nx_image_feature_extract.nx source

1// nx_image_feature_extract.nx -- fixed-length feature vector emitter. 2// 3// Composes existing image-quality detectors (nx_aesthetics + 4// nx_self_similarity + nx_symmetry_group) into a 16-element Q10 5// feature vector. Pair with nx_cosine_similarity for image-to-image 6// comparison, concept-embedding lookup, clustering, recommendation. 7// 8// FOUNDATION FOR THE "AI GROWS BY INGESTING IMAGE" ROADMAP: 9// user uploads N images of a new outfit 10// -> nx_image_feature_extract on each -> N feature vectors 11// -> average them (centroid) -> "outfit_X" concept embedding 12// -> store as content-addressed manifest in nishi-library 13// later prompt mentions "outfit_X" 14// -> blend stored embedding into prompt embedding (IP-adapter path) 15// -> render conditioned on the new concept 16// No LoRA training, no base-model fine-tune. The substrate GROWS. 17// 18// Idea-provenance (patent-clean): 19// CLIP image-encoder pattern (Radford 2021) 20// IP-Adapter (Ye 2023) 21// Textual Inversion (Gal 2022) 22// The architecture absorbs the IDEA -- fixed-length embedding per 23// image -- without copying any third-party code. Today's 24// features are substrate-native (aesthetics + fractal + 25// multi-axis symmetry) rather than CLIP-encoder weights. 26// 27// VECTOR LAYOUT (each Q10): 28// [ 0] COLOR_HARMONY hue distribution narrowness 29// [ 1] LIGHTING exposure / range / clipping composite 30// [ 2] COMPOSITION thirds + symmetry + balance composite 31// [ 3] CLARITY salience peakedness 32// [ 4] SHARPNESS mean Sobel magnitude 33// [ 5] AESTHETIC_COMPOSITE mean of [0..4] 34// [ 6] FRACTAL_DIM box-counting Q10 of (D-1) for D in [1, 2] 35// [ 7] FRACTAL_AESTHETIC Spehar 2003 D~1.4 peak-fit 36// [ 8] EDGE_DENSITY Sobel-thresh fraction 37// [ 9] SYM_COMPOSITE max across 7 symmetry axes 38// [10] SYM_H horizontal bilateral 39// [11] SYM_V vertical bilateral 40// [12] SYM_DIAG diagonal mirror 41// [13] SYM_ROT_180 180-deg rotational 42// [14] SYM_N_AXES_PRESENT how many symmetry axes >= 70% threshold 43// [15] SYM_TRANS_PERIOD translational periodic best-period 44// 45// Substrate cardinal: this vector is the IMAGE's signature. Two 46// images with similar substrate signatures should look "alike" in 47// the broad-aesthetic sense; nx_cosine_similarity over these 48// vectors operationalizes "looks like" mathematically. 49// 50// genealogy_id: salton_1971_smart + radford_2021_clip + 51// gal_2022_textual_inversion + ye_2023_ip_adapter + 52// birkhoff_1933_aesthetic_measure + 53// spehar_2003_universal_aesthetic_fractals 54// lineage_id: image_feature_vector_q10_composite 55 56// nx_safety_envelope: 57// intended_use: AUTO_APPLIED -- primitive-specific tuning queued 58// sil_target: SIL1 59// evidence: [bulk_applied_2026-05-16, see-file-comment-for-detail] 60// verdict: NOT_YET_EVALUATED 61 62import "nx_syscalls.nx" 63import "nx_tier.nx" 64import "nx_image.nx" 65import "nx_aesthetics.nx" 66import "nx_self_similarity.nx" 67import "nx_symmetry_group.nx" 68 69// Feature vector length. Power of 2 for downstream-friendly layouts 70// (cache lines, SIMD width when nx_simd lands, etc.). 71const NX_IMAGE_FEATURE_LEN: nx_int = 16 72 73// Named indices into the feature vector for downstream clarity. 74const NX_IMG_FEAT_COLOR_HARMONY: nx_int = 0 75const NX_IMG_FEAT_LIGHTING: nx_int = 1 76const NX_IMG_FEAT_COMPOSITION: nx_int = 2 77const NX_IMG_FEAT_CLARITY: nx_int = 3 78const NX_IMG_FEAT_SHARPNESS: nx_int = 4 79const NX_IMG_FEAT_AESTHETIC_COMPOSITE: nx_int = 5 80const NX_IMG_FEAT_FRACTAL_DIM: nx_int = 6 81const NX_IMG_FEAT_FRACTAL_AESTHETIC: nx_int = 7 82const NX_IMG_FEAT_EDGE_DENSITY: nx_int = 8 83const NX_IMG_FEAT_SYM_COMPOSITE: nx_int = 9 84const NX_IMG_FEAT_SYM_H: nx_int = 10 85const NX_IMG_FEAT_SYM_V: nx_int = 11 86const NX_IMG_FEAT_SYM_DIAG: nx_int = 12 87const NX_IMG_FEAT_SYM_ROT_180: nx_int = 13 88const NX_IMG_FEAT_SYM_N_AXES_PRESENT: nx_int = 14 89const NX_IMG_FEAT_SYM_TRANS_PERIOD: nx_int = 15 90 91// ===== Public emitter ================================================= 92// 93// rgb 3-channel image (color features) 94// gray 1-channel image (composition / sharpness / fractal) 95// out caller-allocated nx_int array of length NX_IMAGE_FEATURE_LEN 96// 97// Returns: overall fidelity Q10 (mean of upstream detector fidelities 98// where available; 0 = totally untrustworthy, Q = full confidence). 99// Caller refuses to act on the feature vector below a chosen 100// fidelity floor (honest-perf-verdict cardinal). 101 102func nx_image_feature_extract(rgb: *Image, gray: *Image, out: *nx_int) -> nx_int { 103 // ---- Aesthetics composite (5 axes + composite) ---- 104 let aest: *AestheticsReport = (sys_mmap(8 * NX_SIZEOF_NX_INT)) as *AestheticsReport 105 nx_aesthetics_compute(rgb, gray, aest) 106 out[NX_IMG_FEAT_COLOR_HARMONY] = aest.color_harmony 107 out[NX_IMG_FEAT_LIGHTING] = aest.lighting_quality 108 out[NX_IMG_FEAT_COMPOSITION] = aest.composition 109 out[NX_IMG_FEAT_CLARITY] = aest.subject_clarity 110 out[NX_IMG_FEAT_SHARPNESS] = aest.sharpness 111 out[NX_IMG_FEAT_AESTHETIC_COMPOSITE] = aest.composite 112 113 // ---- Self-similarity (fractal dim + Spehar fit + edge density) ---- 114 let ss: *SelfSimilarityReport = (sys_mmap(4 * NX_SIZEOF_NX_INT)) as *SelfSimilarityReport 115 nx_self_similarity_compute(gray, ss) 116 out[NX_IMG_FEAT_FRACTAL_DIM] = ss.fractal_dim_q10 117 out[NX_IMG_FEAT_FRACTAL_AESTHETIC] = ss.aesthetic_q10 118 out[NX_IMG_FEAT_EDGE_DENSITY] = ss.edge_density_q10 119 120 // ---- Symmetry (multi-axis + composite) ---- 121 let sym: *SymmetryReport = (sys_mmap(12 * NX_SIZEOF_NX_INT)) as *SymmetryReport 122 nx_symmetry_compute(gray, sym) 123 out[NX_IMG_FEAT_SYM_COMPOSITE] = sym.composite_q10 124 out[NX_IMG_FEAT_SYM_H] = sym.h_bilateral_q10 125 out[NX_IMG_FEAT_SYM_V] = sym.v_bilateral_q10 126 out[NX_IMG_FEAT_SYM_DIAG] = sym.diagonal_q10 127 out[NX_IMG_FEAT_SYM_ROT_180] = sym.rot_180_q10 128 out[NX_IMG_FEAT_SYM_N_AXES_PRESENT] = sym.n_axes_present 129 out[NX_IMG_FEAT_SYM_TRANS_PERIOD] = sym.translation_period 130 131 // ---- Fidelity (substrate-side honest signal) ---- 132 // Self-similarity has explicit fidelity_q10; aesthetics and 133 // symmetry don't carry one yet so we conservatively gate by 134 // the self-similarity reading. When upstream detectors widen 135 // their reports, this composes the mean. 136 return ss.fidelity_q10 137} 138 139// ===== Convenience: feature-vector centroid for N images ============= 140// 141// Computes the per-axis mean of N feature vectors into out_centroid. 142// Used to derive a "concept embedding" from N example images: average 143// their features, normalize, store as the concept's canonical 144// signature. 145 146func nx_image_feature_centroid(vectors: *nx_int, n_vectors: nx_int, 147 out_centroid: *nx_int) -> nx_int { 148 if n_vectors <= 0 { return 0 } 149 var axis: nx_int = 0 150 while axis < NX_IMAGE_FEATURE_LEN { 151 var sum: nx_int = 0 152 var i: nx_int = 0 153 while i < n_vectors { 154 sum = sum + vectors[i * NX_IMAGE_FEATURE_LEN + axis] 155 i = i + 1 156 } 157 out_centroid[axis] = sum / n_vectors 158 axis = axis + 1 159 } 160 return 0 161}