#include "ccv.h" #include "ccv_internal.h" #ifdef HAVE_GSL #include #include #endif #ifdef USE_DISPATCH #include #endif const ccv_icf_param_t ccv_icf_default_params = { .min_neighbors = 2, .threshold = 0, .step_through = 2, .flags = 0, .interval = 8, }; // this uses a look up table for cubic root computation because rgb to luv only requires data within range of 0~1 static inline float fast_cube_root(const float d) { static const float cube_root[2048] = { 0.000000e+00, 7.875788e-02, 9.922871e-02, 1.135885e-01, 1.250203e-01, 1.346741e-01, 1.431126e-01, 1.506584e-01, 1.575158e-01, 1.638230e-01, 1.696787e-01, 1.751560e-01, 1.803105e-01, 1.851861e-01, 1.898177e-01, 1.942336e-01, 1.984574e-01, 2.025087e-01, 2.064040e-01, 2.101577e-01, 2.137818e-01, 2.172870e-01, 2.206827e-01, 2.239769e-01, 2.271770e-01, 2.302894e-01, 2.333199e-01, 2.362736e-01, 2.391553e-01, 2.419692e-01, 2.447191e-01, 2.474085e-01, 2.500407e-01, 2.526186e-01, 2.551450e-01, 2.576222e-01, 2.600528e-01, 2.624387e-01, 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9.965686e-01, 9.967325e-01, 9.968964e-01, 9.970602e-01, 9.972240e-01, 9.973878e-01, 9.975514e-01, 9.977150e-01, 9.978786e-01, 9.980421e-01, 9.982055e-01, 9.983689e-01, 9.985323e-01, 9.986956e-01, 9.988588e-01, 9.990220e-01, 9.991851e-01, 9.993482e-01, 9.995112e-01, 9.996742e-01, 9.998372e-01, 1.000000e+00, }; int i = (int)(d * 2047); assert(i >= 0 && i < 2048); return cube_root[i]; } static inline void _ccv_rgb_to_luv(const float r, const float g, const float b, float* pl, float* pu, float* pv) { const float x = 0.412453f * r + 0.35758f * g + 0.180423f * b; const float y = 0.212671f * r + 0.71516f * g + 0.072169f * b; const float z = 0.019334f * r + 0.119193f * g + 0.950227f * b; const float x_n = 0.312713f, y_n = 0.329016f; const float uv_n_divisor = -2.f * x_n + 12.f * y_n + 3.f; const float u_n = 4.f * x_n / uv_n_divisor; const float v_n = 9.f * y_n / uv_n_divisor; const float uv_divisor = ccv_max((x + 15.f * y + 3.f * z), FLT_EPSILON); const float u = 4.f * x / uv_divisor; const float v = 9.f * y / uv_divisor; const float y_cube_root = fast_cube_root(y); const float l_value = ccv_max(0.f, ((116.f * y_cube_root) - 16.f)); const float u_value = 13.f * l_value * (u - u_n); const float v_value = 13.f * l_value * (v - v_n); // L in [0, 100], U in [-134, 220], V in [-140, 122] *pl = l_value * (255.f / 100.f); *pu = (u_value + 134.f) * (255.f / (220.f + 134.f)); *pv = (v_value + 140.f) * (255.f / (122.f + 140.f)); } // generating the integrate channels features (which combines the grayscale, gradient magnitude, and 6-direction HOG) void ccv_icf(ccv_dense_matrix_t* a, ccv_dense_matrix_t** b, int type) { int ch = CCV_GET_CHANNEL(a->type); assert(ch == 1 || ch == 3); int nchr = (ch == 1) ? 8 : 10; ccv_declare_derived_signature(sig, a->sig != 0, ccv_sign_with_literal("ccv_icf"), a->sig, CCV_EOF_SIGN); ccv_dense_matrix_t* db = *b = ccv_dense_matrix_renew(*b, a->rows, a->cols, CCV_32F | nchr, CCV_32F | nchr, sig); ccv_object_return_if_cached(, db); ccv_dense_matrix_t* ag = 0; ccv_dense_matrix_t* mg = 0; ccv_gradient(a, &ag, 0, &mg, 0, 1, 1); float* agp = ag->data.f32; float* mgp = mg->data.f32; float* dbp = db->data.f32; ccv_zero(db); int i, j, k; unsigned char* a_ptr = a->data.u8; float magnitude_scaling = 1 / sqrtf(2); // regularize it to 0~1 if (ch == 1) { #define for_block(_, _for_get) \ for (i = 0; i < a->rows; i++) \ { \ for (j = 0; j < a->cols; j++) \ { \ dbp[0] = _for_get(a_ptr, j, 0); \ dbp[1] = mgp[j] * magnitude_scaling; \ float agr = (ccv_clamp(agp[j] <= 180 ? agp[j] : agp[j] - 180, 0, 179.99) / 180.0) * 6; \ int ag0 = (int)agr; \ int ag1 = ag0 < 5 ? ag0 + 1 : 0; \ agr = agr - ag0; \ dbp[2 + ag0] = dbp[1] * (1 - agr); \ dbp[2 + ag1] = dbp[1] * agr; \ dbp += 8; \ } \ a_ptr += a->step; \ agp += a->cols; \ mgp += a->cols; \ } ccv_matrix_getter(a->type, for_block); #undef for_block } else { // color one, luv, gradient magnitude, and 6-direction HOG #define for_block(_, _for_get) \ for (i = 0; i < a->rows; i++) \ { \ for (j = 0; j < a->cols; j++) \ { \ _ccv_rgb_to_luv(_for_get(a_ptr, j * ch, 0) / 255.0, \ _for_get(a_ptr, j * ch + 1, 0) / 255.0, \ _for_get(a_ptr, j * ch + 2, 0) / 255.0, \ dbp, dbp + 1, dbp + 2); \ float agv = agp[j * ch]; \ float mgv = mgp[j * ch]; \ for (k = 1; k < ch; k++) \ { \ if (mgp[j * ch + k] > mgv) \ { \ mgv = mgp[j * ch + k]; \ agv = agp[j * ch + k]; \ } \ } \ dbp[3] = mgv * magnitude_scaling; \ float agr = (ccv_clamp(agv <= 180 ? agv : agv - 180, 0, 179.99) / 180.0) * 6; \ int ag0 = (int)agr; \ int ag1 = ag0 < 5 ? ag0 + 1 : 0; \ agr = agr - ag0; \ dbp[4 + ag0] = dbp[3] * (1 - agr); \ dbp[4 + ag1] = dbp[3] * agr; \ dbp += 10; \ } \ a_ptr += a->step; \ agp += a->cols * ch; \ mgp += a->cols * ch; \ } ccv_matrix_getter(a->type, for_block); #undef for_block } ccv_matrix_free(ag); ccv_matrix_free(mg); } static inline float _ccv_icf_run_feature(ccv_icf_feature_t* feature, float* ptr, int cols, int ch, int x, int y) { float c = feature->beta; int q; for (q = 0; q < feature->count; q++) c += (ptr[(feature->sat[q * 2 + 1].x + x + 1 + (feature->sat[q * 2 + 1].y + y + 1) * cols) * ch + feature->channel[q]] - ptr[(feature->sat[q * 2].x + x + (feature->sat[q * 2 + 1].y + y + 1) * cols) * ch + feature->channel[q]] + ptr[(feature->sat[q * 2].x + x + (feature->sat[q * 2].y + y) * cols) * ch + feature->channel[q]] - ptr[(feature->sat[q * 2 + 1].x + x + 1 + (feature->sat[q * 2].y + y) * cols) * ch + feature->channel[q]]) * feature->alpha[q]; return c; } static inline int _ccv_icf_run_weak_classifier(ccv_icf_decision_tree_t* weak_classifier, float* ptr, int cols, int ch, int x, int y) { float c = _ccv_icf_run_feature(weak_classifier->features, ptr, cols, ch, x, y); if (c > 0) { if (!(weak_classifier->pass & 0x1)) return 1; return _ccv_icf_run_feature(weak_classifier->features + 2, ptr, cols, ch, x, y) > 0; } else { if (!(weak_classifier->pass & 0x2)) return 0; return _ccv_icf_run_feature(weak_classifier->features + 1, ptr, cols, ch, x, y) > 0; } } #ifdef HAVE_GSL static void _ccv_icf_randomize_feature(gsl_rng* rng, ccv_size_t size, int minimum, ccv_icf_feature_t* feature, int grayscale) { feature->count = gsl_rng_uniform_int(rng, CCV_ICF_SAT_MAX) + 1; assert(feature->count <= CCV_ICF_SAT_MAX); int i; feature->beta = 0; for (i = 0; i < feature->count; i++) { int x0, y0, x1, y1; do { x0 = gsl_rng_uniform_int(rng, size.width); x1 = gsl_rng_uniform_int(rng, size.width); y0 = gsl_rng_uniform_int(rng, size.height); y1 = gsl_rng_uniform_int(rng, size.height); } while ((ccv_max(x0, x1) - ccv_min(x0, x1) + 1) * (ccv_max(y0, y1) - ccv_min(y0, y1) + 1) < (minimum + 1) * (minimum + 1) || (ccv_max(x0, x1) - ccv_min(x0, x1) + 1) < minimum || (ccv_max(y0, y1) - ccv_min(y0, y1) + 1) < minimum); feature->sat[i * 2].x = ccv_min(x0, x1); feature->sat[i * 2].y = ccv_min(y0, y1); feature->sat[i * 2 + 1].x = ccv_max(x0, x1); feature->sat[i * 2 + 1].y = ccv_max(y0, y1); feature->channel[i] = gsl_rng_uniform_int(rng, grayscale ? 8 : 10); // 8-channels for grayscale, and 10-channels for rgb assert(feature->channel[i] >= 0 && feature->channel[i] < (grayscale ? 8 : 10)); feature->alpha[i] = gsl_rng_uniform(rng) / (float)((feature->sat[i * 2 + 1].x - feature->sat[i * 2].x + 1) * (feature->sat[i * 2 + 1].y - feature->sat[i * 2].y + 1)); } } static void _ccv_icf_check_params(ccv_icf_new_param_t params) { assert(params.size.width > 0 && params.size.height > 0); assert(params.deform_shift >= 0); assert(params.deform_angle >= 0); assert(params.deform_scale >= 0 && params.deform_scale < 1); assert(params.feature_size > 0); assert(params.acceptance > 0 && params.acceptance < 1.0); } static ccv_dense_matrix_t* _ccv_icf_capture_feature(gsl_rng* rng, ccv_dense_matrix_t* image, ccv_decimal_pose_t pose, ccv_size_t size, ccv_margin_t margin, float deform_angle, float deform_scale, float deform_shift) { float rotate_x = (deform_angle * 2 * gsl_rng_uniform(rng) - deform_angle) * CCV_PI / 180 + pose.pitch; float rotate_y = (deform_angle * 2 * gsl_rng_uniform(rng) - deform_angle) * CCV_PI / 180 + pose.yaw; float rotate_z = (deform_angle * 2 * gsl_rng_uniform(rng) - deform_angle) * CCV_PI / 180 + pose.roll; float scale = gsl_rng_uniform(rng); // to make the scale evenly distributed, for example, when deforming of 1/2 ~ 2, we want it to distribute around 1, rather than any average of 1/2 ~ 2 scale = (1 + deform_scale * scale) / (1 + deform_scale * (1 - scale)); float scale_ratio = sqrtf((float)(size.width * size.height) / (pose.a * pose.b * 4)); float m00 = cosf(rotate_z) * scale; float m01 = cosf(rotate_y) * sinf(rotate_z) * scale; float m02 = (deform_shift * 2 * gsl_rng_uniform(rng) - deform_shift) / scale_ratio + pose.x + (margin.right - margin.left) / scale_ratio - image->cols * 0.5; float m10 = (sinf(rotate_y) * cosf(rotate_z) - cosf(rotate_x) * sinf(rotate_z)) * scale; float m11 = (sinf(rotate_y) * sinf(rotate_z) + cosf(rotate_x) * cosf(rotate_z)) * scale; float m12 = (deform_shift * 2 * gsl_rng_uniform(rng) - deform_shift) / scale_ratio + pose.y + (margin.bottom - margin.top) / scale_ratio - image->rows * 0.5; float m20 = (sinf(rotate_y) * cosf(rotate_z) + sinf(rotate_x) * sinf(rotate_z)) * scale; float m21 = (sinf(rotate_y) * sinf(rotate_z) - sinf(rotate_x) * cosf(rotate_z)) * scale; float m22 = cosf(rotate_x) * cosf(rotate_y); ccv_dense_matrix_t* b = 0; ccv_perspective_transform(image, &b, 0, m00, m01, m02, m10, m11, m12, m20, m21, m22); ccv_dense_matrix_t* resize = 0; // have 1px border around the grayscale image because we need these to compute correct gradient feature ccv_size_t scale_size = { .width = (int)((size.width + margin.left + margin.right + 2) / scale_ratio + 0.5), .height = (int)((size.height + margin.top + margin.bottom + 2) / scale_ratio + 0.5), }; assert(scale_size.width > 0 && scale_size.height > 0); ccv_slice(b, (ccv_matrix_t**)&resize, 0, (int)(b->rows * 0.5 - (size.height + margin.top + margin.bottom + 2) / scale_ratio * 0.5 + 0.5), (int)(b->cols * 0.5 - (size.width + margin.left + margin.right + 2) / scale_ratio * 0.5 + 0.5), scale_size.height, scale_size.width); ccv_matrix_free(b); b = 0; if (scale_ratio > 1) ccv_resample(resize, &b, 0, size.height + margin.top + margin.bottom + 2, size.width + margin.left + margin.right + 2, CCV_INTER_CUBIC); else ccv_resample(resize, &b, 0, size.height + margin.top + margin.bottom + 2, size.width + margin.left + margin.right + 2, CCV_INTER_AREA); ccv_matrix_free(resize); return b; } typedef struct { uint8_t correct:1; double weight; float rate; } ccv_icf_example_state_t; typedef struct { uint8_t classifier:1; uint8_t positives:1; uint8_t negatives:1; uint8_t features:1; uint8_t example_state:1; uint8_t precomputed:1; } ccv_icf_classifier_cascade_persistence_state_t; typedef struct { uint32_t index; float value; } ccv_icf_value_index_t; typedef struct { ccv_function_state_reserve_field; int i; int bootstrap; ccv_icf_new_param_t params; ccv_icf_classifier_cascade_t* classifier; ccv_array_t* positives; ccv_array_t* negatives; ccv_icf_feature_t* features; ccv_size_t size; ccv_margin_t margin; ccv_icf_example_state_t* example_state; uint8_t* precomputed; ccv_icf_classifier_cascade_persistence_state_t x; } ccv_icf_classifier_cascade_state_t; static void _ccv_icf_write_classifier_cascade_state(ccv_icf_classifier_cascade_state_t* state, const char* directory) { char filename[1024]; snprintf(filename, 1024, "%s/state", directory); FILE* w = fopen(filename, "w+"); fprintf(w, "%d %d %d\n", state->line_no, state->i, state->bootstrap); fprintf(w, "%d %d %d\n", state->params.feature_size, state->size.width, state->size.height); fprintf(w, "%d %d %d %d\n", state->margin.left, state->margin.top, state->margin.right, state->margin.bottom); fclose(w); int i, q; if (!state->x.positives) { snprintf(filename, 1024, "%s/positives", directory); w = fopen(filename, "wb+"); fwrite(&state->positives->rnum, sizeof(state->positives->rnum), 1, w); fwrite(&state->positives->rsize, sizeof(state->positives->rsize), 1, w); for (i = 0; i < state->positives->rnum; i++) { ccv_dense_matrix_t* a = (ccv_dense_matrix_t*)ccv_array_get(state->positives, i); assert(a->rows == state->size.height + state->margin.top + state->margin.bottom + 2 && a->cols == state->size.width + state->margin.left + state->margin.right + 2); fwrite(a, 1, state->positives->rsize, w); } fclose(w); state->x.positives = 1; } if (!state->x.negatives) { assert(state->negatives->rsize == state->positives->rsize); snprintf(filename, 1024, "%s/negatives", directory); w = fopen(filename, "wb+"); fwrite(&state->negatives->rnum, sizeof(state->negatives->rnum), 1, w); fwrite(&state->negatives->rsize, sizeof(state->negatives->rsize), 1, w); for (i = 0; i < state->negatives->rnum; i++) { ccv_dense_matrix_t* a = (ccv_dense_matrix_t*)ccv_array_get(state->negatives, i); assert(a->rows == state->size.height + state->margin.top + state->margin.bottom + 2 && a->cols == state->size.width + state->margin.left + state->margin.right + 2); fwrite(a, 1, state->negatives->rsize, w); } fclose(w); state->x.negatives = 1; } if (!state->x.features) { snprintf(filename, 1024, "%s/features", directory); w = fopen(filename, "w+"); for (i = 0; i < state->params.feature_size; i++) { ccv_icf_feature_t* feature = state->features + i; fprintf(w, "%d %a\n", feature->count, feature->beta); for (q = 0; q < feature->count; q++) fprintf(w, "%d %a %d %d %d %d\n", feature->channel[q], feature->alpha[q], feature->sat[q * 2].x, feature->sat[q * 2].y, feature->sat[q * 2 + 1].x, feature->sat[q * 2 + 1].y); } fclose(w); state->x.features = 1; } if (!state->x.example_state) { snprintf(filename, 1024, "%s/example_state", directory); w = fopen(filename, "w+"); for (i = 0; i < state->positives->rnum + state->negatives->rnum; i++) fprintf(w, "%u %la %a\n", (uint32_t)state->example_state[i].correct, state->example_state[i].weight, state->example_state[i].rate); fclose(w); state->x.example_state = 1; } if (!state->x.precomputed) { size_t step = (3 * (state->positives->rnum + state->negatives->rnum) + 3) & -4; snprintf(filename, 1024, "%s/precomputed", directory); w = fopen(filename, "wb+"); fwrite(state->precomputed, 1, step * state->params.feature_size, w); fclose(w); state->x.precomputed = 1; } if (!state->x.classifier) { snprintf(filename, 1024, "%s/cascade", directory); ccv_icf_write_classifier_cascade(state->classifier, filename); state->x.classifier = 1; } } static void _ccv_icf_read_classifier_cascade_state(const char* directory, ccv_icf_classifier_cascade_state_t* state) { char filename[1024]; state->line_no = state->i = 0; state->bootstrap = 0; snprintf(filename, 1024, "%s/state", directory); FILE* r = fopen(filename, "r"); if (r) { int feature_size; fscanf(r, "%d %d %d", &state->line_no, &state->i, &state->bootstrap); fscanf(r, "%d %d %d", &feature_size, &state->size.width, &state->size.height); fscanf(r, "%d %d %d %d", &state->margin.left, &state->margin.top, &state->margin.right, &state->margin.bottom); assert(feature_size == state->params.feature_size); fclose(r); } int i, q; snprintf(filename, 1024, "%s/positives", directory); r = fopen(filename, "rb"); state->x.precomputed = state->x.features = state->x.example_state = state->x.classifier = state->x.positives = state->x.negatives = 1; if (r) { int rnum, rsize; fread(&rnum, sizeof(rnum), 1, r); fread(&rsize, sizeof(rsize), 1, r); state->positives = ccv_array_new(rsize, rnum, 0); ccv_dense_matrix_t* a = (ccv_dense_matrix_t*)alloca(rsize); for (i = 0; i < rnum; i++) { fread(a, 1, rsize, r); assert(a->rows == state->size.height + state->margin.top + state->margin.bottom + 2 && a->cols == state->size.width + state->margin.left + state->margin.right + 2); ccv_array_push(state->positives, a); } fclose(r); } snprintf(filename, 1024, "%s/negatives", directory); r = fopen(filename, "rb"); if (r) { int rnum, rsize; fread(&rnum, sizeof(rnum), 1, r); fread(&rsize, sizeof(rsize), 1, r); state->negatives = ccv_array_new(rsize, rnum, 0); ccv_dense_matrix_t* a = (ccv_dense_matrix_t*)alloca(rsize); for (i = 0; i < rnum; i++) { fread(a, 1, rsize, r); assert(a->rows == state->size.height + state->margin.top + state->margin.bottom + 2 && a->cols == state->size.width + state->margin.left + state->margin.right + 2); ccv_array_push(state->negatives, a); } fclose(r); } snprintf(filename, 1024, "%s/features", directory); r = fopen(filename, "r"); if (r) { state->features = (ccv_icf_feature_t*)ccmalloc(state->params.feature_size * sizeof(ccv_icf_feature_t)); for (i = 0; i < state->params.feature_size; i++) { ccv_icf_feature_t* feature = state->features + i; fscanf(r, "%d %a", &feature->count, &feature->beta); for (q = 0; q < feature->count; q++) fscanf(r, "%d %a %d %d %d %d", &feature->channel[q], &feature->alpha[q], &feature->sat[q * 2].x, &feature->sat[q * 2].y, &feature->sat[q * 2 + 1].x, &feature->sat[q * 2 + 1].y); } fclose(r); } snprintf(filename, 1024, "%s/example_state", directory); r = fopen(filename, "r"); if (r) { state->example_state = (ccv_icf_example_state_t*)ccmalloc((state->positives->rnum + state->negatives->rnum) * sizeof(ccv_icf_example_state_t)); for (i = 0; i < state->positives->rnum + state->negatives->rnum; i++) { uint32_t correct; double weight; float rate; fscanf(r, "%u %la %a", &correct, &weight, &rate); state->example_state[i].correct = correct; state->example_state[i].weight = weight; state->example_state[i].rate = rate; } fclose(r); } else state->example_state = 0; snprintf(filename, 1024, "%s/precomputed", directory); r = fopen(filename, "rb"); if (r) { size_t step = (3 * (state->positives->rnum + state->negatives->rnum) + 3) & -4; state->precomputed = (uint8_t*)ccmalloc(sizeof(uint8_t) * state->params.feature_size * step); fread(state->precomputed, 1, step * state->params.feature_size, r); fclose(r); } else state->precomputed = 0; snprintf(filename, 1024, "%s/cascade", directory); state->classifier = ccv_icf_read_classifier_cascade(filename); if (!state->classifier) { state->classifier = (ccv_icf_classifier_cascade_t*)ccmalloc(sizeof(ccv_icf_classifier_cascade_t)); state->classifier->count = 0; state->classifier->grayscale = state->params.grayscale; state->classifier->weak_classifiers = (ccv_icf_decision_tree_t*)ccmalloc(sizeof(ccv_icf_decision_tree_t) * state->params.weak_classifier); } else { if (state->classifier->count < state->params.weak_classifier) state->classifier->weak_classifiers = (ccv_icf_decision_tree_t*)ccrealloc(state->classifier->weak_classifiers, sizeof(ccv_icf_decision_tree_t) * state->params.weak_classifier); } } #define less_than(s1, s2, aux) ((s1).value < (s2).value) static CCV_IMPLEMENT_QSORT(_ccv_icf_precomputed_ordering, ccv_icf_value_index_t, less_than) #undef less_than static inline void _ccv_icf_3_uint8_to_1_uint1_1_uint23(uint8_t* u8, uint8_t* u1, uint32_t* uint23) { *u1 = (u8[0] >> 7); *uint23 = (((uint32_t)(u8[0] & 0x7f)) << 16) | ((uint32_t)(u8[1]) << 8) | u8[2]; } static inline uint32_t _ccv_icf_3_uint8_to_1_uint23(uint8_t* u8) { return (((uint32_t)(u8[0] & 0x7f)) << 16) | ((uint32_t)(u8[1]) << 8) | u8[2]; } static inline void _ccv_icf_1_uint1_1_uint23_to_3_uint8(uint8_t u1, uint32_t u23, uint8_t* u8) { u8[0] = ((u1 << 7) | (u23 >> 16)) & 0xff; u8[1] = (u23 >> 8) & 0xff; u8[2] = u23 & 0xff; } static float _ccv_icf_run_feature_on_example(ccv_icf_feature_t* feature, ccv_dense_matrix_t* a) { ccv_dense_matrix_t* icf = 0; // we have 1px padding around the image ccv_icf(a, &icf, 0); ccv_dense_matrix_t* sat = 0; ccv_sat(icf, &sat, 0, CCV_PADDING_ZERO); ccv_matrix_free(icf); float* ptr = sat->data.f32; int ch = CCV_GET_CHANNEL(sat->type); float c = _ccv_icf_run_feature(feature, ptr, sat->cols, ch, 1, 1); ccv_matrix_free(sat); return c; } static uint8_t* _ccv_icf_precompute_features(ccv_icf_feature_t* features, int feature_size, ccv_array_t* positives, ccv_array_t* negatives) { int i, j; // we use 3 bytes to represent the sorted index, and compute feature result (float) on fly size_t step = (3 * (positives->rnum + negatives->rnum) + 3) & -4; uint8_t* precomputed = (uint8_t*)ccmalloc(sizeof(uint8_t) * feature_size * step); ccv_icf_value_index_t* sortkv = (ccv_icf_value_index_t*)ccmalloc(sizeof(ccv_icf_value_index_t) * (positives->rnum + negatives->rnum)); PRINT(CCV_CLI_INFO, " - precompute features using %uM memory temporarily\n", (uint32_t)((sizeof(float) * (positives->rnum + negatives->rnum) * feature_size + sizeof(uint8_t) * feature_size * step) / (1024 * 1024))); float* featval = (float*)ccmalloc(sizeof(float) * feature_size * (positives->rnum + negatives->rnum)); ccv_disable_cache(); // clean up cache so we have enough space to run it #ifdef USE_DISPATCH dispatch_semaphore_t sema = dispatch_semaphore_create(1); dispatch_apply(positives->rnum + negatives->rnum, dispatch_get_global_queue(DISPATCH_QUEUE_PRIORITY_DEFAULT, 0), ^(size_t i) { #else for (i = 0; i < positives->rnum + negatives->rnum; i++) { #endif #ifdef USE_DISPATCH dispatch_semaphore_wait(sema, DISPATCH_TIME_FOREVER); #endif if (i % 37 == 0 || i == positives->rnum + negatives->rnum - 1) // don't flush too fast FLUSH(CCV_CLI_INFO, " - precompute %d features through %d%% (%d / %d) examples", feature_size, (int)(i + 1) * 100 / (positives->rnum + negatives->rnum), (int)i + 1, positives->rnum + negatives->rnum); #ifdef USE_DISPATCH dispatch_semaphore_signal(sema); int j; #endif ccv_dense_matrix_t* a = (ccv_dense_matrix_t*)ccv_array_get(i < positives->rnum ? positives : negatives, i < positives->rnum ? i : i - positives->rnum); a->data.u8 = (unsigned char*)(a + 1); // re-host the pointer to the right place ccv_dense_matrix_t* icf = 0; // we have 1px padding around the image ccv_icf(a, &icf, 0); ccv_dense_matrix_t* sat = 0; ccv_sat(icf, &sat, 0, CCV_PADDING_ZERO); ccv_matrix_free(icf); float* ptr = sat->data.f32; int ch = CCV_GET_CHANNEL(sat->type); for (j = 0; j < feature_size; j++) { ccv_icf_feature_t* feature = features + j; float c = _ccv_icf_run_feature(feature, ptr, sat->cols, ch, 1, 1); assert(isfinite(c)); featval[(size_t)j * (positives->rnum + negatives->rnum) + i] = c; } ccv_matrix_free(sat); #ifdef USE_DISPATCH }); dispatch_release(sema); #else } #endif PRINT(CCV_CLI_INFO, "\n"); uint8_t* computed = precomputed; float* pfeatval = featval; for (i = 0; i < feature_size; i++) { if (i % 37 == 0 || i == feature_size - 1) // don't flush too fast FLUSH(CCV_CLI_INFO, " - precompute %d examples through %d%% (%d / %d) features", positives->rnum + negatives->rnum, (i + 1) * 100 / feature_size, i + 1, feature_size); for (j = 0; j < positives->rnum + negatives->rnum; j++) sortkv[j].value = pfeatval[j], sortkv[j].index = j; _ccv_icf_precomputed_ordering(sortkv, positives->rnum + negatives->rnum, 0); // the first flag denotes if the subsequent one are equal to the previous one (if so, we have to skip both of them) for (j = 0; j < positives->rnum + negatives->rnum - 1; j++) _ccv_icf_1_uint1_1_uint23_to_3_uint8(sortkv[j].value == sortkv[j + 1].value, sortkv[j].index, computed + j * 3); j = positives->rnum + negatives->rnum - 1; _ccv_icf_1_uint1_1_uint23_to_3_uint8(0, sortkv[j].index, computed + j * 3); computed += step; pfeatval += positives->rnum + negatives->rnum; } ccfree(featval); ccfree(sortkv); PRINT(CCV_CLI_INFO, "\n - features are precomputed on examples and will occupy %uM memory\n", (uint32_t)((feature_size * step) / (1024 * 1024))); return precomputed; } typedef struct { uint32_t pass; double weigh[4]; int first_feature; uint8_t* lut; } ccv_icf_decision_tree_cache_t; static inline float _ccv_icf_compute_threshold_between(ccv_icf_feature_t* feature, uint8_t* computed, ccv_array_t* positives, ccv_array_t* negatives, int index0, int index1) { float c[2]; uint32_t b[2] = { _ccv_icf_3_uint8_to_1_uint23(computed + index0 * 3), _ccv_icf_3_uint8_to_1_uint23(computed + index1 * 3), }; ccv_dense_matrix_t* a = (ccv_dense_matrix_t*)ccv_array_get(b[0] < positives->rnum ? positives : negatives, b[0] < positives->rnum ? b[0] : b[0] - positives->rnum); a->data.u8 = (unsigned char*)(a + 1); // re-host the pointer to the right place c[0] = _ccv_icf_run_feature_on_example(feature, a); a = (ccv_dense_matrix_t*)ccv_array_get(b[1] < positives->rnum ? positives : negatives, b[1] < positives->rnum ? b[1] : b[1] - positives->rnum); a->data.u8 = (unsigned char*)(a + 1); // re-host the pointer to the right place c[1] = _ccv_icf_run_feature_on_example(feature, a); return (c[0] + c[1]) * 0.5; } static inline void _ccv_icf_example_correct(ccv_icf_example_state_t* example_state, uint8_t* computed, uint8_t* lut, int leaf, ccv_array_t* positives, ccv_array_t* negatives, int start, int end) { int i; for (i = start; i <= end; i++) { uint32_t index = _ccv_icf_3_uint8_to_1_uint23(computed + i * 3); if (!lut || lut[index] == leaf) example_state[index].correct = (index < positives->rnum); } } typedef struct { int error_index; double error_rate; double weigh[2]; int count[2]; } ccv_icf_first_feature_find_t; static ccv_icf_decision_tree_cache_t _ccv_icf_find_first_feature(ccv_icf_feature_t* features, int feature_size, ccv_array_t* positives, ccv_array_t* negatives, uint8_t* precomputed, ccv_icf_example_state_t* example_state, ccv_icf_feature_t* feature) { int i; assert(feature != 0); ccv_icf_decision_tree_cache_t intermediate_cache; double aweigh0 = 0, aweigh1 = 0; for (i = 0; i < positives->rnum; i++) aweigh1 += example_state[i].weight, example_state[i].correct = 0; // assuming positive examples we get wrong for (i = positives->rnum; i < positives->rnum + negatives->rnum; i++) aweigh0 += example_state[i].weight, example_state[i].correct = 1; // assuming negative examples we get right size_t step = (3 * (positives->rnum + negatives->rnum) + 3) & -4; ccv_icf_first_feature_find_t* feature_find = (ccv_icf_first_feature_find_t*)ccmalloc(sizeof(ccv_icf_first_feature_find_t) * feature_size); parallel_for(i, feature_size) { ccv_icf_first_feature_find_t min_find = { .error_rate = 1.0, .error_index = 0, .weigh = {0, 0}, .count = {0, 0}, }; double weigh[2] = {0, 0}; int count[2] = {0, 0}; int j; uint8_t* computed = precomputed + step * i; for (j = 0; j < positives->rnum + negatives->rnum; j++) { uint8_t skip; uint32_t index; _ccv_icf_3_uint8_to_1_uint1_1_uint23(computed + j * 3, &skip, &index); conditional_assert(j == positives->rnum + negatives->rnum - 1, !skip); assert(index >= 0 && index < positives->rnum + negatives->rnum); weigh[index < positives->rnum] += example_state[index].weight; assert(example_state[index].weight > 0); assert(weigh[0] <= aweigh0 + 1e-10 && weigh[1] <= aweigh1 + 1e-10); ++count[index < positives->rnum]; if (skip) // the current index is equal to the next one, we cannot differentiate, therefore, skip continue; double error_rate = ccv_min(weigh[0] + aweigh1 - weigh[1], weigh[1] + aweigh0 - weigh[0]); assert(error_rate > 0); if (error_rate < min_find.error_rate) { min_find.error_index = j; min_find.error_rate = error_rate; min_find.weigh[0] = weigh[0]; min_find.weigh[1] = weigh[1]; min_find.count[0] = count[0]; min_find.count[1] = count[1]; } } feature_find[i] = min_find; } parallel_endfor ccv_icf_first_feature_find_t best = { .error_rate = 1.0, .error_index = -1, .weigh = {0, 0}, .count = {0, 0}, }; int feature_index = 0; for (i = 0; i < feature_size; i++) if (feature_find[i].error_rate < best.error_rate) { best = feature_find[i]; feature_index = i; } ccfree(feature_find); *feature = features[feature_index]; uint8_t* computed = precomputed + step * feature_index; intermediate_cache.lut = (uint8_t*)ccmalloc(positives->rnum + negatives->rnum); assert(best.error_index < positives->rnum + negatives->rnum - 1 && best.error_index >= 0); if (best.weigh[0] + aweigh1 - best.weigh[1] < best.weigh[1] + aweigh0 - best.weigh[0]) { for (i = 0; i < positives->rnum + negatives->rnum; i++) intermediate_cache.lut[_ccv_icf_3_uint8_to_1_uint23(computed + i * 3)] = (i <= best.error_index); feature->beta = _ccv_icf_compute_threshold_between(feature, computed, positives, negatives, best.error_index, best.error_index + 1); // revert the sign of alpha, after threshold is computed for (i = 0; i < feature->count; i++) feature->alpha[i] = -feature->alpha[i]; intermediate_cache.weigh[0] = aweigh0 - best.weigh[0]; intermediate_cache.weigh[1] = aweigh1 - best.weigh[1]; intermediate_cache.weigh[2] = best.weigh[0]; intermediate_cache.weigh[3] = best.weigh[1]; intermediate_cache.pass = 3; if (best.count[0] == 0) intermediate_cache.pass &= 2; // only positive examples in the right, no need to build right leaf if (best.count[1] == positives->rnum) intermediate_cache.pass &= 1; // no positive examples in the left, no need to build left leaf if (!(intermediate_cache.pass & 1)) // mark positives in the right as correct, if we don't have right leaf _ccv_icf_example_correct(example_state, computed, 0, 0, positives, negatives, 0, best.error_index); } else { for (i = 0; i < positives->rnum + negatives->rnum; i++) intermediate_cache.lut[_ccv_icf_3_uint8_to_1_uint23(computed + i * 3)] = (i > best.error_index); feature->beta = -_ccv_icf_compute_threshold_between(feature, computed, positives, negatives, best.error_index, best.error_index + 1); intermediate_cache.weigh[0] = best.weigh[0]; intermediate_cache.weigh[1] = best.weigh[1]; intermediate_cache.weigh[2] = aweigh0 - best.weigh[0]; intermediate_cache.weigh[3] = aweigh1 - best.weigh[1]; intermediate_cache.pass = 3; if (best.count[0] == negatives->rnum) intermediate_cache.pass &= 2; // only positive examples in the right, no need to build right leaf if (best.count[1] == 0) intermediate_cache.pass &= 1; // no positive examples in the left, no need to build left leaf if (!(intermediate_cache.pass & 1)) // mark positives in the right as correct if we don't have right leaf _ccv_icf_example_correct(example_state, computed, 0, 0, positives, negatives, best.error_index + 1, positives->rnum + negatives->rnum - 1); } intermediate_cache.first_feature = feature_index; return intermediate_cache; } typedef struct { int error_index; double error_rate; double weigh[2]; } ccv_icf_second_feature_find_t; static double _ccv_icf_find_second_feature(ccv_icf_decision_tree_cache_t intermediate_cache, int leaf, ccv_icf_feature_t* features, int feature_size, ccv_array_t* positives, ccv_array_t* negatives, uint8_t* precomputed, ccv_icf_example_state_t* example_state, ccv_icf_feature_t* feature) { size_t step = (3 * (positives->rnum + negatives->rnum) + 3) & -4; uint8_t* lut = intermediate_cache.lut; double* aweigh = intermediate_cache.weigh + leaf * 2; ccv_icf_second_feature_find_t* feature_find = (ccv_icf_second_feature_find_t*)ccmalloc(sizeof(ccv_icf_second_feature_find_t) * feature_size); parallel_for(i, feature_size) { ccv_icf_second_feature_find_t min_find = { .error_rate = 1.0, .error_index = 0, .weigh = {0, 0}, }; double weigh[2] = {0, 0}; uint8_t* computed = precomputed + step * i; int j, k; for (j = 0; j < positives->rnum + negatives->rnum; j++) { uint8_t skip; uint32_t index; _ccv_icf_3_uint8_to_1_uint1_1_uint23(computed + j * 3, &skip, &index); conditional_assert(j == positives->rnum + negatives->rnum - 1, !skip); assert(index >= 0 && index < positives->rnum + negatives->rnum); // only care about part of the data if (lut[index] == leaf) { uint8_t leaf_skip = 0; for (k = j + 1; skip; k++) { uint32_t new_index; _ccv_icf_3_uint8_to_1_uint1_1_uint23(computed + j * 3, &skip, &new_index); // if the next equal one is the same leaf, we cannot distinguish them, skip if ((leaf_skip = (lut[new_index] == leaf))) break; conditional_assert(k == positives->rnum + negatives->rnum - 1, !skip); } weigh[index < positives->rnum] += example_state[index].weight; if (leaf_skip) continue; assert(example_state[index].weight > 0); assert(weigh[0] <= aweigh[0] + 1e-10 && weigh[1] <= aweigh[1] + 1e-10); double error_rate = ccv_min(weigh[0] + aweigh[1] - weigh[1], weigh[1] + aweigh[0] - weigh[0]); if (error_rate < min_find.error_rate) { min_find.error_index = j; min_find.error_rate = error_rate; min_find.weigh[0] = weigh[0]; min_find.weigh[1] = weigh[1]; } } } feature_find[i] = min_find; } parallel_endfor ccv_icf_second_feature_find_t best = { .error_rate = 1.0, .error_index = -1, .weigh = {0, 0}, }; int i; int feature_index = 0; for (i = 0; i < feature_size; i++) if (feature_find[i].error_rate < best.error_rate) { best = feature_find[i]; feature_index = i; } ccfree(feature_find); *feature = features[feature_index]; uint8_t* computed = precomputed + step * feature_index; assert(best.error_index < positives->rnum + negatives->rnum - 1 && best.error_index >= 0); if (best.weigh[0] + aweigh[1] - best.weigh[1] < best.weigh[1] + aweigh[0] - best.weigh[0]) { feature->beta = _ccv_icf_compute_threshold_between(feature, computed, positives, negatives, best.error_index, best.error_index + 1); // revert the sign of alpha, after threshold is computed for (i = 0; i < feature->count; i++) feature->alpha[i] = -feature->alpha[i]; // mark everything on the right properly _ccv_icf_example_correct(example_state, computed, lut, leaf, positives, negatives, 0, best.error_index); return best.weigh[1] + aweigh[0] - best.weigh[0]; } else { feature->beta = -_ccv_icf_compute_threshold_between(feature, computed, positives, negatives, best.error_index, best.error_index + 1); // mark everything on the right properly _ccv_icf_example_correct(example_state, computed, lut, leaf, positives, negatives, best.error_index + 1, positives->rnum + negatives->rnum - 1); return best.weigh[0] + aweigh[1] - best.weigh[1]; } } static double _ccv_icf_find_best_weak_classifier(ccv_icf_feature_t* features, int feature_size, ccv_array_t* positives, ccv_array_t* negatives, uint8_t* precomputed, ccv_icf_example_state_t* example_state, ccv_icf_decision_tree_t* weak_classifier) { // we are building the specific depth-2 decision tree ccv_icf_decision_tree_cache_t intermediate_cache = _ccv_icf_find_first_feature(features, feature_size, positives, negatives, precomputed, example_state, weak_classifier->features); // find the left feature // for the pass, 10 is the left branch, 01 is the right branch weak_classifier->pass = intermediate_cache.pass; double rate = 0; if (weak_classifier->pass & 0x2) rate += _ccv_icf_find_second_feature(intermediate_cache, 0, features, feature_size, positives, negatives, precomputed, example_state, weak_classifier->features + 1); else rate += intermediate_cache.weigh[0]; // the negative weights covered by first feature // find the right feature if (weak_classifier->pass & 0x1) rate += _ccv_icf_find_second_feature(intermediate_cache, 1, features, feature_size, positives, negatives, precomputed, example_state, weak_classifier->features + 2); else rate += intermediate_cache.weigh[3]; // the positive weights covered by first feature ccfree(intermediate_cache.lut); return rate; } static ccv_array_t* _ccv_icf_collect_validates(gsl_rng* rng, ccv_size_t size, ccv_margin_t margin, ccv_array_t* validatefiles, int grayscale) { ccv_array_t* validates = ccv_array_new(ccv_compute_dense_matrix_size(size.height + margin.top + margin.bottom + 2, size.width + margin.left + margin.right + 2, CCV_8U | (grayscale ? CCV_C1 : CCV_C3)), validatefiles->rnum, 0); int i; // collect tests for (i = 0; i < validatefiles->rnum; i++) { ccv_file_info_t* file_info = (ccv_file_info_t*)ccv_array_get(validatefiles, i); ccv_dense_matrix_t* image = 0; ccv_read(file_info->filename, &image, CCV_IO_ANY_FILE | (grayscale ? CCV_IO_GRAY : CCV_IO_RGB_COLOR)); if (image == 0) { PRINT(CCV_CLI_ERROR, "\n - %s: cannot be open, possibly corrupted\n", file_info->filename); continue; } ccv_dense_matrix_t* feature = _ccv_icf_capture_feature(rng, image, file_info->pose, size, margin, 0, 0, 0); feature->sig = 0; ccv_array_push(validates, feature); ccv_matrix_free(feature); ccv_matrix_free(image); } return validates; } static ccv_array_t* _ccv_icf_collect_positives(gsl_rng* rng, ccv_size_t size, ccv_margin_t margin, ccv_array_t* posfiles, int posnum, float deform_angle, float deform_scale, float deform_shift, int grayscale) { ccv_array_t* positives = ccv_array_new(ccv_compute_dense_matrix_size(size.height + margin.top + margin.bottom + 2, size.width + margin.left + margin.right + 2, CCV_8U | (grayscale ? CCV_C1 : CCV_C3)), posnum, 0); int i, j, q; // collect positives (with random deformation) for (i = 0; i < posnum;) { FLUSH(CCV_CLI_INFO, " - collect positives %d%% (%d / %d)", (i + 1) * 100 / posnum, i + 1, posnum); double ratio = (double)(posnum - i) / posfiles->rnum; for (j = 0; j < posfiles->rnum && i < posnum; j++) { ccv_file_info_t* file_info = (ccv_file_info_t*)ccv_array_get(posfiles, j); ccv_dense_matrix_t* image = 0; ccv_read(file_info->filename, &image, CCV_IO_ANY_FILE | (grayscale ? CCV_IO_GRAY : CCV_IO_RGB_COLOR)); if (image == 0) { PRINT(CCV_CLI_ERROR, "\n - %s: cannot be open, possibly corrupted\n", file_info->filename); continue; } for (q = 0; q < ratio; q++) if (q < (int)ratio || gsl_rng_uniform(rng) <= ratio - (int)ratio) { FLUSH(CCV_CLI_INFO, " - collect positives %d%% (%d / %d)", (i + 1) * 100 / posnum, i + 1, posnum); ccv_dense_matrix_t* feature = _ccv_icf_capture_feature(rng, image, file_info->pose, size, margin, deform_angle, deform_scale, deform_shift); feature->sig = 0; ccv_array_push(positives, feature); ccv_matrix_free(feature); ++i; if (i >= posnum) break; } ccv_matrix_free(image); } } PRINT(CCV_CLI_INFO, "\n"); return positives; } static uint64_t* _ccv_icf_precompute_classifier_cascade(ccv_icf_classifier_cascade_t* cascade, ccv_array_t* positives) { int step = ((cascade->count - 1) >> 6) + 1; uint64_t* precomputed = (uint64_t*)ccmalloc(sizeof(uint64_t) * positives->rnum * step); uint64_t* result = precomputed; int i, j; for (i = 0; i < positives->rnum; i++) { ccv_dense_matrix_t* a = (ccv_dense_matrix_t*)(ccv_array_get(positives, i)); a->data.u8 = (uint8_t*)(a + 1); ccv_dense_matrix_t* icf = 0; ccv_icf(a, &icf, 0); ccv_dense_matrix_t* sat = 0; ccv_sat(icf, &sat, 0, CCV_PADDING_ZERO); ccv_matrix_free(icf); float* ptr = sat->data.f32; int ch = CCV_GET_CHANNEL(sat->type); for (j = 0; j < cascade->count; j++) if (_ccv_icf_run_weak_classifier(cascade->weak_classifiers + j, ptr, sat->cols, ch, 1, 1)) precomputed[j >> 6] |= (1UL << (j & 63)); else precomputed[j >> 6] &= ~(1UL << (j & 63)); ccv_matrix_free(sat); precomputed += step; } return result; } #define less_than(s1, s2, aux) ((s1) > (s2)) static CCV_IMPLEMENT_QSORT(_ccv_icf_threshold_rating, float, less_than) #undef less_than static void _ccv_icf_classifier_cascade_soft_with_validates(ccv_array_t* validates, ccv_icf_classifier_cascade_t* cascade, double min_accept) { int i, j; int step = ((cascade->count - 1) >> 6) + 1; uint64_t* precomputed = _ccv_icf_precompute_classifier_cascade(cascade, validates); float* positive_rate = (float*)ccmalloc(sizeof(float) * validates->rnum); uint64_t* computed = precomputed; for (i = 0; i < validates->rnum; i++) { positive_rate[i] = 0; for (j = 0; j < cascade->count; j++) { uint64_t accept = computed[j >> 6] & (1UL << (j & 63)); positive_rate[i] += cascade->weak_classifiers[j].weigh[!!accept]; } computed += step; } _ccv_icf_threshold_rating(positive_rate, validates->rnum, 0); float threshold = positive_rate[ccv_min((int)(min_accept * (validates->rnum + 0.5) - 0.5), validates->rnum - 1)]; ccfree(positive_rate); computed = precomputed; // compute the final acceptance per validates / negatives with final threshold uint64_t* acceptance = (uint64_t*)cccalloc(((validates->rnum - 1) >> 6) + 1, sizeof(uint64_t)); int true_positives = 0; for (i = 0; i < validates->rnum; i++) { float rate = 0; for (j = 0; j < cascade->count; j++) { uint64_t accept = computed[j >> 6] & (1UL << (j & 63)); rate += cascade->weak_classifiers[j].weigh[!!accept]; } if (rate >= threshold) { acceptance[i >> 6] |= (1UL << (i & 63)); ++true_positives; } else acceptance[i >> 6] &= ~(1UL << (i & 63)); computed += step; } PRINT(CCV_CLI_INFO, " - at threshold %f, true positive rate: %f%%\n", threshold, (float)true_positives * 100 / validates->rnum); float* rate = (float*)cccalloc(validates->rnum, sizeof(float)); for (j = 0; j < cascade->count; j++) { computed = precomputed; for (i = 0; i < validates->rnum; i++) { uint64_t correct = computed[j >> 6] & (1UL << (j & 63)); rate[i] += cascade->weak_classifiers[j].weigh[!!correct]; computed += step; } float threshold = FLT_MAX; // find a threshold that keeps all accepted validates still acceptable for (i = 0; i < validates->rnum; i++) { uint64_t correct = acceptance[i >> 6] & (1UL << (i & 63)); if (correct && rate[i] < threshold) threshold = rate[i]; } cascade->weak_classifiers[j].threshold = threshold - 1e-10; } ccfree(rate); ccfree(acceptance); ccfree(precomputed); } typedef struct { ccv_point_t point; float sum; } ccv_point_with_sum_t; static void _ccv_icf_bootstrap_negatives(ccv_icf_classifier_cascade_t* cascade, ccv_array_t* negatives, gsl_rng* rng, ccv_array_t* bgfiles, int negnum, int grayscale, int spread, ccv_icf_param_t params) { #ifdef USE_DISPATCH __block int i; #else int i; #endif #ifdef USE_DISPATCH __block int fppi = 0, is = 0; #else int fppi = 0, is = 0; #endif int t = 0; for (i = 0; i < negnum;) { double ratio = (double)(negnum - i) / bgfiles->rnum; #ifdef USE_DISPATCH dispatch_semaphore_t sem = dispatch_semaphore_create(1); dispatch_apply(bgfiles->rnum, dispatch_get_global_queue(DISPATCH_QUEUE_PRIORITY_DEFAULT, 0), ^(size_t j) { #else size_t j; for (j = 0; j < bgfiles->rnum; j++) { #endif int k, x, y, q, p; ccv_dense_matrix_t* a = (ccv_dense_matrix_t*)ccmalloc(ccv_compute_dense_matrix_size(cascade->size.height + 2, cascade->size.width + 2, (grayscale ? CCV_C1 : CCV_C3) | CCV_8U)); #ifdef USE_DISPATCH dispatch_semaphore_wait(sem, DISPATCH_TIME_FOREVER); #endif if (i >= negnum || (spread && ratio < 1 && gsl_rng_uniform(rng) > ratio)) { ccfree(a); #ifdef USE_DISPATCH dispatch_semaphore_signal(sem); return; #else continue; #endif } FLUSH(CCV_CLI_INFO, " - bootstrap negatives %d%% (%d / %d) [%u / %d] %s", (i + 1) * 100 / negnum, i + 1, negnum, (uint32_t)(j + 1), bgfiles->rnum, spread ? "" : "without statistic balancing"); #ifdef USE_DISPATCH gsl_rng* crng = gsl_rng_alloc(gsl_rng_default); gsl_rng_set(crng, gsl_rng_get(rng)); dispatch_semaphore_signal(sem); #else gsl_rng* crng = rng; #endif ccv_file_info_t* file_info = (ccv_file_info_t*)ccv_array_get(bgfiles, j); ccv_dense_matrix_t* image = 0; ccv_read(file_info->filename, &image, CCV_IO_ANY_FILE | (grayscale ? CCV_IO_GRAY : CCV_IO_RGB_COLOR)); if (image == 0) { PRINT(CCV_CLI_ERROR, "\n - %s: cannot be open, possibly corrupted\n", file_info->filename); ccfree(a); #ifdef USE_DISPATCH gsl_rng_free(crng); return; #else continue; #endif } if (ccv_max(image->rows, image->cols) < 800 || image->rows <= (cascade->size.height - cascade->margin.top - cascade->margin.bottom) || image->cols <= (cascade->size.width - cascade->margin.left - cascade->margin.right)) // background is too small, blow it up to next scale { ccv_dense_matrix_t* blowup = 0; ccv_sample_up(image, &blowup, 0, 0, 0); ccv_matrix_free(image); image = blowup; } if (image->rows <= (cascade->size.height - cascade->margin.top - cascade->margin.bottom) || image->cols <= (cascade->size.width - cascade->margin.left - cascade->margin.right)) // background is still too small, abort { ccv_matrix_free(image); ccfree(a); #ifdef USE_DISPATCH gsl_rng_free(crng); return; #else continue; #endif } double scale = pow(2., 1. / (params.interval + 1.)); int next = params.interval + 1; int scale_upto = (int)(log(ccv_min((double)image->rows / (cascade->size.height - cascade->margin.top - cascade->margin.bottom), (double)image->cols / (cascade->size.width - cascade->margin.left - cascade->margin.right))) / log(scale) - DBL_MIN) + 1; ccv_dense_matrix_t** pyr = (ccv_dense_matrix_t**)ccmalloc(scale_upto * sizeof(ccv_dense_matrix_t*)); memset(pyr, 0, scale_upto * sizeof(ccv_dense_matrix_t*)); #ifdef USE_DISPATCH dispatch_semaphore_wait(sem, DISPATCH_TIME_FOREVER); #endif ++is; // how many images are scanned #ifdef USE_DISPATCH dispatch_semaphore_signal(sem); #endif if (t % 2 != 0) ccv_flip(image, 0, 0, CCV_FLIP_X); if (t % 4 >= 2) ccv_flip(image, 0, 0, CCV_FLIP_Y); pyr[0] = image; for (q = 1; q < ccv_min(params.interval + 1, scale_upto); q++) ccv_resample(pyr[0], &pyr[q], 0, (int)(pyr[0]->rows / pow(scale, q)), (int)(pyr[0]->cols / pow(scale, q)), CCV_INTER_AREA); for (q = next; q < scale_upto; q++) ccv_sample_down(pyr[q - next], &pyr[q], 0, 0, 0); for (q = 0; q < scale_upto; q++) { #ifdef USE_DISPATCH dispatch_semaphore_wait(sem, DISPATCH_TIME_FOREVER); #endif if (i >= negnum) { #ifdef USE_DISPATCH dispatch_semaphore_signal(sem); #endif ccv_matrix_free(pyr[q]); continue; } #ifdef USE_DISPATCH dispatch_semaphore_signal(sem); #endif ccv_dense_matrix_t* bordered = 0; ccv_border(pyr[q], (ccv_matrix_t**)&bordered, 0, cascade->margin); ccv_matrix_free(pyr[q]); ccv_dense_matrix_t* icf = 0; ccv_icf(bordered, &icf, 0); ccv_dense_matrix_t* sat = 0; ccv_sat(icf, &sat, 0, CCV_PADDING_ZERO); ccv_matrix_free(icf); assert(sat->rows == bordered->rows + 1 && sat->cols == bordered->cols + 1); int ch = CCV_GET_CHANNEL(sat->type); float* ptr = sat->data.f32 + sat->cols * ch; ccv_array_t* seq = ccv_array_new(sizeof(ccv_point_with_sum_t), 64, 0); for (y = 1; y < sat->rows - cascade->size.height - 2; y += params.step_through) { for (x = 1; x < sat->cols - cascade->size.width - 2; x += params.step_through) { int pass = 1; float sum = 0; for (p = 0; p < cascade->count; p++) { ccv_icf_decision_tree_t* weak_classifier = cascade->weak_classifiers + p; int c = _ccv_icf_run_weak_classifier(weak_classifier, ptr, sat->cols, ch, x, 0); sum += weak_classifier->weigh[c]; if (sum < weak_classifier->threshold) { pass = 0; break; } } if (pass) { ccv_point_with_sum_t point; point.point = ccv_point(x - 1, y - 1); point.sum = sum; ccv_array_push(seq, &point); } } ptr += sat->cols * ch * params.step_through; } ccv_matrix_free(sat); // shuffle negatives so that we don't have too biased negatives #ifdef USE_DISPATCH dispatch_semaphore_wait(sem, DISPATCH_TIME_FOREVER); #endif fppi += seq->rnum; // how many detections we have in total #ifdef USE_DISPATCH dispatch_semaphore_signal(sem); #endif if (seq->rnum > 0) { gsl_ran_shuffle(crng, ccv_array_get(seq, 0), seq->rnum, seq->rsize); /* so that we at least collect 10 from each scale */ for (p = 0; p < (spread ? ccv_min(10, seq->rnum) : seq->rnum); p++) // collect enough negatives from this scale { a = ccv_dense_matrix_new(cascade->size.height + 2, cascade->size.width + 2, (grayscale ? CCV_C1 : CCV_C3) | CCV_8U, a, 0); ccv_point_with_sum_t* point = (ccv_point_with_sum_t*)ccv_array_get(seq, p); ccv_slice(bordered, (ccv_matrix_t**)&a, 0, point->point.y, point->point.x, a->rows, a->cols); assert(bordered->rows >= point->point.y + a->rows && bordered->cols >= point->point.x + a->cols); a->sig = 0; // verify the data we sliced is worthy negative ccv_dense_matrix_t* icf = 0; ccv_icf(a, &icf, 0); ccv_dense_matrix_t* sat = 0; ccv_sat(icf, &sat, 0, CCV_PADDING_ZERO); ccv_matrix_free(icf); float* ptr = sat->data.f32; int ch = CCV_GET_CHANNEL(sat->type); int pass = 1; float sum = 0; for (k = 0; k < cascade->count; k++) { ccv_icf_decision_tree_t* weak_classifier = cascade->weak_classifiers + k; int c = _ccv_icf_run_weak_classifier(weak_classifier, ptr, sat->cols, ch, 1, 1); sum += weak_classifier->weigh[c]; if (sum < weak_classifier->threshold) { pass = 0; break; } } ccv_matrix_free(sat); if (pass) { #ifdef USE_DISPATCH dispatch_semaphore_wait(sem, DISPATCH_TIME_FOREVER); #endif if (i < negnum) ccv_array_push(negatives, a); ++i; if (i >= negnum) { #ifdef USE_DISPATCH dispatch_semaphore_signal(sem); #endif break; } #ifdef USE_DISPATCH dispatch_semaphore_signal(sem); #endif } } } ccv_array_free(seq); ccv_matrix_free(bordered); } ccfree(pyr); ccfree(a); #ifdef USE_DISPATCH gsl_rng_free(crng); }); dispatch_release(sem); #else } #endif if ((double)fppi / is <= (double)negnum / bgfiles->rnum) // if the targeted negative per image is bigger than our fppi, we don't prob anymore spread = 0; ++t; if (t > (spread ? 4 : 3) && !spread) // we've go over 4 or 3 transformations (original, flip x, flip y, flip x & y, [and original again]), and nothing we can do now break; } PRINT(CCV_CLI_INFO, "\n"); } static ccv_array_t* _ccv_icf_collect_negatives(gsl_rng* rng, ccv_size_t size, ccv_margin_t margin, ccv_array_t* bgfiles, int negnum, float deform_angle, float deform_scale, float deform_shift, int grayscale) { ccv_array_t* negatives = ccv_array_new(ccv_compute_dense_matrix_size(size.height + margin.top + margin.bottom + 2, size.width + margin.left + margin.right + 2, CCV_8U | (grayscale ? CCV_C1 : CCV_C3)), negnum, 0); int i, j, q; // randomly collect negatives (with random deformation) for (i = 0; i < negnum;) { FLUSH(CCV_CLI_INFO, " - collect negatives %d%% (%d / %d)", (i + 1) * 100 / negnum, i + 1, negnum); double ratio = (double)(negnum - i) / bgfiles->rnum; for (j = 0; j < bgfiles->rnum && i < negnum; j++) { ccv_file_info_t* file_info = (ccv_file_info_t*)ccv_array_get(bgfiles, j); ccv_dense_matrix_t* image = 0; ccv_read(file_info->filename, &image, CCV_IO_ANY_FILE | (grayscale ? CCV_IO_GRAY : CCV_IO_RGB_COLOR)); if (image == 0) { PRINT(CCV_CLI_ERROR, "\n - %s: cannot be open, possibly corrupted\n", file_info->filename); continue; } double max_scale_ratio = ccv_min((double)image->rows / size.height, (double)image->cols / size.width); if (max_scale_ratio <= 0.5) // too small to be interesting continue; for (q = 0; q < ratio; q++) if (q < (int)ratio || gsl_rng_uniform(rng) <= ratio - (int)ratio) { FLUSH(CCV_CLI_INFO, " - collect negatives %d%% (%d / %d)", (i + 1) * 100 / negnum, i + 1, negnum); ccv_decimal_pose_t pose; double scale_ratio = gsl_rng_uniform(rng) * (max_scale_ratio - 0.5) + 0.5; pose.a = size.width * 0.5 * scale_ratio; pose.b = size.height * 0.5 * scale_ratio; pose.x = gsl_rng_uniform_int(rng, ccv_max((int)(image->cols - pose.a * 2 + 1.5), 1)) + pose.a; pose.y = gsl_rng_uniform_int(rng, ccv_max((int)(image->rows - pose.b * 2 + 1.5), 1)) + pose.b; pose.roll = pose.pitch = pose.yaw = 0; ccv_dense_matrix_t* feature = _ccv_icf_capture_feature(rng, image, pose, size, margin, deform_angle, deform_scale, deform_shift); feature->sig = 0; ccv_array_push(negatives, feature); ccv_matrix_free(feature); ++i; if (i >= negnum) break; } ccv_matrix_free(image); } } PRINT(CCV_CLI_INFO, "\n"); return negatives; } #ifdef USE_SANITY_ASSERTION static double _ccv_icf_rate_weak_classifier(ccv_icf_decision_tree_t* weak_classifier, ccv_array_t* positives, ccv_array_t* negatives, ccv_icf_example_state_t* example_state) { int i; double rate = 0; for (i = 0; i < positives->rnum + negatives->rnum; i++) { ccv_dense_matrix_t* a = (ccv_dense_matrix_t*)ccv_array_get(i < positives->rnum ? positives : negatives, i < positives->rnum ? i : i - positives->rnum); a->data.u8 = (uint8_t*)(a + 1); // re-host the pointer to the right place ccv_dense_matrix_t* icf = 0; // we have 1px padding around the image ccv_icf(a, &icf, 0); ccv_dense_matrix_t* sat = 0; ccv_sat(icf, &sat, 0, CCV_PADDING_ZERO); ccv_matrix_free(icf); float* ptr = sat->data.f32; int ch = CCV_GET_CHANNEL(sat->type); if (i < positives->rnum) { if (_ccv_icf_run_weak_classifier(weak_classifier, ptr, sat->cols, ch, 1, 1)) { assert(example_state[i].correct); rate += example_state[i].weight; } else { assert(!example_state[i].correct); } } else { if (!_ccv_icf_run_weak_classifier(weak_classifier, ptr, sat->cols, ch, 1, 1)) { assert(example_state[i].correct); rate += example_state[i].weight; } else { assert(!example_state[i].correct); } } ccv_matrix_free(sat); } return rate; } #endif #endif ccv_icf_classifier_cascade_t* ccv_icf_classifier_cascade_new(ccv_array_t* posfiles, int posnum, ccv_array_t* bgfiles, int negnum, ccv_array_t* validatefiles, const char* dir, ccv_icf_new_param_t params) { #ifdef HAVE_GSL _ccv_icf_check_params(params); assert(posfiles->rnum > 0); assert(bgfiles->rnum > 0); assert(posnum > 0 && negnum > 0); PRINT(CCV_CLI_INFO, "with %d positive examples and %d negative examples\n" "positive examples are going to be collected from %d positive images\n" "negative examples are are going to be collected from %d background images\n", posnum, negnum, posfiles->rnum, bgfiles->rnum); PRINT(CCV_CLI_INFO, "use color? %s\n", params.grayscale ? "no" : "yes"); PRINT(CCV_CLI_INFO, "feature pool size : %d\n" "weak classifier count : %d\n" "soft cascade acceptance : %lf\n" "minimum dimension of ICF feature : %d\n" "number of bootstrap : %d\n" "distortion on translation : %f\n" "distortion on rotation : %f\n" "distortion on scale : %f\n" "learn ICF classifier cascade at size %dx%d with margin (%d,%d,%d,%d)\n" "------------------------\n", params.feature_size, params.weak_classifier, params.acceptance, params.min_dimension, params.bootstrap, params.deform_shift, params.deform_angle, params.deform_scale, params.size.width, params.size.height, params.margin.left, params.margin.top, params.margin.right, params.margin.bottom); gsl_rng_env_setup(); gsl_rng* rng = gsl_rng_alloc(gsl_rng_default); // we will keep all states inside this structure for easier save / resume across process // this should work better than ad-hoc one we used in DPM / BBF implementation ccv_icf_classifier_cascade_state_t z; z.params = params; ccv_function_state_begin(_ccv_icf_read_classifier_cascade_state, z, dir); z.classifier->grayscale = params.grayscale; z.size = params.size; z.margin = params.margin; z.classifier->size = ccv_size(z.size.width + z.margin.left + z.margin.right, z.size.height + z.margin.top + z.margin.bottom); z.features = (ccv_icf_feature_t*)ccmalloc(sizeof(ccv_icf_feature_t) * params.feature_size); // generate random features for (z.i = 0; z.i < params.feature_size; z.i++) _ccv_icf_randomize_feature(rng, z.classifier->size, params.min_dimension, z.features + z.i, params.grayscale); z.x.features = 0; ccv_function_state_resume(_ccv_icf_write_classifier_cascade_state, z, dir); z.positives = _ccv_icf_collect_positives(rng, z.size, z.margin, posfiles, posnum, params.deform_angle, params.deform_scale, params.deform_shift, params.grayscale); z.x.positives = 0; ccv_function_state_resume(_ccv_icf_write_classifier_cascade_state, z, dir); z.negatives = _ccv_icf_collect_negatives(rng, z.size, z.margin, bgfiles, negnum, params.deform_angle, params.deform_scale, params.deform_shift, params.grayscale); z.x.negatives = 0; ccv_function_state_resume(_ccv_icf_write_classifier_cascade_state, z, dir); for (z.bootstrap = 0; z.bootstrap <= params.bootstrap; z.bootstrap++) { z.example_state = (ccv_icf_example_state_t*)ccmalloc(sizeof(ccv_icf_example_state_t) * (z.negatives->rnum + z.positives->rnum)); memset(z.example_state, 0, sizeof(ccv_icf_example_state_t) * (z.negatives->rnum + z.positives->rnum)); for (z.i = 0; z.i < z.positives->rnum + z.negatives->rnum; z.i++) z.example_state[z.i].weight = (z.i < z.positives->rnum) ? 0.5 / z.positives->rnum : 0.5 / z.negatives->rnum; z.x.example_state = 0; ccv_function_state_resume(_ccv_icf_write_classifier_cascade_state, z, dir); z.precomputed = _ccv_icf_precompute_features(z.features, params.feature_size, z.positives, z.negatives); z.x.precomputed = 0; ccv_function_state_resume(_ccv_icf_write_classifier_cascade_state, z, dir); for (z.i = 0; z.i < params.weak_classifier; z.i++) { z.classifier->count = z.i + 1; PRINT(CCV_CLI_INFO, " - boost weak classifier %d of %d\n", z.i + 1, params.weak_classifier); int j; ccv_icf_decision_tree_t weak_classifier; double rate = _ccv_icf_find_best_weak_classifier(z.features, params.feature_size, z.positives, z.negatives, z.precomputed, z.example_state, &weak_classifier); assert(rate > 0.5); // it has to be better than random chance #ifdef USE_SANITY_ASSERTION double confirm_rate = _ccv_icf_rate_weak_classifier(&weak_classifier, z.positives, z.negatives, z.example_state); #endif double alpha = sqrt((1 - rate) / rate); double beta = 1.0 / alpha; double c = log(rate / (1 - rate)); weak_classifier.weigh[0] = -c; weak_classifier.weigh[1] = c; weak_classifier.threshold = 0; double reweigh = 0; for (j = 0; j < z.positives->rnum + z.negatives->rnum; j++) { z.example_state[j].weight *= (z.example_state[j].correct) ? alpha : beta; z.example_state[j].rate += weak_classifier.weigh[!((j < z.positives->rnum) ^ z.example_state[j].correct)]; reweigh += z.example_state[j].weight; } reweigh = 1.0 / reweigh; #ifdef USE_SANITY_ASSERTION PRINT(CCV_CLI_INFO, " - on all examples, best feature at rate %lf, confirm rate %lf\n", rate, confirm_rate); #else PRINT(CCV_CLI_INFO, " - on all examples, best feature at rate %lf\n", rate); #endif // balancing the weight to sum 1.0 for (j = 0; j < z.positives->rnum + z.negatives->rnum; j++) z.example_state[j].weight *= reweigh; z.classifier->weak_classifiers[z.i] = weak_classifier; // compute the threshold at given acceptance float threshold = z.example_state[0].rate; for (j = 1; j < z.positives->rnum; j++) if (z.example_state[j].rate < threshold) threshold = z.example_state[j].rate; int true_positives = 0, false_positives = 0; for (j = 0; j < z.positives->rnum; j++) if (z.example_state[j].rate >= threshold) ++true_positives; for (j = z.positives->rnum; j < z.positives->rnum + z.negatives->rnum; j++) if (z.example_state[j].rate >= threshold) ++false_positives; PRINT(CCV_CLI_INFO, " - at threshold %f, true positive rate: %f%%, false positive rate: %f%% (%d)\n", threshold, (float)true_positives * 100 / z.positives->rnum, (float)false_positives * 100 / z.negatives->rnum, false_positives); PRINT(CCV_CLI_INFO, " - first feature :\n"); for (j = 0; j < weak_classifier.features[0].count; j++) PRINT(CCV_CLI_INFO, " - %d - (%d, %d) - (%d, %d)\n", weak_classifier.features[0].channel[j], weak_classifier.features[0].sat[j * 2].x, weak_classifier.features[0].sat[j * 2].y, weak_classifier.features[0].sat[j * 2 + 1].x, weak_classifier.features[0].sat[j * 2 + 1].y); if (weak_classifier.pass & 0x2) { PRINT(CCV_CLI_INFO, " - second feature, on left :\n"); for (j = 0; j < weak_classifier.features[1].count; j++) PRINT(CCV_CLI_INFO, " - | - %d - (%d, %d) - (%d, %d)\n", weak_classifier.features[1].channel[j], weak_classifier.features[1].sat[j * 2].x, weak_classifier.features[1].sat[j * 2].y, weak_classifier.features[1].sat[j * 2 + 1].x, weak_classifier.features[1].sat[j * 2 + 1].y); } if (weak_classifier.pass & 0x1) { PRINT(CCV_CLI_INFO, " - second feature, on right :\n"); for (j = 0; j < weak_classifier.features[2].count; j++) PRINT(CCV_CLI_INFO, " - | - %d - (%d, %d) - (%d, %d)\n", weak_classifier.features[2].channel[j], weak_classifier.features[2].sat[j * 2].x, weak_classifier.features[2].sat[j * 2].y, weak_classifier.features[2].sat[j * 2 + 1].x, weak_classifier.features[2].sat[j * 2 + 1].y); } z.classifier->count = z.i + 1; // update count z.classifier->size = ccv_size(z.size.width + z.margin.left + z.margin.right, z.size.height + z.margin.top + z.margin.bottom); z.classifier->margin = z.margin; if (z.i + 1 < params.weak_classifier) { z.x.example_state = 0; z.x.classifier = 0; ccv_function_state_resume(_ccv_icf_write_classifier_cascade_state, z, dir); } } if (z.bootstrap < params.bootstrap) // collecting negatives, again { // free expensive memory ccfree(z.example_state); z.example_state = 0; ccfree(z.precomputed); z.precomputed = 0; _ccv_icf_classifier_cascade_soft_with_validates(z.positives, z.classifier, 1); // assuming perfect score, what's the soft cascading will be int exists = z.negatives->rnum; int spread_policy = z.bootstrap < 2; // we don't spread bootstrapping anymore after the first two bootstrappings // try to boostrap half negatives from perfect scoring _ccv_icf_bootstrap_negatives(z.classifier, z.negatives, rng, bgfiles, (negnum + 1) / 2, params.grayscale, spread_policy, params.detector); int leftover = negnum - (z.negatives->rnum - exists); if (leftover > 0) { // if we cannot get enough negative examples, now will use the validates data set to extract more ccv_array_t* validates = _ccv_icf_collect_validates(rng, z.size, z.margin, validatefiles, params.grayscale); _ccv_icf_classifier_cascade_soft_with_validates(validates, z.classifier, params.acceptance); ccv_array_free(validates); _ccv_icf_bootstrap_negatives(z.classifier, z.negatives, rng, bgfiles, leftover, params.grayscale, spread_policy, params.detector); } PRINT(CCV_CLI_INFO, " - after %d bootstrapping, learn with %d positives and %d negatives\n", z.bootstrap + 1, z.positives->rnum, z.negatives->rnum); z.classifier->count = 0; // reset everything z.x.negatives = 0; } else { z.x.example_state = 0; z.x.classifier = 0; ccv_function_state_resume(_ccv_icf_write_classifier_cascade_state, z, dir); } } if (z.precomputed) ccfree(z.precomputed); if (z.example_state) ccfree(z.example_state); ccfree(z.features); ccv_array_free(z.positives); ccv_array_free(z.negatives); gsl_rng_free(rng); ccv_function_state_finish(); return z.classifier; #else assert(0 && "ccv_icf_classifier_cascade_new requires GSL library support"); return 0; #endif } void ccv_icf_classifier_cascade_soft(ccv_icf_classifier_cascade_t* cascade, ccv_array_t* posfiles, double acceptance) { #ifdef HAVE_GSL PRINT(CCV_CLI_INFO, "with %d positive examples\n" "going to accept %.2lf%% positive examples\n", posfiles->rnum, acceptance * 100); ccv_size_t size = ccv_size(cascade->size.width - cascade->margin.left - cascade->margin.right, cascade->size.height - cascade->margin.top - cascade->margin.bottom); PRINT(CCV_CLI_INFO, "use color? %s\n", cascade->grayscale ? "no" : "yes"); PRINT(CCV_CLI_INFO, "compute soft cascading thresholds for ICF classifier cascade at size %dx%d with margin (%d,%d,%d,%d)\n" "------------------------\n", size.width, size.height, cascade->margin.left, cascade->margin.top, cascade->margin.right, cascade->margin.bottom); gsl_rng_env_setup(); gsl_rng* rng = gsl_rng_alloc(gsl_rng_default); /* collect positives */ double weigh[2] = { 0, 0 }; int i; for (i = 0; i < cascade->count; i++) { ccv_icf_decision_tree_t* weak_classifier = cascade->weak_classifiers + i; weigh[0] += weak_classifier->weigh[0]; weigh[1] += weak_classifier->weigh[1]; } weigh[0] = 1 / fabs(weigh[0]), weigh[1] = 1 / fabs(weigh[1]); for (i = 0; i < cascade->count; i++) { ccv_icf_decision_tree_t* weak_classifier = cascade->weak_classifiers + i; weak_classifier->weigh[0] = weak_classifier->weigh[0] * weigh[0]; weak_classifier->weigh[1] = weak_classifier->weigh[1] * weigh[1]; } ccv_array_t* validates = _ccv_icf_collect_validates(rng, size, cascade->margin, posfiles, cascade->grayscale); /* compute soft cascading thresholds */ _ccv_icf_classifier_cascade_soft_with_validates(validates, cascade, acceptance); ccv_array_free(validates); gsl_rng_free(rng); #else assert(0 && "ccv_icf_classifier_cascade_soft requires GSL library support"); #endif } static void _ccv_icf_read_classifier_cascade_with_fd(FILE* r, ccv_icf_classifier_cascade_t* cascade) { cascade->type = CCV_ICF_CLASSIFIER_TYPE_A; fscanf(r, "%d %d %d %d", &cascade->count, &cascade->size.width, &cascade->size.height, &cascade->grayscale); fscanf(r, "%d %d %d %d", &cascade->margin.left, &cascade->margin.top, &cascade->margin.right, &cascade->margin.bottom); cascade->weak_classifiers = (ccv_icf_decision_tree_t*)ccmalloc(sizeof(ccv_icf_decision_tree_t) * cascade->count); int i, q; for (i = 0; i < cascade->count; i++) { ccv_icf_decision_tree_t* weak_classifier = cascade->weak_classifiers + i; fscanf(r, "%u %a %a %a", &weak_classifier->pass, &weak_classifier->weigh[0], &weak_classifier->weigh[1], &weak_classifier->threshold); fscanf(r, "%d %a", &weak_classifier->features[0].count, &weak_classifier->features[0].beta); for (q = 0; q < weak_classifier->features[0].count; q++) fscanf(r, "%d %a %d %d %d %d", &weak_classifier->features[0].channel[q], &weak_classifier->features[0].alpha[q], &weak_classifier->features[0].sat[q * 2].x, &weak_classifier->features[0].sat[q * 2].y, &weak_classifier->features[0].sat[q * 2 + 1].x, &weak_classifier->features[0].sat[q * 2 + 1].y); if (weak_classifier->pass & 0x2) { fscanf(r, "%d %a", &weak_classifier->features[1].count, &weak_classifier->features[1].beta); for (q = 0; q < weak_classifier->features[1].count; q++) fscanf(r, "%d %a %d %d %d %d", &weak_classifier->features[1].channel[q], &weak_classifier->features[1].alpha[q], &weak_classifier->features[1].sat[q * 2].x, &weak_classifier->features[1].sat[q * 2].y, &weak_classifier->features[1].sat[q * 2 + 1].x, &weak_classifier->features[1].sat[q * 2 + 1].y); } if (weak_classifier->pass & 0x1) { fscanf(r, "%d %a", &weak_classifier->features[2].count, &weak_classifier->features[2].beta); for (q = 0; q < weak_classifier->features[2].count; q++) fscanf(r, "%d %a %d %d %d %d", &weak_classifier->features[2].channel[q], &weak_classifier->features[2].alpha[q], &weak_classifier->features[2].sat[q * 2].x, &weak_classifier->features[2].sat[q * 2].y, &weak_classifier->features[2].sat[q * 2 + 1].x, &weak_classifier->features[2].sat[q * 2 + 1].y); } } } static void _ccv_icf_write_classifier_cascade_with_fd(ccv_icf_classifier_cascade_t* cascade, FILE* w) { int i, q; fprintf(w, "%d %d %d %d\n", cascade->count, cascade->size.width, cascade->size.height, cascade->grayscale); fprintf(w, "%d %d %d %d\n", cascade->margin.left, cascade->margin.top, cascade->margin.right, cascade->margin.bottom); for (i = 0; i < cascade->count; i++) { ccv_icf_decision_tree_t* weak_classifier = cascade->weak_classifiers + i; fprintf(w, "%u %a %a %a\n", weak_classifier->pass, weak_classifier->weigh[0], weak_classifier->weigh[1], weak_classifier->threshold); fprintf(w, "%d %a\n", weak_classifier->features[0].count, weak_classifier->features[0].beta); for (q = 0; q < weak_classifier->features[0].count; q++) fprintf(w, "%d %a\n%d %d %d %d\n", weak_classifier->features[0].channel[q], weak_classifier->features[0].alpha[q], weak_classifier->features[0].sat[q * 2].x, weak_classifier->features[0].sat[q * 2].y, weak_classifier->features[0].sat[q * 2 + 1].x, weak_classifier->features[0].sat[q * 2 + 1].y); if (weak_classifier->pass & 0x2) { fprintf(w, "%d %a\n", weak_classifier->features[1].count, weak_classifier->features[1].beta); for (q = 0; q < weak_classifier->features[1].count; q++) fprintf(w, "%d %a\n%d %d %d %d\n", weak_classifier->features[1].channel[q], weak_classifier->features[1].alpha[q], weak_classifier->features[1].sat[q * 2].x, weak_classifier->features[1].sat[q * 2].y, weak_classifier->features[1].sat[q * 2 + 1].x, weak_classifier->features[1].sat[q * 2 + 1].y); } if (weak_classifier->pass & 0x1) { fprintf(w, "%d %a\n", weak_classifier->features[2].count, weak_classifier->features[2].beta); for (q = 0; q < weak_classifier->features[2].count; q++) fprintf(w, "%d %a\n%d %d %d %d\n", weak_classifier->features[2].channel[q], weak_classifier->features[2].alpha[q], weak_classifier->features[2].sat[q * 2].x, weak_classifier->features[2].sat[q * 2].y, weak_classifier->features[2].sat[q * 2 + 1].x, weak_classifier->features[2].sat[q * 2 + 1].y); } } } ccv_icf_classifier_cascade_t* ccv_icf_read_classifier_cascade(const char* filename) { FILE* r = fopen(filename, "r"); ccv_icf_classifier_cascade_t* cascade = 0; if (r) { cascade = (ccv_icf_classifier_cascade_t*)ccmalloc(sizeof(ccv_icf_classifier_cascade_t)); _ccv_icf_read_classifier_cascade_with_fd(r, cascade); fclose(r); } return cascade; } void ccv_icf_write_classifier_cascade(ccv_icf_classifier_cascade_t* cascade, const char* filename) { FILE* w = fopen(filename, "w+"); if (w) { _ccv_icf_write_classifier_cascade_with_fd(cascade, w); fclose(w); } } void ccv_icf_classifier_cascade_free(ccv_icf_classifier_cascade_t* classifier) { ccfree(classifier->weak_classifiers); ccfree(classifier); } ccv_icf_multiscale_classifier_cascade_t* ccv_icf_read_multiscale_classifier_cascade(const char* directory) { char filename[1024]; snprintf(filename, 1024, "%s/multiscale", directory); FILE* r = fopen(filename, "r"); if (r) { int octave = 0, count = 0, grayscale = 0; fscanf(r, "%d %d %d", &octave, &count, &grayscale); fclose(r); ccv_icf_multiscale_classifier_cascade_t* classifier = (ccv_icf_multiscale_classifier_cascade_t*)ccmalloc(sizeof(ccv_icf_multiscale_classifier_cascade_t) + sizeof(ccv_icf_classifier_cascade_t) * count); classifier->type = CCV_ICF_CLASSIFIER_TYPE_B; classifier->octave = octave; classifier->count = count; classifier->grayscale = grayscale; classifier->cascade = (ccv_icf_classifier_cascade_t*)(classifier + 1); int i; for (i = 0; i < count; i++) { snprintf(filename, 1024, "%s/cascade-%d", directory, i + 1); r = fopen(filename, "r"); if (r) { ccv_icf_classifier_cascade_t* cascade = classifier->cascade + i; _ccv_icf_read_classifier_cascade_with_fd(r, cascade); fclose(r); } } return classifier; } return 0; } void ccv_icf_write_multiscale_classifier_cascade(ccv_icf_multiscale_classifier_cascade_t* classifier, const char* directory) { char filename[1024]; snprintf(filename, 1024, "%s/multiscale", directory); FILE* w = fopen(filename, "w+"); fprintf(w, "%d %d %d\n", classifier->octave, classifier->count, classifier->grayscale); fclose(w); int i; for (i = 0; i < classifier->count; i++) { snprintf(filename, 1024, "%s/cascade-%d", directory, i + 1); w = fopen(filename, "w+"); _ccv_icf_write_classifier_cascade_with_fd(classifier->cascade + i, w); fclose(w); } } void ccv_icf_multiscale_classifier_cascade_free(ccv_icf_multiscale_classifier_cascade_t* classifier) { int i; for (i = 0; i < classifier->count; i++) ccfree(classifier->cascade[i].weak_classifiers); ccfree(classifier); } static int _ccv_is_equal_same_class(const void* _r1, const void* _r2, void* data) { const ccv_comp_t* r1 = (const ccv_comp_t*)_r1; const ccv_comp_t* r2 = (const ccv_comp_t*)_r2; int distance = (int)(ccv_min(r1->rect.width, r1->rect.height) * 0.25 + 0.5); return r2->classification.id == r1->classification.id && r2->rect.x <= r1->rect.x + distance && r2->rect.x >= r1->rect.x - distance && r2->rect.y <= r1->rect.y + distance && r2->rect.y >= r1->rect.y - distance && r2->rect.width <= (int)(r1->rect.width * 1.5 + 0.5) && (int)(r2->rect.width * 1.5 + 0.5) >= r1->rect.width && r2->rect.height <= (int)(r1->rect.height * 1.5 + 0.5) && (int)(r2->rect.height * 1.5 + 0.5) >= r1->rect.height; } static void _ccv_icf_detect_objects_with_classifier_cascade(ccv_dense_matrix_t* a, ccv_icf_classifier_cascade_t** cascades, int count, ccv_icf_param_t params, ccv_array_t* seq[]) { int i, j, k, q, x, y; int scale_upto = 1; for (i = 0; i < count; i++) scale_upto = ccv_max(scale_upto, (int)(log(ccv_min((double)a->rows / (cascades[i]->size.height - cascades[i]->margin.top - cascades[i]->margin.bottom), (double)a->cols / (cascades[i]->size.width - cascades[i]->margin.left - cascades[i]->margin.right))) / log(2.) - DBL_MIN) + 1); ccv_dense_matrix_t** pyr = (ccv_dense_matrix_t**)alloca(sizeof(ccv_dense_matrix_t*) * scale_upto); pyr[0] = a; for (i = 1; i < scale_upto; i++) { pyr[i] = 0; ccv_sample_down(pyr[i - 1], &pyr[i], 0, 0, 0); } for (i = 0; i < scale_upto; i++) { // run it for (j = 0; j < count; j++) { double scale_ratio = pow(2., 1. / (params.interval + 1)); double scale = 1; ccv_icf_classifier_cascade_t* cascade = cascades[j]; for (k = 0; k <= params.interval; k++) { int rows = (int)(pyr[i]->rows / scale + 0.5); int cols = (int)(pyr[i]->cols / scale + 0.5); if (rows < cascade->size.height || cols < cascade->size.width) break; ccv_dense_matrix_t* image = k == 0 ? pyr[i] : 0; if (k > 0) ccv_resample(pyr[i], &image, 0, rows, cols, CCV_INTER_AREA); ccv_dense_matrix_t* bordered = 0; ccv_border(image, (ccv_matrix_t**)&bordered, 0, cascade->margin); if (k > 0) ccv_matrix_free(image); rows = bordered->rows; cols = bordered->cols; ccv_dense_matrix_t* icf = 0; ccv_icf(bordered, &icf, 0); ccv_matrix_free(bordered); ccv_dense_matrix_t* sat = 0; ccv_sat(icf, &sat, 0, CCV_PADDING_ZERO); ccv_matrix_free(icf); int ch = CCV_GET_CHANNEL(sat->type); float* ptr = sat->data.f32; for (y = 0; y < rows; y += params.step_through) { if (y >= sat->rows - cascade->size.height - 1) break; for (x = 0; x < cols; x += params.step_through) { if (x >= sat->cols - cascade->size.width - 1) break; int pass = 1; float sum = 0; for (q = 0; q < cascade->count; q++) { ccv_icf_decision_tree_t* weak_classifier = cascade->weak_classifiers + q; int c = _ccv_icf_run_weak_classifier(weak_classifier, ptr, sat->cols, ch, x, 0); sum += weak_classifier->weigh[c]; if (sum < weak_classifier->threshold) { pass = 0; break; } } if (pass) { ccv_comp_t comp; comp.rect = ccv_rect((int)((x + 0.5) * scale * (1 << i) - 0.5), (int)((y + 0.5) * scale * (1 << i) - 0.5), (cascade->size.width - cascade->margin.left - cascade->margin.right) * scale * (1 << i), (cascade->size.height - cascade->margin.top - cascade->margin.bottom) * scale * (1 << i)); comp.neighbors = 1; comp.classification.id = j + 1; comp.classification.confidence = sum; ccv_array_push(seq[j], &comp); } } ptr += sat->cols * ch * params.step_through; } ccv_matrix_free(sat); scale *= scale_ratio; } } } for (i = 1; i < scale_upto; i++) ccv_matrix_free(pyr[i]); } static void _ccv_icf_detect_objects_with_multiscale_classifier_cascade(ccv_dense_matrix_t* a, ccv_icf_multiscale_classifier_cascade_t** multiscale_cascade, int count, ccv_icf_param_t params, ccv_array_t* seq[]) { int i, j, k, q, x, y, ix, iy, py; assert(multiscale_cascade[0]->count % multiscale_cascade[0]->octave == 0); ccv_margin_t margin = multiscale_cascade[0]->cascade[multiscale_cascade[0]->count - 1].margin; for (i = 1; i < count; i++) { assert(multiscale_cascade[i]->count % multiscale_cascade[i]->octave == 0); assert(multiscale_cascade[i - 1]->grayscale == multiscale_cascade[i]->grayscale); assert(multiscale_cascade[i - 1]->count == multiscale_cascade[i]->count); assert(multiscale_cascade[i - 1]->octave == multiscale_cascade[i]->octave); ccv_icf_classifier_cascade_t* cascade = multiscale_cascade[i]->cascade + multiscale_cascade[i]->count - 1; margin.top = ccv_max(margin.top, cascade->margin.top); margin.right = ccv_max(margin.right, cascade->margin.right); margin.bottom = ccv_max(margin.bottom, cascade->margin.bottom); margin.left = ccv_max(margin.left, cascade->margin.left); } int scale_upto = 1; for (i = 0; i < count; i++) scale_upto = ccv_max(scale_upto, (int)(log(ccv_min((double)a->rows / (multiscale_cascade[i]->cascade[0].size.height - multiscale_cascade[i]->cascade[0].margin.top - multiscale_cascade[i]->cascade[0].margin.bottom), (double)a->cols / (multiscale_cascade[i]->cascade[0].size.width - multiscale_cascade[i]->cascade[0].margin.left - multiscale_cascade[i]->cascade[0].margin.right))) / log(2.) - DBL_MIN) + 2 - multiscale_cascade[i]->octave); ccv_dense_matrix_t** pyr = (ccv_dense_matrix_t**)alloca(sizeof(ccv_dense_matrix_t*) * scale_upto); pyr[0] = a; for (i = 1; i < scale_upto; i++) { pyr[i] = 0; ccv_sample_down(pyr[i - 1], &pyr[i], 0, 0, 0); } for (i = 0; i < scale_upto; i++) { ccv_dense_matrix_t* bordered = 0; ccv_border(pyr[i], (ccv_matrix_t**)&bordered, 0, margin); ccv_dense_matrix_t* icf = 0; ccv_icf(bordered, &icf, 0); ccv_matrix_free(bordered); ccv_dense_matrix_t* sat = 0; ccv_sat(icf, &sat, 0, CCV_PADDING_ZERO); ccv_matrix_free(icf); int ch = CCV_GET_CHANNEL(sat->type); assert(CCV_GET_DATA_TYPE(sat->type) == CCV_32F); // run it for (j = 0; j < count; j++) { double scale_ratio = pow(2., (double)multiscale_cascade[j]->octave / multiscale_cascade[j]->count); int starter = i > 0 ? multiscale_cascade[j]->count - (multiscale_cascade[j]->count / multiscale_cascade[j]->octave) : 0; double scale = pow(scale_ratio, starter); for (k = starter; k < multiscale_cascade[j]->count; k++) { ccv_icf_classifier_cascade_t* cascade = multiscale_cascade[j]->cascade + k; int rows = (int)(pyr[i]->rows / scale + cascade->margin.top + 0.5); int cols = (int)(pyr[i]->cols / scale + cascade->margin.left + 0.5); int top = margin.top - cascade->margin.top; int right = margin.right - cascade->margin.right; int bottom = margin.bottom - cascade->margin.bottom; int left = margin.left - cascade->margin.left; if (sat->rows - top - bottom <= cascade->size.height || sat->cols - left - right <= cascade->size.width) break; float* ptr = sat->data.f32 + top * sat->cols * ch; for (y = 0, iy = py = top; y < rows; y += params.step_through) { iy = (int)((y + 0.5) * scale + top); if (iy >= sat->rows - cascade->size.height - 1) break; if (iy > py) { ptr += sat->cols * ch * (iy - py); py = iy; } for (x = 0; x < cols; x += params.step_through) { ix = (int)((x + 0.5) * scale + left); if (ix >= sat->cols - cascade->size.width - 1) break; int pass = 1; float sum = 0; for (q = 0; q < cascade->count; q++) { ccv_icf_decision_tree_t* weak_classifier = cascade->weak_classifiers + q; int c = _ccv_icf_run_weak_classifier(weak_classifier, ptr, sat->cols, ch, ix, 0); sum += weak_classifier->weigh[c]; if (sum < weak_classifier->threshold) { pass = 0; break; } } if (pass) { ccv_comp_t comp; comp.rect = ccv_rect((int)((x + 0.5) * scale * (1 << i)), (int)((y + 0.5) * scale * (1 << i)), (cascade->size.width - cascade->margin.left - cascade->margin.right) << i, (cascade->size.height - cascade->margin.top - cascade->margin.bottom) << i); comp.neighbors = 1; comp.classification.id = j + 1; comp.classification.confidence = sum; ccv_array_push(seq[j], &comp); } } } scale *= scale_ratio; } } ccv_matrix_free(sat); } for (i = 1; i < scale_upto; i++) ccv_matrix_free(pyr[i]); } ccv_array_t* ccv_icf_detect_objects(ccv_dense_matrix_t* a, void* cascade, int count, ccv_icf_param_t params) { assert(count > 0); int i, j, k; int type = *(((int**)cascade)[0]); for (i = 1; i < count; i++) { // check all types to be the same assert(*(((int**)cascade)[i]) == type); } ccv_array_t** seq = (ccv_array_t**)alloca(sizeof(ccv_array_t*) * count); for (i = 0; i < count; i++) seq[i] = ccv_array_new(sizeof(ccv_comp_t), 64, 0); switch (type) { case CCV_ICF_CLASSIFIER_TYPE_A: _ccv_icf_detect_objects_with_classifier_cascade(a, (ccv_icf_classifier_cascade_t**)cascade, count, params, seq); break; case CCV_ICF_CLASSIFIER_TYPE_B: _ccv_icf_detect_objects_with_multiscale_classifier_cascade(a, (ccv_icf_multiscale_classifier_cascade_t**)cascade, count, params, seq); break; } ccv_array_t* result_seq = ccv_array_new(sizeof(ccv_comp_t), 64, 0); ccv_array_t* seq2 = ccv_array_new(sizeof(ccv_comp_t), 64, 0); for (k = 0; k < count; k++) { /* the following code from OpenCV's haar feature implementation */ if(params.min_neighbors == 0) { for (i = 0; i < seq[k]->rnum; i++) { ccv_comp_t* comp = (ccv_comp_t*)ccv_array_get(seq[k], i); ccv_array_push(result_seq, comp); } } else { ccv_array_t* idx_seq = 0; ccv_array_clear(seq2); // group retrieved rectangles in order to filter out noise int ncomp = ccv_array_group(seq[k], &idx_seq, _ccv_is_equal_same_class, 0); ccv_comp_t* comps = (ccv_comp_t*)cccalloc(ncomp + 1, sizeof(ccv_comp_t)); // count number of neighbors for (i = 0; i < seq[k]->rnum; i++) { ccv_comp_t r1 = *(ccv_comp_t*)ccv_array_get(seq[k], i); int idx = *(int*)ccv_array_get(idx_seq, i); comps[idx].classification.id = r1.classification.id; if (r1.classification.confidence > comps[idx].classification.confidence || comps[idx].neighbors == 0) { comps[idx].rect = r1.rect; comps[idx].classification.confidence = r1.classification.confidence; } ++comps[idx].neighbors; } // calculate average bounding box for (i = 0; i < ncomp; i++) { int n = comps[i].neighbors; if (n >= params.min_neighbors) ccv_array_push(seq2, comps + i); } // filter out large object rectangles contains small object rectangles for (i = 0; i < seq2->rnum; i++) { ccv_comp_t* r2 = (ccv_comp_t*)ccv_array_get(seq2, i); int distance = (int)(ccv_min(r2->rect.width, r2->rect.height) * 0.25 + 0.5); for (j = 0; j < seq2->rnum; j++) { ccv_comp_t r1 = *(ccv_comp_t*)ccv_array_get(seq2, j); if (i != j && abs(r1.classification.id) == r2->classification.id && r1.rect.x >= r2->rect.x - distance && r1.rect.y >= r2->rect.y - distance && r1.rect.x + r1.rect.width <= r2->rect.x + r2->rect.width + distance && r1.rect.y + r1.rect.height <= r2->rect.y + r2->rect.height + distance && // if r1 (the smaller one) is better, mute r2 (r2->classification.confidence <= r1.classification.confidence && r2->neighbors < r1.neighbors)) { r2->classification.id = -r2->classification.id; break; } } } // filter out small object rectangles inside large object rectangles for (i = 0; i < seq2->rnum; i++) { ccv_comp_t r1 = *(ccv_comp_t*)ccv_array_get(seq2, i); if (r1.classification.id > 0) { int flag = 1; for (j = 0; j < seq2->rnum; j++) { ccv_comp_t r2 = *(ccv_comp_t*)ccv_array_get(seq2, j); int distance = (int)(ccv_min(r2.rect.width, r2.rect.height) * 0.25 + 0.5); if (i != j && abs(r1.classification.id) == abs(r2.classification.id) && r1.rect.x >= r2.rect.x - distance && r1.rect.y >= r2.rect.y - distance && r1.rect.x + r1.rect.width <= r2.rect.x + r2.rect.width + distance && r1.rect.y + r1.rect.height <= r2.rect.y + r2.rect.height + distance && // if r2 is better, we mute r1 (r2.classification.confidence > r1.classification.confidence || r2.neighbors >= r1.neighbors)) { flag = 0; break; } } if (flag) ccv_array_push(result_seq, &r1); } } ccv_array_free(idx_seq); ccfree(comps); } ccv_array_free(seq[k]); } ccv_array_free(seq2); return result_seq; }