Dan Biagini commited on
Commit
38d30c3
·
1 Parent(s): 7efb926

polish on breeds and menu

Browse files
src/Hockey_Breeds.py CHANGED
@@ -57,7 +57,7 @@ st.image("src/images/artifacts/confusion_matrix.png", caption="Confusion Matrix
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  st.subheader("Try It Out")
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- img = image_select(label="Select an image and hockey breeds will guess a label", images=list(st.session_state.sample.values()))
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  st.button("Re-roll Samples", on_click=reroll_sample_images)
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  st.subheader("Try It Out")
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+ img = image_select(label="Select an image and hockey breeds will guess a label. See if you can find some incorrect guesses...", images=list(st.session_state.sample.values()))
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  st.button("Re-roll Samples", on_click=reroll_sample_images)
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src/app.py CHANGED
@@ -5,8 +5,8 @@ import logging
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  app = st.navigation(
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  {"App": [st.Page("Home.py", title="Home", icon=":material/home:"),
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  st.Page("About.py", icon="🥅")],
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- "Models": [st.Page("Hockey_Breeds.py", icon=":material/gradient:"),
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- st.Page("hockey_object_detection.py", title="Hockey Object Detection", icon=":material/filter_b_and_w:")]
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  }
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  )
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  app.run()
 
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  app = st.navigation(
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  {"App": [st.Page("Home.py", title="Home", icon=":material/home:"),
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  st.Page("About.py", icon="🥅")],
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+ "Models": [st.Page("Hockey_Breeds.py", title="v1 - Hockey Breeds", icon=":material/gradient:"),
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+ st.Page("hockey_object_detection.py", title="v2 - Hockey Object Detection", icon=":material/filter_b_and_w:")]
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  }
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  )
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  app.run()
src/hockey_object_detection.py CHANGED
@@ -0,0 +1,15 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ import streamlit as st
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+
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+ st.set_page_config(page_title='Hockey Breeds v2 - Objects', layout="wide",
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+ page_icon=":frame_with_picture:")
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+
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+ st.title('Hockey Breeds v2 - Objects')
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+ st.markdown('The first version of Hockey Breeds was fun and educational, but not useful for analyzing hockey videos. The second version needs to be able to recognize individual "objects" within an image and ultimately track those objects through game play')
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+ st.subheader('Object Detection')
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+
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+ desc = '''Hockey Breed detector v2 uses a state of the art (circa 2023) computer vision approach.
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+
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+ I used the same training images as the first version of the Hockey Breeds model, but change the ML algorithm to use YOLO object detection (YOLO v8).
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+ The output will be a set of hockey objects (defined by "bounding boxes") with labels for any hockey image uploaded.'''
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+
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+ st.markdown(desc)