Super simple example to test OpenCV inside a Jupyter notebook running through a docker container - https://ricardodeazambuja.com/deep_learning/2018/05/04/tuning_tensorflow_docker/

opencv_live_webcam.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy\n",
    "import cv2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "cv2.startWindowThread()\n",
    "cv2.namedWindow(\"Image window\",cv2.WINDOW_NORMAL) # cv2.WINDOW_NORMAL lets us resize the window"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's test it with some old school analog tv static (aka noise)\n",
    "while True:\n",
    "    cv2.imshow(\"Image window\", numpy.random.rand(600,600,4))\n",
    "    key = cv2.waitKey(1) & 0xFF\n",
    "    # Pressing 'q' stops the loop\n",
    "    if key == ord(\"q\"):\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now, we need to get to the webcam\n",
    "capture = cv2.VideoCapture(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Grabs frames from webcam and sends to the window created\n",
    "while True:\n",
    "    grabbed, frame = capture.read()\n",
    "    cv2.imshow(\"Image window\", frame)\n",
    "    key = cv2.waitKey(1) & 0xFF\n",
    "    # Pressing 'q' stops the loop\n",
    "    if key == ord(\"q\"):\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "capture.release()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "cv2.destroyAllWindows()"
   ]
  }
 ],
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