Create app.py

app.py

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+import numpy as np
+import av
+import torch
+# from transformers.models.auto import AutoImageProcessor, AutoModelForVideoClassification
+from transformers import AutoImageProcessor, AutoModelForVideoClassification
+import streamlit as st
+
+
+def read_video_pyav(container, indices):
+    '''
+    Decode the video with PyAV decoder.
+    Args:
+        container (`av.container.input.InputContainer`): PyAV container.
+        indices (`List[int]`): List of frame indices to decode.
+    Returns:
+        result (np.ndarray): np array of decoded frames of shape (num_frames, height, width, 3).
+    '''
+    frames = []
+    container.seek(0)
+    start_index = indices[0]
+    end_index = indices[-1]
+    for i, frame in enumerate(container.decode(video=0)):
+        if i > end_index:
+            break
+        if i >= start_index and i in indices:
+            frames.append(frame)
+    return np.stack([x.to_ndarray(format="rgb24") for x in frames])
+
+
+def sample_frame_indices(clip_len, frame_sample_rate, seg_len):
+    '''
+    Sample a given number of frame indices from the video.
+    Args:
+        clip_len (`int`): Total number of frames to sample.
+        frame_sample_rate (`int`): Sample every n-th frame.
+        seg_len (`int`): Maximum allowed index of sample's last frame.
+    Returns:
+        indices (`List[int]`): List of sampled frame indices
+    '''
+    converted_len = int(clip_len * frame_sample_rate)
+    end_idx = np.random.randint(converted_len, seg_len)
+    start_idx = end_idx - converted_len
+    indices = np.linspace(start_idx, end_idx, num=clip_len)
+    indices = np.clip(indices, start_idx, end_idx - 1).astype(np.int64)
+    return indices
+
+
+
+def classify(file):
+    container = av.open(file)
+
+    # sample 16 frames
+    indices = sample_frame_indices(clip_len=16, frame_sample_rate=4, seg_len=container.streams.video[0].frames)
+    video = read_video_pyav(container, indices)
+
+    if container.streams.video[0].frames < 16:
+        return 'Video trop courte'
+
+    inputs = image_processor(list(video), return_tensors="pt")
+
+    with torch.no_grad():
+        outputs = model(**inputs)
+        logits = outputs.logits
+
+    # model predicts one of the 400 Kinetics-400 classes
+    predicted_label = logits.argmax(-1).item()
+    print(model.config.id2label[predicted_label])
+    
+    return model.config.id2label[predicted_label]
+
+
+model_ckpt = '2nzi/videomae-surf-analytics'
+# pipe = pipeline("video-classification", model="2nzi/videomae-surf-analytics")
+image_processor = AutoImageProcessor.from_pretrained(model_ckpt)
+model = AutoModelForVideoClassification.from_pretrained(model_ckpt)
+
+
+
+
+
+st.subheader("Surf Analytics")
+
+st.markdown("""
+    Bienvenue sur le projet Surf Analytics réalisé par Walid, Guillaume, Valentine, et Antoine.
+            
+    <a href="https://github.com/2nzi/M09-FinalProject-Surf-Analytics" style="text-decoration: none;">@Surf-Analytics-Github</a>.
+""", unsafe_allow_html=True)
+
+st.title("Surf Maneuver Classification")
+
+uploaded_file = st.file_uploader("Upload a video file", type=["mp4", "avi", "mov"])
+
+if uploaded_file is not None:
+    video_bytes = uploaded_file.read()
+    st.video(video_bytes)
+    predicted_label = classify(uploaded_file)
+    st.success(f"Predicted Label: {predicted_label}")