kind of working
parent
a36ab62de9
commit
e78275f0c6
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@ -93,6 +93,63 @@
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"model = load_model(model_file)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"%%capture\n",
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"def gen_image(test_data_gen,test_dir):\n",
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" test_image_gen = ImageDataGenerator(rescale=1./255);\n",
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" test_data_gen = test_image_gen.flow_from_directory(batch_size=1,\n",
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" directory=test_dir,\n",
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" target_size=(IMG_HEIGHT, IMG_WIDTH),\n",
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" shuffle=True,\n",
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" class_mode='binary');\n",
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" return test_data_gen"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"%%capture --no-stderr --no-stdout\n",
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"def gen_image_tmp(obs_waterfall_path):\n",
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" tmp_dir = tempfile.mkdtemp()\n",
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" test_dir = os.path.join(tmp_dir)\n",
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" os.makedirs(test_dir + '/unvetted', exist_ok=True)\n",
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" shutil.copy(obs_waterfall_path, test_dir + '/unvetted/') \n",
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" \n",
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" img = im.open(obs_waterfall_path).resize( (100,200))\n",
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" display(img)\n",
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"\n",
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" # XXX delete tmp dir down below\n",
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" #print(test_dir)\n",
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" #shutil.rmtree(test_dir) \n",
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" return test_dir"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"%%capture\n",
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"def obs_wutsay(test_data_gen):\n",
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" prediction = model.predict(\n",
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" x=test_data_gen,\n",
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" verbose=0)\n",
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" predictions=[]\n",
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" prediction_bool = (prediction >0.8)\n",
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" predictions = prediction_bool.astype(int)\n",
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" \n",
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" return prediction_bool"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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@ -118,26 +175,12 @@
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" \n",
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" obs_waterfall=os.path.basename(obs_waterfallurl)\n",
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" obs_waterfall_path = os.path.join('/srv/satnogs/download', str(obs_id), obs_waterfall)\n",
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" \n",
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" test_dir=gen_image_tmp(obs_waterfall_path)\n",
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" test_data_gen=gen_image(obs_waterfall_path, test_dir)\n",
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" \n",
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" tmp_dir = tempfile.mkdtemp()\n",
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" test_dir = os.path.join(tmp_dir)\n",
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" os.makedirs(test_dir + '/unvetted', exist_ok=True)\n",
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" shutil.copy(obs_waterfall_path, test_dir + '/unvetted/')\n",
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" img = im.open(obs_waterfall_path).resize( (100,200) )\n",
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" test_image_generator = ImageDataGenerator(\n",
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" rescale=1./255\n",
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" )\n",
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" test_data_gen = test_image_generator.flow_from_directory(batch_size=1,\n",
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" directory=test_dir,\n",
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" target_size=(IMG_HEIGHT, IMG_WIDTH),\n",
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" shuffle=True,\n",
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" class_mode='binary')\n",
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" prediction = model.predict(\n",
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" x=test_data_gen,\n",
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" verbose=0)\n",
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" predictions=[]\n",
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" prediction_bool = (prediction >0.8)\n",
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" predictions = prediction_bool.astype(int)\n",
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" prediction_bool=obs_wutsay(test_data_gen);\n",
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"\n",
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" print()\n",
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" print('Observation ID: ', obs_id)\n",
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" print('Encoding: ', obs_transmitter_mode)\n",
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@ -146,19 +189,13 @@
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" rating = 'bad'\n",
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" else:\n",
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" rating = 'good'\n",
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" print('wut AI rating: %s' % (rating))\n",
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" \n",
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" print('wut AI rating: %s' % (rating)) \n",
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" print()\n",
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" if obs_transmitter_mode == 'DUV':\n",
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" print(\"Using DUV training model.\")\n",
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" else:\n",
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" print(\"NOTE: wut has not been trained on\", obs_transmitter_mode, \"encodings.\")\n",
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"\n",
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" print('https://network.satnogs.org/observations/' + str(obs_id))\n",
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" print()\n",
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" \n",
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" display(img)\n",
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" shutil.rmtree(test_dir)\n",
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" #!cat $obsjsonfile"
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]
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},
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@ -168,7 +205,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"print('Enter an Observation ID between', minobsid, 'and', maxobsid)\n",
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"print('Enter an Observation ID between', minobsid, 'and', maxobsid);\n",
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"wutObs_slide = wg.IntText(value='1292461');\n",
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"wg.interact(wutObs, datObs=wutObs_slide);"
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]
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