Merge branch 'Automatica-3' of https://git.sibsci.ru/SergeyRevyakin/DroneDetector into Automatica-3

.dockerignore

@@ -1,17 +1,50 @@
-.git
+# Default-deny: include only files required by Docker images.
-.vscode
+*
-.venv-sdr
+
+!.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
-
+*.tar
-# Heavy host-only SDR sources
+*.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

@@ -187,3 +187,8 @@ cython_debug/
 runtime/
 
 /.venv-*/*
+
+/train_scripts/models
+
+logs/nn_results_*.csv
+.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=[])
+            if ind_inference is None:
-            self._prepare_data(data=data)
+                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])
+            print('RESULT' + self._shablon + ': ' + str(prediction) + ' (probability=' + str(probability) + ')')
-            self._post_data(prediction=prediction)
+            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

@@ -0,0 +1,265 @@
+from torchvision import models
+import torch.nn as nn
+import matplotlib
+import numpy as np
+import torch
+import cv2
+import gc
+import io
+import os
+import re
+
+
+def _render_signal_channel(values, figsize=(16, 16), dpi=16, resize=(256, 256)):
+    import matplotlib.pyplot as plt
+
+    fig = plt.figure(figsize=figsize)
+    plt.axes(ylim=(-1, 1))
+    plt.plot(values, color="black")
+    plt.gca().set_axis_off()
+    plt.subplots_adjust(top=1, bottom=0, right=1, left=0, hspace=0, wspace=0)
+    plt.margins(0, 0)
+
+    buf = io.BytesIO()
+    fig.savefig(buf, format="png", dpi=dpi)
+    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 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()
+    plt.close(fig)
+
+    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
+
+        matplotlib.use("Agg")
+        plt.ioff()
+
+        real = np.asarray(data[0], dtype=np.float32)
+        imag = np.asarray(data[1], dtype=np.float32)
+        signal = real + 1j * imag
+
+        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()
+
+        print("Подготовка данных завершена")
+        print()
+        return [img_real, img_mag]
+
+    except Exception as exc:
+        print(str(exc))
+        return None
+
+
+def build_func_ensemble(file_model="", file_config="", num_classes=None):
+    try:
+        import matplotlib.pyplot as plt
+
+        matplotlib.use("Agg")
+        plt.ioff()
+        torch.cuda.empty_cache()
+
+        num_classes = 2
+        model1 = models.resnet18(pretrained=False)
+        model2 = models.resnet50(pretrained=False)
+
+        model1.fc = nn.Linear(model1.fc.in_features, num_classes)
+        model2.fc = nn.Linear(model2.fc.in_features, num_classes)
+
+        class Ensemble(nn.Module):
+            def __init__(self, model1, model2):
+                super().__init__()
+                self.model1 = model1
+                self.model2 = model2
+                self.fc = nn.Linear(2 * num_classes, num_classes)
+
+            def forward(self, x):
+                if isinstance(x, (list, tuple)):
+                    x1 = x[0]
+                    x2 = x[1] if len(x) > 1 else x[0]
+                else:
+                    x1 = x
+                    x2 = x
+                y1 = self.model1(x1)
+                y2 = self.model2(x2)
+                y = torch.cat((y1, y2), dim=1)
+                return self.fc(y)
+
+        model = Ensemble(model1, model2)
+
+        device = "cuda" if torch.cuda.is_available() else "cpu"
+        if device != "cpu":
+            model = model.to(device)
+        model.load_state_dict(torch.load(file_model, map_location=device))
+        model.eval()
+
+        cv2.destroyAllWindows()
+        gc.collect()
+
+        print("Инициализация модели завершена")
+        print()
+        return model
+
+    except Exception as exc:
+        print(str(exc))
+        return None
+
+
+def inference_func_ensemble(data=None, model=None, mapping=None, shablon=""):
+    try:
+        cv2.destroyAllWindows()
+        gc.collect()
+        torch.cuda.empty_cache()
+
+        device = "cuda" if torch.cuda.is_available() else "cpu"
+        if isinstance(data, (list, tuple)) and len(data) >= 2:
+            inputs = [
+                torch.unsqueeze(torch.tensor(data[0]).cpu(), 0).to(device).float(),
+                torch.unsqueeze(torch.tensor(data[1]).cpu(), 0).to(device).float(),
+            ]
+        else:
+            tensor = torch.unsqueeze(torch.tensor(data).cpu(), 0).to(device).float()
+            inputs = [tensor, tensor]
+
+        with torch.no_grad():
+            output = model(inputs)
+            _, predict = torch.max(output.data, 1)
+
+        prediction = mapping[int(np.asarray(predict.cpu())[0])]
+        print("PREDICTION" + shablon + ": " + prediction)
+
+        output = output.cpu()
+        label = np.asarray(np.argmax(output, axis=1))[0]
+        output = np.asarray(torch.squeeze(output, 0))
+        expon = np.exp(output - np.max(output))
+        probability = round((expon / expon.sum())[label], 2)
+
+        cv2.destroyAllWindows()
+        gc.collect()
+        print("Уверенность" + shablon + " в предсказании: " + str(probability))
+        print("Инференс завершен")
+        print()
+        return [prediction, probability]
+
+    except Exception as exc:
+        print(str(exc))
+        return None
+
+
+def post_func_ensemble(src="", model_type="", prediction="", model_id=0, ind_inference=0, data=None):
+    try:
+        import matplotlib.pyplot as plt
+
+        matplotlib.use("Agg")
+        plt.ioff()
+
+        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")
+            plt.clf()
+            plt.cla()
+            plt.close(fig)
+            cv2.destroyAllWindows()
+            gc.collect()
+
+            fig, ax = plt.subplots()
+            ax.imshow(np.moveaxis(data[1], 0, -1))
+            plt.savefig(src + "_inference_" + str(ind_inference) + "_" + prediction + "_mod_" + str(model_id) + "_" + model_type + ".png")
+            plt.clf()
+            plt.cla()
+            plt.close(fig)
+            cv2.destroyAllWindows()
+            gc.collect()
+
+            _prune_old_inference_images(src, model_type, model_id)
+
+        plt.clf()
+        plt.cla()
+        plt.close()
+        cv2.destroyAllWindows()
+        gc.collect()
+
+        print("Постобработка завершена")
+        print()
+
+    except Exception as exc:
+        print(str(exc))
+        return None

NN_server/Models/ensemble_2400_v44.py

@@ -0,0 +1,265 @@
+from torchvision import models
+import torch.nn as nn
+import matplotlib
+import numpy as np
+import torch
+import cv2
+import gc
+import io
+import os
+import re
+
+
+def _render_signal_channel(values, figsize=(16, 16), dpi=16, resize=(256, 256)):
+    import matplotlib.pyplot as plt
+
+    fig = plt.figure(figsize=figsize)
+    plt.axes(ylim=(-1, 1))
+    plt.plot(values, color="black")
+    plt.gca().set_axis_off()
+    plt.subplots_adjust(top=1, bottom=0, right=1, left=0, hspace=0, wspace=0)
+    plt.margins(0, 0)
+
+    buf = io.BytesIO()
+    fig.savefig(buf, format="png", dpi=dpi)
+    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 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()
+    plt.close(fig)
+
+    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
+
+        matplotlib.use("Agg")
+        plt.ioff()
+
+        real = np.asarray(data[0], dtype=np.float32)
+        imag = np.asarray(data[1], dtype=np.float32)
+        signal = real + 1j * imag
+
+        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()
+
+        print("Подготовка данных завершена")
+        print()
+        return [img_real, img_mag]
+
+    except Exception as exc:
+        print(str(exc))
+        return None
+
+
+def build_func_ensemble(file_model="", file_config="", num_classes=None):
+    try:
+        import matplotlib.pyplot as plt
+
+        matplotlib.use("Agg")
+        plt.ioff()
+        torch.cuda.empty_cache()
+
+        num_classes = 2
+        model1 = models.resnet18(pretrained=False)
+        model2 = models.resnet50(pretrained=False)
+
+        model1.fc = nn.Linear(model1.fc.in_features, num_classes)
+        model2.fc = nn.Linear(model2.fc.in_features, num_classes)
+
+        class Ensemble(nn.Module):
+            def __init__(self, model1, model2):
+                super().__init__()
+                self.model1 = model1
+                self.model2 = model2
+                self.fc = nn.Linear(2 * num_classes, num_classes)
+
+            def forward(self, x):
+                if isinstance(x, (list, tuple)):
+                    x1 = x[0]
+                    x2 = x[1] if len(x) > 1 else x[0]
+                else:
+                    x1 = x
+                    x2 = x
+                y1 = self.model1(x1)
+                y2 = self.model2(x2)
+                y = torch.cat((y1, y2), dim=1)
+                return self.fc(y)
+
+        model = Ensemble(model1, model2)
+
+        device = "cuda" if torch.cuda.is_available() else "cpu"
+        if device != "cpu":
+            model = model.to(device)
+        model.load_state_dict(torch.load(file_model, map_location=device))
+        model.eval()
+
+        cv2.destroyAllWindows()
+        gc.collect()
+
+        print("Инициализация модели завершена")
+        print()
+        return model
+
+    except Exception as exc:
+        print(str(exc))
+        return None
+
+
+def inference_func_ensemble(data=None, model=None, mapping=None, shablon=""):
+    try:
+        cv2.destroyAllWindows()
+        gc.collect()
+        torch.cuda.empty_cache()
+
+        device = "cuda" if torch.cuda.is_available() else "cpu"
+        if isinstance(data, (list, tuple)) and len(data) >= 2:
+            inputs = [
+                torch.unsqueeze(torch.tensor(data[0]).cpu(), 0).to(device).float(),
+                torch.unsqueeze(torch.tensor(data[1]).cpu(), 0).to(device).float(),
+            ]
+        else:
+            tensor = torch.unsqueeze(torch.tensor(data).cpu(), 0).to(device).float()
+            inputs = [tensor, tensor]
+
+        with torch.no_grad():
+            output = model(inputs)
+            _, predict = torch.max(output.data, 1)
+
+        prediction = mapping[int(np.asarray(predict.cpu())[0])]
+        print("PREDICTION" + shablon + ": " + prediction)
+
+        output = output.cpu()
+        label = np.asarray(np.argmax(output, axis=1))[0]
+        output = np.asarray(torch.squeeze(output, 0))
+        expon = np.exp(output - np.max(output))
+        probability = round((expon / expon.sum())[label], 2)
+
+        cv2.destroyAllWindows()
+        gc.collect()
+        print("Уверенность" + shablon + " в предсказании: " + str(probability))
+        print("Инференс завершен")
+        print()
+        return [prediction, probability]
+
+    except Exception as exc:
+        print(str(exc))
+        return None
+
+
+def post_func_ensemble(src="", model_type="", prediction="", model_id=0, ind_inference=0, data=None):
+    try:
+        import matplotlib.pyplot as plt
+
+        matplotlib.use("Agg")
+        plt.ioff()
+
+        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")
+            plt.clf()
+            plt.cla()
+            plt.close(fig)
+            cv2.destroyAllWindows()
+            gc.collect()
+
+            fig, ax = plt.subplots()
+            ax.imshow(np.moveaxis(data[1], 0, -1))
+            plt.savefig(src + "_inference_" + str(ind_inference) + "_" + prediction + "_mod_" + str(model_id) + "_" + model_type + ".png")
+            plt.clf()
+            plt.cla()
+            plt.close(fig)
+            cv2.destroyAllWindows()
+            gc.collect()
+
+            _prune_old_inference_images(src, model_type, model_id)
+
+        plt.clf()
+        plt.cla()
+        plt.close()
+        cv2.destroyAllWindows()
+        gc.collect()
+
+        print("Постобработка завершена")
+        print()
+
+    except Exception as exc:
+        print(str(exc))
+        return None

NN_server/server.py

@@ -15,6 +15,8 @@ import shutil
 import json
 import gc
 import logging
+import time
+import re
 
 TORCHSIG_PATH = "/app/torchsig"
 if TORCHSIG_PATH not in sys.path:
@@ -28,6 +30,7 @@ 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 = {}
@@ -51,20 +54,85 @@ config = dict(dotenv_values(ROOT_ENV))
 
 if not config:
     raise RuntimeError("[NN_server/server.py] .env was loaded but no keys were parsed")
-if not any(key.startswith("NN_") for key in config):
+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)
 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
+
 
 def init_data_for_inference():
     try:
-        if os.path.isdir(config['SRC_RESULT']):
+        reset_directory_contents(config['SRC_RESULT'])
-            shutil.rmtree(config['SRC_RESULT'])
+        reset_directory_contents(config['SRC_EXAMPLE'])
-        os.mkdir(config['SRC_RESULT'])
-        if os.path.isdir(config['SRC_EXAMPLE']):
-            shutil.rmtree(config['SRC_EXAMPLE'])
-        os.mkdir(config['SRC_EXAMPLE'])
     except Exception as exc:
         print(str(exc))
         print()
@@ -72,7 +140,7 @@ def init_data_for_inference():
     try:
         global model_list
         for key in config.keys():
-            if key.startswith('NN_'):
+            if MODEL_ENV_RE.match(key):
                 params = config[key].split(' && ')
                 module = importlib.import_module('Models.' + params[4])
                 classes = {}
@@ -109,11 +177,19 @@ 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']))
@@ -129,26 +205,48 @@ 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 = 0
-                    if (int(freq) == 2400 and (prediction_list[0] in ['drone', 'drone_noise'] or (prediction_list[0] == 'wifi' and float(probability) >= 0.95))) or (int(freq) == 1200 and (prediction_list[0] in ['drone'] and float(probability) >= 0.95)):
+                    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 += 8
-                    if int(freq) in [915]:
+
+                    elif freq_int == 915:
                         result = 0
-                    if int(freq) in []:
+
-                        result = 8
                     data_to_send = {
-                        'freq': str(freq),
+                        "freq": str(freq),
-                        'amplitude': result
+                        "amplitude": result,
-                        #'triggered': False if result < 7 else True,
-                        #'light_len': 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:

README.md

@@ -211,3 +211,12 @@ 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

@@ -1,84 +1,140 @@
 #!/usr/bin/env bash
 set -Eeuo pipefail
 
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+SCRIPT_OWNER="${CAPTURE_USER:-$(stat -c %U "$SCRIPT_DIR")}"
+SCRIPT_OWNER_HOME="$(getent passwd "$SCRIPT_OWNER" | cut -d: -f6 || true)"
+cd "$SCRIPT_DIR"
+
 ############################
 # НАСТРОЙКИ
 ############################
 
-# ЗАМЕНИ на реальную точку монтирования nvme1n1
+BASE_DIR="/mnt/data/dataset_6_5_26"
-BASE_DIR="/mnt/nvme1/dataset"
+
+PYTHON_BIN="${PYTHON_BIN:-$SCRIPT_DIR/.venv-sdr/bin/python}"
+SCRIPT_PATH="${SCRIPT_PATH:-$SCRIPT_DIR/scripts_nn/data_saver_headless.py}"
+ENV_FILE="${ENV_FILE:-$SCRIPT_DIR/.env}"
 
-# Путь к python из venv
+RUN_ONCE="${RUN_ONCE:-0}"
-PYTHON_BIN="${PYTHON_BIN:-$PWD/.venv-sdr/bin/python}"
+CAPTURE_LOG_FILE="${CAPTURE_LOG_FILE:-$BASE_DIR/capture_hourly.log}"
 
-# Путь к headless скрипту
+SYSTEMCTL_BIN=(systemctl)
-SCRIPT_PATH="${SCRIPT_PATH:-$PWD/scripts_nn/data_saver_headless.py}"
 
-# Проверка, что каталог реально на nvme1n1
+CURRENT_CAPTURE_PID=""
-EXPECTED_DEVICE_PREFIX="/dev/nvme1n1"
+CURRENT_MOCK_PID=""
+CURRENT_SERVICE_UNIT=""
+STOPPED_SERVICE_UNIT=""
 
 # Лимиты
-PER_FREQ_LIMIT_BYTES=$((3 * 1024 * 1024 * 1024))     # 3 GiB на частоту за запуск
+PER_FREQ_LIMIT_GIB="${PER_FREQ_LIMIT_GIB:-12}"
-TOTAL_LIMIT_BYTES=$((128 * 1024 * 1024 * 1024))      # общий лимит 512 GiB
+TOTAL_LIMIT_GIB="${TOTAL_LIMIT_GIB:-266}"
-CYCLE_SECONDS=36                                # один цикл в час
+
+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:-1}"
 
 # Параметры SDR
-SAMP_RATE="20e6"
+SAMP_RATE="${SAMP_RATE:-20e6}"
-SPLIT_SIZE="400000"
+SPLIT_SIZE="${SPLIT_SIZE:-400000}"
-DELAY="0.1"
+DELAY="${DELAY:-0.25}"
-RF_GAIN="12"
+RF_GAIN="${RF_GAIN:-12}"
-IF_GAIN="30"
+IF_GAIN="${IF_GAIN:-12}"
-BB_GAIN="36"
+BB_GAIN="${BB_GAIN:-0}"
 
 ############################
-# ЧАСТОТЫ И SERIAL ИЗ ENV
+# MOCK SENDER
 ############################
 
-ORDER=(433 750 915 1200 2400 3300 4500 5200 5800)
+CAPTURE_MOCK_SEND_ENABLED="${CAPTURE_MOCK_SEND_ENABLED:-1}"
+CAPTURE_MOCK_HOST="${CAPTURE_MOCK_HOST:-127.0.0.1}"
+CAPTURE_MOCK_INTERVAL_SECONDS="${CAPTURE_MOCK_INTERVAL_SECONDS:-1}"
+CAPTURE_MOCK_TIMEOUT_SECONDS="${CAPTURE_MOCK_TIMEOUT_SECONDS:-0.3}"
+CAPTURE_MOCK_LOG_SUCCESS="${CAPTURE_MOCK_LOG_SUCCESS:-0}"
 
-declare -A SERIAL
+############################
-declare -A FREQ_HZ
+# ВСПОМОГАТЕЛЬНОЕ
+############################
 
-SERIAL[433]="0000000000000000114864dc382a8e1b"
+log() {
-FREQ_HZ[433]="433000000"
+  printf '[%s] %s\n' "$(date '+%F %T')" "$*"
+}
 
-SERIAL[750]="0000000000000000114864dc383d0d1b"
+systemctl_run() {
-FREQ_HZ[750]="750000000"
+  "${SYSTEMCTL_BIN[@]}" "$@"
+}
 
-SERIAL[868]="000000000000000026a468dc36066b63"
+env_get() {
-FREQ_HZ[868]="868000000"
+  local key="$1"
+  local default="${2:-}"
 
-SERIAL[915]="000000000000000026a468dc36066b63"
+  if [[ ! -f "$ENV_FILE" ]]; then
-FREQ_HZ[915]="915000000"
+    printf '%s\n' "$default"
+    return 0
+  fi
 
-SERIAL[1200]="0000000000000000518864dc32660d83"
+  awk -v key="$key" -v default="$default" '
-FREQ_HZ[1200]="1200000000"
+    BEGIN { found = 0 }
 
-SERIAL[2400]="0000000000000000919068dc3437b31f"
+    $0 ~ "^[[:space:]]*" key "=" {
-FREQ_HZ[2400]="2400000000"
+      value = $0
 
-SERIAL[3300]="0000000000000000114864dc325069a3"
+      sub("^[[:space:]]*" key "=", "", value)
-FREQ_HZ[3300]="3300000000"
+      sub("[[:space:]]+#.*$", "", value)
+      gsub("^[[:space:]]+|[[:space:]]+$", "", value)
 
-SERIAL[4500]="0000000000000000518864dc33833183"
+      if ((substr(value, 1, 1) == "\"" && substr(value, length(value), 1) == "\"") ||
-FREQ_HZ[4500]="4500000000"
+          (substr(value, 1, 1) == "'"'"'" && substr(value, length(value), 1) == "'"'"'")) {
+        value = substr(value, 2, length(value) - 2)
+      }
 
-SERIAL[5200]="0000000000000000874461dc241c7857"
+      print value
-FREQ_HZ[5200]="5200000000"
+      found = 1
+      exit
+    }
 
-SERIAL[5800]="0000000000000000919068dc35781c1f"
+    END {
-FREQ_HZ[5800]="5800000000"
+      if (!found) {
+        print default
+      }
+    }
+  ' "$ENV_FILE"
+}
 
-############################
+mock_bool_enabled() {
-# ВСПОМОГАТЕЛЬНОЕ
+  case "${1,,}" in
-############################
+    1|true|yes|on) return 0 ;;
+    *) return 1 ;;
+  esac
+}
 
-log() {
+mock_url() {
-  printf '[%s] %s\n' "$(date '+%F %T')" "$*"
+  local port endpoint
+
+  port="${CAPTURE_MOCK_PORT:-$(env_get locport "$(env_get GENERAL_SERVER_PORT 5010)")}"
+  endpoint="${CAPTURE_MOCK_ENDPOINT:-$(env_get freq_endpoint process_data)}"
+
+  endpoint="${endpoint#/}"
+
+  printf 'http://%s:%s/%s' "$CAPTURE_MOCK_HOST" "$port" "$endpoint"
+}
+
+service_exists() {
+  local unit="$1"
+  systemctl_run show "$unit" >/dev/null 2>&1
+}
+
+service_is_active() {
+  local unit="$1"
+  systemctl_run is-active --quiet "$unit" >/dev/null 2>&1
 }
 
 dir_size_bytes() {
   local path="$1"
+
   if [[ -e "$path" ]]; then
     du -sb "$path" 2>/dev/null | awk '{print $1}'
   else
@@ -90,6 +146,246 @@ total_size_bytes() {
   dir_size_bytes "$BASE_DIR"
 }
 
+terminate_pid() {
+  local pid="${1:-}"
+
+  if [[ -z "$pid" ]]; then
+    return 0
+  fi
+
+  if ! kill -0 "$pid" 2>/dev/null; then
+    return 0
+  fi
+
+  kill -TERM "$pid" 2>/dev/null || true
+
+  for _ in {1..10}; do
+    if ! kill -0 "$pid" 2>/dev/null; then
+      break
+    fi
+    sleep 1
+  done
+
+  if kill -0 "$pid" 2>/dev/null; then
+    log "PID=$pid не завершился по TERM, отправляю KILL"
+    kill -KILL "$pid" 2>/dev/null || true
+  fi
+
+  wait "$pid" 2>/dev/null || true
+}
+
+stop_band_service() {
+  local band="$1"
+  local unit="${SERVICE_UNIT[$band]:-}"
+
+  STOPPED_SERVICE_UNIT=""
+  CURRENT_SERVICE_UNIT=""
+
+  if [[ -z "$unit" ]]; then
+    log "Для band=$band не найден service unit"
+    return 0
+  fi
+
+  if ! service_exists "$unit"; then
+    log "Service unit $unit не установлен, пропускаю stop/start"
+    return 0
+  fi
+
+  log "Останавливаю service $unit перед записью band=$band"
+
+  if systemctl_run stop "$unit"; then
+    log "Service $unit остановлен или уже был остановлен"
+  else
+    log "Не удалось остановить service $unit"
+  fi
+
+  STOPPED_SERVICE_UNIT="$unit"
+  CURRENT_SERVICE_UNIT="$unit"
+
+  return 0
+}
+
+start_stopped_service() {
+  local unit="${STOPPED_SERVICE_UNIT:-}"
+
+  if [[ -z "$unit" ]]; then
+    return 0
+  fi
+
+  log "Запускаю service $unit после записи"
+
+  if systemctl_run start "$unit"; then
+    log "Service $unit запущен"
+  else
+    log "Не удалось запустить service $unit"
+    systemctl_run status "$unit" --no-pager || true
+  fi
+
+  STOPPED_SERVICE_UNIT=""
+  CURRENT_SERVICE_UNIT=""
+}
+
+start_mock_sender() {
+  local band="$1"
+  local log_file="$2"
+  local url
+
+  CURRENT_MOCK_PID=""
+
+  if ! mock_bool_enabled "$CAPTURE_MOCK_SEND_ENABLED"; then
+    log "Mock sender отключен CAPTURE_MOCK_SEND_ENABLED=$CAPTURE_MOCK_SEND_ENABLED"
+    return 0
+  fi
+
+  url="$(mock_url)"
+
+  log "Старт mock sender band=$band url=$url amplitude=0 interval=${CAPTURE_MOCK_INTERVAL_SECONDS}s"
+
+  "$PYTHON_BIN" - \
+    "$url" \
+    "$band" \
+    "$CAPTURE_MOCK_INTERVAL_SECONDS" \
+    "$CAPTURE_MOCK_TIMEOUT_SECONDS" \
+    "$CAPTURE_MOCK_LOG_SUCCESS" \
+    >>"$log_file" 2>&1 <<'PY' &
+
+import json
+import sys
+import time
+import urllib.request
+
+
+url = sys.argv[1]
+freq = str(sys.argv[2])
+interval = float(sys.argv[3])
+timeout = float(sys.argv[4])
+log_success = sys.argv[5].lower() in {"1", "true", "yes", "on"}
+
+payload = json.dumps({"freq": freq, "amplitude": 0}).encode("utf-8")
+headers = {"Content-Type": "application/json"}
+
+print(
+    f"[capture-mock] started url={url} freq={freq} amplitude=0 interval={interval}",
+    flush=True,
+)
+
+while True:
+    try:
+        req = urllib.request.Request(
+            url,
+            data=payload,
+            headers=headers,
+            method="POST",
+        )
+
+        with urllib.request.urlopen(req, timeout=timeout) as response:
+            if log_success:
+                print(f"[capture-mock] sent status={response.status}", flush=True)
+
+    except Exception as exc:
+        print(f"[capture-mock] send failed: {exc}", flush=True)
+
+    time.sleep(interval)
+PY
+
+  CURRENT_MOCK_PID=$!
+  log "Mock sender PID=$CURRENT_MOCK_PID"
+}
+
+stop_mock_sender() {
+  local pid="${1:-}"
+
+  if [[ -z "$pid" ]]; then
+    return 0
+  fi
+
+  if kill -0 "$pid" 2>/dev/null; then
+    log "Останавливаю mock sender PID=$pid"
+    terminate_pid "$pid"
+  fi
+}
+
+cleanup_capture() {
+  local rc=$?
+
+  if [[ -n "${CURRENT_CAPTURE_PID:-}" ]] && kill -0 "$CURRENT_CAPTURE_PID" 2>/dev/null; then
+    log "Останавливаю текущий capture PID=$CURRENT_CAPTURE_PID"
+    terminate_pid "$CURRENT_CAPTURE_PID"
+  fi
+  CURRENT_CAPTURE_PID=""
+
+  if [[ -n "${CURRENT_MOCK_PID:-}" ]] && kill -0 "$CURRENT_MOCK_PID" 2>/dev/null; then
+    log "Останавливаю текущий mock sender PID=$CURRENT_MOCK_PID"
+    terminate_pid "$CURRENT_MOCK_PID"
+  fi
+  CURRENT_MOCK_PID=""
+
+  start_stopped_service
+
+  exit "$rc"
+}
+
+on_signal() {
+  local sig="$1"
+  log "Получен сигнал $sig, завершаю работу"
+  exit 130
+}
+
+############################
+# ЧАСТОТЫ И SERIAL
+############################
+
+ORDER=(2400)
+
+declare -A SERIAL
+declare -A FREQ_HZ
+declare -A SERVICE_UNIT
+
+SERIAL[433]="$(env_get hack_433)"
+FREQ_HZ[433]="433000000"
+
+SERIAL[750]="$(env_get hack_750)"
+FREQ_HZ[750]="750000000"
+
+SERIAL[868]="$(env_get hack_868)"
+FREQ_HZ[868]="868000000"
+
+SERIAL[915]="$(env_get hack_915)"
+FREQ_HZ[915]="915000000"
+
+SERIAL[1200]="$(env_get hack_1200)"
+FREQ_HZ[1200]="1200000000"
+
+SERIAL[2400]="$(env_get hack_2400)"
+FREQ_HZ[2400]="2400000000"
+
+SERIAL[3300]="$(env_get hack_3300)"
+FREQ_HZ[3300]="3300000000"
+
+SERIAL[4500]="$(env_get hack_4500)"
+FREQ_HZ[4500]="4500000000"
+
+SERIAL[5200]="$(env_get hack_5200)"
+FREQ_HZ[5200]="5200000000"
+
+SERIAL[5800]="$(env_get hack_5800)"
+FREQ_HZ[5800]="5800000000"
+
+SERVICE_UNIT[433]="dronedetector-sdr-433.service"
+SERVICE_UNIT[750]="dronedetector-sdr-750.service"
+SERVICE_UNIT[868]="dronedetector-sdr-868.service"
+SERVICE_UNIT[915]="dronedetector-sdr-915.service"
+SERVICE_UNIT[1200]="dronedetector-sdr-1200.service"
+SERVICE_UNIT[2400]="dronedetector-sdr-2400.service"
+SERVICE_UNIT[3300]="dronedetector-sdr-3300.service"
+SERVICE_UNIT[4500]="dronedetector-sdr-4500.service"
+SERVICE_UNIT[5200]="dronedetector-sdr-5200.service"
+SERVICE_UNIT[5800]="dronedetector-sdr-5800.service"
+
+############################
+# ОСНОВНАЯ ЛОГИКА
+############################
+
 ensure_requirements() {
   if [[ ! -x "$PYTHON_BIN" ]]; then
     echo "Не найден python: $PYTHON_BIN" >&2
@@ -101,32 +397,78 @@ ensure_requirements() {
     exit 1
   fi
 
+  if ! command -v systemctl >/dev/null 2>&1; then
+    echo "Не найден systemctl" >&2
+    exit 1
+  fi
+
+  if [[ "${EUID}" -ne 0 ]]; then
+    if command -v sudo >/dev/null 2>&1; then
+      SYSTEMCTL_BIN=(sudo systemctl)
+    else
+      echo "Для управления service нужен root или sudo" >&2
+      exit 1
+    fi
+  fi
+
   mkdir -p "$BASE_DIR"
+  mkdir -p "$(dirname "$CAPTURE_LOG_FILE")"
 
-  local dev
+  if [[ -n "${CAPTURE_ORDER:-}" ]]; then
-  dev="$(df -P "$BASE_DIR" | awk 'NR==2 {print $1}')"
+    ORDER=()
+    local band
 
-  if [[ "$dev" != ${EXPECTED_DEVICE_PREFIX}* ]]; then
+    for band in ${CAPTURE_ORDER//,/ }; do
-    echo "BASE_DIR=$BASE_DIR сейчас находится на $dev, а ожидался ${EXPECTED_DEVICE_PREFIX}*" >&2
+      ORDER+=("$band")
-    echo "Исправь BASE_DIR на точку монтирования nvme1n1" >&2
+    done
+  fi
+
+  if [[ "${#ORDER[@]}" -eq 0 ]]; then
+    echo "ORDER пустой. Укажите частоты в ORDER или задайте CAPTURE_ORDER." >&2
     exit 1
   fi
+
+  log "Рабочая директория: $SCRIPT_DIR"
+  log "BASE_DIR: $BASE_DIR"
+  log "ORDER: ${ORDER[*]}"
+  log "RUN_ONCE: $RUN_ONCE"
+  log "PER_FREQ_LIMIT_GIB: $PER_FREQ_LIMIT_GIB"
+  log "TOTAL_LIMIT_GIB: $TOTAL_LIMIT_GIB"
+  log "CYCLE_SECONDS: $CYCLE_SECONDS"
+  log "CAPTURE_MOCK_SEND_ENABLED: $CAPTURE_MOCK_SEND_ENABLED"
+  log "PYTHON_BIN: $PYTHON_BIN"
+  log "SCRIPT_PATH: $SCRIPT_PATH"
 }
 
 run_one_freq() {
   local band="$1"
-  local serial="${SERIAL[$band]}"
+  local serial="${SERIAL[$band]:-}"
-  local freq="${FREQ_HZ[$band]}"
+  local freq="${FREQ_HZ[$band]:-}"
+
+  if [[ -z "$serial" ]]; then
+    log "Для band=$band пустой serial, пропускаю"
+    return 0
+  fi
+
+  if [[ -z "$freq" ]]; then
+    log "Для band=$band не задана частота, пропускаю"
+    return 0
+  fi
 
   local ts out_dir log_file
   ts="$(date '+%F_%H-%M-%S')"
   out_dir="$BASE_DIR/$band/$ts"
   log_file="$out_dir/run.log"
 
-  mkdir -p "$out_dir"
-
   log "Старт band=$band serial=$serial freq=$freq dir=$out_dir"
 
+  if ! mkdir -p "$out_dir"; then
+    log "Не удалось создать каталог $out_dir"
+    return 1
+  fi
+
+  stop_band_service "$band"
+
   "$PYTHON_BIN" "$SCRIPT_PATH" \
     --serial "$serial" \
     --freq "$freq" \
@@ -141,60 +483,72 @@ run_one_freq() {
     >"$log_file" 2>&1 &
 
   local pid=$!
-  log "PID=$pid"
+  CURRENT_CAPTURE_PID="$pid"
+  log "Capture PID=$pid"
+
+  start_mock_sender "$band" "$log_file"
+  local mock_pid="$CURRENT_MOCK_PID"
 
   while kill -0 "$pid" 2>/dev/null; do
     local cur_dir_size cur_total_size
+
     cur_dir_size="$(dir_size_bytes "$out_dir")"
     cur_total_size="$(total_size_bytes)"
 
     if (( cur_total_size >= TOTAL_LIMIT_BYTES )); then
-      log "Достигнут общий лимит 512 GiB. Останавливаю PID=$pid"
+      log "Достигнут общий лимит ${TOTAL_LIMIT_GIB} GiB. Останавливаю PID=$pid"
-      kill -TERM "$pid" 2>/dev/null || true
+
-      wait "$pid" 2>/dev/null || true
+      terminate_pid "$pid"
+      CURRENT_CAPTURE_PID=""
+
+      stop_mock_sender "$mock_pid"
+      CURRENT_MOCK_PID=""
+
+      start_stopped_service
+
       return 2
     fi
 
     if (( cur_dir_size >= PER_FREQ_LIMIT_BYTES )); then
-      log "Для band=$band достигнут лимит 3 GiB. Останавливаю PID=$pid"
+      log "Для band=$band достигнут лимит ${PER_FREQ_LIMIT_GIB} GiB. Останавливаю PID=$pid"
-      kill -TERM "$pid" 2>/dev/null || true
+
+      terminate_pid "$pid"
+      CURRENT_CAPTURE_PID=""
 
-      for _ in {1..10}; do
-        if ! kill -0 "$pid" 2>/dev/null; then
       break
     fi
+
     sleep 1
   done
 
-      if kill -0 "$pid" 2>/dev/null; then
-        log "PID=$pid не завершился по TERM, отправляю KILL"
-        kill -KILL "$pid" 2>/dev/null || true
-      fi
-
   wait "$pid" 2>/dev/null || true
-      break
+  CURRENT_CAPTURE_PID=""
-    fi
 
-    sleep 1
+  stop_mock_sender "$mock_pid"
-  done
+  CURRENT_MOCK_PID=""
+
+  start_stopped_service
 
   log "Завершен band=$band, размер=$(du -sh "$out_dir" | awk '{print $1}')"
+
   return 0
 }
 
 main_loop() {
   while true; do
     local total_before cycle_start elapsed sleep_left
+
     total_before="$(total_size_bytes)"
 
     if (( total_before >= TOTAL_LIMIT_BYTES )); then
-      log "Общий размер уже >= 512 GiB, выхожу"
+      log "Общий размер уже >= ${TOTAL_LIMIT_GIB} GiB, выхожу"
       break
     fi
 
     cycle_start="$(date +%s)"
     log "Новый цикл"
 
+    local band
     for band in "${ORDER[@]}"; do
       if (( $(total_size_bytes) >= TOTAL_LIMIT_BYTES )); then
         log "Общий лимит достигнут внутри цикла, выхожу"
@@ -202,19 +556,28 @@ main_loop() {
       fi
 
       run_one_freq "$band" || {
-        rc=$?
+        local rc=$?
-        if [[ $rc -eq 2 ]]; then
+
+        if [[ "$rc" -eq 2 ]]; then
           log "Остановка по общему лимиту"
           return 0
         fi
+
+        log "Ошибка записи band=$band rc=$rc"
+        return "$rc"
       }
     done
 
+    if [[ "$RUN_ONCE" == "1" ]]; then
+      log "RUN_ONCE=1, завершаю работу после одного цикла"
+      break
+    fi
+
     elapsed=$(( $(date +%s) - cycle_start ))
     sleep_left=$(( CYCLE_SECONDS - elapsed ))
 
     if (( sleep_left > 0 )); then
-      log "Цикл занял ${elapsed} сек, жду ${sleep_left} сек до следующего часа"
+      log "Цикл занял ${elapsed} сек, жду ${sleep_left} сек"
       sleep "$sleep_left"
     else
       log "Цикл занял ${elapsed} сек, паузы нет"
@@ -222,5 +585,9 @@ main_loop() {
   done
 }
 
+trap cleanup_capture EXIT
+trap 'on_signal INT' INT
+trap 'on_signal TERM' TERM
+
 ensure_requirements
 main_loop

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
@@ -50,6 +52,7 @@ services:
       - ../../.env:/app/.env:ro
       - ../../NN_server:/app/NN_server
       - ../../common:/app/common
+      - ../../train_scripts:/app/train_scripts:ro
     gpus: all
     networks:
       - dronedetector-net
@@ -74,6 +77,7 @@ 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_gain(0, 0)
-        self.rtlsdr_source_0.set_if_gain(16, 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_gain(0, 0)
-        self.rtlsdr_source_0.set_if_gain(24, 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 import FastAPI, HTTPException, Query, WebSocket, WebSocketDisconnect
-from fastapi.responses import HTMLResponse
+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": 2,
+   "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",
@@ -16,10 +24,10 @@
     {
      "data": {
       "text/plain": [
-       "<contextlib.ExitStack at 0x7d183e4bd220>"
+       "<contextlib.ExitStack at 0x7dbe9e0bc080>"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 1,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -64,7 +72,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "id": "4848b066-2e09-4c1c-b8fa-8e3fa84d907a",
    "metadata": {},
    "outputs": [],
@@ -74,7 +82,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "id": "9267fbe1",
    "metadata": {},
    "outputs": [],
@@ -147,20 +155,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 16,
    "id": "448da74a-e0ae-44d8-9877-8dd1f257a24f",
    "metadata": {},
    "outputs": [],
    "source": [
-    "selected_freq=750\n",
+    "selected_freq=2400\n",
     "\n",
-    "path_to_binaries = f'/mnt/nvme1/dataset/{selected_freq}'\n",
+    "path_to_binaries = f'/mnt/data/Dataset/noise/{selected_freq}'\n",
-    "path_to_pictures = f'/mnt/nvme1/dataset_img/noise/{selected_freq}_jpg'"
+    "path_to_pictures = f'/mnt/data/Dataset_img/noise/{selected_freq}_jpg'"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 14,
    "id": "ac4945a8-29c4-4da4-945f-08658953e3e5",
    "metadata": {},
    "outputs": [],
@@ -170,7 +178,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 17,
    "id": "6f226f86-5d72-4573-8af6-750128b70263",
    "metadata": {},
    "outputs": [
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      ]
     },
     {
@@ -482,7 +245,7 @@
     "        continue\n",
     "\n",
     "    files = os.listdir(filepath)\n",
-    "    k = max(1, int(len(files) * 0.04))\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",
@@ -542,7 +305,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": ".venv-train (3.12.3)",
+   "display_name": ".venv-train",
    "language": "python",
    "name": "python3"
   },

train_scripts/Training_models2pic_val_loss.ipynb

@@ -0,0 +1,523 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "e1db882b",
+   "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",
+     "text": [
+      "cuda\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "220"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "from torch.utils.data import Dataset, DataLoader\n",
+    "from torch import default_generator, randperm\n",
+    "from torch.utils.data.dataset import Subset\n",
+    "import torchvision.transforms as transforms\n",
+    "from torchvision.io import read_image\n",
+    "from importlib import import_module\n",
+    "import matplotlib.pyplot as plt\n",
+    "from torchvision import models\n",
+    "import torch, torchvision\n",
+    "from pathlib import Path\n",
+    "from PIL import Image\n",
+    "import torch.nn as nn\n",
+    "from tqdm import tqdm\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib\n",
+    "import os, shutil\n",
+    "import mlconfig\n",
+    "import random\n",
+    "import shutil\n",
+    "import timeit\n",
+    "import copy\n",
+    "import time\n",
+    "import cv2\n",
+    "import csv\n",
+    "import sys\n",
+    "import io\n",
+    "import gc\n",
+    "\n",
+    "plt.rcParams[\"savefig.bbox\"] = 'tight'\n",
+    "torch.manual_seed(1)\n",
+    "#matplotlib.use('Agg')\n",
+    "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
+    "print(device)\n",
+    "torch.cuda.empty_cache()\n",
+    "cv2.destroyAllWindows()\n",
+    "gc.collect()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "8e009995",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def prepare_and_learning_detection(num_classes, num_samples, path_dataset, model_name, config_name, model,selected_freq):\n",
+    "    num_samples_per_class = num_samples // num_classes\n",
+    "\n",
+    "    #----------Создаём папку для сохранения результатов обучения--------------\n",
+    "    os.makedirs(\"models\", exist_ok=True)\n",
+    "    ind = 1\n",
+    "    while True:\n",
+    "        if os.path.exists(\"models/\" + model_name + str(ind)):\n",
+    "            ind += 1\n",
+    "        else:\n",
+    "            os.mkdir(\"models/\" + model_name + str(ind))\n",
+    "            path_res = \"models/\" + model_name + str(ind) + '/'\n",
+    "            break\n",
+    "    \n",
+    "    #----------Создаём файл dataset.csv для обучения--------------\n",
+    "    \n",
+    "    pd_columns = ['file_name']\n",
+    "    df = pd.DataFrame(columns=pd_columns)\n",
+    "    \n",
+    "    subdirs = os.listdir(path_dataset)\n",
+    "    \n",
+    "    for subdir in subdirs:\n",
+    "        freq_dir = os.path.join(path_dataset, subdir, str(selected_freq)+\"_jpg\")\n",
+    "        if not os.path.isdir(freq_dir):\n",
+    "            print(\"Error1\")\n",
+    "            continue\n",
+    "        \n",
+    "        files_k=[f for f in os.listdir(freq_dir)]\n",
+    "        print(len(files_k))\n",
+    "        \n",
+    "        files = [\n",
+    "                f for f in os.listdir(freq_dir)\n",
+    "                if os.path.isfile(os.path.join(freq_dir, f)) and f.endswith('imag.png')\n",
+    "            ]\n",
+    "        num_samples_per_class = min(num_samples_per_class, len(files))\n",
+    "        print(f\"num_samples per class {subdir} is {num_samples_per_class}\")\n",
+    "\n",
+    "    for subdir in subdirs:\n",
+    "        freq_dir = os.path.join(path_dataset, subdir, str(selected_freq)+\"_jpg\")\n",
+    "        if not os.path.isdir(freq_dir):\n",
+    "            print(\"Error1\")\n",
+    "            continue\n",
+    "\n",
+    "        files = [\n",
+    "            f for f in os.listdir(freq_dir)\n",
+    "            if os.path.isfile(os.path.join(freq_dir, f)) and f.endswith('imag.png')\n",
+    "        ]\n",
+    "        random.shuffle(files)\n",
+    "        files_to_process = files[:num_samples_per_class]\n",
+    "\n",
+    "        for file in files_to_process:\n",
+    "            row = pd.DataFrame({\n",
+    "                pd_columns[0]: [str(os.path.join(freq_dir, file))]\n",
+    "            })\n",
+    "            df = pd.concat([df, row], ignore_index=True)\n",
+    "\n",
+    "    dataset_csv_path = os.path.join(path_res, 'dataset.csv')\n",
+    "    df.to_csv(dataset_csv_path, index=False)\n",
+    "\n",
+    "    if not os.path.exists(dataset_csv_path):\n",
+    "        raise RuntimeError(f'dataset.csv was not created: {dataset_csv_path}')\n",
+    "    #----------Импортируем параметры для обучения--------------\n",
+    "    \n",
+    "    def load_function(attr):\n",
+    "        module_, func = attr.rsplit('.', maxsplit=1)\n",
+    "        return getattr(import_module(module_), func)\n",
+    "    \n",
+    "    config = mlconfig.load('config_' + config_name + '.yaml')\n",
+    "    \n",
+    "    #----------Создаём класс датасета--------------\n",
+    "    \n",
+    "    class MyDataset(Dataset):\n",
+    "        def __init__(self, path_dataset, csv_file):\n",
+    "            data=[]\n",
+    "            with open(os.path.join(path_dataset, csv_file), newline='') as csvfile:\n",
+    "                reader = csv.reader(csvfile, delimiter=' ', quotechar='|')\n",
+    "                for row in list(reader)[1:]:\n",
+    "                    row = str(row)\n",
+    "                    data.append(row[2: len(row)-2])\n",
+    "            self.path_dataset = path_dataset\n",
+    "            self.target_shape = None\n",
+    "            self.target_hw = None\n",
+    "            self.sig_filenames = self._validate_files(data)\n",
+    "\n",
+    "        def _pair_paths(self, filename):\n",
+    "            base = os.path.splitext(filename)[0]\n",
+    "            if base.endswith(\"real\"):\n",
+    "                return base + \".png\", base[:-4] + \"imag.png\"\n",
+    "            if base.endswith(\"imag\"):\n",
+    "                return base[:-4] + \"real.png\", base + \".png\"\n",
+    "            return None, None\n",
+    "\n",
+    "        @staticmethod\n",
+    "        def _read_shape(path):\n",
+    "            img = cv2.imread(path)\n",
+    "            if img is None:\n",
+    "                return None\n",
+    "            return img.shape\n",
+    "\n",
+    "        def _validate_files(self, filenames):\n",
+    "            from collections import Counter\n",
+    "\n",
+    "            candidates = []\n",
+    "            dropped = []\n",
+    "            shape_counter = Counter()\n",
+    "\n",
+    "            for filename in filenames:\n",
+    "                real_path, imag_path = self._pair_paths(filename)\n",
+    "                if real_path is None or imag_path is None:\n",
+    "                    dropped.append((filename, \"bad file suffix\"))\n",
+    "                    continue\n",
+    "\n",
+    "                real_shape = self._read_shape(real_path)\n",
+    "                imag_shape = self._read_shape(imag_path)\n",
+    "                if real_shape is None or imag_shape is None:\n",
+    "                    dropped.append((filename, f\"read failed real={real_shape} imag={imag_shape}\"))\n",
+    "                    continue\n",
+    "                if real_shape != imag_shape:\n",
+    "                    dropped.append((filename, f\"pair shape mismatch real={real_shape} imag={imag_shape}\"))\n",
+    "                    continue\n",
+    "\n",
+    "                shape_counter[real_shape] += 1\n",
+    "                candidates.append((filename, real_shape))\n",
+    "\n",
+    "            if not candidates:\n",
+    "                raise RuntimeError(\"No valid image pairs left after shape validation\")\n",
+    "\n",
+    "            preferred_shape = (1600, 1600, 3)\n",
+    "            if shape_counter.get(preferred_shape, 0) > 0:\n",
+    "                target_shape = preferred_shape\n",
+    "            else:\n",
+    "                target_shape = shape_counter.most_common(1)[0][0]\n",
+    "\n",
+    "            self.target_shape = target_shape\n",
+    "            self.target_hw = target_shape[:2]\n",
+    "            valid = [filename for filename, _shape in candidates]\n",
+    "            resized_count = sum(1 for _filename, shape in candidates if shape != target_shape)\n",
+    "\n",
+    "            print(f\"[dataset-shape-filter] shape_distribution={dict(shape_counter)}\")\n",
+    "            print(\n",
+    "                f\"[dataset-shape-filter] target_shape={target_shape} \"\n",
+    "                f\"kept={len(valid)} dropped={len(dropped)} will_resize={resized_count}\"\n",
+    "            )\n",
+    "            for filename, reason in dropped[:20]:\n",
+    "                print(f\"[dataset-shape-filter] drop {filename}: {reason}\")\n",
+    "            if len(dropped) > 20:\n",
+    "                print(f\"[dataset-shape-filter] ... {len(dropped) - 20} more dropped\")\n",
+    "\n",
+    "            return valid\n",
+    "\n",
+    "        def __len__(self):\n",
+    "            return len(self.sig_filenames)\n",
+    "\n",
+    "        def _read_image_chw(self, path):\n",
+    "            img = cv2.imread(path)\n",
+    "            if img is None:\n",
+    "                raise RuntimeError(f\"failed to read image: {path}\")\n",
+    "            if img.shape[:2] != self.target_hw:\n",
+    "                img = cv2.resize(\n",
+    "                    img,\n",
+    "                    (self.target_hw[1], self.target_hw[0]),\n",
+    "                    interpolation=cv2.INTER_AREA,\n",
+    "                )\n",
+    "            return np.asarray(cv2.split(img), dtype=np.float32)\n",
+    "\n",
+    "        def __getitem__(self, idx):\n",
+    "            real_path, imag_path = self._pair_paths(self.sig_filenames[idx])\n",
+    "            image_real = self._read_image_chw(real_path)\n",
+    "            image_imag = self._read_image_chw(imag_path)\n",
+    "\n",
+    "            path_parts = set(self.sig_filenames[idx].split('/'))\n",
+    "            if 'drone' in path_parts:\n",
+    "                label = torch.tensor(0)\n",
+    "            elif 'noise' in path_parts:\n",
+    "                label = torch.tensor(1)\n",
+    "            else:\n",
+    "                raise RuntimeError(f\"cannot infer label from path: {self.sig_filenames[idx]}\")\n",
+    "\n",
+    "            return image_real, image_imag, label\n",
+    "\n",
+    "    #----------Создаём датасет--------------\n",
+    "    \n",
+    "    dataset = MyDataset(path_dataset=path_res, csv_file='dataset.csv')\n",
+    "    train_set, valid_set = torch.utils.data.random_split(dataset, [0.7, 0.3], generator=torch.Generator().manual_seed(42))\n",
+    "    batch_size = config.batch_size\n",
+    "    train_dataloader = torch.utils.data.DataLoader(train_set, batch_size=batch_size, shuffle=True, drop_last=True)\n",
+    "    valid_dataloader = torch.utils.data.DataLoader(valid_set, batch_size=batch_size, shuffle=False, drop_last=False)\n",
+    "    \n",
+    "    dataloaders = {}\n",
+    "    dataloaders['train'] = train_dataloader\n",
+    "    dataloaders['val'] = valid_dataloader\n",
+    "    dataset_sizes = {}\n",
+    "    dataset_sizes['train'] = len(train_set)\n",
+    "    dataset_sizes['val'] = len(valid_set)\n",
+    "\n",
+    "    #----------Обучаем модель--------------\n",
+    "\n",
+    "    val_loss = []\n",
+    "    val_acc = []\n",
+    "    train_loss = []\n",
+    "    train_acc = []\n",
+    "    epochs = config.epoch\n",
+    "    min_delta = 1e-4\n",
+    "    \n",
+    "    best_val_loss = float('inf')\n",
+    "    best_model = copy.deepcopy(model.state_dict())\n",
+    "    limit = config.limit\n",
+    "    ind_limit = 0\n",
+    "    epoch_limit = epochs\n",
+    "    \n",
+    "    start = timeit.default_timer()\n",
+    "    for epoch in range(1, epochs+1):\n",
+    "        print(f\"Epoch : {epoch}\\n\")\n",
+    "        \n",
+    "        for phase in ['train', 'val']:\n",
+    "            if phase == 'train':\n",
+    "                model.train()\n",
+    "            else:\n",
+    "                model.eval()\n",
+    "\n",
+    "            running_loss = 0.0\n",
+    "            running_corrects = 0\n",
+    "    \n",
+    "            for (img1, img2, label) in tqdm(dataloaders[phase]):\n",
+    "                img1, img2, label = img1.to(device), img2.to(device), label.to(device)\n",
+    "                optimizer.zero_grad()\n",
+    "    \n",
+    "                with torch.set_grad_enabled(phase == 'train'):\n",
+    "                    output = model([img1, img2])\n",
+    "                    _, pred = torch.max(output.data, 1)\n",
+    "                    loss = criterion(output, label)\n",
+    "                    if phase == 'train':\n",
+    "                        loss.backward()\n",
+    "                        optimizer.step()\n",
+    "                \n",
+    "                running_loss += loss.item() * img1.size(0)\n",
+    "                running_corrects += torch.sum(pred == label.data)\n",
+    "    \n",
+    "            epoch_loss = running_loss / dataset_sizes[phase]\n",
+    "            epoch_acc = running_corrects.double() / dataset_sizes[phase]\n",
+    "                \n",
+    "            print('{} Loss: {:.4f} Acc: {:.4f}'.format(phase, epoch_loss, epoch_acc))\n",
+    "                    \n",
+    "            if phase == 'train':\n",
+    "                train_loss.append(epoch_loss)\n",
+    "                train_acc.append(epoch_acc)\n",
+    "            else:\n",
+    "                val_loss.append(epoch_loss)\n",
+    "                val_acc.append(epoch_acc)\n",
+    "                scheduler.step(epoch_loss)\n",
+    "\n",
+    "                current_lr = optimizer.param_groups[0]['lr']\n",
+    "                print(f'val lr: {current_lr:.8f}')\n",
+    "\n",
+    "                if epoch_loss < (best_val_loss - min_delta):\n",
+    "                    ind_limit = 0\n",
+    "                    best_val_loss = epoch_loss\n",
+    "                    best_model = copy.deepcopy(model.state_dict())\n",
+    "                    torch.save(best_model, path_res + model_name + '.pth')\n",
+    "                    print(f'saved best model with val_loss={best_val_loss:.4f}')\n",
+    "                else:\n",
+    "                    ind_limit += 1\n",
+    "                    print(f'early stopping patience: {ind_limit}/{limit}')\n",
+    "                    \n",
+    "                if ind_limit >= limit:\n",
+    "                    break\n",
+    "                \n",
+    "        if ind_limit >= limit:\n",
+    "            epoch_limit = epoch\n",
+    "            break\n",
+    "            \n",
+    "        print()\n",
+    "    \n",
+    "    end = timeit.default_timer()\n",
+    "    print(f\"Total time elapsed = {end - start} seconds\")\n",
+    "    epoch_limit += 1\n",
+    "    \n",
+    "    #----------Вывод графиков и сохранение результатов обучения--------------\n",
+    "    \n",
+    "    train_acc = np.asarray(list(map(lambda x: x.item(), train_acc)))\n",
+    "    val_acc = np.asarray(list(map(lambda x: x.item(), val_acc)))\n",
+    "    \n",
+    "    np.save(path_res+'train_acc.npy', train_acc)\n",
+    "    np.save(path_res+'val_acc.npy', val_acc)\n",
+    "    np.save(path_res+'train_loss.npy', train_loss)\n",
+    "    np.save(path_res+'val_loss.npy', val_loss)\n",
+    "    \n",
+    "    plt.figure()\n",
+    "    plt.plot(range(1,epoch_limit), train_loss, color='blue')\n",
+    "    plt.plot(range(1,epoch_limit), val_loss, color='red')\n",
+    "    plt.xlabel('Epoch')\n",
+    "    plt.ylabel('Loss')  \n",
+    "             \n",
+    "    plt.title('Loss Curve')\n",
+    "    plt.legend(['Train Loss', 'Validation Loss'])\n",
+    "    plt.show()\n",
+    "    plt.clf()\n",
+    "    plt.cla()\n",
+    "    plt.close()\n",
+    "    \n",
+    "    plt.figure()\n",
+    "    plt.plot(range(1,epoch_limit), train_acc, color='blue')\n",
+    "    plt.plot(range(1,epoch_limit), val_acc, color='red')\n",
+    "    plt.xlabel('Epoch')\n",
+    "    plt.ylabel('Accuracy')\n",
+    "    plt.title('Accuracy Curve')\n",
+    "    plt.legend(['Train Accuracy', 'Validation Accuracy'])\n",
+    "    plt.show()\n",
+    "    \n",
+    "    plt.clf()\n",
+    "    plt.cla()\n",
+    "    plt.close()\n",
+    "    torch.cuda.empty_cache()\n",
+    "    cv2.destroyAllWindows()\n",
+    "    del model\n",
+    "    gc.collect()\n",
+    "\n",
+    "    return path_res, model_name"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bbbe7fea",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/sibscience-4/from_ssh/DroneDetector/.venv-train/lib/python3.12/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
+      "  warnings.warn(\n",
+      "/home/sibscience-4/from_ssh/DroneDetector/.venv-train/lib/python3.12/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=None`.\n",
+      "  warnings.warn(msg)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Error1\n",
+      "Error1\n",
+      "Error1\n",
+      "Error1\n",
+      "Error1\n",
+      "Error1\n"
+     ]
+    },
+    {
+     "ename": "RuntimeError",
+     "evalue": "No valid image pairs left after shape validation",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mRuntimeError\u001b[39m                              Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[4]\u001b[39m\u001b[32m, line 45\u001b[39m\n\u001b[32m     41\u001b[39m     model = model.to(device)\n\u001b[32m     42\u001b[39m \n\u001b[32m     43\u001b[39m \u001b[38;5;66;03m#----------Создания датасета и обучение модели--------------\u001b[39;00m\n\u001b[32m     44\u001b[39m \n\u001b[32m---> \u001b[39m\u001b[32m45\u001b[39m path_res, model_name = prepare_and_learning_detection(num_classes = num_classes, num_samples = 10000, path_dataset = \"/mnt/data/Dataset_overlay\", \n\u001b[32m     46\u001b[39m                      selected_freq=\u001b[32m1200\u001b[39m,model_name = config_name+\u001b[33m\"1200_\"\u001b[39m, config_name = config_name, model=model)\n\u001b[32m     47\u001b[39m \n\u001b[32m     48\u001b[39m \n",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[2]\u001b[39m\u001b[32m, line 183\u001b[39m, in \u001b[36mprepare_and_learning_detection\u001b[39m\u001b[34m(num_classes, num_samples, path_dataset, model_name, config_name, model, selected_freq)\u001b[39m\n\u001b[32m    179\u001b[39m             \u001b[38;5;28;01mreturn\u001b[39;00m image_real, image_imag, label\n\u001b[32m    180\u001b[39m \n\u001b[32m    181\u001b[39m     \u001b[38;5;66;03m#----------Создаём датасет--------------\u001b[39;00m\n\u001b[32m    182\u001b[39m \n\u001b[32m--> \u001b[39m\u001b[32m183\u001b[39m     dataset = MyDataset(path_dataset=path_res, csv_file=\u001b[33m'dataset.csv'\u001b[39m)\n\u001b[32m    184\u001b[39m     train_set, valid_set = torch.utils.data.random_split(dataset, [\u001b[32m0.7\u001b[39m, \u001b[32m0.3\u001b[39m], generator=torch.Generator().manual_seed(\u001b[32m42\u001b[39m))\n\u001b[32m    185\u001b[39m     batch_size = config.batch_size\n\u001b[32m    186\u001b[39m     train_dataloader = torch.utils.data.DataLoader(train_set, batch_size=batch_size, shuffle=\u001b[38;5;28;01mTrue\u001b[39;00m, drop_last=\u001b[38;5;28;01mTrue\u001b[39;00m)\n",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[2]\u001b[39m\u001b[32m, line 83\u001b[39m, in \u001b[36mprepare_and_learning_detection.<locals>.MyDataset.__init__\u001b[39m\u001b[34m(self, path_dataset, csv_file)\u001b[39m\n\u001b[32m     79\u001b[39m                     data.append(row[\u001b[32m2\u001b[39m: len(row)-\u001b[32m2\u001b[39m])\n\u001b[32m     80\u001b[39m             self.path_dataset = path_dataset\n\u001b[32m     81\u001b[39m             self.target_shape = \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m     82\u001b[39m             self.target_hw = \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m83\u001b[39m             self.sig_filenames = self._validate_files(data)\n",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[2]\u001b[39m\u001b[32m, line 126\u001b[39m, in \u001b[36mprepare_and_learning_detection.<locals>.MyDataset._validate_files\u001b[39m\u001b[34m(self, filenames)\u001b[39m\n\u001b[32m    122\u001b[39m                 shape_counter[real_shape] += \u001b[32m1\u001b[39m\n\u001b[32m    123\u001b[39m                 candidates.append((filename, real_shape))\n\u001b[32m    124\u001b[39m \n\u001b[32m    125\u001b[39m             \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28;01mnot\u001b[39;00m candidates:\n\u001b[32m--> \u001b[39m\u001b[32m126\u001b[39m                 \u001b[38;5;28;01mraise\u001b[39;00m RuntimeError(\u001b[33m\"No valid image pairs left after shape validation\"\u001b[39m)\n\u001b[32m    127\u001b[39m \n\u001b[32m    128\u001b[39m             preferred_shape = (\u001b[32m1600\u001b[39m, \u001b[32m1600\u001b[39m, \u001b[32m3\u001b[39m)\n\u001b[32m    129\u001b[39m             \u001b[38;5;28;01mif\u001b[39;00m shape_counter.get(preferred_shape, \u001b[32m0\u001b[39m) > \u001b[32m0\u001b[39m:\n",
+      "\u001b[31mRuntimeError\u001b[39m: No valid image pairs left after shape validation"
+     ]
+    }
+   ],
+   "source": [
+    "torch.cuda.empty_cache()\n",
+    "cv2.destroyAllWindows()\n",
+    "gc.collect()\n",
+    "\n",
+    "config_name = \"ensemble\"\n",
+    "    \n",
+    "def load_function(attr):\n",
+    "    module_, func = attr.rsplit('.', maxsplit=1)\n",
+    "    return getattr(import_module(module_), func)\n",
+    "    \n",
+    "config = mlconfig.load('config_' + config_name + '.yaml')\n",
+    "\n",
+    "model1 = models.resnet18(pretrained=False)\n",
+    "model2 = models.resnet50(pretrained=False)\n",
+    "\n",
+    "num_classes = 2\n",
+    "\n",
+    "model1.fc = nn.Linear(model1.fc.in_features, num_classes)\n",
+    "model2.fc = nn.Linear(model2.fc.in_features, num_classes)\n",
+    "\n",
+    "class Ensemble(nn.Module):\n",
+    "    def __init__(self, model1, model2):\n",
+    "        super(Ensemble, self).__init__()\n",
+    "        self.model1 = model1\n",
+    "        self.model2 = model2\n",
+    "        self.fc = nn.Linear(2 * num_classes, num_classes)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        x1 = self.model1(x[0])\n",
+    "        x2 = self.model2(x[1])\n",
+    "        x = torch.cat((x1, x2), dim=1)\n",
+    "        x = self.fc(x)\n",
+    "        return x\n",
+    "model = Ensemble(model1, model2)\n",
+    "\n",
+    "optimizer = load_function(config.optimizer.name)(model.parameters(), lr=config.optimizer.lr)\n",
+    "criterion = load_function(config.loss_function.name)()\n",
+    "scheduler = load_function(config.scheduler.name)(optimizer, step_size=config.scheduler.step_size, gamma=config.scheduler.gamma)\n",
+    "\n",
+    "if device != 'cpu':\n",
+    "    model = model.to(device)\n",
+    "\n",
+    "#----------Создания датасета и обучение модели--------------\n",
+    "\n",
+    "path_res, model_name = prepare_and_learning_detection(num_classes = num_classes, num_samples = 10000, path_dataset = \"/mnt/data/Dataset_overlay\", \n",
+    "                     selected_freq=2400,model_name = config_name+\"2400_\", config_name = config_name, model=model)\n",
+    "\n",
+    "\n",
+    "torch.cuda.empty_cache()\n",
+    "cv2.destroyAllWindows()\n",
+    "del model\n",
+    "gc.collect()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "usr",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

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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