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import torch
from transformers import AutoProcessor, AutoModelForImageTextToText
from typing import List, Dict, Generator
import logging
import os
import subprocess
import json
import tempfile
import time
logger = logging.getLogger(__name__)
def _grab_best_device(use_gpu=True):
if torch.cuda.device_count() > 0 and use_gpu:
device = "cuda"
else:
device = "cpu"
return device
def get_video_duration_seconds(video_path: str) -> float:
"""Use ffprobe to get video duration in seconds."""
cmd = [
"ffprobe",
"-v", "quiet",
"-print_format", "json",
"-show_format",
video_path
]
result = subprocess.run(cmd, capture_output=True, text=True)
info = json.loads(result.stdout)
return float(info["format"]["duration"])
def format_duration(seconds: int) -> str:
minutes = seconds // 60
secs = seconds % 60
return f"{minutes:02d}:{secs:02d}"
DEVICE = _grab_best_device()
logger.info(f"Using device: {DEVICE}")
class VideoAnalyzer:
def __init__(self):
if not torch.cuda.is_available():
raise RuntimeError("CUDA is required but not available!")
logger.info("Initializing VideoAnalyzer")
self.model_path = "HuggingFaceTB/SmolVLM2-500M-Video-Instruct"
logger.info(f"Loading model from {self.model_path} - Using device: {DEVICE}")
# Load processor and model
self.processor = AutoProcessor.from_pretrained(self.model_path)
self.model = AutoModelForImageTextToText.from_pretrained(
self.model_path,
torch_dtype=torch.bfloat16,
device_map=DEVICE,
_attn_implementation="flash_attention_2"
).to(DEVICE)
logger.info(f"Model loaded on device: {self.model.device}")
def analyze_segment(self, video_path: str, start_time: float) -> str:
"""Analyze a single video segment."""
messages = [
{
"role": "system",
"content": [{"type": "text", "text": """You are a detailed video analysis assistant with expertise in scene description. Your task is to:
1. Describe the visual content with precise details
2. Note any significant actions or movements
3. Describe important objects, people, or elements in the scene
4. Capture the mood, atmosphere, or emotional content if present
5. Mention any scene transitions or camera movements
Be specific and thorough, but focus only on what is visually present in this segment."""}]
},
{
"role": "user",
"content": [
{"type": "video", "path": video_path},
{"type": "text", "text": """Describe this video segment in detail. Focus on:
- What objects, people, or elements are visible?
- What actions or movements are occurring?
- What is the setting or environment?
- Are there any notable visual effects or transitions?
- What is the overall mood or atmosphere?
Be specific about visual details but stay concise."""}
]
}
]
inputs = self.processor.apply_chat_template(
messages,
add_generation_prompt=True,
tokenize=True,
return_dict=True,
return_tensors="pt"
).to(DEVICE, dtype=torch.bfloat16)
outputs = self.model.generate(
**inputs,
do_sample=True,
temperature=0.7,
max_new_tokens=256
)
return self.processor.batch_decode(outputs, skip_special_tokens=True)[0].split("Assistant: ")[-1]
def process_video(self, video_path: str, segment_length: int = 10) -> Generator[Dict, None, None]:
try:
# Create temp directory for segments
temp_dir = tempfile.mkdtemp()
# Get video duration
duration = get_video_duration_seconds(video_path)
total_segments = (int(duration) + segment_length - 1) // segment_length
logger.info(f"Processing {total_segments} segments for video of length {duration:.2f} seconds")
# Process video in segments
for segment_idx in range(total_segments):
segment_start_time = time.time()
start_time = segment_idx * segment_length
end_time = min(start_time + segment_length, duration)
# Skip if we've reached the end
if start_time >= duration:
break
# Create segment - Optimized ffmpeg settings
segment_path = os.path.join(temp_dir, f"segment_{start_time}.mp4")
cmd = [
"ffmpeg",
"-y", # Overwrite output files
"-hwaccel", "cuda", # Use CUDA hardware acceleration
"-hwaccel_output_format", "cuda", # Keep frames in GPU memory
"-threads", "0", # Use all available CPU threads
"-thread_type", "frame", # Frame-level multi-threading
"-i", video_path,
"-ss", str(start_time), # Seek position
"-t", str(end_time - start_time), # Duration
"-c:v", "h264_nvenc", # Use NVIDIA hardware encoder
"-preset", "p1", # Lowest latency preset for NVENC
"-tune", "ll", # Low latency tuning
"-rc", "vbr", # Variable bitrate mode
"-cq", "28", # Quality-based VBR
"-b:v", "0", # Let VBR control bitrate
"-vf", "scale_cuda=640:-2", # GPU-accelerated scaling
"-an", # Remove audio
segment_path
]
ffmpeg_start = time.time()
try:
result = subprocess.run(cmd, check=True, capture_output=True, text=True)
logger.debug(f"FFmpeg output: {result.stderr}")
except subprocess.CalledProcessError as e:
logger.error(f"FFmpeg error: {e.stderr}")
# Fallback to CPU if GPU encoding fails
logger.warning("Falling back to CPU encoding")
cmd = [
"ffmpeg",
"-y",
"-threads", "0",
"-i", video_path,
"-ss", str(start_time),
"-t", str(end_time - start_time),
"-c:v", "libx264",
"-preset", "ultrafast",
"-tune", "fastdecode",
"-crf", "28",
"-vf", "scale=640:-2",
"-an",
"-pix_fmt", "yuv420p",
segment_path
]
subprocess.run(cmd, check=True, capture_output=True)
ffmpeg_time = time.time() - ffmpeg_start
# Analyze segment
inference_start = time.time()
description = self.analyze_segment(segment_path, start_time)
inference_time = time.time() - inference_start
# Add segment info with timestamp
yield {
"timestamp": format_duration(int(start_time)),
"description": description,
"processing_times": {
"ffmpeg": ffmpeg_time,
"inference": inference_time,
"total": time.time() - segment_start_time
}
}
# Clean up segment file
os.remove(segment_path)
logger.info(
f"Segment {segment_idx + 1}/{total_segments} ({start_time}-{end_time}s) - "
f"FFmpeg: {ffmpeg_time:.2f}s, Inference: {inference_time:.2f}s"
)
# Clean up temp directory
os.rmdir(temp_dir)
except Exception as e:
logger.error(f"Error processing video: {str(e)}", exc_info=True)
raise |