notebooks/2_Prediccion.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## L3-Landslides\n",
    "\n",
    "# 2_Prediction Notebook\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,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import glob\n",
    "import subprocess\n",
    "from unetseg.predict import PredictConfig, predict\n",
    "from unetseg.evaluate import plot_data_results\n",
    "from tqdm import tqdm\n",
    "from satproc.postprocess.polygonize import polygonize \n",
    "from satproc.filter import filter_by_max_prob"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Variables statement**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#General\n",
    "ZONE = \"TEGUCIGALPA\"\n",
    "RESO = \"planet\"\n",
    "VERSION=\"V1\"\n",
    "PROYECT='landslides'\n",
    " #We use the trained model in Tegucigalpa to predict over Tegucigalpa and Sula Valley\n",
    "\n",
    "#Bucket\n",
    "BUCKET_IMG_SULA ='gs://dym-quilmes-trucks-temp/I2UD/landslides/images/GLOBAL/SULA/V1/dataset_sula_global_epsg4326.tif'#gs://dym-quilmes-trucks-temp/I2UD/landslides/images/LOCAL/SULA/V1/dataset_sula_local_epsg4326.tif' #f\"gs://dym-quilmes-trucks-temp/I2UD/{PROYECT}/images/LOCAL/{ZONE}/{VERSION}/*.tif\" #CHECK\n",
    "\n",
    "#Paths\n",
    "BASE_PATH = \"./..\"\n",
    "PATH_PREDICT_IMAGES = f'{BASE_PATH}/images/{ZONE}/{RESO}/'\n",
    "PATH_AOI=f'{BASE_PATH}/data/shp/gt/{ZONE}/'\n",
    "AOI_PREDICT=\"municipios_sula_disolve.geojson\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "CLASS_WEIGHT = [1]\n",
    "N_CLASSES = 1\n",
    "INPUT_SIZE=160\n",
    "N_CHANNELS=10\n",
    "INFO = \"classD\"\n",
    "\n",
    "if ZONE='TEGUCIGALPA':\n",
    "    if RESO=\"planet\":\n",
    "        #satproc\n",
    "        SIZE =160\n",
    "        STEP_SIZE = 40\n",
    "        step_size=STEP_SIZE\n",
    "        RESCALE_MODE = \"percentiles --lower-cut 0 --upper-cut 100\"\n",
    "        #unet\n",
    "        MODEL_NAME=\"UNet_TEGUCIGALPA_planet_160x160_160_40_spe90_10N_classD_v3.h5\"\n",
    "        STEP_PER_EPOCH = 90\n",
    "        VERSION_UNET='v3'\n",
    "        #Post-process\n",
    "        SMOOTH_FILTER=91\n",
    "    if RESO=\"low\":\n",
    "        #satproc\n",
    "        SIZE =160\n",
    "        STEP_SIZE = 40\n",
    "        step_size=SIZE\n",
    "        RESCALE_MODE = \"percentiles --lower-cut 0 --upper-cut 100\"           \n",
    "        #unet\n",
    "        MODEL_NAME = 'UNet_TEGUCIGALPA_low_160x160_160_40_spe85_10N_classD_v1.h5'\n",
    "        STEP_PER_EPOCH = 85\n",
    "        VERSION_UNET='v1'\n",
    "        #Post-process\n",
    "        THR=0.3\n",
    "        min_area=20000\n",
    "        \n",
    "if ZONE='SULA':\n",
    "        if RESO=\"planet\":\n",
    "        #satproc\n",
    "        SIZE =256\n",
    "        STEP_SIZE = 64\n",
    "        step_size=STEP_SIZE\n",
    "        RESCALE_MODE = \"percentiles --lower-cut 0 --upper-cut 100\"\n",
    "        #unet\n",
    "        MODEL_NAME=\"UNet_TEGUCIGALPA_planet_160x160_160_40_spe90_10N_classD_v3.h5\"\n",
    "        STEP_PER_EPOCH = 90\n",
    "        VERSION_UNET='v3'\n",
    "        #Post-process\n",
    "        SMOOTH_FILTER=201\n",
    "    if RESO=\"low\":\n",
    "        #satproc\n",
    "        SIZE =160\n",
    "        STEP_SIZE = 40\n",
    "        step_size=STEP_SIZE\n",
    "        RESCALE_MODE = \"percentiles --lower-cut 0 --upper-cut 100\"           \n",
    "        #unet\n",
    "        MODEL_NAME = 'UNet_TEGUCIGALPA_low_160x160_160_40_spe85_10N_classD_v1.h5'\n",
    "        STEP_PER_EPOCH = 85\n",
    "        VERSION_UNET='v1'    \n",
    "        SMOOTH_FILTER=201"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Download from buckets\n",
    "**Sula Valley images download**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!mkdir -p $PATH_SULA_IMAGES\n",
    "\n",
    "!gsutil -m cp -r $BUCKET_IMG_SULA   $PATH_PREDICT_IMAGES"
   ]
  },
  {
   "cell_type": "markdown",
   "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",
    "* Tegucigalpa global:\n",
    "The prediction is done over the whole image and without overlap, then the size and step_size must be equals.\n",
    "\n",
    "* Tegucigalpa local & Sula Valley:\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.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "path_to_files   = f'{PATH_PREDICT_IMAGES}*.tif' #carpeta a las imagenes\n",
    "output_folder   = f'{BASE_PATH}/dataset/data_predict/{ZONE}/{RESO}/{str(SIZE)}_{str(STEP_SIZE)}/' #carpeta de destino del dataset\n",
    "#vector_file_aoi = f'{PATH_AOI}{AOI_PREDICT}'  #archivo vectorial con las zonas de interes\n",
    "print(path_to_files, output_folder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!satproc_extract_chips --help"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Satproc execution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Rasters: 100%|###################################| 1/1 [01:54<00:00, 114.48s/it]\n"
     ]
    }
   ],
   "source": [
    "!satproc_extract_chips \\\n",
    "                $path_to_files \\\n",
    "                -o  $output_folder \\\n",
    "                --size $SIZE \\\n",
    "                --step-size $step_size \\\n",
    "                --rescale \\\n",
    "                --rescale-mode $RESCALE_MODE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The prediction configuration is defined. We have to pass the prediction dataset path and the model path."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "predict_config = PredictConfig(\n",
    "                            images_path=output_folder, # ruta a las imagenes sobre las cuales queremos predecir\n",
    "                            results_path=os.path.join(BASE_PATH,'dataset/data_results',ZONE,RESO,VERSION_UNET,str(SIZE)+\"_\"+str(STEP_SIZE)), #'modelo_'+VERSION ruta de destino para nuestra predicción\n",
    "                            batch_size=16,\n",
    "                            model_path=os.path.join(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",
   "metadata": {},
   "source": [
    "Running the prediction process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total images to predict (./../dataset/data_predict/SULA/low/160_40/images/*.tif): 2124\n",
      "After skipping existing results: 2124\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-08-24 13:11:37.098163: 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",
      "2022-08-24 13:11:37.111256: 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",
      "2022-08-24 13:11:37.112195: 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",
      "2022-08-24 13:11:37.114694: 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",
      "2022-08-24 13:11:37.115234: 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",
      "2022-08-24 13:11:37.116073: 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",
      "2022-08-24 13:11:37.116879: 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",
      "2022-08-24 13:11:37.861530: 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",
      "2022-08-24 13:11:37.862434: 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",
      "2022-08-24 13:11:37.863283: 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",
      "2022-08-24 13:11:37.864032: 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%|                                                   | 0/133 [00:00<?, ?it/s]2022-08-24 13:11:41.663729: I tensorflow/stream_executor/cuda/cuda_dnn.cc:384] Loaded cuDNN version 8202\n",
      "2022-08-24 13:11:42.089194: I tensorflow/core/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory\n",
      "2022-08-24 13:11:42.091446: I tensorflow/core/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory\n",
      "2022-08-24 13:11:42.091504: W tensorflow/stream_executor/gpu/asm_compiler.cc:80] Couldn't get ptxas version string: INTERNAL: Couldn't invoke ptxas --version\n",
      "2022-08-24 13:11:42.092126: I tensorflow/core/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory\n",
      "2022-08-24 13:11:42.092428: 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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 5s 5s/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  1%|▎                                          | 1/133 [00:06<14:32,  6.61s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 48ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  2%|▋                                          | 2/133 [00:07<07:42,  3.53s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 50ms/step\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "  2%|▉                                          | 3/133 [00:09<05:29,  2.53s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 48ms/step\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "  3%|█▎                                         | 4/133 [00:10<04:26,  2.06s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 46ms/step\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "  4%|█▌                                         | 5/133 [00:12<03:51,  1.81s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 42ms/step\n"
     ]
    },
    {
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      "  5%|█▉                                         | 6/133 [00:13<03:28,  1.64s/it]"
     ]
    },
    {
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     "text": [
      "1/1 [==============================] - 0s 37ms/step\n"
     ]
    },
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      "  5%|██▎                                        | 7/133 [00:14<03:14,  1.54s/it]"
     ]
    },
    {
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     "text": [
      "1/1 [==============================] - 0s 37ms/step\n"
     ]
    },
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      "  6%|██▌                                        | 8/133 [00:16<03:04,  1.47s/it]"
     ]
    },
    {
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     "text": [
      "1/1 [==============================] - 0s 43ms/step\n"
     ]
    },
    {
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     "text": [
      "  7%|██▉                                        | 9/133 [00:17<03:00,  1.46s/it]"
     ]
    },
    {
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      "1/1 [==============================] - 0s 46ms/step\n"
     ]
    },
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      "  8%|███▏                                      | 10/133 [00:18<02:56,  1.44s/it]"
     ]
    },
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      "1/1 [==============================] - 0s 37ms/step\n"
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    },
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      "  8%|███▍                                      | 11/133 [00:20<02:51,  1.41s/it]"
     ]
    },
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     "text": [
      "1/1 [==============================] - 0s 42ms/step\n"
     ]
    },
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     "text": [
      "  9%|███▊                                      | 12/133 [00:21<02:47,  1.39s/it]"
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    },
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      "1/1 [==============================] - 0s 39ms/step\n"
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    },
    {
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     "output_type": "stream",
     "text": [
      " 10%|████                                      | 13/133 [00:22<02:45,  1.38s/it]"
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    },
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      " 11%|████▍                                     | 14/133 [00:24<02:43,  1.37s/it]"
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    {
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     "text": [
      "Done!\n"
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    }
   ],
   "source": [
    "predict(predict_config)  # Ejecuta la predicción"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!ls ../data/weights/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The prediction results can be visualized. We see the image on the left side and the prediction result on the right side."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x2880 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x2880 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_data_results(num_samples=2, fig_size=(2, 2), predict_config=predict_config, img_ch =2, n_bands=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Post processing\n",
    "\n",
    "In this stage, we aplied the following post proccessing methods to the prediction results:\n",
    "\n",
    "* Tegucigalpa global model:\n",
    "    1. Threshold filtering: In this process we filter the results images by applying a threshold over the probabilities values, saving those which are higher than the threshold value.\n",
    "    2. Polygonized: the filtered results are converted to polygons into a vector file.\n",
    "    3. Minimum area: We select those polygons which area is bigger than the minimum.\n",
    "* Tegucigalpa local model & Sula Valley models:\n",
    "    1. Smoothing: 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.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Threshold filtering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "thr=str(int(10*THR))\n",
    "filtered_folder=f'./../dataset/data_results/TEGUCIGALPA/low/v1/filtered_up/{str(SIZE)}_{str(STEP_SIZE)}_thr{thr}'\n",
    "filter_by_max_prob(input_dir  = f'./../dataset/data_results/{ZONE}/{RESO}/{VERSION_UNET}/{str(SIZE)}_{str(STEP_SIZE)}_3',#predict_config.results_path,\n",
    "                   output_dir = filtered_folder,\n",
    "                   threshold  = THR)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Polygonized"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "thr=str(int(10*THR))\n",
    "output_path=f'./../dataset/data_results/TEGUCIGALPA/low/v1/poligonize/poligonize_{ZONE}_{RESO}_{PROYECT}_classD_thr{thr}_{str(SIZE)}_{str(STEP_SIZE)}_{VERSION_UNET}.gpkg'\n",
    "polygonize(threshold=THR,\n",
    "           input_dir=filtered_folder,\n",
    "           output=output_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Minimum area "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "src_file = './../dataset/data_results/TEGUCIGALPA/low/v1/poligonize/poligonize_TEGUCIGALPA_low_landslides_classD_thr3_160_40_v1.gpkg'#output_path\n",
    "dst_file = src_file[:-5] + \"_utm.gpkg\"\n",
    "\n",
    "!ogr2ogr -s_srs EPSG:4326 -t_srs EPSG:32616 -f 'GPKG' $dst_file $src_file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_ = dst_file\n",
    "output_ = input_[:-5] + \"_up\"+str(min_area)+\".gpkg\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO: Open of `./../dataset/data_results/SULA/planet/v3/poligonize/poligonize_SULA_planet_landslides_classD_thr5_256_40_3_v3_utm.gpkg'\n",
      "      using driver `GPKG' successful.\n",
      "\n",
      "Layer name: out\n",
      "Geometry: Polygon\n",
      "Feature Count: 39615\n",
      "Extent: (385055.717256, 1697189.752631) - (418893.502815, 1734170.793931)\n",
      "Layer SRS WKT:\n",
      "PROJCRS[\"WGS 84 / UTM zone 16N\",\n",
      "    BASEGEOGCRS[\"WGS 84\",\n",
      "        DATUM[\"World Geodetic System 1984\",\n",
      "            ELLIPSOID[\"WGS 84\",6378137,298.257223563,\n",
      "                LENGTHUNIT[\"metre\",1]]],\n",
      "        PRIMEM[\"Greenwich\",0,\n",
      "            ANGLEUNIT[\"degree\",0.0174532925199433]],\n",
      "        ID[\"EPSG\",4326]],\n",
      "    CONVERSION[\"UTM zone 16N\",\n",
      "        METHOD[\"Transverse Mercator\",\n",
      "            ID[\"EPSG\",9807]],\n",
      "        PARAMETER[\"Latitude of natural origin\",0,\n",
      "            ANGLEUNIT[\"degree\",0.0174532925199433],\n",
      "            ID[\"EPSG\",8801]],\n",
      "        PARAMETER[\"Longitude of natural origin\",-87,\n",
      "            ANGLEUNIT[\"degree\",0.0174532925199433],\n",
      "            ID[\"EPSG\",8802]],\n",
      "        PARAMETER[\"Scale factor at natural origin\",0.9996,\n",
      "            SCALEUNIT[\"unity\",1],\n",
      "            ID[\"EPSG\",8805]],\n",
      "        PARAMETER[\"False easting\",500000,\n",
      "            LENGTHUNIT[\"metre\",1],\n",
      "            ID[\"EPSG\",8806]],\n",
      "        PARAMETER[\"False northing\",0,\n",
      "            LENGTHUNIT[\"metre\",1],\n",
      "            ID[\"EPSG\",8807]]],\n",
      "    CS[Cartesian,2],\n",
      "        AXIS[\"(E)\",east,\n",
      "            ORDER[1],\n",
      "            LENGTHUNIT[\"metre\",1]],\n",
      "        AXIS[\"(N)\",north,\n",
      "            ORDER[2],\n",
      "            LENGTHUNIT[\"metre\",1]],\n",
      "    USAGE[\n",
      "        SCOPE[\"unknown\"],\n",
      "        AREA[\"World - N hemisphere - 90°W to 84°W - by country\"],\n",
      "        BBOX[0,-90,84,-84]],\n",
      "    ID[\"EPSG\",32616]]\n",
      "Data axis to CRS axis mapping: 1,2\n",
      "FID Column = fid\n",
      "Geometry Column = geom\n",
      "DN: Integer (0.0)\n"
     ]
    }
   ],
   "source": [
    "!ogrinfo -al -so $dst_file "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "!ogr2ogr \\\n",
    "    -t_srs EPSG:32616 \\\n",
    "    -f \"GPKG\" \\\n",
    "    -sql \"SELECT * FROM out m WHERE (ST_Area(geom) > $min_area)\" \\\n",
    "    -dialect SQLITE \\\n",
    "    -nln results \\\n",
    "    $output_ \\\n",
    "    $input_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Smoothing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Smooth chips: 100%|#########################| 1285/1285 [00:29<00:00, 43.38it/s]\n",
      "Build bounds R-Tree index: 100%|###########| 1285/1285 [00:02<00:00, 569.33it/s]\n",
      "Merge chips: 100%|############################| 100/100 [00:25<00:00,  3.98it/s]\n"
     ]
    }
   ],
   "source": [
    "output=os.path.join(f'./../dataset/data_results/{ZONE}/{RESO}/{VERSION_UNET}/SMOOTH',str(size)+\"_\"+str(step_size)+\"_aoi\",'output')\n",
    "!satproc_smooth_stitch $predict_config.results_path -o $output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Smooth filter**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2022-08-30 15:36:40] INFO:satproc.console.spatial_filter:Num. input files: 100\n",
      "[2022-08-30 15:36:40] INFO:satproc.postprocess.spatial_filter:Merge input images into a virtual raster /tmp/tmp_k5xi03y/merged.vrt\n",
      "Apply filter:   0%|                                       | 0/1 [00:00<?, ?it/s][2022-08-30 15:36:41] INFO:rasterio._env:GDAL signalled an error: err_no=4, msg='./../dataset/data_results/TEGUCIGALPA/planet/v3/SMOOTH/landslides_TEGUCIGALPA_planet_v3_160_40_aoi_smooth_filter_83.tif: No such file or directory'\n",
      "Apply filter: 100%|###############################| 1/1 [00:00<00:00,  1.74it/s]\n"
     ]
    }
   ],
   "source": [
    "filt_output=f'./../dataset/data_results/{ZONE}/{RESO}/{VERSION_UNET}/SMOOTH/landslides_{ZONE}_{RESO}_{VERSION_UNET}_{str(SIZE)}_{str(step_size)}_aoi_smooth_filter_{SMOOTH_FILTER}.tif'\n",
    "!satproc_spatial_filter -v --input-dir $output --size $SMOOTH_FILTER -o $filt_output"
   ]
  }
 ],
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