diff --git a/.gitignore b/.gitignore index 6a0589e..d0cc217 100644 --- a/.gitignore +++ b/.gitignore @@ -63,7 +63,8 @@ cover/ local_settings.py db.sqlite3 db.sqlite3-journal - +# autres +test.py # Flask stuff: instance/ .webassets-cache @@ -170,4 +171,8 @@ images/ labels/ failed_downloads.csv metadata.csv -data.yaml \ No newline at end of file +data.yaml + +# orchestrateur - tests Timothée +orchestrateur/ +flows/ \ No newline at end of file diff --git a/biolit/__init__.py b/biolit/__init__.py index cb20b13..a112390 100644 --- a/biolit/__init__.py +++ b/biolit/__init__.py @@ -7,6 +7,18 @@ RAWDIR = DATADIR / "raw" EXPORTDIR = DATADIR / "exports" +DATA_GOUV_CONTOUR_COMMUNES_URL = ( + "https://www.data.gouv.fr/api/1/datasets/r/00c0c560-3ad1-4a62-9a29-c34c98c3701e" +) +DATA_GOUV_INFO_COMMUNES_URL = ( + "https://www.data.gouv.fr/api/1/datasets/r/f5df602b-3800-44d7-b2df-fa40a0350325" +) +WORLD_COAST_LINES_URL = ( + "https://osmdata.openstreetmap.de/download/coastlines-split-4326.zip" +) +# /!\ URL emporaire de l'archive le temps que le museum corrige sa cybersec... +TAXREFURL = "https://assets.patrinat.fr/files/referentiel/TAXREF_v18_2025.zip" + mpl.rcParams["axes.spines.right"] = False mpl.rcParams["axes.spines.top"] = False diff --git a/biolit/export_api.py b/biolit/export_api.py new file mode 100644 index 0000000..6c4a510 --- /dev/null +++ b/biolit/export_api.py @@ -0,0 +1,89 @@ +import requests +import polars as pl +import structlog +import re +import os + +LOGGER = structlog.get_logger() + +# ------------------------------ +# FETCH API +# ------------------------------ +def fetch_biolit_from_api(): + + url = os.getenv("BIOLIT_API_URL") + + response = requests.get(url) + response.raise_for_status() + + data = response.json() + + print(f"{len(data)} observations récupérées") + return data + +# ------------------------------ +# RENAME OF COLUMNS +# ------------------------------ + + +def normalize_column_name(col: str) -> str: + """Convertit les noms API en snake_case propre FR""" + col = col.lower() + col = col.replace("-", "_") + col = col.replace(" ", "_") + col = col.replace("é", "e").replace("è", "e").replace("à", "a") + col = col.replace("ù", "u").replace("ô", "o") + col = re.sub(r"[^a-z0-9_]", "", col) + return col + + +COLUMN_MAPPING = { + "id": "id_observation", + "date": "date_observation", + "link": "lien_observation", + "author": "observateur", + "_url_sortie": "url_sortie", + "espece-identifiee": "espece_identifiee", + "heure-debut": "heure_debut", + "heure-fin": "heure_fin", + "latitude": "latitude", + "longitude": "longitude", + "photos": "photos", + "relais": "relais", + "espece_id": "id_espece", + "espece": "nom_scientifique", + "common": "nom_commun", + "categorie-programme": "categorie_programme", + "programme": "programme", +} + + +# ------------------------------ +# ADAPT API -> PARQUET +# ------------------------------ +def adapt_api_to_dataframe(data: list) -> pl.DataFrame: + rows = [] + + for item in data: + new_row = {} + + for key, value in item.items(): + # mapping si connu, sinon normalisation auto + new_key = COLUMN_MAPPING.get(key, normalize_column_name(key)) + new_row[new_key] = value + + rows.append(new_row) + + df = pl.DataFrame(rows) + + return df + + +# ------------------------------ +# LOAD (Fetch + Adapt) +# ------------------------------ +def load_biolit_from_api() -> pl.DataFrame: + raw_data = fetch_biolit_from_api() + df = adapt_api_to_dataframe(raw_data) + return df + diff --git a/biolit/geoloc.py b/biolit/geoloc.py new file mode 100644 index 0000000..9747a10 --- /dev/null +++ b/biolit/geoloc.py @@ -0,0 +1,298 @@ +import geopandas as gpd +from shapely.geometry import Point +from typing import Tuple, Optional +import polars as pl +import pandas as pd +import requests +import structlog +from pathlib import Path +import zipfile +import os +import folium +from folium.plugins import MarkerCluster + +from biolit import DATADIR, DATA_GOUV_INFO_COMMUNES_URL, DATA_GOUV_CONTOUR_COMMUNES_URL, WORLD_COAST_LINES_URL + +LOGGER = structlog.get_logger() + + +def geoloc_enrichie_data_biolit(file: Path): + """ + Fichier à enrichir : DATADIR / "biolit_valid_observations.parquet" + Fonction finale : + - Enrichissement de la commune la plus proche + - Création de la colonne "is_coastal" -> est ce que le point est proche du littoral + - Export vers fichier parquet + """ + if not _check_file_existence(file): + return + df_biolit = get_biolit_df(file) + df = get_info_nearest_commune(df_biolit) + df_coastal = get_info_distance_to_coast(df, 8000) + + export_path_geoloc_enrichie = DATADIR / "data_enrichi_with_communes_and_is_coastal.parquet" + export_path_geoloc_enrichie.parent.mkdir(parents=True, exist_ok=True) + df_coastal.to_parquet(export_path_geoloc_enrichie) + LOGGER.info(f"Files exported: {export_path_geoloc_enrichie}") + +def get_biolit_df(file: Path) -> pd.DataFrame: + """ Fonction qu'il faudra modifier une fois qu'on aura l'export clean de la donnée biolit """ + biolit_df = ( + pl.read_parquet(file) + ) + biolit_df = biolit_df.with_columns([ + pl.col("longitude_-_n1") + .str.replace(",", ".") + .cast(pl.Float64), + pl.col("latitude_-_n1") + .str.replace(",", ".") + .cast(pl.Float64) + ]) + + biolit_df = biolit_df.to_pandas() + + LOGGER.info("biolit df uploaded", count=len(biolit_df)) + return biolit_df + +def download_geometry_communes(targetpath: Path): + """ + Fonction permettant de récupérer les géométries des communes depuis : + DATA_GOUV_CONTOUR_COMMUNES_URL = "https://www.data.gouv.fr/api/1/datasets/r/00c0c560-3ad1-4a62-9a29-c34c98c3701e" + """ + targetpath.parent.mkdir(parents=True, exist_ok=True) + _download_file_from_url(DATA_GOUV_CONTOUR_COMMUNES_URL, targetpath) + if not targetpath.is_file(): + LOGGER.fatal("Didn't manage to properly extract the file with municipalities borders") + +def get_geometry_communes(file: Path) -> gpd.GeoDataFrame: + if _check_file_existence(file): + geometry_communes = ( + gpd.read_file(file, layer="a_com2022") + .rename(columns={"codgeo": "code_insee", "libgeo": "nom_communes"}) + ) + LOGGER.info("geometry_communes_loaded", count=len(geometry_communes)) + return geometry_communes + +def download_info_communes(targetpath: Path): + """ + Fonction permettant de récupérer les géométries des communes depuis : + DATA_GOUV_INFO_COMMUNES_URL = "https://www.data.gouv.fr/api/1/datasets/r/f5df602b-3800-44d7-b2df-fa40a0350325" + """ + targetpath.parent.mkdir(parents=True, exist_ok=True) + _download_file_from_url(DATA_GOUV_INFO_COMMUNES_URL, targetpath) + if not targetpath.is_file(): + LOGGER.fatal("Didn't manage to properly extract the file with municipalities information") + +def get_info_communes(file: Path) -> pd.DataFrame: + if _check_file_existence(file): + info_communes = ( + pl.read_csv(file, ignore_errors = True, schema_overrides={"code_insee": pl.Utf8}) + ) + # Filtre sur les colonnes qui nous intéressent + info_communes = info_communes.select(["code_insee", "code_postal", "reg_nom", "dep_nom"]).to_pandas() + + LOGGER.info("info_communes_loaded", count=len(info_communes)) + return info_communes + +def download_trace_littoral(targetpath: Path): + """ + Fonction permettant de récupérer le tracé du littoral (monde) depuis OpenStreetMap : + WORLD_COAST_LINES_URL = "https://osmdata.openstreetmap.de/download/coastlines-split-4326.zip" + """ + targetpath.parent.mkdir(parents=True, exist_ok=True) + base_path = DATADIR / "geoloc" / "open_street_map" + zip_path = base_path / "coastlines.zip" + extract_path = base_path / "coastlines-split-4326" + + if not zip_path.exists(): + _download_file_from_url(WORLD_COAST_LINES_URL, zip_path) + else: + LOGGER.info("download_skipped", reason="zip already exists") + + if not zip_path.is_file(): + LOGGER.fatal("Download failed") + return + with zipfile.ZipFile(zip_path, "r") as zip_ref: + zip_ref.extractall(base_path) + LOGGER.info("Extraction successful") + shp_files = list(extract_path.glob("*.shp")) + if not shp_files: + LOGGER.fatal("No shapefile found after extraction") + else: + LOGGER.info("Shapefile ready", file=str(shp_files[0])) + +def get_trace_littoral(file: Path) -> gpd.GeoDataFrame: + if not _check_file_existence(file): + LOGGER.info(f"This file does not exist: {file}") + return + coast_gdf = gpd.read_file(file) + coast_gdf = coast_gdf.to_crs(epsg=2154) + LOGGER.info("info_coast_loaded", count=len(coast_gdf)) + return coast_gdf + +def _check_file_existence(file: Path): + if not file.exists(): + LOGGER.info("file_missing", path=str(file)) + return False + + if not file.is_file(): + LOGGER.fatal( + "The following path has been created, but it is not a standard file", + path=file + ) + return True + +def _download_file_from_url(url: str, targetpath: Path): + LOGGER.info("download_start", url=url) + + with requests.get(url, stream=True) as r: + r.raise_for_status() + with open(targetpath, "wb") as f: + for chunk in r: + if chunk: + f.write(chunk) + + LOGGER.info("download_success", path=str(targetpath)) + +def distance_to_communes(point: Point, communes_gdf: gpd.GeoDataFrame, sindex, search_radius: float = 20000) -> Tuple[Optional[float], Optional[str], Optional[str]]: + """ + Fonction permettant de déterminer le polygon le plus proche du point + """ + candidate_idx = list( + sindex.intersection(point.buffer(search_radius).bounds) + ) + + if not candidate_idx: + return None, None, None + + candidates = communes_gdf.iloc[candidate_idx] + distances = candidates.distance(point) + min_idx = distances.idxmin() + return ( + distances.min(), + communes_gdf.loc[min_idx, "nom_communes"], + communes_gdf.loc[min_idx, "code_insee"] + ) + +def get_info_nearest_commune(frame: pd.DataFrame) -> pd.DataFrame: + """ + Fonction permettant d'attribuer à un point Biolit la commune la plus proche + info departement / region + """ + # Points DB Biolit + biolit_df = frame + gdf = gpd.GeoDataFrame( + biolit_df, + geometry=gpd.points_from_xy(biolit_df["longitude_-_n1"], biolit_df["latitude_-_n1"]), + crs="EPSG:4326" + ).to_crs(epsg=2154) + + # Information Géometrie Communes + path_geometry_communes = DATADIR / "geoloc" / "data_gouv" / "geometry_communes.json" + if not _check_file_existence(path_geometry_communes): + download_geometry_communes(path_geometry_communes) + + communes = get_geometry_communes(path_geometry_communes) + sindex = communes.sindex + + # Recherche de la commune la plus proche + results = gdf.geometry.apply( + lambda p: distance_to_communes(p, communes, sindex, search_radius=20000) + ) + gdf["distance_commune_m"] = results.apply(lambda x: x[0]) + gdf["nearest_commune"] = results.apply(lambda x: x[1]) + gdf["code_insee"] = results.apply(lambda x: x[2]) + + df_export = gdf.drop(columns="geometry") + + # Informations sur la commune la plus proche + path_info_communes = DATADIR / "geoloc" / "data_gouv" / "info_communes.csv" + if not _check_file_existence(path_info_communes): + download_info_communes(path_info_communes) + info_communes = get_info_communes(path_info_communes) + + df_export = df_export.merge( + info_communes, + on = "code_insee", + how="left" + ) + + LOGGER.info("Nearest Municipality enriched with dep_name & region_name", count=len(df_export)) + return df_export + +def distance_to_coast(point: Point, coast_gdf: gpd.GeoDataFrame, sindex, search_radius: float = 20000) -> Optional[float]: + """ Fonction de Calcul de distance entre le point et la ligne de côte """ + candidate_idx = list( + sindex.intersection(point.buffer(search_radius).bounds) + ) + + if not candidate_idx: + return + + candidates = coast_gdf.iloc[candidate_idx] + return candidates.distance(point).min() + +def get_info_distance_to_coast(frame: pd.DataFrame, distance_max: float = 8000) -> pd.DataFrame: + # Récupération Tracé Littoral + path_trace_littoral = DATADIR / "geoloc" / "open_street_map" / "coastlines-split-4326" / "lines.shp" + if not _check_file_existence(path_trace_littoral): + download_trace_littoral(path_trace_littoral) + + coast_gdf = get_trace_littoral(path_trace_littoral) + coast_sindex = coast_gdf.sindex + + # Points Biolit + biolit_df = frame + gdf = gpd.GeoDataFrame(biolit_df, geometry=gpd.points_from_xy(biolit_df["longitude_-_n1"], biolit_df["latitude_-_n1"]), crs="EPSG:4326").to_crs(epsg=2154) + distances = [] + + for p in gdf.geometry: + d = distance_to_coast(p, coast_gdf, coast_sindex, search_radius=20000) + distances.append(d) + + gdf["distance_to_coast"] = distances + + gdf["is_coastal"] = ( + gdf["distance_to_coast"].notna() + & (gdf["distance_to_coast"] <= distance_max) + ) + + gdf_export = gdf.drop(columns="geometry", errors="ignore") + + LOGGER.info("Biolit Data Points enriched with distance to coast", nb_not_coastal = (~gdf_export["is_coastal"]).sum(), nb_coastal = gdf_export["is_coastal"].sum()) + return gdf_export + +def carte_points_biolit_checks_geoloc(file_to_map: Path): + """ + Si besoin de checks - possibilité de créer une carte avec en vert les points proches de la côte et en rouge les points éloignés de la côte + """ + if not os.path.exists(file_to_map): + LOGGER.info("The file you want to map does not exist") + return + + df = pl.read_parquet(file_to_map) + gdf = gpd.GeoDataFrame(df.to_pandas(), geometry=gpd.points_from_xy(df["longitude_-_n1"], df["latitude_-_n1"]), crs="EPSG:4326").dropna(subset=["latitude_-_n1", "longitude_-_n1"]) + + carte = folium.Map(location=[48.95, 2.29], zoom_start=6) + + marker_cluster = MarkerCluster().add_to(carte) + + for _, row in gdf.iterrows(): + color = "green" if row["is_coastal"] else "red" + + popup_text = ( + f"ID: {row['id']}
" + f"Côtier: {row['is_coastal']}
" + f"Distance : {row['distance_to_coast']} m" + ) + + folium.CircleMarker( + location=[row["latitude_-_n1"], row["longitude_-_n1"]], + radius=3, + color=color, + fill=True, + fill_color=color, + fill_opacity=0.7, + popup=popup_text, + ).add_to(marker_cluster) + carte.save(DATADIR / 'carte_points_biolit.html') + LOGGER.info('Map created') \ No newline at end of file diff --git a/biolit/lien_doris.py b/biolit/lien_doris.py new file mode 100644 index 0000000..0a4a04e --- /dev/null +++ b/biolit/lien_doris.py @@ -0,0 +1,58 @@ +import requests +import time +from bs4 import BeautifulSoup +import polars as pl + +import structlog + +from biolit import DATADIR + +LOGGER = structlog.get_logger() + + +def scrapping_site_lien_doris() -> pl.DataFrame: + offset = 0 + lien_doris_all_data = [] + + while True: + url = f"https://doris.ffessm.fr/find/species/(offset)/{offset}/(state)/*/(sortby)/recent/(manualSort)/1/(view)/list" + LOGGER.info(f"Scraping offset = {offset}") + + try: + response = requests.get(url, timeout=10) + + if response.status_code != 200: + LOGGER.info(f"Erreur à l'offset : {offset}") + break + + soup = BeautifulSoup(response.text, "html.parser") + species = soup.find_all("div", class_="specieSearchResult resultLine") + if not species: + LOGGER.info("Fin des pages.") + break + + lien_doris_page_data = [] + for specie in species: + try: + a_tag = specie.find('a', href=True) + lien_doris = a_tag.get("href") + nom_scientifique = a_tag.find("em").get_text(strip=True) + lien_doris_page_data.append({ + "nom_scientifique": nom_scientifique, + "lien_doris": lien_doris, + }) + except Exception as e: + LOGGER.info(f"Erreur parsing espèce : {e}") + continue + lien_doris_all_data.extend(lien_doris_page_data) + offset += len(lien_doris_page_data) + + df = pl.DataFrame(lien_doris_all_data) + df.write_csv(DATADIR / "doris_data.csv") + time.sleep(1) + + except Exception as e: + LOGGER.info(f"Erreur requête : {e}") + break + + return pl.DataFrame(lien_doris_all_data) diff --git a/biolit/observations.py b/biolit/observations.py index a081d67..847883a 100644 --- a/biolit/observations.py +++ b/biolit/observations.py @@ -22,7 +22,7 @@ def format_observations(): fn = DATADIR / "export_biolit.csv" taxref = pl.read_parquet(DATADIR / "taxref.parquet") biolit = ( - pl.read_csv(fn) + pl.read_csv(fn, separator=";", truncate_ragged_lines=True) .rename(lambda c: c.replace(" - observation", "").lower().replace(" ", "_")) .with_columns( col("nom_scientifique").str.to_lowercase(), diff --git a/biolit/postgres.py b/biolit/postgres.py new file mode 100644 index 0000000..247df20 --- /dev/null +++ b/biolit/postgres.py @@ -0,0 +1,124 @@ +import os +import polars as pl +from sqlalchemy import create_engine, text + + +# ------------------------- +# Connexion DB +# ------------------------- + +def get_engine(): + postgres_url = os.getenv("POSTGRES_URL") + + if not postgres_url: + raise ValueError("Missing POSTGRES_URL") + + return create_engine(postgres_url) + + +# ------------------------- +# Préparation des données +# ------------------------- +def prepare_dataframe_for_postgres(df: pl.DataFrame) -> pl.DataFrame: + return df.with_columns([ + + # ------------------------- + # IDs + # ------------------------- + pl.col("id_observation") + .cast(pl.Int64), + + pl.col("id_espece") + .cast(pl.Float64, strict=False) + .fill_nan(None) + .cast(pl.Int64, strict=False), + + pl.col("categorie_programme") + .cast(pl.Float64, strict=False) + .fill_nan(None) + .cast(pl.Int64, strict=False), + + pl.col("relais") + .cast(pl.Utf8) + .replace("", None) + .cast(pl.Float64, strict=False) + .fill_nan(None) + .cast(pl.Int64, strict=False), + + # ------------------------- + # Coordonnées + # ------------------------- + pl.col("latitude") + .cast(pl.Utf8) + .str.strip_chars() + .cast(pl.Float64, strict=False), + + pl.col("longitude") + .cast(pl.Utf8) + .str.strip_chars() + .cast(pl.Float64, strict=False), + + # ------------------------- + # Dates + # ------------------------- + pl.col("date_observation") + .str.strptime(pl.Datetime, strict=False), + + pl.col("heure_debut") + .str.strptime(pl.Time, strict=False), + + pl.col("heure_fin") + .str.strptime(pl.Time, strict=False), + ]) + +# ------------------------- +# Insert avec sécurité (UPSERT) +# ------------------------- + +def insert_dataframe(df: pl.DataFrame): + engine = get_engine() + + rows = df.to_dicts() + + with engine.begin() as conn: + for row in rows: + conn.execute(text(""" + INSERT INTO observations ( + id_observation, + date_observation, + lien_observation, + observateur, + url_sortie, + espece_identifiee, + heure_debut, + heure_fin, + latitude, + longitude, + photos, + relais, + id_espece, + nom_scientifique, + nom_commun, + categorie_programme, + programme + ) VALUES ( + :id_observation, + :date_observation, + :lien_observation, + :observateur, + :url_sortie, + :espece_identifiee, + :heure_debut, + :heure_fin, + :latitude, + :longitude, + :photos, + :relais, + :id_espece, + :nom_scientifique, + :nom_commun, + :categorie_programme, + :programme + ) + ON CONFLICT (id_observation) DO NOTHING + """), row) \ No newline at end of file diff --git a/biolit/taxref.py b/biolit/taxref.py index 4dcf3a5..08989a5 100644 --- a/biolit/taxref.py +++ b/biolit/taxref.py @@ -1,15 +1,23 @@ import polars as pl import structlog from polars import col +from pathlib import Path +import requests +import zipfile +import shutil -from biolit import DATADIR +from biolit import DATADIR, TAXREFURL TAXREF_HIERARCHY = ["regne", "phylum", "classe", "ordre", "famille", "sous_famille"] LOGGER = structlog.get_logger() def format_taxref(): - fn = DATADIR / "TAXREF_v18_2025" / "TAXREFv18.txt" + fn = DATADIR / "TAXREFv18.txt" + + if not _check_file_existence(fn): + _download_taxref(fn) + taxref = ( pl.read_csv(fn, separator="\t") .rename(str.lower) @@ -50,3 +58,52 @@ def _check_duplicates(frame: pl.DataFrame): n_species=len(frame), n_names=frame["species_name"].n_unique(), ) + + +def _check_file_existence(file: Path): + if not file.exists(): + return False + + if not file.is_file(): + LOGGER.fatal( + "The following path has been created, but it is not a standard file", + path=file + ) + + +def _download_taxref(targetpath: Path): + # Download and save the TaxRef zip file to a temp file + LOGGER.warning( + "Currently downloading the TaxRef file from the following URL, "+ + " known to be temporary ! It will have to be changed at a later date.", + url = TAXREFURL + ) + + Path("data/temp").mkdir(exist_ok=True) + _download_file_from_url(TAXREFURL, "data/temp/tmp_taxref.zip") + _get_file_from_zip("data/temp/tmp_taxref.zip", 'TAXREFv18.txt', targetpath) + + # Cleanup the zip archive and check all went well + Path("data/temp/tmp_taxref.zip").unlink() + + if not targetpath.is_file(): + LOGGER.fatal("Didn't manage to properly extract the TaxRef") + + +def _download_file_from_url(url: str, targetpath: Path): + # Download a given file from the web and store it to target path + r = requests.get(url) + + Path("data/temp").mkdir(exist_ok=True) + tmpfile = Path(targetpath) + with open(tmpfile, 'wb') as f: + for chunk in r: + if chunk: + f.write(chunk) + + +def _get_file_from_zip(input_zip: Path, filename: str, output_file: Path): + # Extract a given file from a zip archive to a target path + with zipfile.ZipFile(input_zip) as z: + with z.open(filename) as zf, open(output_file, 'wb') as f: + shutil.copyfileobj(zf, f) \ No newline at end of file diff --git a/ml/classification/BioClipv2/Readme.md b/ml/classification/BioClipv2/Readme.md new file mode 100644 index 0000000..cee7826 --- /dev/null +++ b/ml/classification/BioClipv2/Readme.md @@ -0,0 +1,165 @@ +# BioModel — Classification hiérarchique d'espèces marines côtières + +Le modèle prédit le **niveau taxonomique le plus fin possible** — espèce, famille, ordre, classe, phylum ou règne — en s'arrêtant dès qu'il est suffisamment confiant + +--- + +## Architecture + +``` +Image + ↓ +BioCLIP2 (frozen) backbone pré-entraîné sur 200M images biologiques + ↓ +Whitening PCA (512d → 256d) rend les distances cosine plus fiables + ↓ +MLP supervisé par niveau un MLP par niveau taxonomique + ├── MLP règne (3 classes) + ├── MLP phylum (~6 classes) + ├── MLP classe (~9 classes) + ├── MLP ordre (~12 classes) + └── MLP famille(~50 classes) + + +Proto-CLIP espèce (232 classes) few-shot : prototype = α × visuel + (1-α) × texte + ↓ +margin score rejette si score_top1 - score_top2 < MARGIN_MIN + ↓ +Décision hiérarchique cohérente + ├── Espèce confiante → espèce + lookup taxref pour niveaux supérieurs + └── Sinon → MLP du niveau le plus fin confiant +``` + +--- + +## Fichiers du projet + +``` +classifier_train.py Entraînement — extraction features, MLP, Proto-CLIP +classifier_infer.py Inférence — charge le modèle et prédit +build_classify_dataset.py Charge les images et construit le DataFrame labellisé +config.py Tous les paramètres — source de vérité unique +README.md +.gitignore +``` + +--- + +## Données d'entrée + +### Structure attendue + +``` +data/ +├── export_biolit.csv métadonnées terrain +├── taxonomy.parquet référentiel taxonomique +└── images/ + └── identifiable/ + ├── 1234_fucus-spiralis_42.jpg + └── ... +``` + +### Format des noms de fichiers images + +Les images doivent respecter le format établi : + +``` +{id_n1}_{nom_commun}_{index}.{ext} +``` + +| Segment | Description | Exemple | +|---------|-------------|---------| +| `id_n1` | Identifiant de l'observation (correspond à `ID - N1` dans observations.csv) | `1234` | +| `nom_commun` | Nom commun normalisé (espaces → `_`, `/` → `-`) | `fucus-spiralis` | +| `index` | Numéro de l'image pour cette observation | `42` | +| `ext` | Extension : jpg, jpeg, png ou webp | `jpg` | + + + + +## Requirements + +```bash +pip install open-clip-torch torch torchvision scikit-learn pandas pillow pyarrow +``` + +--- + +## Usage + +### 1. Entraîner le modèle + +```bash +# Entraîner + évaluer sur split 80/20 +python classifier_train.py --fit --eval --images data/images/identifiable + +# Entraîner sur tout le dataset +python classifier_train.py --fit --images data/images/identifiable +``` + +### 2. Prédire sur de nouvelles images + +```bash +python classifier_infer.py --images mon_dossier/ +``` + +Résultats dans `results/predictions.csv`. + +### 4. Ajouter de nouvelles espèces sans réentraîner + +```bash +python classifier_train.py --update --images nouvelles_images/ +``` + +Seuls les prototypes Proto-CLIP des nouvelles espèces sont recalculés. +Les MLP par niveau et le whitening restent inchangés. + +--- + +## Utiliser le modèle depuis Python + +```python +from classifier_infer import load_model, predict_image + +# Charger le modèle (une seule fois) +model = load_model() + +# Prédire sur une image +result = predict_image("ma_photo.jpg", model) + +# Scores à tous les niveaux +for level, preds in result["all_scores"].items(): + print(f"{level}: {preds[0]['label']} ({preds[0]['score']:.0%})") +``` + +## Fichiers sauvegardés après l'entraînement + +``` +results/ +├── proto_model.npz prototypes Proto-CLIP + whitening PCA +├── tax_lookup.pkl hiérarchie taxonomique espèce → niveaux +├── mlp_model.pt poids MLP par niveau (règne → famille) +└── bioclip_features.npz cache features BioCLIP2 (~1 GB, non versionné) +``` +--- + +## Paramètres — config.py + +Tous les paramètres sont centralisés dans `config.py`. * +`CONFIDENCE_THRESHOLD` Seuil d'acceptation. Baisser → plus de prédictions, plus d'erreurs. Monter → moins de prédictions, plus fiables. + `MARGIN_MIN` Seuil faux positifs. Si top1=91% et top2=89%, margin=0.02 → rejeté. Baisser pour plus de couverture. Plage : 0.05–0.20 | + `PROTO_ALPHA` Poids visuel dans Proto-CLIP. `0.7` si images de qualité. `0.4` si espèces très rares. | + `MLP_EPOCHS` Epochs par MLP. Monter si underfitting, baisser si overfitting. +`MLP_DROPOUT` Régularisation MLP. Monter si overfitting + + +## Dépendances + +``` +open-clip-torch BioCLIP2 backbone +torch PyTorch +torchvision transforms +scikit-learn PCA, LabelEncoder, metrics +pandas manipulation données +pillow lecture images +pyarrow lecture taxonomy.parquet +``` \ No newline at end of file diff --git a/ml/classification/BioClipv2/build_classify_dataset.py b/ml/classification/BioClipv2/build_classify_dataset.py new file mode 100644 index 0000000..b3651de --- /dev/null +++ b/ml/classification/BioClipv2/build_classify_dataset.py @@ -0,0 +1,146 @@ +""" +Le DataFrame produit a toujours les mêmes colonnes : + image_path : chemin absolu vers l'image + source_image : nom du fichier + id_n1 : extrait du nom de fichier + species_name, regne, phylum, classe, ordre, famille, sous_famille +""" + +import re +import pandas as pd +from pathlib import Path + +from config import IMAGES_DIR, DATA_DIR, TAXONOMY_FILE, TAXONOMY_LEVELS + + +VALID_EXTS = {".jpg", ".jpeg", ".png", ".webp"} + + +def _parse_id(filename: str) -> str | None: + """Extrait l'id_n1 depuis {id_n1}_{nom}_{idx}.{ext}""" + m = re.match(r'^(\d+)_', filename) + return m.group(1) if m else None + + +def _parse_nom_commun(filename: str) -> str | None: + """ + Extrait le nom commun normalisé depuis {id_n1}_{nom_commun}_{idx}.{ext} + """ + stem = Path(filename).stem # "1234_fucus_spiralis_42" + parts = stem.split("_") + if len(parts) < 3: + return None + # Tout entre le premier segment (id) et le dernier (idx pandas) + return "_".join(parts[1:-1]) + + +def _normalize_nom(nom: str) -> str: + + return str(nom).strip().replace(" ", "_").replace("/", "-") + + +def build_dataset( + images_dir: Path = None, + observations_csv: Path = None, + taxonomy_path: Path = None, +) -> pd.DataFrame: + """ + Construit le DataFrame labellisé depuis un dossier d'images. + + Paramètres : + images_dir : dossier contenant les images (défaut: IMAGES_DIR/identifiable) + export_biolit : CSV avec ID - N1 + Nom scientifique + taxonomy_path : parquet taxref + + Retourne un DataFrame avec image_path + colonnes taxonomiques. + """ + images_dir = Path(images_dir) if images_dir else IMAGES_DIR / "identifiable" + observations_csv = Path(observations_csv) if observations_csv else DATA_DIR / "export_biolit.csv" + taxonomy_path = Path(taxonomy_path) if taxonomy_path else TAXONOMY_FILE + + # ── Collecter les images ────────────────────────────────────────────────── + image_paths = [p for p in images_dir.iterdir() + if p.suffix.lower() in VALID_EXTS] + print(f"{len(image_paths)} images trouvées dans {images_dir}") + + df = pd.DataFrame({ + "image_path": [str(p) for p in image_paths], + "source_image": [p.name for p in image_paths], + }) + df["id_n1"] = df["source_image"].apply(_parse_id) + df["nom_commun"] = df["source_image"].apply(_parse_nom_commun) + + # ── Jointure observations → nom scientifique ────────────────────────────── + obs = pd.read_csv(observations_csv) + obs["id_n1"] = obs["ID - N1"].astype(str).str.strip() + obs["_species"] = obs["Nom scientifique - observation"].str.strip() + obs["nom_commun"] = obs["Nom commun - observation"].apply( + lambda x: _normalize_nom(x) if pd.notna(x) else None + ) + + obs_valid = obs[obs["_species"].notna()][["id_n1", "nom_commun", "_species"]] + + # Jointure composite id_n1 + nom_commun — gère les doublons d'id_n1 + merged = df.merge(obs_valid, on=["id_n1", "nom_commun"], how="left") + + # Fallback id_n1 seul pour les cas non matchés + # (ex: nom commun manquant ou légèrement différent) + no_match = merged["_species"].isna() + if no_match.any(): + obs_id_only = obs_valid.drop_duplicates("id_n1")[["id_n1", "_species"]] + fallback = df[no_match][["id_n1"]].merge(obs_id_only, on="id_n1", how="left") + merged.loc[no_match, "_species"] = fallback["_species"].values + n_fallback = no_match.sum() - merged["_species"].isna().sum() + if n_fallback > 0: + print(f" ⚠ {n_fallback} images matchées via id_n1 seul (nom commun non trouvé)") + + df = merged.rename(columns={"_species": "Nom scientifique - observation"}) + + n_with_species = df["Nom scientifique - observation"].notna().sum() + print(f"Observations matchées : {n_with_species}/{len(df)}") + + # ── Jointure taxonomie ──────────────────────────────────────────────────── + tax = pd.read_parquet(taxonomy_path) + tax.columns = tax.columns.str.strip().str.lower().str.replace(" ", "_") + for col in tax.select_dtypes("object").columns: + tax[col] = tax[col].str.strip() + + tax["_key"] = tax["species_name"].str.lower() + df["_key"] = df["Nom scientifique - observation"].str.lower() + + df = df.merge(tax[["_key"] + TAXONOMY_LEVELS], on="_key", how="left") + df.drop(columns=["_key"], inplace=True) + + n_matched = df["species_name"].notna().sum() + no_obs = df["Nom scientifique - observation"].isna().sum() + no_tax = (df["Nom scientifique - observation"].notna() & df["species_name"].isna()).sum() + + print(f"Taxonomie matchée : {n_matched}/{len(df)}") + print(f"\nDétail des {len(df) - n_matched} images non utilisables :") + print(f" - Sans observation CSV : {no_obs}") + print(f" - Observation sans taxref : {no_tax}") + + unmatched = (df[df["species_name"].isna()]["Nom scientifique - observation"] + .dropna().unique()) + if len(unmatched): + print(f"\nEspèces absentes du taxref ({len(unmatched)}) :") + for u in sorted(unmatched): + print(f" - {u}") + + if n_matched > 0: + print(f"\nDistribution par règne :") + print(df.groupby("regne").size().sort_values(ascending=False).to_string()) + print(f"\nTop 10 espèces :") + print(df.groupby("species_name").size().sort_values(ascending=False).head(10).to_string()) + rare = (df.groupby("species_name").size() < 5).sum() + print(f"\nEspèces avec < 5 images : {rare} / {df['species_name'].nunique()}") + + return df + + +if __name__ == "__main__": + df = build_dataset() + out = DATA_DIR / "classify_dataset.csv" + df.to_csv(out, index=False) + print(f"\nDataset sauvegardé : {out}") + print(df[["source_image", "species_name", "regne", "famille"]].head(10).to_string()) \ No newline at end of file diff --git a/ml/classification/BioClipv2/classifier_infer.py b/ml/classification/BioClipv2/classifier_infer.py new file mode 100644 index 0000000..08abc2a --- /dev/null +++ b/ml/classification/BioClipv2/classifier_infer.py @@ -0,0 +1,420 @@ +""" +classifier_infer.py — Inférence uniquement +========================================== +Ce fichier est le seul nécessaire en production. +Il charge le modèle sauvegardé et prédit sur de nouvelles images. + +Dépendances minimales : + pip install torch numpy pandas pillow + +BioCLIP2 n'est PAS nécessaire ici — les prototypes et le whitening +sont déjà calculés et sauvegardés dans results/. + +Fichiers requis dans results/ : + proto_model.npz → prototypes + whitening PCA + tax_lookup.pkl → hiérarchie taxonomique + mlp_model.pt → MLP par niveau (règne → famille) + +Usage : + from classifier_infer import load_model, predict_image + + model = load_model() + result = predict_image("mon_image.jpg", model) + print(result["best_level"], result["best_label"], result["best_score"]) + + # Ou en ligne de commande + python classifier_infer.py --images mon_dossier/ + python classifier_infer.py --images mon_dossier/ --threshold 0.6 +""" + +import pickle +import time +import numpy as np +import pandas as pd +import torch +import torch.nn as nn +import torch.nn.functional as F +from dataclasses import dataclass +from pathlib import Path +from PIL import Image +from torchvision import transforms + +from config import ( + DEVICE, + RESULTS_DIR, + CONFIDENCE_THRESHOLD as THRESHOLD, + PROTO_WHITENING_DIM as WHITENING_DIM, + PROTO_MODEL_FILE as MODEL_FILE, + PROTO_TAX_FILE as TAX_LOOKUP_FILE, + MARGIN_MIN, + MLP_MODEL_FILE, +) + +# Niveaux du plus fin au plus large +ALL_LEVELS = ["species_name", "famille", "ordre", "classe", "phylum", "regne"] +SUPERVISED_LEVELS = ["regne", "phylum", "classe", "ordre", "famille"] + +# Normalisation BioCLIP +CLIP_MEAN = [0.48145466, 0.4578275, 0.40821073] +CLIP_STD = [0.26862954, 0.26130258, 0.27577711] + +IMG_TRANSFORM = transforms.Compose([ + transforms.Resize((224, 224)), + transforms.ToTensor(), + transforms.Normalize(mean=CLIP_MEAN, std=CLIP_STD), +]) + + +# ── Architecture MLP (doit être identique à classifier_train.py) ────────────── + +class LevelMLP(nn.Module): + """MLP léger pour un niveau taxonomique. Doit être identique à classifier_train.""" + def __init__(self, input_dim: int, n_classes: int, dropout: float = 0.3): + super().__init__() + hidden = max(256, n_classes * 8) + self.net = nn.Sequential( + nn.Linear(input_dim, hidden), + nn.BatchNorm1d(hidden), + nn.ReLU(), + nn.Dropout(dropout), + nn.Linear(hidden, n_classes), + ) + + def forward(self, x): + return self.net(x) + + +# ── Structure du modèle chargé ──────────────────────────────────────────────── + +@dataclass +class BioModel: + """ + Conteneur pour tous les artefacts du modèle. + Passé tel quel à predict_image() — pas besoin de déstructurer. + """ + prototypes: dict # {espèce → vecteur 256d} + tax_lookup: dict # {espèce → {famille, ordre, classe, phylum, règne}} + whitening: tuple # (mean, components, variance) pour apply_whitening + temperature: float # température calibrée pour Proto-CLIP + mlp_dict: dict # {niveau → (LevelMLP, LabelEncoder)} + + +# ════════════════════════════════════════════════════════════════════════════ +# CHARGEMENT DU MODÈLE +# ════════════════════════════════════════════════════════════════════════════ + +def load_model(results_dir: Path = RESULTS_DIR) -> BioModel: + """ + Charge le modèle depuis results/. + BioCLIP2 n'est PAS nécessaire. + + Paramètre : + results_dir : dossier contenant les fichiers sauvegardés + (défaut : results/ depuis config.py) + + Retourne un BioModel prêt pour predict_image(). + """ + model_file = results_dir / MODEL_FILE.name + tax_file = results_dir / TAX_LOOKUP_FILE.name + mlp_file = results_dir / MLP_MODEL_FILE.name + + if not model_file.exists(): + raise FileNotFoundError( + f"{model_file} introuvable.\n" + "Entraînez d'abord : python classifier_train.py --fit --images ..." + ) + + # Proto-CLIP + data = np.load(model_file, allow_pickle=True) + species_list = list(data["species_list"]) + proto_mat = data["prototypes"] + prototypes = {sp: proto_mat[i] for i, sp in enumerate(species_list)} + whitening = (data["wh_mean"], data["wh_components"], data["wh_variance"]) + temperature = float(data["temperature"][0]) + + with open(tax_file, "rb") as f: + tax_lookup = pickle.load(f) + + # MLP par niveau + mlp_dict = {} + if mlp_file.exists(): + mlp_save = torch.load(mlp_file, map_location=DEVICE, weights_only=False) + for level, ckpt in mlp_save.items(): + mlp = LevelMLP(ckpt["input_dim"], ckpt["n_classes"]).to(DEVICE) + mlp.load_state_dict(ckpt["state_dict"]) + mlp.eval() + mlp_dict[level] = (mlp, ckpt["encoder"]) + + print(f"Modèle chargé : {len(prototypes)} espèces | " + f"MLP : {list(mlp_dict.keys())} | T={temperature:.2f}") + + return BioModel( + prototypes = prototypes, + tax_lookup = tax_lookup, + whitening = whitening, + temperature = temperature, + mlp_dict = mlp_dict, + ) + + +# ════════════════════════════════════════════════════════════════════════════ +# WHITENING (appliqué aux features extraites) +# ════════════════════════════════════════════════════════════════════════════ + +def apply_whitening(features: np.ndarray, mean, components, variance) -> np.ndarray: + x = (features - mean) @ components.T / np.sqrt(variance + 1e-8) + norms = np.linalg.norm(x, axis=1, keepdims=True) + return (x / np.clip(norms, 1e-8, None)).astype(np.float32) + + +# ════════════════════════════════════════════════════════════════════════════ +# PRÉDICTION +# ════════════════════════════════════════════════════════════════════════════ + +def predict(feat_w: np.ndarray, + model: BioModel, + threshold: float = THRESHOLD, + margin_min: float = MARGIN_MIN) -> dict: + """ + Prédit le niveau taxonomique le plus fin possible depuis un vecteur de features. + + Paramètre d'entrée : + feat_w : vecteur numpy 256d whitened (sortie de apply_whitening) + model : BioModel chargé avec load_model() + threshold : seuil de confiance (défaut config: CONFIDENCE_THRESHOLD) + margin_min : garde-fou faux positifs (défaut config: MARGIN_MIN) + + Retourne : + { + "best_level": "famille", ← niveau le plus fin accepté + "best_label": "Fucaceae", ← label prédit + "best_score": 0.87, ← score de confiance + "margin": 0.34, ← écart top1/top2 (qualité) + "path": "proto_clip", ← chemin utilisé + "all_scores": { ← scores à tous les niveaux + "species_name": [...], + "famille": [...], + ... + } + } + """ + feat_t = torch.tensor(feat_w[None], dtype=torch.float32).to(DEVICE) + + # ── Proto-CLIP espèce ────────────────────────────────────────────────────── + species_list = list(model.prototypes.keys()) + proto_mat = np.vstack([model.prototypes[sp] for sp in species_list]) + sims = feat_w @ proto_mat.T + logits = sims * model.temperature - sims.max() * model.temperature + proto_proba = np.exp(logits) / np.exp(logits).sum() + + top_idx = proto_proba.argsort()[::-1] + sp_top1 = species_list[top_idx[0]] + sp_score = float(proto_proba[top_idx[0]]) + sp_margin = sp_score - float(proto_proba[top_idx[1]]) if len(top_idx) > 1 else sp_score + + # ── MLP niveaux supérieurs ───────────────────────────────────────────────── + mlp_scores = {} + with torch.no_grad(): + for level, (mlp, enc) in model.mlp_dict.items(): + logits_l = mlp(feat_t).squeeze(0) + proba_l = F.softmax(logits_l, dim=-1).detach().cpu().numpy() + top_l = proba_l.argsort()[::-1] + mlp_scores[level] = { + "label": enc.classes_[top_l[0]], + "score": float(proba_l[top_l[0]]), + "margin": float(proba_l[top_l[0]]) - float(proba_l[top_l[1]]) + if len(top_l) > 1 else float(proba_l[top_l[0]]), + "top3": [{"label": enc.classes_[i], "score": round(float(proba_l[i]), 4)} + for i in top_l[:3]], + } + + # ── Construire all_scores ────────────────────────────────────────────────── + all_scores = { + "species_name": [ + {"label": species_list[i], "score": round(float(proto_proba[i]), 4)} + for i in top_idx[:3] + ] + } + for level, ms in mlp_scores.items(): + all_scores[level] = ms["top3"] + + # ── Décision hiérarchique cohérente ─────────────────────────────────────── + sp_confident = (sp_score >= threshold and sp_margin >= margin_min) + + if sp_confident: + # Espèce confiante → lookup taxo (cohérence garantie) + tax = model.tax_lookup.get(sp_top1, {}) + best_level = "species_name" + best_label = sp_top1 + best_score = sp_score + path = "proto_clip" + for level in SUPERVISED_LEVELS: + true_val = tax.get(level) + if true_val and pd.notna(true_val): + existing = [e for e in all_scores.get(level, []) + if e["label"] != str(true_val)] + all_scores[level] = [ + {"label": str(true_val), "score": round(best_score, 4), "inherited": True} + ] + existing[:2] + else: + # Pas assez confiant → MLP du niveau le plus fin confiant + path = "mlp" + best_level = None + best_label = None + best_score = 0.0 + for level in ["famille", "ordre", "classe", "phylum", "regne"]: + if level not in mlp_scores: + continue + ms = mlp_scores[level] + if ms["score"] >= threshold and ms["margin"] >= margin_min: + best_level = level + best_label = ms["label"] + best_score = ms["score"] + break + + return { + "best_level": best_level, + "best_label": best_label, + "best_score": best_score, + "margin": round(sp_margin, 4), + "path": path, + "all_scores": all_scores, + } + + +def predict_image(image_path: str, + model: BioModel, + bioclip = None, + threshold: float = THRESHOLD, + margin_min: float = MARGIN_MIN) -> dict: + """ + Prédit directement depuis un chemin d'image. + + Si bioclip est None, importe BioCLIP2 à la volée (un peu plus lent). + Pour traiter beaucoup d'images, passer bioclip explicitement pour + ne le charger qu'une fois. + + Exemple : + model = load_model() + result = predict_image("photo.jpg", model) + + # Ou pour un lot d'images (plus efficace) : + from classifier_train import load_bioclip + bioclip = load_bioclip() + for path in images: + result = predict_image(path, model, bioclip) + """ + if bioclip is None: + try: + import open_clip + bc, _, _ = open_clip.create_model_and_transforms( + "hf-hub:imageomics/bioclip-2" + ) + bc = bc.to(DEVICE).eval() + bioclip = bc + except ImportError: + raise ImportError( + "BioCLIP2 requis pour lire les images.\n" + "pip install open-clip-torch\n" + "Ou pré-extrayez les features avec classifier_train.py." + ) + + with torch.no_grad(): + img = Image.open(image_path).convert("RGB") + x = IMG_TRANSFORM(img).unsqueeze(0).to(DEVICE) + feat = bioclip.encode_image(x) + feat = F.normalize(feat, dim=-1).squeeze(0).cpu().numpy() + + feat_w = apply_whitening(feat[None], *model.whitening)[0] + return predict(feat_w, model, threshold, margin_min) + + +# ════════════════════════════════════════════════════════════════════════════ +# POINT D'ENTRÉE — PRÉDICTION EN BATCH +# ════════════════════════════════════════════════════════════════════════════ + +def main(): + import argparse + parser = argparse.ArgumentParser( + description="Inférence — classifier Biolit", + formatter_class=argparse.RawDescriptionHelpFormatter, + epilog=""" +Exemples : + python classifier_infer.py --images mon_dossier/ + python classifier_infer.py --images mon_dossier/ --threshold 0.6 --margin 0.15 + +Paramètres dans config.py : + CONFIDENCE_THRESHOLD = 0.5 (seuil d'acceptation) + MARGIN_MIN = 0.10 (garde-fou faux positifs) + """ + ) + parser.add_argument("--images", type=str, required=True, + help="Dossier contenant les images à prédire") + parser.add_argument("--threshold", type=float, default=THRESHOLD, + help=f"Seuil de confiance (défaut {THRESHOLD})") + parser.add_argument("--margin", type=float, default=MARGIN_MIN, + help=f"Margin min top1/top2 (défaut {MARGIN_MIN})") + parser.add_argument("--output", type=str, default=None, + help="Chemin CSV de sortie (défaut results/predictions.csv)") + args = parser.parse_args() + + images_dir = Path(args.images) + if not images_dir.exists(): + print(f"Dossier introuvable : {images_dir}") + return + + output = Path(args.output) if args.output else RESULTS_DIR / "predictions.csv" + + print("Chargement du modèle...") + model = load_model() + + print("Chargement BioCLIP2 pour l'extraction features...") + try: + import open_clip + bc, _, _ = open_clip.create_model_and_transforms("hf-hub:imageomics/bioclip-2") + bioclip = bc.to(DEVICE).eval() + except ImportError: + print("⚠ open_clip manquant — pip install open-clip-torch") + return + + VALID_EXTS = {".jpg", ".jpeg", ".png", ".webp"} + images = [p for p in images_dir.iterdir() if p.suffix.lower() in VALID_EXTS] + print(f"\n{len(images)} images à prédire...") + + rows = [] + t0 = time.time() + for img_path in images: + try: + result = predict_image(str(img_path), model, bioclip, + args.threshold, args.margin) + sp = result["all_scores"].get("species_name", [{}]) + rows.append({ + "image": img_path.name, + "best_level": result["best_level"] or "rejeté", + "best_label": result["best_label"] or "—", + "best_score": f"{result['best_score']:.0%}", + "margin": f"{result['margin']:.3f}", + "path": result["path"], + "top1_espece": sp[0].get("label", "—") if sp else "—", + "conf_espece": f"{sp[0].get('score', 0):.0%}" if sp else "0%", + "top2_espece": sp[1].get("label", "—") if len(sp) > 1 else "—", + }) + except Exception as e: + rows.append({"image": img_path.name, "best_level": "erreur", "erreur": str(e)}) + + elapsed = time.time() - t0 + df_res = pd.DataFrame(rows) + RESULTS_DIR.mkdir(parents=True, exist_ok=True) + df_res.to_csv(output, index=False) + + accepted = df_res[df_res["best_level"] != "rejeté"] + print(f"\n{len(images)} images en {elapsed:.1f}s " + f"({elapsed/max(len(images),1)*1000:.0f}ms/image)") + print(f"Acceptées : {len(accepted)}/{len(images)} " + f"({len(accepted)/max(len(images),1):.0%})") + print(f"Niveaux : {df_res['best_level'].value_counts().to_dict()}") + print(f"Résultats → {output}") + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/ml/classification/BioClipv2/classifier_train.py b/ml/classification/BioClipv2/classifier_train.py new file mode 100644 index 0000000..79c5620 --- /dev/null +++ b/ml/classification/BioClipv2/classifier_train.py @@ -0,0 +1,627 @@ +""" +classifier_train.py — Entraînement du modèle +============================================= +Ce fichier gère tout ce qui touche à l'entraînement. +Une fois le modèle sauvegardé, seul classifier_infer.py est nécessaire. + +Ce qui est fait ici : + - Extraction des features BioCLIP2 (avec cache sur disque) + - Whitening PCA (512d → 256d) + - Entraînement d'un MLP par niveau taxonomique (règne → famille) + - Calcul des prototypes Proto-CLIP (espèce, few-shot) + - Apprentissage de la température + - Sauvegarde de tous les artefacts dans results/ + - Évaluation propre (split 80/20 sans data leakage) + - Mise à jour du modèle pour de nouvelles espèces + +Dépendances : + pip install open-clip-torch torch torchvision scikit-learn pandas pillow + +Usage : + python classifier_train.py --fit --eval --images data/images/identifiable + python classifier_train.py --fit --images data/images/identifiable + python classifier_train.py --eval --images data/images/identifiable + python classifier_train.py --update --images nouvelles_images/ +""" + +import argparse +import time +import numpy as np +import pandas as pd +import torch +import torch.nn as nn +import torch.nn.functional as F +from pathlib import Path +from PIL import Image +from sklearn.decomposition import PCA +from sklearn.metrics import accuracy_score +from sklearn.model_selection import train_test_split +from sklearn.preprocessing import LabelEncoder +from torch.utils.data import DataLoader, Dataset, WeightedRandomSampler +from torchvision import transforms + +try: + import open_clip + BIOCLIP_AVAILABLE = True +except ImportError: + BIOCLIP_AVAILABLE = False + print("⚠ open_clip manquant — pip install open-clip-torch") + +from config import ( + DEVICE, + RESULTS_DIR, + TAXONOMY_LEVELS, + BIOCLIP_MODEL_ID as BIOCLIP_MODEL, + CONFIDENCE_THRESHOLD as THRESHOLD, + PROTO_ALPHA as ALPHA, + PROTO_WHITENING_DIM as WHITENING_DIM, + PROTO_BATCH_SIZE as BATCH_SIZE, + PROTO_CACHE_FILE as CACHE_FILE, + MARGIN_MIN, + MLP_EPOCHS, + MLP_DROPOUT, + PROTO_MODEL_FILE as MODEL_FILE, + PROTO_TAX_FILE as TAX_LOOKUP_FILE, + MLP_MODEL_FILE, +) +from classifier_infer import ( + LevelMLP, BioModel, apply_whitening, + predict, ALL_LEVELS, SUPERVISED_LEVELS, + IMG_TRANSFORM, +) +from build_classify_dataset import build_dataset + +import pickle + + +# ── Descriptions textuelles pour Proto-CLIP ─────────────────────────────────── +# Pour toute espèce absente → template automatique depuis taxonomy.parquet + +SPECIES_DESCRIPTIONS = { + "Actinia equina": "a photo of Actinia equina, beadlet anemone, red or green column with blue acrorhagi beads, rocky intertidal shore", + "Anemonia viridis": "a photo of Anemonia viridis, snakelocks anemone, long green tentacles with purple tips, shallow rocky coast", + "Asterias rubens": "a photo of Asterias rubens, common starfish, five-armed orange brown seastar on mussel beds", + "Carcinus maenas": "a photo of Carcinus maenas, European green shore crab, five frontal teeth, olive to dark green carapace", + "Eriphia verrucosa": "a photo of Eriphia verrucosa, warty crab, robust dark brown crab with unequal claws and knobbly carapace", + "Fucus serratus": "a photo of Fucus serratus, serrated wrack, brown seaweed with finely serrated frond edges no bladders", + "Fucus spiralis": "a photo of Fucus spiralis, spiral wrack, twisted narrow brown fronds at top of shore without bladders", + "Fucus vesiculosus": "a photo of Fucus vesiculosus, bladder wrack, brown seaweed with paired spherical air bladders on rocky shore", + "Ascophyllum nodosum": "a photo of Ascophyllum nodosum, egg wrack, long yellowish-brown seaweed with single egg-shaped air bladders", + "Mytilus edulis": "a photo of Mytilus edulis, blue mussel, elongated blue-black bivalve shell in dense clusters on rocks", + "Mytilus galloprovincialis":"a photo of Mytilus galloprovincialis, Mediterranean mussel, elongated blue-black bivalve in dense clusters on rocks", + "Nucella lapillus": "a photo of Nucella lapillus, dog whelk, robust spiral shell banded white grey brown near barnacles", + "Octopus vulgaris": "a photo of Octopus vulgaris, common octopus, eight-armed mollusc with large rounded head and suckers", + "Pachygrapsus marmoratus": "a photo of Pachygrapsus marmoratus, marbled rock crab, square dark carapace with white marbled pattern on rocks", + "Palaemon elegans": "a photo of Palaemon elegans, rock pool prawn, small translucent slim prawn in tide pools", + "Palaemon serratus": "a photo of Palaemon serratus, common prawn, transparent shrimp with red-brown bands on body and claws", + "Paracentrotus lividus": "a photo of Paracentrotus lividus, purple sea urchin, dark purple-brown spiny echinoderm on rocky shores", + "Patella vulgata": "a photo of Patella vulgata, common limpet, oval ribbed conical shell clamped on rock, intertidal gastropod", + "Patella ulyssiponensis": "a photo of Patella ulyssiponensis, chinaman hat limpet, flat ribbed limpet with orange foot on Atlantic rocks", + "Pecten maximus": "a photo of Pecten maximus, great scallop, large fan-shaped ribbed bivalve with equal ears on sandy seabed", + "Corallina officinalis": "a photo of Corallina officinalis, coral weed, jointed pink calcified alga forming tufts in rock pools", + "Ulva lactuca": "a photo of Ulva lactuca, sea lettuce, bright green flat translucent alga sheets on rocky shore", + "Semibalanus balanoides": "a photo of Semibalanus balanoides, acorn barnacle, white volcano-shaped encrusting shell in dense colonies on rock", + "Littorina littorea": "a photo of Littorina littorea, common periwinkle, small dark rounded snail shell on rocks or seaweed", + "Cancer pagurus": "a photo of Cancer pagurus, edible crab, large brown oval crab with pie-crust edge carapace", + "Necora puber": "a photo of Necora puber, velvet swimming crab, dark blue-brown crab with red eyes and velvety hairy legs", + "Maja brachydactyla": "a photo of Maja brachydactyla, spiny spider crab, triangular spiny carapace with long thin legs", + "Marthasterias glacialis": "a photo of Marthasterias glacialis, spiny starfish, large pale starfish with prominent spines on each arm", + "Sepia officinalis": "a photo of Sepia officinalis, common cuttlefish, broad flat cephalopod with brown zebra-striped pattern", + "Himanthalia elongata": "a photo of Himanthalia elongata, thongweed, long strap-like brown seaweed from button-shaped holdfast on lower shore", + "Pelvetia canaliculata": "a photo of Pelvetia canaliculata, channelled wrack, yellowish-brown seaweed with inrolled channel-shaped fronds above tide", + "Sabellaria alveolata": "a photo of Sabellaria alveolata, honeycomb worm, reef-building polychaete forming honeycomb-textured sand tubes on rock", + "Posidonia oceanica": "a photo of Posidonia oceanica, Neptune seagrass, long ribbon-like green leaves in Mediterranean underwater meadows", + "Gibbula umbilicalis": "a photo of Gibbula umbilicalis, purple top shell, flattened spiral shell with purple streaks under rocks", + "Galathea squamifera": "a photo of Galathea squamifera, squat lobster, flattened brown crustacean with broad abdomen under rocks", + "Codium tomentosum": "a photo of Codium tomentosum, velvet horn, dark green spongy dichotomously branching alga on rocks", + "Cerastoderma edule": "a photo of Cerastoderma edule, common cockle, ribbed rounded bivalve with 22 to 28 ribs on sandy intertidal sediment", + "Magallana gigas": "a photo of Magallana gigas, Pacific oyster, large irregularly shaped craggy cupped bivalve in intertidal clusters", + "Haliotis tuberculata tuberculata": "a photo of Haliotis tuberculata, green ormer, flat ear-shaped shell with row of holes and iridescent nacre", + "Ligia oceanica": "a photo of Ligia oceanica, sea slater, large grey isopod crustacean running on rocky supralittoral", + "Padina pavonica": "a photo of Padina pavonica, peacock tail, fan-shaped brown alga with concentric white bands Mediterranean", + "Sargassum muticum": "a photo of Sargassum muticum, wireweed, bushy brown invasive alga with long main axis and small oval air bladders on rock", + "Phorcus lineatus": "a photo of Phorcus lineatus, toothed top shell, small conical snail with zebra-striped shell on wet rocks", + "Aplysia punctata": "a photo of Aplysia punctata, spotted sea hare, large soft purple-brown mollusc with wing-like parapodia", + "Zostera marina": "a photo of Zostera marina, common eelgrass, narrow ribbon-like green seagrass in sheltered bays and estuaries", +} + + +# ════════════════════════════════════════════════════════════════════════════ +# 1. BIOCLIP2 — EXTRACTION FEATURES +# ════════════════════════════════════════════════════════════════════════════ + +def load_bioclip(): + """Charge BioCLIP2. Nécessaire uniquement pour l'entraînement.""" + print(f" Chargement BioCLIP2 sur {DEVICE}...") + t0 = time.time() + model, _, _ = open_clip.create_model_and_transforms(BIOCLIP_MODEL) + model = model.to(DEVICE).eval() + print(f" BioCLIP2 prêt en {time.time() - t0:.1f}s") + return model + + +@torch.no_grad() +def extract_features(df: pd.DataFrame, bioclip, use_cache: bool = True) -> np.ndarray: + """ + Extrait les features BioCLIP2 et les cache sur disque. + Empreinte : ~0.5 kWh pour 2000 images sur GPU — fait UNE SEULE FOIS. + """ + if use_cache and CACHE_FILE.exists(): + cache = np.load(CACHE_FILE, allow_pickle=True) + if list(cache["image_paths"]) == list(df["image_path"]): + print(" Features chargées depuis le cache") + return cache["features"] + print(" Cache obsolète — réextraction...") + + print(f" Extraction BioCLIP2 ({len(df)} images) " + f"— ~{len(df) * 0.00025:.3f} kWh estimés...") + t0, all_feats = time.time(), [] + + for start in range(0, len(df), BATCH_SIZE): + batch = df.iloc[start:start + BATCH_SIZE] + imgs = [] + for _, row in batch.iterrows(): + try: + imgs.append(IMG_TRANSFORM(Image.open(row["image_path"]).convert("RGB"))) + except Exception: + imgs.append(torch.zeros(3, 224, 224)) + feats = bioclip.encode_image(torch.stack(imgs).to(DEVICE)) + all_feats.append(F.normalize(feats, dim=-1).cpu().numpy()) + if (start // BATCH_SIZE + 1) % 10 == 0: + print(f" {start + len(batch)}/{len(df)}...") + + features = np.vstack(all_feats).astype(np.float32) + print(f" Terminé en {time.time() - t0:.1f}s") + RESULTS_DIR.mkdir(parents=True, exist_ok=True) + np.savez(CACHE_FILE, features=features, + image_paths=np.array(list(df["image_path"]))) + print(f" Cache → {CACHE_FILE}") + return features + + +# ════════════════════════════════════════════════════════════════════════════ +# 2. WHITENING PCA +# ════════════════════════════════════════════════════════════════════════════ + +def fit_whitening(features: np.ndarray) -> tuple: + """Whitening PCA : 512d → 256d. Ajusté sur le train uniquement.""" + print(f" Whitening PCA : {features.shape[1]}d → {WHITENING_DIM}d...") + pca = PCA(n_components=WHITENING_DIM, whiten=True) + pca.fit(features) + print(f" Variance expliquée : {pca.explained_variance_ratio_.sum():.1%}") + return pca.mean_, pca.components_, pca.explained_variance_ + + +# ════════════════════════════════════════════════════════════════════════════ +# 3. MLP PAR NIVEAU TAXONOMIQUE +# ════════════════════════════════════════════════════════════════════════════ + +class FeaturesDataset(Dataset): + """Dataset sur features pré-calculées.""" + def __init__(self, features: np.ndarray, labels: np.ndarray): + self.features = torch.tensor(features, dtype=torch.float32) + self.labels = torch.tensor(labels, dtype=torch.long) + + def __len__(self): + return len(self.features) + + def __getitem__(self, idx): + return self.features[idx], self.labels[idx] + + +def train_level_mlps(features_w: np.ndarray, df: pd.DataFrame) -> dict: + """ + Entraîne un MLP par niveau taxonomique (règne → famille). + Retourne {niveau → (LevelMLP, LabelEncoder)}. + """ + print(f"\n Entraînement MLP par niveau taxonomique...") + df_lab = df[df["species_name"].notna()].reset_index(drop=True) + mlp_dict = {} + + for level in SUPERVISED_LEVELS: + if level not in df_lab.columns: + continue + + df_lvl = df_lab[df_lab[level].notna()].reset_index(drop=True) + if len(df_lvl) == 0: + continue + + enc = LabelEncoder() + labels = enc.fit_transform(df_lvl[level]) + n_cls = len(enc.classes_) + feats_l = features_w[df_lvl.index] + + counts = df_lvl[level].value_counts() + weights = df_lvl[level].map(lambda x: 1.0 / max(counts[x], 1)).values + sampler = WeightedRandomSampler( + torch.tensor(weights, dtype=torch.float32), + num_samples=len(df_lvl), replacement=True + ) + + ds = FeaturesDataset(feats_l, labels) + loader = DataLoader(ds, batch_size=64, sampler=sampler) + + mlp = LevelMLP(WHITENING_DIM, n_cls).to(DEVICE) + opt = torch.optim.AdamW(mlp.parameters(), lr=1e-3, weight_decay=1e-4) + sch = torch.optim.lr_scheduler.CosineAnnealingLR(opt, T_max=MLP_EPOCHS) + + mlp.train() + for epoch in range(MLP_EPOCHS): + total, n = 0.0, 0 + for feats_b, labels_b in loader: + feats_b, labels_b = feats_b.to(DEVICE), labels_b.to(DEVICE) + loss = F.cross_entropy(mlp(feats_b), labels_b) + opt.zero_grad(); loss.backward() + nn.utils.clip_grad_norm_(mlp.parameters(), 1.0) + opt.step(); total += loss.item(); n += 1 + sch.step() + if (epoch + 1) % 10 == 0 or epoch == 0: + print(f" [{level} — {n_cls} cls] " + f"epoch {epoch+1}/{MLP_EPOCHS} loss={total/max(n,1):.4f}") + + mlp.eval() + mlp_dict[level] = (mlp, enc) + print(f" {level} ✓ ({n_cls} classes)") + + return mlp_dict + + +# ════════════════════════════════════════════════════════════════════════════ +# 4. PROTO-CLIP — NIVEAU ESPÈCE +# ════════════════════════════════════════════════════════════════════════════ + +def get_description(species: str, tax_info: dict = None) -> str: + if species in SPECIES_DESCRIPTIONS: + return SPECIES_DESCRIPTIONS[species] + if tax_info: + parts = [f"a photo of {species}"] + for lvl in ["famille", "ordre", "classe"]: + v = tax_info.get(lvl) + if v and pd.notna(v): + parts.append(str(v)) + parts.append("marine coastal species") + return ", ".join(parts) + return f"a photo of {species}, marine species" + + +@torch.no_grad() +def encode_text_descriptions(species_list, tax_lookup, bioclip) -> np.ndarray: + tokenizer = open_clip.get_tokenizer(BIOCLIP_MODEL) + feats = [] + for sp in species_list: + desc = get_description(sp, tax_lookup.get(sp, {})) + tokens = tokenizer([desc]).to(DEVICE) + feat = bioclip.encode_text(tokens) + feats.append(F.normalize(feat, dim=-1).squeeze(0).cpu().numpy()) + return np.vstack(feats).astype(np.float32) + + +def compute_prototypes(df, features_w, bioclip, alpha=ALPHA) -> tuple: + """Calcule les prototypes Proto-CLIP = α × visuel + (1-α) × textuel.""" + print(f"\n Prototypes Proto-CLIP (α={alpha})...") + df_lab = df[df["species_name"].notna()].reset_index(drop=True) + species_list = sorted(df_lab["species_name"].unique()) + + tax_cols = [l for l in TAXONOMY_LEVELS if l != "species_name" and l in df_lab.columns] + tax_lookup = {} + for _, row in df_lab.drop_duplicates("species_name").iterrows(): + tax_lookup[row["species_name"]] = {lvl: row.get(lvl) for lvl in tax_cols} + + # Prototypes visuels : centroïde pondéré + visual_protos = {} + for sp in species_list: + mask = (df_lab["species_name"] == sp).values + feats = features_w[mask] + if len(feats) == 0: + continue + if len(feats) == 1: + visual_protos[sp] = feats[0] / (np.linalg.norm(feats[0]) + 1e-8) + else: + centroid = feats.mean(0) + centroid = centroid / (np.linalg.norm(centroid) + 1e-8) + weights = np.exp(feats @ centroid / 0.1) + weights /= weights.sum() + proto = (weights[:, None] * feats).sum(0) + visual_protos[sp] = proto / (np.linalg.norm(proto) + 1e-8) + + # Prototypes textuels : projetés dans l'espace whitened + print(" Encodage descriptions textuelles...") + text_feats = encode_text_descriptions(species_list, tax_lookup, bioclip) + vis_mat = np.vstack([visual_protos[sp] for sp in species_list if sp in visual_protos]) + W = np.linalg.lstsq(text_feats, vis_mat, rcond=None)[0] + text_proj = text_feats @ W + norms = np.linalg.norm(text_proj, axis=1, keepdims=True) + text_proj = text_proj / np.clip(norms, 1e-8, None) + + prototypes = {} + for i, sp in enumerate(species_list): + if sp not in visual_protos: + continue + fused = alpha * visual_protos[sp] + (1 - alpha) * text_proj[i] + prototypes[sp] = fused / (np.linalg.norm(fused) + 1e-8) + + n_manual = sum(1 for sp in species_list if sp in SPECIES_DESCRIPTIONS) + print(f" {len(prototypes)} prototypes " + f"({n_manual} manuelles, {len(species_list)-n_manual} templates)") + return prototypes, tax_lookup + + +def learn_temperature(prototypes, features_w, df) -> float: + """Calibre la température T pour des scores de confiance honnêtes.""" + print(" Apprentissage température...") + df_lab = df[df["species_name"].notna()].reset_index(drop=True) + species_list = list(prototypes.keys()) + proto_mat = np.vstack([prototypes[sp] for sp in species_list]) + proto_t = torch.tensor(proto_mat, dtype=torch.float32) + + feats, labels = [], [] + for i, (_, row) in enumerate(df_lab.iterrows()): + sp = row.get("species_name") + if sp in prototypes: + feats.append(features_w[i]) + labels.append(species_list.index(sp)) + + if not feats: + return 10.0 + + feats_t = torch.tensor(np.vstack(feats), dtype=torch.float32) + labels_t = torch.tensor(labels, dtype=torch.long) + log_T = nn.Parameter(torch.tensor(np.log(10.0), dtype=torch.float32)) + opt = torch.optim.Adam([log_T], lr=0.05) + + for _ in range(300): + loss = F.cross_entropy(feats_t @ proto_t.T * log_T.exp(), labels_t) + opt.zero_grad(); loss.backward(); opt.step() + with torch.no_grad(): + log_T.clamp_(np.log(1.0), np.log(100.0)) + + T = float(log_T.exp()) + print(f" Température : T={T:.2f}") + return T + + +# ════════════════════════════════════════════════════════════════════════════ +# 5. SAUVEGARDE +# ════════════════════════════════════════════════════════════════════════════ + +def save_model(prototypes, tax_lookup, whitening, temperature, alpha, mlp_dict): + """Sauvegarde tous les artefacts dans results/.""" + mean, components, variance = whitening + RESULTS_DIR.mkdir(parents=True, exist_ok=True) + + np.savez(MODEL_FILE, + prototypes = np.vstack([prototypes[sp] for sp in prototypes]), + species_list = np.array(list(prototypes.keys())), + wh_mean = mean, + wh_components = components, + wh_variance = variance, + temperature = np.array([temperature]), + alpha = np.array([alpha])) + with open(TAX_LOOKUP_FILE, "wb") as f: + pickle.dump(tax_lookup, f) + + mlp_save = {} + for level, (mlp, enc) in mlp_dict.items(): + mlp_save[level] = { + "state_dict": mlp.state_dict(), + "encoder": enc, + "input_dim": WHITENING_DIM, + "n_classes": len(enc.classes_), + } + torch.save(mlp_save, MLP_MODEL_FILE) + + print(f"\n Sauvegardé :") + print(f" Proto-CLIP → {MODEL_FILE}") + print(f" Taxo lookup → {TAX_LOOKUP_FILE}") + print(f" MLP niveaux → {MLP_MODEL_FILE} ({list(mlp_dict.keys())})") + + +# ════════════════════════════════════════════════════════════════════════════ +# 6. ÉVALUATION +# ════════════════════════════════════════════════════════════════════════════ + +def evaluate(features_w, df, prototypes, tax_lookup, temperature, + mlp_dict, threshold=THRESHOLD, margin_min=MARGIN_MIN) -> dict: + """Évalue sur df. Retourne dict de résultats par niveau.""" + print(f"\n Évaluation ({len(df)} images, seuil={threshold:.0%}, margin≥{margin_min:.2f})...") + df_lab = df[df["species_name"].notna()].reset_index(drop=True) + records = {lvl: [] for lvl in ALL_LEVELS} + path_counts = {"proto_clip": 0, "mlp": 0} + + model = BioModel(prototypes, tax_lookup, + (features_w,), temperature, mlp_dict) + # On reconstruit un BioModel léger juste pour passer à predict() + # (whitening déjà appliqué donc on le bypasse) + + for i, (_, row) in enumerate(df_lab.iterrows()): + # predict() attend un feat_w déjà whitened — c'est le cas ici + result = predict(features_w[i], BioModel( + prototypes, tax_lookup, + (np.zeros(WHITENING_DIM), np.eye(WHITENING_DIM, features_w.shape[1]), + np.ones(features_w.shape[1])), + temperature, mlp_dict + ), threshold, margin_min) + path_counts[result["path"]] += 1 + + for level in ALL_LEVELS: + true_val = row.get(level) + if not true_val or (isinstance(true_val, float) and pd.isna(true_val)): + continue + preds = result["all_scores"].get(level, []) + if not preds: + continue + records[level].append({ + "true": str(true_val), + "pred": preds[0]["label"], + "score": preds[0]["score"], + "accepted": preds[0]["score"] >= threshold, + "path": result["path"], + }) + + n_total = len(df_lab) + print(f"\n proto_clip : {path_counts['proto_clip']} ({path_counts['proto_clip']/max(n_total,1):.0%})") + print(f" mlp : {path_counts['mlp']} ({path_counts['mlp']/max(n_total,1):.0%})") + print(f"\n {'Niveau':<15} {'Accuracy':>10} {'Coverage':>10} {'F-cov':>8} {'N':>12}") + print(" " + "─" * 60) + + results = {} + for level in ALL_LEVELS: + recs = records.get(level, []) + if not recs: + continue + accepted = [r for r in recs if r["accepted"]] + n_tot = len(recs) + n_acc = len(accepted) + coverage = n_acc / n_tot if n_tot else 0 + acc = accuracy_score( + [r["true"] for r in accepted], + [r["pred"] for r in accepted], + ) if n_acc else 0 + fcov = round(acc * coverage, 3) + print(f" {level:<15} {acc:>10.1%} {coverage:>10.1%} {fcov:>8.3f} {n_acc:>5}/{n_tot}") + results[level] = {"accuracy": acc, "coverage": coverage, + "n_accepted": n_acc, "n_total": n_tot, "records": recs} + + results["_paths"] = path_counts + return results + + +# ════════════════════════════════════════════════════════════════════════════ +# 7. PIPELINE PRINCIPALE +# ════════════════════════════════════════════════════════════════════════════ + +def run_fit(images_dir: Path, with_eval: bool = False, no_cache: bool = False): + """Pipeline complète d'entraînement.""" + print("\n" + "=" * 60) + print("ENTRAÎNEMENT — MLP par niveau + Proto-CLIP espèce") + print("=" * 60) + + df = build_dataset(images_dir=images_dir) + df_lab = df[df["species_name"].notna()].reset_index(drop=True) + + if len(df_lab) == 0: + print("⚠ Aucune image labellisée.") + return + + bioclip = load_bioclip() + + if with_eval: + try: + train_df, test_df = train_test_split( + df_lab, test_size=0.2, random_state=42, + stratify=df_lab["species_name"] + ) + except ValueError: + train_df, test_df = train_test_split(df_lab, test_size=0.2, random_state=42) + train_df = train_df.reset_index(drop=True) + test_df = test_df.reset_index(drop=True) + print(f"\n Split : {len(train_df)} train | {len(test_df)} test") + else: + train_df, test_df = df_lab, None + + feats_train = extract_features(train_df, bioclip, use_cache=not no_cache) + wh = fit_whitening(feats_train) + feats_w = apply_whitening(feats_train, *wh) + + mlp_dict = train_level_mlps(feats_w, train_df) + protos, tax = compute_prototypes(train_df, feats_w, bioclip) + temp = learn_temperature(protos, feats_w, train_df) + + save_model(protos, tax, wh, temp, ALPHA, mlp_dict) + + if with_eval and test_df is not None: + print("\n Extraction features test set...") + feats_test = extract_features(test_df, bioclip, use_cache=False) + feats_test_w = apply_whitening(feats_test, *wh) + evaluate(feats_test_w, test_df, protos, tax, temp, mlp_dict) + + print("\n Entraînement terminé ✓") + return protos, tax, wh, temp, mlp_dict + + +def run_update(images_dir: Path): + """Ajoute de nouvelles espèces Proto-CLIP sans réentraîner les MLP.""" + print("\n" + "=" * 60) + print("MISE À JOUR — nouvelles espèces") + print("=" * 60) + + from classifier_infer import load_model + model = load_model() + bioclip = load_bioclip() + + df = build_dataset(images_dir=images_dir) + df_new = df[df["species_name"].notna()].reset_index(drop=True) + new_sp = set(df_new["species_name"]) - set(model.prototypes.keys()) + + if not new_sp: + print(" Aucune nouvelle espèce.") + return + + print(f" {len(new_sp)} nouvelles espèces détectées") + df_only = df_new[df_new["species_name"].isin(new_sp)].reset_index(drop=True) + feats = extract_features(df_only, bioclip, use_cache=False) + feats_w = apply_whitening(feats, *model.whitening) + new_p, new_t = compute_prototypes(df_only, feats_w, bioclip) + + model.prototypes.update(new_p) + model.tax_lookup.update(new_t) + save_model(model.prototypes, model.tax_lookup, model.whitening, + model.temperature, ALPHA, model.mlp_dict) + print(f" {len(model.prototypes)} espèces au total ✓") + + +# ════════════════════════════════════════════════════════════════════════════ +# 8. POINT D'ENTRÉE +# ════════════════════════════════════════════════════════════════════════════ + +def main(): + parser = argparse.ArgumentParser( + description="Entraînement — classifier Biolit", + formatter_class=argparse.RawDescriptionHelpFormatter, + epilog=""" +Tous les paramètres sont dans config.py. + +Exemples : + python classifier_train.py --fit --eval --images data/images/identifiable + python classifier_train.py --fit --images data/images/identifiable + python classifier_train.py --eval --images data/images/identifiable + python classifier_train.py --update --images nouvelles_images/ + """ + ) + parser.add_argument("--fit", action="store_true", help="Entraîner") + parser.add_argument("--eval", action="store_true", help="Évaluer (split 80/20)") + parser.add_argument("--update", action="store_true", help="Ajouter nouvelles espèces") + parser.add_argument("--images", type=str, required=True) + parser.add_argument("--no-cache", action="store_true", + help="Forcer réextraction features") + args = parser.parse_args() + + if not BIOCLIP_AVAILABLE: + print("pip install open-clip-torch") + return + + images_dir = Path(args.images) + if not images_dir.exists(): + print(f"Dossier introuvable : {images_dir}") + return + + if args.fit: + run_fit(images_dir, with_eval=args.eval, no_cache=args.no_cache) + elif args.eval: + from classifier_infer import load_model + model = load_model() + bioclip = load_bioclip() + df = build_dataset(images_dir=images_dir) + df_lab = df[df["species_name"].notna()].reset_index(drop=True) + feats = extract_features(df_lab, bioclip, use_cache=not args.no_cache) + feats_w = apply_whitening(feats, *model.whitening) + evaluate(feats_w, df_lab, model.prototypes, model.tax_lookup, + model.temperature, model.mlp_dict) + elif args.update: + run_update(images_dir) + else: + parser.print_help() + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/ml/classification/BioClipv2/config.py b/ml/classification/BioClipv2/config.py new file mode 100644 index 0000000..a6bba97 --- /dev/null +++ b/ml/classification/BioClipv2/config.py @@ -0,0 +1,55 @@ +""" +config.py — Configuration centrale de la pipeline +""" + +import os +import torch +from pathlib import Path + +# ── Chemins ─────────────────────────────────────────────────────────────────── +ROOT_DIR = Path(__file__).parent +DATA_DIR = ROOT_DIR / "data/dataset_biolit" +IMAGES_DIR = DATA_DIR / "images" +LABELS_DIR = DATA_DIR / "labels" +TAXONOMY_FILE = DATA_DIR / "taxref.parquet" +RESULTS_DIR = ROOT_DIR / "results" +CROPS_DIR = RESULTS_DIR / "crops" +REJECTED_DIR = RESULTS_DIR / "rejected" +REPORTS_DIR = RESULTS_DIR / "reports" + + +# ── Device ──────────────────────────────────────────────────────────────────── +DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu") + + +# ── Niveaux taxonomiques ─────────────────────────────────────────────────────── +TAXONOMY_LEVELS = ["regne", "phylum", "classe", "ordre", "famille", "sous_famille", "species_name"] + +# ── Proto-CLIP classifier (classifier.py) ───────────────────────────────────── +BIOCLIP_MODEL_ID = "hf-hub:imageomics/bioclip-2" +CONFIDENCE_THRESHOLD = 0.5 # seuil arrêt hiérarchique +PROTO_ALPHA = 0.6 # fusion visuel/textuel Proto-CLIP +PROTO_WHITENING_DIM = 256 # 512d → 256d après PCA whitening +PROTO_BATCH_SIZE = 32 # batch extraction features BioCLIP2 + +# ── MLP par niveau taxonomique ──────────────────────────────────────────────── +MLP_EPOCHS = 40 # epochs par MLP (règne, phylum, classe, ordre, famille) +MLP_DROPOUT = 0.3 # régularisation MLP + +# ── margin score ───────────────────────────────────────────────────── +# Rejette une prédiction si score_top1 - score_top2 < MARGIN_MIN +# même si score_top1 ≥ CONFIDENCE_THRESHOLD +# Évite les faux positifs à haute confiance (ex: 91% mais faux) +# Recommandé : 0.05 (permissif) à 0.20 (strict) +MARGIN_MIN = 0.10 + +# ── Fichiers de sortie ───────────────────────────────────────────────────────── +PROTO_CACHE_FILE = RESULTS_DIR / "bioclip_features.npz" +PROTO_MODEL_FILE = RESULTS_DIR / "proto_model.npz" +PROTO_TAX_FILE = RESULTS_DIR / "tax_lookup.pkl" +MLP_MODEL_FILE = RESULTS_DIR / "mlp_model.pt" + +# ── Rapport HTML ─────────────────────────────────────────────────────────────── +CLASSIFY_IMAGES_DIR = IMAGES_DIR / "identifiable" +PROTO_REPORT_FILE = RESULTS_DIR / "report_proto_clip.html" +PROTO_REPORT_GALLERY = 60 \ No newline at end of file diff --git a/ml/classification/BioClipv2/results/bioclip_features.npz b/ml/classification/BioClipv2/results/bioclip_features.npz new file mode 100644 index 0000000..37bf009 Binary files /dev/null and b/ml/classification/BioClipv2/results/bioclip_features.npz differ diff --git a/ml/classification/BioClipv2/results/mlp_model.pt b/ml/classification/BioClipv2/results/mlp_model.pt new file mode 100644 index 0000000..7c1ceb4 Binary files /dev/null and b/ml/classification/BioClipv2/results/mlp_model.pt differ diff --git a/ml/classification/BioClipv2/results/predictions.csv b/ml/classification/BioClipv2/results/predictions.csv new file mode 100644 index 0000000..cd907b9 --- /dev/null +++ b/ml/classification/BioClipv2/results/predictions.csv @@ -0,0 +1,237 @@ +image,best_level,best_label,best_score,margin,path,top1_espece,conf_espece,top2_espece +102210_Tomate_de_mer_de_l'Atlantique_1802_animal fish snail_0.32.jpg,species_name,mytilus galloprovincialis,67%,0.347,proto_clip,mytilus galloprovincialis,67%,mytilus edulis +102210_Tomate_de_mer_de_l'Atlantique_1802_marine plant_0.35.jpg,species_name,anemonia viridis,50%,0.466,proto_clip,anemonia viridis,50%,fucus vesiculosus +105118_Botrylle_de_San_Diego_1831_plant_0.35.jpg,classe,Hydrozoa,68%,0.159,mlp,elysia viridis,23%,flabellia petiolata +105118_Botrylle_de_San_Diego_1831_starfish_0.51.jpg,species_name,botrylloides diegensis,100%,1.000,proto_clip,botrylloides diegensis,100%,watersipora subatra +106074_Crabe_de_pierre_1874_crab_0.79.jpg,species_name,xantho hydrophilus,100%,0.999,proto_clip,xantho hydrophilus,100%,sacculina carcini +106074_Crabe_de_pierre_1874_plant_0.18.jpg,phylum,Cnidaria,59%,0.005,mlp,alcedo atthis,8%,caulerpa cylindracea +106176_algae brown algae_0.23.jpg,ordre,Neogastropoda,54%,0.033,mlp,bifurcaria bifurcata,10%,eunicella verrucosa +106176_algae_0.35.jpg,species_name,codium fragile subsp. fragile,71%,0.641,proto_clip,codium fragile subsp. fragile,71%,eunicella cavolini +106176_flower_0.19.jpg,species_name,symsagittifera roscoffensis,59%,0.568,proto_clip,symsagittifera roscoffensis,59%,lagurus ovatus +106830_Anémone_parasite_1899_mussel mollusk_0.38.jpg,regne,Animalia,100%,0.042,mlp,patella caerulea,15%,clavelina lepadiformis +106830_Anémone_parasite_1899_white oval bone_0.19.jpg,regne,Animalia,100%,0.042,mlp,symsagittifera roscoffensis,11%,sterna hirundo +107658_2_flower_0.56.jpg,species_name,crithmum maritimum,100%,1.000,proto_clip,crithmum maritimum,100%,cakile maritima +107847_2_plant_0.49.jpg,famille,Posidoniaceae,97%,0.074,mlp,posidonia oceanica,17%,egretta garzetta +109229_Vélelle_2026_amp marine plant_0.40.jpg,species_name,velella velella,99%,0.994,proto_clip,velella velella,99%,ardea cinerea +109229_Vélelle_2026_white oval bone_0.27.jpg,species_name,velella velella,94%,0.936,proto_clip,velella velella,94%,coscinasterias tenuispina +110038_Anatife_commun_2073_snail_0.42.jpg,species_name,lepas (anatifa) anatifera,100%,1.000,proto_clip,lepas (anatifa) anatifera,100%,lithophaga lithophaga +26110_flower_0.19.jpg,phylum,Echinodermata,53%,0.004,mlp,marthasterias glacialis,7%,asparagopsis armata +26362_animal mollusk_0.21.jpg,species_name,magallana gigas,100%,0.999,proto_clip,magallana gigas,100%,ostrea edulis +26364_plant sea_0.42.jpg,species_name,codium fragile subsp. fragile,100%,1.000,proto_clip,codium fragile subsp. fragile,100%,bifurcaria bifurcata +58008_flower_0.28.jpg,regne,Animalia,97%,0.087,mlp,eunicella singularis,14%,apogon imberbis +58008_plant flower_0.20.jpg,phylum,Mollusca,67%,0.091,mlp,acetabularia acetabulum,18%,himanthalia elongata +58008_plant_0.26.jpg,species_name,halimione portulacoides,100%,1.000,proto_clip,halimione portulacoides,100%,atriplex halimus +58419_snail mussel_0.42.jpg,species_name,stramonita haemastoma,99%,0.993,proto_clip,stramonita haemastoma,99%,ocenebra erinaceus +58419_snail_0.15.jpg,phylum,Cnidaria,52%,0.025,mlp,zeus faber,10%,donax trunculus +62433_13_insect_0.23.jpg,species_name,palaemon elegans,79%,0.590,proto_clip,palaemon elegans,79%,palaemon serratus +62434_7_animal fish_0.60.jpg,species_name,gaidropsarus vulgaris,76%,0.528,proto_clip,gaidropsarus vulgaris,76%,conger conger +62536_10_animal fish_0.37.jpg,phylum,Mollusca,89%,0.001,mlp,diplodus puntazzo,11%,halocynthia papillosa +62536_10_insect_0.21.jpg,regne,Animalia,100%,0.014,mlp,amphipholis squamata,43%,eunicella verrucosa +62536_10_plant_0.20.jpg,classe,Hydrozoa,72%,0.283,mlp,elysia viridis,43%,parablennius incognitus +62731_6_crab_0.78.jpg,species_name,xantho hydrophilus,90%,0.891,proto_clip,xantho hydrophilus,90%,phorcus turbinatus +62731_6_plant_0.34.jpg,phylum,Cnidaria,65%,0.025,mlp,cymbulia peronii,9%,aporrhais pespelecani +62793_2_animal crab_0.23.jpg,famille,Trochidae,100%,0.086,mlp,steromphala umbilicalis,19%,parablennius pilicornis +63063_animal fish_0.39.jpg,species_name,sepia officinalis,61%,0.582,proto_clip,sepia officinalis,61%,lutraria lutraria +63063_insect_0.16.jpg,ordre,Neogastropoda,58%,0.244,mlp,cymbulia peronii,35%,fucus vesiculosus +63063_plant_0.29.jpg,famille,Posidoniaceae,63%,0.002,mlp,lagurus ovatus,20%,cymbulia peronii +63094_1_animal fish_0.50.jpg,famille,Rajidae,100%,0.012,mlp,raja undulata,51%,raja brachyura +63094_1_insect_0.16.jpg,classe,Hydrozoa,50%,0.003,mlp,pelvetia canaliculata,17%,amphipholis squamata +63445_2_animal fish_0.56.jpg,species_name,seriola rivoliana,91%,0.840,proto_clip,seriola rivoliana,91%,sparus aurata 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officinalis,99%,0.988,proto_clip,sepia officinalis,99%,donax trunculus +63833_Os_de_seiche_85_animal fish_0.30.jpg,species_name,sepia officinalis,100%,0.997,proto_clip,sepia officinalis,100%,clavelina lepadiformis +63833_Os_de_seiche_85_oval_0.15.jpg,famille,Sepiidae,53%,0.258,mlp,sterna hirundo,31%,dasyatis pastinaca +63833_Os_de_seiche_85_white oval bone_0.55.jpg,species_name,sepia officinalis,100%,0.998,proto_clip,sepia officinalis,100%,clavelina lepadiformis +64082_Huître_plate_114_white oval bone_0.52.jpg,species_name,ostrea edulis,100%,0.999,proto_clip,ostrea edulis,100%,lutraria lutraria +64100_Os_de_seiche_rose_121_white oval bone_0.53.jpg,species_name,sepia officinalis,81%,0.795,proto_clip,sepia officinalis,81%,sparus aurata +64116_Os_de_seiche_rose_125_animal fish_0.29.jpg,species_name,sepia officinalis,96%,0.926,proto_clip,sepia officinalis,96%,ensis magnus +64116_Os_de_seiche_rose_125_animal_0.15.jpg,species_name,buccinum undatum,65%,0.606,proto_clip,buccinum undatum,65%,pleurobrachia pileus +64116_Os_de_seiche_rose_125_white oval bone_0.54.jpg,species_name,sepia officinalis,97%,0.946,proto_clip,sepia officinalis,97%,ensis magnus +64394_2_flower_0.28.jpg,species_name,mytilus edulis,57%,0.502,proto_clip,mytilus edulis,57%,sparus aurata +64394_2_insect mussel_0.24.jpg,species_name,mytilus edulis,86%,0.848,proto_clip,mytilus edulis,86%,mytilus galloprovincialis +64490_9_animal fish crab snail flower_0.29.jpg,famille,Carcinidae,100%,0.169,mlp,carcinus maenas,36%,portumnus latipes +64490_9_flower_0.17.jpg,regne,Animalia,100%,0.049,mlp,ciona intestinalis,17%,tethya citrina +64490_9_plant_0.23.jpg,regne,Animalia,81%,0.348,mlp,zostera marina,45%,caulerpa cylindracea +64792_Gibbule_ombiliquée_154_animal snail_0.58.jpg,famille,Littorinidae,81%,0.115,mlp,calliostoma zizyphinum,20%,delphinus delphis +64798_7_animal snail_0.58.jpg,famille,Littorinidae,81%,0.115,mlp,calliostoma zizyphinum,20%,delphinus delphis +65405_flower_0.45.jpg,species_name,arbacia lixula,77%,0.554,proto_clip,arbacia lixula,77%,paracentrotus lividus +65855_14_brown algae_0.18.jpg,ordre,Actiniaria,60%,0.327,mlp,atriplex halimus,38%,alcedo atthis +65855_14_insect_0.17.jpg,phylum,Ochrophyta,84%,0.111,mlp,eunicella singularis,19%,littorina obtusata +65855_14_plant_0.18.jpg,species_name,crithmum maritimum,73%,0.696,proto_clip,crithmum maritimum,73%,euphorbia paralias +65855_16_flower_0.46.jpg,species_name,cakile maritima,92%,0.910,proto_clip,cakile maritima,92%,bolinus brandaris +65855_6_insect marine plant_0.22.jpg,classe,Ascidiacea,60%,0.265,mlp,cymbulia peronii,36%,euphorbia paralias +65855_6_insect_0.24.jpg,phylum,Chordata,56%,0.133,mlp,galathea intermedia,18%,clavelina lepadiformis +65855_6_plant seaweed algae_0.34.jpg,classe,Malacostraca,51%,0.184,mlp,dictyota fasciola,39%,sphaerococcus coronopifolius +65855_6_plant seaweed_0.36.jpg,ordre,Neogastropoda,58%,0.119,mlp,sphaerococcus coronopifolius,23%,sparus aurata 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lumbriciformis,93%,0.855,proto_clip,nerophis lumbriciformis,93%,nerophis ophidion +81771_Béret_basque_820_marine plant_0.27.jpg,species_name,codium bursa,99%,0.992,proto_clip,codium bursa,99%,barbatia barbata +81832_flower_0.18.jpg,ordre,Neogastropoda,50%,0.029,mlp,halocynthia papillosa,12%,apogon imberbis +81832_mushroom_0.17.jpg,regne,Animalia,100%,0.012,mlp,doris pseudoargus,13%,gaidropsarus vulgaris +81832_plant flower_0.36.jpg,species_name,peyssonnelia squamaria,99%,0.980,proto_clip,peyssonnelia squamaria,99%,watersipora subatra +82055_2_flower_0.51.jpg,species_name,sphaerococcus coronopifolius,100%,1.000,proto_clip,sphaerococcus coronopifolius,100%,asparagopsis armata +82243_plant_0.45.jpg,regne,Animalia,89%,0.086,mlp,flabellia petiolata,17%,callinectes sapidus +82287_2_snail mollusk_0.29.jpg,species_name,crepidula fornicata,100%,1.000,proto_clip,crepidula fornicata,100%,phoenicopterus roseus +82537_insect_0.50.jpg,species_name,rugulopteryx okamurae,98%,0.971,proto_clip,rugulopteryx okamurae,98%,sphaerococcus coronopifolius +82721_Crabe_vert_888_crab_0.75.jpg,species_name,carcinus maenas,100%,1.000,proto_clip,carcinus maenas,100%,carcinus aestuarii +82766_Nérophis_lombric_897_reptile_0.47.jpg,species_name,nerophis ophidion,83%,0.651,proto_clip,nerophis ophidion,83%,nerophis lumbriciformis +82838_crab_0.78.jpg,species_name,carcinus maenas,98%,0.966,proto_clip,carcinus maenas,98%,carcinus aestuarii +83197_14_bird_0.53.jpg,classe,Bivalvia,58%,0.059,mlp,alcedo atthis,21%,gaidropsarus vulgaris +84226_plant flower_0.34.jpg,species_name,rugulopteryx okamurae,99%,0.983,proto_clip,rugulopteryx okamurae,99%,fucus serratus +84867_algae_0.16.jpg,species_name,dictyota fasciola,71%,0.636,proto_clip,dictyota fasciola,71%,parablennius incognitus +84867_plant brown algae_0.19.jpg,species_name,sphaerococcus coronopifolius,65%,0.426,proto_clip,sphaerococcus coronopifolius,65%,parablennius incognitus +84867_plant_0.20.jpg,species_name,caulerpa cylindracea,100%,1.000,proto_clip,caulerpa cylindracea,100%,euphorbia paralias +85342_Datte_de_mer_985_white oval bone_0.41.jpg,species_name,lithophaga lithophaga,94%,0.929,proto_clip,lithophaga lithophaga,94%,palaemon elegans +85342_Udotée_987_mushroom_0.30.jpg,famille,Porpitidae,54%,0.137,mlp,scyliorhinus stellaris,25%,physalia physalis +85342_Udotée_987_plant_0.47.jpg,species_name,flabellia petiolata,100%,1.000,proto_clip,flabellia petiolata,100%,codium fragile subsp. fragile +85914_2_plant_0.37.jpg,regne,Animalia,56%,0.003,mlp,sphaerococcus coronopifolius,5%,symsagittifera roscoffensis +86149_1_fish_0.82.jpg,species_name,seriola rivoliana,100%,1.000,proto_clip,seriola rivoliana,100%,parablennius pilicornis +87172_2_flower_0.59.jpg,species_name,carpobrotus acinaciformis,99%,0.986,proto_clip,carpobrotus acinaciformis,99%,buccinum undatum +87172_2_plant flower_0.38.jpg,species_name,carpobrotus acinaciformis,100%,1.000,proto_clip,carpobrotus acinaciformis,100%,carpobrotus edulis +87172_2_plant_0.36.jpg,species_name,carpobrotus acinaciformis,100%,1.000,proto_clip,carpobrotus acinaciformis,100%,carpobrotus edulis +87320_plant algae_0.25.jpg,famille,Posidoniaceae,99%,0.157,mlp,symsagittifera roscoffensis,22%,posidonia oceanica +87320_plant_0.38.jpg,species_name,zostera marina,74%,0.692,proto_clip,zostera marina,74%,posidonia oceanica +87357_Voleuse_d'huîtres_1084_animal snail mushroom_0.41.jpg,phylum,Mollusca,70%,0.315,mlp,codium bursa,41%,himanthalia elongata +87357_Voleuse_d'huîtres_1084_brown algae_0.32.jpg,famille,Scytosiphonaceae,99%,0.132,mlp,colpomenia peregrina,25%,codium bursa +87411_1_insect_0.36.jpg,species_name,patella caerulea,98%,0.970,proto_clip,patella caerulea,98%,patella vulgata +87438_Porcellane_grise_1097_insect_0.64.jpg,species_name,porcellana platycheles,100%,1.000,proto_clip,porcellana platycheles,100%,pisidia longicornis +87453_3_animal snail_0.38.jpg,species_name,amphipholis squamata,72%,0.610,proto_clip,amphipholis squamata,72%,ciona intestinalis +87453_3_brown algae_0.24.jpg,famille,Littorinidae,75%,0.070,mlp,aporrhais pespelecani,11%,elysia viridis +87453_3_insect_0.17.jpg,phylum,Chordata,58%,0.063,mlp,doris pseudoargus,14%,alcedo atthis +87453_3_mammal_0.21.jpg,famille,Trochidae,54%,0.154,mlp,elysia viridis,25%,amphipholis squamata +87662_2_animal snail mollusk_0.23.jpg,famille,Trochidae,96%,0.010,mlp,sterna hirundo,8%,delphinus delphis +87727_Monodonte_1142_mollusk_0.44.jpg,famille,Trochidae,97%,0.034,mlp,parablennius incognitus,14%,tethya citrina +88051_Ponte_de_nasse_réticulée_1145_fish flower_0.20.jpg,regne,Animalia,100%,0.012,mlp,ardea alba,9%,serranus hepatus +88051_Ponte_de_nasse_réticulée_1145_fish_0.48.jpg,regne,Animalia,100%,0.206,mlp,lutraria lutraria,26%,luidia ciliaris +88051_Ponte_de_nasse_réticulée_1145_flower_0.39.jpg,classe,Cephalopoda,56%,0.000,mlp,clavelina lepadiformis,9%,homarus gammarus +88051_Ponte_de_nasse_réticulée_1145_plant tree_0.18.jpg,phylum,Mollusca,59%,0.001,mlp,sargassum muticum,7%,crithmum maritimum +88407_10_insect_0.25.jpg,phylum,Mollusca,84%,0.025,mlp,ensis magnus,13%,nerophis lumbriciformis +88559_Vélelle_1186_white oval bone_0.21.jpg,regne,Animalia,100%,0.128,mlp,sargassum muticum,19%,himanthalia elongata +89482_Patelle_commune_1259_cuttle white oval bone_0.38.jpg,species_name,patella vulgata,57%,0.223,proto_clip,patella vulgata,57%,patella caerulea +89482_Patelle_commune_1259_white oval bone_0.37.jpg,famille,Trochidae,57%,0.207,mlp,serranus hepatus,25%,pleurobrachia pileus +89728_Bigorneau_1292_insect mollusk_0.40.jpg,famille,Littorinidae,99%,0.058,mlp,cerastoderma edule,13%,numenius arquata +89728_Bigorneau_1292_insect_0.15.jpg,ordre,Fucales,79%,0.002,mlp,alcedo atthis,5%,mnemiopsis leidyi +89728_Bigorneau_1292_plant_0.16.jpg,famille,Trochidae,57%,0.031,mlp,jania rubens,11%,elysia viridis +90310_Gibbule_ombiliquée_1362_animal snail_0.53.jpg,famille,Trochidae,88%,0.181,mlp,steromphala umbilicalis,38%,sabella pavonina +90628_brown algae_0.22.jpg,species_name,parablennius pilicornis,100%,0.997,proto_clip,parablennius pilicornis,100%,uranoscopus scaber +90628_fish_0.71.jpg,species_name,parablennius pilicornis,100%,1.000,proto_clip,parablennius pilicornis,100%,zeus faber +90628_plant brown algae_0.37.jpg,species_name,parablennius pilicornis,100%,0.998,proto_clip,parablennius pilicornis,100%,muraena helena +91954_Anatife_commun_1464_white oval bone_0.43.jpg,species_name,lepas (anatifa) anatifera,100%,0.996,proto_clip,lepas (anatifa) anatifera,100%,lutraria lutraria +91995_Crabe_marbré_1469_crab_0.74.jpg,famille,Sacculinidae,94%,0.154,mlp,sacculina carcini,38%,pachygrapsus marmoratus +92030_Ver_plat_orange_1480_animal fish_0.46.jpg,species_name,yungia aurantiaca,100%,1.000,proto_clip,yungia aurantiaca,100%,antiopella cristata +92030_Ver_plat_orange_1480_brown algae_0.29.jpg,species_name,yungia aurantiaca,100%,1.000,proto_clip,yungia aurantiaca,100%,watersipora subatra +92030_Ver_plat_orange_1480_plant_0.24.jpg,species_name,yungia aurantiaca,100%,1.000,proto_clip,yungia aurantiaca,100%,hermaea variopicta +92289_Sargasse_japonaise_1487_plant tree sea_0.38.jpg,species_name,sargassum muticum,100%,1.000,proto_clip,sargassum muticum,100%,sphaerococcus coronopifolius +92289_Sargasse_japonaise_1487_plant tree_0.21.jpg,regne,Animalia,67%,0.020,mlp,sargassum muticum,9%,ascophyllum nodosum +92289_Sargasse_japonaise_1487_plant_0.17.jpg,classe,Hydrozoa,81%,0.006,mlp,cymbulia peronii,6%,atriplex halimus +93257_Sterne_pierregarin_1539_bird_0.73.jpg,species_name,sterna hirundo,100%,1.000,proto_clip,sterna hirundo,100%,delphinus delphis +93361_Puffin_de_Yelkouan_1559_bird_0.63.jpg,species_name,puffinus yelkouan,87%,0.850,proto_clip,puffinus yelkouan,87%,lophius piscatorius +94064_Crabe_de_pierre_1599_animal crab_0.34.jpg,species_name,xantho hydrophilus,98%,0.979,proto_clip,xantho hydrophilus,98%,sacculina carcini +94064_Crabe_de_pierre_1599_crab_0.63.jpg,species_name,xantho hydrophilus,100%,1.000,proto_clip,xantho hydrophilus,100%,eriphia verrucosa +94064_Crabe_de_pierre_1599_plant_0.28.jpg,phylum,Cnidaria,75%,0.127,mlp,parablennius incognitus,17%,styela clava +94179_Crabe_verruqueux_1616_crab_0.79.jpg,species_name,eriphia verrucosa,100%,0.998,proto_clip,eriphia verrucosa,100%,xantho hydrophilus +96188_Huître_creuse_1715_white oval bone_0.50.jpg,species_name,ostrea edulis,87%,0.855,proto_clip,ostrea edulis,87%,pleurobrachia pileus +96544_Grande_claveline_1730_brown algae_0.16.jpg,species_name,clavelina lepadiformis,100%,1.000,proto_clip,clavelina lepadiformis,100%,ciona intestinalis +96544_Grande_claveline_1730_white oval bone_0.19.jpg,classe,Actinopterygii,69%,0.017,mlp,amphipholis squamata,16%,alcedo atthis +96544_Grande_claveline_1730_white oval_0.17.jpg,classe,Actinopterygii,54%,0.009,mlp,pleurobrachia pileus,8%,clavelina lepadiformis +96549_brown algae_0.26.jpg,species_name,bifurcaria bifurcata,100%,1.000,proto_clip,bifurcaria bifurcata,100%,himanthalia elongata +96549_fish_0.36.jpg,ordre,Anthoathecata,56%,0.020,mlp,symsagittifera roscoffensis,8%,halimione portulacoides +96960_flower_0.49.jpg,species_name,crithmum maritimum,100%,1.000,proto_clip,crithmum maritimum,100%,euphorbia paralias +96960_plant_0.44.jpg,species_name,crithmum maritimum,100%,1.000,proto_clip,crithmum maritimum,100%,halimione portulacoides diff --git a/ml/classification/BioClipv2/results/proto_model.npz b/ml/classification/BioClipv2/results/proto_model.npz new file mode 100644 index 0000000..40e0cc4 Binary files /dev/null and b/ml/classification/BioClipv2/results/proto_model.npz differ diff --git a/ml/classification/BioClipv2/results/report_proto_clip_sans_crop.html b/ml/classification/BioClipv2/results/report_proto_clip_sans_crop.html new file mode 100644 index 0000000..b472b7e --- /dev/null +++ b/ml/classification/BioClipv2/results/report_proto_clip_sans_crop.html @@ -0,0 +1,3160 @@ + + + + + +Rapport Proto-CLIP — Biolit + + + + + +

🔬 Rapport Proto-CLIP — Biolit

+
+ BioCLIP2 frozen + Whitening PCA + Proto-CLIP · + seuil 50% · 2273 images · + généré le 31/03/2026 01:23 +
+ + +
+
2273
Images évaluées
+
90.7%
Accuracy espèce
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67.8%
Coverage espèce
+
1542
Espèces acceptées
+
2.273
kWh estimés
+
+ + +

Métriques par niveau taxonomique

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+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NiveauAccuracyCoverageF-covN
species_name90.7%67.8%0.6151542/2273
famille94.9%98.6%0.9352241/2273
ordre72.0%90.9%0.6542047/2253
classe78.7%87.6%0.6891987/2267
phylum77.0%94.1%0.7251995/2119
regne98.7%100.0%0.9872273/2273
+
+ +
+
+ + +

Distribution des scores de confiance

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+ +
+ + +

Galerie (60 observations)

+
+ + + +
+ + + +

Top erreurs par espèce

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
EspèceTaux erreurN
urosalpinx cinerea100%1/1
grateloupia turuturu100%1/1
cygnus olor100%1/1
scorpaena scrofa100%1/1
serranus scriba100%1/1
lithophyllum incrustans100%1/1
watersipora subtorquata100%1/1
polycera quadrilineata100%2/2
crangon crangon100%1/1
buccinum undatum67%2/3
carpobrotus edulis50%2/4
homarus gammarus50%1/2
carcinus aestuarii38%3/8
parablennius incognitus33%1/3
ostrea edulis33%4/12
raja undulata33%1/3
sacculina carcini29%2/7
doris pseudoargus29%2/7
actinia fragacea20%3/15
stramonita haemastoma20%1/5
+ + + + \ No newline at end of file diff --git a/ml/classification/BioClipv2/results/tax_lookup.pkl b/ml/classification/BioClipv2/results/tax_lookup.pkl new file mode 100644 index 0000000..ed023a2 Binary files /dev/null and b/ml/classification/BioClipv2/results/tax_lookup.pkl differ diff --git a/ml/yolov8_DINO/README.md b/ml/yolov8_DINO/README.md index 64781c8..7005c4b 100644 --- a/ml/yolov8_DINO/README.md +++ b/ml/yolov8_DINO/README.md @@ -16,7 +16,7 @@ Pipeline de constitution du dataset Biolit pour inférence YOLO / Grounding DINO ├── images/ │ ├── identifiable/ │ └── non_identifiable/ # à valider - ├── labels/ + ├── labels/ │ ├── identifiable/ │ └── non_identifiable/ # à valider ├── metadata.csv # GroundingDINO diff --git a/pipelines/README.md b/pipelines/README.md index a568cb6..e0fbc58 100644 --- a/pipelines/README.md +++ b/pipelines/README.md @@ -5,7 +5,30 @@ Les pipelines utilisent le dossier `data/` (non versionné) comme espace de trav ## Flux quotidien (API → ML → Label Studio) -1. **Récupération quotidienne** depuis l'API (à venir) ou CSV local. +1. **Ingestion & préparation des données** depuis l'API. + 1. **Objectifs** + - Récupérer les données depuis l’API Biolit + - Standardiser et nettoyer les données + - Les stocker dans une base PostgreSQL + - Mettre les données à disposition des autres systèmes (ML, dataviz) + 2. **Étapes du pipeline** + 1. Ingestion + - appel à l’API Biolit + - récupération des observations + + 2. Transformation + - normalisation des noms de colonnes + - typage des champs + - nettoyage des données (dates, coordonnées, identifiants) + + 3. Chargement + - insertion dans PostgreSQL + - gestion des doublons via un mécanisme d’UPSERT (ON CONFLICT DO NOTHING) + 3. **Variables d'environnement** + - POSTGRES_URL=postgresql://user:password@host:port/dbname + - BIOLIT_API_URL=https://biolit.fr/wp-json/biolit/v1/observations?token=XXX + 4. **Lancer la pipeline** + - uv run python -m pipelines.run 2. **Qualité** : si l'image est mauvaise → stop. 3. **YOLOv8** : détection + crop. - si aucune détection → **Label Studio (CROP)** diff --git a/pipelines/run.py b/pipelines/run.py new file mode 100644 index 0000000..e63d662 --- /dev/null +++ b/pipelines/run.py @@ -0,0 +1,22 @@ +from biolit.export_api import fetch_biolit_from_api, adapt_api_to_dataframe +from biolit.postgres import prepare_dataframe_for_postgres, insert_dataframe + + +def run_pipeline(): + print("Fetching data...") + data = fetch_biolit_from_api() + + print("Transforming...") + df = adapt_api_to_dataframe(data) + + print("Preparing for Postgres...") + df = prepare_dataframe_for_postgres(df) + + print("Loading into Postgres...") + insert_dataframe(df) + + print("DONE ✅") + + +if __name__ == "__main__": + run_pipeline() \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index 2536fd8..6aa78c6 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -5,9 +5,13 @@ description = "Add your description here" readme = "README.md" requires-python = ">=3.12" dependencies = [ + "bs4>=0.0.2", "click>=8.1.8", "duckdb>=1.1.3", "duckdb-engine>=0.15.0", + "folium>=0.20.0", + "geopandas>=1.1.2", + "geopy>=2.4.1", "ipykernel>=6.29.5", "ipython>=8.31.0", "ipython-sql>=0.5.0", @@ -21,9 +25,15 @@ dependencies = [ "plotly>=6.5.0", "polars>=1.36.1", "pre-commit>=4.5.1", + "psycopg2-binary>=2.9.11", + "pyarrow>=23.0.1", "pytest>=9.0.2", "requests>=2.32.3", "ruff>=0.14.10", + "sqlalchemy>=2.0.44", + "prefect>=3.6.22", + "requests>=2.32.3", + "shapely>=2.1.2", "structlog>=25.5.0", "tqdm>=4.67.3", "transformers>=5.2.0", diff --git a/tests/test_export_api.py b/tests/test_export_api.py new file mode 100644 index 0000000..19ca0f0 --- /dev/null +++ b/tests/test_export_api.py @@ -0,0 +1,163 @@ +from biolit.export_api import fetch_biolit_from_api, adapt_api_to_dataframe + +# ------------------------- +# Tests API +# ------------------------- + +def test_fetch_api_returns_data(): + """Vérifie que l'API retourne bien des données""" + data = fetch_biolit_from_api() + + assert isinstance(data, list) + assert len(data) > 0 + + print(f"\n✅ {len(data)} observations récupérées") + + +def test_fetch_api_structure(): + """Vérifie la structure des données API""" + data = fetch_biolit_from_api() + sample = data[0] + + expected_keys = { + "id", + "date", + "link", + "author", + "_url_sortie", + "espece-identifiee", + "heure-debut", + "heure-fin", + "latitude", + "longitude", + "photos", + "relais", + "espece_id", + "espece", + "common", + } + + missing_keys = expected_keys - set(sample.keys()) + + assert len(missing_keys) == 0, f"Champs manquants: {missing_keys}" + + print("\n✅ Structure API valide") + + +# ------------------------- +# Tests transformation +# ------------------------- + +def test_adapt_to_dataframe(): + """Vérifie la transformation en DataFrame""" + data = fetch_biolit_from_api() + df = adapt_api_to_dataframe(data) + + assert df.shape[0] > 0 + assert df.shape[1] > 0 + + print(f"\n✅ DataFrame: {df.shape[0]} lignes, {df.shape[1]} colonnes") + + +def test_expected_columns_present(): + """Vérifie les colonnes critiques""" + data = fetch_biolit_from_api() + df = adapt_api_to_dataframe(data) + + expected_columns = { + "id_observation", + "date_observation", + "nom_scientifique", + "nom_commun", + "latitude", + "longitude", + } + + missing = expected_columns - set(df.columns) + + assert len(missing) == 0, f"Colonnes manquantes: {missing}" + + print("\n✅ Colonnes critiques présentes") + + +# ------------------------- +# Tests qualité des données +# ------------------------- + +def test_unique_ids(): + """Vérifie qu'il n'y a pas de doublons""" + data = fetch_biolit_from_api() + df = adapt_api_to_dataframe(data) + + total = df.shape[0] + unique = df.select("id_observation").n_unique() + + assert total == unique, "Doublons détectés sur id_observation" + + print("\n✅ Pas de doublons") + + +def test_no_null_coordinates(): + """Vérifie que les coordonnées sont présentes""" + data = fetch_biolit_from_api() + df = adapt_api_to_dataframe(data) + + null_lat = df.filter(df["latitude"].is_null()).shape[0] + null_lon = df.filter(df["longitude"].is_null()).shape[0] + + print(f"\nNull latitude: {null_lat}") + print(f"Null longitude: {null_lon}") + + # tolérance possible, donc pas assert strict + assert null_lat < df.shape[0] + assert null_lon < df.shape[0] + + +def test_id_is_numeric(): + """Vérifie que les IDs sont bien numériques""" + data = fetch_biolit_from_api() + + ids = [item["id"] for item in data] + + # doit pouvoir être cast en int + for i in ids[:100]: # test sur sample + int(i) + + print("\n✅ IDs valides") + + +# ------------------------- +# Test global pipeline +# ------------------------- + +def test_full_pipeline(): + """Test end-to-end""" + data = fetch_biolit_from_api() + df = adapt_api_to_dataframe(data) + + assert df.shape[0] > 0 + + print("\n=== PIPELINE OK ===") + print(df.head(5)) + + +# ------------------------- +# Execution directe +# ------------------------- + +if __name__ == "__main__": + print("=== TEST FETCH API ===") + test_fetch_api_returns_data() + test_fetch_api_structure() + + print("\n=== TEST TRANSFORMATION ===") + test_adapt_to_dataframe() + test_expected_columns_present() + + print("\n=== TEST QUALITE ===") + test_unique_ids() + test_no_null_coordinates() + test_id_is_numeric() + + print("\n=== TEST PIPELINE COMPLET ===") + test_full_pipeline() \ No newline at end of file diff --git a/tests/test_geoloc.py b/tests/test_geoloc.py new file mode 100644 index 0000000..c41da35 --- /dev/null +++ b/tests/test_geoloc.py @@ -0,0 +1,54 @@ +import pandas as pd +import numpy as np +from pandas.testing import assert_frame_equal + +from biolit.geoloc import get_info_nearest_commune, get_info_distance_to_coast + +class TestDistanceToCommunes: + def test_get_info_nearest_commune(self): + inp = pd.DataFrame( + { + "id_-_n1": [1, 2, 3], + "nom_du_lieu_-_n1": ["paris", "pornic", "ocean atlantique"], + "longitude_-_n1": [2.33333, -2.108881, -9.864458], + "latitude_-_n1": [48.866669, 47.111202, 47.934747], + } + ) + out = get_info_nearest_commune(inp) + exp = pd.DataFrame( + { + "id_-_n1": [1, 2, 3], + "nom_du_lieu_-_n1": ["paris", "pornic", "ocean atlantique"], + "longitude_-_n1": [2.33333, -2.108881, -9.864458], + "latitude_-_n1": [48.866669, 47.111202, 47.934747], + "distance_commune_m": [0, 0, np.nan], + "nearest_commune": ["Paris", "Pornic", None], + "code_insee": ["75056", "44131", None], + "code_postal": [75000, 44210, np.nan], + "reg_nom": ["Île-de-France", "Pays de la Loire", None], + "dep_nom": ["Paris", "Loire-Atlantique", None] + } + ) + assert_frame_equal(out, exp, check_dtype=False) + + def test_get_info_distance_to_coast(self): + inp = pd.DataFrame( + { + "id_-_n1": [1, 2, 3], + "nom_du_lieu_-_n1": ["paris", "pornic", "ocean atlantique"], + "longitude_-_n1": [2.33333, -2.108881, -9.864458], + "latitude_-_n1": [48.866669, 47.111202, 47.934747], + } + ) + out = get_info_distance_to_coast(inp) + exp = pd.DataFrame( + { + "id_-_n1": [1, 2, 3], + "nom_du_lieu_-_n1": ["paris", "pornic", "ocean atlantique"], + "longitude_-_n1": [2.33333, -2.108881, -9.864458], + "latitude_-_n1": [48.866669, 47.111202, 47.934747], + "distance_to_coast": [np.nan, 0.314727, np.nan], + "is_coastal": [False, True, False], + } + ) + assert_frame_equal(out, exp, check_dtype=False) diff --git a/uv.lock b/uv.lock index 91483cd..938f1ae 100644 --- a/uv.lock +++ b/uv.lock @@ -2,8 +2,15 @@ version = 1 revision = 3 requires-python = ">=3.12" resolution-markers = [ - "python_full_version >= '3.14'", - "python_full_version < '3.14'", + 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