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Browse files- app.py +68 -36
- requirements.txt +0 -0
app.py
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@@ -3,7 +3,8 @@ import av
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import torch
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from transformers import AutoImageProcessor, AutoModelForVideoClassification
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import streamlit as st
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def read_video_pyav(container, indices):
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'''
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@@ -43,61 +44,92 @@ def sample_frame_indices(clip_len, frame_sample_rate, seg_len):
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indices = np.clip(indices, start_idx, end_idx - 1).astype(np.int64)
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return indices
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container = av.open(file)
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# sample 16 frames
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indices = sample_frame_indices(clip_len=16, frame_sample_rate=4, seg_len=container.streams.video[0].frames)
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video = read_video_pyav(container, indices)
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if container.streams.video[0].frames < 16:
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return 'Video trop courte'
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inputs = image_processor(list(video), return_tensors="pt")
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with torch.no_grad():
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outputs =
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logits = outputs.logits
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# model predicts one of the 400 Kinetics-400 classes
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predicted_label = logits.argmax(-1).item()
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print(
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image_processor = AutoImageProcessor.from_pretrained(model_ckpt)
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model = AutoModelForVideoClassification.from_pretrained(model_ckpt)
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st.markdown("""
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Bienvenue sur le projet Surf Analytics réalisé par Walid, Guillaume, Valentine, et Antoine.
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<a href="https://github.com/2nzi/M09-FinalProject-Surf-Analytics" style="text-decoration: none;">@Surf-Analytics-Github</a>.
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""", unsafe_allow_html=True)
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st.
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predicted_label = classify(uploaded_file)
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st.success(f"Predicted Label: {predicted_label}")
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import torch
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from transformers import AutoImageProcessor, AutoModelForVideoClassification
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import streamlit as st
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import torch.nn as nn
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from streamlit_navigation_bar import st_navbar
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def read_video_pyav(container, indices):
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'''
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indices = np.clip(indices, start_idx, end_idx - 1).astype(np.int64)
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return indices
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def victoire():
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gif_url = "https://i.postimg.cc/rDp7xRJY/Happy-Birthday-Confetti.gif"
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html_gif = f"""
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<div style="display: flex; justify-content: center; align-items: center;">
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<img src="{gif_url}" height="auto" style="margin: 0px;">
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<img src="{gif_url}" height="auto" style="margin: 0px;">
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<img src="{gif_url}" height="auto" style="margin: 0px;">
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<img src="{gif_url}" height="auto" style="margin: 0px;">
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</div>
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"""
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st.markdown(html_gif, unsafe_allow_html=True)
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def classify(model_maneuver,model_Surf_notSurf,file):
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container = av.open(file)
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# sample 16 frames
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indices = sample_frame_indices(clip_len=16, frame_sample_rate=4, seg_len=container.streams.video[0].frames)
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video = read_video_pyav(container, indices)
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inputs = image_processor(list(video), return_tensors="pt")
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with torch.no_grad():
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outputs = model_Surf_notSurf(**inputs)
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logits = outputs.logits
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predicted_label = logits.argmax(-1).item()
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print(model_Surf_notSurf.config.id2label[predicted_label])
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if model_Surf_notSurf.config.id2label[predicted_label]!='Surfing':
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return model_Surf_notSurf.config.id2label[predicted_label]
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else:
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with torch.no_grad():
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outputs = model_maneuver(**inputs)
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logits = outputs.logits
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predicted_label = logits.argmax(-1).item()
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print(model_maneuver.config.id2label[predicted_label])
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# st.write(f'Les labels: {model_maneuver.config.id2label}')
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# st.write(f'répartiton des probilités {logits}')
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# st.write(f'répartiton des probilités {nn.Softmax(dim=-1)(logits)}')
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return model_maneuver.config.id2label[predicted_label]
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model_maneuver = '2nzi/videomae-surf-analytics'
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model_Surf_notSurf = '2nzi/videomae-surf-analytics-surfNOTsurf'
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image_processor = AutoImageProcessor.from_pretrained(model_maneuver)
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model_maneuver = AutoModelForVideoClassification.from_pretrained(model_maneuver)
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model_Surf_notSurf = AutoModelForVideoClassification.from_pretrained(model_Surf_notSurf)
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# Define the navigation bar and its pages
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page = st_navbar(["Home", "Documentation", "Examples", "About Us"])
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# Main application code
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if page == "Home":
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st.subheader("Surf Analytics")
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st.markdown("""
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Bienvenue sur le projet Surf Analytics réalisé par Walid, Guillaume, Valentine, et Antoine.
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<a href="https://github.com/2nzi/M09-FinalProject-Surf-Analytics" style="text-decoration: none;">@Surf-Analytics-Github</a>.
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""", unsafe_allow_html=True)
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st.title("Surf Maneuver Classification")
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uploaded_file = st.file_uploader("Upload a video file", type=["mp4"])
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if uploaded_file is not None:
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video_bytes = uploaded_file.read()
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st.video(video_bytes)
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predicted_label = classify(model_maneuver, model_Surf_notSurf, uploaded_file)
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st.success(f"Predicted Label: {predicted_label}")
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victoire()
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elif page == "Documentation":
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st.title("Documentation")
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st.markdown("Here you can add your documentation content.")
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elif page == "Examples":
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st.title("Examples")
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st.markdown("Here you can add examples related to your project.")
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elif page == "About Us":
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st.title("About")
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st.markdown("Here you can add information about the project and the team.")
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requirements.txt
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Binary files a/requirements.txt and b/requirements.txt differ
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