{ "cells": [ { "cell_type": "code", "execution_count": null, "id": "a1b2c3d4", "metadata": {}, "outputs": [], "source": [ "import ast\n", "import logging\n", "from collections import Counter, defaultdict\n", "from pathlib import Path\n", "\n", "import pandas as pd\n", "\n", "from histopathai.config import _ensure_env_loaded\n", "_ensure_env_loaded()\n", "\n", "from histopathai.dataset.builder import DatasetBuilder\n", "from histopathai.core.model.entities import (\n", " Workspace, Patient, Image, Annotation, AnnotationType\n", ")\n", "from histopathai.core.model.constants import (\n", " OrganType, TagType, AnnotationResource,\n", " ImageStatus, ProcessingVersion\n", ")\n", "from histopathai.core.model.vobj import ParentRef, ProcessingInfo, Point\n", "\n", "logging.basicConfig(level=logging.INFO, format=\"%(levelname)s — %(message)s\")\n", "print(\"Importlar başarılı.\")" ] }, { "cell_type": "code", "execution_count": null, "id": "b2c3d4e5", "metadata": {}, "outputs": [], "source": [ "RESOURCE_ROOT = Path(\".\")\n", "DATASET_ROOT = RESOURCE_ROOT / \"dataset\"\n", "\n", "builder = DatasetBuilder(\n", " root=DATASET_ROOT,\n", " overwrite=False,\n", ")\n", "print(f\"Dataset root: {builder.root.resolve()}\")" ] }, { "cell_type": "markdown", "id": "c3d4e5f6", "metadata": {}, "source": [ "## Workspace" ] }, { "cell_type": "code", "execution_count": null, "id": "d4e5f6a7", "metadata": {}, "outputs": [], "source": [ "workspace = Workspace(\n", " name=\"Gleason_CNN\",\n", " creator_id=\"111111111111111111111\",\n", " parent=ParentRef.none(),\n", " organ_type=OrganType.PROSTATE,\n", " organization=\"Harvard Dataverse\",\n", " description=(\n", " \"H&E stained images from five prostate cancer Tissue Microarrays (TMAs) \"\n", " \"with corresponding Gleason annotation masks. \"\n", " \"Pixel indices: 0=Benign, 1=Gleason_3, 2=Gleason_4, 3=Gleason_5, 4=unlabelled.\"\n", " ),\n", " license=\"CC-BY-4.0\",\n", " resource_url=\"https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/OCYCMP\",\n", " release_year=2018,\n", ")\n", "print(f\"Workspace ID : {workspace.id}\")\n", "print(f\"Workspace adı: {workspace.name}\")" ] }, { "cell_type": "markdown", "id": "e5f6a7b8", "metadata": {}, "source": [ "## Annotation Types" ] }, { "cell_type": "code", "execution_count": null, "id": "f6a7b8c9", "metadata": {}, "outputs": [], "source": [ "from histopathai.cloud.adapter.db.firestore import FirestoreAdapter\n", "\n", "db = FirestoreAdapter()\n", "\n", "# workspace.id ile bağlı annotation type'ları Firestore'dan çek\n", "at_docs = db.get_all(\n", " \"annotation_types\",\n", " filters=[\n", " (\"is_deleted\", \"==\", False),\n", " ],\n", ")\n", "\n", "at_by_name = {d[\"name\"]: d for d in at_docs}\n", "print(\"Bulunan annotation type'lar:\")\n", "for name, doc in at_by_name.items():\n", " print(f\" {doc['id']} → {name}\")\n", "\n", "AT_GLEASON_PATTERN_ID = at_by_name[\"Gleason Pattern\"][\"id\"]\n", "AT_TUMOR_BOLGESI_ID = at_by_name[\"Tümör Bölgesi\"][\"id\"]\n", "AT_GLEASON_PATTERN_NAME = at_by_name[\"Gleason Pattern\"][\"name\"]\n", "AT_TUMOR_BOLGESI_NAME = at_by_name[\"Tümör Bölgesi\"][\"name\"]\n", "\n", "print(f\"\\nGleason Pattern ID : {AT_GLEASON_PATTERN_ID}\")\n", "print(f\"Tümör Bölgesi ID : {AT_TUMOR_BOLGESI_ID}\")" ] }, { "cell_type": "code", "execution_count": null, "id": "ca1a3c62", "metadata": {}, "outputs": [], "source": [ "workspace.annotation_types = [AT_GLEASON_PATTERN_ID, AT_TUMOR_BOLGESI_ID]" ] }, { "cell_type": "markdown", "id": "a7b8c9d0", "metadata": {}, "source": [ "## Polygons CSV — yükle ve filtrele\n", "\n", "- **train** → eğitim görüntüleri \n", "- **test_pathologist1** → test görüntüleri \n", "- **test_pathologist2** → dışarıda bırakılır" ] }, { "cell_type": "code", "execution_count": null, "id": "b8c9d0e1", "metadata": {}, "outputs": [], "source": [ "polygons = pd.read_csv(RESOURCE_ROOT / \"polygons.csv\")\n", "\n", "# test_pathologist2'yi çıkar\n", "polygons = polygons[polygons[\"creator\"] != \"test_pathologist2\"].copy()\n", "polygons = polygons.reset_index(drop=True)\n", "\n", "print(f\"Kullanılan polygon satırı: {len(polygons)}\")\n", "print()\n", "print(polygons[\"creator\"].value_counts().to_string())\n", "print()\n", "print(polygons[\"label\"].value_counts().to_string())" ] }, { "cell_type": "markdown", "id": "c9d0e1f2", "metadata": {}, "source": [ "## Patients — TMA bazında (ZT klasörü adı)" ] }, { "cell_type": "code", "execution_count": null, "id": "d0e1f2a3", "metadata": {}, "outputs": [], "source": [ "def tma_id(image_name: str) -> str:\n", " \"\"\"ZT111_4_A_1_12 → ZT111_4_A (ilk 3 segment)\"\"\"\n", " return \"_\".join(image_name.split(\"_\")[:3])\n", "\n", "polygons[\"tma\"] = polygons[\"image_name\"].apply(tma_id)\n", "\n", "patients = []\n", "patient_by_tma = {}\n", "\n", "for tma in sorted(polygons[\"tma\"].unique()):\n", " p = Patient(\n", " name=tma,\n", " creator_id=\"111111111111111111111\",\n", " parent=ParentRef.workspace(workspace.id),\n", " )\n", " patients.append(p)\n", " patient_by_tma[tma] = p\n", "\n", "print(f\"{len(patients)} hasta (TMA) oluşturuldu:\")\n", "for p in patients:\n", " print(f\" {p.name} → {p.id}\")" ] }, { "cell_type": "markdown", "id": "e1f2a3b4", "metadata": {}, "source": [ "## Images" ] }, { "cell_type": "code", "execution_count": null, "id": "f2a3b4c5", "metadata": {}, "outputs": [], "source": [ "IMAGE_W, IMAGE_H = 3100, 3100\n", "\n", "images = []\n", "image_paths = []\n", "image_by_name = {}\n", "\n", "for image_name in sorted(polygons[\"image_name\"].unique()):\n", " tma = tma_id(image_name)\n", " patient = patient_by_tma[tma]\n", " local_path = RESOURCE_ROOT / \"origin\" / tma / f\"{image_name}.jpg\"\n", "\n", " img = Image(\n", " name=image_name,\n", " creator_id=\"111111111111111111111\",\n", " parent=ParentRef.patient(patient.id),\n", " format=\"jpg\",\n", " width=IMAGE_W,\n", " height=IMAGE_H,\n", " ws_id=workspace.id,\n", " processing=ProcessingInfo(\n", " status=ImageStatus.PENDING,\n", " version=ProcessingVersion.V2,\n", " ),\n", " )\n", " images.append(img)\n", " image_paths.append(str(local_path))\n", " image_by_name[image_name] = img\n", "\n", "print(f\"{len(images)} görüntü oluşturuldu.\")" ] }, { "cell_type": "markdown", "id": "a3b4c5d6", "metadata": {}, "source": [ "## Annotations\n", "\n", "| Label | Tümör Bölgesi (global) | Gleason Pattern (local + polygon) |\n", "|----------|------------------------|------------------------------------|\n", "| Benign | Benign | — |\n", "| G3/G4/G5 | Malign | G3 / G4 / G5 |" ] }, { "cell_type": "code", "execution_count": null, "id": "b4c5d6e7", "metadata": {}, "outputs": [], "source": [ "annotations = []\n", "\n", "for i, row in polygons.iterrows():\n", " image_name = row[\"image_name\"]\n", " label = row[\"label\"]\n", " img = image_by_name.get(image_name)\n", " if img is None:\n", " continue\n", "\n", " pts_raw = ast.literal_eval(row[\"polygon\"])\n", " polygon = [Point(x=pt[0], y=pt[1]) for pt in pts_raw]\n", "\n", " if label == \"Benign\":\n", " annotations.append(Annotation(\n", " name=AT_TUMOR_BOLGESI_NAME,\n", " creator_id=\"111111111111111111111\",\n", " parent=ParentRef.image(img.id),\n", " annotation_type_id=AT_TUMOR_BOLGESI_ID,\n", " ws_id=workspace.id,\n", " value=\"Benign\",\n", " tag_type=TagType.SELECT,\n", " is_global=False,\n", " resource=AnnotationResource.IMPORTED,\n", " polygon=polygon,\n", " ))\n", "\n", " elif label in {\"G3\", \"G4\", \"G5\"}:\n", " annotations.append(Annotation(\n", " name=AT_GLEASON_PATTERN_NAME,\n", " creator_id=\"111111111111111111111\",\n", " parent=ParentRef.image(img.id),\n", " annotation_type_id=AT_GLEASON_PATTERN_ID,\n", " ws_id=workspace.id,\n", " value=label,\n", " tag_type=TagType.SELECT,\n", " is_global=False,\n", " resource=AnnotationResource.IMPORTED,\n", " polygon=polygon,\n", " ))\n", "\n", "print(f\"Toplam annotation: {len(annotations)}\")\n", "val_counts = Counter(a.value for a in annotations)\n", "for v, c in sorted(val_counts.items()):\n", " print(f\" {v:8s}: {c:,}\")" ] }, { "cell_type": "markdown", "id": "c5d6e7f8", "metadata": {}, "source": [ "## Dataset'i yaz" ] }, { "cell_type": "code", "execution_count": null, "id": "d6e7f8a9", "metadata": {}, "outputs": [], "source": [ "builder.add_workspace(workspace)\n", "\n", "for patient in patients:\n", " builder.add_patient(patient)\n", "\n", "for img, img_path in zip(images, image_paths):\n", " builder.add_image_from_local(\n", " image=img,\n", " local_path=img_path,\n", " origin_strategy=\"reference\",\n", " )\n", "\n", "annotations_by_image = defaultdict(list)\n", "for ann in annotations:\n", " annotations_by_image[ann.parent.id].append(ann)\n", "\n", "image_map = {img.id: img for img in images}\n", "for image_id, image_anns in annotations_by_image.items():\n", " builder.add_annotations(image_map[image_id], image_anns)\n", "\n", "print(\"Tüm veriler yazıldı.\")" ] }, { "cell_type": "code", "execution_count": null, "id": "9cd290f7", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python Common", "language": "python", "name": "common" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.14.3" } }, "nbformat": 4, "nbformat_minor": 5 }