app.py

import gradio as gr
import torch
import cv2
import numpy as np
from transformers import VideoMAEForVideoClassification, VideoMAEImageProcessor

# Model IDs for video classification (UCF101 subset)
classification_model_id = "MCG-NJU/videomae-base"

# Object detection model (you can replace this with a more accurate one if needed)
object_detection_model = "yolov5s"

# Parameters for frame extraction
TARGET_FRAME_COUNT = 16
FRAME_SIZE = (224, 224)  # Expected frame size for the model

def analyze_video(video):
    # Extract key frames from the video using OpenCV
    frames = extract_key_frames(video)

    # Load classification model and image processor
    classification_model = VideoMAEForVideoClassification.from_pretrained(classification_model_id)
    processor = VideoMAEImageProcessor.from_pretrained(classification_model_id)

    # Prepare frames for the classification model
    inputs = processor(images=frames, return_tensors="pt")

    # Make predictions using the classification model
    with torch.no_grad():
        outputs = classification_model(**inputs)
    
    logits = outputs.logits
    predictions = torch.argmax(logits, dim=-1)
    
    # Object detection and tracking (ball and baseman)
    object_detection_results = []
    for frame in frames:
        ball_position = detect_object(frame, "ball")
        baseman_position = detect_object(frame, "baseman")
        object_detection_results.append((ball_position, baseman_position))

    # Analyze predictions and object detection results
    analysis_results = []
    for prediction, (ball_position, baseman_position) in zip(predictions, object_detection_results):
        result = analyze_frame(prediction.item(), ball_position, baseman_position)
        analysis_results.append(result)

    # Aggregate analysis results
    final_result = aggregate_results(analysis_results)

    return final_result

def extract_key_frames(video):
    cap = cv2.VideoCapture(video)
    frames = []
    frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
    fps = int(cap.get(cv2.CAP_PROP_FPS))
    interval = max(1, frame_count // TARGET_FRAME_COUNT)
    
    for i in range(frame_count):
        ret, frame = cap.read()
        if ret and i % interval == 0:  # Extract frames at regular intervals
            frame = cv2.resize(frame, FRAME_SIZE)  # Resize frame
            frames.append(frame)
    cap.release()
    return frames

def detect_object(frame, object_type):
    # Placeholder function for object detection (replace with actual implementation)
    # Here, we assume that the object is detected at the center of the frame
    h, w, _ = frame.shape
    if object_type == "ball":
        return (w // 2, h // 2)  # Return center coordinates for the ball
    elif object_type == "baseman":
        return (w // 2, h // 2)  # Return center coordinates for the baseman
    else:
        return None

def analyze_frame(prediction, ball_position, baseman_position):
    # Placeholder function for analyzing a single frame
    # You can replace this with actual logic based on your requirements
    action_labels = {
        0: "running",
        1: "sliding",
        2: "jumping",
        # Add more labels as necessary
    }
    action = action_labels.get(prediction, "unknown")
    return {"action": action, "ball_position": ball_position, "baseman_position": baseman_position}

def aggregate_results(results):
    # Placeholder function for aggregating analysis results
    # You can implement this based on your specific requirements
    return results

# Gradio interface
interface = gr.Interface(
    fn=analyze_video,
    inputs="video",
    outputs="text",
    title="Baseball Play Analysis",
    description="Upload a video of a baseball play to analyze.",
)

interface.launch()