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

.gitignore

@@ -191,3 +191,4 @@ runtime/
 /train_scripts/models
 
 logs/nn_results_*.csv
+.codex

NN_server/Models/ensemble_1200_v44.py

@@ -10,7 +10,7 @@ import os
 import re
 
 
-def _render_plot(values, figsize=(16, 16), dpi=16):
+def _render_signal_channel(values, figsize=(16, 16), dpi=16, resize=(256, 256)):
     import matplotlib.pyplot as plt
 
     fig = plt.figure(figsize=figsize)
@@ -30,6 +30,34 @@ def _render_plot(values, figsize=(16, 16), dpi=16):
     if img is None:
         raise RuntimeError("failed to decode plot image")
 
+    img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    if resize is not None:
+        img = cv2.resize(img, resize)
+
+    plt.clf()
+    plt.cla()
+    plt.close()
+    plt.close(fig)
+
+    return img
+
+
+def _render_training_png(image):
+    import matplotlib.pyplot as plt
+
+    fig = plt.figure()
+    plt.imshow(image)
+
+    buf = io.BytesIO()
+    fig.savefig(buf, format="png")
+    buf.seek(0)
+    img_arr = np.frombuffer(buf.getvalue(), dtype=np.uint8)
+    buf.close()
+
+    img = cv2.imdecode(img_arr, 1)
+    if img is None:
+        raise RuntimeError("failed to decode training-style image")
+
     plt.clf()
     plt.cla()
     plt.close()
@@ -85,8 +113,8 @@ def pre_func_ensemble(data=None, src="", ind_inference=0):
         imag = np.asarray(data[1], dtype=np.float32)
         signal = real + 1j * imag
 
-        img_real = _render_plot(signal.real)
-        img_mag = _render_plot(np.abs(signal))
+        img_real = _render_training_png(_render_signal_channel(signal.real))
+        img_mag = _render_training_png(_render_signal_channel(np.abs(signal)))
 
         cv2.destroyAllWindows()
         gc.collect()

NN_server/Models/ensemble_2400_v44.py

@@ -10,7 +10,7 @@ import os
 import re
 
 
-def _render_plot(values, figsize=(16, 16), dpi=16):
+def _render_signal_channel(values, figsize=(16, 16), dpi=16, resize=(256, 256)):
     import matplotlib.pyplot as plt
 
     fig = plt.figure(figsize=figsize)
@@ -30,6 +30,34 @@ def _render_plot(values, figsize=(16, 16), dpi=16):
     if img is None:
         raise RuntimeError("failed to decode plot image")
 
+    img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    if resize is not None:
+        img = cv2.resize(img, resize)
+
+    plt.clf()
+    plt.cla()
+    plt.close()
+    plt.close(fig)
+
+    return img
+
+
+def _render_training_png(image):
+    import matplotlib.pyplot as plt
+
+    fig = plt.figure()
+    plt.imshow(image)
+
+    buf = io.BytesIO()
+    fig.savefig(buf, format="png")
+    buf.seek(0)
+    img_arr = np.frombuffer(buf.getvalue(), dtype=np.uint8)
+    buf.close()
+
+    img = cv2.imdecode(img_arr, 1)
+    if img is None:
+        raise RuntimeError("failed to decode training-style image")
+
     plt.clf()
     plt.cla()
     plt.close()
@@ -85,8 +113,8 @@ def pre_func_ensemble(data=None, src="", ind_inference=0):
         imag = np.asarray(data[1], dtype=np.float32)
         signal = real + 1j * imag
 
-        img_real = _render_plot(signal.real)
-        img_mag = _render_plot(np.abs(signal))
+        img_real = _render_training_png(_render_signal_channel(signal.real))
+        img_mag = _render_training_png(_render_signal_channel(np.abs(signal)))
 
         cv2.destroyAllWindows()
         gc.collect()

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
+```