metrics

test_tube_scanner/modules/capture_interface.py

@@ -21,6 +21,7 @@ import logging
 from datetime import datetime, timezone
 from pathlib import Path
 from typing import Optional, Callable, TYPE_CHECKING
+from asgiref.sync import async_to_sync
 
 from django.conf import settings
 from modules.planarian_tracker import PlanarianTracker
@@ -85,6 +86,7 @@ class VideoCaptureInterface(abc.ABC):
         self._tracker = None
         self._metrics = None
         self._params = None
+        self._clientDB = self.parent.metricDB
 
         # Tracker générique, pour simulation
         self.on_test_well_change(**self.DEFAULT_TRACKER_CONFIG)
@@ -106,7 +108,8 @@ class VideoCaptureInterface(abc.ABC):
             logger.error(f"Error creating tracker with conf {cfg}: {e}")
             self._tracker = None
     
-    def on_well_change(self, cfg, draw_contours=False):
+
+    def on_well_change(self, cfg, uuid="", draw_contours=False):
         """
         Appelé par la CNC lors du changement de puits.
         Réinitialise le fond appris et l'état inter-frame du tracker.
@@ -115,10 +118,20 @@ class VideoCaptureInterface(abc.ABC):
         if not self.use_tracking or not cfg:
             return
         
+        # 1. Sauvegarder les résumés du puits qu'on quitte
+        if self._metrics and self._params:
+            for pid, m in enumerate(self._metrics):               
+                async_to_sync(self._clientDB.store_summary)(
+                    summary    = m.summary(),
+                    experiment = self._params.experiment,
+                    well       = self._params.well,
+                    planarian  = pid,
+                    uuid       = uuid,
+                )      
+        
+        # 2. Reconstruire pour le nouveau puits
         params = cfg.to_params_dict()        
         self._params = ExperimentParams(params)        
-        #self._metrics = self._params.build_metrics()
-        
         self._metrics = [self._params.build_metrics() for _ in range(self._params.planarian_count)]
         
         self._tracker = PlanarianTracker(

test_tube_scanner/modules/planarian_metrics.py

@@ -19,22 +19,10 @@ Métriques résumé (summary) :
     Social      : social_pct_time_avoiding, social_pct_time_aggregating,
                   social_mean_nn_mm, social_contact_events
 
-  Architecture :
-    PlanarianTracker.process()   → dict brut (cx, cy, speed_px_s, ...)
-    EthoVisionMetrics.update()   → enrichit avec métriques EthoVision
-    ReductStoreClient.store()    → stocke dans ReductStore avec labels
-    ReductStoreClient.export_csv() → exporte vers CSV
-
-  Schéma des labels ReductStore :
-    experiment  : identifiant de l'expérience (ex: "exp_2026_04_25")
-    well        : identifiant du puits (ex: "A1", "B3")
-    planarian   : index du planaire dans le puits (ex: "0", "1")
-    bucket      : nom du bucket (ex: "planarian_metrics")                  
-                  
 Created on 25 avr. 2026
 @author: denis
 """
-#import asyncio
+
 import csv
 import io
 import json
@@ -42,8 +30,8 @@ import logging
 import math
 import os
 import time
-from datetime import datetime, timezone
 
+from datetime import datetime, timezone
 from typing import Optional
 
 logger = logging.getLogger(__name__)
@@ -618,7 +606,7 @@ class ExperimentParams:
     def from_csv_file(cls, filepath: str) -> list:
         """Charge toutes les expériences d'un fichier CSV."""
         results = []
-        with open(filepath, newline="", encoding="utf-8-sig") as f:
+        with open(filepath, newline="", encoding="utf-8") as f:
             for row in csv.DictReader(f):
                 try:
                     results.append(cls.from_csv_row(row))
@@ -667,7 +655,6 @@ class ReductStoreClient:
         self.bucket_name  = bucket
         self.quota_type = quota_type
         self.quota_size = quota_size 
-        self.entry_name   = "metrics"
         self._client      = None
         self._bucket      = None
         
@@ -682,12 +669,24 @@ class ReductStoreClient:
         )
         return await self._client.create_bucket(self.bucket_name, settings, exist_ok=True)
     
-
     async def connect(self):
         """Initialise la connexion et crée le bucket si nécessaire."""
         self._bucket = await self._create_bucket()
         logger.info(f"ReductStore connecté : {self.url} / {self.bucket_name}")
         
+    '''
+    async def connect(self):
+        """Initialise la connexion et crée le bucket si nécessaire."""
+        from reduct import Client, BucketSettings, QuotaType
+        self._client = Client(self.url, api_token=self.token)
+        self._bucket = await self._client.create_bucket(
+            self.bucket_name,
+            BucketSettings(quota_type=QuotaType.NONE),
+            exist_ok=True,
+        )
+        logger.info(f"ReductStore connecté : {self.url} / {self.bucket_name}")
+    '''
+        
     async def store_metric(
         self,
         record:      dict,
@@ -704,48 +703,99 @@ class ReductStoreClient:
         Le timestamp est rendu unique par planaire en ajoutant l'index
         du planaire comme offset sub-microseconde — évite le 409 Conflict
         quand plusieurs planaires du même puits écrivent dans la même frame.
+
+        Args:
+            record      : dict de métriques (issu de EthoVisionMetrics.update())
+            experiment  : identifiant de l'expérience
+            well        : identifiant du puits
+            planarian   : index du planaire (défaut 0)
+            record_type : "frame" ou "summary"
+            uuid        : identifiant unique de session (permet de filtrer
+                          plusieurs sessions d'un même puits/expérience)
+            ts_us       : timestamp en microsecondes (défaut : maintenant)
         """
         if self._bucket is None:
             await self.connect()
         # ts_us de base + offset planaire (0, 1, 2…) pour unicité garantie
         base_ts   = ts_us or int(time.time() * 1_000_000)
         unique_ts = base_ts + planarian
-        
-        await self._bucket.write(
-            entry_name   = "metrics",
-            data         = json.dumps(record).encode("utf-8"),
-            timestamp    = unique_ts,
         labels    = {
             "experiment":  experiment,
             "well":        well,
             "planarian":   str(planarian),
             "record_type": record_type,
-                "uuid": uuid,
+        }
-            },
+        if uuid:
+            labels["uuid"] = uuid
+        await self._bucket.write(
+            entry_name   = "metrics",
+            data         = json.dumps(record).encode("utf-8"),
+            timestamp    = unique_ts,
+            labels       = labels,
             content_type = "application/json",
         )
 
-    async def store_summary(self, summary: dict, experiment: str,
+    async def store_summary(
-                            well: str, planarian: int = 0):
+        self,
-        """Stocke le résumé de fin de session."""
+        summary:    dict,
-        await self.store_metric(summary, experiment, well, planarian, "summary")
+        experiment: str,
+        well:       str,
+        planarian:  int = 0,
+        uuid:       str = "",
+    ):
+        """
+        Stocke le résumé de fin de session dans ReductStore.
+
+        Args:
+            summary    : dict issu de EthoVisionMetrics.summary()
+            experiment : identifiant de l'expérience
+            well       : identifiant du puits
+            planarian  : index du planaire
+            uuid       : identifiant unique de session (même valeur que
+                         celle utilisée dans store_metric pour cette session)
+        """
+        await self.store_metric(
+            record      = summary,
+            experiment  = experiment,
+            well        = well,
+            planarian   = planarian,
+            record_type = "summary",
+            uuid        = uuid,
+        )
 
     async def get_tracking_data(
         self,
         experiment:  str,
         well:        str,
         planarian:   int = 0,
-        record_type: str = "metrics",
+        record_type: str = "frame",
+        uuid:        str = "",
         start:       Optional[datetime] = None,
         stop:        Optional[datetime] = None,
     ) -> list:
-        """Récupère les enregistrements filtrés par labels."""
+        """
+        Récupère les enregistrements filtrés par labels.
+
+        Args:
+            experiment  : identifiant de l'expérience
+            well        : identifiant du puits
+            planarian   : index du planaire
+            record_type : "frame" | "summary"
+            uuid        : filtre sur une session spécifique (optionnel —
+                          si vide, retourne toutes les sessions)
+            start, stop : plage temporelle (datetime UTC, optionnel)
+        """
         if self._bucket is None:
             await self.connect()
-        kwargs = {"include": {
+        labels = {
-            "experiment": experiment, "well": well,
+            "experiment":  experiment,
-            "planarian": str(planarian), "record_type": record_type,
+            "well":        well,
-        }}
+            "planarian":   str(planarian),
+            "record_type": record_type,
+        }
+        if uuid:
+            labels["uuid"] = uuid
+        kwargs = {"include": labels}
         if start:
             kwargs["start"] = int(start.timestamp() * 1_000_000)
         if stop:
@@ -812,7 +862,8 @@ class ReductStoreClient:
         experiment:  str,
         well:        str,
         planarian:   int = 0,
-        record_type: str = "metrics",
+        record_type: str = "frame",
+        uuid:        str = "",
         output_dir:  str = ".",
         start:       Optional[datetime] = None,
         stop:        Optional[datetime] = None,
@@ -828,13 +879,15 @@ class ReductStoreClient:
             planarian   : index du planaire
             record_type : "frame" | "summary"
             output_dir  : répertoire de sortie (défaut : répertoire courant)
+            uuid        : filtre sur une session spécifique (optionnel —
+                          si vide, retourne toutes les sessions)
             start, stop : plage temporelle (datetime UTC, optionnel)
 
         Returns:
             tuple (filepath, nb_lignes)
         """
         records = await self.get_tracking_data(
-            experiment, well, planarian, record_type, start, stop)
+            experiment, well, planarian, record_type, uuid, start, stop)
         if not records:
             logger.warning(f"Aucune donnée pour {experiment}/{well}/{planarian}")
             return "", 0
@@ -857,7 +910,8 @@ class ReductStoreClient:
         experiment:  str,
         well:        str,
         planarian:   int = 0,
-        record_type: str = "metrics",
+        record_type: str = "frame",
+        uuid:        str = "",
         start:       Optional[datetime] = None,
         stop:        Optional[datetime] = None,
     ) -> tuple:
@@ -869,7 +923,7 @@ class ReductStoreClient:
             tuple (contenu_csv_str, nb_lignes)
         """
         records = await self.get_tracking_data(
-            experiment, well, planarian, record_type, start, stop)
+            experiment, well, planarian, record_type, uuid, start, stop)
         if not records:
             return "", 0
         records    = self._convert_timestamps(records)
@@ -889,4 +943,3 @@ class ReductStoreClient:
         self._client = None
         self._bucket = None
         logger.info("ReductStore déconnecté")
-        

test_tube_scanner/modules/planarian_metrics2.py

@@ -0,0 +1,891 @@
+"""
+modules/planarian_metrics.py
+
+Intégration des métriques EthoVision XT + comportementales dans PlanarianScanner.
+
+Métriques par frame :
+    Mobilité    : velocity, distance, moving, mobility_state
+    Thigmo      : dist_to_wall_mm, near_wall
+    Photo       : dist_to_light_mm, heading_to_light_deg, fleeing_light
+    Chemo       : dist_to_food_mm, heading_to_food_deg, approaching_food, in_food_zone
+    Social      : nearest_neighbour_mm, in_avoid_zone, in_aggreg_zone, chem_repulsion_level
+
+Métriques résumé (summary) :
+    Mobilité    : movedCenter_pointTotal_mm, velocity_mean_mm_s, durations par état
+    Thigmo      : thigmotaxis_pct_time_near_wall
+    Photo       : photo_pct_time_fleeing, photo_mean_dist_mm, photo_latency_s
+    Chemo       : chemo_pct_time_approaching, chemo_pct_time_in_zone,
+                  chemo_latency_s, chemo_mean_dist_mm
+    Social      : social_pct_time_avoiding, social_pct_time_aggregating,
+                  social_mean_nn_mm, social_contact_events
+                  
+  Architecture :
+    PlanarianTracker.process()   → dict brut (cx, cy, speed_px_s, ...)
+    EthoVisionMetrics.update()   → enrichit avec métriques EthoVision
+    ReductStoreClient.store()    → stocke dans ReductStore avec labels
+    ReductStoreClient.export_csv() → exporte vers CSV
+
+  Schéma des labels ReductStore :
+    experiment  : identifiant de l'expérience (ex: "exp_2026_04_25")
+    well        : identifiant du puits (ex: "A1", "B3")
+    planarian   : index du planaire dans le puits (ex: "0", "1")
+    bucket      : nom du bucket (ex: "planarian_metrics")                  
+                  
+Created on 25 avr. 2026
+@author: denis
+"""
+#import asyncio
+import csv
+import io
+import json
+import logging
+import math
+import os
+import time
+from datetime import datetime, timezone
+
+from typing import Optional
+
+logger = logging.getLogger(__name__)
+
+
+# ---------------------------------------------------------------------------
+# Constantes EthoVision (seuils de mobilité par défaut)
+# ---------------------------------------------------------------------------
+
+THRESH_IMMOBILE_DEFAULT = 0.2   # en-dessous : Immobile (mm/s)
+THRESH_MOBILE_DEFAULT   = 1.5   # entre les deux : Mobile, au-delà : Highly mobile
+
+STATE_IMMOBILE    = "Immobile"
+STATE_MOBILE      = "Mobile"
+STATE_HIGH_MOBILE = "Highly mobile"
+
+# Paramètres comportementaux (défauts)
+BEHAVIOUR_DEFAULTS = {
+    # Thigmotactisme
+    "thigmotaxis_wall_dist_mm":  1.0,
+    # Phototactisme
+    "photo_mode":                "none",
+    "photo_strength":            0.0,
+    "photo_x":                   0.5,
+    "photo_y":                   0.5,
+    "photo_flee_angle_deg":      90.0,   # angle max tête/source pour considérer "fuite"
+    # Chimiotactisme
+    "chemo_strength":            0.0,
+    "chemo_x":                   0.5,
+    "chemo_y":                   0.5,
+    "chemo_radius_mm":           2.0,
+    "chemo_approach_angle_deg":  90.0,   # angle max tête/nourriture pour considérer "approche"
+    # Interactions inter-individus
+    "avoid_radius_mm":           3.0,
+    "aggreg_radius_mm":          6.0,
+}
+
+
+# ---------------------------------------------------------------------------
+# Helpers géométriques
+# ---------------------------------------------------------------------------
+
+def _angle_between_deg(vx1: float, vy1: float, vx2: float, vy2: float) -> float:
+    """
+    Calcule l'angle en degrés entre deux vecteurs 2D.
+    Retourne 0.0 si l'un des vecteurs est nul.
+
+    Args:
+        vx1, vy1 : premier vecteur
+        vx2, vy2 : second vecteur
+
+    Returns:
+        angle en degrés [0, 180]
+    """
+    n1 = math.sqrt(vx1**2 + vy1**2)
+    n2 = math.sqrt(vx2**2 + vy2**2)
+    if n1 < 1e-9 or n2 < 1e-9:
+        return 0.0
+    cos_a = max(-1.0, min(1.0, (vx1 * vx2 + vy1 * vy2) / (n1 * n2)))
+    return math.degrees(math.acos(cos_a))
+
+
+def _heading_to_target_deg(
+    cx: float, cy: float,
+    tx: float, ty: float,
+    dx: float, dy: float,
+) -> float:
+    """
+    Calcule l'angle entre la direction de déplacement et le vecteur vers une cible.
+
+    Args:
+        cx, cy : position courante
+        tx, ty : position cible
+        dx, dy : vecteur de déplacement (cx - prev_cx, cy - prev_cy)
+
+    Returns:
+        angle en degrés [0, 180] — 0 = va droit vers la cible, 180 = fuit
+    """
+    to_target_x = tx - cx
+    to_target_y = ty - cy
+    return _angle_between_deg(dx, dy, to_target_x, to_target_y)
+
+
+# ---------------------------------------------------------------------------
+# Classe EthoVisionMetrics
+# ---------------------------------------------------------------------------
+
+class EthoVisionMetrics:
+    """
+    Calcule et accumule toutes les métriques comportementales compatibles
+    EthoVision XT à partir des données brutes de PlanarianTracker.
+
+    Métriques calculées :
+        - Mobilité EthoVision (distance, vitesse, états Immobile/Mobile/Très mobile)
+        - Thigmotactisme (distance paroi, % temps près du bord)
+        - Phototactisme (distance source, orientation, % fuite, latence)
+        - Chimiotactisme (distance nourriture, % approche, % zone, latence)
+        - Interactions inter-individus (voisin le plus proche, évitement,
+          agrégation, répulsion chimique, événements de contact)
+
+    Une instance par planaire suivi.
+
+    Usage :
+        metrics = EthoVisionMetrics(px_per_mm=26.25, fps=10, behaviour={...})
+        for frame in capture:
+            raw = tracker.process(frame, ts)
+            record = metrics.update(
+                raw,
+                well_radius_mm   = 8.0,
+                arena_center_px  = (250, 250),
+                photo_source_px  = (100, 100),
+                others_pos_mm    = [(x1,y1), (x2,y2)],
+                chem_level       = 0.3,
+            )
+            await client.store_metric(record, ...)
+        summary = metrics.summary()
+    """
+
+    def __init__(
+        self,
+        px_per_mm:       float,
+        fps:             float,
+        thresh_immobile: float = THRESH_IMMOBILE_DEFAULT,
+        thresh_mobile:   float = THRESH_MOBILE_DEFAULT,
+        behaviour:       Optional[dict] = None,
+    ):
+        """
+        Args:
+            px_per_mm       : facteur de conversion pixels → mm
+            fps             : fréquence de capture (images/s)
+            thresh_immobile : seuil vitesse Immobile/Mobile (mm/s)
+            thresh_mobile   : seuil vitesse Mobile/Très mobile (mm/s)
+            behaviour       : dict de paramètres comportementaux (cf. BEHAVIOUR_DEFAULTS)
+        """
+        self.px_per_mm       = px_per_mm
+        self.fps             = fps
+        self.dt              = 1.0 / fps
+        self.thresh_immobile = thresh_immobile
+        self.thresh_mobile   = thresh_mobile
+        self.beh             = {**BEHAVIOUR_DEFAULTS, **(behaviour or {})}
+
+        # --- Accumulateurs mobilité ---
+        self.total_distance_mm  = 0.0
+        self.duration_moving_s  = 0.0
+        self.duration_stopped_s = 0.0
+        self.frame_count        = 0
+
+        self._mob_counts = {STATE_IMMOBILE: 0, STATE_MOBILE: 0, STATE_HIGH_MOBILE: 0}
+        self._mob_durations = {STATE_IMMOBILE: 0.0, STATE_MOBILE: 0.0, STATE_HIGH_MOBILE: 0.0}
+        self._current_state = None
+
+        # --- Accumulateurs thigmotactisme ---
+        self._near_wall_frames = 0
+
+        # --- Accumulateurs phototactisme ---
+        self._flee_frames      = 0          # frames en fuite
+        self._photo_dist_sum   = 0.0        # somme distances source
+        self._photo_dist_count = 0
+        self._photo_latency_s  = None       # temps avant 1ère fuite (s)
+
+        # --- Accumulateurs chimiotactisme ---
+        self._approach_frames  = 0          # frames en approche nourriture
+        self._in_zone_frames   = 0          # frames dans la zone nourriture
+        self._chemo_dist_sum   = 0.0
+        self._chemo_dist_count = 0
+        self._chemo_latency_s  = None       # temps avant 1ère entrée zone (s)
+
+        # --- Accumulateurs interactions inter-individus ---
+        self._avoid_frames     = 0          # frames en zone d'évitement
+        self._aggreg_frames    = 0          # frames en zone d'agrégation
+        self._nn_sum           = 0.0        # somme distances voisin le plus proche
+        self._nn_count         = 0
+        self._contact_events   = 0          # transitions False→True de in_avoid_zone
+        self._prev_in_avoid    = False
+
+        # --- Position précédente (vecteur de déplacement) ---
+        self._prev_cx_mm = None
+        self._prev_cy_mm = None
+        self._prev_ts    = None
+
+    # ------------------------------------------------------------------ #
+    # Helpers internes
+    # ------------------------------------------------------------------ #
+
+    def _px_to_mm(self, px: float) -> float:
+        """Convertit des pixels en millimètres."""
+        return px / self.px_per_mm
+
+    def _classify(self, v: float) -> str:
+        """Classifie la vitesse en état de mobilité EthoVision."""
+        if v <= self.thresh_immobile:
+            return STATE_IMMOBILE
+        elif v <= self.thresh_mobile:
+            return STATE_MOBILE
+        return STATE_HIGH_MOBILE
+
+    def _elapsed_s(self) -> float:
+        """Temps écoulé depuis le début de la session (s)."""
+        return self.frame_count * self.dt
+
+    # ------------------------------------------------------------------ #
+    # Méthode principale
+    # ------------------------------------------------------------------ #
+
+    def update(
+        self,
+        raw:              dict,
+        well_radius_mm:   float                    = 8.0,
+        arena_center_px:  tuple                    = (250, 250),
+        photo_source_px:  Optional[tuple]          = None,
+        others_pos_mm:    Optional[list]           = None,
+        chem_level:       float                    = 0.0,
+    ) -> dict:
+        """
+        Calcule toutes les métriques comportementales pour une frame.
+
+        Args:
+            raw             : dict brut de PlanarianTracker.process()
+                              clés : detected, cx, cy, speed_px_s, ts
+            well_radius_mm  : rayon du puits en mm
+            arena_center_px : centre de l'arène en pixels (cx, cy)
+            photo_source_px : position de la source lumineuse en pixels (ou None)
+            others_pos_mm   : liste de (x_mm, y_mm) des autres planaires
+            chem_level      : concentration chimique locale [0-1] (depuis ChemicalMap)
+
+        Returns:
+            dict complet prêt pour ReductStore
+        """
+        self.frame_count += 1
+        ts = raw.get("timestamp", time.time())
+
+        if not raw.get("detected", False):
+            self.duration_stopped_s += self.dt
+            state = self._current_state or STATE_IMMOBILE
+            self._mob_durations[state] += self.dt
+            return {"timestamp": ts, "detected": False}
+
+        # --- Position en mm (relative au centre de l'arène) ---
+        cx_px = raw["cx"] - arena_center_px[0]
+        cy_px = raw["cy"] - arena_center_px[1]
+        cx_mm = self._px_to_mm(cx_px)
+        cy_mm = self._px_to_mm(cy_px)
+
+        # --- Vitesse / distance ---
+        speed_px_s    = raw.get("speed_px_s", 0.0)
+        velocity_mm_s = self._px_to_mm(speed_px_s)
+        dist_mm       = velocity_mm_s * self.dt
+        self.total_distance_mm += dist_mm
+
+        # Vecteur de déplacement (pour calculs d'angle)
+        if self._prev_cx_mm is not None:
+            move_dx = cx_mm - self._prev_cx_mm
+            move_dy = cy_mm - self._prev_cy_mm
+        else:
+            move_dx, move_dy = 0.0, 0.0
+
+        # --- Mobilité ---
+        is_moving = velocity_mm_s > self.thresh_immobile
+        if is_moving:
+            self.duration_moving_s  += self.dt
+        else:
+            self.duration_stopped_s += self.dt
+
+        new_state = self._classify(velocity_mm_s)
+        if new_state != self._current_state:
+            self._mob_counts[new_state] += 1
+            self._current_state = new_state
+        self._mob_durations[new_state] += self.dt
+
+        # ================================================================
+        # THIGMOTACTISME
+        # ================================================================
+        well_radius_px = well_radius_mm * self.px_per_mm
+        dist_center_px = math.sqrt(cx_px**2 + cy_px**2)
+        dist_wall_mm   = self._px_to_mm(well_radius_px - dist_center_px)
+        near_wall_thr  = self.beh.get("thigmotaxis_wall_dist_mm", 1.0)
+        is_near_wall   = dist_wall_mm < near_wall_thr
+        if is_near_wall:
+            self._near_wall_frames += 1
+
+        # ================================================================
+        # PHOTOTACTISME
+        # ================================================================
+        photo_mode = self.beh.get("photo_mode", "none")
+        dist_light_mm      = 0.0
+        heading_light_deg  = 0.0
+        fleeing_light      = False
+
+        if photo_mode != "none" and photo_source_px is not None:
+            lx_px = photo_source_px[0] - arena_center_px[0]
+            ly_px = photo_source_px[1] - arena_center_px[1]
+            lx_mm = self._px_to_mm(lx_px)
+            ly_mm = self._px_to_mm(ly_px)
+
+            dl = math.sqrt((cx_mm - lx_mm)**2 + (cy_mm - ly_mm)**2)
+            dist_light_mm = dl
+
+            self._photo_dist_sum   += dl
+            self._photo_dist_count += 1
+
+            # Angle entre déplacement et direction vers la source
+            heading_light_deg = _heading_to_target_deg(
+                cx_mm, cy_mm, lx_mm, ly_mm, move_dx, move_dy
+            )
+            # Fuite = planaire s'éloigne de la source (angle > seuil)
+            flee_thr  = self.beh.get("photo_flee_angle_deg", 90.0)
+            fleeing_light = (heading_light_deg > flee_thr) and is_moving
+
+            if fleeing_light:
+                self._flee_frames += 1
+                if self._photo_latency_s is None:
+                    self._photo_latency_s = self._elapsed_s()
+
+        # ================================================================
+        # CHIMIOTACTISME
+        # ================================================================
+        chemo_x_frac  = self.beh.get("chemo_x", 0.5)
+        chemo_y_frac  = self.beh.get("chemo_y", 0.5)
+        chemo_r_mm    = self.beh.get("chemo_radius_mm", 2.0)
+        chemo_strength= self.beh.get("chemo_strength", 0.0)
+
+        dist_food_mm      = 0.0
+        heading_food_deg  = 0.0
+        approaching_food  = False
+        in_food_zone      = False
+
+        if chemo_strength > 0.0:
+            # Position nourriture en mm relative au centre
+            fx_mm = (chemo_x_frac - 0.5) * 2.0 * well_radius_mm
+            fy_mm = (chemo_y_frac - 0.5) * 2.0 * well_radius_mm
+
+            df = math.sqrt((cx_mm - fx_mm)**2 + (cy_mm - fy_mm)**2)
+            dist_food_mm = df
+
+            self._chemo_dist_sum   += df
+            self._chemo_dist_count += 1
+
+            in_food_zone = df <= chemo_r_mm
+            if in_food_zone:
+                self._in_zone_frames += 1
+                if self._chemo_latency_s is None:
+                    self._chemo_latency_s = self._elapsed_s()
+
+            heading_food_deg = _heading_to_target_deg(
+                cx_mm, cy_mm, fx_mm, fy_mm, move_dx, move_dy
+            )
+            approach_thr = self.beh.get("chemo_approach_angle_deg", 90.0)
+            approaching_food = (heading_food_deg < approach_thr) and is_moving
+
+            if approaching_food:
+                self._approach_frames += 1
+
+        # ================================================================
+        # INTERACTIONS INTER-INDIVIDUS
+        # ================================================================
+        avoid_r_mm  = self.beh.get("avoid_radius_mm", 3.0)
+        aggreg_r_mm = self.beh.get("aggreg_radius_mm", 6.0)
+
+        nearest_nn_mm    = float("inf")
+        in_avoid_zone    = False
+        in_aggreg_zone   = False
+
+        if others_pos_mm:
+            for ox_mm, oy_mm in others_pos_mm:
+                d = math.sqrt((cx_mm - ox_mm)**2 + (cy_mm - oy_mm)**2)
+                if d < nearest_nn_mm:
+                    nearest_nn_mm = d
+
+            if nearest_nn_mm < avoid_r_mm:
+                in_avoid_zone = True
+                self._avoid_frames += 1
+            elif nearest_nn_mm < aggreg_r_mm:
+                in_aggreg_zone = True
+                self._aggreg_frames += 1
+
+            self._nn_sum   += nearest_nn_mm
+            self._nn_count += 1
+
+            # Événement de contact : transition vers zone d'évitement
+            if in_avoid_zone and not self._prev_in_avoid:
+                self._contact_events += 1
+        else:
+            nearest_nn_mm = 0.0
+
+        self._prev_in_avoid = in_avoid_zone
+
+        # --- Mise à jour position précédente ---
+        self._prev_cx_mm = cx_mm
+        self._prev_cy_mm = cy_mm
+        self._prev_ts    = ts
+
+        # ================================================================
+        # RECORD COMPLET
+        # ================================================================
+        return {
+            # Identification
+            "timestamp":                       ts,
+            "detected":                        True,
+            # Position (mm, relative au centre)
+            "x_mm":                            round(cx_mm, 4),
+            "y_mm":                            round(cy_mm, 4),
+            # Position brute pixels
+            "cx_px":                           raw["cx"],
+            "cy_px":                           raw["cy"],
+            # Mobilité EthoVision
+            "velocity_mm_s":                   round(velocity_mm_s, 4),
+            "distance_mm":                     round(dist_mm, 4),
+            "total_distance_mm":               round(self.total_distance_mm, 4),
+            "moving":                          int(is_moving),
+            "duration_moving_s":               round(self.duration_moving_s, 3),
+            "duration_stopped_s":              round(self.duration_stopped_s, 3),
+            "mobility_state":                  new_state,
+            "mobility_immobile_freq":          self._mob_counts[STATE_IMMOBILE],
+            "mobility_immobile_duration_s":    round(self._mob_durations[STATE_IMMOBILE], 3),
+            "mobility_mobile_freq":            self._mob_counts[STATE_MOBILE],
+            "mobility_mobile_duration_s":      round(self._mob_durations[STATE_MOBILE], 3),
+            "mobility_high_mobile_freq":       self._mob_counts[STATE_HIGH_MOBILE],
+            "mobility_high_mobile_duration_s": round(self._mob_durations[STATE_HIGH_MOBILE], 3),
+            # Thigmotactisme
+            "dist_to_wall_mm":                 round(dist_wall_mm, 4),
+            "near_wall":                       int(is_near_wall),
+            # Phototactisme
+            "dist_to_light_mm":                round(dist_light_mm, 4),
+            "heading_to_light_deg":            round(heading_light_deg, 2),
+            "fleeing_light":                   int(fleeing_light),
+            # Chimiotactisme
+            "dist_to_food_mm":                 round(dist_food_mm, 4),
+            "heading_to_food_deg":             round(heading_food_deg, 2),
+            "approaching_food":                int(approaching_food),
+            "in_food_zone":                    int(in_food_zone),
+            # Interactions inter-individus
+            "nearest_neighbour_mm":            round(nearest_nn_mm, 4) if nearest_nn_mm != float("inf") else 0.0,
+            "in_avoid_zone":                   int(in_avoid_zone),
+            "in_aggreg_zone":                  int(in_aggreg_zone),
+            "chem_repulsion_level":            round(chem_level, 4),
+            # Passthrough tracker
+            "area_px":                         raw.get("area_px", 0),
+            "axial_pos":                       raw.get("axial_pos", 0.0),
+            "axial_speed":                     raw.get("axial_speed", 0.0),
+        }
+
+    # ------------------------------------------------------------------ #
+    # Résumé de session
+    # ------------------------------------------------------------------ #
+
+    def summary(self) -> dict:
+        """
+        Retourne le résumé global de la session.
+        Nomenclature EthoVision XT + métriques comportementales.
+        À appeler en fin d'expérience.
+
+        Returns:
+            dict avec toutes les métriques agrégées
+        """
+        total_s = self.frame_count * self.dt
+        det     = max(self._photo_dist_count, 1)   # frames avec détection
+
+        return {
+            # Identification session
+            "total_frames":                        self.frame_count,
+            "total_duration_s":                    round(total_s, 3),
+            # --- Mobilité EthoVision ---
+            "movedCenter_pointTotal_mm":           round(self.total_distance_mm, 4),
+            "velocity_mean_mm_s":                  round(
+                self.total_distance_mm / total_s if total_s > 0 else 0.0, 4),
+            "movement_moving_duration_s":          round(self.duration_moving_s, 3),
+            "movement_not_moving_duration_s":      round(self.duration_stopped_s, 3),
+            "mobility_immobile_frequency":         self._mob_counts[STATE_IMMOBILE],
+            "mobility_immobile_duration_s":        round(self._mob_durations[STATE_IMMOBILE], 3),
+            "mobility_mobile_frequency":           self._mob_counts[STATE_MOBILE],
+            "mobility_mobile_duration_s":          round(self._mob_durations[STATE_MOBILE], 3),
+            "mobility_highly_mobile_frequency":    self._mob_counts[STATE_HIGH_MOBILE],
+            "mobility_highly_mobile_duration_s":   round(self._mob_durations[STATE_HIGH_MOBILE], 3),
+            # --- Thigmotactisme ---
+            "thigmotaxis_pct_time_near_wall":      round(
+                100.0 * self._near_wall_frames / max(self.frame_count, 1), 2),
+            # --- Phototactisme ---
+            "photo_pct_time_fleeing":              round(
+                100.0 * self._flee_frames / max(self.frame_count, 1), 2),
+            "photo_mean_dist_mm":                  round(
+                self._photo_dist_sum / max(self._photo_dist_count, 1), 4),
+            "photo_latency_s":                     round(self._photo_latency_s, 3)
+                                                   if self._photo_latency_s is not None else None,
+            # --- Chimiotactisme ---
+            "chemo_pct_time_approaching":          round(
+                100.0 * self._approach_frames / max(self.frame_count, 1), 2),
+            "chemo_pct_time_in_zone":              round(
+                100.0 * self._in_zone_frames / max(self.frame_count, 1), 2),
+            "chemo_latency_s":                     round(self._chemo_latency_s, 3)
+                                                   if self._chemo_latency_s is not None else None,
+            "chemo_mean_dist_mm":                  round(
+                self._chemo_dist_sum / max(self._chemo_dist_count, 1), 4),
+            # --- Interactions inter-individus ---
+            "social_pct_time_avoiding":            round(
+                100.0 * self._avoid_frames / max(self.frame_count, 1), 2),
+            "social_pct_time_aggregating":         round(
+                100.0 * self._aggreg_frames / max(self.frame_count, 1), 2),
+            "social_mean_nn_mm":                   round(
+                self._nn_sum / max(self._nn_count, 1), 4),
+            "social_contact_events":               self._contact_events,
+        }
+
+    def reset(self):
+        """Réinitialise tous les accumulateurs (changement de puits ou planaire)."""
+        self.__init__(
+            self.px_per_mm, self.fps,
+            self.thresh_immobile, self.thresh_mobile, self.beh,
+        )
+
+    @staticmethod
+    def _empty_record(ts: float) -> dict:
+        """Enregistrement vide (planaire non détecté)."""
+        return {"timestamp": ts, "detected": False}
+
+
+# ---------------------------------------------------------------------------
+# Paramètres expérimentaux
+# ---------------------------------------------------------------------------
+
+class ExperimentParams:
+    """
+    Conteneur des paramètres d'une expérience.
+    Instanciable depuis un dict, un fichier CSV ou un modèle Django.
+    """
+
+    REQUIRED = {"experiment", "well", "px_per_mm", "fps"}
+
+    DEFAULTS = {
+        "well_radius_mm":         8.0,
+        "thresh_immobile":        THRESH_IMMOBILE_DEFAULT,
+        "thresh_mobile":          THRESH_MOBILE_DEFAULT,
+        "planarian_count":        1,
+        "tube_axis":              "vertical",
+        "min_area_px":            20,
+        "max_area_ratio":         0.10,
+        **BEHAVIOUR_DEFAULTS,
+    }
+
+    def __init__(self, data: dict):
+        missing = self.REQUIRED - set(data.keys())
+        if missing:
+            raise ValueError(f"Paramètres manquants : {missing}")
+        merged = {**self.DEFAULTS, **data}
+        for k, v in merged.items():
+            setattr(self, k, self._cast(k, v))
+
+    @staticmethod
+    def _cast(key: str, value):
+        """Cast automatique des valeurs CSV (toutes en string) vers le bon type."""
+        float_keys = {
+            "px_per_mm", "fps", "well_radius_mm", "thresh_immobile", "thresh_mobile",
+            "photo_strength", "photo_x", "photo_y", "photo_flee_angle_deg",
+            "chemo_strength", "chemo_x", "chemo_y", "chemo_radius_mm",
+            "chemo_approach_angle_deg", "thigmotaxis_wall_dist_mm",
+            "avoid_radius_mm", "aggreg_radius_mm", "max_area_ratio",
+        }
+        int_keys = {"planarian_count", "min_area_px"}
+        if key in float_keys:
+            return float(value)
+        if key in int_keys:
+            return int(value)
+        if isinstance(value, str) and value.lower() in ("true", "false"):
+            return value.lower() == "true"
+        return value
+
+    @classmethod
+    def from_csv_row(cls, row: dict) -> "ExperimentParams":
+        """Instancie depuis une ligne de csv.DictReader."""
+        return cls(row)
+
+    @classmethod
+    def from_csv_file(cls, filepath: str) -> list:
+        """Charge toutes les expériences d'un fichier CSV."""
+        results = []
+        with open(filepath, newline="", encoding="utf-8-sig") as f:
+            for row in csv.DictReader(f):
+                try:
+                    results.append(cls.from_csv_row(row))
+                except ValueError as e:
+                    logger.warning(f"Ligne ignorée : {e} — {row}")
+        return results
+
+    def to_dict(self) -> dict:
+        """Sérialise en dict."""
+        return {k: getattr(self, k)
+                for k in {**self.DEFAULTS, **{r: None for r in self.REQUIRED}}}
+
+    def build_metrics(self) -> EthoVisionMetrics:
+        """Construit l'instance EthoVisionMetrics pour ces paramètres."""
+        behaviour = {k: getattr(self, k) for k in BEHAVIOUR_DEFAULTS if hasattr(self, k)}
+        return EthoVisionMetrics(
+            px_per_mm       = self.px_per_mm,
+            fps             = self.fps,
+            thresh_immobile = self.thresh_immobile,
+            thresh_mobile   = self.thresh_mobile,
+            behaviour       = behaviour,
+        )
+
+
+# ---------------------------------------------------------------------------
+# Client ReductStore
+# ---------------------------------------------------------------------------
+
+class ReductStoreClient:
+    """
+    Interface asynchrone avec ReductStore pour PlanarianScanner.
+
+    Labels : experiment | well | planarian | record_type (frame|summary)
+    """
+
+    def __init__(
+        self,
+        url:    str = "http://localhost:8383",
+        token:  str = "",
+        bucket: str = "planarian_metrics",
+        quota_type=None, 
+        quota_size=1000_000_000
+    ):
+        self.url          = url
+        self.token        = token
+        self.bucket_name  = bucket
+        self.quota_type = quota_type
+        self.quota_size = quota_size
+        self.entry_name   = "metrics"
+        self._client = None
+        self._bucket = None
+        
+
+    async def _create_bucket(self):
+        from reduct import Client, BucketSettings
+        self._client = Client(self.url, api_token=self.token)
+        settings = BucketSettings(
+            quota_type=self.quota_type,
+            quota_size=self.quota_size,
+            exist_ok=True,
+        )
+        return await self._client.create_bucket(self.bucket_name, settings, exist_ok=True)
+
+
+    async def connect(self):
+        """Initialise la connexion et crée le bucket si nécessaire."""
+        self._bucket = await self._create_bucket()
+        logger.info(f"ReductStore connecté : {self.url} / {self.bucket_name}")
+        
+    async def store_metric(
+        self,
+        record:      dict,
+        experiment:  str,
+        well:        str,
+        planarian:   int = 0,
+        record_type: str = "frame",
+        uuid:        str = "",
+        ts_us:       Optional[int] = None,
+    ):
+        """
+        Stocke un enregistrement dans ReductStore.
+
+        Le timestamp est rendu unique par planaire en ajoutant l'index
+        du planaire comme offset sub-microseconde — évite le 409 Conflict
+        quand plusieurs planaires du même puits écrivent dans la même frame.
+        """
+        if self._bucket is None:
+            await self.connect()
+        # ts_us de base + offset planaire (0, 1, 2…) pour unicité garantie
+        base_ts = ts_us or int(time.time() * 1_000_000)
+        unique_ts = base_ts + planarian
+        
+        await self._bucket.write(
+            entry_name   = "metrics",
+            data         = json.dumps(record).encode("utf-8"),
+            timestamp    = unique_ts,
+            labels       = {
+                "experiment":  experiment,
+                "well":        well,
+                "planarian":   str(planarian),
+                "record_type": record_type,
+                "uuid": uuid,
+            },
+            content_type = "application/json",
+        )
+
+    async def store_summary(self, summary: dict, experiment: str, well: str, planarian: int = 0):
+        """Stocke le résumé de fin de session."""
+        await self.store_metric(summary, experiment, well, planarian, "summary")
+
+    async def get_tracking_data(
+        self,
+        experiment:  str,
+        well:        str,
+        planarian:   int = 0,
+        record_type: str = "metrics",
+        start:       Optional[datetime] = None,
+        stop:        Optional[datetime] = None,
+    ) -> list:
+        """Récupère les enregistrements filtrés par labels."""
+        if self._bucket is None:
+            await self.connect()
+        kwargs = {"include": {
+            "experiment": experiment, "well": well,
+            "planarian": str(planarian), "record_type": record_type,
+        }}
+        if start:
+            kwargs["start"] = int(start.timestamp() * 1_000_000)
+        if stop:
+            kwargs["stop"]  = int(stop.timestamp() * 1_000_000)
+        records = []
+        async for rec in self._bucket.query("metrics", **kwargs):
+            try:
+                records.append(json.loads(await rec.read_all()))
+            except Exception as e:
+                logger.warning(f"Entrée illisible ignorée : {e}")
+        return records
+
+    @staticmethod
+    def _convert_timestamps(records: list) -> list:
+        """
+        Convertit le champ 'timestamp' (epoch float secondes) en ISO 8601 UTC
+        dans chaque enregistrement.
+
+        Args:
+            records : liste de dicts issus de ReductStore
+
+        Returns:
+            nouvelle liste avec timestamp converti (originaux non modifiés)
+        """
+        converted = []
+        for r in records:
+            row = dict(r)
+            ts  = row.get("timestamp")
+            if ts is not None:
+                try:
+                    row["timestamp"] = (
+                        datetime.fromtimestamp(float(ts), tz=timezone.utc)
+                        .strftime("%Y-%m-%dT%H:%M:%S.%f") + "Z"
+                    )
+                except (ValueError, TypeError, OSError):
+                    pass
+            converted.append(row)
+        return converted
+
+    @staticmethod
+    def _build_filepath(output_dir: str, experiment: str,
+                        well: str, planarian: int, record_type: str) -> str:
+        """
+        Construit le chemin du fichier CSV de sortie.
+        Nom : <experiment>_<well>_planaire<NN>_<record_type>.csv
+
+        Args:
+            output_dir  : répertoire de sortie (créé si absent)
+            experiment  : identifiant de l'expérience
+            well        : identifiant du puits
+            planarian   : index du planaire
+            record_type : "frame" ou "summary"
+
+        Returns:
+            chemin absolu du fichier CSV
+        """
+        dirpath  = os.path.abspath(output_dir)
+        os.makedirs(dirpath, exist_ok=True)
+        filename = f"{experiment}_{well}_planaire{planarian:02d}_{record_type}.csv"
+        return os.path.join(dirpath, filename)
+
+    async def export_csv(
+        self,
+        experiment:  str,
+        well:        str,
+        planarian:   int = 0,
+        record_type: str = "metrics",
+        output_dir:  str = ".",
+        start:       Optional[datetime] = None,
+        stop:        Optional[datetime] = None,
+    ) -> tuple:
+        """
+        Exporte les données depuis ReductStore vers un fichier CSV.
+        Le répertoire de sortie est choisi via output_dir.
+        Le champ timestamp est converti en ISO 8601 UTC.
+
+        Args:
+            experiment  : identifiant de l'expérience
+            well        : identifiant du puits
+            planarian   : index du planaire
+            record_type : "frame" | "summary"
+            output_dir  : répertoire de sortie (défaut : répertoire courant)
+            start, stop : plage temporelle (datetime UTC, optionnel)
+
+        Returns:
+            tuple (filepath, nb_lignes)
+        """
+        records = await self.get_tracking_data(
+            experiment, well, planarian, record_type, start, stop)
+        if not records:
+            logger.warning(f"Aucune donnée pour {experiment}/{well}/{planarian}")
+            return "", 0
+
+        records    = self._convert_timestamps(records)
+        filepath   = self._build_filepath(output_dir, experiment, well,
+                                          planarian, record_type)
+        fieldnames = list(dict.fromkeys(k for r in records for k in r.keys()))
+
+        with open(filepath, "w", newline="", encoding="utf-8") as f:
+            writer = csv.DictWriter(f, fieldnames=fieldnames, extrasaction="ignore")
+            writer.writeheader()
+            writer.writerows(records)
+
+        logger.info(f"Export CSV : {len(records)} lignes → {filepath}")
+        return filepath, len(records)
+
+    async def export_csv_response(
+        self,
+        experiment:  str,
+        well:        str,
+        planarian:   int = 0,
+        record_type: str = "metrics",
+        start:       Optional[datetime] = None,
+        stop:        Optional[datetime] = None,
+    ) -> tuple:
+        """
+        Génère le contenu CSV en mémoire (pour réponse HTTP Django).
+        Le champ timestamp est converti en ISO 8601 UTC.
+
+        Returns:
+            tuple (contenu_csv_str, nb_lignes)
+        """
+        records = await self.get_tracking_data(
+            experiment, well, planarian, record_type, start, stop)
+        if not records:
+            return "", 0
+        records    = self._convert_timestamps(records)
+        fieldnames = list(dict.fromkeys(k for r in records for k in r.keys()))
+        out = io.StringIO()
+        writer = csv.DictWriter(out, fieldnames=fieldnames, extrasaction="ignore")
+        writer.writeheader()
+        writer.writerows(records)
+        return out.getvalue(), len(records)
+
+    async def close(self):
+        """
+        Ferme la connexion ReductStore.
+        Note : reduct-py >= 1.x ne nécessite pas de fermeture explicite —
+        la méthode est conservée pour compatibilité d'interface.
+        """
+        self._client = None
+        self._bucket = None
+        logger.info("ReductStore déconnecté")        
+        

test_tube_scanner/planarian/views.py

@@ -21,7 +21,7 @@ from modules.planarian_metrics import ExperimentParams, ReductStoreClient
 from modules.system_stats import get_cached_stats, start_background_updater
 from scanner.constants import ScannerConstants
 from .tasks import export_experiment_metrics, export_session_metrics
-from scanner import models
+from scanner import models, views as scanner_views
 from .models import ExperimentConfig
 
 
@@ -64,30 +64,17 @@ def global_context(request, **ctx):
     )
 
 
-def get_active_experiments(session, expid=None):
-    if session:
-        experiments = models.SessionExperiment.experiment_by_session(session.id, active=True) or []
-        eid = [str(e.id) for e in experiments]        
-        if experiments and not expid or expid not in eid:
-            return experiments, experiments[0]
-        
-        for e in experiments:
-            if expid == str(e.id):
-                return experiments, e
-    return [], None
-
-
 def get_active_session(request, session_id=None, experiment_id=None):
     cursid = session_id or request.POST.get('_sid')
     expid = experiment_id or request.POST.get('_expid')
 
     current_session = models.Session.get_session(cursid)
-    experiments, current_experiment = get_active_experiments(current_session, expid) 
+    experiments, current_experiment = scanner_views.get_not_active_experiments(current_session, expid) 
     context = dict(
         current_session = current_session,
         current_experiment = current_experiment,
         experiments=experiments or [],
-        sessions=models.Session.objects.filter(active=True).all(),
+        sessions=models.Session.objects.filter(active=False).all(),
         well_choices=models.Well.objects.order_by('name').all(),
     )
     return context    
@@ -118,6 +105,8 @@ def export_metrics(request):
 def export_csv(request):
     d = request.POST
     
+    print("export_csv===========", d)
+    
     @async_to_sync
     async def _do_export():
         client = _get_reduct_client()
@@ -141,7 +130,7 @@ def export_csv(request):
     csv_content, n = _do_export()
     logger.info(f"Export CSV: {n} lignes, content size={len(csv_content)}")
     filename = (
-        f"{d['experiment']}_{d['well']}-planaire{d['planarian']}"
+        f"{d['experiment']}_{d['well']}-planaire-{d['planarian']}"
         f"_{d['record_type']}.csv"
     )
     return csv_content, filename

test_tube_scanner/scanner/models.py

@@ -53,7 +53,8 @@ class Configuration(models.Model):
     grbl_xmax = models.FloatField(_("Grbl Xmax"), help_text=_("CNC Grbl Xmax en mm"), blank=False, default=350.0)
     grbl_ymax = models.FloatField(_("Grbl Ymax"), help_text=_("CNC Grbl Ymax en mm"), blank=False, default=250.0)
     # camera configuration
-    capture_type = models.CharField(_("Capture"), help_text=_("Type de capture"), default='rpi', max_length=8, choices=CAPTURE_TYPE, null=True, blank=False)
+    capture_type = models.CharField(_("Capture"), help_text=_("Type de capture. Nécessite un redémarrage en cas de modification à chaud!"), default='rpi', max_length=8, choices=CAPTURE_TYPE, null=True, blank=False)
+    
     webcam_device_index = models.PositiveSmallIntegerField(_("Index de la webcam"), help_text=_("Index de la webcam (0, 1, ...) si présente"), default=2)
     image_quality = models.PositiveSmallIntegerField(_("Qualité JPEG"), help_text=_("Qualité JPEG (1-100) pour les images exportées"), default=90)
     video_jpeg_quality = models.PositiveSmallIntegerField(_("Qualité JPEG pour les vidéos"), help_text=_("Qualité JPEG (1-100) pour les images extraites des vidéos"), default=90)
@@ -278,8 +279,8 @@ class Session(models.Model):
     name = models.CharField(_("Nom de la session"), help_text=_("Session d'expérience. 4 Multi-puits maximum"), max_length=100, null=True, blank=False)
     author = models.ForeignKey(User, on_delete=models.SET_NULL, verbose_name="Auteur", null=True, blank=True)
     active = models.BooleanField(_("Active"), default=True)
-    expected_export = models.DateTimeField(_("Date d'exportation"), help_text=_("Date d'exportation prévue"), null=True, blank=True)
+    expected_export = models.DateTimeField(_("Exportation auto"), help_text=_("Date d'exportation prévue"), null=True, blank=True)
-    expected_scanning = models.DateTimeField(_("Date du balayage"), help_text=_("Date du balayage prévue"), null=True, blank=True)    
+    expected_scanning = models.DateTimeField(_("Bbalayage auto"), help_text=_("Date du balayage prévue"), null=True, blank=True)    
     
     created = models.DateTimeField(_("Date de création"), default=timezone.now)
     finished = models.DateTimeField (_("Date de fin"), null=True, blank=True)

test_tube_scanner/scanner/multiwell.py

@@ -238,6 +238,9 @@ class MultiWellManager:
     def _start_scanning(self, session, experiments, simulate=False):
         result = False
         try:
+            conf = self.process.cam.get_config()
+            self.process.cam.use_tracking = conf.use_tracking
+            
             self.process.cam._aligner.debug = False
             self.stop_playing.clear()
             

test_tube_scanner/scanner/process.py

@@ -160,6 +160,7 @@ class ScannerProcess(Task):
         self.data = ProcessData()
         self.manager = None
         self.recordDB = CameraRecordManager(cameraDB)
+        self.metricDB = planarianDB
 
     def __call__(self, *args, **kwargs):
         return self.start(*args, **kwargs)
@@ -167,8 +168,11 @@ class ScannerProcess(Task):
     def set_crop_radius(self, radius):
         return CircularCrop(radius=radius, strategy=CropStrategy.CROP_JPEG, jpeg_quality=self.image_quality)
     
-    def start(self, *args, **kwargs):
+    def get_config(self):
-        try:            
+        '''
+        Constants:
+            reset si besoin les constantes vidéo
+        '''
         self.conf = ScannerConstants().get()
         self.use_tracking = self.conf.tracking
         
@@ -181,6 +185,12 @@ class ScannerProcess(Task):
 
         self.video_jpg_quality = [int(cv2.IMWRITE_JPEG_QUALITY), self.video_quality]
         self.image_jpg_quality = [int(cv2.IMWRITE_JPEG_QUALITY), self.image_quality]
+        return self.conf
+    
+
+    def start(self, *args, **kwargs):
+        try:            
+            self.get_config()
             self.grbl_xmax = self.conf.grbl_xmax
             self.grbl_ymax = self.conf.grbl_ymax