From 940d426a376efdc8208799ecd3660d4f226792ae Mon Sep 17 00:00:00 2001 From: zaiun xu Date: Tue, 14 Apr 2026 22:05:52 +0800 Subject: [PATCH] whole process --- FishMeasure/generate_video_with_labels.py | 143 ++++++++++++++------- FishMeasure/run_predict_from_svo2_fish9.sh | 2 +- README.md | 69 +--------- fish_api/README.md | 16 ++- fish_api/app/db.py | 22 ++++ fish_api/app/prestart_fresh.py | 27 +++- fish_api/start_fresh.sh | 5 +- 7 files changed, 161 insertions(+), 123 deletions(-) diff --git a/FishMeasure/generate_video_with_labels.py b/FishMeasure/generate_video_with_labels.py index 5526a03..539a02c 100644 --- a/FishMeasure/generate_video_with_labels.py +++ b/FishMeasure/generate_video_with_labels.py @@ -1,8 +1,10 @@ #!/usr/bin/env python3 """Generate labeled preview video from SVO + weight prediction JSON. -Reads the SVO with YOLO tracking, overlays DGCNN weight/length on each -detection box, and writes ``_preview.mp4`` into ``--save-output``. +Per-frame labeling: each detection box shows the weight/length predicted +for that specific frame's PLY (from ``per_cloud`` / ``per_file`` in the +DGCNN JSON). Frames without a corresponding PLY carry forward the last +known value. The final aggregated result is shown at top-right. Called by ``predict_weigth_from_svo2.py`` after DGCNN completes. Replaces any existing preview video so the final published file has labels. @@ -13,9 +15,10 @@ from __future__ import annotations import argparse import json import math +import re import sys from pathlib import Path -from typing import Any, Dict, Optional, Tuple +from typing import Any, Dict, List, Optional, Tuple import cv2 import numpy as np @@ -27,45 +30,74 @@ except ImportError: ZED_AVAILABLE = False -def _extract_weight_length(weight_json: Path) -> Tuple[Optional[float], Optional[float]]: - """Return (weight_g, length_mm) from a weight prediction JSON.""" +def _parse_weight_json(weight_json: Path) -> Tuple[ + Dict[int, Tuple[float, float]], + Optional[float], + Optional[float], + bool, +]: + """Parse weight JSON → per-frame map + summary + confidence. + + Returns: + per_frame: {frame_number: (weight_g, length_mm)} from per_cloud/per_file + summary_weight_g: final aggregated weight + summary_length_mm: final aggregated length + is_confident: True when ``*`` should be shown (avg > 440g OR length band fraction >= 25%) + """ data = json.loads(weight_json.read_text(encoding="utf-8")) summary = data.get("dgcnn_summary") or data.get("weight_summary") or data.get("summary") or {} - w_candidates = [ + def _first_finite(*candidates): + for c in candidates: + if c is not None: + try: + v = float(c) + if math.isfinite(v): + return v + except (TypeError, ValueError): + pass + return None + + summary_wg = _first_finite( summary.get("pred_weight_g"), summary.get("avg_predicted_weight_g"), data.get("pred_weight_g"), data.get("avg_predicted_weight_g"), - ] - weight_g = None - for c in w_candidates: - if c is not None: - try: - v = float(c) - if math.isfinite(v): - weight_g = v - break - except (TypeError, ValueError): - continue - - l_candidates = [ + ) + summary_lmm = _first_finite( summary.get("avg_length_input_topk"), summary.get("avg_length_input"), data.get("avg_length_input"), - ] - length_mm = None - for c in l_candidates: - if c is not None: - try: - v = float(c) - if math.isfinite(v): - length_mm = v - break - except (TypeError, ValueError): - continue + ) - return weight_g, length_mm + CONFIDENT_AVG_G = 440.0 + MIN_FRAC_LARGEST_LENGTH_GROUP = 0.25 + + mean_g = _first_finite( + summary.get("mean_all_pred_g_after_filters"), + summary.get("avg_predicted_weight_g"), + ) + frac = _first_finite(summary.get("fraction_in_near_max_length_band")) + + is_confident = False + if mean_g is not None and mean_g > CONFIDENT_AVG_G: + is_confident = True + elif frac is not None and frac >= MIN_FRAC_LARGEST_LENGTH_GROUP: + is_confident = True + + per_frame: Dict[int, Tuple[float, float]] = {} + for item in data.get("per_cloud") or data.get("per_file") or []: + ply = item.get("ply", "") + m = re.search(r"frame_(\d+)", Path(str(ply)).stem) + if not m: + continue + fnum = int(m.group(1)) + wg = _first_finite(item.get("predicted_weight_g")) + lmm = _first_finite(item.get("length_input")) + if wg is not None: + per_frame[fnum] = (wg, lmm if lmm is not None else float("nan")) + + return per_frame, summary_wg, summary_lmm, is_confident def _draw_label_on_box( @@ -79,8 +111,8 @@ def _draw_label_on_box( x1, y1, x2, y2 = map(int, box) cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255, 0), 2) - w_str = f"{weight_g:.0f}g" if weight_g is not None else "--g" - l_str = f"{length_mm:.0f}mm" if length_mm is not None else "--mm" + w_str = f"{weight_g:.0f}g" if weight_g is not None and math.isfinite(weight_g) else "--g" + l_str = f"{length_mm:.0f}mm" if length_mm is not None and math.isfinite(length_mm) else "--mm" label = f"ID:{tid} {class_name} weight: {w_str} len: {l_str}" (tw, th), bl = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.55, 1) @@ -93,13 +125,15 @@ def _draw_large_summary( image: np.ndarray, weight_g: Optional[float], length_mm: Optional[float], + is_confident: bool = False, ) -> None: + star = " *" if is_confident else "" lines = [] - lines.append(f"Weight: {weight_g:.0f}g" if weight_g is not None else "Weight: --g") + lines.append(f"Final: {weight_g:.0f}g{star}" if weight_g is not None else f"Final: --g") lines.append(f"Length: {length_mm:.0f}mm" if length_mm is not None else "Length: --mm") font = cv2.FONT_HERSHEY_SIMPLEX - scale = 2.75 + scale = 2.0 thickness = 2 pad = 10 h, w = image.shape[:2] @@ -138,11 +172,13 @@ def generate_video( print("ERROR: pyzed not available, cannot generate labeled video") return None - weight_g, length_mm = _extract_weight_length(weight_json) - print(f" Labeling with weight={weight_g}g, length={length_mm}mm from {weight_json.name}") + per_frame, summary_wg, summary_lmm, is_confident = _parse_weight_json(weight_json) + star_s = " *" if is_confident else "" + print(f" Per-frame predictions: {len(per_frame)} PLYs mapped") + print(f" Summary: weight={summary_wg}g, length={summary_lmm}mm{star_s}") - if weight_g is None and length_mm is None: - print(" WARNING: No valid weight/length in JSON, video will show '--'") + if not per_frame and summary_wg is None: + print(" WARNING: No weight data in JSON, video will show '--'") from ultralytics import YOLO yolo = YOLO(yolo_model_path) @@ -162,8 +198,10 @@ def generate_video( images_dir = output_dir / "images" images_dir.mkdir(parents=True, exist_ok=True) - frames = [] + frames: List[np.ndarray] = [] idx = 0 + last_wg: Optional[float] = None + last_lmm: Optional[float] = None try: while True: @@ -179,6 +217,15 @@ def generate_video( idx += 1 continue + frame_number = idx + 1 + if frame_number in per_frame: + cur_wg, cur_lmm = per_frame[frame_number] + last_wg = cur_wg + last_lmm = cur_lmm if math.isfinite(cur_lmm) else last_lmm + else: + cur_wg = last_wg + cur_lmm = last_lmm + results = yolo.track(img, conf=conf, imgsz=imgsz, verbose=False, persist=True)[0] num_dets = len(results.boxes) if results.boxes is not None else 0 @@ -196,13 +243,13 @@ def generate_video( tid = int(tids[i]) if i < len(tids) else 0 cid = int(cls_ids[i]) if i < len(cls_ids) else 0 cname = class_names.get(cid, "fish") - _draw_label_on_box(left_disp, box, tid, cname, weight_g, length_mm) + _draw_label_on_box(left_disp, box, tid, cname, cur_wg, cur_lmm) - if show_large: - _draw_large_summary(left_disp, weight_g, length_mm) + if show_large or summary_wg is not None: + _draw_large_summary(left_disp, summary_wg, summary_lmm, is_confident) - frame_name = f"frame_{idx + 1:06d}" - info = f"[{idx + 1}] {frame_name} | Detections: {num_dets}" + frame_name = f"frame_{frame_number:06d}" + info = f"[{frame_number}] {frame_name} | Detections: {num_dets}" cv2.putText(left_disp, info, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2, cv2.LINE_AA) cv2.putText(left_disp, "Detection", (10, left_disp.shape[0] - 20), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255, 255, 255), 2, cv2.LINE_AA) @@ -216,7 +263,9 @@ def generate_video( frames.append(combined) if idx % 30 == 0: - print(f" [{idx + 1}] {frame_name} dets={num_dets} frames_collected={len(frames)}") + w_s = f"{cur_wg:.0f}g" if cur_wg is not None else "--" + l_s = f"{cur_lmm:.0f}mm" if cur_lmm is not None else "--" + print(f" [{frame_number}] {frame_name} dets={num_dets} w={w_s} l={l_s} collected={len(frames)}") idx += 1 finally: @@ -232,7 +281,7 @@ def generate_video( for f in frames: writer.write(f) writer.release() - print(f" ✓ Labeled video: {video_path.name} ({len(frames)} frames, weight={weight_g}g len={length_mm}mm)") + print(f" ✓ Labeled video: {video_path.name} ({len(frames)} frames, {len(per_frame)} PLY labels)") return video_path diff --git a/FishMeasure/run_predict_from_svo2_fish9.sh b/FishMeasure/run_predict_from_svo2_fish9.sh index ed11636..17eaf85 100755 --- a/FishMeasure/run_predict_from_svo2_fish9.sh +++ b/FishMeasure/run_predict_from_svo2_fish9.sh @@ -10,7 +10,7 @@ SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" cd "$SCRIPT_DIR" SESSION_ROOT="/home/ubuntu/data/fish/2016-1-22-last" -FISH_NAME="fish9" +FISH_NAME="fish1" fish_dir="${SESSION_ROOT}/${FISH_NAME}/" OUT_PARENT="output_weight_estimator" save_out="${OUT_PARENT}/${FISH_NAME}" diff --git a/README.md b/README.md index 402394f..c7c88f8 100644 --- a/README.md +++ b/README.md @@ -1,68 +1,7 @@ -# FishServer 核心仓库（已瘦身） +# How To Run -本目录面向 **FishMeasure（SVO2 称重/点云）**、**FishAction（MP4 行为 X3D）** 与 **fish_api（FastAPI 网关）** 的部署与运行，已去掉训练数据、历史推理产物、旧版 Django 前端和 SlowFast 训练栈等大体积非运行时内容。 +In repo root, use ./scripts/start_fresh.sh to run the server -## 目录结构 +# About Configs -| 路径 | 说明 | -|------|------| -| `fish_api/` | FastAPI：`uv sync` 后 `uv run uvicorn app.main:app`，见其中 `README.md` | -| `FishMeasure/` | 双目链路：`predict_weigth_from_svo2.py`、`fish_video_weight_evaluation.py`、`weight_estimator/`、`pointcloud_classifier/` 等 | -| `FishAction/` | 行为推断：`predict_video_x3d_3class.py`、`train_pytorchvideo_x3d.py`、`checkpoints/`（X3D） | -| `packaging/` | **单一 Conda 环境**：网关 + 两条算法依赖定义，见 [`packaging/README.md`](packaging/README.md) | -| `scripts/start_fresh.sh` | 在已激活的 `fishserver` 环境中清空缓存后启动 uvicorn | -| `scripts/start_no_fresh.sh` | 保留 SQLite 与推理缓存启动 uvicorn | - -### 一键打包成「单环境」运行（推荐服务器） - -```bash -bash packaging/bootstrap_fishserver.sh -conda activate fishserver -bash packaging/patch_cuda_torch.sh # Linux + NVIDIA 时建议 -# 再按 packaging/README.md 安装 ZED SDK 与 pyzed -PORT=8001 bash scripts/start_fresh.sh -``` - -多 Conda 环境、分别设置 `PYTHON_FISH_MEASURE` / `PYTHON_FISH_ACTION` 的方式仍支持，见 `fish_api/README.md`。 - -## 已删除内容（需训练/旧功能时可从备份找回） - -- `FishMeasure/output_weight_estimator/`、`output-yolo-sam/`：推理输出 -- `FishMeasure/datasets/`：训练集 -- `FishMeasure/project_jiuzhou01/`：九州 Django + 前端工程 -- `FishMeasure/measure/`、`detect_refbox/dataset`、`detect_refbox/runs`：独立实验数据与跑次 -- `FishMeasure/runs/predict`、`runs/segment`：旧预测/分割输出 -- `FishMeasure/utils/data/`：工具附带大数据 -- `FishMeasure/weight_estimator/runs/` 中除 `dgcnn_20260312_171043/` 外的历史训练目录 -- `FishAction/slowfast/`：SlowFast 训练代码（当前网关仅走 PyTorchVideo X3D） -- 根目录重复 `yolo*.pt`、`fish_video_weight_evaluation__v1.py` 等 - -## 仍占空间的大文件（运行时一般需要） - -- **`FishMeasure/sam_vit_h_4b8939.pth`**（约 2.4GB）：SAM `vit_h`。若未放置，可从 [Segment Anything 官方权重](https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth) 下载到 `FishMeasure/` 下同名文件。 -- **`FishMeasure/runs/train/fish_detection_20251127_104658/weights/best.pt`**：YOLO 检测，与 `fish_api` 默认环境变量一致。 -- **`FishMeasure/weight_estimator/runs/dgcnn_20260312_171043/best.pt`**：DGCNN 体重估计。 -- **`FishAction/checkpoints/ptv_x3d_m/checkpoint_best.pt`**：行为分类（已删除其它实验版 checkpoint 目录以省空间；若需恢复请从备份拿回）。 - -## Git 与体积 - -`FishMeasure/`、`FishAction/` 内嵌各自 `.git`，其中 FishMeasure 历史对象可能很大（含 LFS）。**仅部署运行时**可用 rsync 排除版本库： - -```bash -rsync -avz --exclude '.git' --exclude 'fish_api/.venv' ... -``` - -## 接口说明 - -- 业务 API 契约可参考仓库根目录 `接口文档.docx`（若仍保留）。 -- HTTP 网关路径以 `fish_api/app/routers/` 为准。 - -## 同步到服务器示例 - -```bash -rsync -avz --delete \ - --exclude '.venv' --exclude 'fish_api/.venv' --exclude '__pycache__' \ - ./ ubuntu@192.168.10.93:/home/ubuntu/projects/FishServer/ -``` - -（按实际 IP、用户与路径修改；`--delete` 慎用，会删远端多余文件。） +You can set input file location in .env and other settings. \ No newline at end of file diff --git a/fish_api/README.md b/fish_api/README.md index 3fef7c3..2e6dc49 100644 --- a/fish_api/README.md +++ b/fish_api/README.md @@ -25,7 +25,7 @@ FastAPI 网关：分块接收 **SVO2**（FishMeasure）与 **MP4**（FishAction | `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 | `FishMeasure/output_weight_estimator` | +| `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` | 可在 `fish_api/.env` 中填写上述变量（`pydantic-settings` 会读取）。 @@ -37,7 +37,8 @@ cd fish_api uv sync # 可选：包含 httpx，便于本地用 FastAPI TestClient 做冒烟测试 # uv sync --group dev -bash start_fresh.sh # 清空 SQLite / 缓存后启动；保留缓存用 start_no_fresh.sh +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 # 或：uv run uvicorn app.main:app --host 0.0.0.0 --port 8000（需自行 prestart） ``` @@ -85,6 +86,17 @@ MP4 将 `svo` 换成 `mp4`，本地文件换成 `clip.mp4`，轮询 `GET /api/v1 FishMeasure 跑完后在输出目录查找 `*preview*.mp4`，复制到 `MEDIA_ROOT/`，文件名为 `{UTC时间戳}_{svo_stem}_left.mp4` / `_right.mp4`（每次测量不覆盖；仅一个预览文件时可能左右 URL 指向同一逻辑源经 SBS 拆分）。确保 `PUBLIC_BASE_URL` 与前端/文档中的域名端口一致。 +## Weight Rule (Current) + +最终体重 `pred_weight_g` 由以下规则链决定（按优先级从高到低）： + +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"`。 +4. **Top-K 聚合**（默认路径）：按 `--top-by-length`（默认开）选 top-K 帧，candidates < 5 用 max 否则用 mean，`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。 + ## 演进建议 - RTSP：用 `ffmpeg` 切段写入 MP4 后调用现有 `finalize` 逻辑 diff --git a/fish_api/app/db.py b/fish_api/app/db.py index 350c62d..875cc7a 100644 --- a/fish_api/app/db.py +++ b/fish_api/app/db.py @@ -750,6 +750,28 @@ def remove_sqlite_database_files(settings: Settings) -> None: pass +def reset_delivery_client_progress(settings: Settings) -> None: + """仅重置客户端投递游标（保留历史快照与 watch 缓存）。""" + init_db(settings) + conn = _connect(settings.sqlite_path) + try: + # 清空所有客户端游标，避免沿用旧 client_id 的消费进度。 + conn.execute("UPDATE delivery_client_cursor SET last_delivered_id = 0") + # 确保默认客户端行存在（历史库升级场景）。 + for kind in ("measure", "health"): + conn.execute( + """ + INSERT INTO delivery_client_cursor (client_id, kind, last_delivered_id) + VALUES (?, ?, 0) + ON CONFLICT(client_id, kind) DO NOTHING + """, + (DEFAULT_CLIENT_ID, kind), + ) + conn.commit() + finally: + conn.close() + + def clear_watch_cache_and_snapshots(settings: Settings) -> None: """清空 watch 已处理路径与对应快照，便于重新跑推理（与 measure/action_watch 的 use_state_file 开关一致）。""" init_db(settings) diff --git a/fish_api/app/prestart_fresh.py b/fish_api/app/prestart_fresh.py index acad203..1b05f6e 100644 --- a/fish_api/app/prestart_fresh.py +++ b/fish_api/app/prestart_fresh.py @@ -1,6 +1,8 @@ -"""启动前清空状态：SQLite（客户端数据）、watch 旧 JSON。 +"""启动前清空状态：默认仅重置客户端游标，保留 SQLite 历史快照。 由 start_fresh.sh 在 uvicorn 之前调用。 +- 默认保留 SQLite 历史数据，仅重置 client_id 投递游标（fresh 语义） +- 设置 CLEAR_SQLITE_DATABASE=1 可强制清空 SQLite（主库 + wal/shm） - 默认保留 measure_output 以复用中间步骤（点云等） - 设置 CLEAR_MEASURE_OUTPUT=1 清空测量输出目录 - 设置 CLEAR_ACTION_OUTPUT=1 清空行为输出目录 @@ -11,7 +13,7 @@ from __future__ import annotations import os from pathlib import Path -from app.db import _safe_rm_tree, remove_sqlite_database_files +from app.db import _safe_rm_tree, remove_sqlite_database_files, reset_delivery_client_progress from app.settings import get_settings @@ -29,12 +31,23 @@ def _rm_legacy_json(path: Path | None) -> None: def run_prestart_fresh() -> None: s = get_settings() - # 始终清空 SQLite（客户端数据） - remove_sqlite_database_files(s) - print( - f"[prestart-fresh] removed SQLite at {s.sqlite_path} (and -wal/-shm if present).", - flush=True, + clear_sqlite_database = os.environ.get("CLEAR_SQLITE_DATABASE", "").strip() in ( + "1", + "true", + "yes", ) + if clear_sqlite_database: + remove_sqlite_database_files(s) + print( + f"[prestart-fresh] removed SQLite at {s.sqlite_path} (and -wal/-shm if present).", + flush=True, + ) + else: + reset_delivery_client_progress(s) + print( + f"[prestart-fresh] kept SQLite history, reset delivery client progress in {s.sqlite_path}.", + flush=True, + ) # 检查是否清空中间输出目录（默认保留以复用点云等中间步骤） clear_measure_output = os.environ.get("CLEAR_MEASURE_OUTPUT", "").strip() in ("1", "true", "yes") diff --git a/fish_api/start_fresh.sh b/fish_api/start_fresh.sh index f2521fc..21a3cce 100755 --- a/fish_api/start_fresh.sh +++ b/fish_api/start_fresh.sh @@ -1,10 +1,13 @@ #!/usr/bin/env bash -# 清空 SQLite（客户端数据）后启动 Fish API（uvicorn）。 +# 默认重置 client_id 投递游标后启动 Fish API（uvicorn），保留 SQLite 历史快照。 # 默认保留 measure_output 中间步骤（点云等）以加速重新处理。 # # bash fish_api/start_fresh.sh # PORT=8001 HOST=0.0.0.0 bash fish_api/start_fresh.sh # +# 强制清空 SQLite（谨慎）： +# CLEAR_SQLITE_DATABASE=1 bash fish_api/start_fresh.sh +# # 强制清空中间输出目录（重新生成点云等）： # CLEAR_MEASURE_OUTPUT=1 bash fish_api/start_fresh.sh # CLEAR_MEASURE_OUTPUT=1 CLEAR_ACTION_OUTPUT=1 bash fish_api/start_fresh.sh