Relative paths in `labels.json`

dan/datasets/analyze/__init__.py

@@ -6,7 +6,7 @@ Analyze dataset and display statistics in markdown format.
 from pathlib import Path
 
 from dan.datasets.analyze.statistics import run
-from dan.utils import read_json, read_yaml
+from dan.utils import read_yaml
 
 
 def add_analyze_parser(subcommands) -> None:
@@ -17,7 +17,7 @@ def add_analyze_parser(subcommands) -> None:
     )
     parser.add_argument(
         "--labels",
-        type=read_json,
+        type=Path,
         help="Path to the formatted labels in JSON format.",
         required=True,
     )

dan/datasets/analyze/statistics.py

@@ -10,6 +10,8 @@ import numpy as np
 from mdutils.mdutils import MdUtils
 from prettytable import MARKDOWN, PrettyTable
 
+from dan.utils import read_json
+
 logger = logging.getLogger(__name__)
 
 METRIC_COLUMN = "Metric"
@@ -157,14 +159,18 @@ class Statistics:
             level=3,
         )
 
-    def run(self, labels: Dict, tokens: Dict | None):
+    def run(self, labels_path: Path, tokens: Dict | None):
+        labels = read_json(labels_path)
+
         # Iterate over each split
         for split_name, split_data in labels.items():
             self.document.new_header(level=1, title=split_name.capitalize())
 
             # Image statistics
             # Path to the images are the key of the dict
-            self.create_image_statistics(images=split_data.keys())
+            self.create_image_statistics(
+                images=[labels_path.parent / image_path for image_path in split_data]
+            )
 
             labels = list(split_data.values())
             # Text statistics
@@ -175,8 +181,8 @@ class Statistics:
         self.document.create_md_file()
 
 
-def run(labels: Dict, tokens: Dict | None, output: Path) -> None:
+def run(labels: Path, tokens: Dict | None, output: Path) -> None:
     """
     Compute and save a dataset statistics.
     """
-    Statistics(filename=str(output)).run(labels=labels, tokens=tokens)
+    Statistics(filename=str(output)).run(labels_path=labels, tokens=tokens)

dan/datasets/download/images.py

@@ -49,7 +49,7 @@ class ImageDownloader:
         self.image_extension = image_extension
 
         # Load split file
-        split_file = output / "split.json" if output else None
+        split_file = self.output / "split.json" if self.output else None
         self.split: Dict = (
             json.loads(split_file.read_text())
             if split_file and split_file.is_file()
@@ -127,7 +127,10 @@ class ImageDownloader:
                 image_path = destination / values["dataset_id"] / filename
                 image_path.parent.mkdir(parents=True, exist_ok=True)
 
-                self.data[split][str(image_path)] = values["text"]
+                # Store a relative path to the label file in case we need to move the data elsewhere
+                self.data[split][str(image_path.relative_to(self.output))] = values[
+                    "text"
+                ]
 
                 # Create task for multithreading pool if image does not exist yet
                 if image_path.exists():

dan/ocr/manager/dataset.py

 # -*- coding: utf-8 -*-
 import copy
 import json
-from pathlib import Path
 
 import numpy as np
 from torch.utils.data import Dataset
@@ -95,7 +94,7 @@ class OCRDataset(Dataset):
             set_name = path_and_set["set_name"]
             gt = gt_per_set[set_name]
             for filename in natural_sort(gt):
-                filepath = Path(filename)
+                filepath = path / filename
                 samples.append(
                     {
                         "name": filepath.name,
@@ -105,7 +104,7 @@ class OCRDataset(Dataset):
                 )
                 if self.load_in_memory:
                     samples[-1]["img"] = self.preprocessing_transforms(
-                        read_image(filename)
+                        read_image(str(filepath))
                     )
         return samples
 

tests/data/prediction/labels.json

 {
     "train": {
-        "tests/data/prediction/images/0a56e8b3-95cd-4fa5-a17b-5b0ff9e6ea84.png": "ⓈBellisson ⒻGeorges Ⓑ91 ⓁP ⒸM ⓀCh ⓄPlombier Ⓟ12241",
-        "tests/data/prediction/images/0dfe8bcd-ed0b-453e-bf19-cc697012296e.png": "ⓈTemplié ⒻMarcelle Ⓑ93 ⓁJ Ⓚch ⓄE dachyle"
+        "images/0a56e8b3-95cd-4fa5-a17b-5b0ff9e6ea84.png": "ⓈBellisson ⒻGeorges Ⓑ91 ⓁP ⒸM ⓀCh ⓄPlombier Ⓟ12241",
+        "images/0dfe8bcd-ed0b-453e-bf19-cc697012296e.png": "ⓈTemplié ⒻMarcelle Ⓑ93 ⓁJ Ⓚch ⓄE dachyle"
     },
     "val": {
-        "tests/data/prediction/images/2c242f5c-e979-43c4-b6f2-a6d4815b651d.png": "ⓈA ⒻCharles Ⓑ11 ⓁP ⒸC ⓀF ⓄA Ⓟ14331"
+        "images/2c242f5c-e979-43c4-b6f2-a6d4815b651d.png": "ⓈA ⒻCharles Ⓑ11 ⓁP ⒸC ⓀF ⓄA Ⓟ14331"
     },
     "test": {
-        "tests/data/prediction/images/ffdec445-7f14-4f5f-be44-68d0844d0df1.png": "ⓈNaudin ⒻMarie Ⓑ53 ⓁS ⒸV ⓀBelle mère"
+        "images/ffdec445-7f14-4f5f-be44-68d0844d0df1.png": "ⓈNaudin ⒻMarie Ⓑ53 ⓁS ⒸV ⓀBelle mère"
     }
 }

tests/data/training/training_dataset/labels.json

 {
     "train": {
-        "tests/data/training/training_dataset/images/0a34e13a-4ab0-4a91-8d7c-b1d8fee32628.png": "The latter do not regard",
-        "tests/data/training/training_dataset/images/0a70e14f-feda-4607-989c-36cf581ddff5.png": "At the beginning of"
+        "images/0a34e13a-4ab0-4a91-8d7c-b1d8fee32628.png": "The latter do not regard",
+        "images/0a70e14f-feda-4607-989c-36cf581ddff5.png": "At the beginning of"
     },
     "val": {
-        "tests/data/training/training_dataset/images/0a576062-303c-4893-a729-c09c92865d31.png": "One can remember with",
-        "tests/data/training/training_dataset/images/0b2457c8-81f1-4600-84d9-f8bf2822a991.png": "The play was no more"
+        "images/0a576062-303c-4893-a729-c09c92865d31.png": "One can remember with",
+        "images/0b2457c8-81f1-4600-84d9-f8bf2822a991.png": "The play was no more"
     },
     "test": {
-        "tests/data/training/training_dataset/images/fb3edb59-3678-49f8-8e16-8e32e3b0f051.png": "Both her wrists bore",
-        "tests/data/training/training_dataset/images/fe498de2-ece4-4fbe-8b53-edfce1b820f0.png": "SOME years ago a contemporary"
+        "images/fb3edb59-3678-49f8-8e16-8e32e3b0f051.png": "Both her wrists bore",
+        "images/fe498de2-ece4-4fbe-8b53-edfce1b820f0.png": "SOME years ago a contemporary"
     }
 }

tests/test_analyze.py

@@ -3,7 +3,7 @@
 import pytest
 from mdutils.mdutils import MdUtils
 
-from dan.datasets.analyze import read_json, read_yaml
+from dan.datasets.analyze import read_yaml
 from dan.datasets.analyze.statistics import Statistics
 from tests.conftest import FIXTURES
 
@@ -74,7 +74,7 @@ def test_run(full_statistics, tmp_path):
     output_file = tmp_path / "stats.md"
     stats = Statistics(filename=str(output_file))
     stats.run(
-        labels=read_json(FIXTURES / "training" / "training_dataset" / "labels.json"),
+        labels_path=FIXTURES / "training" / "training_dataset" / "labels.json",
         tokens=read_yaml(FIXTURES / "training" / "training_dataset" / "tokens.yaml"),
     )
     assert output_file.read_text() == full_statistics

tests/test_download.py

@@ -90,26 +90,26 @@ def test_download(split_content, monkeypatch, tmp_path):
     # Check "labels.json"
     expected_labels = {
         "test": {
-            str(TEST_DIR / "test-page_1-line_1.jpg"): "ⓢCou⁇e⁇  ⓕBouis  ⓑ⁇.12.14",
-            str(TEST_DIR / "test-page_1-line_2.jpg"): "ⓢ⁇outrain  ⓕA⁇ol⁇⁇e  ⓑ9.4.13",
-            str(TEST_DIR / "test-page_1-line_3.jpg"): "ⓢ⁇abale  ⓕ⁇ran⁇ais  ⓑ26.3.11",
-            str(TEST_DIR / "test-page_2-line_1.jpg"): "ⓢ⁇urosoy  ⓕBouis  ⓑ22⁇4⁇18",
-            str(TEST_DIR / "test-page_2-line_2.jpg"): "ⓢColaiani  ⓕAn⁇els  ⓑ28.11.1⁇",
-            str(TEST_DIR / "test-page_2-line_3.jpg"): "ⓢRenouar⁇  ⓕMaurice  ⓑ2⁇.⁇.04",
+            "images/test/dataset_id/test-page_1-line_1.jpg": "ⓢCou⁇e⁇  ⓕBouis  ⓑ⁇.12.14",
+            "images/test/dataset_id/test-page_1-line_2.jpg": "ⓢ⁇outrain  ⓕA⁇ol⁇⁇e  ⓑ9.4.13",
+            "images/test/dataset_id/test-page_1-line_3.jpg": "ⓢ⁇abale  ⓕ⁇ran⁇ais  ⓑ26.3.11",
+            "images/test/dataset_id/test-page_2-line_1.jpg": "ⓢ⁇urosoy  ⓕBouis  ⓑ22⁇4⁇18",
+            "images/test/dataset_id/test-page_2-line_2.jpg": "ⓢColaiani  ⓕAn⁇els  ⓑ28.11.1⁇",
+            "images/test/dataset_id/test-page_2-line_3.jpg": "ⓢRenouar⁇  ⓕMaurice  ⓑ2⁇.⁇.04",
         },
         "train": {
-            str(TRAIN_DIR / "train-page_1-line_1.jpg"): "ⓢCaillet  ⓕMaurice  ⓑ28.9.06",
-            str(TRAIN_DIR / "train-page_1-line_2.jpg"): "ⓢReboul  ⓕJean  ⓑ30.9.02",
-            str(TRAIN_DIR / "train-page_1-line_3.jpg"): "ⓢBareyre  ⓕJean  ⓑ28.3.11",
-            str(TRAIN_DIR / "train-page_1-line_4.jpg"): "ⓢRoussy  ⓕJean  ⓑ4.11.14",
-            str(TRAIN_DIR / "train-page_2-line_1.jpg"): "ⓢMarin  ⓕMarcel  ⓑ10.8.06",
-            str(TRAIN_DIR / "train-page_2-line_2.jpg"): "ⓢAmical  ⓕEloi  ⓑ11.10.04",
-            str(TRAIN_DIR / "train-page_2-line_3.jpg"): "ⓢBiros  ⓕMael  ⓑ30.10.10",
+            "images/train/dataset_id/train-page_1-line_1.jpg": "ⓢCaillet  ⓕMaurice  ⓑ28.9.06",
+            "images/train/dataset_id/train-page_1-line_2.jpg": "ⓢReboul  ⓕJean  ⓑ30.9.02",
+            "images/train/dataset_id/train-page_1-line_3.jpg": "ⓢBareyre  ⓕJean  ⓑ28.3.11",
+            "images/train/dataset_id/train-page_1-line_4.jpg": "ⓢRoussy  ⓕJean  ⓑ4.11.14",
+            "images/train/dataset_id/train-page_2-line_1.jpg": "ⓢMarin  ⓕMarcel  ⓑ10.8.06",
+            "images/train/dataset_id/train-page_2-line_2.jpg": "ⓢAmical  ⓕEloi  ⓑ11.10.04",
+            "images/train/dataset_id/train-page_2-line_3.jpg": "ⓢBiros  ⓕMael  ⓑ30.10.10",
         },
         "val": {
-            str(VAL_DIR / "val-page_1-line_1.jpg"): "ⓢMonar⁇  ⓕBouis  ⓑ29⁇⁇⁇04",
-            str(VAL_DIR / "val-page_1-line_2.jpg"): "ⓢAstier  ⓕArt⁇ur  ⓑ11⁇2⁇13",
-            str(VAL_DIR / "val-page_1-line_3.jpg"): "ⓢ⁇e ⁇lie⁇er  ⓕJules  ⓑ21⁇11⁇11",
+            "images/val/dataset_id/val-page_1-line_1.jpg": "ⓢMonar⁇  ⓕBouis  ⓑ29⁇⁇⁇04",
+            "images/val/dataset_id/val-page_1-line_2.jpg": "ⓢAstier  ⓕArt⁇ur  ⓑ11⁇2⁇13",
+            "images/val/dataset_id/val-page_1-line_3.jpg": "ⓢ⁇e ⁇lie⁇er  ⓕJules  ⓑ21⁇11⁇11",
         },
     }