GENERATE_VIDEO.ipynb

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   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
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     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "# show images inline\n",
    "%matplotlib inline\n",
    "\n",
    "# automatically reload modules when they have changed\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "# import keras\n",
    "import keras\n",
    "# import miscellaneous modules\n",
    "import matplotlib.pyplot as plt\n",
    "import cv2\n",
    "import os\n",
    "import numpy as np\n",
    "import time\n",
    "import mmcv\n",
    "# set tf backend to allow memory to grow, instead of claiming everything\n",
    "import tensorflow as tf\n",
    "# import keras_retinanet\n",
    "from keras_retinanet import models\n",
    "from keras_retinanet.utils.image import read_image_bgr, preprocess_image, resize_image\n",
    "from keras_retinanet.utils.visualization import draw_box, draw_caption\n",
    "from keras_retinanet.utils.colors import label_color\n",
    "from keras_retinanet.utils.gpu import setup_gpu\n",
    "from keras_retinanet.utils.visual import draw_caption1\n",
    "\n",
    "\n",
    "\n",
    "def get_session():\n",
    "    config = tf.ConfigProto()\n",
    "    config.gpu_options.allow_growth = True\n",
    "    return tf.Session(config=config)\n",
    "\n",
    "# use this to change which GPU to use\n",
    "gpu = 0\n",
    "\n",
    "# set the modified tf session as backend in keras\n",
    "setup_gpu(gpu)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
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      "tracking <tf.Variable 'Variable:0' shape=(9, 4) dtype=float32, numpy=\n",
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   "source": [
    "# adjust this to point to your downloaded/trained model\n",
    "# models can be downloaded here: https://github.com/fizyr/keras-retinanet/releases\n",
    "model_path = os.path.join('/home/victor/keras-retinanet/', 'snapshots', 'resnet50_csv_49.h5')\n",
    "\n",
    "# load retinanet model\n",
    "model = models.load_model(model_path, backbone_name='resnet50')\n",
    "\n",
    "# if the model is not converted to an inference model, use the line below\n",
    "# see: https://github.com/fizyr/keras-retinanet#converting-a-training-model-to-inference-model\n",
    "model = models.convert_model(model)\n",
    "\n",
    "#print(model.summary())\n",
    "\n",
    "# load label to names mapping for visualization purposes\n",
    "#labels_to_names = {0: 'person', 1: 'cyclist', 2: 'Car', 3: 'motorcyclist', 4: 'tricyclist', 5: 'Bus', 6: 'wheelchairuser', 7: 'Truck', 8: 'Ambulance'}\n",
    "labels_to_names = {0: 'Airplane'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "path='/home/victor/Frame/Airplane/720p - CARBONITE 2 video Mumbai airport - YouTube.webm' \n",
    "video = mmcv.VideoReader(path)"
   ]
  },
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      "processing time:  1.2197272777557373\n"
     ]
    }
   ],
   "source": [
    "i=0\n",
    "for image in video:\n",
    "    # copy to draw on\n",
    "    draw = image.copy()\n",
    "    draw = cv2.cvtColor(draw, cv2.COLOR_BGR2RGB)\n",
    "\n",
    "# preprocess image for network\n",
    "    image = preprocess_image(image)\n",
    "    image, scale = resize_image(image)\n",
    "\n",
    "# process image\n",
    "    start = time.time()\n",
    "    boxes, scores, labels = model.predict_on_batch(np.expand_dims(image, axis=0))\n",
    "    print(\"processing time: \", time.time() - start)\n",
    "\n",
    "# correct for image scale\n",
    "    boxes /= scale\n",
    "\n",
    "# visualize detections\n",
    "    for box, score, label in zip(boxes[0], scores[0], labels[0]):\n",
    "    # scores are sorted so we can break\n",
    "        if score < 0.5:\n",
    "            break\n",
    "        \n",
    "        color = label_color(label)\n",
    "    \n",
    "        b = box.astype(int)\n",
    "        draw_box(draw, b, color=color,thickness=3)\n",
    "    \n",
    "        caption = \"{} {:.3f}\".format(labels_to_names[label], score)\n",
    "        #print(caption)\n",
    "        draw_caption1(draw, b, caption,size=1,thickness=1,R=255,G=255,B=0)\n",
    "    #cv2.imwrite('/home/victor/keras-retinanet/result'+caption+'.jpg',draw1)\n",
    "    \n",
    "\n",
    "    save_img=cv2.cvtColor(draw, cv2.COLOR_BGR2RGB)\n",
    "    cv2.imwrite('/home/victor/Frame/cvt_frame/'+str(i).zfill(6)+'.jpg',save_img)\n",
    "    i=i+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 1335/1335, 130.5 task/s, elapsed: 10s, ETA:     0s\n"
     ]
    }
   ],
   "source": [
    "mmcv.frames2video('/home/victor/Frame/cvt_frame/', '/home/victor/Frame/video/airport2.mp4')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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