initial commit

README.md

@@ -11,4 +11,31 @@ license: apache-2.0
 short_description: Fall detector with TimeSformer
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Video Human Detection Demo using TimeSformer
+
+This is a Hugging Face Spaces demo project that uses TimeSformer – a video transformer model – for video-based human detection (action recognition). In this demo, we use the pre-trained model `microsoft/timesformer-base-finetuned-k400` from Hugging Face, which has been fine‑tuned on the Kinetics‑400 dataset. The model is capable of classifying a video into one of 400 human action categories.
+
+## Overview
+
+- **Model:** We use a TimeSformer model (`microsoft/timesformer-base-finetuned-k400`) to classify video clips.
+- **Feature Extractor:** The demo employs the Hugging Face `AutoFeatureExtractor` for video to process and prepare video frames.
+- **Inference:** The model outputs a set of predicted action labels with scores. These predictions help detect human actions in the video.
+- **Interface:** Built with Gradio, the demo lets the user upload a video file. The application extracts frames from the video, processes them with the model, and displays the top action predictions.
+
+## Setup and Deployment
+
+1. **Requirements:** See `requirements.txt` for the list of required packages.
+2. **Run Locally:** You can run the demo locally using:
+    ```bash
+    python app.py
+    ```
+3. **Deploy on Hugging Face Spaces:**  
+   Simply push these files to a new repository under HF Spaces. The app is designed to run with ZeroGPU if available and it is fully compatible with CPU-only environments.
+
+## Notes
+
+- **Video Preprocessing:** The demo extracts frames using OpenCV and passes them to the feature extractor. The number of frames and the resolution are set to default values that can be adjusted.
+- **Model Performance:** TimeSformer is computationally heavy – for real-time use, consider using a smaller or distilled variant, or reduce the number of frames processed.
+- **ZeroGPU Support:** The app uses the `@spaces.GPU` decorator (from the HF Spaces ZeroGPU environment) if available; otherwise, it will run on CPU.
+
+Enjoy testing human detection in videos with this demo!

app.py

@@ -0,0 +1,106 @@
+import spaces  # Import spaces immediately for HF ZeroGPU support.
+import os
+import cv2
+import torch
+import yt_dlp  # (Retained in requirements for potential video fetching use)
+import numpy as np
+from PIL import Image
+import gradio as gr
+
+from transformers import AutoFeatureExtractor, AutoModelForVideoClassification
+
+# Specify the model checkpoint for TimeSformer.
+MODEL_NAME = "microsoft/timesformer-base-finetuned-k400"
+
+def extract_frames(video_path, num_frames=16, target_size=(224, 224)):
+    """
+    Extract up to `num_frames` uniformly-sampled frames from the video.
+    If the video has fewer frames, all frames are returned.
+    """
+    cap = cv2.VideoCapture(video_path)
+    total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+    frames = []
+    if total_frames <= 0:
+        cap.release()
+        return frames
+    indices = np.linspace(0, total_frames - 1, num_frames, dtype=int)
+    current_frame = 0
+    while True:
+        ret, frame = cap.read()
+        if not ret:
+            break
+        if current_frame in indices:
+            frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+            frame = cv2.resize(frame, target_size)
+            frames.append(Image.fromarray(frame))
+        current_frame += 1
+    cap.release()
+    return frames
+
+@spaces.GPU
+def classify_video(video_path):
+    """
+    Loads the TimeSformer model and feature extractor inside the GPU context,
+    extracts frames from the video, runs inference, and returns the top 5 predicted actions.
+    """
+    # Load the feature extractor and model inside the GPU context.
+    feature_extractor = AutoFeatureExtractor.from_pretrained(MODEL_NAME)
+    model = AutoModelForVideoClassification.from_pretrained(MODEL_NAME)
+    model.eval()
+
+    # Determine the device.
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    model.to(device)
+    
+    # Extract frames from the video (here we sample 16 frames).
+    frames = extract_frames(video_path, num_frames=16, target_size=(224, 224))
+    if len(frames) == 0:
+        return "No frames extracted from video."
+
+    # Preprocess the frames.
+    inputs = feature_extractor(frames, return_tensors="pt")
+    inputs = {key: val.to(device) for key, val in inputs.items()}
+    
+    # Run inference.
+    with torch.no_grad():
+        outputs = model(**inputs)
+    
+    # Compute probabilities from logits.
+    logits = outputs.logits  # Shape: [batch_size, num_classes]; batch_size is 1 here.
+    probs = torch.nn.functional.softmax(logits, dim=-1)[0]
+    
+    # Get the top 5 predictions.
+    top_probs, top_indices = torch.topk(probs, k=5)
+    top_probs = top_probs.cpu().numpy()
+    top_indices = top_indices.cpu().numpy()
+    
+    # Retrieve the label mapping from the feature extractor's config.
+    id2label = feature_extractor.feature_extractor_config.get("id2label", {})
+    results = []
+    for idx, prob in zip(top_indices, top_probs):
+        label = id2label.get(str(idx), f"Class {idx}")
+        results.append(f"{label}: {prob:.3f}")
+    
+    return "\n".join(results)
+
+def process_video(video_file):
+    if video_file is None:
+        return "No video provided."
+    result = classify_video(video_file)
+    return result
+
+# Gradio interface definition.
+demo = gr.Interface(
+    fn=process_video,
+    inputs=gr.Video(source="upload", label="Upload Video Clip"),
+    outputs=gr.Textbox(label="Predicted Actions"),
+    title="Video Human Detection Demo using TimeSformer",
+    description=(
+        "Upload a video clip to see the top predicted human action labels using the TimeSformer model "
+        "(fine-tuned on Kinetics-400). This demo loads the model and feature extractor within the GPU context "
+        "for optimized inference in Hugging Face ZeroGPU Spaces while also supporting CPU-only environments."
+    )
+)
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

@@ -0,0 +1,6 @@
+gradio>=4.0
+torch>=2.0.1
+transformers>=4.25.0
+opencv-python>=4.5.5
+Pillow>=8.4.0
+yt-dlp>=2022.12.1