after fg

natanielruiz

2017-10-26 f415df3448622f30c3d1eb680596871672b38dac

 practice/.ipynb_checkpoints/smoothing_ypr-checkpoint.ipynb

@@ -2,9 +2,9 @@
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 156,
   "execution_count": 8,
   "metadata": {
    "collapsed": false
    "collapsed": true
   },
   "outputs": [],
   "source": [
@@ -17,22 +17,22 @@
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "execution_count": 9,
   "metadata": {
    "collapsed": false
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "video_path = '../data/video/SGT036_2016_07_25_pivothead_AVI.avi'\n",
    "bbox_path = '../data/video/annotations/SGT036_childface.txt'\n",
    "video_path = '../data/video/jacob_collier_live.mp4'\n",
    "bbox_path = '../data/video/annotations/video-det-fold-jacob_collier_live.txt'\n",
    "\n",
    "annot_path = '../output/video/output-SGT036_resnet18_cr_epoch_1.txt'\n",
    "output_string = 'SGT036_resnet18_cr_epoch_1_flat_smoothed'"
    "annot_path = '../output/video/output-jacob_normal_a1.txt'\n",
    "output_string = 'jacob_smooth'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
@@ -41,9 +41,9 @@
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-6.069214 -0.831665  0.53318  ..., -3.836042 -3.868275 -2.377155]\n",
      "(8508,)\n",
      "(53464,)\n"
      "[ -2.073635 -10.835449   8.318871 ..., -20.763079 -12.68498   19.257992]\n",
      "(19987,)\n",
      "(21373,)\n"
     ]
    }
   ],
@@ -67,11 +67,15 @@
    "    list_p.append(float(line[2]))\n",
    "    list_r.append(float(line[3]))\n",
    "    \n",
    "last_frame = 0\n",
    "for line in bbox:\n",
    "    line = line.strip('\\n')\n",
    "    line = line.split(' ')\n",
    "    frame = int(line[0])\n",
    "    x_min, y_min, x_max, y_max = int(line[1]), int(line[2]), int(line[3]), int(line[4])\n",
    "    if frame == last_frame:\n",
    "        continue\n",
    "    last_frame = frame\n",
    "    x_min, y_min, x_max, y_max = int(float(line[1])), int(float(line[2])), int(float(line[3])), int(float(line[4]))\n",
    "    list_x_min.append(x_min)\n",
    "    list_x_max.append(x_max)\n",
    "    list_y_min.append(y_min)\n",
@@ -93,7 +97,7 @@
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
@@ -107,31 +111,39 @@
    }
   ],
   "source": [
    "window_len = 6\n",
    "window_len = 1\n",
    "pad = window_len / 2\n",
    "window = 'flat'\n",
    "window_2 = 'flat'\n",
    "window_len_2 = 7\n",
    "pad_2 = window_len_2 / 2\n",
    "\n",
    "s = np.r_[y[window_len-1:0:-1],y,y[-2:-window_len-1:-1]]\n",
    "t = np.r_[p[window_len-1:0:-1],p,p[-2:-window_len-1:-1]]\n",
    "u = np.r_[r[window_len-1:0:-1],r,r[-2:-window_len-1:-1]]\n",
    "\n",
    "xa = np.r_[x_min_arr[window_len-1:0:-1],x_min_arr,x_min_arr[-2:-window_len-1:-1]]\n",
    "xb = np.r_[x_max_arr[window_len-1:0:-1],x_max_arr,x_max_arr[-2:-window_len-1:-1]]\n",
    "ya = np.r_[y_min_arr[window_len-1:0:-1],y_min_arr,y_min_arr[-2:-window_len-1:-1]]\n",
    "yb = np.r_[y_max_arr[window_len-1:0:-1],y_max_arr,y_max_arr[-2:-window_len-1:-1]]\n",
    "xa = np.r_[x_min_arr[window_len_2-1:0:-1],x_min_arr,x_min_arr[-2:-window_len_2-1:-1]]\n",
    "xb = np.r_[x_max_arr[window_len_2-1:0:-1],x_max_arr,x_max_arr[-2:-window_len_2-1:-1]]\n",
    "ya = np.r_[y_min_arr[window_len_2-1:0:-1],y_min_arr,y_min_arr[-2:-window_len_2-1:-1]]\n",
    "yb = np.r_[y_max_arr[window_len_2-1:0:-1],y_max_arr,y_max_arr[-2:-window_len_2-1:-1]]\n",
    "\n",
    "if window == 'flat':\n",
    "    w=np.ones(window_len, 'd')\n",
    "else:\n",
    "    w=eval('np.' + window + '(window_len)')\n",
    "    \n",
    "if window_2 == 'flat':\n",
    "    w_2=np.ones(window_len_2, 'd')\n",
    "else:\n",
    "    w_2=eval('np.' + window_2 + '(window_len_2)')    \n",
    "\n",
    "y = np.convolve(w / w.sum(), s, mode='valid')[pad:-pad]\n",
    "p = np.convolve(w / w.sum(), t, mode='valid')[pad:-pad]\n",
    "r = np.convolve(w / w.sum(), u, mode='valid')[pad:-pad]\n",
    "x_min_arr = np.convolve(w / w.sum(), xa, mode='valid')[pad:-pad]\n",
    "x_max_arr = np.convolve(w / w.sum(), xb, mode='valid')[pad:-pad]\n",
    "y_min_arr = np.convolve(w / w.sum(), ya, mode='valid')[pad:-pad]\n",
    "y_max_arr = np.convolve(w / w.sum(), yb, mode='valid')[pad:-pad]\n",
    "x_min_arr = np.convolve(w_2 / w_2.sum(), xa, mode='valid')[pad_2:-pad_2]\n",
    "x_max_arr = np.convolve(w_2 / w_2.sum(), xb, mode='valid')[pad_2:-pad_2]\n",
    "y_min_arr = np.convolve(w_2 / w_2.sum(), ya, mode='valid')[pad_2:-pad_2]\n",
    "y_max_arr = np.convolve(w_2 / w_2.sum(), yb, mode='valid')[pad_2:-pad_2]\n",
    "\n",
    "pose_dict = {}\n",
    "bbox_dict = {}\n",
@@ -151,7 +163,7 @@
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
@@ -166,12 +178,21 @@
   ],
   "source": [
    "video = cv2.VideoCapture(video_path)\n",
    "# New cv2\n",
    "width = int(video.get(cv2.CAP_PROP_FRAME_WIDTH))   # float\n",
    "height = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT)) # float\n",
    "\n",
    "# Define the codec and create VideoWriter object\n",
    "fourcc = cv2.VideoWriter_fourcc(*'MJPG')\n",
    "out = cv2.VideoWriter('../output/video/output-%s.avi' % output_string, fourcc, 30.0, (width, height))\n",
    "\n",
    "# Old cv2\n",
    "# width = int(video.get(cv2.cv.CV_CAP_PROP_FRAME_WIDTH))   # float\n",
    "# height = int(video.get(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT)) # float\n",
    "\n",
    "# # Define the codec and create VideoWriter object\n",
    "# fourcc = cv2.cv.CV_FOURCC(*'MJPG')\n",
    "# out = cv2.VideoWriter('../output/video/output-%s.avi' % output_string, fourcc, 30.0, (width, height))\n",
    "\n",
    "txt_out = open('../output/video/output-%s.txt' % output_string, 'w')\n",
    "\n",
@@ -196,7 +217,7 @@
    "\n",
    "        # Print new frame with cube and TODO: axis\n",
    "        txt_out.write(str(frame_num) + ' %f %f %f\\n' % (yaw_predicted, pitch_predicted, roll_predicted))\n",
    "        utils.plot_pose_cube(frame, yaw_predicted, pitch_predicted, roll_predicted, (x_min + x_max) / 2, (y_min + y_max) / 2, size = 200)\n",
    "        utils.plot_pose_cube(frame, yaw_predicted, pitch_predicted, roll_predicted, (x_min + x_max) / 2, (y_min + y_max) / 2, size = 80)\n",
    "        out.write(frame)\n",
    "    else:\n",
    "        out.write(frame)\n",
@@ -226,9 +247,9 @@
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda root]",
   "display_name": "Python [default]",
   "language": "python",
   "name": "conda-root-py"
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {