File size: 7,299 Bytes
70fbcf2 dbc3c35 70fbcf2 dbc3c35 70fbcf2 dbc3c35 70fbcf2 dbc3c35 70fbcf2 dbc3c35 70fbcf2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 | import os
import subprocess
import tempfile
from app.core.config import Settings
from app.models.schemas import ClipCandidate
_DEMO_VISUALS = [
("High-energy scene with strong visual contrast and clear subject focus.", 88.0),
("Close-up with expressive reactions — excellent engagement framing.", 92.0),
("Dynamic motion sequence; subject well-lit with clean background.", 84.0),
("Text-overlay-friendly composition with natural colour grading.", 79.0),
("Wide establishing shot; strong emotional beat in middle frames.", 81.0),
]
class QwenVisualAnalyzer:
def __init__(self, settings: Settings) -> None:
self.settings = settings
self._model = None
self._processor = None
def enrich(self, video_path: str, clips: list[ClipCandidate]) -> list[ClipCandidate]:
if self.settings.demo_mode:
return self._demo_enrich(clips)
try:
return self._qwen_enrich(video_path, clips)
except Exception:
return clips
# ------------------------------------------------------------------
# Demo mode
# ------------------------------------------------------------------
def _demo_enrich(self, clips: list[ClipCandidate]) -> list[ClipCandidate]:
enriched = []
for i, clip in enumerate(clips):
note, vscore = _DEMO_VISUALS[i % len(_DEMO_VISUALS)]
enriched.append(
clip.model_copy(
update={
"metadata": {
**clip.metadata,
"visual_model": "demo",
"visual_note": note,
"visual_score": vscore,
}
}
)
)
return enriched
# ------------------------------------------------------------------
# Production mode — Qwen2-VL on ROCm
# ------------------------------------------------------------------
def _load_model(self) -> None:
try:
import torch
from transformers import AutoProcessor, Qwen2VLForConditionalGeneration
except ImportError as exc:
raise RuntimeError("transformers + ROCm PyTorch are required for Qwen2-VL") from exc
dtype = getattr(torch, self.settings.preferred_torch_dtype, torch.bfloat16)
self._model = Qwen2VLForConditionalGeneration.from_pretrained(
self.settings.qwen_vl_model_id,
torch_dtype=dtype,
device_map="auto",
trust_remote_code=True,
token=self.settings.hf_token or None,
)
self._processor = AutoProcessor.from_pretrained(
self.settings.qwen_vl_model_id,
trust_remote_code=True,
token=self.settings.hf_token or None,
)
def _qwen_enrich(self, video_path: str, clips: list[ClipCandidate]) -> list[ClipCandidate]:
if self._model is None:
self._load_model()
enriched = []
for clip in clips:
try:
frames = _sample_frames(video_path, clip.start_seconds, clip.end_seconds, self.settings.ffmpeg_binary)
if not frames:
enriched.append(clip)
continue
note, vscore = self._analyze(frames, clip.title)
enriched.append(
clip.model_copy(
update={
"metadata": {
**clip.metadata,
"visual_model": self.settings.qwen_vl_model_id,
"visual_note": note,
"visual_score": vscore,
}
}
)
)
except Exception:
enriched.append(
clip.model_copy(
update={
"metadata": {
**clip.metadata,
"visual_model": self.settings.qwen_vl_model_id,
"visual_status": "analysis_failed",
}
}
)
)
return enriched
def _analyze(self, frames: list, title: str) -> tuple[str, float]:
import torch
messages = [
{
"role": "user",
"content": [
*[{"type": "image", "image": f} for f in frames],
{
"type": "text",
"text": (
f'These frames are from a clip titled "{title}". '
"Describe the visual quality and short-form engagement potential in 1-2 sentences. "
"Then output exactly: SCORE: <integer 0-100>"
),
},
],
}
]
text = self._processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = self._processor(text=[text], images=frames, return_tensors="pt").to(self._model.device)
with torch.no_grad():
ids = self._model.generate(**inputs, max_new_tokens=140)
reply = self._processor.batch_decode(
ids[:, inputs["input_ids"].shape[1]:],
skip_special_tokens=True,
)[0].strip()
vscore = 75.0
for line in reversed(reply.splitlines()):
upper = line.strip().upper()
if upper.startswith("SCORE:"):
try:
vscore = float(upper.split(":", 1)[1].strip())
except ValueError:
pass
break
note = reply.split("SCORE:")[0].strip() or reply
return note, min(max(vscore, 0.0), 100.0)
# ------------------------------------------------------------------
# Frame extraction helper
# ------------------------------------------------------------------
def _sample_frames(video_path: str, start: float, end: float, ffmpeg: str, n: int = 4) -> list:
try:
from PIL import Image
except ImportError:
return []
duration = max(end - start, 1.0)
timestamps = [start + duration * i / max(n - 1, 1) for i in range(n)]
frames = []
tmp_files = []
try:
for ts in timestamps:
fd, tmp = tempfile.mkstemp(suffix=".jpg")
os.close(fd)
tmp_files.append(tmp)
result = subprocess.run(
[
ffmpeg,
"-ss", f"{ts:.3f}",
"-i", video_path,
"-vframes", "1",
"-q:v", "2",
"-y", tmp,
],
capture_output=True,
timeout=15,
)
if result.returncode == 0:
try:
frames.append(Image.open(tmp).convert("RGB"))
except Exception:
pass
finally:
for tmp in tmp_files:
try:
os.unlink(tmp)
except OSError:
pass
return frames
|