Camera Traps Skill

Use this skill when you need to process a collection of camera trap images, run species classification, extract vector representation embeddings, and store them inside a Hoplite vector database.

Workflow Overview

Follow these sequential steps:

1. Identify Dataset and Limits:
   • Locate the target camera trap images directory.
   • Assess if a GPU is available on the system. If running on CPU-only, discuss with the user or apply a processing limit (e.g., first 1000 images) to prevent the ingestion pipeline from running excessively long.
2. Initialize Hoplite Database:
   • Create a Hoplite database (SQLiteUSearchDB) at the destination folder.
   • Configure it with an embedding dimension of 1280 (EfficientNet-V2 M feature size), the metric set to Cos, and the data type set to float16.
3. Run Ingestion Pipeline:
   • Instantiate the SpeciesNetDetector and SpeciesNetClassifier models (if running in an environment with pre-mounted read-only models like /kaggle/input/ on Colab, copy the model directory to a local writable path first; see the technical reference).
   • Register a PyTorch forward hook on the classifier's average pooling layer (SpeciesNet/efficientnetv2-m/avg_pool/Mean_Squeeze__3825) to intercept raw embeddings.
   • For each image:
     • Insert it into the database as a recording.
     • Run the detector model to get bounding box coords for animal detections.
     • Crop the PIL image to the bounding box, preprocess it, and run the classifier to extract the 1280-dim embedding vector.
     • Cast the vector to float16 and insert it into the database as a window.
4. Agile Modeling and Search:
   • Once populated, use the Hoplite database to perform vector searches (ranking by similarity) or train active learning classifiers on top of the embeddings.
5. Camera Trap Visualization Guidelines (M3 UI):
   • Context Preservation: Avoid displaying raw cropped images in the result cards. Instead, display the original (uncropped) image inside the card container and draw the animal detection as a red border box overlay dynamically using CSS absolute positioning and percentages (e.g., left: xmin * 100%, top: ymin * 100%, etc.).
   • Full-Resolution Modal Preview: Implement a click handler on the card media that triggers a floating fullscreen modal containing the uncropped image and the aligned bounding box overlay to allow the user to verify low-confidence detections.
   • Custom Query Search Support: The backend server supporting the Web UI must implement on-the-fly embedding extraction for custom HTTP/S query URIs by downloading the image, running the detector to identify target bounding boxes, preprocessing the crop, and capturing the embedding vector using the PyTorch forward hook on the classifier.