49f982611a
Hybrid matching combines text recognition with CLIP similarity: - If reference logo has text and detection matches: lower CLIP threshold - If reference has text but detection doesn't match: higher threshold - If reference has no text: standard threshold Image preprocessing adds letterbox/stretch modes for CLIP input to preserve aspect ratio instead of center cropping. New files: - run_hybrid_test.sh: Test hybrid matching configurations - run_preprocess_test.sh: Compare preprocessing modes Changes to logo_detection_detr.py: - Add preprocess_mode parameter (default/letterbox/stretch) - Add set_text_detector() for hybrid matching - Add extract_text() using EasyOCR - Add compute_text_similarity() with fuzzy matching - Add find_best_match_hybrid() with tiered thresholds Changes to test_logo_detection.py: - Add --matching-method hybrid option - Add --preprocess-mode option - Add hybrid threshold arguments
169 lines
4.9 KiB
Bash
Executable File
169 lines
4.9 KiB
Bash
Executable File
#!/bin/bash
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#
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# Test the hybrid text+CLIP matching approach for logo detection.
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#
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# This approach uses text recognition to improve logo matching:
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# - If reference logo has text and detection matches it: use lower CLIP threshold
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# - If reference logo has text but detection doesn't match: use higher CLIP threshold
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# - If reference logo has no text: use standard CLIP threshold
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#
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# Usage:
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# ./run_hybrid_test.sh
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#
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SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
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OUTPUT_FILE="${SCRIPT_DIR}/test_results/hybrid_matching_results.txt"
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# Model - baseline CLIP
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MODEL="openai/clip-vit-large-patch14"
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# Fixed parameters
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NUM_LOGOS=20
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REFS_PER_LOGO=10
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POSITIVE_SAMPLES=20
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NEGATIVE_SAMPLES=100
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SEED=42
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# Create output directory if needed
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mkdir -p "${SCRIPT_DIR}/test_results"
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# Clear output file and write header
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cat > "$OUTPUT_FILE" << EOF
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Hybrid Text+CLIP Matching Test Results
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======================================
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Date: $(date)
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Model: ${MODEL}
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Fixed Parameters:
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Number of logo brands: ${NUM_LOGOS}
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Refs per logo: ${REFS_PER_LOGO}
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Positive samples/logo: ${POSITIVE_SAMPLES}
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Negative samples/logo: ${NEGATIVE_SAMPLES}
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Seed: ${SEED}
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EOF
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echo "Hybrid Text+CLIP Matching Test"
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echo "==============================="
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echo "Model: ${MODEL}"
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echo ""
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# Test 1: Compare hybrid vs multi-ref baseline
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echo "=== Test 1: Multi-ref baseline (for comparison) ==="
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echo "" >> "$OUTPUT_FILE"
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echo "=== BASELINE: Multi-ref (max) at threshold 0.70 ===" >> "$OUTPUT_FILE"
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uv run python "$SCRIPT_DIR/test_logo_detection.py" \
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--num-logos $NUM_LOGOS \
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--refs-per-logo $REFS_PER_LOGO \
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--positive-samples $POSITIVE_SAMPLES \
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--negative-samples $NEGATIVE_SAMPLES \
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--matching-method multi-ref \
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--min-matching-refs 1 \
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--use-max-similarity \
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--threshold 0.70 \
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--margin 0.05 \
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--seed $SEED \
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--embedding-model "$MODEL" \
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--output-file "$OUTPUT_FILE" \
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--no-cache
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echo ""
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# Test 2: Hybrid with default thresholds
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echo "=== Test 2: Hybrid with default thresholds ==="
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echo "" >> "$OUTPUT_FILE"
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echo "=== HYBRID: default thresholds (0.70/0.60/0.80) ===" >> "$OUTPUT_FILE"
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uv run python "$SCRIPT_DIR/test_logo_detection.py" \
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--num-logos $NUM_LOGOS \
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--refs-per-logo $REFS_PER_LOGO \
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--positive-samples $POSITIVE_SAMPLES \
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--negative-samples $NEGATIVE_SAMPLES \
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--matching-method hybrid \
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--threshold 0.70 \
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--hybrid-text-threshold 0.60 \
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--hybrid-no-text-threshold 0.80 \
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--text-similarity-threshold 0.5 \
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--margin 0.05 \
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--seed $SEED \
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--embedding-model "$MODEL" \
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--output-file "$OUTPUT_FILE" \
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--no-cache
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echo ""
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# Test 3: Hybrid with more aggressive text bonus
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echo "=== Test 3: Hybrid with lower text-match threshold ==="
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echo "" >> "$OUTPUT_FILE"
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echo "=== HYBRID: aggressive text bonus (0.70/0.55/0.80) ===" >> "$OUTPUT_FILE"
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uv run python "$SCRIPT_DIR/test_logo_detection.py" \
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--num-logos $NUM_LOGOS \
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--refs-per-logo $REFS_PER_LOGO \
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--positive-samples $POSITIVE_SAMPLES \
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--negative-samples $NEGATIVE_SAMPLES \
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--matching-method hybrid \
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--threshold 0.70 \
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--hybrid-text-threshold 0.55 \
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--hybrid-no-text-threshold 0.80 \
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--text-similarity-threshold 0.5 \
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--margin 0.05 \
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--seed $SEED \
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--embedding-model "$MODEL" \
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--output-file "$OUTPUT_FILE" \
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--no-cache
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echo ""
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# Test 4: Hybrid with stricter text mismatch penalty
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echo "=== Test 4: Hybrid with stricter text mismatch penalty ==="
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echo "" >> "$OUTPUT_FILE"
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echo "=== HYBRID: strict mismatch (0.70/0.60/0.85) ===" >> "$OUTPUT_FILE"
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uv run python "$SCRIPT_DIR/test_logo_detection.py" \
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--num-logos $NUM_LOGOS \
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--refs-per-logo $REFS_PER_LOGO \
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--positive-samples $POSITIVE_SAMPLES \
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--negative-samples $NEGATIVE_SAMPLES \
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--matching-method hybrid \
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--threshold 0.70 \
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--hybrid-text-threshold 0.60 \
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--hybrid-no-text-threshold 0.85 \
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--text-similarity-threshold 0.5 \
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--margin 0.05 \
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--seed $SEED \
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--embedding-model "$MODEL" \
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--output-file "$OUTPUT_FILE" \
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--no-cache
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echo ""
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# Test 5: Hybrid with lower text similarity threshold (more lenient OCR matching)
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echo "=== Test 5: Hybrid with lenient text matching ==="
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echo "" >> "$OUTPUT_FILE"
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echo "=== HYBRID: lenient text matching (text_sim=0.4) ===" >> "$OUTPUT_FILE"
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uv run python "$SCRIPT_DIR/test_logo_detection.py" \
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--num-logos $NUM_LOGOS \
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--refs-per-logo $REFS_PER_LOGO \
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--positive-samples $POSITIVE_SAMPLES \
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--negative-samples $NEGATIVE_SAMPLES \
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--matching-method hybrid \
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--threshold 0.70 \
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--hybrid-text-threshold 0.60 \
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--hybrid-no-text-threshold 0.80 \
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--text-similarity-threshold 0.4 \
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--margin 0.05 \
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--seed $SEED \
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--embedding-model "$MODEL" \
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--output-file "$OUTPUT_FILE" \
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--no-cache
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echo ""
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echo "======================================="
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echo "Tests complete!"
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echo "Results saved to: $OUTPUT_FILE"
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echo "======================================="
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