{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Images of type float must be between -1 and 1.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-1-3aa8fce9f976>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     21\u001b[0m \u001b[1;31m# so we first tranpose to the RGB image and then convert to the gray scale\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     22\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 23\u001b[1;33m \u001b[0mframe_a\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mimg_as_uint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrgb2gray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mframe_a\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     24\u001b[0m \u001b[0mframe_b\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mimg_as_uint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrgb2gray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mframe_b\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     25\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mc_\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mframe_a\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mframe_b\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mcmap\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgray\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\openpiv\\lib\\site-packages\\skimage\\util\\dtype.py\u001b[0m in \u001b[0;36mimg_as_uint\u001b[1;34m(image, force_copy)\u001b[0m\n\u001b[0;32m    452\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    453\u001b[0m     \"\"\"\n\u001b[1;32m--> 454\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mconvert\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimage\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0muint16\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mforce_copy\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    455\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    456\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\envs\\openpiv\\lib\\site-packages\\skimage\\util\\dtype.py\u001b[0m in \u001b[0;36mconvert\u001b[1;34m(image, dtype, force_copy, uniform)\u001b[0m\n\u001b[0;32m    275\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    276\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimage\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m<\u001b[0m \u001b[1;33m-\u001b[0m\u001b[1;36m1.0\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimage\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1.0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 277\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Images of type float must be between -1 and 1.\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    278\u001b[0m         \u001b[1;31m# floating point -> integer\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    279\u001b[0m         \u001b[1;31m# use float type that can represent output integer type\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Images of type float must be between -1 and 1."
     ]
    }
   ],
   "source": [
    "# %load red_Cell.py\n",
    "from openpiv import tools, pyprocess, scaling, filters, \\\n",
    "                    validation, process\n",
    "import numpy as np\n",
    "from skimage import data\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from scipy import ndimage\n",
    "from skimage import feature\n",
    "from PIL import Image\n",
    "from pylab import *\n",
    "%matplotlib inline\n",
    "from skimage.color import rgb2gray\n",
    "from skimage import img_as_uint\n",
    "\n",
    "frame_a  = tools.imread('../test3/Y4-S3_Camera000398.tif')  \n",
    "frame_b  = tools.imread('../test3/Y4-S3_Camera000399.tif')\n",
    "\n",
    "\n",
    "# for whatever reason the shape of frame_a is (3, 284, 256)\n",
    "# so we first tranpose to the RGB image and then convert to the gray scale\n",
    "\n",
    "frame_a = img_as_uint(rgb2gray(frame_a))\n",
    "frame_b = img_as_uint(rgb2gray(frame_b))\n",
    "plt.imshow(np.c_[frame_a,frame_b],cmap=plt.cm.gray)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# frame_a.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#u, v, sig2noise = openpiv.process.extended_search_area_piv( frame_a, frame_b, window_size=24, overlap=12, dt=0.02, search_area_size=64, sig2noise_method='peak2peak' )\n",
    "u, v, sig2noise = pyprocess.extended_search_area_piv( frame_a, frame_b, window_size=32, overlap=8, dt=.1, sig2noise_method='peak2peak' )\n",
    "x, y = pyprocess.get_coordinates( image_size=frame_a.shape, window_size=32, overlap=8 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "u, v, mask = validation.sig2noise_val( u, v, sig2noise, threshold = 1.3 )\n",
    "u, v = filters.replace_outliers( u, v, method='localmean', max_iter=10, kernel_size=2)\n",
    "x, y, u, v = scaling.uniform(x, y, u, v, scaling_factor = 96.52 )\n",
    "quiver(x,y,u,v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tools.save(x, y, u, v, mask, 'Y4-S3_Camera000398.txt' )\n",
    "tools.display_vector_field('Y4-S3_Camera000398.txt', scale=3, width=0.0125)\n",
    "# frame_vectors  = io.imshow(vectors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x,y,u,v, mask = process.WiDIM(frame_a.astype(np.int32), frame_b.astype(np.int32), ones_like(frame_a).astype(np.int32), min_window_size=32, overlap_ratio=0.25, coarse_factor=0, dt=0.1, validation_method='mean_velocity', trust_1st_iter=0, validation_iter=0, tolerance=0.7, nb_iter_max=1, sig2noise_method='peak2peak')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tools.save(x, y, u, v, zeros_like(u), 'Y4-S3_Camera000398.txt' )\n",
    "tools.display_vector_field('Y4-S3_Camera000398.txt', scale=300, width=0.005)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x,y,u,v, mask = process.WiDIM(frame_a.astype(np.int32), frame_b.astype(np.int32), ones_like(frame_a).astype(np.int32), min_window_size=16, overlap_ratio=0.25, coarse_factor=2, dt=0.1, validation_method='mean_velocity', trust_1st_iter=1, validation_iter=2, tolerance=0.7, nb_iter_max=4, sig2noise_method='peak2peak')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tools.save(x, y, u, v, zeros_like(u), 'Y4-S3_Camera000398.txt' )\n",
    "tools.display_vector_field('Y4-S3_Camera000398.txt', scale=300, width=0.005)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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