Merge branch 'main' of https://huggingface.co/spaces/SEA-AI/PanopticQuality

README.md

@@ -29,55 +29,153 @@ To get started with PanopticQuality, make sure you have the necessary dependenci
 >>>                         models=MODEL_FIELD,
 >>>                         sequence_list=["Trip_55_Seq_2", "Trip_197_Seq_1", "Trip_197_Seq_68"],
 >>>                         excluded_classes=[""]).payload
->>> module = evaluate.load("SEA-AI/PanopticQuality")
+>>> module = evaluate.load("SEA-AI/PanopticQuality", area_rng=[(0, 100),(100, 1e9)])
 >>> module.add_payload(payload, model_name=MODEL_FIELD[0])
 >>> module.compute()
 100%|██████████| 3/3 [00:03<00:00,  1.30s/it]
 Added data ...
 Start computing ...
 Finished!
-{'scores': {'MOTORBOAT': [0.18632257426639526,
-   0.698709617058436,
-   0.2666666805744171],
-  'FAR_AWAY_OBJECT': [0.0, 0.0, 0.0],
-  'SAILING_BOAT_WITH_CLOSED_SAILS': [0.0, 0.0, 0.0],
-  'SHIP': [0.3621737026917471, 0.684105846616957, 0.529411792755127],
-  'WATERCRAFT': [0.0, 0.0, 0.0],
-  'SPHERICAL_BUOY': [0.0, 0.0, 0.0],
-  'FLOTSAM': [0.0, 0.0, 0.0],
-  'SAILING_BOAT_WITH_OPEN_SAILS': [0.0, 0.0, 0.0],
-  'CONTAINER': [0.0, 0.0, 0.0],
-  'PILLAR_BUOY': [0.0, 0.0, 0.0],
-  'AERIAL_ANIMAL': [0.0, 0.0, 0.0],
-  'HUMAN_IN_WATER': [0.0, 0.0, 0.0],
-  'WOODEN_LOG': [0.0, 0.0, 0.0],
-  'MARITIME_ANIMAL': [0.0, 0.0, 0.0],
-  'WATER': [0.9397601008415222, 0.9397601008415222, 1.0],
-  'SKY': [0.9674496332804362, 0.9674496332804362, 1.0],
-  'LAND': [0.30757412078761204, 0.8304501533508301, 0.37037035822868347],
-  'CONSTRUCTION': [0.0, 0.0, 0.0],
-  'OWN_BOAT': [0.0, 0.0, 0.0],
-  'ALL': [0.14543579641409013, 0.21686712374464112, 0.16665520166095935]},
- 'numbers': {'MOTORBOAT': [6, 15, 18, 4.1922577023506165],
-  'FAR_AWAY_OBJECT': [0, 8, 9, 0.0],
-  'SAILING_BOAT_WITH_CLOSED_SAILS': [0, 2, 0, 0.0],
-  'SHIP': [9, 1, 15, 6.156952619552612],
-  'WATERCRAFT': [0, 9, 12, 0.0],
-  'SPHERICAL_BUOY': [0, 4, 22, 0.0],
-  'FLOTSAM': [0, 0, 1, 0.0],
-  'SAILING_BOAT_WITH_OPEN_SAILS': [0, 6, 0, 0.0],
-  'CONTAINER': [0, 0, 0, 0.0],
-  'PILLAR_BUOY': [0, 0, 9, 0.0],
-  'AERIAL_ANIMAL': [0, 0, 0, 0.0],
-  'HUMAN_IN_WATER': [0, 0, 0, 0.0],
-  'WOODEN_LOG': [0, 0, 0, 0.0],
-  'MARITIME_ANIMAL': [0, 0, 0, 0.0],
-  'WATER': [15, 0, 0, 14.096401512622833],
-  'SKY': [15, 0, 0, 14.511744499206543],
-  'LAND': [5, 9, 8, 4.15225076675415],
-  'CONSTRUCTION': [0, 0, 0, 0.0],
-  'OWN_BOAT': [0, 0, 8, 0.0],
-  'ALL': [50, 54, 102, 43.109607100486755]}}
+{'scores': {'MOTORBOAT': array([[0.        , 0.25889117],
+         [0.        , 0.79029936],
+         [0.        , 0.3275862 ]]),
+  'FAR_AWAY_OBJECT': array([[0., 0.],
+         [0., 0.],
+         [0., 0.]]),
+  'SAILING_BOAT_WITH_CLOSED_SAILS': array([[0.        , 0.35410052],
+         [0.        , 0.75246359],
+         [0.        , 0.47058824]]),
+  'SHIP': array([[0.        , 0.47743301],
+         [0.        , 0.90181785],
+         [0.        , 0.52941179]]),
+  'WATERCRAFT': array([[0., 0.],
+         [0., 0.],
+         [0., 0.]]),
+  'SPHERICAL_BUOY': array([[0., 0.],
+         [0., 0.],
+         [0., 0.]]),
+  'FLOTSAM': array([[0., 0.],
+         [0., 0.],
+         [0., 0.]]),
+  'SAILING_BOAT_WITH_OPEN_SAILS': array([[0., 0.],
+         [0., 0.],
+         [0., 0.]]),
+  'CONTAINER': array([[0., 0.],
+         [0., 0.],
+         [0., 0.]]),
+  'PILLAR_BUOY': array([[0., 0.],
+         [0., 0.],
+         [0., 0.]]),
+  'AERIAL_ANIMAL': array([[0., 0.],
+         [0., 0.],
+         [0., 0.]]),
+  'HUMAN_IN_WATER': array([[0., 0.],
+         [0., 0.],
+         [0., 0.]]),
+  'WOODEN_LOG': array([[0., 0.],
+         [0., 0.],
+         [0., 0.]]),
+  'MARITIME_ANIMAL': array([[0., 0.],
+         [0., 0.],
+         [0., 0.]]),
+  'WATER': array([[0.        , 0.96737861],
+         [0.        , 0.96737861],
+         [0.        , 1.        ]]),
+  'SKY': array([[0.        , 0.93018024],
+         [0.        , 0.93018024],
+         [0.        , 1.        ]]),
+  'LAND': array([[0.        , 0.53552331],
+         [0.        , 0.84447907],
+         [0.        , 0.63414633]]),
+  'CONSTRUCTION': array([[0., 0.],
+         [0., 0.],
+         [0., 0.]]),
+  'OWN_BOAT': array([[0., 0.],
+         [0., 0.],
+         [0., 0.]]),
+  'ALL': array([[0.        , 0.18544773],
+         [0.        , 0.27297993],
+         [0.        , 0.20851224]])},
+ 'numbers': {'MOTORBOAT': array([[ 0.        , 19.        ],
+         [ 6.        , 18.        ],
+         [10.        , 60.        ],
+         [ 0.        , 15.01568782]]),
+  'FAR_AWAY_OBJECT': array([[0., 0.],
+         [6., 6.],
+         [9., 0.],
+         [0., 0.]]),
+  'SAILING_BOAT_WITH_CLOSED_SAILS': array([[0.        , 4.        ],
+         [0.        , 6.        ],
+         [0.        , 3.        ],
+         [0.        , 3.00985438]]),
+  'SHIP': array([[ 0.        ,  9.        ],
+         [ 0.        ,  2.        ],
+         [ 1.        , 14.        ],
+         [ 0.        ,  8.11636066]]),
+  'WATERCRAFT': array([[ 0.,  0.],
+         [ 1.,  9.],
+         [11.,  1.],
+         [ 0.,  0.]]),
+  'SPHERICAL_BUOY': array([[ 0.,  0.],
+         [ 1.,  3.],
+         [36.,  0.],
+         [ 0.,  0.]]),
+  'FLOTSAM': array([[0., 0.],
+         [0., 0.],
+         [7., 4.],
+         [0., 0.]]),
+  'SAILING_BOAT_WITH_OPEN_SAILS': array([[0., 0.],
+         [0., 5.],
+         [0., 0.],
+         [0., 0.]]),
+  'CONTAINER': array([[0., 0.],
+         [0., 0.],
+         [0., 0.],
+         [0., 0.]]),
+  'PILLAR_BUOY': array([[0., 0.],
+         [0., 0.],
+         [5., 3.],
+         [0., 0.]]),
+  'AERIAL_ANIMAL': array([[0., 0.],
+         [0., 0.],
+         [0., 0.],
+         [0., 0.]]),
+  'HUMAN_IN_WATER': array([[0., 0.],
+         [0., 0.],
+         [0., 0.],
+         [0., 0.]]),
+  'WOODEN_LOG': array([[0., 0.],
+         [0., 0.],
+         [0., 0.],
+         [0., 0.]]),
+  'MARITIME_ANIMAL': array([[0., 0.],
+         [0., 0.],
+         [0., 0.],
+         [0., 0.]]),
+  'WATER': array([[ 0.        , 24.        ],
+         [ 0.        ,  0.        ],
+         [ 0.        ,  0.        ],
+         [ 0.        , 23.21708667]]),
+  'SKY': array([[ 0.        , 24.        ],
+         [ 0.        ,  0.        ],
+         [ 0.        ,  0.        ],
+         [ 0.        , 22.32432568]]),
+  'LAND': array([[ 0.        , 13.        ],
+         [ 0.        ,  7.        ],
+         [ 0.        ,  8.        ],
+         [ 0.        , 10.97822797]]),
+  'CONSTRUCTION': array([[0., 0.],
+         [0., 0.],
+         [0., 0.],
+         [0., 0.]]),
+  'OWN_BOAT': array([[ 0.,  0.],
+         [ 0.,  0.],
+         [ 0., 10.],
+         [ 0.,  0.]]),
+  'ALL': array([[  0.        ,  93.        ],
+         [ 14.        ,  56.        ],
+         [ 79.        , 103.        ],
+         [  0.        ,  82.66154319]])}}
 ```
 
 ## Metric Settings
@@ -114,19 +212,24 @@ The metric takes four optional input parameters: __label2id__, __stuff__, __per_
     Setting this to False will aggregate the results (average the _scores_, sum up the _numbers_; see below for explanation of _scores_ and _numbers_)
 * `split_sq_rq: bool = True`: By default, the PQ-score is returned in three parts: the PQ score itself, and split into the segmentation quality (SQ) and recognition quality (RQ) part.
     Setting this to False will return the PQ score only (PQ=RQ*SQ).
+* `area_rng: List[Tuple[float]]`: The list holds all the area ranges for which results are calculated.
+    Each range is represented by a Tuple, where the first element is the lower limit and the second is the upper limit of the area range.
+    Each value represents total number of pixels of a mask.
+    The parameter defaults to [(0, 1e8)].
 
 ## Output Values
 A dictionary containing the following keys:
 * __scores__: This is a dictionary, that contains a key for each label, if `per_class == True`. Otherwise it only contains the key _all_.
-              For each key, it contains a list that holds the scores in the following order: PQ, SQ and RQ. If `split_sq_rq == False`, the list consists of PQ only.
+              For each key, it contains an array that holds the scores in the the rows in following order: PQ, SQ and RQ. If `split_sq_rq == False`, the rows consist of PQ only.
+              The number of columns corresponds to the given area ranges. That means, the results in each column are for a certain size of objects.
 * __numbers__: This is a dictionary, that contains a key for each label, if `per_class == True`. Otherwise it only contains the key _all_.
-               For each key, it contains a list that consists of four elements: TP, FP, FN and IOU:
+               For each key, it contains an array that consists of four elements in the rows: TP, FP, FN and IOU:
   * __TP__: number of true positive predictions
   * __FP__: number of false positive predictions
   * __FN__: number of false negative predictions
   * __IOU__: sum of IOU of all TP predictions with ground truth
 
-  With all these values, it is possible to calculate the final scores.
+  With all these values, it is possible to calculate the final scores. As for the scores, the number of columns corresponds to the given area ranges. That means, the results in each column are for a certain size of objects.
 
 ## Further References