Skip GSD and size filtering without altitude

src/ai_config.pxd

@@ -12,6 +12,7 @@ cdef class AIRecognitionConfig:
 
     cdef public int model_batch_size
 
+    cdef public bint has_altitude
     cdef public double altitude
     cdef public double focal_length
     cdef public double sensor_width

src/ai_config.pyx

@@ -25,7 +25,8 @@ cdef class AIRecognitionConfig:
 
         self.big_image_tile_overlap_percent = big_image_tile_overlap_percent
 
-        self.altitude = altitude
+        self.has_altitude = altitude is not None
+        self.altitude = 0.0 if altitude is None else float(altitude)
         self.focal_length = focal_length
         self.sensor_width = sensor_width
 
@@ -36,7 +37,7 @@ cdef class AIRecognitionConfig:
                 f'frame_period_recognition : {self.frame_period_recognition}, '
                 f'big_image_tile_overlap_percent: {self.big_image_tile_overlap_percent}, '
                 f'model_batch_size: {self.model_batch_size}, '
-                f'altitude: {self.altitude}, '
+                f'altitude: {self.altitude if self.has_altitude else None}, '
                 f'focal_length: {self.focal_length}, '
                 f'sensor_width: {self.sensor_width}'
                 )
@@ -56,7 +57,7 @@ cdef class AIRecognitionConfig:
 
             data.get("big_image_tile_overlap_percent", 20),
 
-            data.get("altitude", 400),
+            data.get("altitude", None),
             data.get("focal_length", 24),
             data.get("sensor_width", 23.5)
         )

src/inference.pyx

@@ -311,13 +311,22 @@ cdef class Inference:
         cdef double ground_sampling_distance
         cdef int model_h, model_w
         cdef int img_h, img_w
+        cdef bint has_gsd
         model_h, model_w = self.engine.get_input_shape()
         img_h, img_w, _ = frame.shape
-        ground_sampling_distance = ai_config.sensor_width * ai_config.altitude / (ai_config.focal_length * img_w)
-        constants_inf.log(<str>f'ground sampling distance: {ground_sampling_distance}')
+        has_gsd = ai_config.has_altitude and ai_config.focal_length > 0 and ai_config.sensor_width > 0 and img_w > 0
+        ground_sampling_distance = 0.0
+        if has_gsd:
+            ground_sampling_distance = ai_config.sensor_width * ai_config.altitude / (ai_config.focal_length * img_w)
+            constants_inf.log(<str>f'ground sampling distance: {ground_sampling_distance}')
+        else:
+            constants_inf.log(<str>'ground sampling distance: skipped (altitude unavailable)')
         if img_h <= 1.5 * model_h and img_w <= 1.5 * model_w:
             all_frame_data.append((frame, original_media_name, f'{original_media_name}_000000', ground_sampling_distance))
         else:
+            if not has_gsd:
+                all_frame_data.append((frame, original_media_name, f'{original_media_name}_000000', ground_sampling_distance))
+                return
             tile_size = int(constants_inf.METERS_IN_TILE / ground_sampling_distance)
             constants_inf.log(<str> f'calc tile size: {tile_size}')
             res = self.split_to_tiles(frame, original_media_name, tile_size, ai_config.big_image_tile_overlap_percent)
@@ -410,6 +419,12 @@ cdef class Inference:
         if annotation.detections:
             constants_inf.log(<str> f'Initial ann: {annotation}')
 
+        if ground_sampling_distance <= 0:
+            if not annotation.detections:
+                return <bint>False
+            constants_inf.log(<str>'Skipping physical-size filtering (ground sampling distance unavailable)')
+            return <bint>True
+
         cdef list[Detection] valid_detections = []
         for det in annotation.detections:
             m_w = det.w * img_w * ground_sampling_distance

src/main.py

@@ -163,7 +163,7 @@ class AIConfigDto(BaseModel):
     tracking_intersection_threshold: float = 0.6
     model_batch_size: int = 8
     big_image_tile_overlap_percent: int = 20
-    altitude: float = 400
+    altitude: Optional[float] = None
     focal_length: float = 24
     sensor_width: float = 23.5