout(margin=dict(l=0, r=0, b=0, t=0))
+        col1.plotly_chart(fig1, use_container_width=True)
+
+    pressed = st.button('Run')
+    if pressed:
+        st.empty()
+        model_output = onnx_segment_membrane(np.array(input), threshold)
+
+        fig2 = px.imshow(model_output[0], binary_string=True, labels=dict(x="Segmentation Map"))
+        fig2.update_layout(margin=dict(l=0, r=0, b=0, t=0))
+        col2.plotly_chart(fig2, use_container_width=True)
+
+        fig3 = px.imshow(model_output[1], binary_string=True, labels=dict(x="Centroid Map"))
+        fig3.update_layout(margin=dict(l=0, r=0, b=0, t=0))
+        col3.plotly_chart(fig3, use_container_width=True)

requirements.txt

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+altair==4.1.0
+argon2-cffi==20.1.0
+astor==0.8.1
+async-generator==1.10
+attrs==21.2.0
+backcall==0.2.0
+base58==2.1.0
+bleach==3.3.1
+blinker==1.4
+cachetools==4.2.2
+certifi==2021.5.30
+cffi==1.14.6
+charset-normalizer==2.0.3
+click==7.1.2
+debugpy==1.4.1
+decorator==5.0.9
+defusedxml==0.7.1
+entrypoints==0.3
+flatbuffers==2.0
+gitdb==4.0.7
+GitPython==3.1.18
+idna==3.2
+imutils==0.5.4
+ipykernel==6.0.3
+ipython==7.25.0
+ipython-genutils==0.2.0
+ipywidgets==7.6.3
+jedi==0.18.0
+Jinja2==3.0.1
+joblib==1.0.1
+jsonschema==3.2.0
+jupyter-client==6.1.12
+jupyter-core==4.7.1
+jupyterlab-pygments==0.1.2
+jupyterlab-widgets==1.0.0
+MarkupSafe==2.0.1
+matplotlib-inline==0.1.2
+mistune==0.8.4
+nbclient==0.5.3
+nbconvert==6.1.0
+nbformat==5.1.3
+nest-asyncio==1.5.1
+notebook==6.4.0
+numpy==1.21.1
+onnxruntime==1.8.1
+opencv-python-headless==4.5.3.56
+packaging==21.0
+pandas==1.3.1
+pandocfilters==1.4.3
+parso==0.8.2
+pexpect==4.8.0
+pickleshare==0.7.5
+Pillow==8.3.1
+plotly==5.1.0
+prometheus-client==0.11.0
+prompt-toolkit==3.0.19
+protobuf==3.17.3
+ptyprocess==0.7.0
+pyarrow==5.0.0
+pycparser==2.20
+pydeck==0.6.2
+Pygments==2.9.0
+pyparsing==2.4.7
+pyrsistent==0.18.0
+python-dateutil==2.8.2
+pytz==2021.1
+pyzmq==22.1.0
+requests==2.26.0
+scikit-learn==0.24.1
+scipy==1.7.0
+Send2Trash==1.7.1
+six==1.16.0
+smmap==4.0.0
+streamlit==0.85.1
+tenacity==8.0.1
+terminado==0.10.1
+testpath==0.5.0
+threadpoolctl==2.2.0
+toml==0.10.2
+toolz==0.11.1
+tornado==6.1
+traitlets==5.0.5
+tzlocal==2.1
+urllib3==1.26.6
+validators==0.18.2
+watchdog==2.1.3
+wcwidth==0.2.5
+webencodings==0.5.1
+widgetsnbextension==3.5.1

setup.sh

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+mkdir -p ~/.streamlit/
+echo "\
+[server]\n\
+headless = true\n\
+port = $PORT\n\
+enableCORS = false\n\
+\n\
+" > ~/.streamlit/config.toml