Merge branch 'Automatica-3' into Automatica_1_v2

.dockerignore

@@ -1,17 +1,50 @@
-.git
-.vscode
-.venv-sdr
+# Default-deny: include only files required by Docker images.
+*
+
+!.dockerignore
+!deploy/
+!deploy/docker/
+!deploy/docker/**
+!deploy/requirements/
+!deploy/requirements/**
+!src/
+!src/**
+!common/
+!common/**
+!NN_server/
+!NN_server/**
+!telemetry/
+!telemetry/**
+!torchsig/
+!torchsig/**
+
+# Drop heavy or host-only artifacts even if their parent directory is included.
+.git/
+.vscode/
+.venv*/
 __pycache__/
 *.pyc
 *.pyo
-
-# Heavy host-only SDR sources
+*.tar
+*.tar.gz
+*.tgz
+*.zip
+*.iq
+logs/
+runtime/
+train_scripts/
+scripts_nn/
+orange_scripts/
+signal/
 gnuradio/
 gr-osmosdr/
 gr-osmosdr-0.2.6/
-
-# Local runtime artifacts
-NN_server/result/
-
-# Legacy install artifacts not needed in docker image
 install_scripts/
+NN_server/NN/
+NN_server/result/
+NN_server/__pycache__/
+src/__pycache__/
+common/__pycache__/
+telemetry/__pycache__/
+torchsig/__pycache__/
+torchsig/docs/

.gitignore

@@ -185,6 +185,8 @@ cython_debug/
 
 *.png
 /logs/*.log
+logs/nn_results_*.csv
+logs/nn_results_*.jsonl
 runtime/
 
 /.venv-*/*
@@ -194,4 +196,6 @@ runtime/
 NN_server/server.py.bak_streak_gate
 
 *.npy
-train_scripts/models/ensemble*/
+train_scripts/models/ensemble*/
+
+.codex

NN_server/Model.py

@@ -154,18 +154,22 @@ class Model(object):
         except Exception as exc:
             print(str(exc))
 
-    def _prepare_data(self, data=None):
+    def _prepare_data(self, data=None, ind_inference=None):
         try:
+            if ind_inference is None:
+                ind_inference = Model.get_ind_inference()
             print('Подготовка данных' + self._shablon)
-            self._data = self._pre_func(data, src=self._src_result, ind_inference=Model.get_ind_inference())
+            self._data = self._pre_func(data, src=self._src_result, ind_inference=ind_inference)
         except Exception as exc:
             print(str(exc))
 
-    def _post_data(self, prediction=None):
+    def _post_data(self, prediction=None, ind_inference=None):
+        if ind_inference is None:
+            ind_inference = Model.get_ind_inference()
         print('Постобработка данных' + self._shablon)
         self._ind_inference += 1
         self._post_func(src=self._src_result, data=self._data, model_id=self._model_id, model_type=self._type_model,
-                        ind_inference=Model.get_ind_inference(), prediction=prediction)
+                        ind_inference=ind_inference, prediction=prediction)
 
     def get_test_inference(self):
         try:
@@ -248,22 +252,25 @@ class Model(object):
         except Exception as exc:
             print(str(exc))
 
-    def get_inference(self, data=None):
+    def get_inference(self, data=None, ind_inference=None):
         try:
-            return self._inference(data=data)
+            return self._inference(data=data, ind_inference=ind_inference)
         except Exception as exc:
             print(str(exc))
             return None
 
-    def _inference(self, data=None):
+    def _inference(self, data=None, ind_inference=None):
         try:
-            Model._add_in_result_list(type_model=self._type_model, ind_inference=self.get_ind_inference(), list_to_add=[])
-            self._prepare_data(data=data)
+            if ind_inference is None:
+                ind_inference = Model.get_ind_inference()
+            Model._add_in_result_list(type_model=self._type_model, ind_inference=ind_inference, list_to_add=[])
+            self._prepare_data(data=data, ind_inference=ind_inference)
             print('Инференс' + self._shablon)
             prediction, probability = self._inference_func(data=self._data, model=self._model, mapping=self._classes,
                                                            shablon=self._shablon)
-            Model._add_in_result_list(type_model=self._type_model, ind_inference=self.get_ind_inference(), list_to_add=[prediction, probability])
-            self._post_data(prediction=prediction)
+            print('RESULT' + self._shablon + ': ' + str(prediction) + ' (probability=' + str(probability) + ')')
+            Model._add_in_result_list(type_model=self._type_model, ind_inference=ind_inference, list_to_add=[prediction, probability])
+            self._post_data(prediction=prediction, ind_inference=ind_inference)
 
             gc.collect()
             return prediction, probability

NN_server/Models/ensemble_1200_v44.py

@@ -6,9 +6,11 @@ import torch
 import cv2
 import gc
 import io
+import os
+import re
 
 
-def _render_plot(values, figsize=(16, 16), dpi=16):
+def _render_signal_channel(values, figsize=(16, 16), dpi=16, resize=(256, 256)):
     import matplotlib.pyplot as plt
 
     fig = plt.figure(figsize=figsize)
@@ -28,6 +30,34 @@ def _render_plot(values, figsize=(16, 16), dpi=16):
     if img is None:
         raise RuntimeError("failed to decode plot image")
 
+    img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    if resize is not None:
+        img = cv2.resize(img, resize)
+
+    plt.clf()
+    plt.cla()
+    plt.close()
+    plt.close(fig)
+
+    return img
+
+
+def _render_training_png(image):
+    import matplotlib.pyplot as plt
+
+    fig = plt.figure()
+    plt.imshow(image)
+
+    buf = io.BytesIO()
+    fig.savefig(buf, format="png")
+    buf.seek(0)
+    img_arr = np.frombuffer(buf.getvalue(), dtype=np.uint8)
+    buf.close()
+
+    img = cv2.imdecode(img_arr, 1)
+    if img is None:
+        raise RuntimeError("failed to decode training-style image")
+
     plt.clf()
     plt.cla()
     plt.close()
@@ -36,6 +66,42 @@ def _render_plot(values, figsize=(16, 16), dpi=16):
     return np.asarray(cv2.split(img), dtype=np.float32)
 
 
+def _prune_old_inference_images(src, model_type, model_id, keep_last=200):
+    try:
+        keep_last = int(os.getenv("INFERENCE_IMAGE_KEEP_LAST", str(keep_last)))
+    except ValueError:
+        keep_last = keep_last
+
+    if keep_last <= 0 or not src or not os.path.isdir(src):
+        return
+
+    pattern = re.compile(
+        r"_inference_(\d+)_.*_"
+        + re.escape(str(model_id))
+        + "_"
+        + re.escape(str(model_type))
+        + r"\.png$"
+    )
+    grouped = {}
+    for name in os.listdir(src):
+        match = pattern.match(name)
+        if match is None:
+            continue
+        grouped.setdefault(int(match.group(1)), []).append(name)
+
+    if len(grouped) <= keep_last:
+        return
+
+    for old_result_id in sorted(grouped)[: len(grouped) - keep_last]:
+        for name in grouped[old_result_id]:
+            try:
+                os.remove(os.path.join(src, name))
+            except FileNotFoundError:
+                pass
+            except OSError as exc:
+                print(f"failed to remove old inference image {name}: {exc}")
+
+
 def pre_func_ensemble(data=None, src="", ind_inference=0):
     try:
         import matplotlib.pyplot as plt
@@ -47,8 +113,8 @@ def pre_func_ensemble(data=None, src="", ind_inference=0):
         imag = np.asarray(data[1], dtype=np.float32)
         signal = real + 1j * imag
 
-        img_real = _render_plot(signal.real)
-        img_mag = _render_plot(np.abs(signal))
+        img_real = _render_training_png(_render_signal_channel(signal.real))
+        img_mag = _render_training_png(_render_signal_channel(np.abs(signal)))
 
         cv2.destroyAllWindows()
         gc.collect()
@@ -164,7 +230,7 @@ def post_func_ensemble(src="", model_type="", prediction="", model_id=0, ind_inf
         matplotlib.use("Agg")
         plt.ioff()
 
-        if int(ind_inference) <= 100 and isinstance(data, (list, tuple)) and len(data) >= 2:
+        if isinstance(data, (list, tuple)) and len(data) >= 2:
             fig, ax = plt.subplots()
             ax.imshow(np.moveaxis(data[0], 0, -1))
             plt.savefig(src + "_inference_" + str(ind_inference) + "_" + prediction + "_real_" + str(model_id) + "_" + model_type + ".png")
@@ -183,6 +249,8 @@ def post_func_ensemble(src="", model_type="", prediction="", model_id=0, ind_inf
             cv2.destroyAllWindows()
             gc.collect()
 
+            _prune_old_inference_images(src, model_type, model_id)
+
         plt.clf()
         plt.cla()
         plt.close()

NN_server/Models/ensemble_2400_v44.py

@@ -6,9 +6,11 @@ import torch
 import cv2
 import gc
 import io
+import os
+import re
 
 
-def _render_plot(values, figsize=(16, 16), dpi=16):
+def _render_signal_channel(values, figsize=(16, 16), dpi=16, resize=(256, 256)):
     import matplotlib.pyplot as plt
 
     fig = plt.figure(figsize=figsize)
@@ -28,6 +30,34 @@ def _render_plot(values, figsize=(16, 16), dpi=16):
     if img is None:
         raise RuntimeError("failed to decode plot image")
 
+    img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    if resize is not None:
+        img = cv2.resize(img, resize)
+
+    plt.clf()
+    plt.cla()
+    plt.close()
+    plt.close(fig)
+
+    return img
+
+
+def _render_training_png(image):
+    import matplotlib.pyplot as plt
+
+    fig = plt.figure()
+    plt.imshow(image)
+
+    buf = io.BytesIO()
+    fig.savefig(buf, format="png")
+    buf.seek(0)
+    img_arr = np.frombuffer(buf.getvalue(), dtype=np.uint8)
+    buf.close()
+
+    img = cv2.imdecode(img_arr, 1)
+    if img is None:
+        raise RuntimeError("failed to decode training-style image")
+
     plt.clf()
     plt.cla()
     plt.close()
@@ -36,6 +66,42 @@ def _render_plot(values, figsize=(16, 16), dpi=16):
     return np.asarray(cv2.split(img), dtype=np.float32)
 
 
+def _prune_old_inference_images(src, model_type, model_id, keep_last=200):
+    try:
+        keep_last = int(os.getenv("INFERENCE_IMAGE_KEEP_LAST", str(keep_last)))
+    except ValueError:
+        keep_last = keep_last
+
+    if keep_last <= 0 or not src or not os.path.isdir(src):
+        return
+
+    pattern = re.compile(
+        r"_inference_(\d+)_.*_"
+        + re.escape(str(model_id))
+        + "_"
+        + re.escape(str(model_type))
+        + r"\.png$"
+    )
+    grouped = {}
+    for name in os.listdir(src):
+        match = pattern.match(name)
+        if match is None:
+            continue
+        grouped.setdefault(int(match.group(1)), []).append(name)
+
+    if len(grouped) <= keep_last:
+        return
+
+    for old_result_id in sorted(grouped)[: len(grouped) - keep_last]:
+        for name in grouped[old_result_id]:
+            try:
+                os.remove(os.path.join(src, name))
+            except FileNotFoundError:
+                pass
+            except OSError as exc:
+                print(f"failed to remove old inference image {name}: {exc}")
+
+
 def pre_func_ensemble(data=None, src="", ind_inference=0):
     try:
         import matplotlib.pyplot as plt
@@ -47,8 +113,8 @@ def pre_func_ensemble(data=None, src="", ind_inference=0):
         imag = np.asarray(data[1], dtype=np.float32)
         signal = real + 1j * imag
 
-        img_real = _render_plot(signal.real)
-        img_mag = _render_plot(np.abs(signal))
+        img_real = _render_training_png(_render_signal_channel(signal.real))
+        img_mag = _render_training_png(_render_signal_channel(np.abs(signal)))
 
         cv2.destroyAllWindows()
         gc.collect()
@@ -164,7 +230,7 @@ def post_func_ensemble(src="", model_type="", prediction="", model_id=0, ind_inf
         matplotlib.use("Agg")
         plt.ioff()
 
-        if int(ind_inference) <= 100 and isinstance(data, (list, tuple)) and len(data) >= 2:
+        if isinstance(data, (list, tuple)) and len(data) >= 2:
             fig, ax = plt.subplots()
             ax.imshow(np.moveaxis(data[0], 0, -1))
             plt.savefig(src + "_inference_" + str(ind_inference) + "_" + prediction + "_real_" + str(model_id) + "_" + model_type + ".png")
@@ -183,6 +249,8 @@ def post_func_ensemble(src="", model_type="", prediction="", model_id=0, ind_inf
             cv2.destroyAllWindows()
             gc.collect()
 
+            _prune_old_inference_images(src, model_type, model_id)
+
         plt.clf()
         plt.cla()
         plt.close()

NN_server/server.py

@@ -20,6 +20,8 @@ import shutil
 import json
 import gc
 import logging
+import time
+import re
 from pathlib import Path
 
 TORCHSIG_PATH = "/app/torchsig"
@@ -34,6 +36,7 @@ loop = asyncio.new_event_loop()
 asyncio.set_event_loop(loop)
 queue = asyncio.Queue()
 semaphore = asyncio.Semaphore(3)
+receive_data_lock = threading.Lock()
 
 prediction_list = []
 result_msg = {}
@@ -91,11 +94,82 @@ def update_drone_streak(freq, prediction):
 
 if not config:
     raise RuntimeError("[NN_server/server.py] .env was loaded but no keys were parsed")
+MODEL_ENV_RE = re.compile(r"^NN_\d+$")
+
+if not any(MODEL_ENV_RE.match(key) for key in config):
+    raise RuntimeError("[NN_server/server.py] no NN_* model entries configured")
 logging.info("NN config loaded from %s", ROOT_ENV)
 if RUNTIME_ENV.exists():
     logging.info("NN runtime overrides loaded from %s", RUNTIME_ENV)
 gen_server_ip = config['GENERAL_SERVER_IP']
 gen_server_port = config['GENERAL_SERVER_PORT']
+INFERENCE_TELEMETRY_HOST = os.getenv('telemetry_host', '127.0.0.1')
+INFERENCE_TELEMETRY_PORT = os.getenv('telemetry_port', '5020')
+INFERENCE_TELEMETRY_ENDPOINT = os.getenv('telemetry_inference_endpoint', 'inference/result')
+INFERENCE_TELEMETRY_TIMEOUT_SEC = float(os.getenv('telemetry_inference_timeout_sec', '0.30'))
+INFERENCE_IMAGE_RE = re.compile(r"_inference_(\d+)_")
+
+
+def get_result_dir():
+    return config.get('SRC_RESULT', '')
+
+
+def collect_inference_images(result_id, model_name=''):
+    result_dir = get_result_dir()
+    if not result_dir or not os.path.isdir(result_dir):
+        return result_id, []
+
+    needle = f"_inference_{result_id}_"
+    model_suffix = f"_{model_name}.png" if model_name else ''
+    exact_images = []
+    grouped_images = {}
+
+    for name in sorted(os.listdir(result_dir)):
+        if not name.endswith('.png'):
+            continue
+        if model_suffix and not name.endswith(model_suffix):
+            continue
+        match = INFERENCE_IMAGE_RE.search(name)
+        if match is None:
+            continue
+
+        image_result_id = int(match.group(1))
+        grouped_images.setdefault(image_result_id, []).append(name)
+        if image_result_id == result_id and needle in name:
+            exact_images.append(name)
+
+    if exact_images:
+        return result_id, exact_images
+    return result_id, []
+
+
+def send_inference_result(payload):
+    try:
+        requests.post(
+            "http://{0}:{1}/{2}".format(
+                INFERENCE_TELEMETRY_HOST,
+                INFERENCE_TELEMETRY_PORT,
+                INFERENCE_TELEMETRY_ENDPOINT.lstrip('/'),
+            ),
+            json=payload,
+            timeout=INFERENCE_TELEMETRY_TIMEOUT_SEC,
+        )
+    except Exception as exc:
+        print(str(exc))
+
+
+def reset_directory_contents(path):
+    os.makedirs(path, exist_ok=True)
+    for name in os.listdir(path):
+        full_path = os.path.join(path, name)
+        try:
+            if os.path.isdir(full_path) and not os.path.islink(full_path):
+                shutil.rmtree(full_path)
+            else:
+                os.remove(full_path)
+        except FileNotFoundError:
+            continue
+
 requested_profile = normalize_profile_name(config.get("NN_ACTIVE_PROFILE"))
 active_profile = resolve_active_profile({k: v for k, v in config.items() if k != "NN_SCHEDULE"})
 if requested_profile != active_profile:
@@ -109,18 +183,37 @@ drone_streaks = {}
 
 def init_data_for_inference():
     try:
-        if os.path.isdir(config['SRC_RESULT']):
-            shutil.rmtree(config['SRC_RESULT'])
-        os.mkdir(config['SRC_RESULT'])
-        if os.path.isdir(config['SRC_EXAMPLE']):
-            shutil.rmtree(config['SRC_EXAMPLE'])
-        os.mkdir(config['SRC_EXAMPLE'])
+        reset_directory_contents(config['SRC_RESULT'])
+        reset_directory_contents(config['SRC_EXAMPLE'])
     except Exception as exc:
         print(str(exc))
         print()
 
     try:
         global model_list
+        for key in config.keys():
+            if MODEL_ENV_RE.match(key):
+                params = config[key].split(' && ')
+                module = importlib.import_module('Models.' + params[4])
+                classes = {}
+                for value in params[9][1:-1].split(','):
+                    classes[len(classes)] = value
+                model = Model(file_model=params[0], file_config=params[1], src_example=params[2], src_result=params[3],
+                              type_model=params[4], build_model_func=getattr(module, params[5]),
+                              pre_func=getattr(module, params[6]), inference_func=getattr(module, params[7]),
+                              post_func=getattr(module, params[8]), classes=classes, number_synthetic_examples=int(params[10]),
+                              number_src_data_for_one_synthetic_example=int(params[11]), path_to_src_dataset=params[12])
+                model_list.append(model)
+            # if key.startswith('ALG_'):
+            #     params = config[key].split(' && ')
+            #     module = importlib.import_module('Algorithms.' + params[2])
+            #     classes = {}
+            #     for value in params[6][1:-1].split(','):
+            #         classes[len(classes)] = value
+            #     alg = Algorithm(src_example=params[0], src_result=params[1], type_alg=params[2], pre_func=getattr(module, params[3]),
+            #                     inference_func=getattr(module, params[4]), post_func=getattr(module, params[5]), classes=classes,
+            #                     number_synthetic_examples=int(params[7]), number_src_data_for_one_synthetic_example=int(params[8]), path_to_src_dataset=params[9])
+            #     alg_list.append(alg)
         model_list = []
         loaded_model_keys = []
         model_entries = get_profile_model_entries(config, active_profile)
@@ -162,11 +255,19 @@ def run_example():
 
 @app.route('/receive_data', methods=['POST'])
 def receive_data():
+    with receive_data_lock:
+        return _receive_data_locked()
+
+
+def _receive_data_locked():
     try:
         print()
-        data = json.loads(request.json)
+        data = request.json
+        if isinstance(data, str):
+            data = json.loads(data)
         print('#' * 100)
         print('Получен пакет ' + str(Model.get_ind_inference()))
+        result_id = Model.get_ind_inference()
         freq = int(data['freq'])
         print('Частота: ' + str(freq))
         # print('Канал: ' + str(data['channel']))
@@ -182,23 +283,49 @@ def receive_data():
                 print('-' * 100)
                 print(str(model))
                 result_msg[str(model.get_model_name())] = {'freq': freq}
-                prediction, probability = model.get_inference([np.asarray(data['data_real'], dtype=np.float32), np.asarray(data['data_imag'], dtype=np.float32)])
+                prediction, probability = model.get_inference([np.asarray(data['data_real'], dtype=np.float32), np.asarray(data['data_imag'], dtype=np.float32)], ind_inference=result_id)
                 result_msg[str(model.get_model_name())]['prediction'] = prediction
                 result_msg[str(model.get_model_name())]['probability'] = str(probability)
                 prediction_list.append(prediction)
+                image_result_id, images = collect_inference_images(result_id, model.get_model_name())
+                send_inference_result({
+                    'result_id': image_result_id,
+                    'ts': time.time(),
+                    'freq': str(freq),
+                    'model': model.get_model_name(),
+                    'prediction': prediction,
+                    'probability': float(probability),
+                    'drone_probability': float(probability) if prediction == 'drone' else 0.0,
+                    'drone_threshold': None,
+                    'images': images,
+                })
                 print('-' * 100)
                 print()
                 
                 try:
-                    result = update_drone_streak(freq, prediction_list[0])
-                    if str(freq)==2400:
-                        result=0
-                    data_to_send={
-                        'freq': str(freq),
-                        'amplitude': result
-                        #'triggered': False if result < 7 else True,
-                        #'light_len': result
-                        }
+                                    
+                    result = 0
+                    freq_int = int(freq)
+                    prediction = prediction_list[0]
+                    prob = float(probability)
+
+                    if freq_int == 2400:
+                        if prediction in ["drone", "drone_noise"]:
+                            result += 0
+                        elif prediction == "wifi" and prob >= 0.95:
+                            result += 0
+
+                    elif freq_int == 1200:
+                        if prediction == "drone" and prob >= 0.95:
+                            result += 0
+
+                    elif freq_int == 915:
+                        result = 0
+
+                    data_to_send = {
+                        "freq": str(freq),
+                        "amplitude": result,
+                    }
                     response = requests.post("http://{0}:{1}/process_data".format(gen_server_ip, gen_server_port), json=data_to_send)
                     if response.status_code == 200:
                         print("Данные успешно отправлены!")

README.md

@@ -256,3 +256,12 @@ sudo hackrf_spiflash -w hackrf_one_usb.bin
 ```
 
 ./.venv-sdr/bin/python scripts_nn/data_saver_headless.py   --serial 000  --freq 4500000000   --save-dir /home/sibscience-4/Dataset/3300   --file-tag DJI_3_   --samp-rate 20000000   --split-size 400000   --delay 0.1   --rf-gain 12   --if-gain 30   --bb-gain 36
+
+### Парсинг логов 
+```  bash
+.venv-train/bin/python scripts/capture_nn_results.py \
+    --output logs/nn_results_live_6gb.csv \
+    --format csv \
+    --max-bytes 6442450944 \
+    --tail 0
+```

capture_hourly.sh

@@ -10,7 +10,7 @@ cd "$SCRIPT_DIR"
 # НАСТРОЙКИ
 ############################
 
-BASE_DIR="${BASE_DIR:-/mnt/nvme1/dataset_5_5_26}"
+BASE_DIR="/mnt/data/dataset_6_5_26"
 
 PYTHON_BIN="${PYTHON_BIN:-$SCRIPT_DIR/.venv-sdr/bin/python}"
 SCRIPT_PATH="${SCRIPT_PATH:-$SCRIPT_DIR/scripts_nn/data_saver_headless.py}"
@@ -28,22 +28,22 @@ STOPPED_SERVICE_UNIT=""
 
 # Лимиты
 PER_FREQ_LIMIT_GIB="${PER_FREQ_LIMIT_GIB:-12}"
-TOTAL_LIMIT_GIB="${TOTAL_LIMIT_GIB:-380}"
+TOTAL_LIMIT_GIB="${TOTAL_LIMIT_GIB:-266}"
 
 PER_FREQ_LIMIT_BYTES=$((PER_FREQ_LIMIT_GIB * 1024 * 1024 * 1024))
 TOTAL_LIMIT_BYTES=$((TOTAL_LIMIT_GIB * 1024 * 1024 * 1024))
 
 # Для обычного hourly можно поставить 3600.
 # Для RUN_ONCE это почти не важно.
-CYCLE_SECONDS="${CYCLE_SECONDS:-100}"
+CYCLE_SECONDS="${CYCLE_SECONDS:-1}"
 
 # Параметры SDR
 SAMP_RATE="${SAMP_RATE:-20e6}"
 SPLIT_SIZE="${SPLIT_SIZE:-400000}"
-DELAY="${DELAY:-0.15}"
+DELAY="${DELAY:-0.25}"
 RF_GAIN="${RF_GAIN:-12}"
-IF_GAIN="${IF_GAIN:-30}"
-BB_GAIN="${BB_GAIN:-36}"
+IF_GAIN="${IF_GAIN:-12}"
+BB_GAIN="${BB_GAIN:-0}"
 
 ############################
 # MOCK SENDER
@@ -335,7 +335,7 @@ on_signal() {
 # ЧАСТОТЫ И SERIAL
 ############################
 
-ORDER=(1200 2400 5200 5800)
+ORDER=(2400)
 
 declare -A SERIAL
 declare -A FREQ_HZ

deploy/docker/docker-compose.yml

@@ -20,6 +20,7 @@ services:
       - ../../runtime:/app/runtime
       - ../../src:/app/src
       - ../../common:/app/common
+      - ../../train_scripts:/app/train_scripts:ro
     networks:
       - dronedetector-net
     extra_hosts:
@@ -36,6 +37,7 @@ services:
     environment:
       - PYTHONPATH=/app:/app/NN_server
       - NN_HOT_RELOAD=${NN_HOT_RELOAD:-1}
+      - telemetry_host=dronedetector-telemetry-server
     working_dir: /app/NN_server
     command:
       - sh
@@ -76,6 +78,7 @@ services:
       - ../../.env:/app/.env:ro
       - ../../telemetry:/app/telemetry
       - ../../common:/app/common
+      - ../../NN_server/result:/app/inference_result:ro
     networks:
       - dronedetector-net
 

logs/analysis.ipynb

@@ -0,0 +1,673 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "215a6c3a",
+   "metadata": {},
+   "source": [
+    "# NN inference analysis\n",
+    "\n",
+    "Анализ CSV с результатами инференса: доля класса `drone`, частоты срабатываний, уверенность модели и интервалы между `drone`-классификациями."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "4e8cff32",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CSV path: /home/sibscience-4/from_ssh/DroneDetector/logs/nn_results_live_6gb.csv\n",
+      "Rows: 27258\n",
+      "Time range: 2026-05-04 17:35:21.019627763+07:00 -> 2026-05-05 12:17:19.369858371+07:00\n",
+      "Missing freq rows: 0\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>docker_timestamp</th>\n",
+       "      <th>event_time_iso</th>\n",
+       "      <th>event_time_epoch</th>\n",
+       "      <th>freq</th>\n",
+       "      <th>model_id</th>\n",
+       "      <th>model_type</th>\n",
+       "      <th>prediction</th>\n",
+       "      <th>probability</th>\n",
+       "      <th>ts</th>\n",
+       "      <th>local_time</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2026-05-04T10:35:21.019627763Z</td>\n",
+       "      <td>2026-05-04T17:35:21+07:00</td>\n",
+       "      <td>1.777891e+09</td>\n",
+       "      <td>2400</td>\n",
+       "      <td>2</td>\n",
+       "      <td>ensemble_2400_v44</td>\n",
+       "      <td>drone</td>\n",
+       "      <td>0.99</td>\n",
+       "      <td>2026-05-04 10:35:21.019627763+00:00</td>\n",
+       "      <td>2026-05-04 17:35:21.019627763+07:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2026-05-04T10:35:21.019631281Z</td>\n",
+       "      <td>2026-05-04T17:35:21+07:00</td>\n",
+       "      <td>1.777891e+09</td>\n",
+       "      <td>1200</td>\n",
+       "      <td>1</td>\n",
+       "      <td>ensemble_1200_v44</td>\n",
+       "      <td>noise</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>2026-05-04 10:35:21.019631281+00:00</td>\n",
+       "      <td>2026-05-04 17:35:21.019631281+07:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2026-05-04T10:35:27.048188525Z</td>\n",
+       "      <td>2026-05-04T17:35:27+07:00</td>\n",
+       "      <td>1.777891e+09</td>\n",
+       "      <td>2400</td>\n",
+       "      <td>2</td>\n",
+       "      <td>ensemble_2400_v44</td>\n",
+       "      <td>drone</td>\n",
+       "      <td>0.99</td>\n",
+       "      <td>2026-05-04 10:35:27.048188525+00:00</td>\n",
+       "      <td>2026-05-04 17:35:27.048188525+07:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2026-05-04T10:35:29.238925690Z</td>\n",
+       "      <td>2026-05-04T17:35:29+07:00</td>\n",
+       "      <td>1.777891e+09</td>\n",
+       "      <td>1200</td>\n",
+       "      <td>1</td>\n",
+       "      <td>ensemble_1200_v44</td>\n",
+       "      <td>noise</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>2026-05-04 10:35:29.238925690+00:00</td>\n",
+       "      <td>2026-05-04 17:35:29.238925690+07:00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2026-05-04T10:35:32.842234116Z</td>\n",
+       "      <td>2026-05-04T17:35:32+07:00</td>\n",
+       "      <td>1.777891e+09</td>\n",
+       "      <td>2400</td>\n",
+       "      <td>2</td>\n",
+       "      <td>ensemble_2400_v44</td>\n",
+       "      <td>drone</td>\n",
+       "      <td>0.92</td>\n",
+       "      <td>2026-05-04 10:35:32.842234116+00:00</td>\n",
+       "      <td>2026-05-04 17:35:32.842234116+07:00</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 docker_timestamp             event_time_iso  \\\n",
+       "0  2026-05-04T10:35:21.019627763Z  2026-05-04T17:35:21+07:00   \n",
+       "1  2026-05-04T10:35:21.019631281Z  2026-05-04T17:35:21+07:00   \n",
+       "2  2026-05-04T10:35:27.048188525Z  2026-05-04T17:35:27+07:00   \n",
+       "3  2026-05-04T10:35:29.238925690Z  2026-05-04T17:35:29+07:00   \n",
+       "4  2026-05-04T10:35:32.842234116Z  2026-05-04T17:35:32+07:00   \n",
+       "\n",
+       "   event_time_epoch  freq  model_id         model_type prediction  \\\n",
+       "0      1.777891e+09  2400         2  ensemble_2400_v44      drone   \n",
+       "1      1.777891e+09  1200         1  ensemble_1200_v44      noise   \n",
+       "2      1.777891e+09  2400         2  ensemble_2400_v44      drone   \n",
+       "3      1.777891e+09  1200         1  ensemble_1200_v44      noise   \n",
+       "4      1.777891e+09  2400         2  ensemble_2400_v44      drone   \n",
+       "\n",
+       "   probability                                  ts  \\\n",
+       "0         0.99 2026-05-04 10:35:21.019627763+00:00   \n",
+       "1         1.00 2026-05-04 10:35:21.019631281+00:00   \n",
+       "2         0.99 2026-05-04 10:35:27.048188525+00:00   \n",
+       "3         1.00 2026-05-04 10:35:29.238925690+00:00   \n",
+       "4         0.92 2026-05-04 10:35:32.842234116+00:00   \n",
+       "\n",
+       "                           local_time  \n",
+       "0 2026-05-04 17:35:21.019627763+07:00  \n",
+       "1 2026-05-04 17:35:21.019631281+07:00  \n",
+       "2 2026-05-04 17:35:27.048188525+07:00  \n",
+       "3 2026-05-04 17:35:29.238925690+07:00  \n",
+       "4 2026-05-04 17:35:32.842234116+07:00  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from pathlib import Path\n",
+    "import pandas as pd\n",
+    "\n",
+    "csv_path = Path('/home/sibscience-4/from_ssh/DroneDetector/logs/nn_results_live_6gb.csv')\n",
+    "df = pd.read_csv(csv_path)\n",
+    "\n",
+    "df['ts'] = pd.to_datetime(df['docker_timestamp'], utc=True)\n",
+    "df['local_time'] = df['ts'].dt.tz_convert('Asia/Novosibirsk')\n",
+    "df['freq'] = pd.to_numeric(df['freq'], errors='coerce').astype('Int64')\n",
+    "df['probability'] = pd.to_numeric(df['probability'], errors='coerce')\n",
+    "df = df.sort_values('ts').reset_index(drop=True)\n",
+    "\n",
+    "print(f'CSV path: {csv_path}')\n",
+    "print(f'Rows: {len(df)}')\n",
+    "print(f'Time range: {df[\"local_time\"].min()} -> {df[\"local_time\"].max()}')\n",
+    "print(f'Missing freq rows: {df[\"freq\"].isna().sum()}')\n",
+    "display(df.head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0fcc6dd6",
+   "metadata": {},
+   "source": [
+    "## Общая сводка по частотам и классам"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "7bf0fc3f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>freq</th>\n",
+       "      <th>prediction</th>\n",
+       "      <th>count</th>\n",
+       "      <th>avg_probability</th>\n",
+       "      <th>min_probability</th>\n",
+       "      <th>max_probability</th>\n",
+       "      <th>freq_total</th>\n",
+       "      <th>class_rate</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1200</td>\n",
+       "      <td>drone</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.840000</td>\n",
+       "      <td>0.78</td>\n",
+       "      <td>0.91</td>\n",
+       "      <td>13632</td>\n",
+       "      <td>0.000220</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1200</td>\n",
+       "      <td>noise</td>\n",
+       "      <td>13629</td>\n",
+       "      <td>0.997436</td>\n",
+       "      <td>0.91</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>13632</td>\n",
+       "      <td>0.999780</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2400</td>\n",
+       "      <td>drone</td>\n",
+       "      <td>11921</td>\n",
+       "      <td>0.868013</td>\n",
+       "      <td>0.50</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>13626</td>\n",
+       "      <td>0.874872</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2400</td>\n",
+       "      <td>noise</td>\n",
+       "      <td>1705</td>\n",
+       "      <td>0.649185</td>\n",
+       "      <td>0.50</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>13626</td>\n",
+       "      <td>0.125128</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   freq prediction  count  avg_probability  min_probability  max_probability  \\\n",
+       "0  1200      drone      3         0.840000             0.78             0.91   \n",
+       "1  1200      noise  13629         0.997436             0.91             1.00   \n",
+       "2  2400      drone  11921         0.868013             0.50             1.00   \n",
+       "3  2400      noise   1705         0.649185             0.50             1.00   \n",
+       "\n",
+       "   freq_total  class_rate  \n",
+       "0       13632    0.000220  \n",
+       "1       13632    0.999780  \n",
+       "2       13626    0.874872  \n",
+       "3       13626    0.125128  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "class_summary = (\n",
+    "    df.groupby(['freq', 'prediction'], dropna=False)\n",
+    "      .agg(\n",
+    "          count=('prediction', 'size'),\n",
+    "          avg_probability=('probability', 'mean'),\n",
+    "          min_probability=('probability', 'min'),\n",
+    "          max_probability=('probability', 'max'),\n",
+    "      )\n",
+    "      .reset_index()\n",
+    ")\n",
+    "\n",
+    "freq_total = df.groupby('freq', dropna=False).size().rename('freq_total').reset_index()\n",
+    "class_summary = class_summary.merge(freq_total, on='freq', how='left')\n",
+    "class_summary['class_rate'] = class_summary['count'] / class_summary['freq_total']\n",
+    "class_summary = class_summary.sort_values(['freq', 'prediction']).reset_index(drop=True)\n",
+    "\n",
+    "display(class_summary)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "039fcfa6",
+   "metadata": {},
+   "source": [
+    "## Статистика по классу drone"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "c1ed2bc4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>freq</th>\n",
+       "      <th>total_inferences</th>\n",
+       "      <th>drone_count</th>\n",
+       "      <th>avg_drone_probability</th>\n",
+       "      <th>median_drone_probability</th>\n",
+       "      <th>min_drone_probability</th>\n",
+       "      <th>max_drone_probability</th>\n",
+       "      <th>first_drone_time</th>\n",
+       "      <th>last_drone_time</th>\n",
+       "      <th>drone_rate</th>\n",
+       "      <th>dataset_duration_min</th>\n",
+       "      <th>drone_per_min</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1200</td>\n",
+       "      <td>13632</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.840000</td>\n",
+       "      <td>0.83</td>\n",
+       "      <td>0.78</td>\n",
+       "      <td>0.91</td>\n",
+       "      <td>2026-05-05 03:18:55.374394280+07:00</td>\n",
+       "      <td>2026-05-05 08:39:23.676669045+07:00</td>\n",
+       "      <td>0.000220</td>\n",
+       "      <td>1121.972504</td>\n",
+       "      <td>0.002674</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2400</td>\n",
+       "      <td>13626</td>\n",
+       "      <td>11921</td>\n",
+       "      <td>0.868013</td>\n",
+       "      <td>0.93</td>\n",
+       "      <td>0.50</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>2026-05-04 17:35:21.019627763+07:00</td>\n",
+       "      <td>2026-05-05 12:17:19.369858371+07:00</td>\n",
+       "      <td>0.874872</td>\n",
+       "      <td>1121.972504</td>\n",
+       "      <td>10.625038</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   freq  total_inferences  drone_count  avg_drone_probability  \\\n",
+       "0  1200             13632            3               0.840000   \n",
+       "1  2400             13626        11921               0.868013   \n",
+       "\n",
+       "   median_drone_probability  min_drone_probability  max_drone_probability  \\\n",
+       "0                      0.83                   0.78                   0.91   \n",
+       "1                      0.93                   0.50                   1.00   \n",
+       "\n",
+       "                     first_drone_time                     last_drone_time  \\\n",
+       "0 2026-05-05 03:18:55.374394280+07:00 2026-05-05 08:39:23.676669045+07:00   \n",
+       "1 2026-05-04 17:35:21.019627763+07:00 2026-05-05 12:17:19.369858371+07:00   \n",
+       "\n",
+       "   drone_rate  dataset_duration_min  drone_per_min  \n",
+       "0    0.000220           1121.972504       0.002674  \n",
+       "1    0.874872           1121.972504      10.625038  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "drone = df[df['prediction'].eq('drone')].copy()\n",
+    "\n",
+    "total_by_freq = df.groupby('freq', dropna=False).size().rename('total_inferences')\n",
+    "drone_by_freq = drone.groupby('freq', dropna=False).agg(\n",
+    "    drone_count=('prediction', 'size'),\n",
+    "    avg_drone_probability=('probability', 'mean'),\n",
+    "    median_drone_probability=('probability', 'median'),\n",
+    "    min_drone_probability=('probability', 'min'),\n",
+    "    max_drone_probability=('probability', 'max'),\n",
+    "    first_drone_time=('local_time', 'min'),\n",
+    "    last_drone_time=('local_time', 'max'),\n",
+    ")\n",
+    "\n",
+    "drone_stats = total_by_freq.to_frame().join(drone_by_freq, how='left').fillna({'drone_count': 0})\n",
+    "drone_stats['drone_count'] = drone_stats['drone_count'].astype(int)\n",
+    "drone_stats['drone_rate'] = drone_stats['drone_count'] / drone_stats['total_inferences']\n",
+    "\n",
+    "if len(df) > 1:\n",
+    "    duration_min = (df['ts'].max() - df['ts'].min()).total_seconds() / 60\n",
+    "else:\n",
+    "    duration_min = 0\n",
+    "\n",
+    "drone_stats['dataset_duration_min'] = duration_min\n",
+    "drone_stats['drone_per_min'] = drone_stats['drone_count'] / duration_min if duration_min > 0 else 0\n",
+    "drone_stats = drone_stats.reset_index().sort_values('freq').reset_index(drop=True)\n",
+    "\n",
+    "display(drone_stats)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "56ae5e2b",
+   "metadata": {},
+   "source": [
+    "## Интервалы между drone-классификациями"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "0c43eb07",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>freq</th>\n",
+       "      <th>interval_count</th>\n",
+       "      <th>avg_interval_sec</th>\n",
+       "      <th>median_interval_sec</th>\n",
+       "      <th>min_interval_sec</th>\n",
+       "      <th>max_interval_sec</th>\n",
+       "      <th>p90_interval_sec</th>\n",
+       "      <th>p95_interval_sec</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1200</td>\n",
+       "      <td>2</td>\n",
+       "      <td>9614.151137</td>\n",
+       "      <td>9614.151137</td>\n",
+       "      <td>619.196112</td>\n",
+       "      <td>18609.106163</td>\n",
+       "      <td>16810.115158</td>\n",
+       "      <td>17709.610661</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2400</td>\n",
+       "      <td>11920</td>\n",
+       "      <td>5.647513</td>\n",
+       "      <td>4.974157</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1210.107950</td>\n",
+       "      <td>9.203728</td>\n",
+       "      <td>10.079097</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   freq  interval_count  avg_interval_sec  median_interval_sec  \\\n",
+       "0  1200               2       9614.151137          9614.151137   \n",
+       "1  2400           11920          5.647513             4.974157   \n",
+       "\n",
+       "   min_interval_sec  max_interval_sec  p90_interval_sec  p95_interval_sec  \n",
+       "0        619.196112      18609.106163      16810.115158      17709.610661  \n",
+       "1          0.000000       1210.107950          9.203728         10.079097  "
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "if drone.empty:\n",
+    "    print('No drone predictions found')\n",
+    "    drone_interval_stats = pd.DataFrame()\n",
+    "else:\n",
+    "    drone = drone.sort_values(['freq', 'ts']).copy()\n",
+    "    drone['dt_drone_freq_sec'] = drone.groupby('freq')['ts'].diff().dt.total_seconds()\n",
+    "    drone_interval_stats = (\n",
+    "        drone.groupby('freq', dropna=False)['dt_drone_freq_sec']\n",
+    "             .agg(\n",
+    "                 interval_count='count',\n",
+    "                 avg_interval_sec='mean',\n",
+    "                 median_interval_sec='median',\n",
+    "                 min_interval_sec='min',\n",
+    "                 max_interval_sec='max',\n",
+    "                 p90_interval_sec=lambda s: s.quantile(0.90),\n",
+    "                 p95_interval_sec=lambda s: s.quantile(0.95),\n",
+    "             )\n",
+    "             .reset_index()\n",
+    "    )\n",
+    "    display(drone_interval_stats)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2ecf96f0",
+   "metadata": {},
+   "source": [
+    "## Drone-события"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ff14a339",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "drone_events = drone[[\n",
+    "    'local_time',\n",
+    "    'docker_timestamp',\n",
+    "    'freq',\n",
+    "    'model_id',\n",
+    "    'model_type',\n",
+    "    'prediction',\n",
+    "    'probability',\n",
+    "]].sort_values('local_time').reset_index(drop=True)\n",
+    "\n",
+    "display(drone_events.head(50))\n",
+    "display(drone_events.tail(50))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "376b84d0",
+   "metadata": {},
+   "source": [
+    "## Частота drone-классификаций по минутам"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5f82f5c1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if drone.empty:\n",
+    "    print('No drone predictions found')\n",
+    "else:\n",
+    "    drone_per_minute = (\n",
+    "        drone.set_index('local_time')\n",
+    "             .groupby('freq')\n",
+    "             .resample('1min')\n",
+    "             .size()\n",
+    "             .rename('drone_count')\n",
+    "             .reset_index()\n",
+    "    )\n",
+    "    display(drone_per_minute.tail(100))\n",
+    "\n",
+    "    pivot = drone_per_minute.pivot_table(\n",
+    "        index='local_time',\n",
+    "        columns='freq',\n",
+    "        values='drone_count',\n",
+    "        fill_value=0,\n",
+    "    )\n",
+    "    display(pivot.tail(50))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ad570d05",
+   "metadata": {},
+   "source": [
+    "## Быстрые графики"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "895550a1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "if not drone.empty:\n",
+    "    ax = drone_stats.set_index('freq')['drone_rate'].plot(kind='bar', figsize=(8, 4), title='Drone rate by frequency')\n",
+    "    ax.set_ylabel('drone_count / total_inferences')\n",
+    "    plt.show()\n",
+    "\n",
+    "    ax = drone.boxplot(column='probability', by='freq', figsize=(8, 4))\n",
+    "    ax.set_title('Drone probability by frequency')\n",
+    "    ax.set_xlabel('freq')\n",
+    "    ax.set_ylabel('probability')\n",
+    "    plt.suptitle('')\n",
+    "    plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "pygments_lexer": "ipython3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

orange_scripts/main_2400.py

@@ -67,8 +67,8 @@ class get_center_freq(gr.top_block):
         self.rtlsdr_source_0.set_sample_rate(samp_rate)
         self.rtlsdr_source_0.set_center_freq(center_freq, 0)
         self.rtlsdr_source_0.set_freq_corr(0, 0)
-        self.rtlsdr_source_0.set_gain(16, 0)
-        self.rtlsdr_source_0.set_if_gain(16, 0)
+        self.rtlsdr_source_0.set_gain(0, 0)
+        self.rtlsdr_source_0.set_if_gain(0, 0)
         self.rtlsdr_source_0.set_bb_gain(0, 0)
         self.rtlsdr_source_0.set_antenna('', 0)
         self.rtlsdr_source_0.set_bandwidth(0, 0)

restart_all.sh

@@ -8,7 +8,7 @@ SDR_UNITS=(
   dronedetector-sdr-433.service
   dronedetector-sdr-750.service
   #dronedetector-sdr-868-915.service
-  #dronedetector-sdr-868.service
+  dronedetector-sdr-868.service
   dronedetector-sdr-1500.service
   dronedetector-sdr-3300.service
   dronedetector-sdr-4500.service

scripts/capture_nn_results.py

@@ -0,0 +1,223 @@
+#!/usr/bin/env python3
+import argparse
+import csv
+import json
+import os
+import re
+import subprocess
+import sys
+import time
+from datetime import datetime, timezone
+
+
+RESULT_RE = re.compile(
+    r"^(?P<docker_ts>\S+)\s+RESULT Модель (?P<model_id>\d+) с типом (?P<model_type>.+?): "
+    r"(?P<prediction>\S+) \(probability=(?P<probability>[-+]?\d+(?:\.\d+)?)\)\s*$"
+)
+FREQ_RE = re.compile(r"(\d{3,5})")
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description="Capture NN inference RESULT logs into CSV or JSONL until time or size limit."
+    )
+    parser.add_argument(
+        "--output",
+        required=True,
+        help="Output file path (.csv or .jsonl recommended).",
+    )
+    parser.add_argument(
+        "--format",
+        choices=("csv", "jsonl"),
+        default="csv",
+        help="Output format.",
+    )
+    parser.add_argument(
+        "--minutes",
+        type=float,
+        default=0.0,
+        help="Stop after this many minutes. 0 means no time limit.",
+    )
+    parser.add_argument(
+        "--max-bytes",
+        type=int,
+        default=3 * 1024 * 1024 * 1024,
+        help="Stop when output file reaches this size in bytes. Default: 3 GiB.",
+    )
+    parser.add_argument(
+        "--since",
+        default=None,
+        help="Optional docker logs --since value, e.g. 20m, 2h, 2026-05-04T12:00:00.",
+    )
+    parser.add_argument(
+        "--tail",
+        type=int,
+        default=0,
+        help="How many previous log lines to include before following. Default: 0.",
+    )
+    parser.add_argument(
+        "--compose-file",
+        default="deploy/docker/docker-compose.yml",
+        help="Path to docker compose file.",
+    )
+    parser.add_argument(
+        "--service",
+        default="dronedetector-nn-server",
+        help="Docker compose service name.",
+    )
+    parser.add_argument(
+        "--follow",
+        action="store_true",
+        help="Follow logs live. By default the script captures a finite history snapshot.",
+    )
+    return parser.parse_args()
+
+
+def extract_freq(model_type):
+    matches = FREQ_RE.findall(model_type)
+    if not matches:
+        return ""
+    known_freqs = {"433", "750", "868", "915", "1200", "1500", "2400", "3300", "4500", "5200", "5800"}
+    for value in matches:
+        if value in known_freqs:
+            return value
+    return matches[0]
+
+
+def parse_docker_timestamp(value):
+    try:
+        return datetime.fromisoformat(value.replace("Z", "+00:00"))
+    except ValueError:
+        return datetime.now(timezone.utc)
+
+
+def open_output(path, fmt):
+    os.makedirs(os.path.dirname(os.path.abspath(path)), exist_ok=True)
+    fh = open(path, "a", encoding="utf-8", newline="")
+    writer = None
+    if fmt == "csv":
+        writer = csv.writer(fh)
+        if fh.tell() == 0:
+            writer.writerow(
+                [
+                    "docker_timestamp",
+                    "event_time_iso",
+                    "event_time_epoch",
+                    "freq",
+                    "model_id",
+                    "model_type",
+                    "prediction",
+                    "probability",
+                ]
+            )
+            fh.flush()
+    return fh, writer
+
+
+def write_record(fh, writer, fmt, record):
+    if fmt == "csv":
+        writer.writerow(
+            [
+                record["docker_timestamp"],
+                record["event_time_iso"],
+                record["event_time_epoch"],
+                record["freq"],
+                record["model_id"],
+                record["model_type"],
+                record["prediction"],
+                record["probability"],
+            ]
+        )
+    else:
+        fh.write(json.dumps(record, ensure_ascii=False) + "\n")
+    fh.flush()
+
+
+def build_command(args):
+    cmd = [
+        "docker",
+        "compose",
+        "-f",
+        args.compose_file,
+        "logs",
+        "--timestamps",
+        "--no-log-prefix",
+        args.service,
+    ]
+    if args.since:
+        cmd[5:5] = ["--since", args.since]
+    if int(args.tail) > 0:
+        cmd[-1:-1] = ["--tail", str(args.tail)]
+    if args.follow:
+        cmd.insert(-1, "-f")
+    return cmd
+
+
+def main():
+    args = parse_args()
+    deadline = time.time() + (args.minutes * 60.0) if args.minutes > 0 else None
+
+    fh, writer = open_output(args.output, args.format)
+    cmd = build_command(args)
+
+    print("Running:", " ".join(cmd), file=sys.stderr)
+    proc = subprocess.Popen(
+        cmd,
+        stdout=subprocess.PIPE,
+        stderr=subprocess.STDOUT,
+        text=True,
+        encoding="utf-8",
+        errors="replace",
+        bufsize=1,
+    )
+
+    captured = 0
+    try:
+        assert proc.stdout is not None
+        for line in proc.stdout:
+            if deadline is not None and time.time() >= deadline:
+                print("Stopping: time limit reached", file=sys.stderr)
+                break
+
+            match = RESULT_RE.match(line.rstrip("\n"))
+            if not match:
+                continue
+
+            docker_dt = parse_docker_timestamp(match.group("docker_ts"))
+            event_dt = docker_dt.astimezone()
+            model_type = match.group("model_type")
+            record = {
+                "docker_timestamp": match.group("docker_ts"),
+                "event_time_iso": event_dt.isoformat(timespec="seconds"),
+                "event_time_epoch": round(docker_dt.timestamp(), 3),
+                "freq": extract_freq(model_type),
+                "model_id": int(match.group("model_id")),
+                "model_type": model_type,
+                "prediction": match.group("prediction"),
+                "probability": float(match.group("probability")),
+            }
+            write_record(fh, writer, args.format, record)
+            captured += 1
+
+            if fh.tell() >= args.max_bytes:
+                print("Stopping: file size limit reached", file=sys.stderr)
+                break
+    finally:
+        try:
+            proc.terminate()
+        except Exception:
+            pass
+        try:
+            proc.wait(timeout=5)
+        except Exception:
+            try:
+                proc.kill()
+            except Exception:
+                pass
+        fh.close()
+
+    print(f"Captured {captured} inference results into {args.output}", file=sys.stderr)
+
+
+if __name__ == "__main__":
+    main()

src/main_2400.py

@@ -40,8 +40,8 @@ class get_center_freq(gr.top_block):
         self.rtlsdr_source_0.set_sample_rate(self.samp_rate)
         self.rtlsdr_source_0.set_center_freq(self.center_freq, 0)
         self.rtlsdr_source_0.set_freq_corr(0, 0)
-        self.rtlsdr_source_0.set_gain(24, 0)
-        self.rtlsdr_source_0.set_if_gain(24, 0)
+        self.rtlsdr_source_0.set_gain(0, 0)
+        self.rtlsdr_source_0.set_if_gain(0, 0)
         self.rtlsdr_source_0.set_bb_gain(100, 0)
         self.rtlsdr_source_0.set_antenna('', 0)
         self.rtlsdr_source_0.set_bandwidth(0, 0)

src/server_to_master.py

@@ -298,7 +298,7 @@ async def agregate_data(data_to_agregate: list):
 
     data = []
 
-    if any(item is not None for item in data_to_agregate):
+    if all(item is not None for item in data_to_agregate):
         for item in data_to_agregate:
             if item is not None:
                 item['freq'] = int(item['freq'])

telemetry/telemetry_server.py

@@ -1,11 +1,13 @@
 import asyncio
 import os
+import re
 import time
 from collections import defaultdict, deque
+from pathlib import Path
 from typing import Any, Deque, Dict, List, Optional
 
-from fastapi import FastAPI, Query, WebSocket, WebSocketDisconnect
-from fastapi.responses import HTMLResponse
+from fastapi import FastAPI, HTTPException, Query, WebSocket, WebSocketDisconnect
+from fastapi.responses import FileResponse, HTMLResponse
 from pydantic import BaseModel, Field
 
 from common.runtime import load_root_env
@@ -16,15 +18,25 @@ TELEMETRY_BIND_HOST = os.getenv('telemetry_bind_host', os.getenv('lochost', '0.0
 TELEMETRY_BIND_PORT = int(os.getenv('telemetry_bind_port', os.getenv('telemetry_port', '5020')))
 TELEMETRY_HISTORY_SEC = int(float(os.getenv('telemetry_history_sec', '900')))
 TELEMETRY_MAX_POINTS_PER_FREQ = int(os.getenv('telemetry_max_points_per_freq', '5000'))
+INFERENCE_HISTORY_SEC = int(float(os.getenv('inference_history_sec', str(TELEMETRY_HISTORY_SEC))))
+INFERENCE_MAX_RESULTS_PER_FREQ = int(os.getenv('inference_max_results_per_freq', '100'))
+INFERENCE_RESULT_DIR = Path(os.getenv('inference_result_dir', '/app/inference_result')).resolve()
+INFERENCE_IMAGE_RE = re.compile(r"_inference_(\d+)_")
 
 
 def _new_buffer() -> Deque[Dict[str, Any]]:
     return deque(maxlen=TELEMETRY_MAX_POINTS_PER_FREQ)
 
 
+def _new_inference_buffer() -> Deque[Dict[str, Any]]:
+    return deque(maxlen=INFERENCE_MAX_RESULTS_PER_FREQ)
+
+
 app = FastAPI(title='DroneDetector Telemetry Server')
 _buffers: Dict[str, Deque[Dict[str, Any]]] = defaultdict(_new_buffer)
 _ws_clients: List[WebSocket] = []
+_inference_buffers: Dict[str, Deque[Dict[str, Any]]] = defaultdict(_new_inference_buffer)
+_inference_ws_clients: List[WebSocket] = []
 _state_lock = asyncio.Lock()
 
 
@@ -41,6 +53,18 @@ class TelemetryPoint(BaseModel):
     alarm_channels: Optional[List[int]] = None
 
 
+class InferenceResult(BaseModel):
+    result_id: int
+    ts: float = Field(default_factory=lambda: time.time())
+    freq: str
+    model: str
+    prediction: str
+    probability: float
+    drone_probability: Optional[float] = None
+    drone_threshold: Optional[str] = None
+    images: List[str] = Field(default_factory=list)
+
+
 def _prune_freq_locked(freq: str, now_ts: float) -> None:
     cutoff = now_ts - TELEMETRY_HISTORY_SEC
     buf = _buffers[freq]
@@ -62,6 +86,71 @@ def _copy_series_locked(seconds: int, freq: Optional[str] = None) -> Dict[str, L
     return series
 
 
+def _prune_inference_freq_locked(freq: str, now_ts: float) -> None:
+    cutoff = now_ts - INFERENCE_HISTORY_SEC
+    buf = _inference_buffers[freq]
+    while buf and float(buf[0].get('ts', 0.0)) < cutoff:
+        buf.popleft()
+
+
+def _copy_inference_series_locked(limit: int, freq: Optional[str] = None) -> Dict[str, List[Dict[str, Any]]]:
+    now_ts = time.time()
+    cutoff = now_ts - INFERENCE_HISTORY_SEC
+
+    def _slice(buf: Deque[Dict[str, Any]]) -> List[Dict[str, Any]]:
+        recent = [item for item in buf if float(item.get('ts', 0.0)) >= cutoff]
+        return recent[-limit:]
+
+    if freq is not None:
+        return {freq: _slice(_inference_buffers.get(freq, deque()))}
+
+    series: Dict[str, List[Dict[str, Any]]] = {}
+    for key, buf in _inference_buffers.items():
+        series[key] = _slice(buf)
+    return series
+
+
+def _sanitize_image_names(names: List[str]) -> List[str]:
+    safe_names: List[str] = []
+    for name in names:
+        base = Path(str(name)).name
+        if not base or not base.endswith('.png'):
+            continue
+        safe_names.append(base)
+    return safe_names
+
+
+def _resolve_latest_images_for_model(payload: Dict[str, Any]) -> Dict[str, Any]:
+    model_name = str(payload.get('model', ''))
+    if not model_name or not INFERENCE_RESULT_DIR.is_dir():
+        payload['images'] = _sanitize_image_names(payload.get('images', []))
+        return payload
+
+    model_suffix = f"_{model_name}.png"
+    grouped: Dict[int, List[str]] = {}
+    for path in INFERENCE_RESULT_DIR.iterdir():
+        if not path.is_file():
+            continue
+        name = path.name
+        if not name.endswith(model_suffix):
+            continue
+        match = INFERENCE_IMAGE_RE.search(name)
+        if match is None:
+            continue
+        grouped.setdefault(int(match.group(1)), []).append(name)
+
+    if not grouped:
+        payload['images'] = _sanitize_image_names(payload.get('images', []))
+        return payload
+
+    current_id = int(payload.get('result_id', 0) or 0)
+    if current_id in grouped:
+        payload['images'] = sorted(grouped[current_id])
+    else:
+        payload['images'] = _sanitize_image_names(payload.get('images', []))
+    return payload
+
+
 async def _broadcast(message: Dict[str, Any]) -> None:
     dead: List[WebSocket] = []
     for ws in list(_ws_clients):
@@ -77,6 +166,21 @@ async def _broadcast(message: Dict[str, Any]) -> None:
                     _ws_clients.remove(ws)
 
 
+async def _broadcast_inference(message: Dict[str, Any]) -> None:
+    dead: List[WebSocket] = []
+    for ws in list(_inference_ws_clients):
+        try:
+            await ws.send_json(message)
+        except Exception:
+            dead.append(ws)
+
+    if dead:
+        async with _state_lock:
+            for ws in dead:
+                if ws in _inference_ws_clients:
+                    _inference_ws_clients.remove(ws)
+
+
 @app.post('/telemetry')
 async def ingest_telemetry(point: TelemetryPoint):
     payload = point.model_dump()
@@ -102,6 +206,31 @@ async def telemetry_history(
     return {'seconds': seconds, 'series': series}
 
 
+@app.post('/inference/result')
+async def ingest_inference_result(result: InferenceResult):
+    payload = result.model_dump()
+    payload = _resolve_latest_images_for_model(payload)
+    freq = str(payload['freq'])
+    now_ts = time.time()
+
+    async with _state_lock:
+        _inference_buffers[freq].append(payload)
+        _prune_inference_freq_locked(freq, now_ts)
+
+    await _broadcast_inference({'type': 'inference_result', 'data': payload})
+    return {'ok': True}
+
+
+@app.get('/inference/history')
+async def inference_history(
+    freq: Optional[str] = Query(default=None),
+    limit: int = Query(default=20, ge=1, le=200),
+):
+    async with _state_lock:
+        series = _copy_inference_series_locked(limit=limit, freq=freq)
+    return {'limit': limit, 'series': series}
+
+
 @app.websocket('/telemetry/ws')
 async def telemetry_ws(websocket: WebSocket):
     await websocket.accept()
@@ -123,6 +252,26 @@ async def telemetry_ws(websocket: WebSocket):
                 _ws_clients.remove(websocket)
 
 
+@app.websocket('/inference/ws')
+async def inference_ws(websocket: WebSocket):
+    await websocket.accept()
+    async with _state_lock:
+        _inference_ws_clients.append(websocket)
+        snapshot = _copy_inference_series_locked(limit=20, freq=None)
+
+    await websocket.send_json({'type': 'snapshot', 'data': snapshot})
+
+    try:
+        while True:
+            await websocket.receive_text()
+    except WebSocketDisconnect:
+        pass
+    finally:
+        async with _state_lock:
+            if websocket in _inference_ws_clients:
+                _inference_ws_clients.remove(websocket)
+
+
 MONITOR_HTML = """
 <!doctype html>
 <html>
@@ -453,12 +602,294 @@ loadInitial().then(connectWs).catch((e) => {
 """
 
 
+INFERENCE_VIEWER_HTML = """
+<!doctype html>
+<html>
+<head>
+  <meta charset="utf-8" />
+  <meta name="viewport" content="width=device-width, initial-scale=1" />
+  <title>DroneDetector Inference Viewer</title>
+  <style>
+    :root {
+      --bg: #f4f6f8;
+      --card: #ffffff;
+      --line: #d7dde5;
+      --text: #1c232e;
+      --muted: #647084;
+      --ok: #16a34a;
+      --warn: #b45309;
+      --accent: #0f6fff;
+    }
+    * { box-sizing: border-box; }
+    body { margin: 0; background: var(--bg); color: var(--text); font-family: system-ui, -apple-system, Segoe UI, sans-serif; }
+    .wrap { max-width: 1800px; margin: 0 auto; padding: 16px; }
+    .head { display: flex; justify-content: space-between; align-items: center; gap: 16px; margin-bottom: 16px; }
+    .meta { color: var(--muted); font-size: 13px; }
+    .actions { display: flex; gap: 8px; align-items: center; }
+    button { border: 1px solid var(--line); background: var(--card); color: var(--text); border-radius: 8px; padding: 8px 12px; cursor: pointer; }
+    button.primary { background: var(--accent); color: #fff; border-color: var(--accent); }
+    .grid { display: grid; grid-template-columns: repeat(auto-fit, minmax(520px, 1fr)); gap: 14px; }
+    .card { background: var(--card); border: 1px solid var(--line); border-radius: 14px; padding: 14px; }
+    .card-head { display: flex; justify-content: space-between; align-items: start; gap: 12px; margin-bottom: 10px; }
+    .freq { font-size: 28px; font-weight: 700; }
+    .pill { display: inline-flex; align-items: center; gap: 6px; border-radius: 999px; padding: 4px 10px; font-size: 12px; }
+    .pill.live { background: #dcfce7; color: #166534; }
+    .pill.paused { background: #fef3c7; color: #92400e; }
+    .summary { display: grid; grid-template-columns: repeat(2, minmax(0, 1fr)); gap: 8px 12px; margin-bottom: 12px; }
+    .summary-item { border: 1px solid var(--line); border-radius: 10px; padding: 8px 10px; }
+    .summary-label { color: var(--muted); font-size: 12px; margin-bottom: 4px; }
+    .summary-value { font-size: 14px; font-weight: 600; word-break: break-word; }
+    .images { display: grid; grid-template-columns: repeat(auto-fit, minmax(150px, 1fr)); gap: 10px; margin-bottom: 12px; }
+    .image-card { border: 1px solid var(--line); border-radius: 10px; overflow: hidden; background: #f8fafc; }
+    .image-card img { width: 100%; height: 180px; object-fit: contain; display: block; background: #fff; }
+    .image-card .cap { padding: 6px 8px; font-size: 12px; color: var(--muted); border-top: 1px solid var(--line); }
+    .history-title { font-size: 13px; font-weight: 600; margin-bottom: 8px; }
+    .history { display: flex; gap: 8px; overflow-x: auto; padding-bottom: 4px; }
+    .thumb { min-width: 110px; max-width: 110px; border: 1px solid var(--line); border-radius: 10px; background: #fff; padding: 6px; cursor: pointer; }
+    .thumb.active { border-color: var(--accent); box-shadow: inset 0 0 0 1px var(--accent); }
+    .thumb img { width: 100%; height: 70px; object-fit: contain; display: block; background: #f8fafc; border-radius: 6px; }
+    .thumb .t1 { font-size: 12px; font-weight: 600; margin-top: 6px; }
+    .thumb .t2 { font-size: 11px; color: var(--muted); }
+    .empty { color: var(--muted); font-size: 13px; padding: 16px 0; }
+  </style>
+</head>
+<body>
+  <div class="wrap">
+    <div class="head">
+      <div>
+        <h2 style="margin:0 0 4px;">DroneDetector Inference Viewer</h2>
+        <div class="meta">Latest inference card per frequency. Browser keeps last 20 results per frequency.</div>
+      </div>
+      <div class="actions">
+        <div class="meta" id="ws-status">connecting...</div>
+        <button id="pause-btn" class="primary">Pause updates</button>
+      </div>
+    </div>
+    <div class="grid" id="cards"></div>
+  </div>
+
+<script>
+const MAX_RESULTS = 20;
+const state = {};
+const selected = {};
+let paused = false;
+let wsKeepalive = null;
+
+function freqSort(a, b) { return Number(a) - Number(b); }
+function formatTs(ts) {
+  return new Date(Number(ts) * 1000).toLocaleString('ru-RU', { hour12: false });
+}
+function escapeHtml(value) {
+  return String(value ?? '').replaceAll('&', '&amp;').replaceAll('<', '&lt;').replaceAll('>', '&gt;').replaceAll('"', '&quot;');
+}
+function imageUrl(name) {
+  return `/inference/images/${encodeURIComponent(name)}`;
+}
+function primaryImage(result) {
+  return (result.images || [])[0] || '';
+}
+function trimResults(freq) {
+  const arr = state[freq] || [];
+  state[freq] = arr.slice(-MAX_RESULTS);
+}
+function ensureCard(freq) {
+  if (document.getElementById(`card-${freq}`)) return;
+  const card = document.createElement('div');
+  card.className = 'card';
+  card.id = `card-${freq}`;
+  card.innerHTML = `
+    <div class="card-head">
+      <div class="freq">${escapeHtml(freq)} MHz</div>
+      <div class="pill ${paused ? 'paused' : 'live'}" id="badge-${freq}">${paused ? 'paused' : 'live'}</div>
+    </div>
+    <div class="summary" id="summary-${freq}"></div>
+    <div class="images" id="images-${freq}"></div>
+    <div class="history-title">Recent results</div>
+    <div class="history" id="history-${freq}"></div>
+  `;
+  document.getElementById('cards').appendChild(card);
+}
+function renderSummary(freq, result) {
+  const el = document.getElementById(`summary-${freq}`);
+  el.innerHTML = `
+    <div class="summary-item"><div class="summary-label">Model</div><div class="summary-value">${escapeHtml(result.model)}</div></div>
+    <div class="summary-item"><div class="summary-label">Prediction</div><div class="summary-value">${escapeHtml(result.prediction)}</div></div>
+    <div class="summary-item"><div class="summary-label">Confidence</div><div class="summary-value">${Number(result.probability).toFixed(3)}</div></div>
+    <div class="summary-item"><div class="summary-label">Time</div><div class="summary-value">${escapeHtml(formatTs(result.ts))}</div></div>
+  `;
+}
+function renderImages(freq, result) {
+  const el = document.getElementById(`images-${freq}`);
+  const images = result.images || [];
+  if (!images.length) {
+    el.innerHTML = '<div class="empty">No images for this inference.</div>';
+    return;
+  }
+  el.innerHTML = images.map((name) => `
+    <div class="image-card">
+      <img loading="lazy" src="${imageUrl(name)}" alt="${escapeHtml(name)}" />
+      <div class="cap">${escapeHtml(name)}</div>
+    </div>
+  `).join('');
+}
+function renderHistory(freq) {
+  const el = document.getElementById(`history-${freq}`);
+  const results = state[freq] || [];
+  if (!results.length) {
+    el.innerHTML = '<div class="empty">No results yet.</div>';
+    return;
+  }
+  el.innerHTML = results.slice().reverse().map((result) => {
+    const active = String(selected[freq]) === String(result.result_id) ? 'active' : '';
+    const image = primaryImage(result);
+    return `
+      <button class="thumb ${active}" data-freq="${escapeHtml(freq)}" data-result-id="${result.result_id}">
+        ${image ? `<img loading="lazy" src="${imageUrl(image)}" alt="${escapeHtml(result.prediction)}" />` : '<div class="empty" style="padding:20px 0;">no image</div>'}
+        <div class="t1">${escapeHtml(result.prediction)}</div>
+        <div class="t2">p=${Number(result.probability).toFixed(2)}</div>
+      </button>
+    `;
+  }).join('');
+  el.querySelectorAll('.thumb').forEach((node) => {
+    node.addEventListener('click', () => {
+      selected[freq] = Number(node.dataset.resultId);
+      render(freq);
+    });
+  });
+}
+function render(freq) {
+  ensureCard(freq);
+  trimResults(freq);
+  const badge = document.getElementById(`badge-${freq}`);
+  badge.textContent = paused ? 'paused' : 'live';
+  badge.className = `pill ${paused ? 'paused' : 'live'}`;
+
+  const results = state[freq] || [];
+  if (!results.length) {
+    document.getElementById(`summary-${freq}`).innerHTML = '<div class="empty">No inference results yet.</div>';
+    document.getElementById(`images-${freq}`).innerHTML = '';
+    document.getElementById(`history-${freq}`).innerHTML = '';
+    return;
+  }
+  const active = results.find((item) => String(item.result_id) === String(selected[freq])) || results[results.length - 1];
+  selected[freq] = active.result_id;
+  renderSummary(freq, active);
+  renderImages(freq, active);
+  renderHistory(freq);
+}
+function renderAll() {
+  Object.keys(state).sort(freqSort).forEach(render);
+}
+function applySnapshot(series) {
+  const next = {};
+  for (const [freq, results] of Object.entries(series || {})) {
+    next[String(freq)] = Array.isArray(results) ? results.slice(-MAX_RESULTS) : [];
+  }
+  for (const freq of Object.keys(next)) {
+    state[freq] = next[freq];
+    if (state[freq].length) {
+      selected[freq] = state[freq][state[freq].length - 1].result_id;
+    }
+  }
+  renderAll();
+}
+function ingestResult(result) {
+  const freq = String(result.freq);
+  if (!state[freq]) state[freq] = [];
+  state[freq].push(result);
+  trimResults(freq);
+  selected[freq] = result.result_id;
+  render(freq);
+}
+async function loadHistory() {
+  const res = await fetch(`/inference/history?limit=${MAX_RESULTS}`);
+  const payload = await res.json();
+  applySnapshot(payload.series || {});
+}
+function setPaused(nextPaused) {
+  paused = nextPaused;
+  const btn = document.getElementById('pause-btn');
+  btn.textContent = paused ? 'Resume updates' : 'Pause updates';
+  btn.className = paused ? '' : 'primary';
+  renderAll();
+}
+function connectWs() {
+  const proto = location.protocol === 'https:' ? 'wss' : 'ws';
+  const ws = new WebSocket(`${proto}://${location.host}/inference/ws`);
+  ws.onopen = () => {
+    document.getElementById('ws-status').textContent = paused ? 'ws connected (paused)' : 'ws connected';
+    if (wsKeepalive) clearInterval(wsKeepalive);
+    wsKeepalive = setInterval(() => {
+      if (ws.readyState === 1) ws.send('ping');
+    }, 20000);
+  };
+  ws.onmessage = (event) => {
+    const msg = JSON.parse(event.data);
+    if (msg.type === 'snapshot' && msg.data) {
+      if (!paused) applySnapshot(msg.data);
+      return;
+    }
+    if (msg.type !== 'inference_result' || paused) return;
+    ingestResult(msg.data);
+  };
+  ws.onclose = () => {
+    document.getElementById('ws-status').textContent = 'ws disconnected, retrying...';
+    if (wsKeepalive) clearInterval(wsKeepalive);
+    setTimeout(connectWs, 1500);
+  };
+  ws.onerror = () => {
+    document.getElementById('ws-status').textContent = 'ws error';
+  };
+}
+document.getElementById('pause-btn').addEventListener('click', async () => {
+  if (paused) {
+    setPaused(false);
+    document.getElementById('ws-status').textContent = 'syncing...';
+    try {
+      await loadHistory();
+      document.getElementById('ws-status').textContent = 'ws connected';
+    } catch (err) {
+      document.getElementById('ws-status').textContent = `history error: ${err}`;
+    }
+  } else {
+    setPaused(true);
+    document.getElementById('ws-status').textContent = 'ws connected (paused)';
+  }
+});
+loadHistory().then(connectWs).catch((err) => {
+  document.getElementById('ws-status').textContent = `init error: ${err}`;
+  connectWs();
+});
+</script>
+</body>
+</html>
+"""
+
+
 @app.get('/', response_class=HTMLResponse)
 @app.get('/monitor', response_class=HTMLResponse)
 async def monitor_page():
     return HTMLResponse(content=MONITOR_HTML)
 
 
+@app.get('/inference-viewer', response_class=HTMLResponse)
+async def inference_viewer_page():
+    return HTMLResponse(content=INFERENCE_VIEWER_HTML)
+
+
+@app.get('/inference/images/{filename}')
+async def inference_image(filename: str):
+    safe_name = Path(filename).name
+    if safe_name != filename:
+        raise HTTPException(status_code=404, detail='image not found')
+
+    image_path = (INFERENCE_RESULT_DIR / safe_name).resolve()
+    if image_path.parent != INFERENCE_RESULT_DIR or not image_path.is_file():
+        raise HTTPException(status_code=404, detail='image not found')
+
+    return FileResponse(image_path)
+
+
 if __name__ == '__main__':
     import uvicorn
 

train_scripts/ImageDatasetCreate_spec_imag_real_915.ipynb

@@ -2,10 +2,18 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "4fdb98fc-65bb-467e-be0c-168fee9b0fca",
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/sibscience-4/from_ssh/DroneDetector/.venv-train/lib/python3.12/site-packages/matplotlib/projections/__init__.py:63: UserWarning: Unable to import Axes3D. This may be due to multiple versions of Matplotlib being installed (e.g. as a system package and as a pip package). As a result, the 3D projection is not available.\n",
+      "  warnings.warn(\"Unable to import Axes3D. This may be due to multiple versions of \"\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -14,32 +22,14 @@
      ]
     },
     {
-     "ename": "NameError",
-     "evalue": "name '_ndimage_api' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
-      "\u001b[31mNameError\u001b[39m                                 Traceback (most recent call last)",
-      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[3]\u001b[39m\u001b[32m, line 29\u001b[39m\n\u001b[32m     27\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mgc\u001b[39;00m\n\u001b[32m     28\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mtorchsig\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mutils\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mu\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m29\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mtorchsig\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mtransforms\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mtransforms\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mT\u001b[39;00m\n\u001b[32m     30\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mtorchsig\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mtransforms\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m functional \u001b[38;5;28;01mas\u001b[39;00m F\n\u001b[32m     31\u001b[39m matplotlib.use(\u001b[33m'\u001b[39m\u001b[33mAgg\u001b[39m\u001b[33m'\u001b[39m)\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m~/from_ssh/DroneDetector/torchsig/torchsig/transforms/transforms.py:1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01mdataset_transforms\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Spectrogram \u001b[38;5;28;01mas\u001b[39;00m _Spectrogram\n\u001b[32m      4\u001b[39m \u001b[38;5;28;01mclass\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mSpectrogram\u001b[39;00m(_Spectrogram):\n\u001b[32m      5\u001b[39m     \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m, nperseg=\u001b[38;5;28;01mNone\u001b[39;00m, fft_size=\u001b[38;5;28;01mNone\u001b[39;00m, nfft=\u001b[38;5;28;01mNone\u001b[39;00m, **kwargs):\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m~/from_ssh/DroneDetector/torchsig/torchsig/transforms/dataset_transforms.py:37\u001b[39m\n\u001b[32m      4\u001b[39m __all__ = [\n\u001b[32m      5\u001b[39m     \u001b[33m\"\u001b[39m\u001b[33mDatasetTransform\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m      6\u001b[39m \n\u001b[32m   (...)\u001b[39m\u001b[32m     33\u001b[39m     \u001b[33m\"\u001b[39m\u001b[33mTimeReversal\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m     34\u001b[39m ]\n\u001b[32m     36\u001b[39m \u001b[38;5;66;03m# TorchSig\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m37\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mtorchsig\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mtransforms\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mbase_transforms\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Transform\n\u001b[32m     38\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mtorchsig\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msignals\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msignal_types\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m DatasetSignal, SignalMetadata\n\u001b[32m     39\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mtorchsig\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mtransforms\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mfunctional\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mF\u001b[39;00m\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m~/from_ssh/DroneDetector/torchsig/torchsig/transforms/base_transforms.py:16\u001b[39m\n\u001b[32m      6\u001b[39m __all__ = [\n\u001b[32m      7\u001b[39m     \u001b[33m\"\u001b[39m\u001b[33mTransform\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m      8\u001b[39m     \u001b[33m\"\u001b[39m\u001b[33mCompose\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m   (...)\u001b[39m\u001b[32m     12\u001b[39m     \u001b[33m\"\u001b[39m\u001b[33mRandAugment\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m     13\u001b[39m ]\n\u001b[32m     15\u001b[39m \u001b[38;5;66;03m# TorchSig\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m16\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mtorchsig\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mtransforms\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mfunctional\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mF\u001b[39;00m\n\u001b[32m     17\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mtorchsig\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msignals\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01msignal_types\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Signal, DatasetSignal\n\u001b[32m     18\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mtorchsig\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mutils\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mrandom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Seedable\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m~/from_ssh/DroneDetector/torchsig/torchsig/transforms/functional.py:6\u001b[39m\n\u001b[32m      3\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mtyping\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m Literal, Optional, Tuple\n\u001b[32m      5\u001b[39m \u001b[38;5;66;03m# TorchSig\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m6\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mtorchsig\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mutils\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mdsp\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mdsp\u001b[39;00m\n\u001b[32m      7\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mtorchsig\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mutils\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mdsp\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[32m      8\u001b[39m     torchsig_complex_data_type,\n\u001b[32m      9\u001b[39m     torchsig_float_data_type\n\u001b[32m     10\u001b[39m )\n\u001b[32m     12\u001b[39m \u001b[38;5;66;03m# Third Party\u001b[39;00m\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m~/from_ssh/DroneDetector/torchsig/torchsig/utils/dsp.py:5\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[33;03m\"\"\"Digital Signal Processing (DSP) Utils\u001b[39;00m\n\u001b[32m      2\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m      4\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mtorchsig\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m __version__ \u001b[38;5;28;01mas\u001b[39;00m torchsig_version\n\u001b[32m----> \u001b[39m\u001b[32m5\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mscipy\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m signal \u001b[38;5;28;01mas\u001b[39;00m sp\n\u001b[32m      6\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mnumpy\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mnp\u001b[39;00m\n\u001b[32m      7\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mcopy\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m copy\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m<frozen importlib._bootstrap>:1412\u001b[39m, in \u001b[36m_handle_fromlist\u001b[39m\u001b[34m(module, fromlist, import_, recursive)\u001b[39m\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m~/from_ssh/DroneDetector/.venv-train/lib/python3.12/site-packages/scipy/__init__.py:131\u001b[39m, in \u001b[36m__getattr__\u001b[39m\u001b[34m(name)\u001b[39m\n\u001b[32m    129\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m__getattr__\u001b[39m(name):\n\u001b[32m    130\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01min\u001b[39;00m submodules:\n\u001b[32m--> \u001b[39m\u001b[32m131\u001b[39m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_importlib\u001b[49m\u001b[43m.\u001b[49m\u001b[43mimport_module\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43mf\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mscipy.\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mname\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[33;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m    132\u001b[39m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m    133\u001b[39m         \u001b[38;5;28;01mtry\u001b[39;00m:\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m/usr/lib/python3.12/importlib/__init__.py:90\u001b[39m, in \u001b[36mimport_module\u001b[39m\u001b[34m(name, package)\u001b[39m\n\u001b[32m     88\u001b[39m             \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[32m     89\u001b[39m         level += \u001b[32m1\u001b[39m\n\u001b[32m---> \u001b[39m\u001b[32m90\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_bootstrap\u001b[49m\u001b[43m.\u001b[49m\u001b[43m_gcd_import\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m[\u001b[49m\u001b[43mlevel\u001b[49m\u001b[43m:\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpackage\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlevel\u001b[49m\u001b[43m)\u001b[49m\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m~/from_ssh/DroneDetector/.venv-train/lib/python3.12/site-packages/scipy/signal/__init__.py:306\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m      2\u001b[39m \u001b[33;03m=======================================\u001b[39;00m\n\u001b[32m      3\u001b[39m \u001b[33;03mSignal processing (:mod:`scipy.signal`)\u001b[39;00m\n\u001b[32m   (...)\u001b[39m\u001b[32m    300\u001b[39m \n\u001b[32m    301\u001b[39m \u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m    302\u001b[39m \u001b[38;5;66;03m# bring in the public functionality from private namespaces\u001b[39;00m\n\u001b[32m    303\u001b[39m \n\u001b[32m    304\u001b[39m \u001b[38;5;66;03m# mypy: ignore-errors\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m306\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01m_support_alternative_backends\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m *\n\u001b[32m    307\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m _support_alternative_backends\n\u001b[32m    308\u001b[39m __all__ = _support_alternative_backends.__all__\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m~/from_ssh/DroneDetector/.venv-train/lib/python3.12/site-packages/scipy/signal/_support_alternative_backends.py:7\u001b[39m\n\u001b[32m      2\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mtypes\u001b[39;00m\n\u001b[32m      3\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mscipy\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01m_lib\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01m_array_api\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[32m      4\u001b[39m     is_cupy, is_jax, scipy_namespace_for, SCIPY_ARRAY_API, xp_capabilities\n\u001b[32m      5\u001b[39m )\n\u001b[32m----> \u001b[39m\u001b[32m7\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01m_signal_api\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m *   \u001b[38;5;66;03m# noqa: F403\u001b[39;00m\n\u001b[32m      8\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m _signal_api\n\u001b[32m      9\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m _delegators\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m~/from_ssh/DroneDetector/.venv-train/lib/python3.12/site-packages/scipy/signal/_signal_api.py:16\u001b[39m\n\u001b[32m     12\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01m_upfirdn\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m upfirdn         \u001b[38;5;66;03m# noqa: F401\u001b[39;00m\n\u001b[32m     14\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01m_spline\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m sepfir2d          \u001b[38;5;66;03m# noqa: F401\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m16\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01m_spline_filters\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m *         \u001b[38;5;66;03m# noqa: F403\u001b[39;00m\n\u001b[32m     17\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01m_filter_design\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m *         \u001b[38;5;66;03m# noqa: F403\u001b[39;00m\n\u001b[32m     18\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01m_fir_filter_design\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m *         \u001b[38;5;66;03m# noqa: F403\u001b[39;00m\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m~/from_ssh/DroneDetector/.venv-train/lib/python3.12/site-packages/scipy/signal/_spline_filters.py:14\u001b[39m\n\u001b[32m     12\u001b[39m \u001b[38;5;66;03m# From splinemodule.c\u001b[39;00m\n\u001b[32m     13\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01m_spline\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m sepfir2d, symiirorder1_ic, symiirorder2_ic_fwd, symiirorder2_ic_bwd\n\u001b[32m---> \u001b[39m\u001b[32m14\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01m_signaltools\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m lfilter, sosfilt, lfiltic\n\u001b[32m     15\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01m_arraytools\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m axis_slice, axis_reverse\n\u001b[32m     17\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mscipy\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01minterpolate\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m BSpline\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m~/from_ssh/DroneDetector/.venv-train/lib/python3.12/site-packages/scipy/signal/_signaltools.py:19\u001b[39m\n\u001b[32m     17\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01m_upfirdn\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m upfirdn, _output_len, _upfirdn_modes\n\u001b[32m     18\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mscipy\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m linalg, fft \u001b[38;5;28;01mas\u001b[39;00m sp_fft\n\u001b[32m---> \u001b[39m\u001b[32m19\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mscipy\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m ndimage\n\u001b[32m     20\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mscipy\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mfft\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01m_helper\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m _init_nd_shape_and_axes\n\u001b[32m     21\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mnumpy\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mnp\u001b[39;00m\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m<frozen importlib._bootstrap>:1412\u001b[39m, in \u001b[36m_handle_fromlist\u001b[39m\u001b[34m(module, fromlist, import_, recursive)\u001b[39m\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m~/from_ssh/DroneDetector/.venv-train/lib/python3.12/site-packages/scipy/__init__.py:131\u001b[39m, in \u001b[36m__getattr__\u001b[39m\u001b[34m(name)\u001b[39m\n\u001b[32m    129\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m__getattr__\u001b[39m(name):\n\u001b[32m    130\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01min\u001b[39;00m submodules:\n\u001b[32m--> \u001b[39m\u001b[32m131\u001b[39m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_importlib\u001b[49m\u001b[43m.\u001b[49m\u001b[43mimport_module\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43mf\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mscipy.\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mname\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[33;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m    132\u001b[39m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m    133\u001b[39m         \u001b[38;5;28;01mtry\u001b[39;00m:\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m/usr/lib/python3.12/importlib/__init__.py:90\u001b[39m, in \u001b[36mimport_module\u001b[39m\u001b[34m(name, package)\u001b[39m\n\u001b[32m     88\u001b[39m             \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[32m     89\u001b[39m         level += \u001b[32m1\u001b[39m\n\u001b[32m---> \u001b[39m\u001b[32m90\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_bootstrap\u001b[49m\u001b[43m.\u001b[49m\u001b[43m_gcd_import\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m[\u001b[49m\u001b[43mlevel\u001b[49m\u001b[43m:\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpackage\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlevel\u001b[49m\u001b[43m)\u001b[49m\n",
-      "\u001b[36mFile \u001b[39m\u001b[32m~/from_ssh/DroneDetector/.venv-train/lib/python3.12/site-packages/scipy/ndimage/__init__.py:162\u001b[39m\n\u001b[32m    160\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m _support_alternative_backends\n\u001b[32m    161\u001b[39m __all__ = _support_alternative_backends.__all__\n\u001b[32m--> \u001b[39m\u001b[32m162\u001b[39m \u001b[38;5;28;01mdel\u001b[39;00m _support_alternative_backends, \u001b[43m_ndimage_api\u001b[49m, _delegators  \u001b[38;5;66;03m# noqa: F821\u001b[39;00m\n\u001b[32m    165\u001b[39m \u001b[38;5;66;03m# Deprecated namespaces, to be removed in v2.0.0\u001b[39;00m\n\u001b[32m    166\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m filters\n",
-      "\u001b[31mNameError\u001b[39m: name '_ndimage_api' is not defined"
-     ]
+     "data": {
+      "text/plain": [
+       "<contextlib.ExitStack at 0x7dbe9e0bc080>"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -82,7 +72,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "4848b066-2e09-4c1c-b8fa-8e3fa84d907a",
    "metadata": {},
    "outputs": [],
@@ -92,7 +82,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "9267fbe1",
    "metadata": {},
    "outputs": [],
@@ -165,20 +155,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "id": "448da74a-e0ae-44d8-9877-8dd1f257a24f",
    "metadata": {},
    "outputs": [],
    "source": [
     "selected_freq=2400\n",
     "\n",
-    "path_to_binaries = f'/mnt/nvme1/dataset_5_5_26/noise/{selected_freq}'\n",
-    "path_to_pictures = f'/mnt/nvme1/dataset_5_5_26_img/noise/{selected_freq}_jpg'"
+    "path_to_binaries = f'/mnt/data/Dataset/noise/{selected_freq}'\n",
+    "path_to_pictures = f'/mnt/data/Dataset_img/noise/{selected_freq}_jpg'"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "id": "ac4945a8-29c4-4da4-945f-08658953e3e5",
    "metadata": {},
    "outputs": [],
@@ -188,7 +178,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "id": "6f226f86-5d72-4573-8af6-750128b70263",
    "metadata": {},
    "outputs": [
@@ -196,14 +186,42 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "drone: 100%|██████████| 935/935 [13:33<00:00,  1.15it/s]"
+      "2026-04-08_18-06-46: 100%|██████████| 1013/1013 [19:43<00:00,  1.17s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dir:  2026-04-08_18-06-46  finished!\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2026-04-08_18-10-32: 100%|██████████| 353/353 [06:53<00:00,  1.17s/it]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dir:  2026-04-08_18-10-32  finished!\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2026-04-08_18-08-39: 100%|██████████| 1017/1017 [19:48<00:00,  1.17s/it]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Dir:  drone  finished!\n"
+      "Dir:  2026-04-08_18-08-39  finished!\n"
      ]
     },
     {
@@ -227,7 +245,7 @@
     "        sig2pic_with_spec(path_to_data=filepath, filename=file, specT=s, resize=size)\n",
     "\n",
     "    files = os.listdir(filepath)\n",
-    "    k = max(1, int(len(files) * 0.3))\n",
+    "    k = max(1, int(len(files) * 1))\n",
     "    files = random.sample(files, k)\n",
     "    for file in tqdm(files, desc=subdir):\n",
     "        full_input_path = filepath + file\n",

train_scripts/create_dataset_overlay.py

@@ -0,0 +1,217 @@
+#!/usr/bin/env python3
+import argparse
+import csv
+import os
+import random
+import shutil
+from pathlib import Path
+
+import cv2
+import matplotlib
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+import numpy as np
+from tqdm import tqdm
+
+
+DEFAULT_FREQS = (1200, 2400)
+PNG_SUFFIXES = ("_real.png", "_imag.png", "_spec.png")
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description=(
+            "Build a two-class image dataset with drone signatures overlaid on noise images. "
+            "The output is ready for Training_models2pic_val_loss.ipynb."
+        )
+    )
+    parser.add_argument("--drone-root", default="/mnt/data/Dataset/drone")
+    parser.add_argument("--noise-img-root", default="/mnt/data/Dataset_img/noise")
+    parser.add_argument("--output-root", default="/mnt/data/Dataset_overlay")
+    parser.add_argument("--freqs", default=",".join(str(v) for v in DEFAULT_FREQS))
+    parser.add_argument("--alpha", type=float, default=1.0, help="Overlay strength: 1.0 keeps the darkest drone/noise pixels.")
+    parser.add_argument("--limit-per-freq", type=int, default=0, help="0 means use all available noise images per frequency.")
+    parser.add_argument("--seed", type=int, default=42)
+    parser.add_argument("--copy-noise", action="store_true", help="Copy noise files instead of hardlinking them.")
+    parser.add_argument("--overwrite", action="store_true")
+    parser.add_argument("--dry-run", action="store_true")
+    parser.add_argument("--no-progress", action="store_true", help="Disable tqdm progress bars.")
+    return parser.parse_args()
+
+
+def parse_freqs(value):
+    return [int(item.strip()) for item in value.split(",") if item.strip()]
+
+
+def collect_drone_npys(root, freq):
+    root = Path(root)
+    candidates = []
+    candidates.extend((root / str(freq)).rglob("*.npy"))
+    candidates.extend((root / f"{freq}_jpg").glob("*.npy"))
+    return sorted({p for p in candidates if p.is_file()})
+
+
+def collect_noise_npys(root, freq):
+    root = Path(root)
+    candidates = []
+    candidates.extend((root / f"{freq}_jpg").glob("*.npy"))
+    candidates.extend((root / str(freq)).rglob("*.npy"))
+    return sorted({p for p in candidates if p.is_file()})
+
+
+def load_image_tensor(path):
+    arr = np.load(path)
+    if arr.ndim == 2:
+        arr = np.stack([arr, arr, arr], axis=0)
+    if arr.ndim == 3 and arr.shape[-1] in (1, 3) and arr.shape[0] not in (1, 3):
+        arr = np.moveaxis(arr, -1, 0)
+    if arr.ndim != 3:
+        raise ValueError(f"expected 3D image tensor, got shape={arr.shape} path={path}")
+    if arr.shape[0] == 1:
+        arr = np.repeat(arr, 3, axis=0)
+    if arr.shape[0] < 3:
+        raise ValueError(f"expected at least 3 channels, got shape={arr.shape} path={path}")
+    return arr[:3].astype(np.float32, copy=False)
+
+
+def resize_like(arr, shape):
+    if arr.shape == shape:
+        return arr
+    channels, height, width = shape
+    resized = []
+    for channel in arr[:channels]:
+        resized.append(cv2.resize(channel, (width, height), interpolation=cv2.INTER_LINEAR))
+    return np.asarray(resized, dtype=np.float32)
+
+
+def overlay_tensors(noise, drone, alpha):
+    drone = resize_like(drone, noise.shape)
+    bright_overlay = np.minimum(noise, drone)
+    mixed = (1.0 - alpha) * noise + alpha * bright_overlay
+    return np.clip(mixed, 0, 255).astype(np.float32)
+
+
+def to_uint8(channel):
+    arr = np.asarray(channel, dtype=np.float32)
+    finite = arr[np.isfinite(arr)]
+    if finite.size == 0:
+        return np.zeros(arr.shape, dtype=np.uint8)
+    min_v = float(finite.min())
+    max_v = float(finite.max())
+    if min_v >= 0.0 and max_v <= 255.0:
+        return np.clip(arr, 0, 255).astype(np.uint8)
+    if max_v == min_v:
+        return np.zeros(arr.shape, dtype=np.uint8)
+    norm = (arr - min_v) / (max_v - min_v)
+    return np.clip(norm * 255.0, 0, 255).astype(np.uint8)
+
+
+def save_notebook_style_png(path, channel):
+    fig = plt.figure(figsize=(16, 16))
+    plt.imshow(channel)
+    plt.savefig(path)
+    plt.clf()
+    plt.cla()
+    plt.close()
+    plt.close(fig)
+
+
+def save_sample(base_path, tensor):
+    np.save(str(base_path) + ".npy", tensor.astype(np.float32))
+    for idx, suffix in enumerate(PNG_SUFFIXES):
+        save_notebook_style_png(str(base_path) + suffix, tensor[idx])
+
+
+def link_or_copy(src, dst, copy_file):
+    dst.parent.mkdir(parents=True, exist_ok=True)
+    if dst.exists():
+        return
+    if copy_file:
+        shutil.copy2(src, dst)
+        return
+    try:
+        os.link(src, dst)
+    except OSError:
+        shutil.copy2(src, dst)
+
+
+def copy_noise_family(noise_npy, out_dir, copy_file):
+    base_name = noise_npy.name[:-4] if noise_npy.name.endswith(".npy") else noise_npy.name
+    link_or_copy(noise_npy, out_dir / noise_npy.name, copy_file)
+    for suffix in PNG_SUFFIXES:
+        sidecar = noise_npy.with_name(base_name + suffix)
+        if sidecar.exists():
+            link_or_copy(sidecar, out_dir / sidecar.name, copy_file)
+
+
+def main():
+    args = parse_args()
+    random.seed(args.seed)
+    freqs = parse_freqs(args.freqs)
+    output_root = Path(args.output_root)
+    manifest_rows = []
+
+    if args.overwrite and output_root.exists() and not args.dry_run:
+        shutil.rmtree(output_root)
+
+    for freq in freqs:
+        drone_files = collect_drone_npys(args.drone_root, freq)
+        noise_files = collect_noise_npys(args.noise_img_root, freq)
+        if not drone_files:
+            raise RuntimeError(f"no drone npy files found for freq={freq} under {args.drone_root}")
+        if not noise_files:
+            raise RuntimeError(f"no noise image npy files found for freq={freq} under {args.noise_img_root}")
+
+        count = len(noise_files) if args.limit_per_freq <= 0 else min(args.limit_per_freq, len(noise_files))
+        selected_noise = noise_files[:]
+        random.shuffle(selected_noise)
+        selected_noise = selected_noise[:count]
+
+        out_drone_dir = output_root / "drone" / f"{freq}_jpg"
+        out_noise_dir = output_root / "noise" / f"{freq}_jpg"
+
+        print(f"freq={freq}: drone_source={len(drone_files)} noise_source={len(noise_files)} output_per_class={count}")
+        if args.dry_run:
+            continue
+
+        out_drone_dir.mkdir(parents=True, exist_ok=True)
+        out_noise_dir.mkdir(parents=True, exist_ok=True)
+
+        iterator = tqdm(
+            enumerate(selected_noise),
+            total=count,
+            desc=f"overlay freq={freq}",
+            unit="sample",
+            disable=args.no_progress,
+        )
+        for idx, noise_path in iterator:
+            drone_path = drone_files[idx % len(drone_files)]
+            noise_tensor = load_image_tensor(noise_path)
+            drone_tensor = load_image_tensor(drone_path)
+            mixed = overlay_tensors(noise_tensor, drone_tensor, args.alpha)
+
+            out_base = out_drone_dir / f"overlay_{freq}_{idx:06d}"
+            save_sample(out_base, mixed)
+            copy_noise_family(noise_path, out_noise_dir, args.copy_noise)
+
+            manifest_rows.append({
+                "freq": freq,
+                "output": str(out_base) + ".npy",
+                "noise_source": str(noise_path),
+                "drone_source": str(drone_path),
+                "alpha": args.alpha,
+            })
+
+    if not args.dry_run:
+        manifest_path = output_root / "overlay_manifest.csv"
+        with manifest_path.open("w", newline="", encoding="utf-8") as fh:
+            writer = csv.DictWriter(fh, fieldnames=["freq", "output", "noise_source", "drone_source", "alpha"])
+            writer.writeheader()
+            writer.writerows(manifest_rows)
+        print(f"wrote {len(manifest_rows)} overlay samples")
+        print(f"manifest: {manifest_path}")
+        print(f"dataset: {output_root}")
+
+
+if __name__ == "__main__":
+    main()

train_scripts/models/ensemble_1.2_jpg_10/dataset.csv

@@ -1,964 +0,0 @@
-file_name
-/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_1261.npy.npy
-/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_333.npy.npy
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-/mnt/nvme1/dataset_img/drone/1200/HackA5_1.2_880.npy.npy
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