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Datasets:
hotosm
/
vhr-building-segmentation

Tasks:
Image Segmentation
Modalities:
Geospatial
Image
Text
Formats:
parquet
Sub-tasks:
semantic-segmentation
Languages:
English
Size:
10K - 100K
Tags:
buildings
disaster-mapping
remote-sensing
satellite-imagery
openstreetmap
openaerialmap
Libraries:
Datasets
Dask
Polars
License:
Dataset card Data Studio Files
xet
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2
hotosm commited on
Commit
65adb1e
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verified ·
1 Parent(s): 04876b2

feat : add norm_stats for the train split

Browse files
Files changed (2) hide show
  1. norm_stats.ipynb +965 -0
  2. norm_stats.json +22 -0
norm_stats.ipynb ADDED
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+ "_model_module_version": "1.5.0",
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+ "_model_name": "DescriptionStyleModel",
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+ "_view_count": null,
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+ "_view_module": "@jupyter-widgets/base",
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+ "_view_module_version": "1.2.0",
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+ }
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+ }
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+ }
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+ },
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+ "cells": [
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+ {
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+ "cell_type": "code",
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+ "execution_count": 1,
708
+ "metadata": {
709
+ "id": "4W4f_7evsH2Q"
710
+ },
711
+ "outputs": [],
712
+ "source": [
713
+ "import io\n",
714
+ "import json\n",
715
+ "import tempfile\n",
716
+ "from pathlib import Path\n",
717
+ "\n",
718
+ "import numpy as np\n",
719
+ "import pyarrow.parquet as pq\n",
720
+ "from huggingface_hub import snapshot_download\n",
721
+ "from PIL import Image\n",
722
+ "from tqdm import tqdm\n",
723
+ "\n",
724
+ "REPO_ID = \"hotosm/vhr-building-segmentation\"\n",
725
+ "STATS_FILENAME = \"norm_stats.json\"\n",
726
+ "SPLIT_PREFIX = \"train\"\n",
727
+ "NUM_BANDS = 3\n",
728
+ "CHANNEL_NAMES = (\"R\", \"G\", \"B\")"
729
+ ]
730
+ },
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+ {
732
+ "cell_type": "code",
733
+ "source": [
734
+ "def download_train_shards(target: Path) -> list[Path]:\n",
735
+ " snapshot_download(\n",
736
+ " repo_id=REPO_ID,\n",
737
+ " repo_type=\"dataset\",\n",
738
+ " local_dir=str(target),\n",
739
+ " allow_patterns=[f\"data/{SPLIT_PREFIX}-*.parquet\"],\n",
740
+ " )\n",
741
+ " data_dir = target / \"data\"\n",
742
+ " shards = sorted(data_dir.glob(f\"{SPLIT_PREFIX}-*.parquet\"))\n",
743
+ " if not shards:\n",
744
+ " raise FileNotFoundError(f\"No {SPLIT_PREFIX}-*.parquet shards downloaded to {data_dir}\")\n",
745
+ " return shards"
746
+ ],
747
+ "metadata": {
748
+ "id": "MGAkJoyHsMj7"
749
+ },
750
+ "execution_count": 2,
751
+ "outputs": []
752
+ },
753
+ {
754
+ "cell_type": "code",
755
+ "source": [
756
+ "def decode_rgb_array(cell: dict) -> np.ndarray:\n",
757
+ " img = Image.open(io.BytesIO(cell[\"bytes\"])).convert(\"RGB\")\n",
758
+ " return np.asarray(img, dtype=np.float32) / 255.0"
759
+ ],
760
+ "metadata": {
761
+ "id": "vC-5g3OxsSsO"
762
+ },
763
+ "execution_count": 3,
764
+ "outputs": []
765
+ },
766
+ {
767
+ "cell_type": "code",
768
+ "source": [
769
+ "def accumulate_stats(\n",
770
+ " shard_paths: list[Path],\n",
771
+ ") -> tuple[np.ndarray, np.ndarray, int, int]:\n",
772
+ " sum_px = np.zeros(NUM_BANDS, dtype=np.float64)\n",
773
+ " sum_sq = np.zeros(NUM_BANDS, dtype=np.float64)\n",
774
+ " n_pixels = 0\n",
775
+ " n_samples = 0\n",
776
+ " for shard in tqdm(shard_paths, desc=\"shards\", unit=\"shard\"):\n",
777
+ " table = pq.read_table(str(shard), columns=[\"image\"])\n",
778
+ " column = table[\"image\"]\n",
779
+ " for i in tqdm(range(len(column)), desc=shard.name, leave=False, unit=\"img\"):\n",
780
+ " arr = decode_rgb_array(column[i].as_py())\n",
781
+ " pixels = arr.reshape(-1, NUM_BANDS).astype(np.float64, copy=False)\n",
782
+ " sum_px += pixels.sum(axis=0)\n",
783
+ " sum_sq += np.square(pixels).sum(axis=0)\n",
784
+ " n_pixels += pixels.shape[0]\n",
785
+ " n_samples += 1\n",
786
+ " return sum_px, sum_sq, n_pixels, n_samples\n",
787
+ "\n",
788
+ "\n",
789
+ "def finalize_stats(\n",
790
+ " sum_px: np.ndarray, sum_sq: np.ndarray, n_pixels: int\n",
791
+ ") -> tuple[list[float], list[float]]:\n",
792
+ " mean = sum_px / n_pixels\n",
793
+ " variance = np.maximum(sum_sq / n_pixels - mean**2, 0.0)\n",
794
+ " std = np.sqrt(variance)\n",
795
+ " return mean.tolist(), std.tolist()\n",
796
+ "\n",
797
+ "\n",
798
+ "def write_stats_file(\n",
799
+ " path: Path,\n",
800
+ " mean: list[float],\n",
801
+ " std: list[float],\n",
802
+ " n_samples: int,\n",
803
+ " n_pixels: int,\n",
804
+ ") -> None:\n",
805
+ " payload = {\n",
806
+ " \"mean\": mean,\n",
807
+ " \"std\": std,\n",
808
+ " \"n_samples\": n_samples,\n",
809
+ " \"n_pixels\": n_pixels,\n",
810
+ " \"split\": SPLIT_PREFIX,\n",
811
+ " \"scale\": \"0-1\",\n",
812
+ " \"channels\": list(CHANNEL_NAMES),\n",
813
+ " \"source_repo\": REPO_ID,\n",
814
+ " }\n",
815
+ " path.write_text(json.dumps(payload, indent=2) + \"\\n\")\n"
816
+ ],
817
+ "metadata": {
818
+ "id": "HC_CORGksTdk"
819
+ },
820
+ "execution_count": 5,
821
+ "outputs": []
822
+ },
823
+ {
824
+ "cell_type": "code",
825
+ "source": [
826
+ "with tempfile.TemporaryDirectory(prefix=\"hotbuild_stats_\") as tmp:\n",
827
+ " shards = download_train_shards(Path(tmp))\n",
828
+ " total_rows = sum(pq.read_metadata(str(s)).num_rows for s in shards)\n",
829
+ " print(f\"Downloaded {len(shards)} shards, {total_rows:,} training samples\")\n",
830
+ "\n",
831
+ " sum_px, sum_sq, n_pixels, n_samples = accumulate_stats(shards)\n",
832
+ " mean, std = finalize_stats(sum_px, sum_sq, n_pixels)\n",
833
+ "\n",
834
+ "print(f\"n_samples = {n_samples:,}\")\n",
835
+ "print(f\"n_pixels = {n_pixels:,}\")\n",
836
+ "print(f\"mean = {mean}\")\n",
837
+ "print(f\"std = {std}\")\n",
838
+ "\n",
839
+ "out_path = Path.cwd() / STATS_FILENAME\n",
840
+ "write_stats_file(out_path, mean, std, n_samples, n_pixels)\n",
841
+ "print(f\"Wrote {out_path}\")"
842
+ ],
843
+ "metadata": {
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+ "base_uri": "https://localhost:8080/",
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+ "height": 597,
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+ "outputId": "3092e2b4-562a-4899-ea01-c1ce2b22914c"
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+ },
875
+ "execution_count": 6,
876
+ "outputs": [
877
+ {
878
+ "output_type": "stream",
879
+ "name": "stderr",
880
+ "text": [
881
+ "/usr/local/lib/python3.12/dist-packages/huggingface_hub/utils/_auth.py:93: UserWarning: \n",
882
+ "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
883
+ "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
884
+ "You will be able to reuse this secret in all of your notebooks.\n",
885
+ "Please note that authentication is recommended but still optional to access public models or datasets.\n",
886
+ " warnings.warn(\n"
887
+ ]
888
+ },
889
+ {
890
+ "output_type": "display_data",
891
+ "data": {
892
+ "text/plain": [
893
+ "Downloading (incomplete total...): 0.00B [00:00, ?B/s]"
894
+ ],
895
+ "application/vnd.jupyter.widget-view+json": {
896
+ "version_major": 2,
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+ "version_minor": 0,
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+ "model_id": "786f8f2d2ae54f829af40c5a3dc33752"
899
+ }
900
+ },
901
+ "metadata": {}
902
+ },
903
+ {
904
+ "output_type": "display_data",
905
+ "data": {
906
+ "text/plain": [
907
+ "Fetching 11 files: 0%| | 0/11 [00:00<?, ?it/s]"
908
+ ],
909
+ "application/vnd.jupyter.widget-view+json": {
910
+ "version_major": 2,
911
+ "version_minor": 0,
912
+ "model_id": "0ad39833920d4de7b7aa51f6085a3dec"
913
+ }
914
+ },
915
+ "metadata": {}
916
+ },
917
+ {
918
+ "output_type": "stream",
919
+ "name": "stderr",
920
+ "text": [
921
+ "Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n",
922
+ "WARNING:huggingface_hub.utils._http:Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n"
923
+ ]
924
+ },
925
+ {
926
+ "output_type": "stream",
927
+ "name": "stdout",
928
+ "text": [
929
+ "Downloaded 11 shards, 57,890 training samples\n"
930
+ ]
931
+ },
932
+ {
933
+ "output_type": "stream",
934
+ "name": "stderr",
935
+ "text": [
936
+ "\n",
937
+ " \n",
938
+ " \n",
939
+ " \n",
940
+ " \n",
941
+ " \n",
942
+ " \n",
943
+ " \n",
944
+ " \n",
945
+ " \n",
946
+ " \n",
947
+ " \n",
948
+ "shards: 100%|██████████| 11/11 [03:36<00:00, 19.69s/shard]\n"
949
+ ]
950
+ },
951
+ {
952
+ "output_type": "stream",
953
+ "name": "stdout",
954
+ "text": [
955
+ "n_samples = 57,890\n",
956
+ "n_pixels = 3,793,879,040\n",
957
+ "mean = [0.4296737853453577, 0.4001659668453235, 0.34333372802741474]\n",
958
+ "std = [0.2056069389373208, 0.16738555558380538, 0.1598986422586595]\n",
959
+ "Wrote /content/norm_stats.json\n"
960
+ ]
961
+ }
962
+ ]
963
+ }
964
+ ]
965
+ }
norm_stats.json ADDED
@@ -0,0 +1,22 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "mean": [
3
+ 0.4296737853453577,
4
+ 0.4001659668453235,
5
+ 0.34333372802741474
6
+ ],
7
+ "std": [
8
+ 0.2056069389373208,
9
+ 0.16738555558380538,
10
+ 0.1598986422586595
11
+ ],
12
+ "n_samples": 57890,
13
+ "n_pixels": 3793879040,
14
+ "split": "train",
15
+ "scale": "0-1",
16
+ "channels": [
17
+ "R",
18
+ "G",
19
+ "B"
20
+ ],
21
+ "source_repo": "hotosm/vhr-building-segmentation"
22
+ }