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AIWintermuteAIAIWintermuteAI
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add visual ad outputs (#19)
1 parent 30f026a commit 90d1e68

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3 files changed

+99
-30
lines changed

3 files changed

+99
-30
lines changed

‎source/camera.cpp‎

Lines changed: 18 additions & 6 deletions
Original file line numberDiff line numberDiff line change
@@ -136,8 +136,12 @@ int main(int argc, char** argv) {
136136
return 1;
137137
}
138138

139+
// print the predictions
140+
printf("Predictions (DSP: %d ms., Classification: %d ms., Anomaly: %d ms.): \n",
141+
result.timing.dsp, result.timing.classification, result.timing.anomaly);
142+
139143
#if EI_CLASSIFIER_OBJECT_DETECTION == 1
140-
printf("Classification result (%d ms.):\n", result.timing.dsp + result.timing.classification);
144+
printf("#Object detection results:\n");
141145
bool found_bb = false;
142146
for (size_t ix = 0; ix < result.bounding_boxes_count; ix++) {
143147
auto bb = result.bounding_boxes[ix];
@@ -153,14 +157,22 @@ int main(int argc, char** argv) {
153157
printf(" no objects found\n");
154158
}
155159
#else
156-
printf("%d ms. ", result.timing.dsp + result.timing.classification);
160+
printf("#Classification results:\n");
157161
for (size_t ix = 0; ix < EI_CLASSIFIER_LABEL_COUNT; ix++) {
158-
printf("%s: %.05f", result.classification[ix].label, result.classification[ix].value);
159-
if (ix != EI_CLASSIFIER_LABEL_COUNT - 1) {
160-
printf(", ");
162+
printf("%s: %.05f\n", result.classification[ix].label, result.classification[ix].value);
163+
}
164+
#endif
165+
166+
#if EI_CLASSIFIER_HAS_ANOMALY == 3 // visual AD
167+
printf("#Visual anomaly grid results:\n");
168+
for (uint32_t i = 0; i < result.visual_ad_count; i++) {
169+
ei_impulse_result_bounding_box_t bb = result.visual_ad_grid_cells[i];
170+
if (bb.value == 0) {
171+
continue;
161172
}
173+
printf(" %s (%f) [ x: %u, y: %u, width: %u, height: %u ]\n", bb.label, bb.value, bb.x, bb.y, bb.width, bb.height);
162174
}
163-
printf("\n");
175+
printf("Visual anomaly values: Mean %.3f Max %.3f\n", result.visual_ad_result.mean_value, result.visual_ad_result.max_value);
164176
#endif
165177

166178
// show the image on the window

‎source/custom.cpp‎

Lines changed: 52 additions & 23 deletions
Original file line numberDiff line numberDiff line change
@@ -85,15 +85,13 @@ int main(int argc, char **argv) {
8585
numpy::signal_from_buffer(&raw_features[0], raw_features.size(), &signal);
8686

8787
EI_IMPULSE_ERROR res = run_classifier(&signal, &result, false);
88-
printf("run_classifier returned: %d (DSP %d ms., Classification %d ms., Anomaly %d ms.)\n", res,
89-
result.timing.dsp, result.timing.classification, result.timing.anomaly);
90-
if (res != 0) {
91-
return 1;
92-
}
93-
94-
printf("Begin output\n");
88+
// print the predictions
89+
printf("Predictions (DSP: %d ms., Classification: %d ms., Anomaly: %d ms.): \n",
90+
result.timing.dsp, result.timing.classification, result.timing.anomaly);
9591

9692
#if EI_CLASSIFIER_OBJECT_DETECTION == 1
93+
printf("#Object detection results:\n");
94+
bool bb_found = result.bounding_boxes[0].value > 0;
9795
for (size_t ix = 0; ix < result.bounding_boxes_count; ix++) {
9896
auto bb = result.bounding_boxes[ix];
9997
if (bb.value == 0) {
@@ -102,29 +100,44 @@ int main(int argc, char **argv) {
102100

103101
printf("%s (%f) [ x: %u, y: %u, width: %u, height: %u ]\n", bb.label, bb.value, bb.x, bb.y, bb.width, bb.height);
104102
}
105-
#else
106-
// print the predictions
107-
printf("[");
103+
104+
if (!bb_found) {
105+
printf(" No objects found\n");
106+
}
107+
108+
#elif (EI_CLASSIFIER_LABEL_COUNT == 1) && (!EI_CLASSIFIER_HAS_ANOMALY) // regression
109+
printf("#Regression results:\n");
110+
printf(" %s: ", result.classification[0].label);
111+
printf("%.5f", result.classification[0].value);
112+
printf("\n");
113+
114+
#elif EI_CLASSIFIER_LABEL_COUNT > 1 // if there is only one label, this is an anomaly only
115+
printf("#Classification results:\n");
108116
for (size_t ix = 0; ix < EI_CLASSIFIER_LABEL_COUNT; ix++) {
117+
printf(" %s: ", result.classification[ix].label);
109118
printf("%.5f", result.classification[ix].value);
110-
#if EI_CLASSIFIER_HAS_ANOMALY
111-
printf(", ");
112-
#else
113-
if (ix != EI_CLASSIFIER_LABEL_COUNT - 1) {
114-
printf(", ");
115-
}
116-
#endif
119+
printf("\n");
117120
}
118-
#if EI_CLASSIFIER_HAS_ANOMALY
119-
printf("%.3f", result.anomaly);
120121
#endif
121-
printf("]\n");
122+
#if EI_CLASSIFIER_HAS_ANOMALY == 3 // visual AD
123+
printf("#Visual anomaly grid results:\n");
124+
for (uint32_t i = 0; i < result.visual_ad_count; i++) {
125+
ei_impulse_result_bounding_box_t bb = result.visual_ad_grid_cells[i];
126+
if (bb.value == 0) {
127+
continue;
128+
}
129+
130+
printf("%s (%f) [ x: %u, y: %u, width: %u, height: %u ]\n", bb.label, bb.value, bb.x, bb.y, bb.width, bb.height);
131+
}
132+
printf("Visual anomaly values: Mean %.3f Max %.3f\n", result.visual_ad_result.mean_value, result.visual_ad_result.max_value);
133+
#elif (EI_CLASSIFIER_HAS_ANOMALY > 0) // except for visual AD
134+
printf("Anomaly prediction: %.3f\n", result.anomaly);
122135
#endif
123136

124-
printf("End output\n");
137+
// Add a debug.bmp file for object detection / visual AD results
138+
#if (EI_CLASSIFIER_OBJECT_DETECTION == 1) || (EI_CLASSIFIER_HAS_ANOMALY == 3)
125139

126-
// Add a debug.bmp file for object detection results
127-
#if EI_CLASSIFIER_OBJECT_DETECTION == 1
140+
#if (EI_CLASSIFIER_OBJECT_DETECTION == 1)
128141
for (size_t ix = 0; ix < result.bounding_boxes_count; ix++) {
129142
auto bb = result.bounding_boxes[ix];
130143
if (bb.value == 0) {
@@ -137,6 +150,22 @@ int main(int argc, char **argv) {
137150
}
138151
}
139152
}
153+
#endif
154+
155+
#if (EI_CLASSIFIER_HAS_ANOMALY == 3)
156+
for (size_t ix = 0; ix < result.visual_ad_count; ix++) {
157+
auto bb = result.visual_ad_grid_cells[ix];
158+
if (bb.value == 0) {
159+
continue;
160+
}
161+
162+
for (size_t x = bb.x; x < bb.x + bb.width; x++) {
163+
for (size_t y = bb.y; y < bb.y + bb.height; y++) {
164+
raw_features[(y * EI_CLASSIFIER_INPUT_WIDTH) + x] = (float)0xff0000;
165+
}
166+
}
167+
}
168+
#endif
140169

141170
create_bitmap_file("debug.bmp", raw_features.data(), EI_CLASSIFIER_INPUT_WIDTH, EI_CLASSIFIER_INPUT_HEIGHT);
142171
#endif

‎source/eim.cpp‎

Lines changed: 29 additions & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -91,22 +91,50 @@ void json_send_classification_response(int id,
9191
bb_res.push_back(bb_json);
9292
}
9393
#else
94+
#if EI_CLASSIFIER_LABEL_COUNT > 0
9495
nlohmann::json classify_res;
9596
for (size_t ix = 0; ix < EI_CLASSIFIER_LABEL_COUNT; ix++) {
9697
classify_res[result.classification[ix].label] = result.classification[ix].value;
9798
}
99+
#endif // EI_CLASSIFIER_LABEL_COUNT > 0
98100
#endif
99101

102+
#if EI_CLASSIFIER_HAS_VISUAL_ANOMALY
103+
nlohmann::json visual_ad_res = nlohmann::json::array();
104+
for (size_t ix = 0; ix < result.visual_ad_count; ix++) {
105+
auto bb = result.visual_ad_grid_cells[ix];
106+
if (bb.value == 0) {
107+
continue;
108+
}
109+
nlohmann::json bb_json = {
110+
{"label", bb.label},
111+
{"value", bb.value},
112+
{"x", bb.x},
113+
{"y", bb.y},
114+
{"width", bb.width},
115+
{"height", bb.height},
116+
};
117+
visual_ad_res.push_back(bb_json);
118+
}
119+
#endif // EI_CLASSIFIER_HAS_VISUAL_ANOMALY
120+
100121
nlohmann::json resp = {
101122
{"id", id},
102123
{"success", true},
103124
{"result", {
104125
#if EI_CLASSIFIER_OBJECT_DETECTION == 1
105126
{"bounding_boxes", bb_res},
106127
#else
128+
#if EI_CLASSIFIER_LABEL_COUNT > 0
107129
{"classification", classify_res},
130+
#endif // EI_CLASSIFIER_LABEL_COUNT > 0
108131
#endif // EI_CLASSIFIER_OBJECT_DETECTION
109-
#if EI_CLASSIFIER_HAS_ANOMALY
132+
#if EI_CLASSIFIER_HAS_VISUAL_ANOMALY
133+
{"visual_anomaly_grid", visual_ad_res},
134+
{"visual_anomaly_max", result.visual_ad_result.max_value},
135+
{"visual_anomaly_mean", result.visual_ad_result.mean_value},
136+
#endif // EI_CLASSIFIER_HAS_VISUAL_ANOMALY
137+
#if EI_CLASSIFIER_HAS_ANOMALY > 0
110138
{"anomaly", result.anomaly},
111139
#endif // EI_CLASSIFIER_HAS_ANOMALY == 1
112140
}},

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