notebooks/2_Prediccion.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4b085c75-101a-4d33-8633-271d4acfb181",
   "metadata": {},
   "source": [
    "# Flood area detection\n",
    "# 2_ Predicción\n",
    "\n",
    "At this stage, the already trained model processes new images in search of the patterns that define the object of interest. For each image in the prediction dataset, it returns an image where each pixel represents the probability of having detected the object of interest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "423b174a-689e-44d7-9440-703becbcf061",
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob\n",
    "import os\n",
    "from unetseg.predict import PredictConfig, predict\n",
    "from unetseg.evaluate import plot_data_results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9cbcba25-91db-4f7d-a30d-6ca2de562a8d",
   "metadata": {},
   "source": [
    "## Variables statement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0f83faa2-2066-44e2-a8d9-5b74e756ff24",
   "metadata": {},
   "outputs": [],
   "source": [
    "ZONE = \"TEGU\"\n",
    "\n",
    "if ZONE==\"SULA\":\n",
    "    #General\n",
    "    BASE_PATH = \"./../\"\n",
    "    EVENT=\"moderado\"\n",
    "    RESO='planet'\n",
    "    BASE_PATH = \"./../\"\n",
    "    PATH_PREDICT_IMAGE = f'{BASE_PATH}images/{ZONE}/{RESO}/{EVENTO}/'\n",
    "    \n",
    "    #satproc\n",
    "    SIZE =160\n",
    "    STEP_SIZE = 60    \n",
    "    MUNI_AOI='municipios_SE'\n",
    "    BUCKET_SMOOTH =f'gs://dym-quilmes-trucks-temp/I2UD/results_L2/{ZONE}/{EVENTO}/{RESO}/'\n",
    "    \n",
    "    #unet\n",
    "    INPUT_SIZE=256\n",
    "    N_CHANNELS = 8\n",
    "    VERSION='v1'\n",
    "    CLASS_WEIGHT = [1]\n",
    "    STEP_PER_EPOCH = 110\n",
    "    N_CLASSES = 1\n",
    "     MODEL_NAME = f'UNet_{ZONE}_{INPUT_SIZE}x{INPUT_SIZE}_{EVENT}_{RESO}_{str(SIZE)}_{str(STEP_SIZE)}_spe{STEP_PER_EPOCH}_{VERSION}.h5'\n",
    "    ##post-process\n",
    "    THR = 0.5\n",
    "    MIN_AREA = 50000\n",
    "    \n",
    "if ZONE==\"TEGU\":\n",
    "    \n",
    "    #General\n",
    "    BASE_PATH = \"./../\"\n",
    "    RESO='low'\n",
    "    EVENTO='moderado'\n",
    "    PATH_PREDICT_IMAGE = f'{BASE_PATH}images/{ZONE}/{RESO}/{EVENTO}/'\n",
    "    \n",
    "    #satproc\n",
    "    SIZE =160\n",
    "    STEP_SIZE = 30  \n",
    "    \n",
    "    BUCKET_SMOOTH =f'gs://dym-quilmes-trucks-temp/I2UD/results_L2/{ZONE}/{EVENTO}/{RESO}/'\n",
    "    \n",
    "    #unet\n",
    "    INPUT_SIZE=160\n",
    "    N_CHANNELS = 8\n",
    "    VERSION='v1'\n",
    "    CLASS_WEIGHT = [1]\n",
    "    STEP_PER_EPOCH = 70\n",
    "    N_CLASSES = 1\n",
    "    MODEL_NAME = f'UNet_{ZONE}_{INPUT_SIZE}x{INPUT_SIZE}_{EVENT}_{RESO}_{str(SIZE)}_{str(STEP_SIZE)}_spe{STEP_PER_EPOCH}_{VERSION}.h5'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28da5a8b-7b43-436b-ba75-b18f233c55c5",
   "metadata": {},
   "source": [
    "##  Prediction dataset generation\n",
    "### Satproc\n",
    "\n",
    "*satproc_extract_chips* tool is also used to generate the predict dataset which only has images.\n",
    "\n",
    "Here, we are interested in predict over the whole image. We also want to get a better visualization of the results, so we'll generate chips with overlap, because they are necessary to the smooth tool used in the post-process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "91526e88-fc97-4a01-a035-f0d0f22364e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "size = SIZE\n",
    "step_size = 40\n",
    "path_to_files         = f'{BASE_PATH}{PATH_IMAGENES_PREDICCION}*.tif'\n",
    "predict_output_folder = f'{BASE_PATH}/dataset/data_predict/{ZONE}/{RESO}/{EVENTO}/SMOOTH/'+str(size)+'_'+str(step_size)+'/' #Predict dataset folder\n",
    "print(path_to_files,predict_output_folder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "056c4849-d150-4a54-9fba-9e3c6feadcea",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "dataset_v2_tegu_epsg4326.tif windows: 100%|#| 1710/1710 [01:20<00:00, 21.26it/s]\u001b[A\n",
      "Rasters: 100%|####################################| 1/1 [01:20<00:00, 80.50s/it]\n"
     ]
    }
   ],
   "source": [
    "!satproc_extract_chips \\\n",
    "                $path_to_files \\\n",
    "                -o  $predict_output_folder \\\n",
    "                --size $size \\\n",
    "                --step-size $step_size \\\n",
    "                --rescale \\\n",
    "                --rescale-mode percentiles --lower-cut 0 --upper-cut 100"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "762568b0-0d5b-495e-ba73-17f3feca7811",
   "metadata": {},
   "source": [
    "## Prediction\n",
    "The prediction configuration is defined. We have to pass the prediction dataset path and the model path."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1646bd64-1195-4779-95eb-eb6413e07c9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "predict_config = PredictConfig(\n",
    "                            images_path   = predict_output_folder, # ruta a las imagenes sobre las cuales queremos predecir\n",
    "                            results_path  = os.path.join(BASE_PATH,'dataset/data_results',ZONE,RESO,EVENTO,'SMOOTH',str(size)+\"_\"+str(step_size)), # ruta de destino para nuestra predicción\n",
    "                            batch_size    = 16,\n",
    "                            model_path    = os.path.join(f'{BASE_PATH}data/weights',MODEL_NAME),  #  ruta al modelo (.h5)\n",
    "                            height        = INPUT_SIZE,\n",
    "                            width         = INPUT_SIZE,\n",
    "                            n_channels    = N_CHANNELS,\n",
    "                            n_classes     = N_CLASSES,\n",
    "                            class_weights = CLASS_WEIGHT)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3bed1e6a-42b6-48d1-ad2e-16334109a9e5",
   "metadata": {},
   "source": [
    "Running the prediction process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "61ad08ae-ad36-461a-bfb4-402134b8e012",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total images to predict (./../dataset/data_predict/TEGU/low/moderado/SMOOTH/160_40/images/*.tif): 1710\n",
      "After skipping existing results: 1710\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-01 14:35:01.885836: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-08-01 14:35:01.896982: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-08-01 14:35:01.897875: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-08-01 14:35:01.899724: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2022-08-01 14:35:01.900176: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-08-01 14:35:01.901118: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-08-01 14:35:01.901939: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-08-01 14:35:02.570828: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-08-01 14:35:02.571722: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-08-01 14:35:02.572583: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:975] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2022-08-01 14:35:02.573324: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1532] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 10794 MB memory:  -> device: 0, name: Tesla K80, pci bus id: 0000:00:1e.0, compute capability: 3.7\n",
      "  0%|                                                   | 0/107 [00:00<?, ?it/s]2022-08-01 14:35:06.362601: I tensorflow/stream_executor/cuda/cuda_dnn.cc:384] Loaded cuDNN version 8202\n",
      "2022-08-01 14:35:06.784773: I tensorflow/core/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory\n",
      "2022-08-01 14:35:06.787223: I tensorflow/core/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory\n",
      "2022-08-01 14:35:06.787279: W tensorflow/stream_executor/gpu/asm_compiler.cc:80] Couldn't get ptxas version string: INTERNAL: Couldn't invoke ptxas --version\n",
      "2022-08-01 14:35:06.787884: I tensorflow/core/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory\n",
      "2022-08-01 14:35:06.788075: W tensorflow/stream_executor/gpu/redzone_allocator.cc:314] INTERNAL: Failed to launch ptxas\n",
      "Relying on driver to perform ptx compilation. \n",
      "Modify $PATH to customize ptxas location.\n",
      "This message will be only logged once.\n"
     ]
    },
    {
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      "1/1 [==============================] - 0s 33ms/step\n"
     ]
    },
    {
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      " 89%|█████████████████████████████████████▎    | 95/107 [02:10<00:16,  1.34s/it]"
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    },
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     ]
    },
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    },
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      " 99%|████████████████████████████████████████▌| 106/107 [02:25<00:01,  1.37s/it]"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "1/1 [==============================] - 3s 3s/step\n"
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    },
    {
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     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████| 107/107 [02:29<00:00,  1.40s/it]"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "predict(predict_config)  # Ejecuta la predicción"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3bbb09eb-1ac7-410c-aeed-21b8392ee619",
   "metadata": {},
   "source": [
    "## Smoothing\n",
    "\n",
    "To obtain a better visualization we use the *satproc_smooth_stitch* and *satproc_spatial_filter* tools. The first one merges and smooths overlaped chips generated by the prediction. The second, applies a circular kernel that reduces and filters the low probability values in the boundaries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2d5a5708-c15f-4506-b5de-f3b0ce63c4ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Smooth chips: 100%|#########################| 1710/1710 [00:30<00:00, 56.55it/s]\n",
      "Build bounds R-Tree index: 100%|###########| 1710/1710 [00:03<00:00, 555.66it/s]\n",
      "Merge chips: 100%|############################| 132/132 [00:35<00:00,  3.71it/s]\n"
     ]
    }
   ],
   "source": [
    "output=os.path.join(f'./../dataset/data_results/{ZONE}/{RESO}/{EVENTO}/SMOOTH',str(size)+\"_\"+str(step_size),'output')\n",
    "!satproc_smooth_stitch $predict_config.results_path -o $output"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "747a0a67-68c9-4951-89f9-4bb77e4457ed",
   "metadata": {},
   "source": [
    "**SMOOTH FILTER**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4af3db76-66f0-4c88-87a7-23e7db54dd11",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2022-08-01 14:40:53] INFO:satproc.console.spatial_filter:Num. input files: 132\n",
      "[2022-08-01 14:40:53] INFO:satproc.postprocess.spatial_filter:Merge input images into a virtual raster /tmp/tmp9muplz9o/merged.vrt\n",
      "Apply filter:   0%|                                       | 0/1 [00:00<?, ?it/s][2022-08-01 14:40:54] INFO:rasterio._env:GDAL signalled an error: err_no=4, msg='./../dataset/data_results/TEGU/low/moderado/SMOOTH/floods_TEGU_low_moderado_160_40_smooth_filter.tif: No such file or directory'\n",
      "Apply filter: 100%|###############################| 1/1 [00:01<00:00,  1.01s/it]\n"
     ]
    }
   ],
   "source": [
    "filt_output=f'./../dataset/data_results/{ZONE}/{RESO}/{EVENTO}/SMOOTH/floods_{ZONE}_{RESO}_{EVENTO}_{str(SIZE)}_{str(step_size)}_smooth_filter.tif'\n",
    "!satproc_spatial_filter -v --input-dir $output --size 201 -o $filt_output"
   ]
  }
 ],
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