Remove the quality parameter from lambda function call endpoints

changelog.d/20241111_195229_roman_remove_lambda_quality.md

@@ -0,0 +1,5 @@
+### Removed
+
+- It it no longer possible to run lambda functions on compressed images;
+  original images will always be used
+  (<https://github.com/cvat-ai/cvat/pull/8683>)

cvat/apps/lambda_manager/serializers.py

@@ -24,9 +24,6 @@ class FunctionCallRequestSerializer(serializers.Serializer):
     function = serializers.CharField(help_text="The name of the function to execute")
     task = serializers.IntegerField(help_text="The id of the task to be annotated")
     job = serializers.IntegerField(required=False, help_text="The id of the job to be annotated")
-    quality = serializers.ChoiceField(choices=['compressed', 'original'], default="original",
-        help_text="The quality of the images to use in the model run"
-    )
     max_distance = serializers.IntegerField(required=False)
     threshold = serializers.FloatField(required=False)
     cleanup = serializers.BooleanField(help_text="Whether existing annotations should be removed", default=False)

cvat/apps/lambda_manager/tests/test_lambda.py

@@ -368,7 +368,6 @@ def test_api_v2_lambda_requests_read(self):
             "task": self.main_task["id"],
             "cleanup": True,
             "threshold": 55,
-            "quality": "original",
             "mapping": {
                 "car": { "name": "car" },
             },
@@ -447,7 +446,6 @@ def test_api_v2_lambda_requests_create(self):
                 "task": self.main_task["id"],
                 "cleanup": True,
                 "threshold": 55,
-                "quality": "original",
                 "mapping": {
                     "car": { "name": "car" },
                 },
@@ -456,7 +454,6 @@ def test_api_v2_lambda_requests_create(self):
                 "function": id_func,
                 "task": self.assigneed_to_user_task["id"],
                 "cleanup": False,
-                "quality": "compressed",
                 "max_distance": 70,
                 "mapping": {
                     "car": { "name": "car" },
@@ -769,7 +766,6 @@ def test_api_v2_lambda_functions_create_reid(self):
                 OrderedDict([('attributes', []), ('frame', 1), ('group', None), ('id', 11260), ('label_id', 8), ('occluded', False), ('points', [1076.0, 199.0, 1218.0, 593.0]), ('source', 'auto'), ('type', 'rectangle'), ('z_order', 0)]),
                 OrderedDict([('attributes', []), ('frame', 1), ('group', None), ('id', 11261), ('label_id', 8), ('occluded', False), ('points', [924.0, 177.0, 1090.0, 615.0]), ('source', 'auto'), ('type', 'rectangle'), ('z_order', 0)]),
             ],
-            "quality": None,
             "threshold": 0.5,
             "max_distance": 55,
         }
@@ -785,7 +781,6 @@ def test_api_v2_lambda_functions_create_reid(self):
                 OrderedDict([('attributes', []), ('frame', 1), ('group', None), ('id', 11260), ('label_id', 8), ('occluded', False), ('points', [1076.0, 199.0, 1218.0, 593.0]), ('source', 'auto'), ('type', 'rectangle'), ('z_order', 0)]),
                 OrderedDict([('attributes', []), ('frame', 1), ('group', 0), ('id', 11398), ('label_id', 8), ('occluded', False), ('points', [184.3935546875, 211.5048828125, 331.64968722073354, 97.27792672028772, 445.87667560321825, 126.17873100983161, 454.13404825737416, 691.8087578194827, 180.26452189455085]), ('source', 'manual'), ('type', 'polygon'), ('z_order', 0)]),
             ],
-            "quality": None,
         }
 
         response = self._post_request(f"{LAMBDA_FUNCTIONS_PATH}/{id_function_reid_with_response_data}", self.admin, data_main_task)
@@ -829,42 +824,11 @@ def test_api_v2_lambda_functions_create_negative(self):
                 self.assertEqual(response.status_code, status.HTTP_500_INTERNAL_SERVER_ERROR)
 
 
-    def test_api_v2_lambda_functions_create_quality(self):
-        qualities = [None, "original", "compressed"]
-
-        for quality in qualities:
-            data = {
-                "task": self.main_task["id"],
-                "frame": 0,
-                "cleanup": True,
-                "quality": quality,
-                "mapping": {
-                    "car": { "name": "car" },
-                },
-            }
-
-            response = self._post_request(f"{LAMBDA_FUNCTIONS_PATH}/{id_function_detector}", self.admin, data)
-            self.assertEqual(response.status_code, status.HTTP_200_OK)
-
-        data = {
-            "task": self.main_task["id"],
-            "frame": 0,
-            "cleanup": True,
-            "quality": "test-error-quality",
-            "mapping": {
-                "car": { "name": "car" },
-            },
-        }
-
-        response = self._post_request(f"{LAMBDA_FUNCTIONS_PATH}/{id_function_detector}", self.admin, data)
-        self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)
-
     def test_api_v2_lambda_functions_convert_mask_to_rle(self):
         data_main_task = {
             "function": id_function_detector,
             "task": self.main_task["id"],
             "cleanup": True,
-            "quality": "original",
             "mapping": {
                 "car": { "name": "car" },
             },

cvat/apps/lambda_manager/views.py

@@ -32,7 +32,7 @@
 from rest_framework.request import Request
 
 import cvat.apps.dataset_manager as dm
-from cvat.apps.engine.frame_provider import FrameQuality, TaskFrameProvider
+from cvat.apps.engine.frame_provider import TaskFrameProvider
 from cvat.apps.engine.models import (
     Job, ShapeType, SourceType, Task, Label, RequestAction, RequestTarget
 )
@@ -257,7 +257,6 @@ def mandatory_arg(name: str) -> Any:
         threshold = data.get("threshold")
         if threshold:
             payload.update({ "threshold": threshold })
-        quality = data.get("quality")
         mapping = data.get("mapping", {})
 
         model_labels = self.labels
@@ -387,19 +386,19 @@ def validate_attributes_mapping(attributes_mapping, model_attributes, db_attribu
 
         if self.kind == FunctionKind.DETECTOR:
             payload.update({
-                "image": self._get_image(db_task, mandatory_arg("frame"), quality)
+                "image": self._get_image(db_task, mandatory_arg("frame"))
             })
         elif self.kind == FunctionKind.INTERACTOR:
             payload.update({
-                "image": self._get_image(db_task, mandatory_arg("frame"), quality),
+                "image": self._get_image(db_task, mandatory_arg("frame")),
                 "pos_points": mandatory_arg("pos_points"),
                 "neg_points": mandatory_arg("neg_points"),
                 "obj_bbox": data.get("obj_bbox", None)
             })
         elif self.kind == FunctionKind.REID:
             payload.update({
-                "image0": self._get_image(db_task, mandatory_arg("frame0"), quality),
-                "image1": self._get_image(db_task, mandatory_arg("frame1"), quality),
+                "image0": self._get_image(db_task, mandatory_arg("frame0")),
+                "image1": self._get_image(db_task, mandatory_arg("frame1")),
                 "boxes0": mandatory_arg("boxes0"),
                 "boxes1": mandatory_arg("boxes1")
             })
@@ -410,7 +409,7 @@ def validate_attributes_mapping(attributes_mapping, model_attributes, db_attribu
                 })
         elif self.kind == FunctionKind.TRACKER:
             payload.update({
-                "image": self._get_image(db_task, mandatory_arg("frame"), quality),
+                "image": self._get_image(db_task, mandatory_arg("frame")),
                 "shapes": data.get("shapes", []),
                 "states": data.get("states", [])
             })
@@ -487,19 +486,9 @@ def transform_attributes(input_attributes, attr_mapping, db_attributes):
 
         return response
 
-    def _get_image(self, db_task, frame, quality):
-        if quality is None or quality == "original":
-            quality = FrameQuality.ORIGINAL
-        elif  quality == "compressed":
-            quality = FrameQuality.COMPRESSED
-        else:
-            raise ValidationError(
-                '`{}` lambda function was run '.format(self.id) +
-                'with wrong arguments (quality={})'.format(quality),
-                code=status.HTTP_400_BAD_REQUEST)
-
+    def _get_image(self, db_task, frame):
         frame_provider = TaskFrameProvider(db_task)
-        image = frame_provider.get_frame(frame, quality=quality)
+        image = frame_provider.get_frame(frame)
 
         return base64.b64encode(image.data.getvalue()).decode('utf-8')
 
@@ -523,7 +512,7 @@ def get_jobs(self):
         return [LambdaJob(job) for job in jobs if job and job.meta.get("lambda")]
 
     def enqueue(self,
-        lambda_func, threshold, task, quality, mapping, cleanup, conv_mask_to_poly, max_distance, request,
+        lambda_func, threshold, task, mapping, cleanup, conv_mask_to_poly, max_distance, request,
         *,
         job: Optional[int] = None
     ) -> LambdaJob:
@@ -576,7 +565,6 @@ def enqueue(self,
                     "threshold": threshold,
                     "task": task,
                     "job": job,
-                    "quality": quality,
                     "cleanup": cleanup,
                     "conv_mask_to_poly": conv_mask_to_poly,
                     "mapping": mapping,
@@ -667,7 +655,6 @@ def _call_detector(
         function: LambdaFunction,
         db_task: Task,
         labels: Dict[str, Dict[str, Any]],
-        quality: str,
         threshold: float,
         mapping: Optional[Dict[str, str]],
         conv_mask_to_poly: bool,
@@ -799,7 +786,7 @@ def _map(sublabel_body):
                 continue
 
             annotations = function.invoke(db_task, db_job=db_job, data={
-                "frame": frame, "quality": quality, "mapping": mapping,
+                "frame": frame, "mapping": mapping,
                 "threshold": threshold
             })
 
@@ -854,7 +841,6 @@ def _call_reid(
         cls,
         function: LambdaFunction,
         db_task: Task,
-        quality: str,
         threshold: float,
         max_distance: int,
         *,
@@ -887,7 +873,7 @@ def _call_reid(
             boxes1 = boxes_by_frame[frame1]
             if boxes0 and boxes1:
                 matching = function.invoke(db_task, db_job=db_job, data={
-                    "frame0": frame0, "frame1": frame1, "quality": quality,
+                    "frame0": frame0, "frame1": frame1,
                     "boxes0": boxes0, "boxes1": boxes1, "threshold": threshold,
                     "max_distance": max_distance})
 
@@ -947,7 +933,7 @@ def _call_reid(
                     dm.task.put_task_data(db_task.id, serializer.data)
 
     @classmethod
-    def __call__(cls, function, task: int, quality: str, cleanup: bool, **kwargs):
+    def __call__(cls, function, task: int, cleanup: bool, **kwargs):
         # TODO: need logging
         db_job = None
         if job := kwargs.get('job'):
@@ -977,11 +963,11 @@ def convert_labels(db_labels):
         labels = convert_labels(db_task.get_labels(prefetch=True))
 
         if function.kind == FunctionKind.DETECTOR:
-            cls._call_detector(function, db_task, labels, quality,
+            cls._call_detector(function, db_task, labels,
                 kwargs.get("threshold"), kwargs.get("mapping"), kwargs.get("conv_mask_to_poly"),
                 db_job=db_job)
         elif function.kind == FunctionKind.REID:
-            cls._call_reid(function, db_task, quality,
+            cls._call_reid(function, db_task,
                 kwargs.get("threshold"), kwargs.get("max_distance"), db_job=db_job)
 
 def return_response(success_code=status.HTTP_200_OK):
@@ -1176,7 +1162,6 @@ def create(self, request):
             threshold = request_data.get('threshold')
             task = request_data['task']
             job = request_data.get('job', None)
-            quality = request_data.get("quality")
             cleanup = request_data.get('cleanup', False)
             conv_mask_to_poly = request_data.get('convMaskToPoly', False)
             mapping = request_data.get('mapping')
@@ -1190,7 +1175,7 @@ def create(self, request):
         gateway = LambdaGateway()
         queue = LambdaQueue()
         lambda_func = gateway.get(function)
-        rq_job = queue.enqueue(lambda_func, threshold, task, quality,
+        rq_job = queue.enqueue(lambda_func, threshold, task,
             mapping, cleanup, conv_mask_to_poly, max_distance, request, job=job)
 
         handle_function_call(function, job or task, category="batch")

cvat/schema.yml

@@ -8049,15 +8049,6 @@ components:
         job:
           type: integer
           description: The id of the job to be annotated
-        quality:
-          allOf:
-          - $ref: '#/components/schemas/QualityEnum'
-          default: original
-          description: |-
-            The quality of the images to use in the model run
-
-            * `compressed` - compressed
-            * `original` - original
         max_distance:
           type: integer
         threshold:
@@ -10020,14 +10011,6 @@ components:
         * `AZURE_CONTAINER` - AZURE_CONTAINER
         * `GOOGLE_DRIVE` - GOOGLE_DRIVE
         * `GOOGLE_CLOUD_STORAGE` - GOOGLE_CLOUD_STORAGE
-    QualityEnum:
-      enum:
-      - compressed
-      - original
-      type: string
-      description: |-
-        * `compressed` - compressed
-        * `original` - original
     QualityReport:
       type: object
       properties: