faster rcnn notebook

demo_faster_rcnn_notebook.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "import _init_paths\n",
    "import tensorflow as tf\n",
    "\n",
    "#import matplotlib\n",
    "#matplotlib.use('Agg') # Must be before importing matplotlib.pyplot or pylab!\n",
    "import matplotlib.pyplot as plt\n",
    "%pylab inline\n",
    "\n",
    "from fast_rcnn.config import cfg\n",
    "from fast_rcnn.test import im_detect\n",
    "from fast_rcnn.nms_wrapper import nms\n",
    "from utils.timer import Timer\n",
    "import numpy as np\n",
    "import os, sys, cv2\n",
    "import argparse\n",
    "from networks.factory import get_network\n",
    "\n",
    "CLASSES = ('__background__',\n",
    "           'aeroplane', 'bicycle', 'bird', 'boat',\n",
    "           'bottle', 'bus', 'car', 'cat', 'chair',\n",
    "           'cow', 'diningtable', 'dog', 'horse',\n",
    "           'motorbike', 'person', 'pottedplant',\n",
    "           'sheep', 'sofa', 'train', 'tvmonitor')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "#CLASSES = ('__background__','person','bike','motorbike','car','bus')\n",
    "\n",
    "def vis_detections(im, class_name, dets,ax, thresh=0.5):\n",
    "    \"\"\"Draw detected bounding boxes.\"\"\"\n",
    "    inds = np.where(dets[:, -1] >= thresh)[0]\n",
    "    if len(inds) == 0:\n",
    "        return\n",
    "\n",
    "    for i in inds:\n",
    "        bbox = dets[i, :4]\n",
    "        score = dets[i, -1]\n",
    "\n",
    "        ax.add_patch(\n",
    "            plt.Rectangle((bbox[0], bbox[1]),\n",
    "                          bbox[2] - bbox[0],\n",
    "                          bbox[3] - bbox[1], fill=False,\n",
    "                          edgecolor='red', linewidth=3.5)\n",
    "            )\n",
    "        ax.text(bbox[0], bbox[1] - 2,\n",
    "                '{:s} {:.3f}'.format(class_name, score),\n",
    "                bbox=dict(facecolor='blue', alpha=0.5),\n",
    "                fontsize=14, color='white')\n",
    "\n",
    "    ax.set_title(('{} detections with '\n",
    "                  'p({} | box) >= {:.1f}').format(class_name, class_name,\n",
    "                                                  thresh),\n",
    "                  fontsize=14)\n",
    "    plt.axis('off')\n",
    "    plt.tight_layout()\n",
    "    plt.draw()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "def demo(sess, net, image_name):\n",
    "    \"\"\"Detect object classes in an image using pre-computed object proposals.\"\"\"\n",
    "\n",
    "    # Load the demo image\n",
    "    im_file = os.path.join(cfg.DATA_DIR, 'demo', image_name)\n",
    "    #im_file = os.path.join('/home/corgi/Lab/label/pos_frame/ACCV/training/000001/',image_name)\n",
    "    im = cv2.imread(im_file)\n",
    "\n",
    "    # Detect all object classes and regress object bounds\n",
    "    timer = Timer()\n",
    "    timer.tic()\n",
    "    scores, boxes = im_detect(sess, net, im)\n",
    "    timer.toc()\n",
    "    print ('Detection took {:.3f}s for '\n",
    "           '{:d} object proposals').format(timer.total_time, boxes.shape[0])\n",
    "\n",
    "    # Visualize detections for each class\n",
    "    im = im[:, :, (2, 1, 0)]\n",
    "    fig, ax = plt.subplots(figsize=(12, 12))\n",
    "    ax.imshow(im, aspect='equal')\n",
    "\n",
    "    CONF_THRESH = 0.8\n",
    "    NMS_THRESH = 0.3\n",
    "    for cls_ind, cls in enumerate(CLASSES[1:]):\n",
    "        cls_ind += 1 # because we skipped background\n",
    "        cls_boxes = boxes[:, 4*cls_ind:4*(cls_ind + 1)]\n",
    "        cls_scores = scores[:, cls_ind]\n",
    "        dets = np.hstack((cls_boxes,\n",
    "                          cls_scores[:, np.newaxis])).astype(np.float32)\n",
    "        keep = nms(dets, NMS_THRESH)\n",
    "        dets = dets[keep, :]\n",
    "        vis_detections(im, cls, dets, ax, thresh=CONF_THRESH)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cfg.TEST.HAS_RPN = True  # Use RPN for proposals\n",
    "gpu_id = 1\n",
    "demo_net = \"VGGnet_test\"\n",
    "model = \"/home/ubuntu/dev/Faster-RCNN_TF/VGGnet_fast_rcnn_iter_70000.ckpt\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensor(\"Placeholder:0\", shape=(?, ?, ?, 3), dtype=float32)\n",
      "Tensor(\"conv5_3/conv5_3:0\", shape=(?, ?, ?, 512), dtype=float32)\n",
      "Tensor(\"rpn_conv/3x3/rpn_conv/3x3:0\", shape=(?, ?, ?, 512), dtype=float32)\n",
      "Tensor(\"rpn_cls_score/rpn_cls_score:0\", shape=(?, ?, ?, 18), dtype=float32)\n",
      "Tensor(\"rpn_cls_prob:0\", shape=(?, ?, ?, ?), dtype=float32)\n",
      "Tensor(\"rpn_cls_prob_reshape:0\", shape=(?, ?, ?, 18), dtype=float32)\n",
      "Tensor(\"rpn_bbox_pred/rpn_bbox_pred:0\", shape=(?, ?, ?, 36), dtype=float32)\n",
      "Tensor(\"Placeholder_1:0\", shape=(?, 3), dtype=float32)\n",
      "Tensor(\"conv5_3/conv5_3:0\", shape=(?, ?, ?, 512), dtype=float32)\n",
      "Tensor(\"rois:0\", shape=(?, 5), dtype=float32)\n",
      "[<tf.Tensor 'conv5_3/conv5_3:0' shape=(?, ?, ?, 512) dtype=float32>, <tf.Tensor 'rois:0' shape=(?, 5) dtype=float32>]\n",
      "Tensor(\"fc7/fc7:0\", shape=(?, 4096), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "net = get_network(demo_net)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "saver = tf.train.Saver()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Loaded network /home/ubuntu/dev/Faster-RCNN_TF/VGGnet_fast_rcnn_iter_70000.ckpt\n"
     ]
    }
   ],
   "source": [
    "saver.restore(sess, model)\n",
    "\n",
    "print('\\n\\nLoaded network {:s}'.format(model))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Warmup on a dummy image\n",
    "im = 128 * np.ones((300, 300, 3), dtype=np.uint8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
      "Demo for data/demo/000456.jpg\n",
      "Detection took 1.564s for 300 object proposals\n",
      "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
      "Demo for data/demo/000542.jpg\n",
      "Detection took 0.111s for 261 object proposals\n",
      "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
      "Demo for data/demo/001150.jpg\n",
      "Detection took 0.105s for 232 object proposals\n",
      "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
      "Demo for data/demo/001763.jpg\n",
      "Detection took 0.112s for 265 object proposals\n",
      "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
      "Demo for data/demo/004545.jpg\n",
      "Detection took 0.106s for 300 object proposals\n"
     ]
    },
    {
     "data": {