buildHistograms.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gdal\n",
    "import numpy as np\n",
    "from os import listdir, mkdir\n",
    "from os.path import isfile, join, getsize, expanduser, basename, isdir\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import gdal\n",
    "gdal.SetCacheMax(2**35)\n",
    "\n",
    "CROP_SENTINEL = 12 # if the pixel has value 12 means there is 60% of cultivated territory \n",
    "                   # which is the type of territoy we are interested in\n",
    "NUM_TEMP_BANDS = 2\n",
    "NUM_IMGS_PER_YEAR = 45\n",
    "HIST_BINS_LIST = [np.linspace(-25,60,128),\n",
    "                  np.linspace(-25,60,128)] #suitable temp range for bands 'Day' and 'Night' for building our histograms\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let's read our tif files and build our histograms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def return_tif_filenames(path):\n",
    "    return [f for f in listdir(path) if (isfile(join(path, f)) and not f.startswith('.') and f.endswith('.tif'))]\n",
    "\n",
    "\n",
    "def collect_tif_path_dict(temp_dir, mask_dir, verbose=True):\n",
    "    \"\"\"\n",
    "    Returns a dictionary of form {name of place : (temp path, mask path)}\n",
    "    \"\"\"\n",
    "    all_temp_files = return_tif_filenames(temp_dir)\n",
    "    temp_places = get_places(all_temp_files)\n",
    "    all_mask_files = return_tif_filenames(mask_dir)\n",
    "    mask_places = get_places(all_mask_files)\n",
    "    tif_dict = {}\n",
    "\n",
    "    for t_i, place in enumerate(temp_places):\n",
    "        if place not in mask_places:\n",
    "            if verbose:\n",
    "                print(place, ' missing mask file')\n",
    "            continue\n",
    "        m_i = mask_places.index(place)\n",
    "        tif_dict[place] = (all_temp_files[t_i], all_mask_files[m_i])\n",
    "    return tif_dict\n",
    "\n",
    "\n",
    "def read_tif(tif_path):\n",
    "    \"\"\"\n",
    "    Reads tif image into a tensor\n",
    "    \"\"\"\n",
    "    try:\n",
    "        gdal_dataset = gdal.Open(tif_path)\n",
    "    except:\n",
    "        print('Error opening', tif_path, 'with gdal')\n",
    "        return None\n",
    "\n",
    "    if gdal_dataset is None:\n",
    "        print('gdal returned Non from: ', tif_path)\n",
    "        return None\n",
    "\n",
    "    gdal_result = gdal_dataset.ReadAsArray().astype(np.uint16)\n",
    "\n",
    "    # If a tif file has only 1 band, then the band dimension will be removed.\n",
    "    if len(gdal_result.shape) == 2:\n",
    "        gdal_result = np.reshape(gdal_result, [1] + list(gdal_result.shape))\n",
    "\n",
    "    # gdal_result is a rank 3 tensor as follows (bands, height, width). Transpose.\n",
    "    return np.transpose(gdal_result, [1, 2, 0])\n",
    "\n",
    "\n",
    "def get_places(filenames):\n",
    "    \"\"\"\n",
    "    Gets places of tif imagery from filenames in list. Assumes names are of form\n",
    "    <country>_<img type>_<place name>.tif|_<date info>.tif\n",
    "    \"\"\"\n",
    "    places = []\n",
    "    for f in filenames:\n",
    "        place = f.split('_')[2]\n",
    "        if place.find('tif') != -1:\n",
    "            place = place[:-1]\n",
    "        places.append(place)\n",
    "    return places\n",
    "\n",
    "\n",
    "def mask_image(img, mask, num_bands):\n",
    "    \"\"\"\n",
    "     Masks away non-crop pixels in all 2D slices of 3D image tensor of shape X x Y x (bands/time)\n",
    "    \"\"\"\n",
    "    num_imgs = img.shape[2]/num_bands\n",
    "    assert num_imgs == int(num_imgs)\n",
    "    num_imgs = int(num_imgs)\n",
    "    remainder_imgs = num_imgs % NUM_IMGS_PER_YEAR\n",
    "\n",
    "    for t in range(num_imgs):\n",
    "        if t-remainder_imgs < 0:  # masking all temp images of 2010\n",
    "            mask_slice = mask[:, :, 0]\n",
    "        else:  # masking all remaining temp images\n",
    "            mask_year = int((t-remainder_imgs)/NUM_IMGS_PER_YEAR)\n",
    "            mask_slice = mask[:, :, mask_year+1]\n",
    "        for b in range(num_bands):\n",
    "            img[:, :, t*num_bands +\n",
    "                b] = np.multiply(img[:, :, t*num_bands + b], mask_slice)\n",
    "    return img\n",
    "\n",
    "def build_histograms(image, bin_seq_list):\n",
    "    \"\"\"\n",
    "    input is a 3D image in tensor form [H, W, time/num_bands + band]\n",
    "    Makes a 3D tensor of pixel histograms [normalized bin values, time, band]\n",
    "    \"\"\"\n",
    "    num_bands = len(bin_seq_list)\n",
    "    num_bins = len(bin_seq_list[0])-1\n",
    "    if image.shape[2] % num_bands != 0:\n",
    "        raise Exception('Number of bands does not match image depth.')\n",
    "    num_times = image.shape[2]/num_bands\n",
    "\n",
    "    num_times = int(num_times)\n",
    "    \n",
    "    hist = np.zeros([num_bins, num_times, num_bands])\n",
    "    for i in range(image.shape[2]):\n",
    "        band = i % num_bands\n",
    "        density, _ = np.histogram(\n",
    "            image[:, :, i], bin_seq_list[band], density=False)\n",
    "        total = density.sum()  # normalize over only values in bins\n",
    "        hist[:, int(i / num_bands), band] = density/float(total) if total > 0 else 0\n",
    "    return hist\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp_directory = 'temp/'\n",
    "mask_directory = 'mask/'\n",
    "target_folder_name = 'histograms_celsius'\n",
    "\n",
    "if not isdir(target_folder_name):\n",
    "    mkdir(target_folder_name)\n",
    "\n",
    "tif_dict = collect_tif_path_dict(temp_directory, mask_directory)\n",
    "\n",
    "count = 0\n",
    "num_tifs = len(tif_dict)\n",
    "\n",
    "for place, tif_path_tuple in tif_dict.items():\n",
    "    hist_save_path = join(target_folder_name, place + '_histogram')\n",
    "\n",
    "    if isfile(hist_save_path + '.npy'):\n",
    "        print(place, 'already processed. Continuing...')\n",
    "        count += 1\n",
    "        continue\n",
    "\n",
    "    temp_path, mask_path = tif_path_tuple\n",
    "    temp_tensor = read_tif(temp_directory + temp_path)\n",
    "    assert temp_tensor is not None\n",
    "    mask_tensor = read_tif(mask_directory + mask_path)\n",
    "    assert mask_tensor is not None\n",
    "\n",
    "    #print(\"shape of temp tensor:\", temp_tensor.shape)\n",
    "    #print(\"shape of mask tensor:\", mask_tensor.shape)\n",
    "\n",
    "    if temp_tensor.shape[:2] != mask_tensor.shape[:2]:\n",
    "        print(place, 'slice shapes do not match! temp, mask shapes:',\n",
    "              temp_tensor.shape[:2], mask_tensor.shape[:2])\n",
    "        count += 1\n",
    "        continue\n",
    "\n",
    "    mask_tensor[mask_tensor != CROP_SENTINEL] = 0\n",
    "    mask_tensor[mask_tensor == CROP_SENTINEL] = 1\n",
    "\n",
    "    num_temp_imgs = temp_tensor.shape[2]/NUM_TEMP_BANDS\n",
    "\n",
    "    temp_tensor = mask_image(temp_tensor, mask_tensor, NUM_TEMP_BANDS)\n",
    "    temp_tensor = (temp_tensor*0.02) - 273.15\n",
    "    temp_histograms = build_histograms(temp_tensor, HIST_BINS_LIST)\n",
    "    np.save(hist_save_path, temp_histograms)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check and analyze the histograms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(127, 295, 2)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hist = np.load('histograms_celsius/adair-ia_histogram.npy')\n",
    "\n",
    "hist.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "num_bands = hist.shape[2]\n",
    "print(hist.shape[2])\n",
    "f, axarr = plt.subplots(num_bands, sharex=True)\n",
    "for band in range(num_bands):\n",
    "    axarr[band].imshow(hist[:,:,band])\n",
    "plt.imshow(hist[:,:,1])\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Notice that, on the x axis there is the 295 images, on the y axis the bins of the histograms. The period seems to be around 45, which are the number of images in one year. We are just plotting the histograms in a different way that we are used"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(127, 295, 2)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "histograms = np.load('histograms_celsius/adair-ia_histogram.npy')\n",
    "histograms.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.tight_layout(*, pad=1.08, h_pad=None, w_pad=None, rect=None)>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.bar(np.linspace(-25,60,127), histograms[:,0,0])\n",
    "plt.ylabel('Day')\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.bar(np.linspace(-25,60,127), histograms[:,0,1])\n",
    "plt.ylabel('Night')\n",
    "plt.suptitle('Histogram of an image for day and night')\n",
    "plt.tight_layout\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "SUMMARY_STAT_NAMES = ['max', 'mean', 'min']\n",
    "\n",
    "\n",
    "def visualize_histogram(hist, title, save_folder, show=False):\n",
    "    \"\"\"\n",
    "    Outputs an image of a histogram's multiple bands for a sanity check\n",
    "    \"\"\"\n",
    "    num_bands = hist.shape[2]\n",
    "    f, axarr = plt.subplots(num_bands, sharex=True)\n",
    "    for band in range(num_bands):\n",
    "        axarr[band].imshow(hist[:,:,band])\n",
    "    plt.suptitle(title)\n",
    "    plt.savefig(join(save_folder, title +'.png'))\n",
    "    if show:\n",
    "        plt.show()\n",
    "\n",
    "def return_files(path):\n",
    "    return [f for f in listdir(path) if (isfile(join(path, f)) and not f.startswith('.'))]\n",
    "\n",
    "def analyze_histograms(directory, save_directory):\n",
    "    hist_files = return_files(directory)\n",
    "    count = 0\n",
    "    for idx, f in enumerate(hist_files):\n",
    "        histograms = np.load(join(directory, f))\n",
    "        shape = histograms.shape\n",
    "        if idx == 0:\n",
    "            num_bins = shape[0]\n",
    "            num_bands = shape[2]\n",
    "            histogram_sums = np.zeros((num_bins, num_bands))\n",
    "        histogram_sums += np.sum(histograms, axis=1) # sum along time axis for every bin and every band\n",
    "\n",
    "        place = basename(f)[:-4]\n",
    "        \n",
    "        visualize_histogram(histograms, place, save_directory)\n",
    "        plt.clf()\n",
    "        \n",
    "        count += 1\n",
    "        print (place, shape, str(count) + '/' + str(len(hist_files)))\n",
    "        \n",
    "    #plot summed histograms\n",
    "    plt.figure(figsize=(19,8))\n",
    "    for band in range(num_bands):\n",
    "        plt.subplot(num_bands, 1, band+1)\n",
    "        plt.bar(np.linspace(-25,60,127), histogram_sums[:, band])\n",
    "        plt.ylabel(str(band))\n",
    "        plt.yticks([])\n",
    "        plt.xticks(list(range(-25, 60, 2)))\n",
    "    plt.suptitle('Histogram density sums')\n",
    "    plt.savefig(join(save_directory, '111_density_sums.png'))\n",
    "    plt.tight_layout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "hist_dir = 'histograms_celsius'\n",
    "save_directory = 'hist_plot_celsius'\n",
    "if not isdir(save_directory): mkdir(save_directory)\n",
    "analyze_histograms(hist_dir, save_directory)\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8.13 ('RecSysFramework')",
   "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": "3.8.13"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "b261f49cf4d72dbfc58792abd0b60701ed878094bef7a4d84592b1a8931531b1"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}