agrobot_base/Python/OAK/datasets/oak-fcc-3/_0_capture.py

import os
import time
import json
import argparse
from datetime import datetime

import cv2
import numpy as np

from core.oak_fcc3_client import OakFcc3Client as MultiSpectralClient

with open("config.json", "r", encoding="utf-8") as f:
    config = json.load(f)

RAW_SIZE = config.get("raw_size")  # [W, H]
MODULE_PARAMS = config.get("module_params_json")


# =========================
# Helpers gerais
# =========================

def ts_name() -> str:
    return datetime.now().strftime("%Y%m%d_%H%M%S_%f")[:-3]


def overlay_hud(
    img_bgr: np.ndarray,
    lines: list[str],
    base_h: int = 720,
    base_font_scale: float = 0.75,
    base_line_step: int = 28,
):
    h, w = img_bgr.shape[:2]

    scale = h / float(base_h)
    scale = max(scale, 0.4)

    font_scale = base_font_scale * scale
    line_step = int(base_line_step * scale)

    thick_outline = max(1, int(3 * scale))
    thick_text = max(1, int(2 * scale))

    y = int(24 * scale)
    x = int(12 * scale)

    for s in lines:
        cv2.putText(img_bgr, s, (x, y), cv2.FONT_HERSHEY_SIMPLEX, font_scale, (0, 0, 0), thick_outline, cv2.LINE_AA)
        cv2.putText(img_bgr, s, (x, y), cv2.FONT_HERSHEY_SIMPLEX, font_scale, (255, 255, 255), thick_text, cv2.LINE_AA)
        y += line_step


def save_sample(
    base_dir: str,
    frame_type: str,
    preview_bgr: np.ndarray,
    meta: dict,
    raw_payload: np.ndarray | None = None,
    packed_raw: np.ndarray | None = None,
    packed_raw_by_camera: dict | None = None,
):
    os.makedirs(base_dir, exist_ok=True)
    name = ts_name()

    png_path = os.path.join(base_dir, f"{name}.png")
    json_path = os.path.join(base_dir, f"{name}.json")

    if frame_type in ("RGB", "MULTISPEC"):
        if raw_payload is None:
            raise ValueError(f"raw_payload não pode ser None quando frame_type='{frame_type}'")

        payload_path = os.path.join(base_dir, f"{name}.raw")
        raw_payload.astype(np.float32).tofile(payload_path)

        meta["saved_payload_type"] = frame_type.lower()
        meta["saved_payload_path"] = os.path.basename(payload_path)
        meta["saved_payload_dtype"] = "float32"
        meta["saved_payload_shape"] = list(raw_payload.shape)

    elif frame_type == "RAW_BRUTO":
        if packed_raw_by_camera is not None:
            payload_files = {}
            payload_shapes = {}
            payload_dtypes = {}

            for cam_id, arr in packed_raw_by_camera.items():
                path = os.path.join(base_dir, f"{name}_{cam_id}.bin")
                arr.tofile(path)
                payload_files[cam_id] = os.path.basename(path)
                payload_shapes[cam_id] = list(arr.shape)
                payload_dtypes[cam_id] = str(arr.dtype)

            meta["saved_payload_type"] = "raw_native_multi"
            meta["saved_payload_paths"] = payload_files
            meta["saved_payload_shapes"] = payload_shapes
            meta["saved_payload_dtypes"] = payload_dtypes

        else:
            if packed_raw is None:
                raise ValueError("packed_raw não pode ser None quando frame_type='RAW_BRUTO'")

            payload_path = os.path.join(base_dir, f"{name}.bin")
            packed_raw.tofile(payload_path)

            meta["saved_payload_type"] = "raw_native_single"
            meta["saved_payload_path"] = os.path.basename(payload_path)
            meta["saved_payload_dtype"] = str(packed_raw.dtype)
            meta["saved_payload_shape"] = list(packed_raw.shape)

    else:
        raise ValueError(f"frame_type não suportado para save: {frame_type}")

    cv2.imwrite(png_path, preview_bgr)

    with open(json_path, "w", encoding="utf-8") as f:
        json.dump(meta, f, ensure_ascii=False, indent=2)

    return png_path, json_path


def get_camera_map_from_status(status: dict) -> dict:
    result = {}
    for cam in status.get("cameras", []):
        result[cam.get("id")] = cam
    return result


# =========================
# MAIN
# =========================

def main():
    parser = argparse.ArgumentParser(
        description="Captura de dataset usando módulo multispectral Pi + StreamReceiver.",
        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
    )

    parser.add_argument("--cana", required=True, choices=["baixa", "media", "alta"], help="Estado da cana no momento da coleta.")
    parser.add_argument("--horario", required=True, choices=["cedo", "meio_dia", "entardecer", "nublado"], help="Janela de iluminação / horário da coleta.")
    parser.add_argument("--out_root", default="dataset", help="Pasta raiz do dataset.")
    parser.add_argument("--fps", type=int, default=20, help="FPS desejado.")
    parser.add_argument("--width", type=int, default=RAW_SIZE[0], help="Largura óptica da câmera.")
    parser.add_argument("--height", type=int, default=RAW_SIZE[1], help="Altura óptica da câmera.")
    parser.add_argument("--interval", type=float, default=1.0, help="Intervalo em segundos para auto-save quando ligado.")
    parser.add_argument("--preview_upscale", type=int, default=2, help="Fator de upscale visual do preview.")
    parser.add_argument("--bayer", default="RGGB", choices=["GBRG", "GRBG", "RGGB", "BGGR"], help="Padrão Bayer das câmeras.")
    parser.add_argument("--output_dtype", default="float32", choices=["uint8", "uint16", "float32"], help="Dtype do payload processado no Pi.")
    parser.add_argument("--frame_type", default="RAW_BRUTO", choices=["RAW_BRUTO", "RGB", "MULTISPEC"], help="Tipo de payload pedido ao Pi.")
    parser.add_argument("--capture_mode", default="AUTO", choices=["AUTO", "SINGLE", "DOUBLE", "TRIPLE"], help="Modo de captura desejado no módulo.")
    parser.add_argument("--raw_policy", default="allow_single", choices=["allow_single", "require_triple"], help="Quando frame_type=RAW_BRUTO, define se o script aceita 1 câmera ou exige 3.")
    parser.add_argument("--module_calibration_json", default=MODULE_PARAMS, help="JSON salvo pelo calibrador de sensores com parâmetros fixos por câmera.")

    args = parser.parse_args()

    effective_capture_mode = args.capture_mode

    raw_w = args.width
    raw_h = args.height

    session_dir = os.path.join(
        args.out_root,
        "brutas",
        f"cana_{args.cana}",
        args.horario,
        datetime.now().strftime("%Y%m%d"),
    )
    os.makedirs(session_dir, exist_ok=True)

    print("============================================")
    print("Coleta de dataset - Módulo Multiespectral")
    print(f"Cana         : {args.cana}")
    print(f"Horário      : {args.horario}")
    print(f"Saída        : {session_dir}")
    print(f"Sensor       : {raw_w}x{raw_h} | Bayer={args.bayer}")
    print(f"FrameType    : {args.frame_type}")
    print(f"CaptureMode  : {args.capture_mode} -> efetivo={effective_capture_mode}")
    print(f"RAW policy   : {args.raw_policy}")
    print("============================================")

    beauty_preview = False
    radiometric_ae = False
    auto_save = False
    last_auto_t = 0.0
    preview_upscale = args.preview_upscale

    t_view_fps = time.time()
    view_frames = 0
    fps_view = 0.0

    t_stream_fps = time.time()
    last_stream_frame_id = None
    stream_frames_accum = 0
    fps_stream = 0.0

    last_msg = ""
    last_msg_t = 0.0

    window_name = "Dataset Capture (C/SPACE=save | A=auto-save | M=preview | R=rad | Q=quit)"
    cv2.namedWindow(window_name, cv2.WINDOW_NORMAL)

    last_frame_id = -1
    last_payload_float = None
    last_packed_raw = None
    last_packed_raw_by_camera = None
    last_preview_bgr = None
    last_meta_stream = None

    try:
        with MultiSpectralClient(
            width=raw_w,
            height=raw_h,
            bayer=args.bayer,
            fps=args.fps,
            frame_type=args.frame_type,
            output_dtype=args.output_dtype,
            capture_mode=effective_capture_mode,
            raw_policy=args.raw_policy,
            module_calibration_json=args.module_calibration_json,
            radiometric_enabled=radiometric_ae,
        ) as cam:
            while True:
                t0 = time.time()
                frame, meta, decoded = cam.get_next_decoded(timeout=1.0)
                if meta is not None and frame is not None and meta.get("frame_id") != last_frame_id:
                    last_frame_id = meta["frame_id"]

                    try:
                        frame_type = meta.get("frame_type", "RAW_BRUTO")
                        dtype_str = meta.get("dtype") or meta.get("output_dtype", "uint8")
                        preview_source_id = "rgb"

                        if frame_type == "RAW_BRUTO":
                            if isinstance(frame, dict):
                                packed_by_camera = frame

                                preview_bgr, raw3_preview, preview_source_id = cam.build_preview_from_raw_payload(frame=frame, meta=meta)

                                if beauty_preview:
                                    rgb_preview = None
                                    previews = cam.build_visual_preview_from_raw(frame, meta)
                                    camera_info = meta.get("camera_info", {}) or {}
                                    for cam_id, img in previews.items():
                                        role = camera_info.get(cam_id, {}).get("role")
                                        if role == "rgb":
                                            rgb_preview = img
                                            preview_source_id = cam_id
                                            break
                                    if rgb_preview is not None:
                                        preview_bgr = rgb_preview

                                last_packed_raw = None
                                last_packed_raw_by_camera = {cam_id: arr.copy() for cam_id, arr in packed_by_camera.items()}
                                last_payload_float = raw3_preview.copy()
                        
                        elif frame_type == "RGB":
                            rgb_chw = frame
                            if not isinstance(rgb_chw, np.ndarray) or rgb_chw.ndim != 3:
                                raise RuntimeError(f"Frame RGB inválido: type={type(rgb_chw)}")

                            if dtype_str == "uint8":
                                payload_float = rgb_chw.astype(np.float32) / 255.0
                            elif dtype_str == "float32":
                                payload_float = rgb_chw.astype(np.float32)
                            elif dtype_str == "uint16":
                                payload_float = rgb_chw.astype(np.float32) / 65535.0
                            else:
                                raise RuntimeError(f"dtype RGB não suportado: {dtype_str}")

                            preview_rgb = np.transpose(payload_float, (1, 2, 0))
                            preview_bgr = cv2.cvtColor(
                                np.clip(preview_rgb * 255.0, 0, 255).astype(np.uint8),
                                cv2.COLOR_RGB2BGR
                            )

                            last_payload_float = payload_float.copy()
                            last_packed_raw = None
                            last_packed_raw_by_camera = None

                        elif frame_type == "MULTISPEC":
                            multispec_chw = frame
                            if not isinstance(multispec_chw, np.ndarray) or multispec_chw.ndim != 3 or multispec_chw.shape[0] not in (4, 5):
                                raise RuntimeError(f"Frame MULTISPEC inválido: shape={getattr(multispec_chw, 'shape', None)}")

                            if dtype_str == "uint8":
                                payload_float = multispec_chw.astype(np.float32) / 255.0
                            elif dtype_str == "float32":
                                payload_float = multispec_chw.astype(np.float32)
                            elif dtype_str == "uint16":
                                payload_float = multispec_chw.astype(np.float32) / 65535.0
                            else:
                                raise RuntimeError(f"dtype MULTISPEC não suportado: {dtype_str}")

                            preview_rgb = np.transpose(payload_float[:3], (1, 2, 0))
                            preview_bgr = cv2.cvtColor(
                                np.clip(preview_rgb * 255.0, 0, 255).astype(np.uint8),
                                cv2.COLOR_RGB2BGR
                            )

                            last_payload_float = payload_float.copy()
                            last_packed_raw = None
                            last_packed_raw_by_camera = None

                        else:
                            raise RuntimeError(f"frame_type não suportado neste script: {frame_type}")

                        if preview_upscale and preview_upscale > 1:
                            preview_show = cv2.resize(
                                preview_bgr,
                                (preview_bgr.shape[1] * preview_upscale, preview_bgr.shape[0] * preview_upscale),
                                interpolation=cv2.INTER_NEAREST,
                            )
                        else:
                            preview_show = preview_bgr.copy()

                        curr_frame_id = meta.get("frame_id")

                        if curr_frame_id is not None:
                            if last_stream_frame_id != curr_frame_id:
                                stream_frames_accum += 1

                        last_stream_frame_id = curr_frame_id

                        dt_stream = time.time() - t_stream_fps
                        if dt_stream >= 1.0:
                            fps_stream = stream_frames_accum / dt_stream
                            stream_frames_accum = 0
                            t_stream_fps = time.time()

                        view_frames += 1
                        dt_view = time.time() - t_view_fps
                        if dt_view >= 1.0:
                            fps_view = view_frames / dt_view
                            view_frames = 0
                            t_view_fps = time.time()

                        active_sources = meta.get("payload_sources")

                        rad = getattr(cam, "radiometric_controller", None)
                        if rad and rad.enabled:
                            st = rad.state
                            line_rad = (
                                f"RAD | "
                                f"RGB(exp={st['rgb']['exp']}, g={st['rgb']['gain']:.2f}) | "
                                f"RE(exp={st['re']['exp']}, g={st['re']['gain']:.2f}) | "
                                f"NIR(exp={st['nir']['exp']}, g={st['nir']['gain']:.2f})"
                            )
                        else:
                            line_rad = "RAD | OFF"
                            
                        lines = [
                            f"CANA: {args.cana} | HORA: {args.horario} | Pasta: {os.path.basename(session_dir)}",
                            f"Type={meta.get('frame_type')} | CaptureMode={effective_capture_mode} | RAW policy={args.raw_policy}",
                            f"Sources={active_sources} | FPS_STREAM={fps_stream:.1f} | FPS_VIEW={fps_view:.1f}",
                            f"frame_id={meta.get('frame_id')} | layout={meta.get('output_layout')} | dtype={meta.get('dtype') or meta.get('output_dtype')}",
                            f"codec={meta.get('codec_name', meta.get('codec_family', '-'))} | comp={meta.get('dt_comp', 0):.4f}s | send={meta.get('dt_send_payload_prev', 0):.4f}s",
                            f"CAM_PARAMS={os.path.basename(args.module_calibration_json)} | controles fixos aplicados",
                            line_rad,
                            "Keys: C/SPACE=save | A=auto-save | M=preview | R=rad | Q/Esc=quit"
                        ]
                        overlay_hud(preview_show, lines, base_h=raw_h)

                        if last_msg and (time.time() - last_msg_t) < 2.0:
                            cv2.putText(preview_show, last_msg, (12, preview_show.shape[0] - 18),
                                        cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 0), 2, cv2.LINE_AA)

                        cv2.imshow(window_name, preview_show)

                        last_preview_bgr = preview_bgr.copy()
                        last_meta_stream = dict(meta)

                    except Exception as e:
                        err = np.zeros((500, 1200, 3), dtype=np.uint8)
                        cv2.putText(err, f"Erro ao processar frame: {e}", (20, 60),
                                    cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 0, 255), 2, cv2.LINE_AA)
                        cv2.imshow(window_name, err)
                        print(f"[ERRO FRAME] {e}")

                now = time.time()
                can_save = (
                    last_meta_stream is not None and
                    last_preview_bgr is not None and
                    (
                        (last_meta_stream.get("frame_type") in ("RGB", "MULTISPEC") and last_payload_float is not None) or
                        (last_meta_stream.get("frame_type") == "RAW_BRUTO" and (last_packed_raw is not None or last_packed_raw_by_camera is not None))
                    )
                )

                if auto_save and can_save and (now - last_auto_t) >= args.interval:
                    frame_type_save = last_meta_stream.get("frame_type")

                    meta_save = {
                        "ts": datetime.now().isoformat(timespec="milliseconds"),
                        "cana": args.cana,
                        "horario": args.horario,
                        "sensor_width": raw_w,
                        "sensor_height": raw_h,
                        "bayer_pattern": args.bayer,
                        "fps_target": args.fps,
                        "frame_type": frame_type_save,
                        "capture_mode_requested": args.capture_mode,
                        "capture_mode_effective": effective_capture_mode,
                        "raw_policy": args.raw_policy,
                        "stream_meta": last_meta_stream,
                        "applied_camera_controls": cam.applied_camera_controls,
                        "camera_params_json": args.module_calibration_json,
                        "note": "autosave",
                        "raw_preview_reference_camera": preview_source_id,
                    }

                    save_sample(
                        session_dir,
                        frame_type=frame_type_save,
                        preview_bgr=last_preview_bgr,
                        meta=meta_save,
                        raw_payload=last_payload_float,
                        packed_raw=last_packed_raw,
                        packed_raw_by_camera=last_packed_raw_by_camera,
                    )

                    last_msg = "SALVO (auto)"
                    last_msg_t = now
                    last_auto_t = now

                k = cv2.waitKey(1) & 0xFF
                if k in (ord("q"), ord("Q"), 27):
                    break

                elif k in (ord("a"), ord("A")):
                    auto_save = not auto_save
                    last_msg = f"AutoSave -> {'ON' if auto_save else 'OFF'}"
                    last_msg_t = time.time()

                elif k in (ord("m"), ord("M")):
                    #preview_upscale = 0 if preview_upscale else args.preview_upscale
                    beauty_preview = False if beauty_preview else True
                    last_msg = f"Preview Beauty -> {beauty_preview}"
                    last_msg_t = time.time()

                elif k in (ord("r"), ord("R")):
                    radiometric_ae = False if radiometric_ae else True
                    rad = getattr(cam, "radiometric_controller", None)
                    rad.enabled = radiometric_ae
                    last_msg = f"RAD -> {radiometric_ae}"
                    last_msg_t = time.time()


                elif k in (ord("c"), ord("C"), 32):
                    if can_save:
                        frame_type_save = last_meta_stream.get("frame_type")
                        meta_save = {
                            "ts": datetime.now().isoformat(timespec="milliseconds"),
                            "cana": args.cana,
                            "horario": args.horario,
                            "sensor_width": raw_w,
                            "sensor_height": raw_h,
                            "bayer_pattern": args.bayer,
                            "fps_target": args.fps,
                            "frame_type": frame_type_save,
                            "capture_mode_requested": args.capture_mode,
                            "capture_mode_effective": effective_capture_mode,
                            "raw_policy": args.raw_policy,
                            "stream_meta": last_meta_stream,
                            "applied_camera_controls": cam.applied_camera_controls,
                            "camera_params_json": args.module_calibration_json,
                            "note": "manual",
                            "raw_preview_reference_camera": preview_source_id,
                        }

                        save_sample(
                            session_dir,
                            frame_type=frame_type_save,
                            preview_bgr=last_preview_bgr,
                            meta=meta_save,
                            raw_payload=last_payload_float,
                            packed_raw=last_packed_raw,
                            packed_raw_by_camera=last_packed_raw_by_camera,
                        )

                        last_msg = "SALVO (manual)"
                        last_msg_t = time.time()

                dt_loop = time.time() - t0
                if dt_loop < 0.001:
                    time.sleep(0.001)

    finally:
        cv2.destroyAllWindows()
        print("Fim da captura.")


if __name__ == "__main__":
    main()