Update README.md

README.md

@@ -13,26 +13,33 @@ pinned: false
 emoji: 🕵️
 ---
 
-# Metric Card for Detection Metric
+# Detection Metrics
 
-## Metric Description
+## Description
 This metric can be used to calculate object detection metrics. It has an option to calculate the metrics at different levels of bounding box sizes, so that more insight is provided into the performance for different objects. It is adapted from the base of pycocotools metrics.
 
 ## How to Use
+```python
+import evaluate
+import logging
+from seametrics.payload import PayloadProcessor
+
+logging.basicConfig(level=logging.WARNING)
+
+processor = PayloadProcessor(
+    dataset_name="SAILING_DATASET_QA",
+    gt_field="ground_truth_det",
+    models=["yolov5n6_RGB_D2304-v1_9C"],
+    sequence_list=["Trip_14_Seq_1"],
+    data_type="rgb",
+)
+
+module = evaluate.load("SEA-AI/det-metrics")
+module.add_from_payload(processor.payload)
+module.compute()
 ```
->>> import evaluate
->>> from seametrics.fo_to_payload.utils import fo_to_payload
->>> b = fo_to_payload(
->>>         dataset="SAILING_DATASET_QA",
->>>         gt_field="ground_truth_det",
->>>         models=["yolov5n6_RGB_D2304-v1_9C"],
->>>         sequence_list=["Trip_14_Seq_1"],
->>>         data_type="rgb"
->>>    )
->>> module = evaluate.load("SEA-AI/det-metrics.py")
->>> module.add_batch(b)
->>> res = module.compute()
->>> print(res)
+
+```console
 {'all': {'range': [0, 10000000000.0],
   'iouThr': '0.00',
   'maxDets': 100,
@@ -62,56 +69,103 @@ The format of payload should be as returned by function `fo_to_payload()` define
 An example of how a payload might look like is:
 
 ``` 
-test_payload = {
+{
     'dataset': 'SAILING_DATASET_QA',
-    'models': ['yolov5n6_RGB_D2304-v1_9C'],
+    'models': ['yolov5n6_RGB_D2304-v1_9C'
+    ],
     'gt_field_name': 'ground_truth_det',
     'sequences': {
-        # sequence 1, 1 frame with 1 pred and 1 gt
+        # sequence 1,
+        1 frame with 1 pred and 1 gt
         'Trip_14_Seq_1': {
-            'resolution': (720, 1280),
-            'yolov5n6_RGB_D2304-v1_9C': [[fo.Detection(
+            'resolution': (720,
+            1280),
+            'yolov5n6_RGB_D2304-v1_9C': [
+                [fo.Detection(
                     label='FAR_AWAY_OBJECT',
-                    bounding_box=[0.35107421875, 0.274658203125, 0.0048828125, 0.009765625], # tp nr1
+                    bounding_box=[
+                        0.35107421875,
+                        0.274658203125,
+                        0.0048828125,
+                        0.009765625
+                    ], # tp nr1
                     confidence=0.153076171875
-                )]],
-                'ground_truth_det': [[fo.Detection(
+                )
+                ]
+            ],
+                'ground_truth_det': [
+                [fo.Detection(
                     label='FAR_AWAY_OBJECT',
-                    bounding_box=[0.35107421875, 0.274658203125, 0.0048828125, 0.009765625]
-                )]]
-            },
-        # sequence 2, 2 frames with frame 1: 2 pred, 1 gt; frame 2: 1 pred 1 gt
+                    bounding_box=[
+                        0.35107421875,
+                        0.274658203125,
+                        0.0048828125,
+                        0.009765625
+                    ]
+                )
+                ]
+            ]
+        },
+        # sequence 2,
+        2 frames with frame 1: 2 pred,
+        1 gt; frame 2: 1 pred 1 gt
         'Trip_14_Seq_2': {
-            'resolution': (720, 1280),
+            'resolution': (720,
+            1280),
             'yolov5n6_RGB_D2304-v1_9C': [
                 [
                     fo.Detection(
                         label='FAR_AWAY_OBJECT',
-                        bounding_box=[0.389404296875,0.306640625,0.005126953125,0.0146484375], # tp nr 2
+                        bounding_box=[
+                        0.389404296875,
+                        0.306640625,
+                        0.005126953125,
+                        0.0146484375
+                    ], # tp nr 2
                         confidence=0.153076171875
                         ),
                     fo.Detection(
                         label='FAR_AWAY_OBJECT',
-                        bounding_box=[0.50390625, 0.357666015625, 0.0048828125, 0.00976562], # fp nr 1
+                        bounding_box=[
+                        0.50390625,
+                        0.357666015625,
+                        0.0048828125,
+                        0.00976562
+                    ], # fp nr 1
                         confidence=0.153076171875
                         ),
                     fo.Detection(
                         label='FAR_AWAY_OBJECT',
-                        bounding_box=[0.455078125, 0.31494140625, 0.00390625, 0.0087890625], # fp nr 2
+                        bounding_box=[
+                        0.455078125,
+                        0.31494140625,
+                        0.00390625,
+                        0.0087890625
+                    ], # fp nr 2
                         confidence=0.153076171875
                     )
                 ],
                 [
                     fo.Detection(
                         label='FAR_AWAY_OBJECT',
-                        bounding_box=[0.455078125, 0.31494140625, 0.00390625, 0.0087890625], # tp nr 3
+                        bounding_box=[
+                        0.455078125,
+                        0.31494140625,
+                        0.00390625,
+                        0.0087890625
+                    ], # tp nr 3
                         confidence=0.153076171875
                         )
                 ],
                 [
                     fo.Detection(
                         label='FAR_AWAY_OBJECT',
-                        bounding_box=[0.455078125, 0.31494140625, 0.00390625, 0.0087890625], # fp nr 3
+                        bounding_box=[
+                        0.455078125,
+                        0.31494140625,
+                        0.00390625,
+                        0.0087890625
+                    ], # fp nr 3
                         confidence=0.153076171875
                         )
                 ]
@@ -121,29 +175,45 @@ test_payload = {
                 [
                     fo.Detection(
                         label='FAR_AWAY_OBJECT',
-                        bounding_box=[0.389404296875,0.306640625,0.005126953125,0.0146484375],
+                        bounding_box=[
+                        0.389404296875,
+                        0.306640625,
+                        0.005126953125,
+                        0.0146484375
+                    ],
                         )
                 ],
                 # frame nr 2
                 [
                     fo.Detection(
                         label='FAR_AWAY_OBJECT',
-                        bounding_box=[0.455078125, 0.31494140625, 0.00390625, 0.0087890625],
+                        bounding_box=[
+                        0.455078125,
+                        0.31494140625,
+                        0.00390625,
+                        0.0087890625
+                    ],
                         confidence=0.153076171875
                     ),
                     fo.Detection(
                         label='FAR_AWAY_OBJECT',
-                        bounding_box=[0.35107421875, 0.274658203125, 0.0048828125, 0.009765625], # missed nr 1
+                        bounding_box=[
+                        0.35107421875,
+                        0.274658203125,
+                        0.0048828125,
+                        0.009765625
+                    ], # missed nr 1
                         confidence=0.153076171875
                     )
                 ],
                 # frame nr3
-                [
-                ], 
+                [],
             ]
-            }
-        },
-    "sequence_list": ["Trip_14_Seq_1", 'Trip_14_Seq_2']
+        }
+    },
+    "sequence_list": [
+        "Trip_14_Seq_1", 'Trip_14_Seq_2'
+    ]
 }
 ```
 
@@ -169,88 +239,90 @@ Each sub-dictionary holds performance metrics at the specific area range level:
 
 ### Examples
 We can specify different area range levels, at which we would like to compute the metrics.
-```
->>> import evaluate
->>> from seametrics.fo_to_payload.utils import fo_to_payload
->>> area_ranges_tuples = [
-        ("all", [0, 1e5 ** 2]),
-        ("small", [0 ** 2, 6 ** 2]),
-        ("medium", [6 ** 2, 12 ** 2]),  
-        ("large", [12 ** 2, 1e5 ** 2])
-    ]
->>> payload = fo_to_payload(
-        dataset=dataset, 
-        gt_field=gt_field, 
-        models=model_list
-    )
->>> module = evaluate.load(
-      "./detection_metric.py",
-      iou_threshold=0.9,
-      area_ranges_tuples=area_ranges_tuples
+```python
+import evaluate
+import logging
+from seametrics.payload import PayloadProcessor
+
+logging.basicConfig(level=logging.WARNING)
+
+processor = PayloadProcessor(
+    dataset_name="SAILING_DATASET_QA",
+    gt_field="ground_truth_det",
+    models=["yolov5n6_RGB_D2304-v1_9C"],
+    sequence_list=["Trip_14_Seq_1"],
+    data_type="rgb",
 )
->>> module.add_batch(payload)
->>> result = module.compute()
->>> print(result)
-{'all': 
-    {'range': [0, 10000000000.0],
-    'iouThr': '0.9',
-    'maxDets': 100,
-    'tp': 0, 
-    'fp': 3,
-    'fn': 1,
-    'duplicates': 0,
-    'precision': 0.0,
-    'recall': 0.0,
-    'f1': 0,
-    'support': 1,
-    'fpi': 1,
-    'nImgs': 2
-}, 
-'small': {
-    'range': [0, 36],
-    'iouThr': '0.9',
-    'maxDets': 100,
-    'tp': 0,
-    'fp': 1,
-    'fn': 1,
-    'duplicates': 0,
-    'precision': 0.0,
-    'recall': 0.0,
-    'f1': 0,
-    'support': 1,
-    'fpi': 1, 
-    'nImgs': 2
-}, 
-'medium': {
-    'range': [36, 144],
-    'iouThr': '0.9',
-    'maxDets': 100,
-    'tp': 0,
-    'fp': 2,
-    'fn': 0,
-    'duplicates': 0,
-    'precision': 0.0,
-    'recall': 0,
-    'f1': 0,
-    'support': 0,
-    'fpi': 2,
-    'nImgs': 2
-}, 'large': {
-    'range': [144, 10000000000.0],
-    'iouThr': '0.9',
-    'maxDets': 100,
-    'tp': -1,
-    'fp': -1,
-    'fn': -1,
-    'duplicates': -1,
-    'precision': -1,
-    'recall': -1,
-    'f1': -1,
-    'support': 0,
-    'fpi': 0,
-    'nImgs': 2
-    }
-}
+
+area_ranges_tuples = [
+    ("all", [0, 1e5**2]),
+    ("small", [0**2, 6**2]),
+    ("medium", [6**2, 12**2]),
+    ("large", [12**2, 1e5**2]),
+]
+
+module = evaluate.load(
+    "SEA-AI/det-metrics",
+    iou_thresholds=[0.00001],
+    area_ranges_tuples=area_ranges_tuples,
+)
+module.add_from_payload(processor.payload)
+module.compute()
+```
+
+```
+{'all': {'range': [0, 10000000000.0],
+  'iouThr': '0.00',
+  'maxDets': 100,
+  'tp': 89,
+  'fp': 13,
+  'fn': 15,
+  'duplicates': 1,
+  'precision': 0.8725490196078431,
+  'recall': 0.8557692307692307,
+  'f1': 0.8640776699029126,
+  'support': 104,
+  'fpi': 0,
+  'nImgs': 22},
+ 'small': {'range': [0, 36],
+  'iouThr': '0.00',
+  'maxDets': 100,
+  'tp': 12,
+  'fp': 3,
+  'fn': 8,
+  'duplicates': 0,
+  'precision': 0.8,
+  'recall': 0.6,
+  'f1': 0.6857142857142857,
+  'support': 20,
+  'fpi': 0,
+  'nImgs': 22},
+ 'medium': {'range': [36, 144],
+  'iouThr': '0.00',
+  'maxDets': 100,
+  'tp': 50,
+  'fp': 10,
+  'fn': 7,
+  'duplicates': 1,
+  'precision': 0.8333333333333334,
+  'recall': 0.8771929824561403,
+  'f1': 0.8547008547008548,
+  'support': 57,
+  'fpi': 0,
+  'nImgs': 22},
+ 'large': {'range': [144, 10000000000.0],
+  'iouThr': '0.00',
+  'maxDets': 100,
+  'tp': 27,
+  'fp': 0,
+  'fn': 0,
+  'duplicates': 0,
+  'precision': 1.0,
+  'recall': 1.0,
+  'f1': 1.0,
+  'support': 27,
+  'fpi': 0,
+  'nImgs': 22}}
 ```
 
 ## Further References