From b6a752261a916ce1948d87d8e350ede2ff28ea85 Mon Sep 17 00:00:00 2001 From: alexpetit Date: Sun, 1 Mar 2026 20:07:06 +0100 Subject: [PATCH 01/13] feat: ajout des fonctions pour l'ingestion API --- biolit/export_api.py | 65 ++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 65 insertions(+) create mode 100644 biolit/export_api.py diff --git a/biolit/export_api.py b/biolit/export_api.py new file mode 100644 index 0000000..0697ba0 --- /dev/null +++ b/biolit/export_api.py @@ -0,0 +1,65 @@ +import polars as pl +import requests + +###Test export from API + +def fetch_biolit_api(per_page: int = 1000): + all_data = [] + page = 1 + print("Téléchargement des données depuis l'API Biolit...") + while True: + url = f"https://biolit.fr/wp-json/biolitapi/v1/observations/all?per_page={per_page}&page={page}" + r = requests.get(url) + data = r.json() + if not data: + break + all_data.extend(data) + page += 1 + print(f"Page {page} téléchargée, total observations : {len(all_data)}") + + if not all_data: + return pl.DataFrame([]) + df = pl.DataFrame(all_data) + print(df.head()) + print(df.shape) + return df + + +def adapt_api_to_parquet_schema(df): + return ( + df.rename({ + "id": "id", + "link": "lien", + "author": "auteur", + "date": "date", + "heure-debut": "heure-de-debut", + "heure-fin": "heure-de-fin", + "latitude": "latitude", + "longitude": "longitude", + "photos": "images", + "espece": "nom_scientifique", + "common": "nom_commun", + }) + .with_columns([ + pl.col("lien").str.split("/").list.get(-1).alias("titre"), # dernier segment du lien + pl.lit("").alias("validee"), + pl.lit("TBD").alias("espece_identifiable_?"), + pl.lit("API").alias("protocole"), + ]) + ) +def load_biolit_from_api() -> pl.DataFrame: + df_api = fetch_biolit_api() + if df_api.is_empty(): + return df_api + print(adapt_api_to_parquet_schema(df_api).head()) + print(adapt_api_to_parquet_schema(df_api).columns) + return adapt_api_to_parquet_schema(df_api) + +def format_observations_from_api(): +### A faire plus tard + return + + +if __name__ == "__main__": + print("Script lancé") + load_biolit_from_api() \ No newline at end of file From f2be0e8854d3e3f00cc593a60b54fcc46ff49b4b Mon Sep 17 00:00:00 2001 From: alexpetit Date: Sun, 15 Mar 2026 12:49:14 +0100 Subject: [PATCH 02/13] nouvelle API URL, modification des champs avec tous les champs de l'API, ajout de test(limite aux 5 premieres pages pour le moment) --- biolit/export_api.py | 165 ++++++++++++++++++++++++++------------- tests/test_export_api.py | 69 ++++++++++++++++ 2 files changed, 178 insertions(+), 56 deletions(-) create mode 100644 tests/test_export_api.py diff --git a/biolit/export_api.py b/biolit/export_api.py index 0697ba0..93ae428 100644 --- a/biolit/export_api.py +++ b/biolit/export_api.py @@ -1,65 +1,118 @@ -import polars as pl import requests +import polars as pl +import structlog + +LOGGER = structlog.get_logger() -###Test export from API +# ------------------------------ +# Helper pour récupérer une clé dans meta +# ------------------------------ +def get_meta(meta: dict, key: str): + """Retourne la première valeur d'une clé meta, ou None si absente""" + if not meta: + return None + value = meta.get(key) + if isinstance(value, list) and value: + return value[0] + return value -def fetch_biolit_api(per_page: int = 1000): + +# ------------------------------ +# FETCH API +# ------------------------------ +def fetch_biolit_from_api(per_page=100, max_pages=5): + """ + Récupère les observations depuis l'API Biolit. + Limite par défaut à max_pages pour éviter les 150+ pages. + """ + url_base = "https://biolit.fr/wp-json/biolitapi/v1/observations" all_data = [] - page = 1 - print("Téléchargement des données depuis l'API Biolit...") - while True: - url = f"https://biolit.fr/wp-json/biolitapi/v1/observations/all?per_page={per_page}&page={page}" - r = requests.get(url) - data = r.json() + + for page in range(1, max_pages + 1): + url = f"{url_base}?per_page={per_page}&page={page}" + LOGGER.info(f"Fetching page {page} from API") + response = requests.get(url) + response.raise_for_status() + data = response.json() if not data: break all_data.extend(data) - page += 1 - print(f"Page {page} téléchargée, total observations : {len(all_data)}") - - if not all_data: - return pl.DataFrame([]) - df = pl.DataFrame(all_data) - print(df.head()) - print(df.shape) - return df + LOGGER.info(f"Fetched {len(all_data)} observations total") + return all_data + + +# ------------------------------ +# ADAPT API -> PARQUET +# ------------------------------ +def adapt_api_to_parquet_schema(data: list) -> pl.DataFrame: + """ + Transforme la structure API Biolit en DataFrame pour parquet. + """ + rows = [] + + for item in data: + obs = item.get("observation", {}) + meta = obs.get("meta", {}) + parents = item.get("parents", {}) + especes = item.get("especes", []) + + quadra = parents.get("quadra", {}) + abb = parents.get("abb", {}) + quadra_meta = quadra.get("meta", {}) + abb_meta = abb.get("meta", {}) + + # Gestion des espèces + nom_scientifique = None + nom_commun = None + nombre_mollusques = None + + if especes: + nom_scientifique = especes[0].get("nom") + nombre_mollusques = especes[0].get("nombre_presents") + + row = { + # Niveau N1 (Quadra) + "protocole": get_meta(meta, "jet_tax__protocole"), + "ID - N1": quadra.get("ID"), + "titre - N1": quadra.get("title"), + "lien - N1": get_meta(meta, "_url_sortie"), + "auteur - N1": None, + "images - N1": None, + "date - N1": obs.get("date"), + "heure-de-debut - N1": get_meta(meta, "heure-debut"), + "heure-de-fin - N1": get_meta(meta, "heure-fin"), + "latitude - N1": get_meta(meta, "latitude"), + "longitude - N1": get_meta(meta, "longitude"), + "relais-local - N1": get_meta(abb_meta, "relais-local"), + "nom du lieu - N1": get_meta(abb_meta, "nom-du-lieu-abb"), + + # Observation + "ID - observation": obs.get("ID"), + "titre - observation": obs.get("title"), + "lien - observation": obs.get("link"), + "Nom scientifique - observation": nom_scientifique, + "Nom commun - observation": nom_commun, + "programme espèce": get_meta(meta, "jet_tax__categorie-programme"), + "images - observation": obs.get("images"), + "nombre de mollusques - observation": nombre_mollusques, + "validee - observation": get_meta(meta, "validee"), + "espece identifiable ? - observation": get_meta(meta, "espece-identifiee"), + } + + rows.append(row) + + return pl.DataFrame(rows) + + +# ------------------------------ +# LOAD (Fetch + Adapt) +# ------------------------------ +def load_biolit_from_api(per_page=100, max_pages=5) -> pl.DataFrame: + """ + Récupère et transforme les données Biolit depuis l'API. + """ + raw_data = fetch_biolit_from_api(per_page=per_page, max_pages=max_pages) + df = adapt_api_to_parquet_schema(raw_data) + return df -def adapt_api_to_parquet_schema(df): - return ( - df.rename({ - "id": "id", - "link": "lien", - "author": "auteur", - "date": "date", - "heure-debut": "heure-de-debut", - "heure-fin": "heure-de-fin", - "latitude": "latitude", - "longitude": "longitude", - "photos": "images", - "espece": "nom_scientifique", - "common": "nom_commun", - }) - .with_columns([ - pl.col("lien").str.split("/").list.get(-1).alias("titre"), # dernier segment du lien - pl.lit("").alias("validee"), - pl.lit("TBD").alias("espece_identifiable_?"), - pl.lit("API").alias("protocole"), - ]) - ) -def load_biolit_from_api() -> pl.DataFrame: - df_api = fetch_biolit_api() - if df_api.is_empty(): - return df_api - print(adapt_api_to_parquet_schema(df_api).head()) - print(adapt_api_to_parquet_schema(df_api).columns) - return adapt_api_to_parquet_schema(df_api) - -def format_observations_from_api(): -### A faire plus tard - return - - -if __name__ == "__main__": - print("Script lancé") - load_biolit_from_api() \ No newline at end of file diff --git a/tests/test_export_api.py b/tests/test_export_api.py new file mode 100644 index 0000000..0cea68a --- /dev/null +++ b/tests/test_export_api.py @@ -0,0 +1,69 @@ + +from biolit.export_api import fetch_biolit_from_api, load_biolit_from_api, adapt_api_to_parquet_schema + +# ------------------------- +# Fonctions d'inspection +# ------------------------- + +def inspect_api_structure(raw_data): + """Affiche les clés du niveau supérieur dans l'API""" + keys = set() + for item in raw_data: + keys.update(item.keys()) + print("TOP LEVEL KEYS:") + for k in sorted(keys): + print("-", k) + +def inspect_meta_keys(raw_data): + """Affiche toutes les clés présentes dans le champ 'meta' de chaque observation""" + meta_keys = set() + for item in raw_data: + if "observation" not in item: + continue + meta = item["observation"].get("meta", {}) + meta_keys.update(meta.keys()) + print("META KEYS:") + for k in sorted(meta_keys): + print("-", k) + +def inspect_meta_values(raw_data, field): + """Affiche les valeurs et leur quantité pour un champ du meta""" + values = {} + for item in raw_data: + if "observation" not in item: + continue + meta = item["observation"].get("meta", {}) + val = meta.get(field) + if isinstance(val, list) and val: + val = val[0] + values[val] = values.get(val, 0) + 1 + print(f"\nVALUES FOR {field}:") + for k, v in values.items(): + print(k, ":", v) + +# ------------------------- +# Tests +# ------------------------- + +def test_fetch_small_sample(): + """Test fetch API avec un petit nombre de pages pour debug""" + raw_data = fetch_biolit_from_api(per_page=1000, max_pages=5) # petit sample + assert isinstance(raw_data, list) + print(f"\nNombre total d'éléments récupérés: {len(raw_data)}") + inspect_api_structure(raw_data) + inspect_meta_keys(raw_data) + inspect_meta_values(raw_data, "validee") + +def test_load_and_adapt(): + """Test l'adaptation de l'API vers le schéma parquet""" + raw_data = fetch_biolit_from_api(per_page=1000, max_pages=5) + df = adapt_api_to_parquet_schema(raw_data) + print(f"\nDataframe adapté avec {len(df)} lignes") + print(df.head(10)) # affiche les 10 premières lignes pour vérification + + +if __name__ == "__main__": + print("=== TEST FETCH SMALL SAMPLE ===") + test_fetch_small_sample() + print("\n=== TEST LOAD AND ADAPT ===") + test_load_and_adapt() \ No newline at end of file From a04b0ed253b024c7d2e9c33510d96381d6964452 Mon Sep 17 00:00:00 2001 From: alexpetit Date: Tue, 17 Mar 2026 21:38:07 +0100 Subject: [PATCH 03/13] Fix pre-commit issues and update API processing --- biolit/export_api.py | 1 - tests/test_export_api.py | 2 +- 2 files changed, 1 insertion(+), 2 deletions(-) diff --git a/biolit/export_api.py b/biolit/export_api.py index 93ae428..f3a99a0 100644 --- a/biolit/export_api.py +++ b/biolit/export_api.py @@ -59,7 +59,6 @@ def adapt_api_to_parquet_schema(data: list) -> pl.DataFrame: quadra = parents.get("quadra", {}) abb = parents.get("abb", {}) - quadra_meta = quadra.get("meta", {}) abb_meta = abb.get("meta", {}) # Gestion des espèces diff --git a/tests/test_export_api.py b/tests/test_export_api.py index 0cea68a..8560417 100644 --- a/tests/test_export_api.py +++ b/tests/test_export_api.py @@ -1,5 +1,5 @@ -from biolit.export_api import fetch_biolit_from_api, load_biolit_from_api, adapt_api_to_parquet_schema +from biolit.export_api import fetch_biolit_from_api, adapt_api_to_parquet_schema # ------------------------- # Fonctions d'inspection From c464a0517e84bdda89d1453136b556d741ea5191 Mon Sep 17 00:00:00 2001 From: Timothee Corlay Date: Thu, 19 Mar 2026 15:49:31 +0100 Subject: [PATCH 04/13] Features : Lien Doris scrapping --- .gitignore | 6 +- biolit/lien_doris.py | 58 +++ pyproject.toml | 1 + uv.lock | 1177 +++++++++++++++++++++++++++++++++++++++++- 4 files changed, 1234 insertions(+), 8 deletions(-) create mode 100644 biolit/lien_doris.py diff --git a/.gitignore b/.gitignore index 1284cb0..d0cc217 100644 --- a/.gitignore +++ b/.gitignore @@ -171,4 +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/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/pyproject.toml b/pyproject.toml index 85e6f21..f9c6ec1 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -25,6 +25,7 @@ dependencies = [ "plotly>=6.5.0", "polars>=1.36.1", "pre-commit>=4.5.1", + "prefect>=3.6.22", "pyarrow>=23.0.0", "pytest>=9.0.2", "requests>=2.32.3", diff --git a/uv.lock b/uv.lock index f882065..ffc7011 100644 --- a/uv.lock +++ b/uv.lock @@ -5,9 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++++++++++++++++++++++++++++----------- uv.lock | 90 +++++++++++++++++ 7 files changed, 452 insertions(+), 147 deletions(-) create mode 100644 biolit/postgres.py create mode 100644 pipelines/run.py diff --git a/biolit/export_api.py b/biolit/export_api.py index f3a99a0..6c4a510 100644 --- a/biolit/export_api.py +++ b/biolit/export_api.py @@ -1,117 +1,89 @@ import requests import polars as pl import structlog +import re +import os LOGGER = structlog.get_logger() # ------------------------------ -# Helper pour récupérer une clé dans meta +# FETCH API # ------------------------------ -def get_meta(meta: dict, key: str): - """Retourne la première valeur d'une clé meta, ou None si absente""" - if not meta: - return None - value = meta.get(key) - if isinstance(value, list) and value: - return value[0] - return value +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 # ------------------------------ -# FETCH API +# RENAME OF COLUMNS # ------------------------------ -def fetch_biolit_from_api(per_page=100, max_pages=5): - """ - Récupère les observations depuis l'API Biolit. - Limite par défaut à max_pages pour éviter les 150+ pages. - """ - url_base = "https://biolit.fr/wp-json/biolitapi/v1/observations" - all_data = [] - - for page in range(1, max_pages + 1): - url = f"{url_base}?per_page={per_page}&page={page}" - LOGGER.info(f"Fetching page {page} from API") - response = requests.get(url) - response.raise_for_status() - data = response.json() - if not data: - break - all_data.extend(data) - - LOGGER.info(f"Fetched {len(all_data)} observations total") - return all_data + + +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_parquet_schema(data: list) -> pl.DataFrame: - """ - Transforme la structure API Biolit en DataFrame pour parquet. - """ +def adapt_api_to_dataframe(data: list) -> pl.DataFrame: rows = [] for item in data: - obs = item.get("observation", {}) - meta = obs.get("meta", {}) - parents = item.get("parents", {}) - especes = item.get("especes", []) - - quadra = parents.get("quadra", {}) - abb = parents.get("abb", {}) - abb_meta = abb.get("meta", {}) - - # Gestion des espèces - nom_scientifique = None - nom_commun = None - nombre_mollusques = None - - if especes: - nom_scientifique = especes[0].get("nom") - nombre_mollusques = especes[0].get("nombre_presents") - - row = { - # Niveau N1 (Quadra) - "protocole": get_meta(meta, "jet_tax__protocole"), - "ID - N1": quadra.get("ID"), - "titre - N1": quadra.get("title"), - "lien - N1": get_meta(meta, "_url_sortie"), - "auteur - N1": None, - "images - N1": None, - "date - N1": obs.get("date"), - "heure-de-debut - N1": get_meta(meta, "heure-debut"), - "heure-de-fin - N1": get_meta(meta, "heure-fin"), - "latitude - N1": get_meta(meta, "latitude"), - "longitude - N1": get_meta(meta, "longitude"), - "relais-local - N1": get_meta(abb_meta, "relais-local"), - "nom du lieu - N1": get_meta(abb_meta, "nom-du-lieu-abb"), - - # Observation - "ID - observation": obs.get("ID"), - "titre - observation": obs.get("title"), - "lien - observation": obs.get("link"), - "Nom scientifique - observation": nom_scientifique, - "Nom commun - observation": nom_commun, - "programme espèce": get_meta(meta, "jet_tax__categorie-programme"), - "images - observation": obs.get("images"), - "nombre de mollusques - observation": nombre_mollusques, - "validee - observation": get_meta(meta, "validee"), - "espece identifiable ? - observation": get_meta(meta, "espece-identifiee"), - } - - rows.append(row) - - return pl.DataFrame(rows) + 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(per_page=100, max_pages=5) -> pl.DataFrame: - """ - Récupère et transforme les données Biolit depuis l'API. - """ - raw_data = fetch_biolit_from_api(per_page=per_page, max_pages=max_pages) - df = adapt_api_to_parquet_schema(raw_data) +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/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/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/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..9df7cb0 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -21,9 +21,12 @@ 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", "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 index 8560417..19ca0f0 100644 --- a/tests/test_export_api.py +++ b/tests/test_export_api.py @@ -1,69 +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") -from biolit.export_api import fetch_biolit_from_api, adapt_api_to_parquet_schema # ------------------------- -# Fonctions d'inspection +# Tests qualité des données # ------------------------- -def inspect_api_structure(raw_data): - """Affiche les clés du niveau supérieur dans l'API""" - keys = set() - for item in raw_data: - keys.update(item.keys()) - print("TOP LEVEL KEYS:") - for k in sorted(keys): - print("-", k) - -def inspect_meta_keys(raw_data): - """Affiche toutes les clés présentes dans le champ 'meta' de chaque observation""" - meta_keys = set() - for item in raw_data: - if "observation" not in item: - continue - meta = item["observation"].get("meta", {}) - meta_keys.update(meta.keys()) - print("META KEYS:") - for k in sorted(meta_keys): - print("-", k) - -def inspect_meta_values(raw_data, field): - """Affiche les valeurs et leur quantité pour un champ du meta""" - values = {} - for item in raw_data: - if "observation" not in item: - continue - meta = item["observation"].get("meta", {}) - val = meta.get(field) - if isinstance(val, list) and val: - val = val[0] - values[val] = values.get(val, 0) + 1 - print(f"\nVALUES FOR {field}:") - for k, v in values.items(): - print(k, ":", v) +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") + # ------------------------- -# Tests +# Test global pipeline # ------------------------- -def test_fetch_small_sample(): - """Test fetch API avec un petit nombre de pages pour debug""" - raw_data = fetch_biolit_from_api(per_page=1000, max_pages=5) # petit sample - assert isinstance(raw_data, list) - print(f"\nNombre total d'éléments récupérés: {len(raw_data)}") - inspect_api_structure(raw_data) - inspect_meta_keys(raw_data) - inspect_meta_values(raw_data, "validee") +def test_full_pipeline(): + """Test end-to-end""" + data = fetch_biolit_from_api() + df = adapt_api_to_dataframe(data) + + assert df.shape[0] > 0 -def test_load_and_adapt(): - """Test l'adaptation de l'API vers le schéma parquet""" - raw_data = fetch_biolit_from_api(per_page=1000, max_pages=5) - df = adapt_api_to_parquet_schema(raw_data) - print(f"\nDataframe adapté avec {len(df)} lignes") - print(df.head(10)) # affiche les 10 premières lignes pour vérification + print("\n=== PIPELINE OK ===") + print(df.head(5)) +# ------------------------- +# Execution directe +# ------------------------- + if __name__ == "__main__": - print("=== TEST FETCH SMALL SAMPLE ===") - test_fetch_small_sample() - print("\n=== TEST LOAD AND ADAPT ===") - test_load_and_adapt() \ No newline at end of file + 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/uv.lock b/uv.lock index 91483cd..a43bb38 100644 --- a/uv.lock +++ b/uv.lock @@ -27,9 +27,12 @@ dependencies = [ { name = "plotly" }, { name = "polars" }, { name = "pre-commit" }, + { name = "psycopg2-binary" }, + { name = "pyarrow" }, { name = "pytest" }, { name = "requests" }, { name = "ruff" }, + { name = "sqlalchemy" }, { name = "structlog" }, { name = "tqdm" }, { name = "transformers" }, @@ -53,9 +56,12 @@ requires-dist = [ { name = "plotly", specifier = ">=6.5.0" }, { name = "polars", specifier = ">=1.36.1" }, { name = "pre-commit", specifier = ">=4.5.1" }, + { name = "psycopg2-binary", specifier = 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++++++++++++++++++++++++- 1 file changed, 24 insertions(+), 1 deletion(-) 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)** From 9e976eee09055ef2d469d1411354f68a3cd00237 Mon Sep 17 00:00:00 2001 From: mandresyandri Date: Sun, 29 Mar 2026 20:59:18 +0200 Subject: [PATCH 07/13] =?UTF-8?q?feature:=20t=C3=A9l=C3=A9chargement=20des?= =?UTF-8?q?=20fichiers=20et=20structuration=20du=20code.?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ml/yolov8_DINO/build_dataset.py | 312 +++++++--------------- ml/yolov8_DINO/configs/build_dataset.yaml | 17 ++ ml/yolov8_DINO/crop_pipeline.py | 99 ------- ml/yolov8_DINO/utils/logger.py | 33 +++ 4 files changed, 149 insertions(+), 312 deletions(-) create mode 100644 ml/yolov8_DINO/configs/build_dataset.yaml delete mode 100644 ml/yolov8_DINO/crop_pipeline.py create mode 100644 ml/yolov8_DINO/utils/logger.py diff --git a/ml/yolov8_DINO/build_dataset.py b/ml/yolov8_DINO/build_dataset.py index 9ebb33c..1e782e4 100644 --- a/ml/yolov8_DINO/build_dataset.py +++ b/ml/yolov8_DINO/build_dataset.py @@ -1,246 +1,132 @@ -import logging -import sys +import yaml import requests import pandas as pd from pathlib import Path from tqdm import tqdm - - - -# LOGGING -LOG_FILE = Path("logs/build_dataset.log") -LOG_FILE.parent.mkdir(parents=True, exist_ok=True) - -logger = logging.getLogger("biolit") -logger.setLevel(logging.INFO) - -_stream_handler = logging.StreamHandler(sys.stdout) -_stream_handler.setLevel(logging.INFO) -_stream_handler.setFormatter( - logging.Formatter("%(asctime)s %(levelname)-8s %(message)s", "%H:%M:%S") -) - -_file_handler = logging.FileHandler(LOG_FILE, mode="w", encoding="utf-8") -_file_handler.setLevel(logging.INFO) -_file_handler.setFormatter( - logging.Formatter( - "%(asctime)s %(levelname)-8s %(message)s", - "%Y-%m-%d %H:%M:%S", - ) -) - -logger.addHandler(_stream_handler) -logger.addHandler(_file_handler) - - -# CONFIGS -DATA_PATH = "dataset_biolit/export_biolit.csv" -BASE_DIR = Path("dataset_biolit") -TIMEOUT = 10 -VALID_EXTS = {"jpg", "jpeg", "png", "webp"} - -USE_COLS = [ - "ID - N1", - "images - observation", - "Nom commun - observation", - "validee - observation", - "espece identifiable ? - observation", -] - -IDENTIFIABLE_VALUES = ["Identifiable", "Non identifiable"] - -SUBFOLDER_MAP = { - "Identifiable": "identifiable", - "Non identifiable": "non_identifiable", -} - - -# FONCTION UTILS -def load_and_filter(path: str) -> pd.DataFrame: - """Charge le CSV et filtre les observations validées.""" - logger.info("Chargement du CSV : %s", path) - data = pd.read_csv(path, usecols=USE_COLS) - total = len(data) - filtered = data[data["validee - observation"] == "TRUE"] - logger.info( - "CSV chargé — %d lignes totales, %d après filtrage (validée=TRUE)", - total, len(filtered), - ) - return filtered - - -def explode_image_urls(df: pd.DataFrame) -> pd.DataFrame: - """Split les URLs multiples (séparées par |) en une ligne par image.""" - logger.debug("Explosion des URLs multiples...") - result = ( - df.copy() - .assign(**{ - "images - observation": df["images - observation"] - .str.strip() - .str.split("|") - }) +from concurrent.futures import ThreadPoolExecutor, as_completed +from utils.logger import get_logger + +logger = get_logger("build_dataset") +def load_config(path: str = "configs/build_dataset.yaml") -> dict: + with open(path) as f: + cfg = yaml.safe_load(f) + logger.info(f"Config chargée : {path}") + return cfg + +# CHARGEMENT DU YAML +cfg = load_config() +DATA_PATH = cfg["data_path"] +BASE_DIR = Path(cfg["base_dir"]) +IMAGES_DIR = Path(cfg["images_dir"]) +TIMEOUT = cfg["timeout"] +MAX_WORKERS = cfg["max_workers"] +VALID_EXTS = set(cfg["valid_extensions"]) +USE_COLS = list(cfg["columns"].values()) + + +# PIPELINE TELECHARGEMENT DATASET +def load(path: str) -> pd.DataFrame: + """Charge le CSV, garde uniquement les observations validées et identifiables.""" + logger.info("Chargement : %s", path) + df = pd.read_csv(path, usecols=USE_COLS) + + df = df[ + (df["validee - observation"] == "TRUE") + # (df["espece identifiable ? - observation"] == "Identifiable") + ].reset_index(drop=True) + + logger.info("%d observations valides et identifiables", len(df)) + return df + + +def explode_urls(df: pd.DataFrame) -> pd.DataFrame: + """Une ligne par image.""" + df = ( + df.assign(**{"images - observation": df["images - observation"].str.strip().str.split("|")}) .explode("images - observation") .rename(columns={"images - observation": "image_url"}) .assign(image_url=lambda d: d["image_url"].str.strip()) - .reset_index(drop=True) - ) - logger.info("Explosion terminée — %d lignes (une par image)", len(result)) - return result - - -def prepare_dataset(df: pd.DataFrame) -> pd.DataFrame: - """Sélectionne, renomme et enrichit les colonnes utiles.""" - logger.debug("Préparation du dataset...") - dataset = ( - df[[ - "ID - N1", - "Nom commun - observation", - "espece identifiable ? - observation", - "image_url", - ]] - .copy() - .rename(columns={"espece identifiable ? - observation": "identifiable"}) - .pipe(lambda d: d[d["identifiable"].isin(IDENTIFIABLE_VALUES)]) - .reset_index(drop=True) ) + # Supprimer les URLs vides ou nulles générées par les trailing "|" ou cellules vides + df = df[df["image_url"].notna() & (df["image_url"] != "")].reset_index(drop=True) + logger.info("%d URLs d'images après explosion", len(df)) + return df - dataset["filename"] = [_make_filename(row, i) for i, row in dataset.iterrows()] - dataset["subfolder"] = dataset["identifiable"].map(SUBFOLDER_MAP) - dataset["filepath"] = ( - str(BASE_DIR / "images") + "/" + dataset["subfolder"] + "/" + dataset["filename"] - ) - counts = dataset["subfolder"].value_counts() - logger.info( - "Dataset préparé — identifiable: %d | non_identifiable: %d | total: %d", - counts.get("identifiable", 0), - counts.get("non_identifiable", 0), - len(dataset), - ) - return dataset - - -def _make_filename(row: pd.Series, idx: int) -> str: +def make_filename(row: pd.Series, idx: int) -> str: id_n1 = str(row["ID - N1"]) nom = str(row["Nom commun - observation"]) if pd.notna(row["Nom commun - observation"]) else "inconnu" nom = nom.strip().replace(" ", "_").replace("/", "-") - ext = row["image_url"].split(".")[-1].split("?")[0].lower() + # Garde-fou au cas où une URL NaN passerait quand même + url = row["image_url"] if isinstance(row["image_url"], str) else "" + ext = url.split(".")[-1].split("?")[0].lower() if url else "jpg" ext = ext if ext in VALID_EXTS else "jpg" return f"{id_n1}_{nom}_{idx}.{ext}" -def create_folder_structure() -> None: - """Crée l'arborescence du dataset.""" - logger.debug("Création de l'arborescence des dossiers...") - for sub in [ - "images/identifiable", "images/non_identifiable", - "labels/identifiable", "labels/non_identifiable", - ]: - (BASE_DIR / sub).mkdir(parents=True, exist_ok=True) - logger.info("Arborescence créée dans : %s", BASE_DIR.resolve()) - - -def download_images(dataset: pd.DataFrame) -> list[dict]: - """Télécharge les images, skippe celles déjà présentes.""" - failed = [] - skipped = 0 - success = 0 - - logger.info("Démarrage du téléchargement (%d images)...", len(dataset)) - - for _, row in tqdm(dataset.iterrows(), total=len(dataset), desc="Téléchargement"): - dest = Path(row["filepath"]) - if dest.exists(): - skipped += 1 - logger.debug("Skipped (déjà présente) : %s", dest.name) - continue - try: - response = requests.get(row["image_url"], timeout=TIMEOUT) - response.raise_for_status() - dest.write_bytes(response.content) - success += 1 - logger.debug("OK %s", dest.name) - except Exception as e: - failed.append({"url": row["image_url"], "erreur": str(e)}) - logger.warning("ÉCHEC %s → %s", row["image_url"], e) - - logger.info( - "Téléchargement terminé — succès: %d | skipped: %d | échecs: %d", - success, skipped, len(failed), - ) - return failed - - -def save_metadata(dataset: pd.DataFrame) -> None: - dest = BASE_DIR / "metadata.csv" - dataset.to_csv(dest, index=False) - logger.info("Métadonnées sauvegardées : %s (%d lignes)", dest, len(dataset)) +def _download_one(row: pd.Series, idx: int) -> dict | None: + """Télécharge une image. Retourne un dict si succès, None si échec.""" + filename = make_filename(row, idx) + dest = IMAGES_DIR / filename + if dest.exists(): + logger.debug("Skip (déjà présente) : %s", filename) + return {**row, "filename": filename, "filepath": str(dest)} -def save_yaml(dataset: pd.DataFrame) -> None: - classes = sorted(dataset["Nom commun - observation"].dropna().unique().tolist()) - yaml_content = ( - f"# Dataset Biolit - config YOLO\n" - f"path: {BASE_DIR.resolve()}\n" - f"train: images/identifiable\n" - f"val: images/identifiable # à splitter train/val/test\n\n" - f"nc: {len(classes)}\n" - f"names: {classes}\n" - ) - dest = BASE_DIR / "data.yaml" - dest.write_text(yaml_content) - logger.info("data.yaml sauvegardé : %d classes", len(classes)) - + try: + r = requests.get(row["image_url"], timeout=TIMEOUT) + r.raise_for_status() + dest.write_bytes(r.content) + logger.debug("OK %s", filename) + return {**row, "filename": filename, "filepath": str(dest)} + except Exception as e: + logger.warning("ÉCHEC %s → %s", row["image_url"], e) + return None -def print_report(dataset: pd.DataFrame, failed: list[dict]) -> None: - counts = dataset["subfolder"].value_counts() - report = f""" - Dataset sauvegardé dans : {BASE_DIR.resolve()} - images/identifiable/ -> {counts.get('identifiable', 0):>5} images - images/non_identifiable/ -> {counts.get('non_identifiable', 0):>5} images - labels/ -> prêt pour annotation YOLO - metadata.csv -> {len(dataset):>5} lignes - data.yaml -> {dataset['Nom commun - observation'].nunique():>5} classes - - Succès : {len(dataset) - len(failed)} - Échecs : {len(failed)} +def download(df: pd.DataFrame) -> pd.DataFrame: + """ + Télécharge chaque image en parallèle (ThreadPoolExecutor, I/O-bound). + Retourne uniquement les lignes dont le téléchargement a réussi. """ - print(report) - logger.info("=== RAPPORT FINAL ===") - logger.info("identifiable: %d | non_identifiable: %d", counts.get("identifiable", 0), counts.get("non_identifiable", 0)) - logger.info("Total lignes metadata: %d | Classes: %d", len(dataset), dataset["Nom commun - observation"].nunique()) - logger.info("Succès: %d | Échecs: %d", len(dataset) - len(failed), len(failed)) - logger.info("Logs sauvegardés dans : %s", LOG_FILE.resolve()) + IMAGES_DIR.mkdir(parents=True, exist_ok=True) + futures = {} + rows_ok = [] -# RUNNER L'EXTRACT -def build_dataset(): - logger.info("════════ START build_dataset ════════") + with ThreadPoolExecutor(max_workers=MAX_WORKERS) as executor: + for idx, row in df.iterrows(): + future = executor.submit(_download_one, row, idx) + futures[future] = idx - logger.info("Chargement et filtrage...") - raw = load_and_filter(DATA_PATH) - exploded = explode_image_urls(raw) - dataset = prepare_dataset(exploded) + for future in tqdm(as_completed(futures), total=len(futures), desc="Téléchargement"): + result = future.result() + if result is not None: + rows_ok.append(result) - logger.info("%d images à traiter", len(dataset)) - - create_folder_structure() + downloaded = pd.DataFrame(rows_ok).reset_index(drop=True) + logger.info( + "Téléchargement terminé — %d/%d images récupérées", + len(downloaded), len(df), + ) + return downloaded - logger.info("Téléchargement des images...") - failed = download_images(dataset) - logger.info("Sauvegarde des métadonnées...") - save_metadata(dataset) - save_yaml(dataset) +# MAIN +def build_dataset(): + logger.info("════════ START ════════") - if failed: - dest = BASE_DIR / "failed_downloads.csv" - pd.DataFrame(failed).to_csv(dest, index=False) - logger.warning("%d échec(s) enregistré(s) dans : %s", len(failed), dest) + df = load(DATA_PATH) + df = explode_urls(df) + df_ok = download(df) - print_report(dataset, failed) - logger.info("════════ END build_dataset ════════") + logger.info( + "Terminé — %d images | %d espèces | dossier : %s", + len(df_ok), + df_ok["Nom commun - observation"].nunique(), + IMAGES_DIR.resolve(), + ) + logger.info("════════ END ════════") if __name__ == "__main__": diff --git a/ml/yolov8_DINO/configs/build_dataset.yaml b/ml/yolov8_DINO/configs/build_dataset.yaml new file mode 100644 index 0000000..03e5008 --- /dev/null +++ b/ml/yolov8_DINO/configs/build_dataset.yaml @@ -0,0 +1,17 @@ +# Chemins +data_path: dataset_biolit/export_biolit.csv +base_dir: dataset_biolit +images_dir: dataset_biolit/images + +# Téléchargement +timeout: 10 +max_workers: 16 +valid_extensions: ["jpg", "jpeg", "png", "webp"] + +# Colonnes CSV +columns: + id: "ID - N1" + images: "images - observation" + nom_commun: "Nom commun - observation" + validee: "validee - observation" + espece_identifiable: "espece identifiable ? - observation" diff --git a/ml/yolov8_DINO/crop_pipeline.py b/ml/yolov8_DINO/crop_pipeline.py deleted file mode 100644 index dcd2b29..0000000 --- a/ml/yolov8_DINO/crop_pipeline.py +++ /dev/null @@ -1,99 +0,0 @@ -import os -from pathlib import Path - -import torch -from PIL import Image -from transformers import AutoModelForZeroShotObjectDetection, AutoProcessor - - -# Configuration de base TODO: Ajouter dans config.py -device = torch.device("cuda" if torch.cuda.is_available() else "cpu") -USE_SMALL_MODELS = os.environ.get("USE_SMALL_MODELS", "0") == "1" -ASSETS_DIR = Path(__file__).parent / "assets" - -# Récupération du modèle TODO: Ajouter dans config.py -GDINO_MODEL_ID = "IDEA-Research/grounding-dino-tiny" if USE_SMALL_MODELS else "IDEA-Research/grounding-dino-base" -gdino_processor = AutoProcessor.from_pretrained(GDINO_MODEL_ID) -gdino_model = AutoModelForZeroShotObjectDetection.from_pretrained(GDINO_MODEL_ID).eval().to(device) - -# Code détection avec GroundingDINO TODO: mettre dans detect.py -def _detect_gdino(image: Image.Image, labels: str, threshold: float) -> tuple[Image.Image, list[dict], dict, list]: - text = labels.strip().rstrip(".") - candidate_labels = [part.strip() for part in text.split(",") if part.strip()] - text_prompt = ". ".join(candidate_labels) + "." - - inputs = gdino_processor(images=image, text=text_prompt, return_tensors="pt").to(device) - outputs = gdino_model(**inputs) - results = gdino_processor.post_process_grounded_object_detection( - outputs, - input_ids=inputs["input_ids"], - target_sizes=[(image.height, image.width)], - threshold=threshold, - text_threshold=threshold, - )[0] - - return results - -# Code de crop et annotation TODO: mettre dans img_utils -def crop_and_anotate(image, results, threshold): - annotations = [] - crops = [] - - for score, label, box in zip(results["scores"], results["labels"], results["boxes"], strict=True): - x_min, y_min, x_max, y_max = box.tolist() - annotations.append( - { - "bbox": {"x": x_min, "y": y_min, "width": x_max - x_min, "height": y_max - y_min}, - "score": round(float(score), 3), - "label": label, - } - ) - # Crop de la région détectée - crop = image.crop((x_min, y_min, x_max, y_max)) - crops.append((crop, f"{label} {float(score):.1%}")) - - return image, annotations, {"score_threshold": (threshold, 1.0)}, crops - - -# Script d'inférence TODO: mettre dans main.py -@torch.inference_mode() -def run_detection(): - # Partie détection - detection = _detect_gdino( - image = Image.open(f"{ASSETS_DIR}/Biolit-Espece (12).webp"), - labels="animal, plant", - threshold=0.4 - ) - - # Partie crop - crop_anotation = crop_and_anotate( - image = Image.open(f"{ASSETS_DIR}/Biolit-Espece (12).webp"), - results=detection, - threshold=0.4 - ) - - # Afficher les infos et sauvegarder l'image TODO: afficher dans un DataFrame pour évaluer le modèle - print("Bbox : ", crop_anotation[1][0]["bbox"]) - print("Score : ", crop_anotation[1][0]["score"]) - print("Label : ", crop_anotation[1][0]["label"]) - # print(crop_anotation[2]) - crop_anotation[3][0][0].save("Biolit-Espece_(12)_crop.webp","WEBP") - -# def run_detection(image: Image.Image, labels: str, threshold: float) -> tuple | None: -# if image is None or not labels.strip(): -# return None, [] -# image, annotations, score_threshold, crops = _detect_gdino(image, labels, threshold) - # return (image, annotations, score_threshold), crops - - - -# Exécuter la fonction -if __name__ == "__main__": - run_detection() -# results = run_detection( -# image = Image.open(f"{ASSETS_DIR}/Biolit-Espece (12).webp"), -# labels="animal, plant", -# threshold=0.4 -# ) - -# print(results) \ No newline at end of file diff --git a/ml/yolov8_DINO/utils/logger.py b/ml/yolov8_DINO/utils/logger.py new file mode 100644 index 0000000..e3555ea --- /dev/null +++ b/ml/yolov8_DINO/utils/logger.py @@ -0,0 +1,33 @@ +import logging +import sys +from pathlib import Path +from datetime import datetime + +# FICHIER DES LOGS +def get_logger(name: str, log_dir: str = "logs") -> logging.Logger: + Path(log_dir).mkdir(parents=True, exist_ok=True) + + logger = logging.getLogger(name) + logger.setLevel(logging.INFO) + + if logger.handlers: + return logger + + _stream_handler = logging.StreamHandler(sys.stdout) + _stream_handler.setFormatter( + logging.Formatter("%(asctime)s %(levelname)-8s %(message)s", "%H:%M:%S") + ) + + _file_handler = logging.FileHandler( + Path(log_dir) / f"{name}_{datetime.now().strftime('%Y%m%d_%H%M%S')}.log", + mode="w", + encoding="utf-8", + ) + _file_handler.setFormatter( + logging.Formatter("%(asctime)s %(levelname)-8s %(message)s", "%Y-%m-%d %H:%M:%S") + ) + + logger.addHandler(_stream_handler) + logger.addHandler(_file_handler) + + return logger From 42d1380fef0a615d0200de75ecbf2615856f13c5 Mon Sep 17 00:00:00 2001 From: mandresyandri Date: Mon, 30 Mar 2026 21:10:56 +0200 Subject: [PATCH 08/13] Code for autodistilling Dino --- ml/yolov8_DINO/autodistill_boostrap.py | 127 +++ .../configs/autodistill_boostrap.yaml | 26 + pyproject.toml | 9 +- uv.lock | 984 +++++++++++++++++- 4 files changed, 1103 insertions(+), 43 deletions(-) create mode 100644 ml/yolov8_DINO/autodistill_boostrap.py create mode 100644 ml/yolov8_DINO/configs/autodistill_boostrap.yaml diff --git a/ml/yolov8_DINO/autodistill_boostrap.py b/ml/yolov8_DINO/autodistill_boostrap.py new file mode 100644 index 0000000..e782e02 --- /dev/null +++ b/ml/yolov8_DINO/autodistill_boostrap.py @@ -0,0 +1,127 @@ +import torch +import yaml +import unicodedata +import numpy as np +from pathlib import Path +from autodistill_grounding_dino import GroundingDINO +from autodistill.detection import CaptionOntology +from autodistill_yolov8 import YOLOv8 + +# --------------------------------------------------------------------------- +# Fix PyTorch / Ultralytics / NumPy +# --------------------------------------------------------------------------- +torch.use_deterministic_algorithms(False) +_original_load = torch.load +torch.load = lambda *args, **kwargs: _original_load(*args, **kwargs, weights_only=False) + +# np.trapz supprimé en NumPy 2.0, ultralytics 8.0.81 l'utilise encore +if not hasattr(np, "trapz"): + np.trapz = np.trapezoid + +VALID_IMG_EXT = {".jpg", ".jpeg", ".png", ".bmp", ".tiff", ".webp"} + + +# --------------------------------------------------------------------------- +# Helpers +# --------------------------------------------------------------------------- +def sanitize_filenames(img_dir: str) -> None: + """Renomme les fichiers avec accents → ASCII safe (cv2.imread échoue sur macOS sinon)""" + for f in Path(img_dir).iterdir(): + if f.is_file(): + # Décompose les caractères accentués (NFD) puis supprime les diacritiques + nfkd = unicodedata.normalize("NFKD", f.name) + ascii_name = "".join(c for c in nfkd if not unicodedata.combining(c)) + if ascii_name != f.name: + new_path = f.parent / ascii_name + f.rename(new_path) + print(f"Renommé : {f.name} → {ascii_name}") + + +def clean_non_images(img_dir: str) -> list[Path]: + """Déplace les fichiers non-image (.DS_Store, etc.) dans un sous-dossier temporaire. + Retourne la liste des fichiers déplacés pour restauration ultérieure. + """ + img_path = Path(img_dir) + non_images = [ + f for f in img_path.iterdir() + if f.is_file() and (f.name.startswith(".") or f.suffix.lower() not in VALID_IMG_EXT) + ] + + if not non_images: + return [] + + tmp = img_path / "_non_images" + tmp.mkdir(exist_ok=True) + + for f in non_images: + f.rename(tmp / f.name) + print(f"⚠️ Déplacé (non-image) : {f.name}") + + return non_images + + +def restore_non_images(img_dir: str) -> None: + """Restaure les fichiers non-image déplacés par clean_non_images.""" + tmp = Path(img_dir) / "_non_images" + if not tmp.exists(): + return + for f in tmp.iterdir(): + f.rename(tmp.parent / f.name) + tmp.rmdir() + + +def load_config(path: str = "configs/autodistill_boostrap.yaml") -> dict: + with open(path) as f: + return yaml.safe_load(f) + + +# --------------------------------------------------------------------------- +# Pipeline +# --------------------------------------------------------------------------- +def label(cfg: dict) -> None: + print("[1/2] Pseudo-labeling GroundingDINO") + + ontology = CaptionOntology(cfg["ontology"]) + base_model = GroundingDINO(ontology=ontology) + + # Écarte les fichiers non-image avant le labeling + clean_non_images(cfg["img_path"]) + + try: + base_model.label( + input_folder=cfg["img_path"], + output_folder=cfg["labeled_output"], + ) + finally: + # Restaure quoi qu'il arrive (crash, KeyboardInterrupt…) + restore_non_images(cfg["img_path"]) + + print(f"Labels générés → {cfg['labeled_output']}") + + +def train(cfg: dict) -> None: + print("[2/2] Training YOLOv8 (bootstrap)") + print(f"GPU dispo : {torch.cuda.is_available()}") + if torch.cuda.is_available(): + print(f"Device : {torch.cuda.get_device_name(0)}") + + device = cfg["device"] if torch.cuda.is_available() else "cpu" + model = YOLOv8(cfg["model"]) + # project/name passés directement à ultralytics (non exposés par le wrapper autodistill) + model.yolo.train( + data=cfg["data_yaml"], + epochs=cfg["epochs"], + device=device, + project=cfg["project"], + name=cfg["name"], + ) + + +# --------------------------------------------------------------------------- +# Main +# --------------------------------------------------------------------------- +if __name__ == "__main__": + cfg = load_config() + sanitize_filenames(cfg["img_path"]) + # label(cfg) + train(cfg) diff --git a/ml/yolov8_DINO/configs/autodistill_boostrap.yaml b/ml/yolov8_DINO/configs/autodistill_boostrap.yaml new file mode 100644 index 0000000..a9f174a --- /dev/null +++ b/ml/yolov8_DINO/configs/autodistill_boostrap.yaml @@ -0,0 +1,26 @@ +# Ontologie (TODO: affiner avec Giulia) +ontology: + "plant": "plant" + "animal": "animal" + +# Chemins +img_path: dataset_biolit/images +labeled_output: dataset_biolit/labeled-images +data_yaml: dataset_biolit/labeled-images/data.yaml + +# Modèle +model: yolov8l.pt + +# Hyperparams +epochs: 100 +imgsz: 640 +batch: 16 +device: 0 + +# Robustesse au bruit +label_smoothing: 0.1 +dropout: 0.1 + +# Sorties +project: runs/ +name: biolit_v2_bootstrap \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index 6aa78c6..7e40e82 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -36,7 +36,14 @@ dependencies = [ "shapely>=2.1.2", "structlog>=25.5.0", "tqdm>=4.67.3", - "transformers>=5.2.0", + "transformers==4.41.2", + "torch>=2.11.0", + "roboflow>=1.2.16", + "autodistill>=0.1.29", + "autodistill-grounding-dino>=0.1.4", + "autodistill-yolov8>=0.1.4", + "supervision>=0.27.0.post2", + "scikit-learn>=1.8.0", ] [tool.pytest.ini_options] diff --git a/uv.lock b/uv.lock index 938f1ae..218e0b6 100644 --- a/uv.lock +++ b/uv.lock @@ -4,13 +4,19 @@ requires-python = ">=3.12" resolution-markers = [ "python_full_version >= '3.14' and sys_platform == 'win32'", "python_full_version >= '3.14' and sys_platform == 'emscripten'", - "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version >= '3.14' and sys_platform == 'darwin'", + "python_full_version >= '3.14' and platform_machine == 'aarch64' and sys_platform == 'linux'", + "(python_full_version >= '3.14' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.14' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'linux' and sys_platform != 'win32')", "python_full_version == '3.13.*' and sys_platform == 'win32'", "python_full_version < '3.13' and sys_platform == 'win32'", "python_full_version == '3.13.*' and sys_platform == 'emscripten'", "python_full_version < '3.13' and sys_platform == 'emscripten'", - "python_full_version == '3.13.*' and sys_platform != 'emscripten' and sys_platform != 'win32'", - "python_full_version < '3.13' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version == '3.13.*' and sys_platform == 'darwin'", + "python_full_version == '3.13.*' and platform_machine == 'aarch64' and sys_platform == 'linux'", + "(python_full_version == '3.13.*' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version == '3.13.*' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'linux' and sys_platform != 'win32')", + "python_full_version < '3.13' and sys_platform == 'darwin'", + "python_full_version < '3.13' and platform_machine == 'aarch64' and sys_platform == 'linux'", + "(python_full_version < '3.13' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version < '3.13' and sys_platform != 'darwin' and sys_platform != 'emscripten' and sys_platform != 'linux' and sys_platform != 'win32')", ] [[package]] @@ -18,6 +24,9 @@ name = "14-biolit" version = "0.1.0" source = { virtual = "." } dependencies = [ + { name = "autodistill" }, + { name = "autodistill-grounding-dino" }, + { name = "autodistill-yolov8" }, { name = "bs4" }, { name = "click" }, { name = "duckdb" }, @@ -38,21 +47,28 @@ dependencies = [ { name = "plotly" }, { name = "polars" }, { name = "pre-commit" }, - { name = "psycopg2-binary" }, { name = "prefect" }, + 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sanitize_filenames(img_dir: str) -> None: """Renomme les fichiers avec accents → ASCII safe (cv2.imread échoue sur macOS sinon)""" for f in Path(img_dir).iterdir(): if f.is_file(): - # Décompose les caractères accentués (NFD) puis supprime les diacritiques nfkd = unicodedata.normalize("NFKD", f.name) ascii_name = "".join(c for c in nfkd if not unicodedata.combining(c)) if ascii_name != f.name: @@ -55,7 +54,7 @@ def clean_non_images(img_dir: str) -> list[Path]: for f in non_images: f.rename(tmp / f.name) - print(f"⚠️ Déplacé (non-image) : {f.name}") + print(f"Déplacé (non-image) : {f.name}") return non_images @@ -93,7 +92,6 @@ def label(cfg: dict) -> None: output_folder=cfg["labeled_output"], ) finally: - # Restaure quoi qu'il arrive (crash, KeyboardInterrupt…) restore_non_images(cfg["img_path"]) print(f"Labels générés → {cfg['labeled_output']}") @@ -107,7 +105,6 @@ def train(cfg: dict) -> None: device = cfg["device"] if torch.cuda.is_available() else "cpu" model = YOLOv8(cfg["model"]) - # project/name passés directement à ultralytics (non exposés par le wrapper autodistill) model.yolo.train( data=cfg["data_yaml"], epochs=cfg["epochs"], @@ -123,5 +120,5 @@ def train(cfg: dict) -> None: if __name__ == "__main__": cfg = load_config() sanitize_filenames(cfg["img_path"]) - # label(cfg) + label(cfg) train(cfg) From d002ef7a39ea7267dd6b5164dcde7dd8917575c4 Mon Sep 17 00:00:00 2001 From: mandresyandri Date: Mon, 30 Mar 2026 21:13:21 +0200 Subject: [PATCH 10/13] fix1 code --- ml/yolov8_DINO/autodistill_boostrap.py | 8 -------- 1 file changed, 8 deletions(-) diff --git a/ml/yolov8_DINO/autodistill_boostrap.py b/ml/yolov8_DINO/autodistill_boostrap.py index b1fa8c4..b64a6e2 100644 --- a/ml/yolov8_DINO/autodistill_boostrap.py +++ b/ml/yolov8_DINO/autodistill_boostrap.py @@ -7,9 +7,7 @@ from autodistill.detection import CaptionOntology from autodistill_yolov8 import YOLOv8 -# --------------------------------------------------------------------------- # Fix PyTorch / Ultralytics / NumPy -# --------------------------------------------------------------------------- torch.use_deterministic_algorithms(False) _original_load = torch.load torch.load = lambda *args, **kwargs: _original_load(*args, **kwargs, weights_only=False) @@ -21,9 +19,7 @@ VALID_IMG_EXT = {".jpg", ".jpeg", ".png", ".bmp", ".tiff", ".webp"} -# --------------------------------------------------------------------------- # Helpers -# --------------------------------------------------------------------------- def sanitize_filenames(img_dir: str) -> None: """Renomme les fichiers avec accents → ASCII safe (cv2.imread échoue sur macOS sinon)""" for f in Path(img_dir).iterdir(): @@ -74,9 +70,7 @@ def load_config(path: str = "configs/autodistill_boostrap.yaml") -> dict: return yaml.safe_load(f) -# --------------------------------------------------------------------------- # Pipeline -# --------------------------------------------------------------------------- def label(cfg: dict) -> None: print("[1/2] Pseudo-labeling GroundingDINO") @@ -114,9 +108,7 @@ def train(cfg: dict) -> None: ) -# --------------------------------------------------------------------------- # Main -# --------------------------------------------------------------------------- if __name__ == "__main__": cfg = load_config() sanitize_filenames(cfg["img_path"]) From e97da34e02659b6170848799e409090572438ec5 Mon Sep 17 00:00:00 2001 From: mandresyandri Date: Tue, 31 Mar 2026 22:54:04 +0200 Subject: [PATCH 11/13] =?UTF-8?q?feat:=20code=20autodistill=20pour=20le=20?= =?UTF-8?q?mod=C3=A8le=20bootstrap?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .gitignore | 9 +- ml/yolov8_DINO/README.md | 55 ++++--- ml/yolov8_DINO/autodistill_boostrap.py | 116 ------------- ml/yolov8_DINO/autodistill_label.py | 56 +++++++ ml/yolov8_DINO/autodistill_train.py | 69 ++++++++ ml/yolov8_DINO/build_dataset.py | 154 +++++++++++------- ml/yolov8_DINO/check_dataset.py | 87 ++++++++++ .../configs/autodistill_boostrap.yaml | 4 +- pyproject.toml | 2 + uv.lock | 53 ++++++ 10 files changed, 398 insertions(+), 207 deletions(-) delete mode 100644 ml/yolov8_DINO/autodistill_boostrap.py create mode 100644 ml/yolov8_DINO/autodistill_label.py create mode 100644 ml/yolov8_DINO/autodistill_train.py create mode 100644 ml/yolov8_DINO/check_dataset.py diff --git a/.gitignore b/.gitignore index d0cc217..4a89642 100644 --- a/.gitignore +++ b/.gitignore @@ -175,4 +175,11 @@ data.yaml # orchestrateur - tests Timothée orchestrateur/ -flows/ \ No newline at end of file +flows/ +images/ +labeled-images/ +_non_images_tmp/ +yolov8n.pt +images_test/ +labeled_test/ +runs/ \ No newline at end of file diff --git a/ml/yolov8_DINO/README.md b/ml/yolov8_DINO/README.md index 7005c4b..1cc4149 100644 --- a/ml/yolov8_DINO/README.md +++ b/ml/yolov8_DINO/README.md @@ -1,36 +1,41 @@ -# ML - YOLOv8 (détection + crop) +# ML - YOLOv8 / GroundingDINO -Objectif : détecter l'espèce (végétal/animal) et générer un crop centré sur l'objet. +Approche en deux temps : un premier entraînement sur ~10 000 images annotées automatiquement (quantitatif) +pour bootstrapper le modèle, suivi d'un re-entraînement sur ~1 400 images cropées et annotées manuellement (qualitatif) +pour affiner les performances. L'annotation manuelle ne porte que sur la deuxième partie. -## Entrées -Structure de données généré à partir du fichier `export_biolit.csv`. +## Partie 1 — Bootstrap autodistill -### Récupération propre des données -build_dataset.py -Pipeline de constitution du dataset Biolit pour inférence YOLO / Grounding DINO. +Annotation automatique via GroundingDINO + fine-tuning YOLOv8. Pas d'annotation manuelle. +L'ontologie est à affiner dans `configs/autodistill_boostrap.yaml`. + +```bash +python build_dataset.py # téléchargement + nettoyage images +python check_dataset.py # vérification qualité (résolutions, espèces, corrompues) +python autodistill_label.py # pseudo-labels GroundingDINO +python autodistill_train.py # fine-tuning YOLOv8 +``` + +`--limit N` sur `build_dataset.py` pour tester sur un sous-ensemble. **Structure de sortie :** ```text - dataset_biolit/ - ├── images/ - │ ├── identifiable/ - │ └── non_identifiable/ # à valider - ├── labels/ - │ ├── identifiable/ - │ └── non_identifiable/ # à valider - ├── metadata.csv # GroundingDINO - └── data.yaml #YOLO +dataset_biolit/ +├── images/ +└── labeled-images/ + ├── train/ + │ ├── images/ + │ └── labels/ + ├── valid/ + │ ├── images/ + │ └── labels/ + └── data.yaml ``` +Poids entraînés → `runs/biolit_v2_bootstrap/weights/` -## Sorties - -- Bboxes + classes : `dataset_biolit/exports/yolov8_detections.csv` -- Images crops : `dataset_biolit/crops/images/` - -## Routage +## Partie 2 — Fine-tuning (à venir) - - si détection forte → **Classification** - - si détection faible → **Label Studio (CROP)** - - si pas de détection animal ou végétal → stop +Pris en charge par un autre membre de l'équipe. +Entraînement sur des images cropées et annotées manuellement pour améliorer les performances du modèle bootstrap. diff --git a/ml/yolov8_DINO/autodistill_boostrap.py b/ml/yolov8_DINO/autodistill_boostrap.py deleted file mode 100644 index b64a6e2..0000000 --- a/ml/yolov8_DINO/autodistill_boostrap.py +++ /dev/null @@ -1,116 +0,0 @@ -import torch -import yaml -import unicodedata -import numpy as np -from pathlib import Path -from autodistill_grounding_dino import GroundingDINO -from autodistill.detection import CaptionOntology -from autodistill_yolov8 import YOLOv8 - -# Fix PyTorch / Ultralytics / NumPy -torch.use_deterministic_algorithms(False) -_original_load = torch.load -torch.load = lambda *args, **kwargs: _original_load(*args, **kwargs, weights_only=False) - -# np.trapz supprimé en NumPy 2.0, ultralytics 8.0.81 l'utilise encore -if not hasattr(np, "trapz"): - np.trapz = np.trapezoid - -VALID_IMG_EXT = {".jpg", ".jpeg", ".png", ".bmp", ".tiff", ".webp"} - - -# Helpers -def sanitize_filenames(img_dir: str) -> None: - """Renomme les fichiers avec accents → ASCII safe (cv2.imread échoue sur macOS sinon)""" - for f in Path(img_dir).iterdir(): - if f.is_file(): - nfkd = unicodedata.normalize("NFKD", f.name) - ascii_name = "".join(c for c in nfkd if not unicodedata.combining(c)) - if ascii_name != f.name: - new_path = f.parent / ascii_name - f.rename(new_path) - print(f"Renommé : {f.name} → {ascii_name}") - - -def clean_non_images(img_dir: str) -> list[Path]: - """Déplace les fichiers non-image (.DS_Store, etc.) dans un sous-dossier temporaire. - Retourne la liste des fichiers déplacés pour restauration ultérieure. - """ - img_path = Path(img_dir) - non_images = [ - f for f in img_path.iterdir() - if f.is_file() and (f.name.startswith(".") or f.suffix.lower() not in VALID_IMG_EXT) - ] - - if not non_images: - return [] - - tmp = img_path / "_non_images" - tmp.mkdir(exist_ok=True) - - for f in non_images: - f.rename(tmp / f.name) - print(f"Déplacé (non-image) : {f.name}") - - return non_images - - -def restore_non_images(img_dir: str) -> None: - """Restaure les fichiers non-image déplacés par clean_non_images.""" - tmp = Path(img_dir) / "_non_images" - if not tmp.exists(): - return - for f in tmp.iterdir(): - f.rename(tmp.parent / f.name) - tmp.rmdir() - - -def load_config(path: str = "configs/autodistill_boostrap.yaml") -> dict: - with open(path) as f: - return yaml.safe_load(f) - - -# Pipeline -def label(cfg: dict) -> None: - print("[1/2] Pseudo-labeling GroundingDINO") - - ontology = CaptionOntology(cfg["ontology"]) - base_model = GroundingDINO(ontology=ontology) - - # Écarte les fichiers non-image avant le labeling - clean_non_images(cfg["img_path"]) - - try: - base_model.label( - input_folder=cfg["img_path"], - output_folder=cfg["labeled_output"], - ) - finally: - restore_non_images(cfg["img_path"]) - - print(f"Labels générés → {cfg['labeled_output']}") - - -def train(cfg: dict) -> None: - print("[2/2] Training YOLOv8 (bootstrap)") - print(f"GPU dispo : {torch.cuda.is_available()}") - if torch.cuda.is_available(): - print(f"Device : {torch.cuda.get_device_name(0)}") - - device = cfg["device"] if torch.cuda.is_available() else "cpu" - model = YOLOv8(cfg["model"]) - model.yolo.train( - data=cfg["data_yaml"], - epochs=cfg["epochs"], - device=device, - project=cfg["project"], - name=cfg["name"], - ) - - -# Main -if __name__ == "__main__": - cfg = load_config() - sanitize_filenames(cfg["img_path"]) - label(cfg) - train(cfg) diff --git a/ml/yolov8_DINO/autodistill_label.py b/ml/yolov8_DINO/autodistill_label.py new file mode 100644 index 0000000..e3074ed --- /dev/null +++ b/ml/yolov8_DINO/autodistill_label.py @@ -0,0 +1,56 @@ +""" +Pseudo-labeling GroundingDINO. +Lance après build_dataset.py + inspect_data.py. + +Usage: + python autodistill_label.py + python autodistill_label.py --config configs/autodistill_boostrap.yaml +""" + +import argparse +import sys +from pathlib import Path + +import numpy as np +import torch +import yaml + +# compat —> ajustement version +torch.use_deterministic_algorithms(False) +_orig = torch.load +torch.load = lambda *a, **kw: _orig(*a, **kw, weights_only=False) + +if not hasattr(np, "trapz"): + np.trapz = np.trapezoid + +from autodistill.detection import CaptionOntology +from autodistill_grounding_dino import GroundingDINO + +sys.path.insert(0, str(Path(__file__).parent)) +from utils.logger import get_logger + +log = get_logger("autodistill_label") + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--config", default="configs/autodistill_boostrap.yaml") + args = parser.parse_args() + + with open(args.config) as f: + cfg = yaml.safe_load(f) + + log.info("Labeling GroundingDINO → %s", cfg["labeled_output"]) + + model = GroundingDINO(ontology=CaptionOntology(cfg["ontology"])) + model.label( + input_folder=cfg["img_path"], + output_folder=cfg["labeled_output"], + extension="", + ) + + log.info("Labels générés → %s", cfg["labeled_output"]) + + +if __name__ == "__main__": + main() diff --git a/ml/yolov8_DINO/autodistill_train.py b/ml/yolov8_DINO/autodistill_train.py new file mode 100644 index 0000000..bb5277b --- /dev/null +++ b/ml/yolov8_DINO/autodistill_train.py @@ -0,0 +1,69 @@ +""" +Fine-tuning YOLOv8 sur les pseudo-labels GroundingDINO. +Lance après autodistill_label.py. + +Usage: + python autodistill_train.py + python autodistill_train.py --config configs/autodistill_boostrap.yaml +""" + +import argparse +import sys +from pathlib import Path + +import numpy as np +import torch +import yaml + +# compat —> ajustement version +torch.use_deterministic_algorithms(False) +_orig = torch.load +torch.load = lambda *a, **kw: _orig(*a, **kw, weights_only=False) + +if not hasattr(np, "trapz"): + np.trapz = np.trapezoid + +from autodistill_yolov8 import YOLOv8 + +sys.path.insert(0, str(Path(__file__).parent)) +from utils.logger import get_logger + +log = get_logger("autodistill_train") + + +def _device(): + if torch.cuda.is_available(): + return "cuda" + if torch.backends.mps.is_available(): + return "mps" + return "cpu" + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--config", default="configs/autodistill_boostrap.yaml") + args = parser.parse_args() + + with open(args.config) as f: + cfg = yaml.safe_load(f) + + device = _device() + log.info("Training YOLOv8 | device=%s", device) + + model = YOLOv8(cfg["model"]) + model.yolo.train( + data=cfg["data_yaml"], + epochs=cfg["epochs"], + imgsz=cfg.get("imgsz", 640), + device=device, + project=cfg["project"], + name=cfg["name"], + batch=cfg.get("batch", 16), + exist_ok=True, + ) + + log.info("Entraînement terminé.") + + +if __name__ == "__main__": + main() diff --git a/ml/yolov8_DINO/build_dataset.py b/ml/yolov8_DINO/build_dataset.py index 1e782e4..7c4f2f6 100644 --- a/ml/yolov8_DINO/build_dataset.py +++ b/ml/yolov8_DINO/build_dataset.py @@ -1,4 +1,7 @@ +import argparse +import unicodedata import yaml +import cv2 import requests import pandas as pd from pathlib import Path @@ -7,34 +10,55 @@ from utils.logger import get_logger logger = get_logger("build_dataset") + +VALID_IMG_EXT = {".jpg", ".jpeg", ".png", ".bmp", ".tiff", ".webp"} + + +def sanitize_filenames(img_dir: Path): + """Renomme les fichiers avec accents en ASCII (cv2.imread plante sur macOS sinon).""" + for f in img_dir.iterdir(): + if not f.is_file(): + continue + clean = "".join( + c for c in unicodedata.normalize("NFKD", f.name) + if not unicodedata.combining(c) + ) + if clean != f.name: + f.rename(f.parent / clean) + logger.info("Renommé : %s → %s", f.name, clean) + + +def clean_non_images(img_dir: Path): + """Écarte les fichiers non-image et les images corrompues dans un dossier tmp.""" + tmp = img_dir.parent / "_non_images_tmp" + tmp.mkdir(exist_ok=True) + removed = 0 + for f in img_dir.iterdir(): + if not f.is_file(): + continue + ok = f.suffix.lower() in VALID_IMG_EXT and cv2.imread(str(f)) is not None + if not ok: + f.rename(tmp / f.name) + logger.warning("Écarté : %s", f.name) + removed += 1 + if removed: + logger.info("%d fichier(s) non-image écartés dans _non_images_tmp/", removed) + + def load_config(path: str = "configs/build_dataset.yaml") -> dict: with open(path) as f: cfg = yaml.safe_load(f) - logger.info(f"Config chargée : {path}") + logger.info("Config chargée : %s", path) return cfg -# CHARGEMENT DU YAML -cfg = load_config() -DATA_PATH = cfg["data_path"] -BASE_DIR = Path(cfg["base_dir"]) -IMAGES_DIR = Path(cfg["images_dir"]) -TIMEOUT = cfg["timeout"] -MAX_WORKERS = cfg["max_workers"] -VALID_EXTS = set(cfg["valid_extensions"]) -USE_COLS = list(cfg["columns"].values()) - # PIPELINE TELECHARGEMENT DATASET -def load(path: str) -> pd.DataFrame: + +def load(path: str, use_cols: list) -> pd.DataFrame: """Charge le CSV, garde uniquement les observations validées et identifiables.""" logger.info("Chargement : %s", path) - df = pd.read_csv(path, usecols=USE_COLS) - - df = df[ - (df["validee - observation"] == "TRUE") - # (df["espece identifiable ? - observation"] == "Identifiable") - ].reset_index(drop=True) - + df = pd.read_csv(path, usecols=use_cols) + df = df[df["validee - observation"] == "TRUE"].reset_index(drop=True) logger.info("%d observations valides et identifiables", len(df)) return df @@ -47,87 +71,91 @@ def explode_urls(df: pd.DataFrame) -> pd.DataFrame: .rename(columns={"images - observation": "image_url"}) .assign(image_url=lambda d: d["image_url"].str.strip()) ) - # Supprimer les URLs vides ou nulles générées par les trailing "|" ou cellules vides df = df[df["image_url"].notna() & (df["image_url"] != "")].reset_index(drop=True) logger.info("%d URLs d'images après explosion", len(df)) return df -def make_filename(row: pd.Series, idx: int) -> str: +def make_filename(row: pd.Series, idx: int, valid_exts: set) -> str: id_n1 = str(row["ID - N1"]) nom = str(row["Nom commun - observation"]) if pd.notna(row["Nom commun - observation"]) else "inconnu" nom = nom.strip().replace(" ", "_").replace("/", "-") - # Garde-fou au cas où une URL NaN passerait quand même url = row["image_url"] if isinstance(row["image_url"], str) else "" ext = url.split(".")[-1].split("?")[0].lower() if url else "jpg" - ext = ext if ext in VALID_EXTS else "jpg" + ext = ext if ext in valid_exts else "jpg" return f"{id_n1}_{nom}_{idx}.{ext}" -def _download_one(row: pd.Series, idx: int) -> dict | None: - """Télécharge une image. Retourne un dict si succès, None si échec.""" - filename = make_filename(row, idx) - dest = IMAGES_DIR / filename - - if dest.exists(): - logger.debug("Skip (déjà présente) : %s", filename) - return {**row, "filename": filename, "filepath": str(dest)} - - try: - r = requests.get(row["image_url"], timeout=TIMEOUT) - r.raise_for_status() - dest.write_bytes(r.content) - logger.debug("OK %s", filename) - return {**row, "filename": filename, "filepath": str(dest)} - except Exception as e: - logger.warning("ÉCHEC %s → %s", row["image_url"], e) - return None - - -def download(df: pd.DataFrame) -> pd.DataFrame: - """ - Télécharge chaque image en parallèle (ThreadPoolExecutor, I/O-bound). - Retourne uniquement les lignes dont le téléchargement a réussi. - """ - IMAGES_DIR.mkdir(parents=True, exist_ok=True) +def download(df: pd.DataFrame, images_dir: Path, timeout: int, max_workers: int, valid_exts: set) -> pd.DataFrame: + """Télécharge chaque image en parallèle. Retourne les lignes réussies.""" + images_dir.mkdir(parents=True, exist_ok=True) + + def _download_one(row, idx): + filename = make_filename(row, idx, valid_exts) + dest = images_dir / filename + if dest.exists(): + logger.debug("Skip (déjà présente) : %s", filename) + return {**row, "filename": filename, "filepath": str(dest)} + try: + r = requests.get(row["image_url"], timeout=timeout) + r.raise_for_status() + dest.write_bytes(r.content) + logger.debug("OK %s", filename) + return {**row, "filename": filename, "filepath": str(dest)} + except Exception as e: + logger.warning("ÉCHEC %s → %s", row["image_url"], e) + return None futures = {} rows_ok = [] - - with ThreadPoolExecutor(max_workers=MAX_WORKERS) as executor: + with ThreadPoolExecutor(max_workers=max_workers) as executor: for idx, row in df.iterrows(): - future = executor.submit(_download_one, row, idx) - futures[future] = idx - + futures[executor.submit(_download_one, row, idx)] = idx for future in tqdm(as_completed(futures), total=len(futures), desc="Téléchargement"): result = future.result() if result is not None: rows_ok.append(result) downloaded = pd.DataFrame(rows_ok).reset_index(drop=True) - logger.info( - "Téléchargement terminé — %d/%d images récupérées", - len(downloaded), len(df), - ) + logger.info("Téléchargement terminé — %d/%d images récupérées", len(downloaded), len(df)) return downloaded # MAIN -def build_dataset(): + +def build_dataset(cfg: dict, limit: int = None): logger.info("════════ START ════════") - df = load(DATA_PATH) + images_dir = Path(cfg["images_dir"]) + use_cols = list(cfg["columns"].values()) + + df = load(cfg["data_path"], use_cols) df = explode_urls(df) - df_ok = download(df) + + if limit: + df = df.head(limit) + logger.info("Mode test : limité à %d images", limit) + + df_ok = download(df, images_dir, cfg["timeout"], cfg["max_workers"], set(cfg["valid_extensions"])) logger.info( "Terminé — %d images | %d espèces | dossier : %s", len(df_ok), df_ok["Nom commun - observation"].nunique(), - IMAGES_DIR.resolve(), + images_dir.resolve(), ) + + logger.info("Nettoyage du dossier images…") + sanitize_filenames(images_dir) + clean_non_images(images_dir) + logger.info("════════ END ════════") if __name__ == "__main__": - build_dataset() + parser = argparse.ArgumentParser() + parser.add_argument("--config", default="configs/build_dataset.yaml") + parser.add_argument("--limit", type=int, default=None, help="Limite le nombre d'images téléchargées (test)") + args = parser.parse_args() + + build_dataset(load_config(args.config), limit=args.limit) diff --git a/ml/yolov8_DINO/check_dataset.py b/ml/yolov8_DINO/check_dataset.py new file mode 100644 index 0000000..3c51852 --- /dev/null +++ b/ml/yolov8_DINO/check_dataset.py @@ -0,0 +1,87 @@ +import argparse +from collections import Counter +from pathlib import Path + +import cv2 +import yaml + +VALID_IMG_EXT = {".jpg", ".jpeg", ".png", ".bmp", ".tiff", ".webp"} +MIN_SIZE = 100 # pixels — en dessous c'est inutilisable + + +def inspect(img_dir: str): + path = Path(img_dir) + images = [f for f in path.iterdir() if f.is_file() and f.suffix.lower() in VALID_IMG_EXT] + + print(f"\n── Inspection : {path} ({len(images)} images) ──\n") + + corrupted = [] + too_small = [] + widths, heights = [], [] + species = Counter() + + for f in images: + img = cv2.imread(str(f)) + if img is None: + corrupted.append(f.name) + continue + + h, w = img.shape[:2] + widths.append(w) + heights.append(h) + + if w < MIN_SIZE or h < MIN_SIZE: + too_small.append(f.name) + + # Le format de nom est "{id}_{espece}_{idx}.ext" (cf. build_dataset.py) + parts = f.stem.split("_") + if len(parts) >= 2: + species[parts[1]] += 1 + + ok = len(images) - len(corrupted) + print(f"OK : {ok}") + print(f"Corrompues : {len(corrupted)}") + print(f"Trop petites : {len(too_small)} (< {MIN_SIZE}px)") + + if widths: + print(f"\nRésolution min : {min(widths)} x {min(heights)}") + print(f"Résolution max : {max(widths)} x {max(heights)}") + med_w = sorted(widths)[len(widths) // 2] + med_h = sorted(heights)[len(heights) // 2] + print(f"Résolution médiane : {med_w} x {med_h}") + + if species: + print(f"\nEspèces ({len(species)}) :") + for name, count in species.most_common(): + print(f"{name:<30} {count} image(s)") + + if corrupted: + print(f"\nFichiers corrompus :") + for name in corrupted: + print(f" - {name}") + + if too_small: + print(f"\nFichiers trop petits :") + for name in too_small: + print(f" - {name}") + + print() + + if corrupted or too_small: + print(f"{len(corrupted)} corrompue(s), {len(too_small)} trop petite(s)") + print("Lance build_dataset.py pour nettoyer, ou supprime-les manuellement.\n") + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--config", default="configs/autodistill_boostrap.yaml") + args = parser.parse_args() + + with open(args.config) as f: + cfg = yaml.safe_load(f) + + inspect(cfg["img_path"]) + + +if __name__ == "__main__": + main() diff --git a/ml/yolov8_DINO/configs/autodistill_boostrap.yaml b/ml/yolov8_DINO/configs/autodistill_boostrap.yaml index a9f174a..6ca6ec2 100644 --- a/ml/yolov8_DINO/configs/autodistill_boostrap.yaml +++ b/ml/yolov8_DINO/configs/autodistill_boostrap.yaml @@ -9,7 +9,7 @@ labeled_output: dataset_biolit/labeled-images data_yaml: dataset_biolit/labeled-images/data.yaml # Modèle -model: yolov8l.pt +model: yolov8s.pt # en test utiliser n au lieu de s # Hyperparams epochs: 100 @@ -21,6 +21,6 @@ device: 0 label_smoothing: 0.1 dropout: 0.1 -# Sorties +# Sorties project: runs/ name: biolit_v2_bootstrap \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index 7e40e82..0b97a4b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -44,6 +44,8 @@ dependencies = [ "autodistill-yolov8>=0.1.4", "supervision>=0.27.0.post2", "scikit-learn>=1.8.0", + "boto3>=1.42.80", + "python-dotenv>=1.2.2", ] [tool.pytest.ini_options] diff --git a/uv.lock b/uv.lock index 218e0b6..c67a311 100644 --- a/uv.lock +++ 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"sha256:18e25d66fed509e3868dc1572b3f427ff947dd2c56f844a5bf09481ad3f3b2fe", size = 86830, upload-time = "2025-12-01T02:30:57.729Z" }, +] + [[package]] name = "safetensors" version = "0.7.0" From 9777b545c78c97ec16a5887d5bcbd9c533e99561 Mon Sep 17 00:00:00 2001 From: mandresyandri Date: Tue, 31 Mar 2026 22:56:14 +0200 Subject: [PATCH 12/13] =?UTF-8?q?feat:=20code=20autodistill=20pour=20le=20?= =?UTF-8?q?mod=C3=A8le=20bootstrap=20clean?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- ml/yolov8_DINO/autodistill_label.py | 9 --------- ml/yolov8_DINO/autodistill_train.py | 9 --------- 2 files changed, 18 deletions(-) diff --git a/ml/yolov8_DINO/autodistill_label.py b/ml/yolov8_DINO/autodistill_label.py index e3074ed..403565c 100644 --- a/ml/yolov8_DINO/autodistill_label.py +++ b/ml/yolov8_DINO/autodistill_label.py @@ -1,12 +1,3 @@ -""" -Pseudo-labeling GroundingDINO. -Lance après build_dataset.py + inspect_data.py. - -Usage: - python autodistill_label.py - python autodistill_label.py --config configs/autodistill_boostrap.yaml -""" - import argparse import sys from pathlib import Path diff --git a/ml/yolov8_DINO/autodistill_train.py b/ml/yolov8_DINO/autodistill_train.py index bb5277b..10b1559 100644 --- a/ml/yolov8_DINO/autodistill_train.py +++ b/ml/yolov8_DINO/autodistill_train.py @@ -1,12 +1,3 @@ -""" -Fine-tuning YOLOv8 sur les pseudo-labels GroundingDINO. -Lance après autodistill_label.py. - -Usage: - python autodistill_train.py - python autodistill_train.py --config configs/autodistill_boostrap.yaml -""" - import argparse import sys from pathlib import Path From 18e655e24118a6f9770fbddf57dfe5b6d5890aa2 Mon Sep 17 00:00:00 2001 From: mandresyandri Date: Tue, 31 Mar 2026 23:02:51 +0200 Subject: [PATCH 13/13] fix: ml/yolov8 precommit test --- ml/yolov8_DINO/autodistill_label.py | 6 +++--- ml/yolov8_DINO/autodistill_train.py | 4 ++-- ml/yolov8_DINO/check_dataset.py | 4 ++-- ml/yolov8_DINO/configs/autodistill_boostrap.yaml | 8 ++++---- ml/yolov8_DINO/configs/build_dataset.yaml | 4 ++-- 5 files changed, 13 insertions(+), 13 deletions(-) diff --git a/ml/yolov8_DINO/autodistill_label.py b/ml/yolov8_DINO/autodistill_label.py index 403565c..f5ca50b 100644 --- a/ml/yolov8_DINO/autodistill_label.py +++ b/ml/yolov8_DINO/autodistill_label.py @@ -14,11 +14,11 @@ if not hasattr(np, "trapz"): np.trapz = np.trapezoid -from autodistill.detection import CaptionOntology -from autodistill_grounding_dino import GroundingDINO +from autodistill.detection import CaptionOntology # noqa: E402 +from autodistill_grounding_dino import GroundingDINO # noqa: E402 sys.path.insert(0, str(Path(__file__).parent)) -from utils.logger import get_logger +from utils.logger import get_logger # noqa: E402 log = get_logger("autodistill_label") diff --git a/ml/yolov8_DINO/autodistill_train.py b/ml/yolov8_DINO/autodistill_train.py index 10b1559..03629b7 100644 --- a/ml/yolov8_DINO/autodistill_train.py +++ b/ml/yolov8_DINO/autodistill_train.py @@ -14,10 +14,10 @@ if not hasattr(np, "trapz"): np.trapz = np.trapezoid -from autodistill_yolov8 import YOLOv8 +from autodistill_yolov8 import YOLOv8 # noqa: E402 sys.path.insert(0, str(Path(__file__).parent)) -from utils.logger import get_logger +from utils.logger import get_logger # noqa: E402 log = get_logger("autodistill_train") diff --git a/ml/yolov8_DINO/check_dataset.py b/ml/yolov8_DINO/check_dataset.py index 3c51852..dada2e5 100644 --- a/ml/yolov8_DINO/check_dataset.py +++ b/ml/yolov8_DINO/check_dataset.py @@ -56,12 +56,12 @@ def inspect(img_dir: str): print(f"{name:<30} {count} image(s)") if corrupted: - print(f"\nFichiers corrompus :") + print("\nFichiers corrompus :") for name in corrupted: print(f" - {name}") if too_small: - print(f"\nFichiers trop petits :") + print("\nFichiers trop petits :") for name in too_small: print(f" - {name}") diff --git a/ml/yolov8_DINO/configs/autodistill_boostrap.yaml b/ml/yolov8_DINO/configs/autodistill_boostrap.yaml index 6ca6ec2..8d65686 100644 --- a/ml/yolov8_DINO/configs/autodistill_boostrap.yaml +++ b/ml/yolov8_DINO/configs/autodistill_boostrap.yaml @@ -1,14 +1,14 @@ -# Ontologie (TODO: affiner avec Giulia) +# Ontologie (TODO: affiner avec Giulia) ontology: "plant": "plant" "animal": "animal" -# Chemins +# Chemins img_path: dataset_biolit/images labeled_output: dataset_biolit/labeled-images data_yaml: dataset_biolit/labeled-images/data.yaml -# Modèle +# Modèle model: yolov8s.pt # en test utiliser n au lieu de s # Hyperparams @@ -17,7 +17,7 @@ imgsz: 640 batch: 16 device: 0 -# Robustesse au bruit +# Robustesse au bruit label_smoothing: 0.1 dropout: 0.1 diff --git a/ml/yolov8_DINO/configs/build_dataset.yaml b/ml/yolov8_DINO/configs/build_dataset.yaml index 03e5008..5b0f569 100644 --- a/ml/yolov8_DINO/configs/build_dataset.yaml +++ b/ml/yolov8_DINO/configs/build_dataset.yaml @@ -1,9 +1,9 @@ -# Chemins +# Chemins data_path: dataset_biolit/export_biolit.csv base_dir: dataset_biolit images_dir: dataset_biolit/images -# Téléchargement +# Téléchargement timeout: 10 max_workers: 16 valid_extensions: ["jpg", "jpeg", "png", "webp"]