Merge pull request #15 from Relifest/main

Basic function repair and maintenance.

pytdml/io/tdml_readers.py

@@ -29,13 +29,7 @@
 #
 # ------------------------------------------------------------------------------
 import json
-
-
-# from pytdml.type.basic_types_old import TrainingDataset
-# from pytdml.type.extended_types_old import EOTrainingDataset
-from pytdml.type.basic_types import TrainingDataset
-from pytdml.type.extended_types import EOTrainingDataset
-
+from pytdml.type import TrainingDataset, EOTrainingDataset
 
 
 def read_from_json(file_path: str):
@@ -50,27 +44,9 @@ def read_from_json(file_path: str):
 
 def parse_json(json_dict):
     # Different kinds of training datasets are supported
-    if json_dict["type"] == "TrainingDataset":
-        return TrainingDataset(**json_dict)
-    elif json_dict["type"] == "EOTrainingDataset":
-        return EOTrainingDataset(**json_dict)
+    if json_dict["type"] == "AI_TrainingDataset":
+        return TrainingDataset.from_dict(json_dict)
     elif json_dict["type"] == "AI_EOTrainingDataset":
-        return EOTrainingDataset(**json_dict)
+        return EOTrainingDataset.from_dict(json_dict)
     else:
         raise ValueError("Unknown TDML type: {}".format(json_dict["type"]))
-
-
-# def read_from_json(file_path: str):
-#     """
-#     Reads a TDML JSON file and returns a TrainingDataset object.
-#     """
-#     with open(file_path, "r", encoding='utf-8') as f:
-#         json_dict = json.load(f)
-#         # Different kinds of training datasets are supported
-#         if json_dict["type"] == "TrainingDataset":
-#             return TrainingDataset.from_dict(json_dict)
-#         elif json_dict["type"] == "EOTrainingDataset":
-#             return EOTrainingDataset.from_dict(json_dict)
-#         else:
-#             raise ValueError("Unknown TDML type: {}".format(json_dict["type"]))
-

pytdml/io/tdml_writers.py

@@ -31,21 +31,13 @@
 # ------------------------------------------------------------------------------
 import json
 from typing import Union
-from pytdml.type.basic_types import TrainingDataset
-from pytdml.utils import remove_empty
+from pytdml.type import TrainingDataset, EOTrainingDataset
 
 
-# def write_to_json(td: TrainingDataset, file_path: str, indent: Union[None, int, str] = 4):
-#     """
-#     Writes a TrainingDataset to a JSON file.
-#     """
-#     with open(file_path, "w", encoding='utf-8') as f:
-#         json.dump(remove_empty(td.to_dict()), f, indent=indent, ensure_ascii=False)
-
-def write_to_json(td: TrainingDataset, file_path: str, indent: Union[None, int, str] = 4):
+def write_to_json(td: TrainingDataset or EOTrainingDataset, file_path: str, indent: Union[int, str] = 4):
     """
     Writes a TrainingDataset to a JSON file.
     """
     with open(file_path, "w", encoding='utf-8') as f:
-        json.dump(td.dict(by_alias=True,exclude_none=True), f, indent=indent, ensure_ascii=False)
+        json.dump(td.to_dict(), f, indent=indent, ensure_ascii=False)
         # json.dump(remove_empty(td.dict()), f, indent=indent, ensure_ascii=False)

pytdml/type/UiT_HCD_California_2017.json

@@ -0,0 +1,109 @@
+{
+  "type": "AI_EOTrainingDataset",
+  "id": "uit_hcd_california_2017",
+  "name": "UiT HCD California 2017",
+  "description": "This dataset is composed of two images and a label image.",
+  "license": "CC BY-SA 4.0",
+  "version": "1.0",
+  "amountOfTrainingData": 1,
+  "createdTime": "2017-01-01",
+  "providers": [
+    "LP DAAC",
+    "ESA"
+  ],
+  "classes": [
+    {
+      "key": "change",
+      "value": 1
+    },
+    {
+      "key": "unchanged",
+      "value": 0
+    }
+  ],
+  "numberOfClasses": 2,
+  "bands": [
+    {
+      "name": [
+        {
+          "code": "red"
+        }
+      ]
+    },
+    {
+      "name": [
+        {
+          "code": "green"
+        }
+      ]
+    },
+    {
+      "name": [
+        {
+          "code": "blue"
+        }
+      ]
+    },
+    {
+      "name": [
+        {
+          "code": "VH"
+        }
+      ]
+    },
+    {
+      "name": [
+        {
+          "code": "VV"
+        }
+      ]
+    },
+    {
+      "name": [
+        {
+          "code": "VV/VH"
+        }
+      ]
+    }
+  ],
+  "imageSize": "2000x3500",
+  "tasks": [
+    {
+      "type": "AI_EOTask",
+      "id": "uit_hcd_california_2017-task",
+      "description": "Multi-source images change detection",
+      "taskType": "http://demo#change_detection"
+    }
+  ],
+  "data": [
+    {
+      "type": "AI_EOTrainingData",
+      "id": "0",
+      "dataTime": [
+        "2017-01-05",
+        "2017-02-18"
+      ],
+      "dataURL": [
+        "t1_L8.png",
+        "t2_SAR.png"
+      ],
+      "dataSources": [
+        {
+          "title": "Landsat-8"
+        }
+      ],
+      "numberOfLabels": 1,
+      "labels": [
+        {
+          "type": "AI_PixelLabel",
+          "imageURL": [
+            "change_label.png"
+          ],
+          "imageFormat": [
+            "image/png"
+          ]
+        }
+      ]
+    }
+  ]
+}

pytdml/type/UiT_HCD_California_2017.yml

@@ -0,0 +1,55 @@
+---
+type: AI_EOTrainingDataset
+id: uit_hcd_california_2017
+name: UiT HCD California 2017
+description: This dataset is composed of two images and a label image.
+license: CC BY-SA 4.0
+version: '1.0'
+amountOfTrainingData: 1
+createdTime: '2017-01-01'
+providers:
+  - LP DAAC
+  - ESA
+classes:
+  - key: change
+    value: 1
+  - key: unchanged
+    value: 0
+numberOfClasses: 2
+bands:
+  - name:
+      - code: red
+  - name:
+      - code: green
+  - name:
+      - code: blue
+  - name:
+      - code: VH
+  - name:
+      - code: VV
+  - name:
+      - code: VV/VH
+imageSize: 2000x3500
+tasks:
+  - type: AI_EOTask
+    id: uit_hcd_california_2017-task
+    description: Multi-source images change detection
+    taskType: http://demo#change_detection
+data:
+  - type: AI_EOTrainingData
+    id: '0'
+    dataTime:
+      - '2017-01-05'
+      - '2017-02-18'
+    dataURL:
+      - t1_L8.png
+      - t2_SAR.png
+    dataSources:
+      - title: Landsat-8
+    numberOfLabels: 1
+    labels:
+      - type: AI_PixelLabel
+        imageURL:
+          - change_label.png
+        imageFormat:
+          - image/png

pytdml/type/__init__.py

@@ -29,33 +29,16 @@
 # SOFTWARE.
 #
 # ------------------------------------------------------------------------------
-from .basic_types import BaseCamelModel
-from .basic_types import KeyValuePair
-from .basic_types import MD_ScopeDescription
-from .basic_types import MD_Band
-from .basic_types import MD_Scope
-from .basic_types import CI_Date
-from .basic_types import MD_BrowseGraphic
-from .basic_types import CI_Citation
-from .basic_types import MD_Identifier
-from .basic_types import MetricsPair
-from .basic_types import MetricsInLiterature
-from .basic_types import Task
-from .basic_types import Labeler
-from .basic_types import LabelingProcedure
-from .basic_types import Labeling
-from .basic_types import QualityElement
-from .basic_types import DataQuality
-from .basic_types import Label
-from .basic_types import TrainingData
-from .basic_types import Changeset
-from .basic_types import StatisticsInfo
-from .basic_types import TrainingDataset
-
-
-from .extended_types import EOTrainingDataset
-from .extended_types import EOTrainingData
-from .extended_types import SceneLabel
-from .extended_types import ObjectLabel
-from .extended_types import PixelLabel
-from .extended_types import EOTask
+from .all_types import BaseCamelModel
+from .all_types import KeyValuePair
+from .all_types import MD_ScopeDescription
+from .all_types import MD_Band
+from .all_types import MD_Scope
+from .all_types import CI_Date
+from .all_types import MD_BrowseGraphic
+from .all_types import CI_Citation
+from .all_types import MD_Identifier
+from .all_types import QualityElement
+from .all_types import DataQuality
+from .all_types import TrainingDataset
+from .all_types import EOTrainingDataset

pytdml/type/_utils.py

@@ -34,9 +34,6 @@
 import re
 
 
-
-
-
 class InvalidDatetimeError(ValueError):
     def __init__(self, message):
         self.message = message
@@ -169,17 +166,34 @@ def _validate_image_format(image_format: str):
         "application/x-netcdf",
         "application/geopackage+sqlite3"
     }
-    return image_format in image_format_list
+    if image_format in image_format_list:
+        return image_format
+    else:
+        pass
 
 
 def _valid_methods(labeling_methods: str):
     labeling_methods_list = ["manual", "automatic", "semi-automatic", "unknown"]
-    return labeling_methods in labeling_methods_list
+    if labeling_methods in labeling_methods_list:
+        return labeling_methods
+    else:
+        pass
 
 
 def _validate_training_type(training_type: str):
     training_type_list = ["training", "validation", "test", "retraining"]
-    return training_type in training_type_list
+    if training_type in training_type_list:
+        return training_type
+    else:
+        pass
+
+
+def _validate_evaluation_method_type(evaluation_method_type: str):
+    evaluation_method_type_list = ["directInternal", "directExternal", "indirect"]
+    if evaluation_method_type in evaluation_method_type_list:
+        return evaluation_method_type
+    else:
+        pass
 
 
 def to_camel(string: str) -> str:
@@ -191,4 +205,16 @@ def to_camel(string: str) -> str:
     Returns:
         str: camelCase string
     """
-    return re.sub(r"_(\w)", lambda match: match.group(1).upper(), string)
+    return re.sub(r"_(\w)", lambda match: match.group(1).upper(), string)
+
+
+def to_interior_class(data_dict, name, class_name):
+    new_dic = data_dict[name]
+    new_dic = class_name.from_dict(new_dic)
+    data_dict[name] = new_dic
+
+
+def list_to_interior_class(data_dict, name, class_name):
+    new_dic = data_dict[name]
+    new_dic = [class_name.from_dict(i) for i in new_dic]
+    data_dict[name] = new_dic