{ "cells": [ { "cell_type": "markdown", "metadata": { "_cell_guid": "ebe40fa1-1189-132c-373a-6f612df71742" }, "source": [ "I started writing the notebook to check the effect of moving vehicles vs the time gap in acquisition of sensor bands. The plan was to:\n", "\n", " - choose samples where vehicles were along a road\n", " - compare vehicle positions against different bands\n", "\n", "But in the meantime I realized this is not very important. There are more challenging things to deal with. The images below speak for themselves.\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "_cell_guid": "6a9d48c4-1234-1643-8a39-ba73442725e1" }, "outputs": [], "source": [ "import os\n", "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import cv2\n", "import tifffile as tiff\n", "from shapely.wkt import loads\n", "from shapely import affinity\n", "\n", "# living dangerously\n", "import warnings\n", "warnings.filterwarnings(\"ignore\")\n", "\n", "SVEHICLE_TYPE = 10\n", "SVEHICLE_IMAGES = [ '6120_2_2',\n", " '6100_1_3', '6140_3_1','6110_3_1','6100_2_3',\n", " '6140_1_2','6120_2_0','6100_2_2','6110_1_2',\n", " '6070_2_3','6110_4_0','6090_2_0','6060_2_3'\n", "]\n", "\n", "PADDING = 10\n", "W = 3396\n", "H = 3348\n", "\n", "def P(image_id):\n", " filename = os.path.join('..', 'input', 'sixteen_band', '{}_P.tif'.format(image_id))\n", " img = tiff.imread(filename) \n", " return img\n", "\n", "def RGB(image_id):\n", " filename = os.path.join('..', 'input', 'three_band', '{}.tif'.format(image_id))\n", " img = tiff.imread(filename)\n", " img = np.rollaxis(img, 0, 3) \n", " return img\n", " \n", "def M(image_id):\n", " filename = os.path.join('..', 'input', 'sixteen_band', '{}_M.tif'.format(image_id))\n", " img = tiff.imread(filename) \n", " img = np.rollaxis(img, 0, 3)\n", " return img\n", "\n", "def stretch2(band, lower_percent=2, higher_percent=98):\n", " a = 0 #np.min(band)\n", " b = 255 #np.max(band)\n", " c = np.percentile(band, lower_percent)\n", " d = np.percentile(band, higher_percent) \n", " out = a + (band - c) * (b - a) / (d - c) \n", " out[outb] = b\n", " return out\n", "\n", "def adjust_contrast(x): \n", " for i in range(3):\n", " x[:,:,i] = stretch2(x[:,:,i])\n", " return x.astype(np.uint8)\n", " \n", "def truth_polys(image_id, class_id):\n", " x = pd.read_csv('../input/train_wkt_v4.csv')\n", " rows = x.loc[(x.ImageId==image_id) & (x.ClassType==class_id), 'MultipolygonWKT']\n", " mp = loads(rows.values[0])\n", " \n", " grid_sizes = pd.read_csv('../input/grid_sizes.csv')\n", " xmax, ymin = [(row[1], row[2]) for row in grid_sizes.values if row[0] == image_id][0] \n", " W_ = W * (W/(W+1))\n", " H_ = H * (H/(H+1))\n", " x_scaler = W_ / xmax\n", " y_scaler = H_ / ymin\n", " return affinity.scale(mp, xfact = x_scaler, yfact= y_scaler, origin=(0,0,0))\n", " " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "_cell_guid": "621393a1-5649-c925-99d1-b635f30a25ea" }, "outputs": [], "source": [ " \n", " patches = []\n", " titles = []\n", " for image_id in SVEHICLE_IMAGES:\n", " print(image_id)\n", " polys = truth_polys(image_id, SVEHICLE_TYPE) \n", " rgb = RGB(image_id)\n", " #rgb = adjust_contrast(rgb) \n", " #rgb = rgb.copy().astype(np.uint8) \n", " \n", " img_p = P(image_id)\n", " img_p = cv2.resize(img_p, tuple(reversed(rgb.shape[:2]))) \n", " img_p = cv2.cvtColor(img_p, cv2.COLOR_GRAY2RGB) \n", " img_p = adjust_contrast(img_p)\n", " \n", " img_m = M(image_id)\n", " img_m = cv2.resize(img_m, tuple(reversed(rgb.shape[:2]))) \n", " x = np.zeros_like(rgb)\n", " x[:,:,0] = img_m[:,:,4]\n", " x[:,:,1] = img_m[:,:,2]\n", " x[:,:,2] = img_m[:,:,1]\n", " x = adjust_contrast(x).copy()\n", " \n", " for poly_id, poly in enumerate(polys):\n", " x1,y1,x2,y2 = [int(x) for x in poly.bounds]\n", " cv2.rectangle(x, (x1,y1), (x2,y2), (255,0,0), 1)\n", " cv2.rectangle(img_p, (x1,y1), (x2,y2), (255,0,0), 1)\n", " patches.append(np.hstack([x[y1-PADDING:y2+PADDING, x1-PADDING:x2+PADDING,:], img_p[y1-PADDING:y2+PADDING, x1-PADDING:x2+PADDING,:]]))\n", " titles.append(\"image_id: {} -- poly_id: {}\".format(image_id, poly_id))\n", " \n", " " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "_cell_guid": "c250e212-14c1-b3e3-695c-c6c638df502b" }, "outputs": [], "source": [ " #random = np.arange(len(patches))\n", " #np.random.shuffle(random)\n", " c = 0\n", " for _ in range(15):\n", " rows = 10\n", " cols = 5\n", " f, ax = plt.subplots(rows, cols, figsize=(10,10))\n", " for i in range(rows):\n", " for j in range(cols): \n", " c += 1\n", " if c >= len(patches):\n", " break\n", " ax[i,j].axis('off')\n", " ax[i,j].grid(False)\n", " ax[i,j].imshow(patches[c]) \n", " #ax[i, j].set_title(titles[c])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "_cell_guid": "c585aa87-0281-9213-eaad-0bde52466220" }, "outputs": [], "source": [ "" ] } ], "metadata": { "_change_revision": 0, "_is_fork": false, "kernelspec": { "display_name": "Python 3", "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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }