dtoro
fbeefb24a0
- detect_objects, classify_content, recluster_faces now look up the photo's user_id and set it on created Tag rows — fixes tags being invisible to the owning user due to NULL user_id - Initial admin setup creates source root at the mount root (/photos) instead of a subdirectory, since the admin owns the entire library - Revert to OpenCLIP ViT-B/32 (512-d) as default embedder — SigLIP requires transformers version alignment not yet available in the Docker image. SigLIP2 code remains for future enablement. - Add transformers to requirements for future SigLIP support Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
118 lines
4.0 KiB
Python
118 lines
4.0 KiB
Python
"""
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CLIP zero-shot content-type classifier.
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Uses the native OpenCLIP PyTorch text encoder for high-quality text
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embeddings (the ONNX text encoder has degraded quality due to the
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eot_indices workaround). Image embeddings use the ONNX visual encoder
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which works well.
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"""
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import logging
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import numpy as np
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import torch
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from app.config import VisionSettings
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from app.services.vision.base import ContentClassifier, ClassificationResult
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logger = logging.getLogger(__name__)
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CATEGORY_PROMPTS = {
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"screenshot": [
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"a screenshot of a computer screen",
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"a screenshot of a phone screen",
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"a screen capture of a user interface",
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],
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"document": [
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"a scanned document",
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"a photo of a document with printed text",
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"a photo of a page of text on paper",
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],
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"receipt": [
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"a photo of a receipt",
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"a photo of a bill or invoice",
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],
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"meme": [
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"an internet meme with text overlay",
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"a funny image with caption text",
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],
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"artwork": [
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"a painting or drawing",
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"a sketch or illustration",
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"digital art or graphic design",
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],
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"photograph": [
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"a photograph taken with a camera",
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"a real photo of a real scene or person",
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"a candid photograph",
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],
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}
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class CLIPContentClassifier(ContentClassifier):
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"""Zero-shot content classifier using CLIP text-image similarity.
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Uses native PyTorch for text encoding, ONNX for image encoding."""
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def __init__(self, settings: VisionSettings):
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import open_clip
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self._min_confidence = settings.classifier.min_confidence
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# Load native model for text encoding only.
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# Use whichever model family the embedder is configured for so
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# the classification text vectors live in the same space as the
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# image embeddings.
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embedder_name = settings.embedder.name
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if embedder_name.startswith("siglip2"):
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model_arch = "ViT-B-16-SigLIP-384"
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pretrained = "webli"
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else:
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model_arch = "ViT-B-32"
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pretrained = "laion2b_s34b_b79k"
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logger.info("Loading %s text encoder for content classification", model_arch)
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model, _, _ = open_clip.create_model_and_transforms(
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model_arch, pretrained=pretrained
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)
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model.eval()
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self._model = model
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self._tokenizer = open_clip.get_tokenizer(model_arch)
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# Get the ONNX image embedder from the registry
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from app.services.vision.registry import registry
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self._embedder = registry.get_embedder()
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# Pre-compute text embeddings for each category
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self._category_embeddings: dict[str, np.ndarray] = {}
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for category, prompts in CATEGORY_PROMPTS.items():
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tokens = self._tokenizer(prompts)
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with torch.no_grad():
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text_features = model.encode_text(tokens)
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text_features /= text_features.norm(dim=-1, keepdim=True)
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avg = text_features.mean(dim=0)
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avg /= avg.norm()
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self._category_embeddings[category] = avg.numpy().astype(np.float32)
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logger.info("Content classifier ready with %d categories", len(self._category_embeddings))
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def classify(self, image: np.ndarray) -> list[ClassificationResult]:
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img_vec = self._embedder.embed_image(image)
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# Cosine similarity against each category
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scores = {}
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for category, cat_vec in self._category_embeddings.items():
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scores[category] = float(np.dot(img_vec, cat_vec))
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# Sort by score descending
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ranked = sorted(scores.items(), key=lambda x: -x[1])
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best_cat, best_score = ranked[0]
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second_score = ranked[1][1]
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margin = best_score - second_score
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# Normalize: 0.01 margin → ~0.5 confidence, 0.03+ → ~1.0
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confidence = min(1.0, margin * 30)
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if confidence >= self._min_confidence:
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return [ClassificationResult(label=best_cat, confidence=confidence)]
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return []
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