From 34ecc33ee59a5e9d38d3d5a5668101edaca10605 Mon Sep 17 00:00:00 2001 From: kevin Date: Thu, 16 Apr 2026 14:53:01 +0800 Subject: [PATCH] live sonar feed, and incremental action feed --- FishMeasure/dataset/zed_reader.py | 4 +- FishMeasure/fish_video_weight_evaluation.py | 36 +- .../fish_video_weight_evaluation__v1.py | 32 +- FishMeasure/generate_video_with_labels.py | 25 +- .../optical_flow/visualize_optical_flow.py | 612 +++++++++++++++++ FishMeasure/predict_weigth_from_svo2.py | 2 +- docs/conda-deploy.md | 18 +- fish_api/README.md | 61 +- fish_api/app/db.py | 110 ++-- fish_api/app/main.py | 3 + fish_api/app/routers/ingest.py | 14 +- fish_api/app/services/action.py | 131 ++-- fish_api/app/services/action_watch.py | 100 ++- fish_api/app/services/measure.py | 175 ++++- fish_api/app/services/sonar_optical_flow.py | 77 +++ fish_api/app/services/sonar_video.py | 383 ++++++++--- fish_api/app/services/water_video.py | 264 +------- fish_api/app/services/zed_svo_record.py | 4 +- fish_api/app/settings.py | 33 +- fish_api/app/state.py | 1 + fish_api/pyproject.toml | 4 +- fish_api/requirements.txt | 9 + fish_api/start_fresh.sh | 16 +- fish_api/uv.lock | 619 ------------------ record.sh | 24 + scripts/measure_debug.sh | 5 +- start_recording.sh | 11 +- stop_recording.sh | 9 +- 28 files changed, 1555 insertions(+), 1227 deletions(-) create mode 100644 FishMeasure/optical_flow/visualize_optical_flow.py create mode 100644 fish_api/app/services/sonar_optical_flow.py create mode 100644 fish_api/requirements.txt delete mode 100644 fish_api/uv.lock create mode 100755 record.sh diff --git a/FishMeasure/dataset/zed_reader.py b/FishMeasure/dataset/zed_reader.py index defd906..9d68513 100644 --- a/FishMeasure/dataset/zed_reader.py +++ b/FishMeasure/dataset/zed_reader.py @@ -44,8 +44,8 @@ class ZEDReader: else: # 实时相机模式设置 print("使用实时相机模式") - self.init_params.camera_resolution = sl.RESOLUTION.HD720 - self.init_params.depth_mode = sl.DEPTH_MODE.ULTRA + self.init_params.camera_resolution = sl.RESOLUTION.HD1200 + self.init_params.depth_mode = sl.DEPTH_MODE.NEURAL self.init_params.coordinate_units = sl.UNIT.MILLIMETER self.init_params.camera_fps = 30 # 设置帧率 diff --git a/FishMeasure/fish_video_weight_evaluation.py b/FishMeasure/fish_video_weight_evaluation.py index 811a0bb..4293834 100644 --- a/FishMeasure/fish_video_weight_evaluation.py +++ b/FishMeasure/fish_video_weight_evaluation.py @@ -10,6 +10,28 @@ import json import numpy as np import torch from pathlib import Path +from typing import Tuple + + +def _open_video_writer(path: Path, fps: float, size: Tuple[int, int]) -> cv2.VideoWriter: + """Open a VideoWriter, preferring GStreamer NVENC on Jetson for hardware H.264.""" + w, h = size + try: + if hasattr(cv2, "CAP_GSTREAMER"): + loc = str(path).replace('"', '\\"') + gst_pipe = ( + f'appsrc ! videoconvert ! video/x-raw,format=BGRx ! ' + f'nvvidconv ! video/x-raw(memory:NVMM) ! ' + f'nvv4l2h264enc bitrate=4000000 ! h264parse ! ' + f'mp4mux ! filesink location="{loc}"' + ) + writer = cv2.VideoWriter(gst_pipe, cv2.CAP_GSTREAMER, 0, fps, (w, h)) + if writer.isOpened(): + return writer + writer.release() + except Exception: + pass + return cv2.VideoWriter(str(path), cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h)) if not hasattr(argparse, "BooleanOptionalAction"): class _BooleanOptionalAction(argparse.Action): @@ -1293,8 +1315,7 @@ def finalize_preview_video_with_weights( video_path = output_images_folder / f"{svo_name}_preview.mp4" if out_frames: h, w = out_frames[0].shape[:2] - fourcc = cv2.VideoWriter_fourcc(*'mp4v') - video_writer = cv2.VideoWriter(str(video_path), fourcc, fps_video, (w, h)) + video_writer = _open_video_writer(video_path, fps_video, (w, h)) for frame in out_frames: video_writer.write(frame) video_writer.release() @@ -1930,8 +1951,7 @@ def process_single_svo2(svo_path, output_base, yolo_model, sam_predictor, sam_de video_path = output_images_folder / f"{svo_name}_preview.mp4" h, w = video_frames[0].shape[:2] fps = 10.0 - fourcc = cv2.VideoWriter_fourcc(*'mp4v') - video_writer = cv2.VideoWriter(str(video_path), fourcc, fps, (w, h)) + video_writer = _open_video_writer(video_path, fps, (w, h)) for frame in video_frames: video_writer.write(frame) video_writer.release() @@ -3103,13 +3123,9 @@ def main(): print(f"\nCreating video from {len(video_frames)} frames...") if len(video_frames) > 0: - # Get frame dimensions h, w = video_frames[0].shape[:2] - fps = 10.0 # Frames per second - - # Create video writer - fourcc = cv2.VideoWriter_fourcc(*'mp4v') - video_writer = cv2.VideoWriter(str(video_path), fourcc, fps, (w, h)) + fps = 10.0 + video_writer = _open_video_writer(video_path, fps, (w, h)) for frame in video_frames: video_writer.write(frame) diff --git a/FishMeasure/fish_video_weight_evaluation__v1.py b/FishMeasure/fish_video_weight_evaluation__v1.py index 91130c1..5847f0e 100644 --- a/FishMeasure/fish_video_weight_evaluation__v1.py +++ b/FishMeasure/fish_video_weight_evaluation__v1.py @@ -11,6 +11,27 @@ import numpy as np import torch from pathlib import Path from typing import List, Dict, Any, Optional, Tuple + + +def _open_video_writer(path: Path, fps: float, size: Tuple[int, int]) -> cv2.VideoWriter: + """Open a VideoWriter, preferring GStreamer NVENC on Jetson for hardware H.264.""" + w, h = size + try: + if hasattr(cv2, "CAP_GSTREAMER"): + loc = str(path).replace('"', '\\"') + gst_pipe = ( + f'appsrc ! videoconvert ! video/x-raw,format=BGRx ! ' + f'nvvidconv ! video/x-raw(memory:NVMM) ! ' + f'nvv4l2h264enc bitrate=4000000 ! h264parse ! ' + f'mp4mux ! filesink location="{loc}"' + ) + writer = cv2.VideoWriter(gst_pipe, cv2.CAP_GSTREAMER, 0, fps, (w, h)) + if writer.isOpened(): + return writer + writer.release() + except Exception: + pass + return cv2.VideoWriter(str(path), cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h)) from ultralytics import YOLO from seg import init_models from pointcloud_filter import filter_point_cloud @@ -1185,8 +1206,7 @@ def process_single_svo2(svo_path, output_base, yolo_model, sam_predictor, sam_de video_path = output_images_folder / f"{svo_name}_preview.mp4" h, w = video_frames[0].shape[:2] fps = 10.0 - fourcc = cv2.VideoWriter_fourcc(*'mp4v') - video_writer = cv2.VideoWriter(str(video_path), fourcc, fps, (w, h)) + video_writer = _open_video_writer(video_path, fps, (w, h)) for frame in video_frames: video_writer.write(frame) video_writer.release() @@ -2297,13 +2317,9 @@ def main(): print(f"\nCreating video from {len(video_frames)} frames...") if len(video_frames) > 0: - # Get frame dimensions h, w = video_frames[0].shape[:2] - fps = 10.0 # Frames per second - - # Create video writer - fourcc = cv2.VideoWriter_fourcc(*'mp4v') - video_writer = cv2.VideoWriter(str(video_path), fourcc, fps, (w, h)) + fps = 10.0 + video_writer = _open_video_writer(video_path, fps, (w, h)) for frame in video_frames: video_writer.write(frame) diff --git a/FishMeasure/generate_video_with_labels.py b/FishMeasure/generate_video_with_labels.py index 4b326fa..998f23c 100644 --- a/FishMeasure/generate_video_with_labels.py +++ b/FishMeasure/generate_video_with_labels.py @@ -30,6 +30,27 @@ except ImportError: ZED_AVAILABLE = False +def _open_video_writer(path: Path, fps: float, size: Tuple[int, int]) -> cv2.VideoWriter: + """Open a VideoWriter, preferring GStreamer NVENC on Jetson for hardware H.264.""" + w, h = size + try: + if hasattr(cv2, "CAP_GSTREAMER"): + loc = str(path).replace('"', '\\"') + gst_pipe = ( + f'appsrc ! videoconvert ! video/x-raw,format=BGRx ! ' + f'nvvidconv ! video/x-raw(memory:NVMM) ! ' + f'nvv4l2h264enc bitrate=4000000 ! h264parse ! ' + f'mp4mux ! filesink location="{loc}"' + ) + writer = cv2.VideoWriter(gst_pipe, cv2.CAP_GSTREAMER, 0, fps, (w, h)) + if writer.isOpened(): + return writer + writer.release() + except Exception: + pass + return cv2.VideoWriter(str(path), cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h)) + + def _parse_weight_json(weight_json: Path) -> Tuple[ Dict[int, Tuple[float, float]], Optional[float], @@ -310,7 +331,7 @@ def generate_video( video_path = images_dir / (output_video_name or f"{svo_name}_preview.mp4") h, w = frames[0].shape[:2] - writer = cv2.VideoWriter(str(video_path), cv2.VideoWriter_fourcc(*"mp4v"), 10.0, (w, h)) + writer = _open_video_writer(video_path, 10.0, (w, h)) for f in frames: writer.write(f) writer.release() @@ -332,7 +353,7 @@ def main(): parser.add_argument("--show-large-labels-at-top-right", action="store_true") parser.add_argument( "--summary-star", - action=argparse.BooleanOptionalAction, + action="store_true", default=False, help="Whether to draw * on the Final summary line; caller/DB is the source of truth.", ) diff --git a/FishMeasure/optical_flow/visualize_optical_flow.py b/FishMeasure/optical_flow/visualize_optical_flow.py new file mode 100644 index 0000000..557b173 --- /dev/null +++ b/FishMeasure/optical_flow/visualize_optical_flow.py @@ -0,0 +1,612 @@ +#!/usr/bin/env python3 +""" +从视频读取连续帧，使用 OpenCV Farneback 稠密光流估计运动， +输出带光流伪彩色（方向=色调、速度=亮度）的可视化视频。 + +声呐类画面背景噪声也会在光流里产生响应；可用 --fish-mask 仅在高亮「鱼团」 +区域显示光流，并用连通域面积过滤掉细碎亮点。 +""" + +from __future__ import annotations +import argparse +import logging +import time +from pathlib import Path +from types import SimpleNamespace +from typing import Any + +import cv2 +import numpy as np + +_log = logging.getLogger("optical_flow") + + +def flow_to_bgr( + flow: np.ndarray, + mag_clip_percentile: float = 95.0, + valid_mask: np.ndarray | None = None, +) -> np.ndarray: + """将 (H,W,2) 光流转为 BGR 伪彩色图。若给定 valid_mask，幅值分位数仅在掩膜内统计。""" + fx = flow[..., 0].astype(np.float32) + fy = flow[..., 1].astype(np.float32) + mag = np.sqrt(fx * fx + fy * fy) + ang = np.arctan2(fy, fx) + + if valid_mask is not None and valid_mask.size > 0: + vm = valid_mask > 0 + if np.any(vm): + clip = float(np.percentile(mag[vm], mag_clip_percentile)) + else: + clip = float(np.percentile(mag, mag_clip_percentile)) + else: + clip = float(np.percentile(mag, mag_clip_percentile)) + if clip < 1e-6: + clip = 1e-6 + mag_norm = np.clip(mag / clip, 0.0, 1.0) + + h, w = flow.shape[:2] + hsv = np.zeros((h, w, 3), dtype=np.float32) + hsv[..., 0] = (ang + np.pi) / (2.0 * np.pi) * 179.0 + hsv[..., 1] = 255.0 + hsv[..., 2] = mag_norm * 255.0 + hsv_u8 = hsv.astype(np.uint8) + return cv2.cvtColor(hsv_u8, cv2.COLOR_HSV2BGR) + + +def _odd_k(k: int) -> int: + k = max(1, int(k)) + return k if k % 2 == 1 else k + 1 + + +def build_fish_mask( + gray_u8: np.ndarray, + *, + bright_percentile: float, + min_blob_area: int, + open_k: int, + close_k: int, + dilate_k: int, + blur_sigma: float, + keep_largest_blobs: int = 0, +) -> np.ndarray: + """ + 声呐/暗背景高亮目标：取灰度高分位作为阈值，形态学去噪，保留足够大的连通域。 + """ + g = gray_u8 + if blur_sigma > 1e-6: + k = _odd_k(int(round(blur_sigma * 6)) | 1) + k = max(3, min(k, 31)) + g = cv2.GaussianBlur(g, (k, k), blur_sigma) + + thr = float(np.percentile(g.astype(np.float32), bright_percentile)) + binary = (g.astype(np.float32) >= thr).astype(np.uint8) * 255 + + if open_k > 0: + ok = _odd_k(open_k) + kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (ok, ok)) + binary = cv2.morphologyEx(binary, cv2.MORPH_OPEN, kernel) + if close_k > 0: + ck = _odd_k(close_k) + kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (ck, ck)) + binary = cv2.morphologyEx(binary, cv2.MORPH_CLOSE, kernel) + + num, labels, stats, _ = cv2.connectedComponentsWithStats(binary, connectivity=8) + out = np.zeros_like(binary) + for i in range(1, num): + if stats[i, cv2.CC_STAT_AREA] >= min_blob_area: + out[labels == i] = 255 + + if dilate_k > 0: + dk = _odd_k(dilate_k) + kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (dk, dk)) + out = cv2.dilate(out, kernel) + + if keep_largest_blobs > 0: + num, labels, stats, _ = cv2.connectedComponentsWithStats(out, connectivity=8) + ranked: list[tuple[int, int]] = [] + for i in range(1, num): + ranked.append((int(stats[i, cv2.CC_STAT_AREA]), i)) + ranked.sort(reverse=True) + trimmed = np.zeros_like(out) + for _, comp_idx in ranked[:keep_largest_blobs]: + trimmed[labels == comp_idx] = 255 + out = trimmed + return out + + +def apply_flow_mask(flow_bgr: np.ndarray, mask_u8: np.ndarray) -> np.ndarray: + """掩膜外置黑；三通道与 mask 相乘。""" + m = (mask_u8.astype(np.float32) / 255.0)[..., np.newaxis] + return np.clip(flow_bgr.astype(np.float32) * m, 0, 255).astype(np.uint8) + + +def draw_flow_arrows( + flow: np.ndarray, + canvas_bgr: np.ndarray, + *, + mask_u8: np.ndarray | None, + step: int, + scale: float, + min_magnitude: float, + color: tuple[int, int, int], + thickness: int, +) -> np.ndarray: + """ + 将稠密光流按网格采样绘制为箭头，便于精确观察局部方向与速度。 + """ + out = canvas_bgr.copy() + h, w = flow.shape[:2] + s = max(2, int(step)) + t = max(1, int(thickness)) + mag2_thr = float(min_magnitude) * float(min_magnitude) + + for y in range(s // 2, h, s): + for x in range(s // 2, w, s): + if mask_u8 is not None and mask_u8[y, x] == 0: + continue + fx = float(flow[y, x, 0]) + fy = float(flow[y, x, 1]) + if fx * fx + fy * fy < mag2_thr: + continue + x2 = int(round(x + fx * scale)) + y2 = int(round(y + fy * scale)) + x2 = max(0, min(w - 1, x2)) + y2 = max(0, min(h - 1, y2)) + cv2.arrowedLine( + out, + (x, y), + (x2, y2), + color=color, + thickness=t, + tipLength=0.3, + ) + return out + + +def build_output_frame( + frame_bgr: np.ndarray, + flow_bgr: np.ndarray, + mode: str, +) -> np.ndarray: + if mode == "sidebyside": + return np.hstack([frame_bgr, flow_bgr]) + if mode == "overlay": + return cv2.addWeighted(frame_bgr, 0.55, flow_bgr, 0.45, 0.0) + raise ValueError(f"未知 mode: {mode}") + + +def _try_gst_nvenc_writer( + path: Path, + fps: float, + size: tuple[int, int], +) -> cv2.VideoWriter | None: + """Try to create a cv2.VideoWriter backed by GStreamer + Jetson NVENC. + + Returns None if GStreamer or the hardware encoder is unavailable. + """ + try: + if not hasattr(cv2, "CAP_GSTREAMER"): + return None + w, h = size + loc = str(path).replace('"', '\\"') + gst_pipe = ( + f'appsrc ! videoconvert ! video/x-raw,format=BGRx ! ' + f'nvvidconv ! video/x-raw(memory:NVMM) ! ' + f'nvv4l2h264enc bitrate=4000000 ! h264parse ! ' + f'mp4mux ! filesink location="{loc}"' + ) + writer = cv2.VideoWriter(gst_pipe, cv2.CAP_GSTREAMER, 0, fps, (w, h)) + if writer.isOpened(): + _log.info("[optical-flow] using GStreamer NVENC writer for %s", path.name) + return writer + writer.release() + except Exception: + pass + return None + + +def open_writer( + path: Path, + fps: float, + size: tuple[int, int], + fourcc_str: str, +) -> cv2.VideoWriter: + gst = _try_gst_nvenc_writer(path, fps, size) + if gst is not None: + return gst + fourcc = cv2.VideoWriter_fourcc(*fourcc_str) + writer = cv2.VideoWriter(str(path), fourcc, fps, size) + if not writer.isOpened(): + raise RuntimeError( + f"无法创建输出视频: {path}（codec={fourcc_str}）。" + "可尝试 --fourcc avc1 或 XVID 并配合 .avi 扩展名。" + ) + return writer + + +def _validate_flow_args(args: Any) -> None: + if not isinstance(args.resize, (int, float)) or float(args.resize) <= 0: + raise ValueError("--resize 必须为正数") + if not 0 < float(args.bright_percentile) < 100: + raise ValueError("--bright-percentile 应在 (0, 100) 内") + + +def _run_flow_core(args: Any) -> None: + """稠密光流 + 可视化；``args`` 需含与 CLI 相同的字段（含 ``input`` / ``output`` 路径）。""" + in_path = Path(args.input) + out_path = Path(args.output) + if not in_path.is_file(): + raise FileNotFoundError(f"找不到输入视频: {in_path}") + + src_size_mb = in_path.stat().st_size / (1024 * 1024) + _log.info("[optical-flow] start: %s (%.1f MB), mode=%s, resize=%.2f", + in_path.name, src_size_mb, args.mode, args.resize) + t0 = time.monotonic() + + cap = cv2.VideoCapture(str(in_path)) + if not cap.isOpened(): + raise RuntimeError(f"无法打开视频: {in_path}") + + writer: cv2.VideoWriter | None = None + try: + fps = cap.get(cv2.CAP_PROP_FPS) or 30.0 + w_in = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) + h_in = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) + _log.info("[optical-flow] src %dx%d @ %.1f fps", w_in, h_in, fps) + scale = float(args.resize) + + w = max(1, int(round(w_in * scale))) + h = max(1, int(round(h_in * scale))) + + if args.mode == "sidebyside": + out_w, out_h = w * 2, h + else: + out_w, out_h = w, h + + writer = open_writer(out_path, fps, (out_w, out_h), args.fourcc) + + ret, prev_bgr = cap.read() + if not ret: + raise RuntimeError("视频为空或无法读取首帧") + + if scale != 1.0: + prev_bgr = cv2.resize(prev_bgr, (w, h), interpolation=cv2.INTER_AREA) + prev_gray = cv2.cvtColor(prev_bgr, cv2.COLOR_BGR2GRAY) + + black_flow = np.zeros((h, w, 3), dtype=np.uint8) + first_out = build_output_frame(prev_bgr, black_flow, args.mode) + writer.write(first_out) + written = 1 + frame_idx = 0 + + fb_flags = 0 + progress_log = bool(getattr(args, "progress_log", False)) + while True: + ret, frame_bgr = cap.read() + if not ret: + break + if scale != 1.0: + frame_bgr = cv2.resize(frame_bgr, (w, h), interpolation=cv2.INTER_AREA) + gray = cv2.cvtColor(frame_bgr, cv2.COLOR_BGR2GRAY) + + flow = cv2.calcOpticalFlowFarneback( + prev_gray, + gray, + None, + args.pyr_scale, + args.levels, + args.winsize, + args.iterations, + args.poly_n, + args.poly_sigma, + fb_flags, + ) + mask: np.ndarray | None = None + if args.fish_mask: + mask = build_fish_mask( + gray, + bright_percentile=args.bright_percentile, + min_blob_area=args.min_blob_area, + open_k=args.mask_open, + close_k=args.mask_close, + dilate_k=args.mask_dilate, + blur_sigma=args.mask_blur, + keep_largest_blobs=args.keep_largest_blobs, + ) + has_mask = np.count_nonzero(mask) > 0 + else: + has_mask = False + + if args.viz_style in ("hsv", "hsv_arrows"): + if args.fish_mask and has_mask and mask is not None: + hsv_bgr = flow_to_bgr(flow, valid_mask=mask) + hsv_bgr = apply_flow_mask(hsv_bgr, mask) + elif args.fish_mask: + hsv_bgr = np.zeros((h, w, 3), dtype=np.uint8) + else: + hsv_bgr = flow_to_bgr(flow) + else: + hsv_bgr = np.zeros((h, w, 3), dtype=np.uint8) + + if args.viz_style in ("arrows", "hsv_arrows"): + arrow_base = np.zeros((h, w, 3), dtype=np.uint8) + arrow_bgr = draw_flow_arrows( + flow, + arrow_base, + mask_u8=mask if (args.fish_mask and has_mask) else None, + step=args.arrow_step, + scale=args.arrow_scale, + min_magnitude=args.arrow_threshold, + color=(0, 255, 255), + thickness=args.arrow_thickness, + ) + else: + arrow_bgr = np.zeros((h, w, 3), dtype=np.uint8) + + flow_bgr = cv2.addWeighted(hsv_bgr, 0.8, arrow_bgr, 1.0, 0.0) + out_frame = build_output_frame(frame_bgr, flow_bgr, args.mode) + writer.write(out_frame) + written += 1 + + prev_gray = gray + frame_idx += 1 + if progress_log and frame_idx % 30 == 0: + print(f"已处理 {frame_idx} 帧光流…", flush=True) + + elapsed = time.monotonic() - t0 + _log.info("[optical-flow] done: %d frames in %.1fs (%.1f fps) -> %s", + written, elapsed, written / max(elapsed, 0.001), out_path.name) + if progress_log: + print(f"完成，共写入 {written} 帧（{elapsed:.1f}s），保存至: {out_path}", flush=True) + finally: + cap.release() + if writer is not None: + writer.release() + + +def run_optical_flow_video( + input_path: Path, + output_path: Path, + *, + mode: str = "overlay", + viz_style: str = "hsv", + resize: float = 1.0, + fourcc: str = "mp4v", + pyr_scale: float = 0.5, + levels: int = 3, + winsize: int = 15, + iterations: int = 3, + poly_n: int = 5, + poly_sigma: float = 1.2, + fish_mask: bool = True, + bright_percentile: float = 97.5, + min_blob_area: int = 500, + mask_open: int = 3, + mask_close: int = 11, + mask_dilate: int = 5, + mask_blur: float = 1.0, + keep_largest_blobs: int = 0, + arrow_step: int = 12, + arrow_scale: float = 2.0, + arrow_threshold: float = 0.8, + arrow_thickness: int = 1, + progress_log: bool = False, +) -> bool: + """供 fish_api 等调用：成功写出 ``output_path`` 返回 True，否则 False。""" + args = SimpleNamespace( + input=input_path, + output=output_path, + mode=mode, + viz_style=viz_style, + resize=resize, + fourcc=fourcc, + pyr_scale=pyr_scale, + levels=levels, + winsize=winsize, + iterations=iterations, + poly_n=poly_n, + poly_sigma=poly_sigma, + fish_mask=fish_mask, + bright_percentile=bright_percentile, + min_blob_area=min_blob_area, + mask_open=mask_open, + mask_close=mask_close, + mask_dilate=mask_dilate, + mask_blur=mask_blur, + keep_largest_blobs=keep_largest_blobs, + arrow_step=arrow_step, + arrow_scale=arrow_scale, + arrow_threshold=arrow_threshold, + arrow_thickness=arrow_thickness, + progress_log=progress_log, + ) + try: + _validate_flow_args(args) + _run_flow_core(args) + except Exception: + _log.exception("[optical-flow] run_optical_flow_video failed: %s -> %s", + input_path, output_path) + return False + return output_path.is_file() and output_path.stat().st_size > 0 + + +def main() -> None: + script_dir = Path(__file__).resolve().parent + default_in = script_dir / "fish_echo.MP4" + default_out = script_dir / "fish_echo_flow_vis.mp4" + + p = argparse.ArgumentParser(description="视频稠密光流可视化（OpenCV Farneback）") + p.add_argument( + "--input", + "-i", + type=Path, + default=default_in, + help=f"输入视频路径（默认: {default_in.name}）", + ) + p.add_argument( + "--output", + "-o", + type=Path, + default=default_out, + help=f"输出视频路径（默认: {default_out.name}）", + ) + p.add_argument( + "--mode", + choices=("sidebyside", "overlay"), + default="sidebyside", + help="sidebyside: 原图|光流；overlay: 原图与光流半透明叠加", + ) + p.add_argument( + "--viz-style", + choices=("hsv", "arrows", "hsv_arrows"), + default="hsv", + help="光流可视化风格：hsv 伪彩色、arrows 箭头、hsv_arrows 组合", + ) + p.add_argument( + "--resize", + type=float, + default=1.0, + metavar="SCALE", + help="处理前将帧宽高乘以该比例以加速（例如 0.5）", + ) + p.add_argument( + "--fourcc", + default="mp4v", + help="VideoWriter 四字符编码，常见: mp4v, avc1, XVID", + ) + p.add_argument( + "--pyr-scale", + type=float, + default=0.5, + help="Farneback 金字塔缩放（OpenCV pyr_scale）", + ) + p.add_argument( + "--levels", + type=int, + default=3, + help="Farneback 金字塔层数", + ) + p.add_argument( + "--winsize", + type=int, + default=15, + help="Farneback 窗口大小", + ) + p.add_argument( + "--iterations", + type=int, + default=3, + help="Farneback 每层迭代次数", + ) + p.add_argument( + "--poly-n", + type=int, + default=5, + help="Farneback 像素邻域大小（poly_n）", + ) + p.add_argument( + "--poly-sigma", + type=float, + default=1.2, + help="Farneback 高斯标准差（poly_sigma）", + ) + p.add_argument( + "--fish-mask", + dest="fish_mask", + action="store_true", + default=True, + help="仅在高亮鱼团区域显示光流（默认开启，抑制背景噪声）", + ) + p.add_argument( + "--no-fish-mask", + dest="fish_mask", + action="store_false", + help="关闭鱼团掩膜，整幅画面显示光流（与旧行为一致）", + ) + p.add_argument( + "--bright-percentile", + type=float, + default=97.5, + metavar="P", + help="灰度阈值：取 >= 该分位数的像素作为「亮斑」候选（越高越只保留最亮区域）", + ) + p.add_argument( + "--min-blob-area", + type=int, + default=500, + metavar="PX", + help="连通域最小面积（像素），小于此面积的亮斑视为噪声并丢弃", + ) + p.add_argument( + "--mask-open", + type=int, + default=3, + help="形态学开运算核大小（奇数，0 表示跳过），去小噪点", + ) + p.add_argument( + "--mask-close", + type=int, + default=11, + help="形态学闭运算核大小（奇数，0 表示跳过），连接同一鱼团碎片", + ) + p.add_argument( + "--mask-dilate", + type=int, + default=5, + help="掩膜膨胀核大小（奇数，0 表示不膨胀），略扩大显示区域包住边缘运动", + ) + p.add_argument( + "--mask-blur", + type=float, + default=1.0, + help="阈值前高斯模糊 sigma（0 表示不模糊），可平滑细碎纹理", + ) + p.add_argument( + "--keep-largest-blobs", + type=int, + default=0, + metavar="N", + help="在面积过滤后仅保留面积最大的 N 个连通域（0 表示不限制，适合多鱼场景）", + ) + p.add_argument( + "--arrow-step", + type=int, + default=12, + help="箭头网格步长（像素），越小越密", + ) + p.add_argument( + "--arrow-scale", + type=float, + default=2.0, + help="箭头长度缩放系数", + ) + p.add_argument( + "--arrow-threshold", + type=float, + default=0.8, + help="绘制箭头的最小速度阈值（像素/帧）", + ) + p.add_argument( + "--arrow-thickness", + type=int, + default=1, + help="箭头线宽", + ) + args = p.parse_args() + if not args.input.is_file(): + raise SystemExit(f"找不到输入视频: {args.input}") + try: + _validate_flow_args(args) + except ValueError as e: + raise SystemExit(str(e)) from e + args.progress_log = True + try: + _run_flow_core(args) + except FileNotFoundError as e: + raise SystemExit(str(e)) from e + except RuntimeError as e: + raise SystemExit(str(e)) from e + + +if __name__ == "__main__": + main() diff --git a/FishMeasure/predict_weigth_from_svo2.py b/FishMeasure/predict_weigth_from_svo2.py index 48a178b..e66e6cb 100644 --- a/FishMeasure/predict_weigth_from_svo2.py +++ b/FishMeasure/predict_weigth_from_svo2.py @@ -645,7 +645,7 @@ def main() -> None: ) parser.add_argument( "--summary-star", - action=argparse.BooleanOptionalAction, + action="store_true", default=False, help="Pass to generate_video_with_labels: whether the summary line should draw *.", ) diff --git a/docs/conda-deploy.md b/docs/conda-deploy.md index 26970bf..580aadc 100644 --- a/docs/conda-deploy.md +++ b/docs/conda-deploy.md @@ -32,6 +32,7 @@ conda activate fishserver ```bash cd /path/to/FishServer/fish_api pip install -e . +# 或：pip install -r requirements.txt ``` 可选开发依赖（本地冒烟测试等）： @@ -71,20 +72,11 @@ python -c "import torch; print(torch.__version__)" **方案 A 下不要填写** `PYTHON_FISH_MEASURE`、`PYTHON_FISH_ACTION`（留空），让子进程使用当前环境的 `python`。 ---- - -## 5. 与 `uv` 的优先级 - -`fish_api/start_fresh.sh` 若检测到系统中有 `uv` 命令，会优先使用 `uv run`。若你希望**强制使用当前 conda 环境**： - -- 启动前执行 **`conda activate fishserver`**，并确认 `which python` / `which uvicorn` 指向 `fishserver`；或 -- 在不含 `uv` 的 PATH 下启动；或临时从 PATH 中移除 `uv`。 - -若未安装 `uv`，脚本会自动使用 `python3` 与 `uvicorn`（依赖已激活的 conda）。 +启动前请 **`conda activate fishserver`**，使 `which python` / `which uvicorn` 指向该环境。`fish_api/start_fresh.sh` 使用环境变量 **`PYTHON`**（可选）覆盖解释器，默认 **`python3`**。 --- -## 6. 启动服务 +## 5. 启动服务 在**仓库根目录**执行： @@ -109,7 +101,7 @@ HOST=0.0.0.0 PORT=8000 bash scripts/start_fresh.sh --- -## 7. 自检清单 +## 6. 自检清单 | 检查项 | 说明 | |--------|------| @@ -122,7 +114,7 @@ HOST=0.0.0.0 PORT=8000 bash scripts/start_fresh.sh --- -## 8. 参考 +## 7. 参考 - 根目录 `README.md`、`fish_api/README.md`：环境变量表与启动说明。 - `fish_api/start_fresh.sh`：启动前 `prestart_fresh` 与 uvicorn 参数。 diff --git a/fish_api/README.md b/fish_api/README.md index 50324a3..2b19b0f 100644 --- a/fish_api/README.md +++ b/fish_api/README.md @@ -2,52 +2,54 @@ CLEAR_SQLITE_DATABASE=1 CLEAR_MEASURE_OUTPUT=1 CLEAR_ACTION_OUTPUT=1 CLEAR_MEDIA=1 CLEAR_STREAM_TMP=1 LD_PRELOAD=/lib/aarch64-linux-gnu/libGLdispatch.so.0 bash scripts/start_fresh.sh - - ## 配置（环境变量） -| 变量 | 说明 | 默认 | -|------|------|------| -| `PUBLIC_BASE_URL` | 返回 JSON 中 `video_left` / `video_right` 的前缀（勿带末尾 `/`） | `http://127.0.0.1:8000` | -| `INGEST_API_KEY` | 非空时，`/api/v1/ingest/*` 需请求头 `X-API-Key` | 空（不校验） | -| `STREAM_TMP_DIR` | 分块上传临时目录 | `/fish_api/.data/ingest` | -| `MEDIA_ROOT` | 对外托管每次测量生成的 `*_left.mp4` / `*_right.mp4` | `/fish_api/.data/media` | -| `FISH_MEASURE_ROOT` | `FishMeasure` 根目录 | 自动相对仓库 | -| `FISH_ACTION_ROOT` | `FishAction` 根目录 | 自动相对仓库 | -| `MEASURE_OUTPUT_ROOT` | 传给 `--save-output` 的目录 | `/fish_api/.data/measure_output` | -| `YOLO_MODEL` / `WEIGHT_CHECKPOINT` / `ACTION_CHECKPOINT` | 模型路径 | 与仓库内脚本默认一致 | -| `SAM_DEVICE` | `cuda` 或 `cpu` | `cuda` | -| `MEASURE_FINAL_AGGREGATE_MODE` | 齐套后对各段 former 体重/体长聚合：`median` / `mean` / `trimmed_mean` | `median` | -可在 `fish_api/.env` 中填写上述变量（`pydantic-settings` 会读取）。 + +| 变量 | 说明 | 默认 | +| -------------------------------------------------------- | -------------------------------------------------------- | -------------------------------------- | +| `PUBLIC_BASE_URL` | 返回 JSON 中 `video_left` / `video_right` 的前缀（勿带末尾 `/`） | `http://127.0.0.1:8000` | +| `INGEST_API_KEY` | 非空时，`/api/v1/ingest/*` 需请求头 `X-API-Key` | 空（不校验） | +| `STREAM_TMP_DIR` | 分块上传临时目录 | `/fish_api/.data/ingest` | +| `MEDIA_ROOT` | 对外托管每次测量生成的 `*_left.mp4` / `*_right.mp4` | `/fish_api/.data/media` | +| `FISH_MEASURE_ROOT` | `FishMeasure` 根目录 | 自动相对仓库 | +| `FISH_ACTION_ROOT` | `FishAction` 根目录 | 自动相对仓库 | +| `MEASURE_OUTPUT_ROOT` | 传给 `--save-output` 的目录 | `/fish_api/.data/measure_output` | +| `YOLO_MODEL` / `WEIGHT_CHECKPOINT` / `ACTION_CHECKPOINT` | 模型路径 | 与仓库内脚本默认一致 | +| `SAM_DEVICE` | `cuda` 或 `cpu` | `cuda` | +| `MEASURE_FINAL_AGGREGATE_MODE` | 齐套后对各段 former 体重/体长聚合：`median` / `mean` / `trimmed_mean` | `median` | +| 可在 `fish_api/.env` 中填写上述变量（`pydantic-settings` 会读取）。 | | | + ## 安装与启动 ```bash cd fish_api -python3 -m pip install -e . # 安装 pyproject 依赖（无 venv 时可用 --user 或系统 site-packages） -./scripts/start_fresh.sh # 默认仅重置 client_id 投递进度，保留 SQLite 历史与快照 +python3 -m pip install -e . # 或：python3 -m pip install -r requirements.txt +bash start_fresh.sh # 默认仅重置 client_id 投递进度，保留 SQLite 历史与快照 # CLEAR_SQLITE_DATABASE=1 bash start_fresh.sh # 需要时才彻底清 SQLite # python3 -m uvicorn app.main:app --host 0.0.0.0 --port 8000 # 需自行 prestart ``` -## SVO 输入与 ``measure_watch``（两种来源，同一套目录逻辑） +## SVO 输入与 `measure_watch`（两种来源，同一套目录逻辑） -后台 **[``measure_watch``](app/services/measure_watch.py)** 只认 **`MEASURE_WATCH_DIR`** 下一层的 **`fish{N}/`** 子目录及其中的 **``.svo2``**（见 ``iter_svo2_folders``）。与入口无关： +后台 `**[measure_watch](app/services/measure_watch.py)**` 只认 `**MEASURE_WATCH_DIR**` 下一层的 `**fish{N}/**` 子目录及其中的 `**.svo2**`（见 `iter_svo2_folders`）。与入口无关： -| 方式 | 说明 | -|------|------| -| **ZED 分段录制** | 每次会话分配 ``fish_id``：取库表（``fish_id`` 与 ``output_dir`` 路径）、父目录下 ``fish``+数字 子目录名、以及 ``fish{N}/`` 下已有 ``.svo2`` 路径 四者编号的最大值再加 1（磁盘有数据而库未记时也不冲突）；文件写入 ``{MEASURE_WATCH_DIR}/fish{N}/``（若未配置 ``MEASURE_WATCH_DIR`` 则 ``{STREAM_TMP_DIR}/zed_svo2/fish{N}/``，此时**不会**启用 ``measure_watch``，除非把 ``MEASURE_WATCH_DIR`` 指到 ``…/ingest/zed_svo2``） | -| **手工拷贝** | 将 ``.svo2`` 放入 ``MEASURE_WATCH_DIR/fish{N}/``（自建 ``fish{N}`` 即可） | -**逐段测量与齐套 final**：对每个 ``.svo2`` 稳定后轮询跑 FishMeasure，写入 SQLite；服务端可在 ``calculation_log`` 中区分 segment/final。``GET /api/v1/biomass/real/camera/`` 的 ``data.result[]`` **仅含** ``id``、``type``、``length``、``weight``、``date``（与历史客户端约定一致）；按投递顺序先可能收到多段再收到聚合行。``video_left`` / ``video_right`` 规则不变。 +| 方式 | 说明 | +| ------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| **ZED 分段录制** | 每次会话分配 `fish_id`：取库表（`fish_id` 与 `output_dir` 路径）、父目录下 `fish`+数字 子目录名、以及 `fish{N}/` 下已有 `.svo2` 路径 四者编号的最大值再加 1（磁盘有数据而库未记时也不冲突）；文件写入 `{MEASURE_WATCH_DIR}/fish{N}/`（若未配置 `MEASURE_WATCH_DIR` 则 `{STREAM_TMP_DIR}/zed_svo2/fish{N}/`，此时**不会**启用 `measure_watch`，除非把 `MEASURE_WATCH_DIR` 指到 `…/ingest/zed_svo2`） | +| **手工拷贝** | 将 `.svo2` 放入 `MEASURE_WATCH_DIR/fish{N}/`（自建 `fish{N}` 即可） | -**ingest** 的 ``/api/v1/ingest/svo/...`` 为分块上传流程，落盘与上述 ``fish{N}`` 路径独立；``fish{N}`` 目录的测量以 ``measure_watch`` 为准。 -**ZED 相机**：**fish_api 启动/停止不会自动开关相机录制**；录制由独立服务或仓库根 ``start_recording.sh`` / ``zed_record_cli`` 等自行管理，写入 ``MEASURE_WATCH_DIR/fish{N}/`` 后由 ``measure_watch`` 消费。需要时也可调 ``/api/v1/zed/recording/start|stop``（不经过 uvicorn lifespan）。 +**逐段测量与齐套 final**：对每个 `.svo2` 稳定后轮询跑 FishMeasure，写入 SQLite；服务端可在 `calculation_log` 中区分 segment/final。`GET /api/v1/biomass/real/camera/` 的 `data.result[]` **仅含** `id`、`type`、`length`、`weight`、`date`（与历史客户端约定一致）；按投递顺序先可能收到多段再收到聚合行。`video_left` / `video_right` 规则不变。 + +**ingest** 的 `/api/v1/ingest/svo/...` 为分块上传流程，落盘与上述 `fish{N}` 路径独立；`fish{N}` 目录的测量以 `measure_watch` 为准。 + +**ZED 相机**：**fish_api 启动/停止不会自动开关相机录制**；录制由独立服务或仓库根 `start_recording.sh` / `zed_record_cli` 等自行管理，写入 `MEASURE_WATCH_DIR/fish{N}/` 后由 `measure_watch` 消费。需要时也可调 `/api/v1/zed/recording/start|stop`（不经过 uvicorn lifespan）。 ## ZED 分段录制 CLI -每次 **开始/停止** 录制会在 SQLite 表 ``zed_recording_sessions`` 中记录一行（``fish_id``、``started_at`` / ``stopped_at``、``output_dir``；分配规则见上表）。 +每次 **开始/停止** 录制会在 SQLite 表 `zed_recording_sessions` 中记录一行（`fish_id`、`started_at` / `stopped_at`、`output_dir`；分配规则见上表）。 在 `fish_api` 目录下执行（与 `app` 包路径一致；依赖已 `pip install -e .`）： @@ -79,7 +81,7 @@ python3 -m app.zed_record_cli status 1. **440g 全池均值保护**（规则 B）：若 `avg_g_filtered`（所有 candidates 均值）> `--mean-pool-fallback-max-if-over-g`（默认 440g），则 `pred_weight_g = max_predicted_weight_g_after_filter`，`pred_weight_rule = "max_after_filter_high_mean_pool_over_g"`。 2. **400g mean-all fallback**（规则 A，仅 `--average-all-after-filter` 开启时）：若全池 mean > `--average-all-fallback-max-if-mean-over-g`（默认 400g），则 `pred_weight_g = max_predicted_weight_g_after_filter`，`pred_weight_rule = "max_after_filter_high_mean_all"`。 -3. **`--average-all-after-filter`**（默认关）：全部 candidates 均值作为最终值，`pred_weight_rule = "mean_all_filtered"`。 +3. `**--average-all-after-filter`**（默认关）：全部 candidates 均值作为最终值，`pred_weight_rule = "mean_all_filtered"`。 4. **Top-K 聚合**（默认路径）：按 `--top-by-length`（默认开）选 top-K 帧，对选中帧的预测重量取**算术平均**（与日志中 `top{k}_avg` 一致），`pred_weight_rule = "top_k_aggregate"`。 DGCNN 明细中同时输出 `mean_all_pred_g_after_filters`、`avg_topk_mean_pred_g` 等供对比参考。 @@ -88,7 +90,7 @@ DGCNN 明细中同时输出 `mean_all_pred_g_after_filters`、`avg_topk_mean_pre 不启动 uvicorn、**不写 SQLite**、**不发布** `MEDIA_ROOT`（与 `run_full_measure_batch` 相比仅少快照与媒体发布；FishMeasure 子进程与 `measure_output/fish{N}` 与线上一致）。 -调用 [`app/services/measure.py`](app/services/measure.py) 中的 `run_measure_batch_subprocess`，配置与 `fish_api/.env` 相同（`get_settings()`）。 +调用 `[app/services/measure.py](app/services/measure.py)` 中的 `run_measure_batch_subprocess`，配置与 `fish_api/.env` 相同（`get_settings()`）。 **必须在 `fish_api` 下执行 `python -m app...`，或从仓库根用下面脚本**；若在仓库根直接运行 `python -m app.measure_debug_cli`，会因找不到 `app` 包报错（`ModuleNotFoundError: No module named 'app'`）。 @@ -115,3 +117,4 @@ python3 -m app.measure_debug_cli --batch-folder /path/to/fish1 --fish-id 1 --out - RTSP：用 `ffmpeg` 切段写入 MP4 后调用现有 `finalize` 逻辑 - 任务状态：`finalize` 返回 `job_id`，增加 `GET /jobs/{id}` 查询进度 + diff --git a/fish_api/app/db.py b/fish_api/app/db.py index 5125096..f1392df 100644 --- a/fish_api/app/db.py +++ b/fish_api/app/db.py @@ -23,37 +23,6 @@ from app.state import HealthSnapshot, MeasureSnapshot DEFAULT_CLIENT_ID = "default" MAX_CLIENT_ID_LEN = 128 -# 客户端切片索引起缓存：记录每个 client_id 上次返回的切片索引（用于对齐 water/video 端点） -_client_health_slice_index = {} # type: Dict[str, int] - - -def _parse_slice_index_from_source_path(source_path: Optional[str]) -> int: - """从 source_path 解析切片索引，格式为 video.mp4#slice{N}。 - - Returns: - 切片序号（>=0），如果不是切片则返回 -1 - """ - if not source_path: - return -1 - if "#slice" not in source_path: - return -1 - try: - idx_part = source_path.split("#slice")[-1] - return int(idx_part) - except (ValueError, IndexError): - return -1 - - -def get_last_health_slice_index(client_id: str) -> int: - """获取指定 client_id 上次返回的切片索引（用于 water/video 端点对齐）。 - - Returns: - 切片序号（>=0），如果没有记录则返回 -1 - """ - cid = normalize_client_id(client_id) - return _client_health_slice_index.get(cid, -1) - - def normalize_client_id(raw: Optional[str]) -> str: """供轮询接口使用；过长截断，空值回退为 DEFAULT_CLIENT_ID。""" if raw is None: @@ -103,7 +72,8 @@ def init_db(settings: Settings) -> None: health_result TEXT NOT NULL DEFAULT '', raw_class_en TEXT NOT NULL DEFAULT '', error TEXT, - source_path TEXT + source_path TEXT, + video_url TEXT NOT NULL DEFAULT '' ); CREATE TABLE IF NOT EXISTS watch_processed ( @@ -136,6 +106,7 @@ def init_db(settings: Settings) -> None: _migrate_add_client_id_column(conn) _migrate_add_pred_star_columns(conn) _migrate_add_calculation_log_column(conn) + _migrate_add_health_video_url_column(conn) finally: conn.close() @@ -221,7 +192,7 @@ def begin_zed_recording_session(settings: Settings) -> Tuple[int, int, Path]: fs_dir = _max_numeric_fish_folder_id(parent) fs_svo = _max_fish_id_from_svo2_under_parent(parent) fs_max = max(fs_dir, fs_svo) - fish_id = max(db_max, fs_max) + 1 + fish_id = max(db_max, fs_max, 99) + 1 ts = datetime.now(timezone.utc).isoformat() output_dir = (parent / f"fish{fish_id}").resolve() # 极端情况：并发或其它原因导致目录已存在，则顺延编号 @@ -327,6 +298,22 @@ def _migrate_add_calculation_log_column(conn: sqlite3.Connection) -> None: conn.commit() +def _migrate_add_health_video_url_column(conn: sqlite3.Connection) -> None: + """为旧数据库的 health_snapshots 添加 video_url 列（如果不存在）。""" + row = conn.execute( + "SELECT name FROM sqlite_master WHERE type='table' AND name='health_snapshots'" + ).fetchone() + if row is None: + return + cols = conn.execute("PRAGMA table_info(health_snapshots)").fetchall() + col_names = {col[1] for col in cols} + if "video_url" not in col_names: + conn.execute( + "ALTER TABLE health_snapshots ADD COLUMN video_url TEXT NOT NULL DEFAULT ''" + ) + conn.commit() + + def _migrate_delivery_cursor_from_legacy(conn: sqlite3.Connection) -> None: """旧表 delivery_cursor(kind) → delivery_client_cursor(default, kind)。""" row = conn.execute( @@ -419,6 +406,7 @@ def save_health_snapshot( settings: Settings, snap: HealthSnapshot, source_path: Optional[str] = None, + video_url: str = "", ) -> None: init_db(settings) conn = _connect(settings.sqlite_path) @@ -432,8 +420,8 @@ def save_health_snapshot( """ INSERT INTO health_snapshots ( created_at, behavior_result, health_result, - raw_class_en, error, source_path - ) VALUES (?, ?, ?, ?, ?, ?) + raw_class_en, error, source_path, video_url + ) VALUES (?, ?, ?, ?, ?, ?, ?) """, ( ts, @@ -442,6 +430,7 @@ def save_health_snapshot( snap.raw_class_en, snap.error, source_path, + video_url, ), ) finally: @@ -537,7 +526,7 @@ def list_all_health_snapshots(settings: Settings) -> List[Dict[str, Any]]: rows = conn.execute( """ SELECT id, created_at, behavior_result, health_result, - raw_class_en, error, source_path + raw_class_en, error, source_path, video_url FROM health_snapshots ORDER BY id DESC """ @@ -553,6 +542,7 @@ def list_all_health_snapshots(settings: Settings) -> List[Dict[str, Any]]: "raw_class_en": row["raw_class_en"] or "", "error": row["error"], "source_path": row["source_path"], + "video_path": row["video_url"] or "", } ) return out @@ -567,7 +557,7 @@ def get_latest_health(settings: Settings) -> HealthSnapshot: row = conn.execute( """ SELECT created_at, behavior_result, health_result, - raw_class_en, error + raw_class_en, error, video_url FROM health_snapshots ORDER BY id DESC LIMIT 1 @@ -581,6 +571,7 @@ def get_latest_health(settings: Settings) -> HealthSnapshot: updated_at=_parse_dt(row["created_at"]), error=row["error"], raw_class_en=row["raw_class_en"] or "", + video_path=row["video_url"] or "", ) finally: conn.close() @@ -770,8 +761,6 @@ def pop_next_health( client_id: str = DEFAULT_CLIENT_ID, ) -> Tuple[HealthSnapshot, bool, Optional[int]]: """取该客户端队首未投递且可交付的 health 快照并推进其游标；跳过不可交付行仅推进游标。""" - global _client_health_slice_index - cid = normalize_client_id(client_id) init_db(settings) conn = _connect(settings.sqlite_path) @@ -783,7 +772,7 @@ def pop_next_health( next_row = conn.execute( """ SELECT id, created_at, behavior_result, health_result, - raw_class_en, error, source_path + raw_class_en, error, source_path, video_url FROM health_snapshots WHERE id > ? ORDER BY id ASC @@ -801,7 +790,6 @@ def pop_next_health( hlth = next_row["health_result"] or "" raw_en = next_row["raw_class_en"] or "" err: Optional[str] = next_row["error"] - source_path: Optional[str] = next_row["source_path"] conn.execute( """ @@ -817,16 +805,13 @@ def pop_next_health( conn.commit() - # 解析并记录切片索引（用于与 water/video 端点对齐） - slice_idx = _parse_slice_index_from_source_path(source_path) - _client_health_slice_index[cid] = slice_idx - snap = HealthSnapshot( behavior_result=beh, health_result=hlth, updated_at=_parse_dt(next_row["created_at"]), error=err, raw_class_en=raw_en, + video_path=next_row["video_url"] or "", ) return snap, True, nid except Exception: @@ -836,6 +821,41 @@ def pop_next_health( conn.close() +def peek_last_delivered_health_video_url( + settings: Settings, + client_id: str = DEFAULT_CLIENT_ID, +) -> str: + """返回该客户端上一次 ``pop_next_health`` 投递的行的 ``video_url``（不推进游标）。 + + 用于 ``/water/video/`` 端点对齐：返回与最近投递的 health 快照相同的视频 URL。 + """ + cid = normalize_client_id(client_id) + init_db(settings) + conn = _connect(settings.sqlite_path) + try: + cursor_row = conn.execute( + """ + SELECT last_delivered_id FROM delivery_client_cursor + WHERE kind = ? AND client_id = ? + """, + ("health", cid), + ).fetchone() + if cursor_row is None: + return "" + last_id = int(cursor_row["last_delivered_id"]) + if last_id <= 0: + return "" + row = conn.execute( + "SELECT video_url FROM health_snapshots WHERE id = ?", + (last_id,), + ).fetchone() + if row is None: + return "" + return row["video_url"] or "" + finally: + conn.close() + + def _load_json_processed_set(path: Path) -> Set[str]: if not path.is_file(): return set() diff --git a/fish_api/app/main.py b/fish_api/app/main.py index 2c171d2..9c5858a 100644 --- a/fish_api/app/main.py +++ b/fish_api/app/main.py @@ -12,6 +12,7 @@ from app.db import init_db from app.routers import biomass, debug, ingest, zed from app.services.action_watch import run_action_watch_loop from app.services.measure_watch import run_measure_watch_loop +from app.services.sonar_video import run_sonar_video_watch_loop from app.settings import get_settings setup_logging() @@ -29,6 +30,8 @@ async def lifespan(app: FastAPI): tasks.append(asyncio.create_task(run_action_watch_loop(s))) if s.measure_watch_dir is not None: tasks.append(asyncio.create_task(run_measure_watch_loop(s))) + if s.biomass_sonar_video_dir is not None: + tasks.append(asyncio.create_task(run_sonar_video_watch_loop(s))) yield for t in tasks: diff --git a/fish_api/app/routers/ingest.py b/fish_api/app/routers/ingest.py index 88ae8fd..5dc36d9 100644 --- a/fish_api/app/routers/ingest.py +++ b/fish_api/app/routers/ingest.py @@ -50,15 +50,17 @@ async def _action_job_serial(mp4_path: Path, settings: Settings) -> None: async with app_state.action_lock: app_state.action_status = "running" try: - # 返回 (第一个快照, 所有切片快照列表) - first_snap, all_snaps = await to_thread( - action_svc.run_full_action, mp4_path, settings + slice_files, duration = await to_thread( + action_svc.prepare_action_slices, mp4_path, settings ) + total_slices = len(slice_files) - # 将所有切片作为独立视频保存到数据库 - for i, snap in enumerate(all_snaps): + for i, slice_file in enumerate(slice_files): + snap = await to_thread( + action_svc.run_single_slice_inference, + slice_file, i, total_slices, duration, mp4_path.name, settings, + ) if health_snapshot_deliverable(snap): - # 为每个切片生成独立的 source_path slice_source = f"{mp4_path.resolve()}#slice{i}" save_health_snapshot(settings, snap, source_path=slice_source) diff --git a/fish_api/app/services/action.py b/fish_api/app/services/action.py index ed3eb7a..8a9c963 100644 --- a/fish_api/app/services/action.py +++ b/fish_api/app/services/action.py @@ -102,109 +102,78 @@ def run_action_subprocess(mp4_path: Path, settings: Settings) -> str: Path(out_json).unlink(missing_ok=True) -def run_full_action(mp4_path: Path, settings: Settings) -> Tuple[HealthSnapshot, List[HealthSnapshot]]: - """运行 FishAction 健康检测。如果视频较长，会自动切片后分别检测。 +def prepare_action_slices( + mp4_path: Path, settings: Settings +) -> Tuple[List[Path], float]: + """Check video duration and create slices if needed. - 每个切片被视为独立的视频，返回所有切片的结果列表。 - - Args: - mp4_path: 输入视频路径 - settings: 应用配置 - - Returns: - Tuple[HealthSnapshot, List[HealthSnapshot]]: (第一个切片/完整视频的快照, 所有切片快照列表) - - 如果视频被切片：返回 (第一个切片, 所有切片列表) - - 如果视频未被切片：返回 (完整视频快照, [完整视频快照]) + Returns ``(slice_files, duration)``. If the video is short enough, + returns ``([mp4_path], duration)`` without slicing. """ logger.info("[FishAction] start mp4={}", mp4_path.resolve()) - - # 检查视频时长 duration = get_video_duration(mp4_path) - should_slice = duration > DEFAULT_MIN_DURATION_FOR_SLICE - if should_slice: - # 视频较长，需要切片处理 + if duration > DEFAULT_MIN_DURATION_FOR_SLICE: logger.info( "[FishAction] video duration {}s > {}s, slicing into {}s segments", duration, DEFAULT_MIN_DURATION_FOR_SLICE, DEFAULT_SLICE_DURATION, ) - - slice_files, slice_dir = slice_video(mp4_path, DEFAULT_SLICE_DURATION) - + slice_files, _slice_dir = slice_video(mp4_path, DEFAULT_SLICE_DURATION) if len(slice_files) > 1: logger.info( "[FishAction] processing {} slices for {}", len(slice_files), mp4_path.name, ) + return slice_files, duration - # 处理每个切片 - all_snaps = [] # type: List[HealthSnapshot] - for i, slice_file in enumerate(slice_files): - start_time = i * DEFAULT_SLICE_DURATION - end_time = min(start_time + DEFAULT_SLICE_DURATION, duration) + return [mp4_path], duration - try: - pred_en = run_action_subprocess(slice_file, settings) - zh = BEHAVIOR_EN_TO_ZH[pred_en] - health = behavior_to_health(pred_en) - snap = HealthSnapshot( - behavior_result=zh, - health_result=health, - updated_at=datetime.now(timezone.utc), - raw_class_en=pred_en, - ) +def run_single_slice_inference( + slice_file: Path, + slice_index: int, + total_slices: int, + duration: float, + mp4_name: str, + settings: Settings, +) -> HealthSnapshot: + """Run FishAction inference on a single video file / slice. - logger.info( - "[FishAction] slice {} ({}s-{}s): pred={} behavior={} health={}", - i, start_time, end_time, pred_en, zh, health, - ) - - all_snaps.append(snap) - except Exception as e: - logger.error("[FishAction] failed to process slice {}: {}", i, e) - # 创建一个表示失败的快照 - error_snap = HealthSnapshot( - behavior_result="处理失败", - health_result="未知", - updated_at=datetime.now(timezone.utc), - raw_class_en="error", - error=str(e), - ) - all_snaps.append(error_snap) + Returns a ``HealthSnapshot`` (may contain error info if inference fails). + """ + start_time = slice_index * DEFAULT_SLICE_DURATION + end_time = min(start_time + DEFAULT_SLICE_DURATION, duration) + try: + pred_en = run_action_subprocess(slice_file, settings) + zh = BEHAVIOR_EN_TO_ZH[pred_en] + health = behavior_to_health(pred_en) + snap = HealthSnapshot( + behavior_result=zh, + health_result=health, + updated_at=datetime.now(timezone.utc), + raw_class_en=pred_en, + ) + if total_slices > 1: logger.info( - "[FishAction] done mp4={} total_slices={}", - mp4_path.name, - len(slice_files), + "[FishAction] slice {} ({}s-{}s): pred={} behavior={} health={}", + slice_index, start_time, end_time, pred_en, zh, health, ) - - # 返回第一个切片的结果和所有切片列表 - first_snap = all_snaps[0] if all_snaps else HealthSnapshot( - behavior_result="", - health_result="", - updated_at=datetime.now(timezone.utc), + else: + logger.info( + "[FishAction] done mp4={} pred_3class={} behavior_zh={} health={}", + mp4_name, pred_en, zh, health, ) - return first_snap, all_snaps - - # 视频较短，直接处理（原有逻辑） - pred_en = run_action_subprocess(mp4_path, settings) - zh = BEHAVIOR_EN_TO_ZH[pred_en] - health = behavior_to_health(pred_en) - logger.info( - "[FishAction] done mp4={} pred_3class={} behavior_zh={} health={}", - mp4_path.name, - pred_en, - zh, - health, - ) - snap = HealthSnapshot( - behavior_result=zh, - health_result=health, - updated_at=datetime.now(timezone.utc), - raw_class_en=pred_en, - ) - return snap, [snap] + return snap + except Exception as e: + logger.error("[FishAction] failed to process slice {}: {}", slice_index, e) + return HealthSnapshot( + behavior_result="处理失败", + health_result="未知", + updated_at=datetime.now(timezone.utc), + raw_class_en="error", + error=str(e), + ) diff --git a/fish_api/app/services/action_watch.py b/fish_api/app/services/action_watch.py index fc42ac5..1b4b3e4 100644 --- a/fish_api/app/services/action_watch.py +++ b/fish_api/app/services/action_watch.py @@ -1,6 +1,7 @@ from __future__ import annotations import asyncio +import shutil from pathlib import Path from typing import Dict, List, Set, Tuple @@ -15,6 +16,7 @@ from app.db import ( save_health_snapshot, ) from app.services import action as action_svc +from app.services.measure import transcode_src_to_h264_dst from app.settings import Settings from app.state import app_state from app.watch_idle import IdleWatchWarnState, idle_warn_interval_sec, maybe_warn_idle_watch @@ -30,20 +32,68 @@ def _state_path(settings: Settings) -> Path: return settings.action_watch_dir / ".fishaction_watch_processed.json" +_VIDEO_SUFFIXES = frozenset({".mp4", ".mkv", ".mov"}) + + def iter_mp4(watch_dir: Path, recursive: bool) -> List[Path]: + """List video files (.mp4, .mkv, .mov) in *watch_dir*.""" if recursive: return sorted( p for p in watch_dir.rglob("*") - if p.is_file() and p.suffix.lower() == ".mp4" + if p.is_file() and p.suffix.lower() in _VIDEO_SUFFIXES ) return sorted( p for p in watch_dir.iterdir() - if p.is_file() and p.suffix.lower() == ".mp4" + if p.is_file() and p.suffix.lower() in _VIDEO_SUFFIXES ) +def _public_media_url(settings: Settings, basename: str) -> str: + base = settings.public_base_url.rstrip("/") + return f"{base}/media/{basename}" + + +def _slice_media_basename(base_name: str, slice_index: int) -> str: + """生成切片视频的媒体文件名。""" + base = Path(base_name).stem + return f"{base}_slice_{slice_index:03d}.mp4" + + +def _publish_slice_video( + src: Path, dst: Path, settings: Settings, +) -> str: + """Transcode and publish a single video slice to media_root. + + Returns the public URL on success, empty string on failure. + """ + tmp = dst.with_name(dst.stem + "_tmp.mp4") + tmp.unlink(missing_ok=True) + try: + ok = transcode_src_to_h264_dst(src, tmp) + if ok and tmp.is_file() and tmp.stat().st_size > 0: + tmp.replace(dst) + logger.info( + "[action-watch] published H.264: {} -> {}", src.name, dst.name, + ) + else: + tmp.unlink(missing_ok=True) + shutil.copy2(src, dst) + logger.warning( + "[action-watch] transcode failed, copied raw: {} -> {}", + src.name, + dst.name, + ) + return _public_media_url(settings, dst.name) + except Exception: + logger.exception("[action-watch] publish failed for {}", src.name) + tmp.unlink(missing_ok=True) + if dst.is_file(): + return _public_media_url(settings, dst.name) + return "" + + async def _run_inference_and_state( mp4: Path, settings: Settings, @@ -57,19 +107,43 @@ async def _run_inference_and_state( async with app_state.action_lock: app_state.action_status = "running" try: - # 返回 (第一个快照, 所有切片快照列表) - first_snap, all_snaps = await to_thread( - action_svc.run_full_action, mp4, settings + slice_files, duration = await to_thread( + action_svc.prepare_action_slices, mp4, settings + ) + + settings.media_root.mkdir(parents=True, exist_ok=True) + base_name = ( + settings.biomass_water_video_media_name or "biomass_water_surface.mp4" ) - # 将所有切片作为独立视频保存到数据库 saved_count = 0 - for i, snap in enumerate(all_snaps): - if health_snapshot_deliverable(snap): - # 为每个切片生成独立的 source_path - slice_key = f"{key}#slice{i}" - save_health_snapshot(settings, snap, source_path=slice_key) - saved_count += 1 + first_snap = None + total_slices = len(slice_files) + + for i, slice_file in enumerate(slice_files): + snap = await to_thread( + action_svc.run_single_slice_inference, + slice_file, i, total_slices, duration, mp4.name, settings, + ) + + if first_snap is None: + first_snap = snap + + if not health_snapshot_deliverable(snap): + continue + + video_url = "" + media_basename = _slice_media_basename(base_name, i) + dst = settings.media_root / media_basename + video_url = await to_thread( + _publish_slice_video, slice_file, dst, settings, + ) + + slice_key = f"{key}#slice{i}" + save_health_snapshot( + settings, snap, source_path=slice_key, video_url=video_url, + ) + saved_count += 1 if saved_count == 0: logger.warning( @@ -82,7 +156,7 @@ async def _run_inference_and_state( if settings.action_watch_use_state_file: add_watch_processed(settings, key, "action") - pred = (first_snap.raw_class_en or "").strip() + pred = (first_snap.raw_class_en or "").strip() if first_snap else "" logger.info( "[action-watch] done: {} -> {} (saved {} slices)", mp4.name, diff --git a/fish_api/app/services/measure.py b/fish_api/app/services/measure.py index 96e014e..30f6f63 100644 --- a/fish_api/app/services/measure.py +++ b/fish_api/app/services/measure.py @@ -1030,6 +1030,31 @@ def _run_generate_video_with_labels_cli( return video_path +def _open_cv2_video_writer( + dst: Path, fps: float, size: Tuple[int, int], +) -> "cv2.VideoWriter": + """Open a cv2.VideoWriter, preferring GStreamer NVENC on Jetson.""" + import cv2 + w, h = size + try: + if hasattr(cv2, "CAP_GSTREAMER"): + loc = str(dst).replace('"', '\\"') + gst_pipe = ( + f'appsrc ! videoconvert ! video/x-raw,format=BGRx ! ' + f'nvvidconv ! video/x-raw(memory:NVMM) ! ' + f'nvv4l2h264enc bitrate=4000000 ! h264parse ! ' + f'mp4mux ! filesink location="{loc}"' + ) + writer = cv2.VideoWriter(gst_pipe, cv2.CAP_GSTREAMER, 0, fps, (w, h)) + if writer.isOpened(): + logger.debug("[FishMeasure] cv2 writer using GStreamer NVENC: {}", dst.name) + return writer + writer.release() + except Exception: + pass + return cv2.VideoWriter(str(dst), cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h)) + + def _concat_preview_videos(inputs: List[Path], dst: Path) -> bool: if not inputs: return False @@ -1059,12 +1084,7 @@ def _concat_preview_videos(inputs: List[Path], dst: Path) -> bool: if writer is None: target_size = (width, height) fps = cur_fps if cur_fps > 0.0 else 10.0 - writer = cv2.VideoWriter( - str(dst), - cv2.VideoWriter_fourcc(*"mp4v"), - fps, - target_size, - ) + writer = _open_cv2_video_writer(dst, fps, target_size) while True: ok, frame = cap.read() if not ok: @@ -1224,6 +1244,96 @@ def _get_h264_encoder() -> Tuple[str, List[str], str]: return "", [], ffmpeg_path +_gst_nvenc_available: Optional[bool] = None + + +def _check_gst_nvenc() -> bool: + """Detect whether GStreamer nvv4l2h264enc (Jetson hardware encoder) is usable.""" + global _gst_nvenc_available + if _gst_nvenc_available is not None: + return _gst_nvenc_available + + try: + r = subprocess.run( + ["gst-inspect-1.0", "nvv4l2h264enc"], + capture_output=True, text=True, timeout=5, + ) + _gst_nvenc_available = r.returncode == 0 + except (FileNotFoundError, Exception): + _gst_nvenc_available = False + + if _gst_nvenc_available: + logger.info("[FishMeasure] GStreamer nvv4l2h264enc (Jetson NVENC) available") + else: + logger.debug("[FishMeasure] GStreamer nvv4l2h264enc not available, will use software encoder") + return _gst_nvenc_available + + +_GST_DEMUX_BY_EXT = { + ".mp4": "qtdemux", + ".mov": "qtdemux", + ".m4v": "qtdemux", + ".mkv": "matroskademux", + ".webm": "matroskademux", + ".ts": "tsdemux", + ".avi": "avidemux", +} + + +def _transcode_with_gst_nvenc(src: Path, dst: Path) -> bool: + """Transcode to H.264 using GStreamer with Jetson NVENC hardware encoder. + + Pipeline: filesrc → demux → decode → nvvidconv (to NVMM) → nvv4l2h264enc → mp4mux → filesink + Falls back gracefully: returns False so callers can try software encoding. + """ + if not _check_gst_nvenc(): + return False + + demux = _GST_DEMUX_BY_EXT.get(src.suffix.lower()) + if demux is None: + logger.debug("[FishMeasure] NVENC: unsupported container {}, skipping", src.suffix) + return False + + tmp = dst.with_suffix(".gst_tmp.mp4") + tmp.unlink(missing_ok=True) + + cmd = [ + "gst-launch-1.0", "-e", + "filesrc", f"location={src}", + "!", demux, "name=demux", + "demux.video_0", "!", "decodebin", + "!", "nvvidconv", + "!", "video/x-raw(memory:NVMM)", + "!", "nvv4l2h264enc", "bitrate=4000000", + "!", "h264parse", + "!", "mp4mux", + "!", "filesink", f"location={tmp}", + ] + + logger.info("[FishMeasure] NVENC transcode: {} -> {}", src.name, dst.name) + try: + result = subprocess.run( + cmd, capture_output=True, text=True, timeout=600, + ) + if result.returncode == 0 and tmp.is_file() and tmp.stat().st_size > 0: + tmp.replace(dst) + logger.info( + "[FishMeasure] NVENC transcode SUCCESS: {} ({} bytes)", + dst.name, dst.stat().st_size, + ) + return True + + stderr = (result.stderr or "")[-500:] + logger.warning("[FishMeasure] NVENC transcode FAILED (rc={}): {}", result.returncode, stderr) + except subprocess.TimeoutExpired: + logger.warning("[FishMeasure] NVENC transcode timed out for {}", src.name) + except Exception as e: + logger.warning("[FishMeasure] NVENC transcode exception: {}", e) + + tmp.unlink(missing_ok=True) + return False + + def _get_x264_path() -> Optional[str]: """检测系统上是否有可用的 x264 命令行工具。""" for path in ["/usr/bin/x264", "/usr/local/bin/x264", "x264"]: @@ -1390,17 +1500,56 @@ def _transcode_fallback(src: Path, dst: Path) -> bool: shutil.rmtree(tmp_dir, ignore_errors=True) -def _transcode_to_h264(src: Path, dst: Path) -> bool: - """使用 ffmpeg 将视频转码为 H.264 (浏览器兼容格式)。 +def _is_already_h264(src: Path) -> bool: + """Quick ffprobe check: is the video stream already H.264?""" + try: + from app.services.video_slice import _get_ffprobe_path + r = subprocess.run( + [_get_ffprobe_path(), "-v", "error", + "-select_streams", "v:0", + "-show_entries", "stream=codec_name", + "-of", "default=noprint_wrappers=1:nokey=1", + str(src)], + capture_output=True, text=True, timeout=5, + ) + return r.returncode == 0 and r.stdout.strip() == "h264" + except Exception: + return False - 尝试多种H.264编码器，包括软件编码和硬件加速编码。 - 如果直接转码失败，依次尝试备选方案： - 1. 提取帧重新编码 - 2. 使用 x264 命令行工具（当 ffmpeg 的 H.264 编码器都不可用时） + +def _remux_h264_faststart(src: Path, dst: Path) -> bool: + """Stream-copy an already-H.264 file into an MP4 with moov at the front.""" + ffmpeg_path = _get_ffmpeg_path() + try: + r = subprocess.run( + [ffmpeg_path, "-y", "-i", str(src), + "-c", "copy", "-movflags", "+faststart", "-an", + str(dst)], + capture_output=True, text=True, timeout=60, + ) + if r.returncode == 0 and dst.is_file() and dst.stat().st_size > 0: + logger.info("[FishMeasure] remuxed H.264 (no re-encode): {} -> {} ({} bytes)", + src.name, dst.name, dst.stat().st_size) + return True + except Exception: + pass + return False + + +def _transcode_to_h264(src: Path, dst: Path) -> bool: + """将视频转码为 H.264 (浏览器兼容格式)。 + + If the source is already H.264, remux without re-encoding (instant). + Otherwise try Jetson NVENC hardware, then fall back to ffmpeg software. """ + if _is_already_h264(src) and _remux_h264_faststart(src, dst): + return True + + if _transcode_with_gst_nvenc(src, dst): + return True + encoder, encoder_options, ffmpeg_path = _get_h264_encoder() - # 如果有可用的 ffmpeg H.264 编码器，先尝试直接转码 if encoder: try: # 基础参数 diff --git a/fish_api/app/services/sonar_optical_flow.py b/fish_api/app/services/sonar_optical_flow.py new file mode 100644 index 0000000..df2d6b1 --- /dev/null +++ b/fish_api/app/services/sonar_optical_flow.py @@ -0,0 +1,77 @@ +"""声呐视频发布前：在 FishMeasure ``optical_flow/visualize_optical_flow.py`` 中做稠密光流 overlay。""" + +from __future__ import annotations + +import importlib.util +import threading +import time +from pathlib import Path +from types import ModuleType +from typing import Optional + +from loguru import logger + +from app.settings import Settings + +_mod_lock = threading.Lock() +_cached_mod: Optional[ModuleType] = None +_cached_mod_path: Optional[str] = None + + +def _load_optical_flow_module(settings: Settings) -> Optional[ModuleType]: + global _cached_mod, _cached_mod_path + path = settings.fish_measure_root / "optical_flow" / "visualize_optical_flow.py" + path_str = str(path) + with _mod_lock: + if _cached_mod is not None and _cached_mod_path == path_str: + return _cached_mod + if not path.is_file(): + logger.warning("[sonar-video] optical flow module not found: {}", path) + return None + spec = importlib.util.spec_from_file_location("fishmeasure_optical_flow_viz", path) + if spec is None or spec.loader is None: + return None + mod = importlib.util.module_from_spec(spec) + spec.loader.exec_module(mod) + _cached_mod = mod + _cached_mod_path = path_str + return mod + + +def run_sonar_optical_flow_overlay( + src: Path, + flow_out: Path, + settings: Settings, +) -> bool: + """将 ``src`` 处理为 overlay 光流视频写入 ``flow_out``。失败返回 False（调用方应回退直转码）。""" + if not settings.biomass_sonar_optical_flow: + return False + mod = _load_optical_flow_module(settings) + if mod is None: + return False + run_fn = getattr(mod, "run_optical_flow_video", None) + if not callable(run_fn): + logger.warning("[sonar-video] run_optical_flow_video missing in optical flow module") + return False + src_size_mb = src.stat().st_size / (1024 * 1024) if src.is_file() else 0 + logger.info("[sonar-video] optical flow starting: {} ({:.1f} MB), resize={}", src.name, src_size_mb, settings.biomass_sonar_optical_flow_resize) + t0 = time.monotonic() + try: + ok = run_fn( + src, + flow_out, + mode="overlay", + resize=float(settings.biomass_sonar_optical_flow_resize), + progress_log=False, + ) + elapsed = time.monotonic() - t0 + if ok: + out_size_mb = flow_out.stat().st_size / (1024 * 1024) if flow_out.is_file() else 0 + logger.info("[sonar-video] optical flow done in {:.1f}s: {} -> {} ({:.1f} MB)", elapsed, src.name, flow_out.name, out_size_mb) + else: + logger.warning("[sonar-video] optical flow returned False after {:.1f}s for {}", elapsed, src.name) + return bool(ok) + except Exception: + elapsed = time.monotonic() - t0 + logger.exception("[sonar-video] optical flow raised exception after {:.1f}s for {}", elapsed, src.name) + return False diff --git a/fish_api/app/services/sonar_video.py b/fish_api/app/services/sonar_video.py index 3512185..1149544 100644 --- a/fish_api/app/services/sonar_video.py +++ b/fish_api/app/services/sonar_video.py @@ -1,32 +1,37 @@ -"""声呐视频：由显式文件或专用目录中的最新 MP4 发布 H.264 到 MEDIA_ROOT。 +"""声呐视频：后台处理 ``BIOMASS_SONAR_VIDEO_DIR`` 中的**当前最新**视频文件。 -与水上视频使用相同的转码路径（``transcode_src_to_h264_dst`` / ffmpeg）。 -不切片：整段源转成一个固定文件，每次 GET 返回同一 ``video_path`` URL。 +支持 MP4、MKV、MOV。MP4/MOV 在录制中缺少 ``moov`` atom，须等录完才能处理； +MKV 的元数据写在文件头，录制中即可读取，无需等待。 + +每次轮询取目录中 **mtime 最新** 的文件；当其 **(path, size)** 变化时，用 ffmpeg +``-sseof`` 截取**最后 N 秒**（默认 60，见 ``BIOMASS_SONAR_VIDEO_SLICE_SEC``），再对该 +切片做光流 overlay → H.264 转码并原子替换发布。``GET /api/v1/biomass/sonar/video/`` +立即返回最近成功发布的 URL。 """ from __future__ import annotations import asyncio -import shutil +import datetime +import subprocess +import time from pathlib import Path - from typing import Optional, Tuple from loguru import logger from app.compat import to_thread - from app.services.action_watch import iter_mp4 from app.services.measure import transcode_src_to_h264_dst +from app.services.sonar_optical_flow import run_sonar_optical_flow_overlay +from app.services.video_slice import _get_ffmpeg_path from app.settings import Settings -_publish_lock = asyncio.Lock() - DEFAULT_CLIENT_ID = "default" -# 源路径 + mtime，用于跳过重复转码 -_last_src_key = None # type: Optional[Tuple[str, float]] -_cached_public_url: str = "" +# --- published state (read by GET endpoint) --- +_published_url: str = "" +_published_lock = asyncio.Lock() def _public_media_url(settings: Settings, basename: str) -> str: @@ -41,104 +46,286 @@ def _safe_sonar_media_basename(raw: str) -> str: return Path(n).name or "biomass_sonar.mp4" -def resolve_sonar_video_source(settings: Settings) -> Optional[Path]: - """优先 BIOMASS_SONAR_VIDEO_SOURCE；否则在 BIOMASS_SONAR_VIDEO_DIR 中取 mtime 最新的 .mp4。""" - cfg = settings.biomass_sonar_video_source - if cfg is not None: - if cfg.is_file(): - return cfg - logger.warning( - "[sonar-video] BIOMASS_SONAR_VIDEO_SOURCE is not a file: {}", - cfg, - ) - return None - d = settings.biomass_sonar_video_dir - if d is None or not d.is_dir(): - return None - mp4s = iter_mp4(d, settings.biomass_sonar_video_recursive) - if not mp4s: - return None - try: - return max(mp4s, key=lambda p: p.stat().st_mtime) - except OSError as e: - logger.warning("[sonar-video] could not pick latest mp4: {}", e) - return None +# Formats that store metadata at the start → readable while recording +_STREAMING_SUFFIXES = frozenset({".mkv", ".ts", ".webm"}) +# --------------------------------------------------------------------------- +# Probe: is the video file ready to process? +# --------------------------------------------------------------------------- + +def _is_ready_to_process(path: Path) -> bool: + """MKV/TS/WebM are always readable (metadata at start); MP4/MOV need moov at end.""" + if path.suffix.lower() in _STREAMING_SUFFIXES: + return path.is_file() and path.stat().st_size > 0 + return _probe_moov_readable(path) + + +def _probe_moov_readable(path: Path) -> bool: + """Quick check via ffprobe (fallback cv2): does the MP4/MOV have a moov atom?""" + try: + r = subprocess.run( + ["ffprobe", "-v", "error", "-show_entries", "format=duration", + "-of", "default=noprint_wrappers=1:nokey=1", str(path)], + capture_output=True, timeout=5, + ) + if r.returncode == 0 and r.stdout.strip(): + return True + logger.debug("[sonar-watch] ffprobe: moov missing: {}", path.name) + return False + except FileNotFoundError: + pass + except Exception as e: + logger.debug("[sonar-watch] ffprobe failed ({}), trying cv2", e) + + try: + import cv2 + cap = cv2.VideoCapture(str(path)) + ok = cap.isOpened() and cap.get(cv2.CAP_PROP_FRAME_COUNT) > 0 + cap.release() + if not ok: + logger.debug("[sonar-watch] cv2: unreadable: {}", path.name) + return ok + except Exception: + return False + + +def _extract_tail_slice(src: Path, slice_out: Path, duration_sec: float) -> bool: + """Extract last ``duration_sec`` seconds with ``ffmpeg -sseof`` + stream copy. + + For growing MKV files ``ffprobe`` often returns ``N/A`` for duration, so we + always attempt ``-sseof`` first (ffmpeg clamps to file start when the file is + shorter than the requested window). Only when ``-sseof`` fails do we fall + back to a plain ``-c copy`` of the entire file. + """ + slice_out.parent.mkdir(parents=True, exist_ok=True) + slice_out.unlink(missing_ok=True) + if not src.is_file() or src.stat().st_size <= 0: + return False + + ffmpeg = _get_ffmpeg_path() + sec = float(duration_sec) + + # --- primary: -sseof (works on growing MKV even when duration is unknown) --- + sseof_cmd = [ + ffmpeg, "-y", "-hide_banner", "-loglevel", "error", + "-sseof", f"-{sec}", + "-i", str(src), + "-t", str(sec), + "-c", "copy", + "-avoid_negative_ts", "make_zero", + str(slice_out), + ] + try: + r = subprocess.run(sseof_cmd, capture_output=True, text=True, timeout=600) + if r.returncode == 0 and slice_out.is_file() and slice_out.stat().st_size > 0: + logger.debug("[sonar-watch] tail slice ok (-sseof {:.0f}s): {} -> {}", + sec, src.name, slice_out.name) + return True + except Exception as e: + logger.debug("[sonar-watch] tail slice -sseof error: {} ({})", src.name, e) + + # --- fallback: copy entire file (source shorter than window or -sseof unsupported) --- + slice_out.unlink(missing_ok=True) + copy_cmd = [ + ffmpeg, "-y", "-hide_banner", "-loglevel", "error", + "-i", str(src), + "-c", "copy", + str(slice_out), + ] + try: + r = subprocess.run(copy_cmd, capture_output=True, text=True, timeout=600) + if r.returncode == 0 and slice_out.is_file() and slice_out.stat().st_size > 0: + logger.debug("[sonar-watch] tail slice ok (full copy fallback): {} -> {}", + src.name, slice_out.name) + return True + except Exception as e: + logger.warning("[sonar-watch] tail slice fallback error: {} ({})", src.name, e) + + logger.warning("[sonar-watch] tail slice failed for {} | stderr={}", + src.name, (r.stderr or "")[:500]) + slice_out.unlink(missing_ok=True) + return False + + +# --------------------------------------------------------------------------- +# Publish pipeline: tail slice → optical flow (optional) → H.264 transcode → atomic replace +# --------------------------------------------------------------------------- + async def _publish_video( src: Path, - dst: Path, + media_root: Path, + dst_stem: str, settings: Settings, -) -> str: - """与 water_video._publish_video 相同：ffmpeg H.264，失败则回退复制。""" - tmp = dst.with_name(dst.stem + "_tmp.mp4") +) -> Optional[Path]: + """Extract tail slice → optical-flow → H.264 transcode → verify → save. + + Every output file gets a unique timestamp so successive cycles never + overwrite each other: + + * ``_optical_flow_.mp4`` — optical-flow overlay (kept) + * ``_.mp4`` — final H.264 output (kept, published) + + Returns the path of the published file, or ``None`` on failure. + """ + src_size_mb = src.stat().st_size / (1024 * 1024) if src.is_file() else 0 + slice_sec = float(settings.biomass_sonar_video_slice_sec) + logger.info( + "[sonar-watch] processing: {} ({:.1f} MB), slice_sec={:.0f}, optical_flow={}", + src.name, src_size_mb, slice_sec, settings.biomass_sonar_optical_flow, + ) + t0 = time.monotonic() + + ts = datetime.datetime.now().strftime("%Y%m%d_%H%M%S") + + slice_tmp = media_root / f"{dst_stem}_tail_slice.mkv" + slice_tmp.unlink(missing_ok=True) + tmp = media_root / f"{dst_stem}_tmp.mp4" tmp.unlink(missing_ok=True) try: - ok = await to_thread(transcode_src_to_h264_dst, src, tmp) - if ok and tmp.is_file() and tmp.stat().st_size > 0: - tmp.replace(dst) - logger.info("[sonar-video] published H.264: {} -> {}", src.name, dst.name) - else: - tmp.unlink(missing_ok=True) - await to_thread(shutil.copy2, src, dst) - logger.warning( - "[sonar-video] transcode failed, copied raw: {} -> {}", - src.name, - dst.name, - ) - return _public_media_url(settings, dst.name) - except Exception: - logger.exception("[sonar-video] publish failed") - tmp.unlink(missing_ok=True) - if dst.is_file(): - return _public_media_url(settings, dst.name) - return "" + slice_ok = await to_thread(_extract_tail_slice, src, slice_tmp, slice_sec) + if not slice_ok: + logger.warning("[sonar-watch] tail extract failed, skip publish: {}", src.name) + return None + transcode_src = slice_tmp + if settings.biomass_sonar_optical_flow: + flow_dst = media_root / f"{dst_stem}_optical_flow_{ts}.mp4" + flow_tmp = media_root / f"{dst_stem}_flow_tmp.mp4" + flow_tmp.unlink(missing_ok=True) + flow_ok = await to_thread( + run_sonar_optical_flow_overlay, slice_tmp, flow_tmp, settings, + ) + if flow_ok and flow_tmp.is_file() and flow_tmp.stat().st_size > 0: + flow_tmp.replace(flow_dst) + transcode_src = flow_dst + logger.info("[sonar-watch] optical flow saved: {} ({:.1f} MB)", + flow_dst.name, + flow_dst.stat().st_size / (1024 * 1024)) + else: + flow_tmp.unlink(missing_ok=True) + logger.warning("[sonar-watch] optical flow failed, transcoding raw slice: {}", src.name) + + ok = await to_thread(transcode_src_to_h264_dst, transcode_src, tmp) + if not (ok and tmp.is_file() and tmp.stat().st_size > 0): + tmp.unlink(missing_ok=True) + logger.warning("[sonar-watch] transcode failed for {}", src.name) + return None + + if not _probe_moov_readable(tmp): + logger.warning("[sonar-watch] transcoded file not playable, discarding: {}", src.name) + tmp.unlink(missing_ok=True) + return None + + dst = media_root / f"{dst_stem}_{ts}.mp4" + tmp.replace(dst) + elapsed = time.monotonic() - t0 + dst_mb = dst.stat().st_size / (1024 * 1024) + logger.info("[sonar-watch] published in {:.1f}s: {} -> {} ({:.1f} MB)", + elapsed, src.name, dst.name, dst_mb) + return dst + except Exception: + logger.exception("[sonar-watch] publish exception for {}", src.name) + tmp.unlink(missing_ok=True) + return None + finally: + slice_tmp.unlink(missing_ok=True) + + +# --------------------------------------------------------------------------- +# Background watcher loop (started from lifespan, like action_watch) +# --------------------------------------------------------------------------- + +async def run_sonar_video_watch_loop(settings: Settings) -> None: + """Poll ``BIOMASS_SONAR_VIDEO_DIR``, follow the **latest** file by mtime. + + When that file's ``(resolved_path, size)`` changes and the file is ready to read, + extract tail slice → publish. Each published video gets a unique timestamped + filename; the GET endpoint always returns the most recent one. + """ + global _published_url + + d = settings.biomass_sonar_video_dir + if d is None: + logger.info("[sonar-watch] BIOMASS_SONAR_VIDEO_DIR not set, sonar watch disabled") + return + + poll = max(1.0, settings.biomass_sonar_video_poll_interval) + + basename = _safe_sonar_media_basename(settings.biomass_sonar_video_media_name) + dst_stem = Path(basename).stem # e.g. "biomass_sonar" + media_root = settings.media_root + media_root.mkdir(parents=True, exist_ok=True) + + # Seed published URL from the newest existing published file in media_root + existing = sorted( + media_root.glob(f"{dst_stem}_*.mp4"), + key=lambda p: p.stat().st_mtime, + ) + # Exclude intermediates (_tmp, _flow_tmp, _optical_flow_, _tail_slice) + existing = [ + p for p in existing + if not any(tag in p.stem for tag in ("_tmp", "_flow_tmp", "_optical_flow_", "_tail_slice")) + ] + if existing: + seed = existing[-1] + async with _published_lock: + _published_url = _public_media_url(settings, seed.name) + logger.info("[sonar-watch] seeded from existing: {}", _published_url) + + last_published_key: Optional[Tuple[str, int]] = None + + logger.info("[sonar-watch] watching {} (poll={:.0f}s, recursive={})", + d, poll, settings.biomass_sonar_video_recursive) + + while True: + try: + if d.is_dir(): + all_videos = iter_mp4(d, settings.biomass_sonar_video_recursive) + if all_videos: + latest = max(all_videos, key=lambda p: p.stat().st_mtime) + try: + rp = str(latest.resolve()) + sz = latest.stat().st_size + except OSError: + await asyncio.sleep(poll) + continue + + if sz > 0 and last_published_key != (rp, sz): + ready = await to_thread(_is_ready_to_process, latest) + if ready: + published = await _publish_video( + latest, media_root, dst_stem, settings, + ) + if published is not None: + async with _published_lock: + _published_url = _public_media_url( + settings, published.name, + ) + last_published_key = (rp, sz) + else: + logger.debug( + "[sonar-watch] {} not ready yet (still recording?), waiting", + latest.name, + ) + + except asyncio.CancelledError: + raise + except Exception: + logger.exception("[sonar-watch] unexpected error in watch loop") + + await asyncio.sleep(poll) + + +# --------------------------------------------------------------------------- +# GET endpoint helper (called from biomass router, no change to API contract) +# --------------------------------------------------------------------------- async def get_sonar_video_public_url( settings: Settings, _client_id: str = DEFAULT_CLIENT_ID, ) -> str: - """转码并发布到 MEDIA_ROOT 后返回绝对 URL；无可用源且无已发布文件时返回空串。 - - 始终对应同一发布文件（BIOMASS_SONAR_VIDEO_MEDIA_NAME），所有客户端每次请求返回同一 URL。 - """ - settings.media_root.mkdir(parents=True, exist_ok=True) - - global _last_src_key, _cached_public_url - - async with _publish_lock: - basename = _safe_sonar_media_basename(settings.biomass_sonar_video_media_name) - dst = settings.media_root / basename - src = resolve_sonar_video_source(settings) - - if src is None: - _last_src_key = None - _cached_public_url = "" - if dst.is_file(): - return _public_media_url(settings, dst.name) - return "" - - try: - key = (str(src.resolve()), src.stat().st_mtime) - except OSError: - if dst.is_file(): - return _public_media_url(settings, dst.name) - return "" - - if key == _last_src_key and _cached_public_url: - return _cached_public_url - - url = await _publish_video(src, dst, settings) - if url: - _last_src_key = key - _cached_public_url = url - return url - - if dst.is_file(): - u = _public_media_url(settings, dst.name) - _last_src_key = key - _cached_public_url = u - return u - return "" + """Return the URL of the latest successfully published sonar video, or ``""``.""" + async with _published_lock: + return _published_url diff --git a/fish_api/app/services/water_video.py b/fish_api/app/services/water_video.py index e6232fa..3eeef45 100644 --- a/fish_api/app/services/water_video.py +++ b/fish_api/app/services/water_video.py @@ -1,265 +1,25 @@ -"""水上视频：从 FishAction 输入目录或显式路径发布 H.264 MP4 到 MEDIA_ROOT。 +"""水上视频：返回与最近投递的 health 快照对齐的视频 URL。 -支持长视频切片：如果视频较长，会切分为多个10秒片段并分别转码发布。 -每个切片被视为独立的视频。 - -对齐机制：使用 client_id 区分不同客户端的轮询进度，确保 health/result 和 -water/video 两个端点对齐返回同一切片。 +视频由 action_watch 在推理完成后统一发布到 MEDIA_ROOT，URL 存储在 +health_snapshots.video_url 列中，确保 /health/result/ 与 /water/video/ +两个端点始终返回同一切片的结果。 """ from __future__ import annotations -import asyncio -import shutil -from pathlib import Path -from typing import Dict, List, Optional - -from loguru import logger - -from app.compat import to_thread - -from app.services.action_watch import iter_mp4 -from app.services.measure import transcode_src_to_h264_dst -from app.services.video_slice import get_video_duration, slice_video +from app.db import peek_last_delivered_health_video_url from app.settings import Settings -_publish_lock = asyncio.Lock() - -# 视频切片配置 -SLICE_DURATION = 10.0 # 每个切片的时长（秒） -MIN_DURATION_FOR_SLICE = 15.0 # 超过此时长才切片 - -# 默认客户端ID（与 db.py 保持一致） DEFAULT_CLIENT_ID = "default" -def _public_media_url(settings: Settings, basename: str) -> str: - base = settings.public_base_url.rstrip("/") - return f"{base}/media/{basename}" - - -def _safe_water_media_basename(raw: str) -> str: - n = (raw or "").strip() - if not n: - return "biomass_water_surface.mp4" - return Path(n).name or "biomass_water_surface.mp4" - - -def _slice_media_basename(base_name: str, slice_index: int) -> str: - """生成切片视频的媒体文件名。""" - base = Path(base_name).stem - return f"{base}_slice_{slice_index:03d}.mp4" - - -# 客户端独立的状态：每个 client_id 有自己的切片队列和索引 -# _client_slice_queues[client_id] = [url0, url1, url2, ...] -# _client_slice_indices[client_id] = 当前应该返回的索引 -_client_slice_queues: Dict[str, List[str]] = {} -_client_slice_indices: Dict[str, int] = {} - -# 全局缓存的切片列表（用于检测源文件变化） -_global_slice_urls: List[str] = [] -_last_source_mtime: float = 0.0 - - -def resolve_water_video_source(settings: Settings) -> Optional[Path]: - """优先 BIOMASS_WATER_VIDEO_SOURCE；否则取 ACTION_WATCH_DIR 中 mtime 最新的 .mp4。""" - cfg = settings.biomass_water_video_source - if cfg is not None: - if cfg.is_file(): - return cfg - logger.warning( - "[water-video] BIOMASS_WATER_VIDEO_SOURCE is not a file: {}", - cfg, - ) - return None - aw = settings.action_watch_dir - if aw is None or not aw.is_dir(): - return None - mp4s = iter_mp4(aw, settings.action_watch_recursive) - if not mp4s: - return None - try: - return max(mp4s, key=lambda p: p.stat().st_mtime) - except OSError as e: - logger.warning("[water-video] could not pick latest mp4: {}", e) - return None - - -async def _publish_video( - src: Path, - dst: Path, - settings: Settings, +async def get_water_video_public_url( + settings: Settings, client_id: str = DEFAULT_CLIENT_ID, ) -> str: - """发布视频到 MEDIA_ROOT。 + """返回该客户端上一次 ``/health/result/`` 投递的切片视频 URL。 - Args: - src: 源视频路径 - dst: 目标路径 - settings: 应用配置 - - Returns: - 发布的视频URL，失败返回空串 + 视频文件在 ``action_watch`` 推理完成后即发布到 ``MEDIA_ROOT`` 并将 URL + 写入 ``health_snapshots.video_url``；本函数仅做一次 DB 查询，不再独立 + 解析源文件或切片。 """ - tmp = dst.with_name(dst.stem + "_tmp.mp4") - tmp.unlink(missing_ok=True) - - try: - ok = await to_thread(transcode_src_to_h264_dst, src, tmp) - if ok and tmp.is_file() and tmp.stat().st_size > 0: - tmp.replace(dst) - logger.info("[water-video] published H.264: {} -> {}", src.name, dst.name) - else: - tmp.unlink(missing_ok=True) - await to_thread(shutil.copy2, src, dst) - logger.warning( - "[water-video] transcode failed, copied raw: {} -> {}", - src.name, - dst.name, - ) - return _public_media_url(settings, dst.name) - except Exception: - logger.exception("[water-video] publish failed") - tmp.unlink(missing_ok=True) - if dst.is_file(): - return _public_media_url(settings, dst.name) - return "" - - -async def _prepare_slices(settings: Settings) -> List[str]: - """预处理：如果视频较长，切分为多个片段并发布到 MEDIA_ROOT。 - - 返回切片URL列表（供各客户端使用）。 - """ - global _global_slice_urls, _last_source_mtime - - base_basename = _safe_water_media_basename(settings.biomass_water_video_media_name) - src = resolve_water_video_source(settings) - - if src is None: - return [] - - # 检查是否需要重新切片 - try: - current_mtime = src.stat().st_mtime - except OSError: - current_mtime = 0.0 - - # 如果源文件未变化且已有缓存，直接返回缓存 - if current_mtime == _last_source_mtime and _global_slice_urls: - return _global_slice_urls - - # 检查视频时长 - duration = get_video_duration(src) - should_slice = duration > MIN_DURATION_FOR_SLICE - - new_urls: List[str] = [] - - if should_slice: - # 视频较长，切片处理 - logger.info( - "[water-video] video duration {}s > {}s, slicing into {}s segments", - duration, - MIN_DURATION_FOR_SLICE, - SLICE_DURATION, - ) - - slice_files, slice_dir = slice_video(src, SLICE_DURATION) - - if len(slice_files) > 1: - # 发布每个切片 - for i, slice_file in enumerate(slice_files): - slice_basename = _slice_media_basename(base_basename, i) - dst = settings.media_root / slice_basename - - # 检查是否需要重新发布 - need_publish = True - if dst.is_file(): - try: - if dst.stat().st_mtime >= slice_file.stat().st_mtime: - need_publish = False - except OSError: - pass - - if need_publish: - url = await _publish_video(slice_file, dst, settings) - else: - url = _public_media_url(settings, dst.name) - - if url: - new_urls.append(url) - - logger.info( - "[water-video] prepared {} slices for {}", - len(new_urls), - src.name, - ) - - # 更新全局缓存 - _global_slice_urls = new_urls - _last_source_mtime = current_mtime - return new_urls - - # 视频较短，只保留完整视频 - dst = settings.media_root / base_basename - url = await _publish_video(src, dst, settings) - - if url: - new_urls = [url] - _global_slice_urls = new_urls - _last_source_mtime = current_mtime - return new_urls - - return [] - - -async def get_water_video_public_url(settings: Settings, client_id: str = DEFAULT_CLIENT_ID) -> str: - """转码并发布到 MEDIA_ROOT 后返回绝对 URL；无可用源且无已发布文件时返回空串。 - - 如果视频较长被切片，会根据 health/result 端点的状态返回对应的切片URL。 - - 对齐机制：查询 health 数据库记录的切片索引，确保与 health/result 端点对齐。 - 只有当 health/result 返回了第 N 个切片的行为结果后，本端点才会返回第 N 个切片的视频。 - - Args: - settings: 应用配置 - client_id: 客户端标识，默认为 "default" - - Returns: - 视频URL，失败返回空串 - """ - from app.db import get_last_health_slice_index - - settings.media_root.mkdir(parents=True, exist_ok=True) - - async with _publish_lock: - # 确保切片已准备好 - slice_urls = await _prepare_slices(settings) - - if not slice_urls: - # 没有切片，尝试返回已发布的文件 - basename = _safe_water_media_basename(settings.biomass_water_video_media_name) - dst = settings.media_root / basename - if dst.is_file(): - return _public_media_url(settings, dst.name) - return "" - - # 查询 health 端点上次返回的切片索引 - target_slice_idx = get_last_health_slice_index(client_id) - - if target_slice_idx >= 0 and target_slice_idx < len(slice_urls): - # 返回与 health 结果对齐的切片 - logger.debug( - "[water-video] client_id={} aligned to health slice {}/{}", - client_id, - target_slice_idx, - len(slice_urls), - ) - return slice_urls[target_slice_idx] - else: - # 没有对齐的 health 结果，返回空（等待 health/result 先被调用） - logger.debug( - "[water-video] client_id={} no health index yet, returning empty", - client_id, - ) - return "" + return peek_last_delivered_health_video_url(settings, client_id) diff --git a/fish_api/app/services/zed_svo_record.py b/fish_api/app/services/zed_svo_record.py index b8febd6..ff4a173 100644 --- a/fish_api/app/services/zed_svo_record.py +++ b/fish_api/app/services/zed_svo_record.py @@ -50,9 +50,9 @@ def run_zed_svo_record_loop( zed = sl.Camera() init = sl.InitParameters() - init.camera_resolution = sl.RESOLUTION.HD720 + init.camera_resolution = sl.RESOLUTION.HD1200 init.camera_fps = 30 - init.depth_mode = sl.DEPTH_MODE.ULTRA + init.depth_mode = sl.DEPTH_MODE.NEURAL init.coordinate_units = sl.UNIT.MILLIMETER if settings.zed_serial_number is not None: init.set_from_serial_number(settings.zed_serial_number) diff --git a/fish_api/app/settings.py b/fish_api/app/settings.py index d133d3a..ca86e0d 100644 --- a/fish_api/app/settings.py +++ b/fish_api/app/settings.py @@ -255,12 +255,43 @@ class Settings(BaseSettings): #: 优先作为「声呐视频」源文件；未设置时在 BIOMASS_SONAR_VIDEO_DIR 取最新 .mp4。**BIOMASS_SONAR_VIDEO_SOURCE** biomass_sonar_video_source: Optional[Path] = None - #: 声呐 MP4 目录（与 ACTION_WATCH_DIR 独立，避免与水面视频混用）。**BIOMASS_SONAR_VIDEO_DIR** + #: 声呐 MP4 目录（与 ACTION_WATCH_DIR 独立，避免与水面视频混用）。须**绝对路径**（例如 ``/home/you/shared``）；``/shared`` 与 ``~/shared`` 不是同一目录。**BIOMASS_SONAR_VIDEO_DIR** biomass_sonar_video_dir: Optional[Path] = None #: 是否在 SONAR_VIDEO_DIR 中递归查找 .mp4。**BIOMASS_SONAR_VIDEO_RECURSIVE** biomass_sonar_video_recursive: bool = False + #: 后台轮询间隔（秒）：扫描 BIOMASS_SONAR_VIDEO_DIR 并处理已录完的 MP4。**BIOMASS_SONAR_VIDEO_POLL_INTERVAL** + biomass_sonar_video_poll_interval: float = Field( + default=5.0, + ge=1.0, + validation_alias=AliasChoices( + "BIOMASS_SONAR_VIDEO_POLL_INTERVAL", "biomass_sonar_video_poll_interval" + ), + ) + #: ffmpeg ``-sseof`` 取源码最后 N 秒再送光流/转码（避免处理整段长录像）。**BIOMASS_SONAR_VIDEO_SLICE_SEC** + biomass_sonar_video_slice_sec: float = Field( + default=60.0, + ge=1.0, + validation_alias=AliasChoices( + "BIOMASS_SONAR_VIDEO_SLICE_SEC", "biomass_sonar_video_slice_sec" + ), + ) #: 发布到 MEDIA_ROOT 的 H.264 文件名。**BIOMASS_SONAR_VIDEO_MEDIA_NAME** biomass_sonar_video_media_name: str = "biomass_sonar.mp4" + #: 为 True 时声呐发布管线在 ffmpeg 转 H.264 之前先做 Farneback 光流 overlay（与 ``/sonar/video/`` 返回的仍是同一 ``video_path`` 字段）。**BIOMASS_SONAR_OPTICAL_FLOW** + biomass_sonar_optical_flow: bool = Field( + default=True, + validation_alias=AliasChoices( + "BIOMASS_SONAR_OPTICAL_FLOW", "biomass_sonar_optical_flow" + ), + ) + #: 光流处理前缩放帧（例如 0.5 减轻 Jetson 负载）。**BIOMASS_SONAR_OPTICAL_FLOW_RESIZE** + biomass_sonar_optical_flow_resize: float = Field( + default=1.0, + gt=0, + validation_alias=AliasChoices( + "BIOMASS_SONAR_OPTICAL_FLOW_RESIZE", "biomass_sonar_optical_flow_resize" + ), + ) #: 非空时后台持续扫描该目录中的新 .svo2 并跑 FishMeasure（与 ingest 共用 SQLite 最新结果） measure_watch_dir: Optional[Path] = None diff --git a/fish_api/app/state.py b/fish_api/app/state.py index 346ff97..d93e3c2 100644 --- a/fish_api/app/state.py +++ b/fish_api/app/state.py @@ -28,6 +28,7 @@ class HealthSnapshot: updated_at: Optional[datetime] = None error: Optional[str] = None raw_class_en: str = "" + video_path: str = "" @dataclass diff --git a/fish_api/pyproject.toml b/fish_api/pyproject.toml index 2de57e5..eec5c3e 100644 --- a/fish_api/pyproject.toml +++ b/fish_api/pyproject.toml @@ -7,11 +7,13 @@ requires-python = ">=3.11" dependencies = [ "fastapi>=0.115.0", "loguru>=0.7.0", + "numpy>=1.26.0", + "opencv-python-headless>=4.8.0", "uvicorn[standard]>=0.32.0", "pydantic-settings>=2.6.0", ] -[dependency-groups] +[project.optional-dependencies] dev = ["httpx>=0.28.1"] [project.scripts] diff --git a/fish_api/requirements.txt b/fish_api/requirements.txt new file mode 100644 index 0000000..ab6fc37 --- /dev/null +++ b/fish_api/requirements.txt @@ -0,0 +1,9 @@ +# Install: cd fish_api && python3 -m pip install -r requirements.txt +# Or editable: python3 -m pip install -e . +# (Keep in sync with pyproject.toml [project].dependencies) +fastapi>=0.115.0 +loguru>=0.7.0 +numpy>=1.26.0 +opencv-python-headless>=4.8.0 +uvicorn[standard]>=0.32.0 +pydantic-settings>=2.6.0 diff --git a/fish_api/start_fresh.sh b/fish_api/start_fresh.sh index bb1fb83..066a0f8 100644 --- a/fish_api/start_fresh.sh +++ b/fish_api/start_fresh.sh @@ -16,7 +16,7 @@ # CLEAR_MEDIA=1 bash fish_api/start_fresh.sh # CLEAR_STREAM_TMP=1 bash fish_api/start_fresh.sh # -# 首次使用请先：cd fish_api && uv sync +# 首次使用请先：cd fish_api && python3 -m pip install -e . # set -euo pipefail @@ -27,19 +27,11 @@ cd "$DIR" # 未设置时由 app/settings.py 的默认值或 .env 中的 PUBLIC_BASE_URL 决定。 unset PYTHON_FISH_MEASURE PYTHON_FISH_ACTION 2>/dev/null || true -if command -v uv >/dev/null 2>&1; then - 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exec uv run python -m app.measure_debug_cli "$@" -fi -exec "${PYTHON:-python3}" -m app.measure_debug_cli "$@" +exec "${PYTHON:-python3.8}" -m app.measure_debug_cli "$@" diff --git a/start_recording.sh b/start_recording.sh index 6a82f78..a9c43a1 100755 --- a/start_recording.sh +++ b/start_recording.sh @@ -4,16 +4,11 @@ # ./start_recording.sh # ./start_recording.sh --remote # ./start_recording.sh --segment-sec 300 -# 依赖：fish_api 已 pip install -e .；直连相机时需 pyzed。 +# 依赖：fish_api 已 python3 -m pip install -e .；直连相机时需 pyzed。 set -euo pipefail ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" cd "$ROOT/fish_api" -if command -v uv >/dev/null 2>&1; then - PY=(uv run python) -else - PY=(python3) -fi - -exec "${PY[@]}" -m app.zed_record_cli start "$@" +PY="${PYTHON:-python3.8}" +exec "$PY" -m app.zed_record_cli start "$@" diff --git a/stop_recording.sh b/stop_recording.sh index 87809f3..9cbcd79 100644 --- a/stop_recording.sh +++ b/stop_recording.sh @@ -7,10 +7,5 @@ set -euo pipefail ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" cd "$ROOT/fish_api" -if command -v uv >/dev/null 2>&1; then - PY=(uv run python) -else - PY=(python3) -fi - -exec "${PY[@]}" -m app.zed_record_cli stop "$@" +PY="${PYTHON:-python3.8}" +exec "$PY" -m app.zed_record_cli stop "$@"