print detection results

Azaion.Inference/annotation.pxd

@@ -14,4 +14,5 @@ cdef class Annotation:
 
     cdef format_time(self, ms)
     cdef bytes serialize(self)
+    cdef to_str(self, class_names)
 

Azaion.Inference/annotation.pyx

@@ -32,6 +32,16 @@ cdef class Annotation:
             d.annotation_name = self.name
         self.image = b''
 
+    cdef to_str(self, class_names):
+        if not self.detections:
+            return f"{self.name}: No detections"
+
+        detections_str = ", ".join(
+            f"{class_names[d.cls]} {d.confidence * 100:.1f}% ({d.x:.2f}, {d.y:.2f})"
+            for d in self.detections
+        )
+        return f"{self.name}: {detections_str}"
+
     cdef format_time(self, ms):
         # Calculate hours, minutes, seconds, and hundreds of milliseconds.
         h = ms // 3600000  # Total full hours.

Azaion.Inference/inference.pxd

@@ -7,6 +7,7 @@ cdef class Inference:
     cdef object on_annotation
     cdef Annotation _previous_annotation
     cdef AIRecognitionConfig ai_config
+    cdef object class_names
     cdef bint stop_signal
 
     cdef str model_input

Azaion.Inference/inference.pyx

@@ -26,6 +26,7 @@ cdef class Inference:
         print(f'AI detection model input: {self.model_input} ({self.model_width}, {self.model_height})')
         model_meta = self.session.get_modelmeta()
         print("Metadata:", model_meta.custom_metadata_map)
+        self.class_names = eval(model_meta.custom_metadata_map["names"])
 
     cdef preprocess(self, frames):
         blobs = [cv2.dnn.blobFromImage(frame,
@@ -75,11 +76,9 @@ cdef class Inference:
             if det1_index in filtered_out_indexes:
                 continue
             det1 = detections[det1_index]
-            print(f'det1 size: {det1.w}, {det1.h}')
             res = det1_index
             for det2_index in range(det1_index + 1, len(detections)):
                 det2 = detections[det2_index]
-                print(f'det2 size: {det2.w}, {det2.h}')
                 if det1.overlaps(det2):
                     if det1.confidence > det2.confidence or (
                             det1.confidence == det2.confidence and det1.cls < det2.cls):  # det1 has higher confidence or lower class_id
@@ -158,6 +157,7 @@ cdef class Inference:
                     if self.is_valid_annotation(annotation):
                         _, image = cv2.imencode('.jpg', frame)
                         annotation.image = image.tobytes()
+                        print(annotation.to_str(self.class_names))
                         self.on_annotation(cmd, annotation)
                         self._previous_annotation = annotation
 
@@ -183,6 +183,7 @@ cdef class Inference:
             annotation = Annotation(image_paths[i], timestamps[i], detections)
             _, image = cv2.imencode('.jpg', frames[i])
             annotation.image = image.tobytes()
+            print(annotation.to_str(self.class_names))
             self.on_annotation(cmd, annotation)
 
 

Azaion.Inference/token

@@ -0,0 +1 @@
+eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJuYW1laWQiOiI5N2U5MWI2OC1hNmRlLTQ3YTgtOTgzYi0xOTU3YzViNDQ2MTkiLCJ1bmlxdWVfbmFtZSI6ImFkbWluLXJlbW90ZUBhemFpb24uY29tIiwicm9sZSI6IkFwaUFkbWluIiwibmJmIjoxNzM5MTk5MzM1LCJleHAiOjE3MzkyMTM3MzUsImlhdCI6MTczOTE5OTMzNSwiaXNzIjoiQXphaW9uQXBpIiwiYXVkIjoiQW5ub3RhdG9ycy9PcmFuZ2VQaS9BZG1pbnMifQ.HmfaeFn9T_eJuRZSjBV_EhiSB41ippBVPLRggC7gBZk