rle enconding

vision_agent/tools/tools.py

@@ -200,7 +200,7 @@ def owl_v2_image(
  )
  data = data_obj.model_dump(by_alias=True)
  detections = send_inference_request(data, "tools", v2=False)
- # get the first frame detections
+ # get the first frame
  detection = detections[0]
  bboxes_formatted = [
  ODResponseData(
@@ -429,17 +429,18 @@ def florence2_sam2_image(
  )
  req_data = req_data_obj.model_dump(by_alias=True)
  detections_ft = send_inference_request(req_data, "tools", v2=False)
- # get the first frame detections
+ # get the first frame
  detection = detections_ft[0]
  return_data = []
- all_masks = np.array(detection["masks"])
  for i in range(len(detection["bboxes"])):
  return_data.append(
  {
  "score": 1.0,
  "label": detection["labels"][i],
- "bbox": detection["bboxes"][i],
- "mask": all_masks[i, :, :].astype(np.uint8),
+ "bbox": normalize_bbox(
+ detection["bboxes"][i], detection["masks"][i]["size"]
+ ),
+ "mask": rle_decode_array(detection["masks"][i]),
  }
  )
  return return_data
@@ -453,6 +454,7 @@ def florence2_sam2_image(
  detections: Dict[str, Any] = send_inference_request(
  payload, "florence2-sam2", files=files, v2=True
  )
+
  return_data = []
  for _, data_i in detections["0"].items():
  mask = rle_decode_array(data_i["mask"])
@@ -1189,7 +1191,7 @@ def florence2_phrase_grounding(
  v2=False,
  metadata_payload={"function_name": "florence2_phrase_grounding"},
  )
- # get the first frame detections
+ # get the first frame
  detection = detections[0]
  else:
  data = {
@@ -1607,7 +1609,7 @@ def extract_frames_and_timestamps(
  """
 
  def reformat(
- frames_and_timestamps: List[Tuple[np.ndarray, float]]
+ frames_and_timestamps: List[Tuple[np.ndarray, float]],
  ) -> List[Dict[str, Union[np.ndarray, float]]]:
  return [
  {"frame": frame, "timestamp": timestamp}