2_1_CVAE_interpol.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "title: **C-VAE** Interpolation \n",
    "\n",
    "author: Tiago Ribeiro\n",
    "\n",
    "date: 04/05/2023\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Author: \u001B[1mTiago F. R. Ribeiro\u001B[0m\n",
      "\n",
      "Github username: \u001B[1mTiago1Ribeiro\u001B[0m\n",
      "\n",
      "Last updated: 2023-04-05 08:33:10\n",
      "\n",
      "Python implementation: CPython\n",
      "Python version       : 3.10.0\n",
      "IPython version      : 8.6.0\n",
      "\n",
      "conda environment: tf2_10\n",
      "\n",
      "Compiler    : MSC v.1916 64 bit (AMD64)\n",
      "OS          : Windows\n",
      "Release     : 10\n",
      "Machine     : AMD64\n",
      "Processor   : Intel64 Family 6 Model 165 Stepping 5, GenuineIntel\n",
      "CPU cores   : 16\n",
      "Architecture: 64bit\n",
      "\n",
      "numpy     : 1.23.5\n",
      "re        : 2.2.1\n",
      "matplotlib: 3.5.3\n",
      "cv2       : 4.6.0\n",
      "keras     : 2.10.0\n",
      "shapely   : 2.0.1\n",
      "tensorflow: 2.10.0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from watermark import watermark\n",
    "import tensorflow as tf\n",
    "print(watermark(author=\"\\033[1m\" +\"Tiago F. R. Ribeiro\" + \"\\033[0m\", \n",
    "                github_username=\"\\033[1m\" +\"Tiago1Ribeiro\"+ \"\\033[0m\", \n",
    "                current_date=True, datename=False, iso8601=False, current_time=True, \n",
    "                timezone=False, python=True, conda=True, machine=True, updated=True,\n",
    "                iversions=True,  globals_= globals()))\n",
    "# gpu = !nvidia-smi --query-gpu=gpu_name --format=csv,noheader\n",
    "# cuda = !nvcc --version | grep release | cut -d ' ' -f 5 | cut -d ',' -f 1"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#~ General imports ~\n",
    "import numpy as np\n",
    "import os\n",
    "from glob import glob\n",
    "from tqdm import tqdm\n",
    "import datetime\n",
    "import cv2\n",
    "import re\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.image import imsave\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "#~ geospatial and raster manipulation imports ~     \n",
    "from shapely.geometry import shape, MultiPolygon\n",
    "from rasterio.features import shapes\n",
    "from rasterio import Affine\n",
    "\n",
    "#~ Neural Network related imports and configurations ~\n",
    "from tensorflow.keras import Input, Model\n",
    "from tensorflow.keras.layers import Activation, Dense, Lambda, Input \n",
    "from tensorflow.keras.layers import MaxPooling2D, Flatten, Reshape, Concatenate\n",
    "from tensorflow.keras.layers import SeparableConv2D, Conv2DTranspose\n",
    "from tensorflow.keras import backend as K \n",
    "from tensorflow.keras.preprocessing.image import load_img, img_to_array\n",
    "from tensorflow.python.framework.ops import disable_eager_execution\n",
    "disable_eager_execution()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### C-VAE Configs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "ARGS = dict(\n",
    "    IMG_SIZE = (512, 512),\n",
    "    BATCH_SZ = 2,\n",
    "    REF_FILTERS = 32,\n",
    "    F_INIT = \"he_normal\",\n",
    "    OPTIMIZER = tf.keras.optimizers.Adam(learning_rate = 3e-4),\n",
    "    COST_F = tf.keras.losses.MeanSquaredError(),\n",
    "    CONV_ACTIV = tf.keras.layers.Activation(tf.nn.leaky_relu),\n",
    "    SEED = 42,\n",
    "    LATENT_DIM = 64,\n",
    "    DTYPE = tf.float32\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Auxiliary functions"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### plot_learning_curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function to show learning curves plot\n",
    "def plot_learning_curves(hist_plot):\n",
    "\n",
    "    # figure size in inches optional\n",
    "    plt.figure(figsize=(15, 6))\n",
    "    plt.title(\"Learning Curves\")\n",
    "    plt.grid(True, linestyle='-.', linewidth=0.3)\n",
    "    plt.xticks(np.arange(0, max(hist_plot.epoch) + 50, 50))\n",
    "    # y axis in scientific notation and log scale, showing every 5th label\n",
    "    # plt.ticklabel_format(style='plain', axis='y', scilimits=(0,0), useMathText=True)\n",
    "    plt.yscale('log')\n",
    "    plt.yticks(np.arange(0, max(hist_plot.history['loss']), 0.1), fontsize=8)\n",
    "    plt.xticks(fontsize=8)\n",
    "    # plot the loss with thin line\n",
    "    plt.plot(hist_plot.history['loss'], label='train loss', linewidth=0.7)\n",
    "    # plot the validation loss with thin line and other color\n",
    "    plt.plot(hist_plot.history['val_loss'], label='test loss', linewidth=0.7)\n",
    "    plt.xlabel(\"Epochs\")\n",
    "    plt.ylabel(\"Loss\")\n",
    "    plt.legend()\n",
    "    # find on which epoch the loss is minimum and add a point to the plot\n",
    "    min_loss = min(hist_plot.history['loss'])\n",
    "    min_loss_epoch = hist_plot.history['loss'].index(min_loss)\n",
    "    plt.plot(min_loss_epoch, min_loss, marker='o', markersize=2, color=\"red\")\n",
    "    # add text in gray color with small arrow to the point in scientific notation \n",
    "    plt.annotate(\"min: {:.2e}\".format(min_loss), xy=(min_loss_epoch, min_loss), \n",
    "                xytext=(min_loss_epoch+10, min_loss+0.01),fontsize=7, \n",
    "                arrowprops=dict(arrowstyle=\"->\", connectionstyle=\"arc3\"))\n",
    "    # find on which epoch the validation loss is minimum and add a green point to the plot\n",
    "    min_val_loss = min(hist_plot.history['val_loss'])\n",
    "    min_val_loss_epoch = hist_plot.history['val_loss'].index(min_val_loss)\n",
    "    plt.plot(min_val_loss_epoch, min_val_loss, marker='o', markersize=2, color=\"red\")\n",
    "    # add text in gray color with small arrow to the point in scientific notation\n",
    "    plt.annotate(\"min: {:.2e}\".format(min_val_loss), xy=(min_val_loss_epoch, min_val_loss),\n",
    "                xytext=(min_val_loss_epoch+10, min_val_loss+0.01),fontsize=7,\n",
    "                arrowprops=dict(arrowstyle=\"->\", connectionstyle=\"arc3\"))\n",
    "                \n",
    "    # if ReduceOnPlateau callback is used, add a vertical line and a \n",
    "    # text for each change in learning rate\n",
    "    if 'lr' in hist_plot.history.keys():\n",
    "        # find the learning rate changes\n",
    "        lr_changes = []\n",
    "        for i in range(len(hist_plot.history['lr'])):\n",
    "            if hist_plot.history['lr'][i] != hist_plot.history['lr'][i - 1]:\n",
    "                lr_changes.append(i)\n",
    "        # add a vertical line and a text for each change in learning rate\n",
    "        for i in lr_changes:\n",
    "            plt.axvline(x=i, color='mediumseagreen', linestyle='--', linewidth=0.7)\n",
    "            plt.text(i + 5, min(hist_plot.history['loss']) + 0.03, f\"l.r.: {hist_plot.history['lr'][i]:.0e}\",\n",
    "                     fontsize=7, color='black', rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  wkt2masc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def wkt2masc(wkt_file, images_path, orig_dims, height, width):\n",
    "    \"\"\" \n",
    "    Converts WKT files to segmentation masks.\n",
    "    Parameters:\n",
    "        wkt_file {str} -- path to the WKT file\n",
    "        images_path {str} -- path to the folder where the masks will be saved\n",
    "        orig_dims {tuple} -- original dimensions of the masks\n",
    "        height {int} -- desired height of the masks\n",
    "        width {int} -- desired width of the masks\n",
    "    Returns:\n",
    "        Creates PNG images of the masks\n",
    "    \"\"\"\n",
    "\n",
    "    os.makedirs(images_path, exist_ok=True)\n",
    "\n",
    "    # delete files in the folder, if any\n",
    "    for filename in os.listdir(images_path):\n",
    "        if filename.endswith(\".png\"):\n",
    "            os.remove(os.path.join(images_path, filename))\n",
    "\n",
    "    # open WKT file\n",
    "    wkt = open(wkt_file, 'r')\n",
    "    num_lines = len(wkt.readlines())\n",
    "    cnt = 0\n",
    "    \n",
    "    print(f\"\"\"\n",
    "    {'-'*38}\n",
    "    # \\033[1mProperties of the resulting masks\\033[0m\n",
    "    # Width: {width}, Height: {height}\n",
    "    # Number of masks to create: {num_lines}\n",
    "    {'-'*38}\n",
    "    \"\"\")\n",
    "\n",
    "    pbar = tqdm(total=num_lines)\n",
    "\n",
    "    # process each line of the WKT file\n",
    "    with open(wkt_file) as f:\n",
    "        for line in f:\n",
    "            # extract numbers from the line\n",
    "            points = [int(s) for s in re.findall('[0-9]+', line)]\n",
    "            # create empty mask\n",
    "            mask = np.zeros(orig_dims, dtype=np.uint8)\n",
    "            # create array with polygon points, with 2 columns (x,y)\n",
    "            arr = np.array(points, dtype=np.int32).reshape((-1,2))\n",
    "            # draw mask\n",
    "            cv2.drawContours(image = mask,\n",
    "                             contours=[arr],\n",
    "                             contourIdx=-1,\n",
    "                             color=(255, 255, 255),\n",
    "                             thickness=-1,  # if > 0, thickness of the contour; if -1, fill object\n",
    "                             lineType=cv2.LINE_AA)\n",
    "            # resize frames with Lanczos interpolation\n",
    "            mask_resized = cv2.resize(mask, (width, height), interpolation=cv2.INTER_LANCZOS4)\n",
    "            # save mask and \n",
    "            cv2.imwrite(os.path.join(images_path, f\"mask_{cnt:06d}.png\"), mask_resized)\n",
    "            cnt += 1\n",
    "            pbar.update(1)\n",
    "\n",
    "    pbar.close()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### load_images_from_folder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_images_from_folder(folder_path, target_size=(256,256)):\n",
    "    images = list()\n",
    "    for filename in os.listdir(folder_path):\n",
    "        if filename.endswith('.png'):\n",
    "            # Load the image and convert it to a Numpy array\n",
    "            img = load_img(os.path.join(folder_path, filename), target_size)\n",
    "            img_array = img_to_array(img) / 255.0  # rescale pixel values to [0,1]\n",
    "            # Add the image array to the list of images\n",
    "            images.append(img_array)\n",
    "    # Convert the list of images to a float32 Numpy array\n",
    "    images_array = np.array(images).astype(\"float32\")\n",
    "    \n",
    "    return images_array"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### frame_to_label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def frame_to_label(frame):\n",
    "    label = np.float32(frame /22500)\n",
    "    label = np.expand_dims(label, axis=0)\n",
    "    return label"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### msks_paths_to_polygon_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def msks_paths_to_polygon_list(msks_paths, out_dim = (512, 512)):\n",
    "    \"\"\"\n",
    "    Converts segmentation masks paths list to list of shapely multipolygons.\n",
    "\n",
    "    Parameters:\n",
    "        msks_paths {list} -- list of paths to the masks\n",
    "        out_dim {tuple} -- (width, height) desired dimensions of the masks \n",
    "    Returns:\n",
    "        pol_list {list} -- list of shapely multipolygons\n",
    "    \"\"\"\n",
    "    \n",
    "    pol_list = list()\n",
    "    for img_path in msks_paths:\n",
    "        img = cv2.imread(img_path, cv2.IMREAD_GRAYSCALE)\n",
    "        # image dimensions\n",
    "        h, w = img.shape\n",
    "        if (w, h) != out_dim:\n",
    "            img = cv2.resize(img, out_dim, interpolation=cv2.INTER_CUBIC)\n",
    "        polygon = mask_to_polygons(img)\n",
    "        pol_list.append(polygon)\n",
    "    return pol_list"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### mask_to_polygons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mask_to_polygons(mask_img):\n",
    "    \"\"\"\n",
    "    Converts segmentation mask to shapely multipolygon.\n",
    "    Adapted from: https://rocreguant.com/convert-a-mask-into-a-polygon-for-images-using-shapely-and-rasterio/1786/\n",
    "    \"\"\"\n",
    "    all_polygons = list()\n",
    "    \n",
    "    for shp, _ in shapes(source=mask_img.astype(np.uint8),\n",
    "                         mask=(mask_img>0), \n",
    "                         transform=Affine(1.0, 0, 0, 0, 1.0, 0)):\n",
    "        all_polygons.append(shape(shp))\n",
    "\n",
    "    all_polygons = MultiPolygon(all_polygons)\n",
    "\n",
    "    # Sometimes buffer() converts a simple Multipolygon to just a Polygon,\n",
    "    # need to keep it a Multipolygon throughout\n",
    "    if not all_polygons.is_valid:\n",
    "        all_polygons = all_polygons.buffer(0)\n",
    "        if all_polygons.type == 'Polygon':\n",
    "            all_polygons = MultiPolygon([all_polygons])\n",
    "    \n",
    "    return all_polygons"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### frame2video"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def frames2video(img_list, nome_ficheiro='video', fps_ = 25, titulo: str = \"\",frame_num_text  = False, font_size: int = 1) -> None:\n",
    "    \"\"\" Converts a list of images into an AVI file with the same resolution as the first image in the list. Parameters: - list of images PNG, TIFF, JPEG, BMP, WEBP, STK, LSM or XCF - name of the video file Returns: saves video in the execution directory \"\"\"\n",
    "    #stores the dimensions of the first image\n",
    "    img = cv2.imread(img_list[0])\n",
    "    height, width, _ = img.shape\n",
    "    size = (width, height)\n",
    "    num_frames =  len(img_list)\n",
    "\n",
    "    img_array = list()\n",
    "    for i in range(len(img_list)):\n",
    "        img = cv2.imread(img_list[i])\n",
    "        img_array.append(img)\n",
    "        print(f\"1. Appending frames {i+1}/{num_frames}\", end=\"\\r\")\n",
    "        \n",
    "    print(\"2. Creating video writer...\")\n",
    "    video = cv2.VideoWriter(filename= nome_ficheiro + '.avi',\n",
    "                            fourcc=cv2.VideoWriter_fourcc(*'mp4v'), fps = fps_,\n",
    "                            frameSize=size)\n",
    "\n",
    "    for i in range(len(img_array)):\n",
    "        if frame_num_text:\n",
    "            frame_number_text = f\"frame_{i:06d}\"\n",
    "            cv2.putText(img_array[i], frame_number_text, (width-300, 50), \n",
    "                        cv2.FONT_HERSHEY_SIMPLEX,font_size, (255, 100, 100), \n",
    "                        2, cv2.LINE_AA)\n",
    "        if titulo:\n",
    "            cv2.putText(img_array[i], titulo, (50, 50), cv2.FONT_HERSHEY_SIMPLEX,\n",
    "                        font_size, (255, 255, 255), 2, cv2.LINE_AA)\n",
    "        \n",
    "        video.write(img_array[i])\n",
    "        print(f\"3. Writing frames to file {i+1}/{num_frames}\", end=\"\\r\")\n",
    "    video.release()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Sources\n",
    "\n",
    "**note**: to test on your own machine, please set up the paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "    --------------------------------------\n",
      "    # \u001B[1mProperties of the resulting masks\u001B[0m\n",
      "    # Width: 512, Height: 512\n",
      "    # Number of masks to create: 226\n",
      "    --------------------------------------\n",
      "    \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 226/226 [00:00<00:00, 505.63it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "    --------------------------------------\n",
      "    # \u001B[1mProperties of the resulting masks\u001B[0m\n",
      "    # Width: 512, Height: 512\n",
      "    # Number of masks to create: 23\n",
      "    --------------------------------------\n",
      "    \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 23/23 [00:00<00:00, 518.43it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "    --------------------------------------\n",
      "    # \u001B[1mProperties of the resulting masks\u001B[0m\n",
      "    # Width: 512, Height: 512\n",
      "    # Number of masks to create: 13\n",
      "    --------------------------------------\n",
      "    \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 13/13 [00:00<00:00, 543.11it/s]\n"
     ]
    }
   ],
   "source": [
    "DIR = os.getcwd()\n",
    "IMG_DIR = os.path.normpath('E://BurnedAreaUAV_files//Results')\n",
    "WKT_FILE = os.path.normpath(\"E://BurnedAreaUAV_files//BurnedAreaUAV_dataset//WKT_files//train_valid.wkt\")\n",
    "WKT_FILE_TEST = os.path.normpath(\"E://BurnedAreaUAV_files//BurnedAreaUAV_dataset//WKT_files//test.wkt\")\n",
    "# Sampled masks directory\n",
    "DIR_SAMPLED = os.path.normpath(\"E:/BurnedAreaUAV_files/Interpolation/reference_masks\")\n",
    "TXT_FILE_SAMPLED = os.path.join(DIR_SAMPLED, \"sampled_masks.txt\")\n",
    "WKT_FILE_SAMPLED = os.path.join(DIR_SAMPLED, \"sampled_masks.wkt\")\n",
    "# Train, Test and Checkpoints directories\n",
    "TRAIN_DIR = os.path.join(DIR, \"train_masks\")\n",
    "TRAIN_SAMPLED_DIR = os.path.join(DIR, \"sampled_masks\")\n",
    "os.makedirs(TRAIN_SAMPLED_DIR, exist_ok=True)\n",
    "TEST_DIR = os.path.join(DIR, \"test_masks\")\n",
    "os.makedirs(os.path.join(DIR, \"checkpoints\"), exist_ok=True)\n",
    "CHCKPNT_MODELS = os.path.join(DIR, \"checkpoints\")\n",
    "# Original dimensions of the images\n",
    "ORIG_DIMS = (720, 1280)\n",
    "\n",
    "wkt2masc(wkt_file=WKT_FILE, images_path=TRAIN_DIR, orig_dims=ORIG_DIMS, \n",
    "         height=ARGS[\"IMG_SIZE\"][0], width=ARGS[\"IMG_SIZE\"][1])\n",
    "\n",
    "wkt2masc(wkt_file=WKT_FILE_TEST, images_path=TEST_DIR, orig_dims=ORIG_DIMS, \n",
    "         height=ARGS[\"IMG_SIZE\"][0], width=ARGS[\"IMG_SIZE\"][1])\n",
    "\n",
    "# read txt file \n",
    "with open(TXT_FILE_SAMPLED, 'r') as f:\n",
    "    polygons = f.readlines()\n",
    "    # extract indexes and polygons\n",
    "    indexes = [int(polygon.split(',')[0]) for polygon in polygons]\n",
    "    polygons = [polygon.split(',', 1)[1][:-1] for polygon in polygons]\n",
    "\n",
    "wkt2masc(wkt_file=WKT_FILE_SAMPLED, images_path=TRAIN_SAMPLED_DIR, orig_dims=ORIG_DIMS, \n",
    "         height=ARGS[\"IMG_SIZE\"][0], width=ARGS[\"IMG_SIZE\"][1])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Loading and Preprocessing"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Labels generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "msks_train_path = glob(os.path.join(TRAIN_DIR, \"*.png\"))\n",
    "msks_train_basename = [os.path.basename(m) for m in msks_train_path]\n",
    "msks_train_num = [int(m.split(\"_\")[1].split(\".\")[0]) for m in msks_train_basename]\n",
    "# multiply masks number by 100 to get the same range as the frames\n",
    "msks_train_labels = [m*100 for m in msks_train_num]\n",
    "\n",
    "msks_test_path = glob(os.path.join(TEST_DIR, \"*.png\"))\n",
    "msks_test_basename = [os.path.basename(m) for m in msks_test_path]\n",
    "msks_test_num = [int(m.split(\"_\")[1].split(\".\")[0]) for m in msks_test_basename]\n",
    "# mutiply masks number by 100 to get the same range as the frames\n",
    "msks_test_labels = [(m*100 + 20250) for m in msks_test_num]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For Sampled masks only\n",
    "msks_train_path = glob(os.path.join(TRAIN_SAMPLED_DIR, \"*.png\"))\n",
    "msks_train_labels = [m*100 for m in indexes]\n",
    "msks_test_path = glob(os.path.join(TEST_DIR, \"*.png\"))\n",
    "msks_test_basename = [os.path.basename(m) for m in msks_test_path]\n",
    "msks_test_num = [int(m.split(\"_\")[1].split(\".\")[0]) for m in msks_test_basename]\n",
    "# multiply masks number by 100 to get the same range as the frames\n",
    "msks_test_labels = [(m*100 + 20250) for m in msks_test_num]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data Loading and formatting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train_imgs.shape: (13, 262144), train_labels.shape: (13, 1)\n",
      "test_imgs.shape: (23, 262144), test_labels.shape: (23, 1)\n"
     ]
    }
   ],
   "source": [
    "# train_imgs = load_images_from_folder(TRAIN_DIR, target_size=ARGS[\"IMG_SIZE\"])\n",
    "# For Sampled masks only\n",
    "train_imgs = load_images_from_folder(TRAIN_SAMPLED_DIR, target_size=ARGS[\"IMG_SIZE\"])\n",
    "test_imgs = load_images_from_folder(TEST_DIR, target_size=ARGS[\"IMG_SIZE\"])\n",
    "train_imgs = train_imgs.reshape((-1, ARGS[\"IMG_SIZE\"][0]*ARGS[\"IMG_SIZE\"][1]))\n",
    "test_imgs = test_imgs.reshape((-1, ARGS[\"IMG_SIZE\"][0]*ARGS[\"IMG_SIZE\"][1]))\n",
    "input_shape = (ARGS[\"IMG_SIZE\"][0]*ARGS[\"IMG_SIZE\"][1],)\n",
    "\n",
    "max_val = np.max(msks_train_labels)\n",
    "train_labels = (msks_train_labels/max_val).astype(np.float32)\n",
    "train_labels = np.expand_dims(train_labels, axis=-1)\n",
    "\n",
    "test_labels = (msks_test_labels/max_val).astype(np.float32)\n",
    "test_labels = np.expand_dims(test_labels, axis=-1)\n",
    "\n",
    "print(f\"train_imgs.shape: {train_imgs.shape}, train_labels.shape: {train_labels.shape}\")\n",
    "print(f\"test_imgs.shape: {test_imgs.shape}, test_labels.shape: {test_labels.shape}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### C-VAE Model"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### sampler layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sampler(args):\n",
    "    \"\"\"Reparameterization trick by sampling fr an isotropic unit Gaussian.\n",
    "    # Arguments:\n",
    "        args (tensor): mean and log of variance of Q(z|X)\n",
    "    # Returns:\n",
    "        z (tensor): sampled latent vector\n",
    "    \"\"\"\n",
    "    z_mean, z_log_var = args\n",
    "    batch = K.shape(z_mean)[0]\n",
    "    dim = K.int_shape(z_mean)[1]\n",
    "    # by default, random_normal has mean=0 and std=1.0\n",
    "    epsilon = K.random_normal(shape=(batch, dim))\n",
    "    return z_mean + K.exp(0.5 * z_log_var) * epsilon"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### conv_block"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def conv_block(input, filters, f_init=\"he_normal\"):\n",
    "    \"\"\"\n",
    "    Apply two convolutional layers with ReLU activation function.\n",
    "\n",
    "    Args:\n",
    "        input (tensor): Input tensor to the block.\n",
    "        filters (int): Number of filters in the convolutional layers.\n",
    "\n",
    "    Returns:\n",
    "        tensor: Output tensor of the block with ReLU activation.\n",
    "    \"\"\"\n",
    "    x = SeparableConv2D(filters, \n",
    "                        kernel_size = (4,4), \n",
    "                        depthwise_initializer = f_init,\n",
    "                        pointwise_initializer = f_init,\n",
    "                        padding=\"same\")(input)\n",
    "    x = Activation(tf.nn.leaky_relu)(x)\n",
    "\n",
    "    x = SeparableConv2D(filters, \n",
    "                        kernel_size = (4,4), \n",
    "                        depthwise_initializer = f_init,\n",
    "                        pointwise_initializer = f_init,\n",
    "                        padding=\"same\")(x)\n",
    "    ativ = Activation(tf.nn.leaky_relu)(x)\n",
    "\n",
    "    m_pool = MaxPooling2D(pool_size=(2, 2),\n",
    "                          strides=2,\n",
    "                          data_format=\"channels_last\",\n",
    "                          padding='same')(ativ)\n",
    "    \n",
    "    return m_pool"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### deconv_block"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def deconv_block(input, filters, f_init=\"he_normal\"):\n",
    "   \"\"\"\n",
    "   Apply two convolutional layers with ReLU activation function.\n",
    "\n",
    "   Args:\n",
    "         input (tensor): Input tensor to the block.\n",
    "         filters (int): Number of filters in the convolutional layers.\n",
    "        \n",
    "   Returns:\n",
    "         tensor: Output tensor of the block with ReLU activation.\n",
    "   \"\"\"   \n",
    "   x = Conv2DTranspose(filters, \n",
    "                     kernel_size = (4,4), \n",
    "                     strides=2,\n",
    "                     kernel_initializer = f_init,\n",
    "                     data_format = \"channels_last\", \n",
    "                     padding=\"same\")(input)\n",
    "\n",
    "   x = SeparableConv2D(filters, \n",
    "                     kernel_size = (4,4), \n",
    "                     depthwise_initializer = f_init,\n",
    "                     pointwise_initializer = f_init,\n",
    "                     padding=\"same\")(x)\n",
    "   x = Activation(tf.nn.leaky_relu)(x)\n",
    "\n",
    "   x = SeparableConv2D(filters, \n",
    "                     kernel_size = (4,4), \n",
    "                     depthwise_initializer = f_init,\n",
    "                     pointwise_initializer = f_init,\n",
    "                     padding=\"same\")(x)\n",
    "   activation = Activation(tf.nn.leaky_relu)(x)\n",
    "   \n",
    "   return activation"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Sampling Layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Sampling(tf.keras.layers.Layer):\n",
    "    \"\"\"Uses (z_mean, z_log_var) to sample z, the vector encoding a digit.\"\"\"\n",
    "    def __init__(self, z_mean, z_log_var):\n",
    "        super(Sampling, self).__init__()\n",
    "        self.z_mean = z_mean\n",
    "        self.z_log_var = z_log_var\n",
    "\n",
    "\n",
    "    def call(self, inputs):\n",
    "        z_mean, z_log_var = inputs\n",
    "        batch = tf.shape(z_mean)[0]\n",
    "        dim = tf.shape(z_mean)[1]\n",
    "        epsilon = tf.keras.backend.random_normal(shape=(batch, dim))\n",
    "        return z_mean + tf.exp(0.5 * z_log_var) * epsilon\n",
    "    \n",
    "    def get_config(self):\n",
    "        config = super().get_config().copy()\n",
    "        config.update({\n",
    "            'z_mean': self.z_mean,\n",
    "            'z_log_var': self.z_log_var,\n",
    "        })\n",
    "        return config\n",
    "    \n",
    "    @classmethod\n",
    "    def from_config(cls, config):\n",
    "        return cls(**config)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model construction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"cvae\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                   Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      " input_8 (InputLayer)           [(None, 262144)]     0           []                               \n",
      "                                                                                                  \n",
      " encoder (Functional)           [(None, 64),         33572816    ['input_8[0][0]']                \n",
      "                                 (None, 64),                                                      \n",
      "                                 (None, 64)]                                                      \n",
      "                                                                                                  \n",
      " label (InputLayer)             [(None, 1)]          0           []                               \n",
      "                                                                                                  \n",
      " decoder (Functional)           (None, 262144)       17497793    ['encoder[0][2]',                \n",
      "                                                                  'label[0][0]']                  \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 51,070,609\n",
      "Trainable params: 51,070,609\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "INPUT_SHAPE = (ARGS[\"IMG_SIZE\"][0]*ARGS[\"IMG_SIZE\"][1],)\n",
    "\n",
    "#--------\n",
    "# Encoder\n",
    "#--------\n",
    "\n",
    "encoder_inputs = Input(shape=(INPUT_SHAPE))\n",
    "# Reshape input to 2D image\n",
    "x = Reshape((ARGS[\"IMG_SIZE\"][0], ARGS[\"IMG_SIZE\"][1], 1))(encoder_inputs)\n",
    "x = conv_block(x, ARGS[\"REF_FILTERS\"]*2, ARGS[\"F_INIT\"])\n",
    "x = conv_block(x, ARGS[\"REF_FILTERS\"]*1, ARGS[\"F_INIT\"])\n",
    "x = Flatten()(x)\n",
    "x = Dense(64, activation=\"leaky_relu\")(x)\n",
    "\n",
    "# VAE specific layers for mean and log variance\n",
    "z_mean = Dense(ARGS[\"LATENT_DIM\"], name=\"z_mean\")(x)\n",
    "z_log_var = Dense(ARGS[\"LATENT_DIM\"], name=\"z_log_var\")(x)\n",
    "# Sampling layer to sample z from the latent space\n",
    "# z = Sampling()([z_mean, z_log_var])\n",
    "z = Lambda(sampler, output_shape=(ARGS[\"LATENT_DIM\"],), name='z')([z_mean, z_log_var])\n",
    "# Instantiate encoder model\n",
    "encoder = Model(encoder_inputs, [z_mean, z_log_var, z], name=\"encoder\")\n",
    "\n",
    "#--------\n",
    "# Decoder\n",
    "#--------\n",
    "\n",
    "latent_inputs = Input(shape=(ARGS[\"LATENT_DIM\"],), name='z_sampling')\n",
    "label_inputs = Input(shape=(len(train_labels[0]),), name='label')\n",
    "decoder_inputs = Concatenate()([latent_inputs, label_inputs])\n",
    "x = Dense(64*64*64, activation=\"leaky_relu\")(decoder_inputs)\n",
    "x = Reshape((128,128,16))(x)\n",
    "x = deconv_block(x, ARGS[\"REF_FILTERS\"]*2, ARGS[\"F_INIT\"])\n",
    "x = deconv_block(x, ARGS[\"REF_FILTERS\"]*4, ARGS[\"F_INIT\"])\n",
    "x = Conv2DTranspose(1, 3, activation=\"sigmoid\", padding=\"same\")(x)\n",
    "decoder_output = Reshape(INPUT_SHAPE)(x)\n",
    "decoder = Model([latent_inputs, label_inputs], decoder_output, name=\"decoder\")\n",
    "\n",
    "#-----------------\n",
    "# Conditional VAE\n",
    "#-----------------\n",
    "\n",
    "outputs = decoder([encoder(encoder_inputs)[2], label_inputs])\n",
    "vae = Model([encoder_inputs, label_inputs], outputs, name='cvae')\n",
    "vae.summary()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### VAE Losses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mse_kl_loss(y_true, y_pred):\n",
    "    # E[log P(X|z)]\n",
    "    squared_difference = tf.square(y_true - y_pred)\n",
    "    recontruction = tf.reduce_mean(squared_difference, axis=-1)\n",
    "    # D_KL(Q(z|X) || P(z|X)); calculate in closed from as both dist. are Gaussian\n",
    "    kl_divergence = 0.5 * tf.reduce_sum(tf.exp(z_log_var) + tf.square(z_mean) - 1. - z_log_var, axis=-1)\n",
    "    return recontruction + kl_divergence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ce_kl_loss(y_true, y_pred): \n",
    "    # CE_loss = -∑(y*log(y_hat) + (1-y)*log(1-y_hat))\n",
    "    y_pred = tf.clip_by_value(y_pred, 1e-7, 1.0 - 1e-7)\n",
    "    # Calculate the loss for positive and negative examples\n",
    "    loss = -(y_true * tf.math.log(y_pred) + (1 - y_true) * tf.math.log(1 - y_pred))\n",
    "    recontruction = tf.reduce_mean(loss)\n",
    "    # D_KL(Q(z|X) || P(z|X)); calculate in closed from as both dist. are Gaussian\n",
    "    kl_divergence = 0.5 * tf.reduce_sum(tf.exp(z_log_var) + tf.square(z_mean) - 1. - z_log_var, axis=-1)\n",
    "    return recontruction + kl_divergence"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Callbacks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "reduce_lr = tf.keras.callbacks.ReduceLROnPlateau(\n",
    "    monitor=\"loss\",\n",
    "    factor=0.3,\n",
    "    mode=\"min\",\n",
    "    patience=30,\n",
    "    verbose=1,\n",
    "    min_lr=1e-8\n",
    "    )\n",
    "\n",
    "date = datetime.datetime.now().strftime(\"%d_%m\")\n",
    "checkpoint_filepath = os.path.join(CHCKPNT_MODELS, f'cvae_sampled_{date}.h5')\n",
    "\n",
    "checkpoint = tf.keras.callbacks.ModelCheckpoint(\n",
    "    filepath = checkpoint_filepath,\n",
    "    save_best_only=True,\n",
    "    mode=\"auto\",\n",
    "    verbose=1,\n",
    "    monitor=\"loss\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "vae.compile(optimizer=ARGS[\"OPTIMIZER\"], loss=mse_kl_loss)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training the CVAE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tf.keras.backend.set_value(vae.optimizer.lr, float(3e-4))\n",
    "history = vae.fit([train_imgs, train_labels],\n",
    "                   train_imgs,\n",
    "                   epochs=20000,\n",
    "                #    batch_size=ARGS[\"BATCH_SZ\"],\n",
    "                   batch_size=1,\n",
    "                   callbacks=[reduce_lr, checkpoint],\n",
    "                   validation_data=([test_imgs, test_labels], test_imgs)\n",
    "                   )\n",
    "# plot the learning curves\n",
    "plot_learning_curves(history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_learning_curves(history)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": "### Load the model with lowest loss"
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the model with lowest loss\n",
    "from tensorflow import keras\n",
    "vae = keras.models.load_model(checkpoint_filepath)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Show Grid of Reconstrutions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# Define the number of rows and columns in the grid\n",
    "num_rows = 2\n",
    "num_cols = 8\n",
    "\n",
    "# Generate random samples for the latent space\n",
    "z_samples = np.random.rand(1, ARGS[\"LATENT_DIM\"])\n",
    "\n",
    "# Define the frames to display\n",
    "frames_num = [0, 2800, 5500, 8500, 11000, 14000, 17000, 22500]\n",
    "# divide each element of frames_num by 100\n",
    "idx = list(np.divide(frames_num, 100).astype(int))\n",
    "imgs_paths = glob(os.path.join(TRAIN_DIR, \"*.png\"))\n",
    "images = list()\n",
    "# load images with index = idx\n",
    "for i in range(len(idx)):\n",
    "    img = load_img(imgs_paths[idx[i]], color_mode=\"grayscale\")\n",
    "    img_array = img_to_array(img) / 255.0\n",
    "    images.append(img_array)\n",
    "# convert list to float32 array\n",
    "imgs_array = np.array(images, dtype=np.float32)\n",
    "\n",
    "# Initialize the plot\n",
    "fig, axes = plt.subplots(num_rows, num_cols, figsize=(10, 4))\n",
    "# figure size\n",
    "\n",
    "fig.set_figwidth(25)\n",
    "fig.set_figheight(7)\n",
    "\n",
    "# Loop through each row and column in the grid\n",
    "for j in range(num_cols):\n",
    "\n",
    "    # Get the frame to display\n",
    "    frame_num = frames_num[j]\n",
    "    \n",
    "    # Get the reconstructed image\n",
    "    z_sample = np.random.rand(1, ARGS[\"LATENT_DIM\"])\n",
    "    # array of shape (1, ARGS[\"LATENT_DIM\"]) with 0.5 values\n",
    "    #z_sample = np.full((1, ARGS[\"LATENT_DIM\"]), 0)\n",
    "    reconst = decoder.predict([z_sample, frame_to_label(frame_num)])\n",
    "    reconst = reconst.reshape(512, 512)\n",
    "    \n",
    "    # Display the ground truth mask and the reconstructed mask\n",
    "    axes[0][j].imshow(imgs_array[j], cmap='Greys_r')\n",
    "    axes[0][j].set_title(f\"U-NET Seg. {frame_num}\")\n",
    "    axes[1][j].imshow(reconst, cmap='viridis')\n",
    "    axes[1][j].set_title(f\"C-VAE Reconstrution {frame_num}\")\n",
    "    axes[1][j].set_axis_off()\n",
    "    axes[0][j].set_axis_off()\n",
    "    \n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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un9u3b6+ffvqJL9ABwCTk8fPKeh6PiYmRdH6mtquvvtq1fv369TIMw23MXJg8fvToUZ0+fVqPP/64XnnlFcXExOiHH37QhAkTdOTIEX322Weutjt27NAbb7yhb7/9VkFBQTp9+nSxzwsA4Hnk9vPMyu1Hjx5VSkqKbrvtNj377LOaPHmyNm/erLFjx2rXrl1au3ZtnjO5Hj16VM8884zatWvnmrG1sJxOp+6//37deuutOWaBBQCUHeTr87xpLB4TE6OVK1fq77//Vu3atV3r161bJ6lo33VT3wYzMLM6oJx3iTmdzlzbjRw5UuHh4Rd9tJYkrVy5Ups3b3ZbFi1a5OHIC6dv37451mVlZem1115TTEyMfH19Zbfb5evrq3379mnPnj052v978NqkSRNJyvElcZMmTRQbG+t6VMp3330ni8Wiu+++2+21joqKUsuWLbVq1SoPnaX08ccf64knntBjjz2mlStXaunSpbruuut00003ub7czpbfo1fy2paVlaU5c+aoadOmeT7ePK99C/r4dABAwZHHvSePZ//fpaWlaenSpbrtttt03XXXacGCBWrTpo1efvnlQvdptVr16KOP6ocfftArr7yiU6dOaf/+/br77ruVkpLiagMAKH3k8IqZwzMzM9W/f3/t2LFDH374odasWaM5c+bo6NGj6t69u+Lj43P0P2XKFG3atEmLFy9Wjx491L9/f82bNy9HuypVqmjz5s1at26dPvzwQ8XGxurqq6/W8ePHPXKeAID/Qx73njzesmVLde7cWW+++aYWLlyouLg4rV+/XoMHD5bNZnMbMxcmjzudTiUmJmr69Ol69NFHdfXVV2vcuHEaOnSoPv/8c1fhQ1ZWlu6//371799fPXr08Mg5AQAKj9xe9nO70+lUWlqannvuOT377LPq2rWrnn76aY0fP14///yzfvjhh1z3i42N1fXXXy/DMPTFF18U+Xr4pEmTtG/fPk2ePLlI+wMAio98XfbzdUEVZiz+0EMPycfHR3fddZd2796ts2fPatq0afriiy8kFe+7burbUJqYWR1eyW4//6ud12NJsrKy3Gb4aNCggQ4fPuz6eezYsXrxxRdz7FepUiWNHj1aw4YN008//ZRvDC1btlSVKlWKEH3JuXB2sWzDhw/XtGnTNGrUKHXp0kWVK1eW1WrVgw8+qNTU1Bzt/31Hla+vb77r09LSFBwcrJMnT8owDEVGRuYaW/369Yt0Tv927tw514zqb731lmt9r1691LVrVw0ePFgHDx6UJEVERCgtLU0pKSkKDAx06yc2NlZt27bN9RhLly7ViRMncv1QFxERISn3u9ZiY2NzvSMNAOCOPJ67ipDHs/No48aNVadOHdd6i8WiHj16aPz48Tp16pSqVatWqH5feOEFJSUlady4cXrhhRcknb8QMWjQIH300UeqUaOGR+IHgIqOHJ47crh7Dp85c6aWLVumzZs3q127dpLOz0pz1VVXqUGDBpo8ebLGjh3r1n/Dhg1d/77xxhvVq1cvPfroo+rfv7/bhXi73e7qs2PHjurZs6fq1aunCRMm6J133vHIuQKAtyKP564i5HFJWrhwoe677z7dfvvtrliefPJJrVy5UnFxca52hcnjEREROnHiRI4C9F69emny5Mnatm2bLrnkEk2ePFkHDhzQggULXMdKSEhwvRZxcXEKCQmRzWbz2PkCQEVAbs9dec7tERER2rdvX665ddiwYdq2bZu6devmtu3cuXPq3r27jh49qh9//LHInx/+/vtvvfDCC5owYYJ8fX1dOTu7UDIuLk5+fn4KCAgoUv8AUFGRr3NXnvN1YRR0LN6kSRN98803evjhh9WsWTNJUq1atTRx4kQNHTq0SN91U98GM1CsDq+UnTCOHj2aI3kYhqHjx4+7LqRK0rfffqv09HTXz9HR0Xn2PWTIEL3zzjsaNWqUhgwZ4uHIS1Zudz19+umnuvfee/Xaa6+5rT9z5ozCwsI8duwqVarIYrFo7dq18vPzy7E9t3VF8ccffyg1NVWXXXZZjm3t2rXT6tWrlZSUpODgYDVv3lyStHPnTrVv397V7sSJEzpz5owrwf/bzJkz5evrq3vuuSfHtux9du7cmePxZzt37syzTwDA/yGP564i5PEGDRrkuIEsm2EYkop2Z7jdbtekSZP08ssv6+DBg6pSpYqqV6+uHj16qF69eqpZs2ax4gYAnEcOzx053D2H79ixQzabTW3atHFrV79+fUVERGjXrl0XPd7ll1+u5cuX6/Tp03l+aSBJNWvWVHR0tP7888+CngoAVFjk8dxVhDwuSdWqVdPSpUt16tQpnThxQnXq1FFAQICmT5+ufv36udoVJo+3aNFCJ06cyHGsf3822LVrl+Lj491uTss2ZswYjRkzRtu3b1erVq08caoAUGGQ23NXnnN7ixYttHHjxhzr87p2fu7cOXXr1k0HDx7UDz/8oBYtWhQ59gMHDig1NVVPPPGEnnjiiRzbK1eurCeeeIJZ1wGgkMjXuSvP+bowCjoWl87fnHb48GHt379fWVlZatSokRYsWCBJ6ty5c6GPTX0bzECxOrzSNddcI4vFoi+++CLHRdPly5crISHB7a7i7KLlgvD19dW4ceN01113lbk7y4rCYrHkSKT//e9/dfToUV1yySUeO07v3r01YcIEHT161HVHWEnI/iC2ceNGDRw40LXeMAxt3LhRlStXVlBQkCSpZ8+e8vf31+zZs92K1WfPni2LxaKbb745R/8nTpzQ0qVLdeutt7ruMrtQjRo1dPnll+vTTz/ViBEjXLO9bNy4UX/88YeGDRvmwbMFAO9EHi84b8vjdrtdN910k7788ksdOnRIdevWlXQ+jy9fvlwNGjQo1v/bhTerbdu2TT/88IMmTpzoidABACKHF0ZFzuHR0dFyOBzavHmz21j8zz//1NmzZy96E5lhGFq9erXCwsJyHZdfaP/+/frnn3904403Fu8EAaACII8XnLfl8QtVq1bN9TSzd999V8nJyXrsscdc2wuTx/v27av//e9/WrZsme68807X+qVLl8pqtbomnHnmmWd03333ucVx4sQJDRgwQIMHD1b//v09+roCQEVBbi+48pLb+/btqxkzZmjZsmVq3bq1a/3SpUslSVdccYVrXXah+oEDB7RixQq39kXRqlWrXGfmHTZsmOLj4/Xxxx8zKQwAFAH5uuDKS74uiouNxbNZLBbXjd4ZGRl655131KpVqyIVq1PfBjNQrA6v1KBBAz322GN68803FRcXp+uvv14BAQHavHmzJkyYoHbt2rldHC2sAQMG6K233tKyZcvybLN161aFhobmWB8TE6NKlSoV6nizZ8/WoEGD9PHHH+e4aFtcvXv31uzZs9W4cWO1aNFCW7du1ZtvvunxwWTHjh310EMPadCgQdqyZYs6d+6soKAgHT9+XOvWrVPz5s0veiffsmXLlJycrMTEREnS77//ri+//FKSdP311yswMFC1a9fWrbfeqhkzZsjPz0/XX3+90tPTNWfOHP3888965ZVXXHfghYeHa/To0RozZozCw8N13XXXafPmzXrxxRf14IMPKiYmJkcMc+bMUVZWlh588ME843z99dfVvXt33XbbbXrkkUd06tQpPfPMM2rWrJkGDRpU1JcQACoM8njBeVsel6RXXnlFy5YtU8+ePfXiiy+qUqVK+uijj/Trr7+67g7PtmXLFh06dEjS+UeEG4bh6vOyyy5TnTp1JEmrVq3S5s2b1aJFCxmGoU2bNun1119Xz549cwz0Dx8+rM2bN0uS/vrrL0ly9Vm3bl232QsAAO7I4QVXkXP4oEGD9Pbbb6tv374aPXq0Lr30Uh04cECvvfaagoKCNHjwYFfbm266SS1btlSrVq0UERGhY8eOafbs2Vq9erWmTZvmekzub7/9pieffFL9+vVT/fr1ZbVatXPnTr399tuKiIjQiBEjPPq6AoA3Io8XnDfm8Q8//FDS+d+DuLg4LVu2TDNnztRrr73mVjBRmDw+aNAgffDBB3rkkUd05swZxcTEaOXKlZo2bZoeeeQR15i9cePGaty4sVvc2WP9Bg0aqGvXrkV/EQGgAiO3F1x5ye3XXXed+vTpo5dffllOp1NXXHGFtmzZopdeekm9e/fWVVddJUlKTU1Vjx49tH37dk2ePFlZWVluM7JXrVpVDRo0cP1ckOvsYWFhuebksLAwZWVlka8BoIjI1wVXXvK15PmxuCQNHTpUXbt2VUREhA4cOKB3331X//zzj1avXp3j+NnHOnDggKTzuT44OFiS3GZsp74NpY1idXitd955RzExMZo5c6Y+/fRTZWVlqU6dOnr00Uc1evRo+fr6Frlvi8Wi119/Xdddd12ebXr27Jnr+hUrVrjd9VYQSUlJkqTq1asXar+CeOedd+Tj46Px48crKSlJbdq00ddff63Ro0d7/FgffPCBrrjiCn3wwQeaPn26nE6noqOj1bFjR11++eUX3X/IkCE6fPiw6+eFCxdq4cKFkqSDBw+6Zm777LPPNHXqVM2dO1ezZs2Sj4+PGjVqpE8//TTHh7jnn39eISEhmjZtmt566y1FRUXpmWee0fPPP59rDLNmzVLdunXz/T/s2rWrli5dqhdeeEF9+vRRYGCgevfurTfffNOjj4MBAG9GHi8Yb8zjDRo00Nq1a/XMM8/ooYceUmZmplq1aqUlS5aod+/ebn1OnTpVc+bMcVt32223SZLbRRBfX1999dVXGjdunNLT09WwYUO9/PLLevzxx113iWf76aefcgy+s/scOHCgZs+efdFzBYCKjBxeMBU5h9eqVUubN2/Wyy+/rNdff13Hjx9XZGSkrrzySr3wwgu69NJLXW07duyoL7/8UlOnTlVCQoLCwsLUrl07fffdd7rhhhtc7SIjIxUdHa2JEyfq+PHjysrKUs2aNdW7d28999xzqlWrVnFfRgCoEMjjBeONedwwDE2ePFmHDx+W1WpV69at9c033+i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      "text/plain": [
       "<Figure size 3000x1200 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "segs_base = glob(os.path.join(IMG_DIR, \"segs_base\", \"*.png\"))\n",
    "\n",
    "# random sample array of frames\n",
    "frames_num = np.random.randint(0, 22500, 6)\n",
    "# Initialize the plot\n",
    "fig, axes = plt.subplots(2,len(frames_num), figsize=(10, 3))\n",
    "# figure size\n",
    "fig.set_figwidth(5*len(frames_num))\n",
    "fig.set_figheight(12)\n",
    "\n",
    "for j in range(len(frames_num)):\n",
    "\n",
    "    # Get the frame to display\n",
    "    frame_num = frames_num[j]\n",
    "    \n",
    "    # Get the reconstructed image\n",
    "    z_sample = np.random.rand(1, ARGS[\"LATENT_DIM\"])\n",
    "    reconst = decoder.predict([z_sample, frame_to_label(frame_num)])\n",
    "    reconst = reconst.reshape(512, 512)\n",
    "    # binarize the reconstructed image\n",
    "    ret, thresh_img = cv2.threshold(reconst,0.5, 255,  cv2.THRESH_BINARY)\n",
    "                                \n",
    "    axes[0][j].imshow(load_img(segs_base[frame_num], color_mode=\"grayscale\"), cmap='Greys_r')\n",
    "    axes[0][j].set_title(f\"U-NET, frame {frame_num}\")\n",
    "\n",
    "    # Display the ground truth mask and the reconstructed mask\n",
    "    axes[1][j].imshow(thresh_img, cmap='viridis')\n",
    "    axes[1][j].set_title(f\"CVAE Reconstrution, frame {frame_num}\")\n",
    "\n",
    "# Display the plot\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Output files (various folders for various experiments)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "frames_num = np.arange(0, 22500, 1)\n",
    "# images directories\n",
    "CVAE_DIR = os.path.normpath(\"E:\\BurnedAreaUAV_files\\Interpolation\\cvae_interpol\")\n",
    "CVAE_PNG_DIR = os.path.join(CVAE_DIR, \"PNGs\")\n",
    "CVAE_256_PNG_DIR = os.path.join(CVAE_DIR, \"PNGs_256\")\n",
    "CVAE_PNG_DIR_CE = os.path.join(CVAE_DIR, \"PNGs_celoss\")\n",
    "CVAE_PNG_DIR_SAMPLED = os.path.join(CVAE_DIR, \"PNGs_sampled\")\n",
    "os.makedirs(CVAE_PNG_DIR_SAMPLED, exist_ok=True)\n",
    "\n",
    "# WKT files\n",
    "CVAE_WKT_FILE = os.path.join(CVAE_DIR, \"cvae_interpol.wkt\")\n",
    "CVAE_WKT_FILE_256 = os.path.join(CVAE_DIR, \"cvae_interpol_256.wkt\")\n",
    "CVAE_WKT_FILE_CE = os.path.join(CVAE_DIR, \"cvae_interpol_ce.wkt\")\n",
    "CVAE_WKT_FILE_SAMPLED = os.path.join(CVAE_DIR, \"cvae_interpol_sampled.wkt\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import cm\n",
    "# create binary, black or white colormap\n",
    "cmap_B_W = cm.get_cmap('gray', 2)\n",
    "\n",
    "# generate all the frames and save the images as png\n",
    "for i in range(len(frames_num)):\n",
    "    frame_num = frames_num[i]\n",
    "    # z_sample = np.random.rand(1, ARGS[\"LATENT_DIM\"])\n",
    "    z_sample = np.full((1, ARGS[\"LATENT_DIM\"]), 0.5)\n",
    "    reconst = decoder.predict([z_sample, frame_to_label(frame_num)])\n",
    "    reconst = reconst.reshape(ARGS[\"IMG_SIZE\"])\n",
    "    # binarize the reconstructed image\n",
    "    ret, thresh_img = cv2.threshold(reconst, 0.5, 255, cv2.THRESH_BINARY)\n",
    "    # image resize cv2.INTER_CUBIC\n",
    "    reconst = cv2.resize(thresh_img, (ORIG_DIMS[1], ORIG_DIMS[0]), interpolation=cv2.INTER_CUBIC)\n",
    "    # save the image as png grayscale\n",
    "    imsave(os.path.join(CVAE_PNG_DIR_SAMPLED, f\"frame_{frame_num:06d}.png\"), reconst, cmap = cmap_B_W)\n",
    "    # print progress\n",
    "    print(f\"frames {i+1}/{frames_num[-1]+1} saved\", end=\"\\r\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### frames to video conversion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2. Creating video writer...2501\n",
      "3. Writing frames to file 22501/22501\r"
     ]
    }
   ],
   "source": [
    "# list of ordered frames\n",
    "frames = sorted(glob(os.path.join(CVAE_PNG_DIR_SAMPLED, \"*.png\")))\n",
    "\n",
    "# create the video\n",
    "frames2video(frames, \"cvae_interpol_512_sampled\", fps_ = 25*10, \n",
    "             titulo = \"CVAE Interpolation - Sampled (10x speed)\", \n",
    "             frame_num_text = True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### WKT output file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "polygon_list = msks_paths_to_polygon_list(frames, out_dim = (1280, 720))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert shapely list polygons to WKT format file\n",
    "with open(CVAE_WKT_FILE_SAMPLED, 'w') as f:\n",
    "    for polygon in polygon_list:\n",
    "        f.write(polygon.wkt + '\\n')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "tf2_10",
   "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": "3.10.0"
  },
  "orig_nbformat": 4
 },
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}