Testing-libraries.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from moviepy.editor import VideoFileClip\n",
    "from moviepy.editor import ImageSequenceClip\n",
    "import os\n",
    "\n",
    "import sys, skvideo.io"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define pathname to save the output video\n",
    "mpy_output = 'mpy_new_test_video.mp4'\n",
    "image_folder = 'Test/CameraRGB/'\n",
    "\n",
    "images = sorted([(image_folder+img) for img in os.listdir(image_folder) if img.endswith(\".png\")])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30\n",
      "30\n",
      "[MoviePy] >>>> Building video mpy_new_test_video.mp4\n",
      "[MoviePy] Writing video mpy_new_test_video.mp4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 31/31 [00:00<00:00, 57.56it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[MoviePy] Done.\n",
      "[MoviePy] >>>> Video ready: mpy_new_test_video.mp4 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "print (len(images))\n",
    "\n",
    "clip = ImageSequenceClip(images, fps=10)\n",
    "\n",
    "print (len(clip.sequence))\n",
    "\n",
    "clip.write_videofile(mpy_output, audio=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "sk_output = 'sk_new_test_video.mp4'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30, 600, 800, 3)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import cv2\n",
    "\n",
    "np_images = np.array([cv2.cvtColor(cv2.imread(i), cv2.COLOR_BGR2RGB) for i in images]); np_images.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "skvideo.io.vwrite(sk_output, np_images)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30, 600, 800, 3)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sk_res = skvideo.io.vread(sk_output); sk_res.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(31, 600, 800, 3)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mpy_res = skvideo.io.vread(mpy_output); mpy_res.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}

testing_video_libs.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from moviepy.editor import VideoFileClip\n",
    "from moviepy.editor import ImageSequenceClip\n",
    "import os\n",
    "\n",
    "import sys, skvideo.io"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define pathname to save the output video\n",
    "mpy_output = 'mpy_new_test_video.mp4'\n",
    "image_folder = 'Test/CameraRGB/'\n",
    "\n",
    "images = sorted([(image_folder+img) for img in os.listdir(image_folder) if img.endswith(\".png\")])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30\n",
      "30\n",
      "[MoviePy] >>>> Building video mpy_new_test_video.mp4\n",
      "[MoviePy] Writing video mpy_new_test_video.mp4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 31/31 [00:00<00:00, 57.56it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[MoviePy] Done.\n",
      "[MoviePy] >>>> Video ready: mpy_new_test_video.mp4 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "print (len(images))\n",
    "\n",
    "clip = ImageSequenceClip(images, fps=10)\n",
    "\n",
    "print (len(clip.sequence))\n",
    "\n",
    "clip.write_videofile(mpy_output, audio=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "sk_output = 'sk_new_test_video.mp4'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30, 600, 800, 3)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import cv2\n",
    "\n",
    "np_images = np.array([cv2.cvtColor(cv2.imread(i), cv2.COLOR_BGR2RGB) for i in images]); np_images.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "skvideo.io.vwrite(sk_output, np_images)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30, 600, 800, 3)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sk_res = skvideo.io.vread(sk_output); sk_res.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(31, 600, 800, 3)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mpy_res = skvideo.io.vread(mpy_output); mpy_res.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.6.4"
  }
 },
 "nbformat": 4,
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}