920 lines
1.1 MiB
Plaintext
920 lines
1.1 MiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"nbgrader": {
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"grade": false,
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"grade_id": "cell-1d6811de1e292425",
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"locked": true,
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"schema_version": 3,
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"solution": false,
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"task": false
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}
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},
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"source": [
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"In this notebook, we explore how to detect a moving object on a stationary background."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"nbgrader": {
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"grade": false,
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"grade_id": "cell-c101bf39d596ef9a",
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"locked": true,
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"schema_version": 3,
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"solution": false,
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"task": false
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}
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},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"from scipy import ndimage\n",
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"\n",
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"# If import imageio fails, you can install it like this in Jupyter:\n",
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"# !pip install 'imageio[ffmpeg]'\n",
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"# or like this at the command line:\n",
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"# python -m pip install 'imageio[ffmpeg]'\n",
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"import imageio"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"nbgrader": {
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"grade": false,
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"grade_id": "cell-7161f550b1a10a31",
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"locked": true,
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"schema_version": 3,
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"solution": false,
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"task": false
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}
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},
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"outputs": [],
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"source": [
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"# Here we define a helper function which we call below to make sure\n",
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"# an image is an \"unsigned 8 bit integer\". This way, we know they\n",
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"# take only a single byte per pixel and have a value from 0..255.\n",
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"\n",
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"def ensure_dtype_uint8(arr):\n",
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" return arr.astype(np.uint8)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"nbgrader": {
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"grade": false,
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"grade_id": "cell-108186f7f14d851d",
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"locked": true,
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"schema_version": 3,
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"solution": false,
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"task": false
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}
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},
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"source": [
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"Here we load the first frame of a movie file saved in the Straw Lab of a fly walking."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"nbgrader": {
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"grade": false,
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"grade_id": "cell-721f647abba6218f",
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"locked": true,
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"schema_version": 3,
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"solution": false,
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"task": false
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}
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},
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"outputs": [],
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"source": [
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"fname = \"data/short-movie20170810_182130.mp4\"\n",
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"reader = imageio.get_reader(fname)\n",
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"for frame in reader:\n",
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" frame0 = frame[:,:,1] # take only green channel\n",
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" break"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"nbgrader": {
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"grade": false,
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"grade_id": "cell-6589753ba9570c6b",
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"locked": true,
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"schema_version": 3,
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"solution": false,
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"task": false
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}
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},
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"source": [
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"Now, we plot the first frame (the first image) in the video. We do this once in grayscale and once in false color."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {
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"nbgrader": {
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"grade": false,
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"grade_id": "cell-f8d6f9c412400ac4",
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"locked": true,
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"schema_version": 3,
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"solution": false,
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"task": false
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}
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},
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"outputs": [
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{
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"data": {
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"image/png": "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"text/plain": [
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"<Figure size 640x480 with 2 Axes>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"plt.imshow(frame0, cmap='gray')\n",
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"plt.colorbar();"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {
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"nbgrader": {
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"grade": false,
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"grade_id": "cell-79ff5ac403dbec51",
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"locked": true,
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"schema_version": 3,
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"solution": false,
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"task": false
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}
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},
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"outputs": [
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{
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"data": {
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|
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"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 2 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plt.imshow(frame0, cmap='jet')\n",
|
||
|
|
"plt.colorbar();"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {
|
||
|
|
"nbgrader": {
|
||
|
|
"grade": false,
|
||
|
|
"grade_id": "cell-b4d51617498cdf00",
|
||
|
|
"locked": true,
|
||
|
|
"schema_version": 3,
|
||
|
|
"solution": false,
|
||
|
|
"task": false
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"Note how the false color image lets you see more easily approximately what pixel intensity values are present."
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {
|
||
|
|
"nbgrader": {
|
||
|
|
"grade": false,
|
||
|
|
"grade_id": "cell-bb6b6c1075212bfa",
|
||
|
|
"locked": true,
|
||
|
|
"schema_version": 3,
|
||
|
|
"solution": false,
|
||
|
|
"task": false
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"Now, list make a histogram of the pixel intensity values."
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 6,
|
||
|
|
"metadata": {
|
||
|
|
"nbgrader": {
|
||
|
|
"grade": false,
|
||
|
|
"grade_id": "cell-1f03feb12c388aa0",
|
||
|
|
"locked": true,
|
||
|
|
"schema_version": 3,
|
||
|
|
"solution": false,
|
||
|
|
"task": false
|
||
|
|
},
|
||
|
|
"scrolled": true
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plt.hist(frame0.flat, bins=30);"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {
|
||
|
|
"nbgrader": {
|
||
|
|
"grade": false,
|
||
|
|
"grade_id": "cell-b8ef9c796dcc7da1",
|
||
|
|
"locked": true,
|
||
|
|
"schema_version": 3,
|
||
|
|
"solution": false,
|
||
|
|
"task": false
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"Now we will load all the frames into a single large data structure from disk into memory."
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 7,
|
||
|
|
"metadata": {
|
||
|
|
"nbgrader": {
|
||
|
|
"grade": false,
|
||
|
|
"grade_id": "cell-d8d4b833e722c306",
|
||
|
|
"locked": true,
|
||
|
|
"schema_version": 3,
|
||
|
|
"solution": false,
|
||
|
|
"task": false
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"(102, 1024, 1280)\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# get all frames into big 3D array\n",
|
||
|
|
"(height,width) = frame0.shape\n",
|
||
|
|
"all_frames = []\n",
|
||
|
|
"reader.set_image_index(0) # return to start of file\n",
|
||
|
|
"for frame in reader:\n",
|
||
|
|
" all_frames.append( frame[:,:,1] )\n",
|
||
|
|
"all_frames = np.array(all_frames)\n",
|
||
|
|
"print(all_frames.shape)\n",
|
||
|
|
"n_frames = len(all_frames)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {
|
||
|
|
"nbgrader": {
|
||
|
|
"grade": false,
|
||
|
|
"grade_id": "cell-3ef9393de49f1ec7",
|
||
|
|
"locked": true,
|
||
|
|
"schema_version": 3,
|
||
|
|
"solution": false,
|
||
|
|
"task": false
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"Now the entire movie is stored as a large 3D array of size: number of frames x width x height."
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {
|
||
|
|
"nbgrader": {
|
||
|
|
"grade": false,
|
||
|
|
"grade_id": "cell-c2722d719e790f09",
|
||
|
|
"locked": true,
|
||
|
|
"schema_version": 3,
|
||
|
|
"solution": false,
|
||
|
|
"task": false
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"Now, we want to find a fly in the image. The fly is moving, but the background is stationary, so we should be able to determine a single \"background image\". We can try to find the background image in at least two ways: with a \"mean image\" (also \"average image\"), which should average over the fly's position. And with a \"median image\" which should completely eliminate the fly from the backround image if the fly was absent for most of the time from each pixel."
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 8,
|
||
|
|
"metadata": {
|
||
|
|
"nbgrader": {
|
||
|
|
"grade": false,
|
||
|
|
"grade_id": "cell-2f4ea267c6c2ccd9",
|
||
|
|
"locked": true,
|
||
|
|
"schema_version": 3,
|
||
|
|
"solution": false,
|
||
|
|
"task": false
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"mean_frame = np.mean(all_frames, axis=0)\n",
|
||
|
|
"median_frame = np.median(all_frames, axis=0)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 9,
|
||
|
|
"metadata": {
|
||
|
|
"nbgrader": {
|
||
|
|
"grade": false,
|
||
|
|
"grade_id": "cell-e2365cb18d5b20d1",
|
||
|
|
"locked": true,
|
||
|
|
"schema_version": 3,
|
||
|
|
"solution": false,
|
||
|
|
"task": false
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 2 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plt.imshow(mean_frame, cmap='jet')\n",
|
||
|
|
"plt.colorbar();"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 10,
|
||
|
|
"metadata": {
|
||
|
|
"nbgrader": {
|
||
|
|
"grade": false,
|
||
|
|
"grade_id": "cell-58ec1fa04a85c5db",
|
||
|
|
"locked": true,
|
||
|
|
"schema_version": 3,
|
||
|
|
"solution": false,
|
||
|
|
"task": false
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAh0AAAGOCAYAAAAzerGXAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOz9e5RcZ3XnD39a53SdunaXutrdrZbaalmSZWQJy0hEji/YgMFOcIy5DCQZZoaZZF7y4zLhJfxmTYZZ75BZmZBkMhNmQi6TIQNJHAITwj2EiwM2NhqLSCAjI1uWZLXdckvd6mpVq6qr6lSd0/3+sZ/9nKdKMtj4gi3VXqtWVZ379Xm+z97f/d19KysrK/SsZz3rWc961rOePce26id9AD3rWc961rOe9ezisB7o6FnPetaznvWsZ8+L9UBHz3rWs571rGc9e16sBzp61rOe9axnPevZ82I90NGznvWsZz3rWc+eF+uBjp71rGc961nPeva8WA909KxnPetZz3rWs+fFeqCjZz3rWc961rOePS/m/6QPoGc961nPetazi8WazSatVusZbyeVSpFOp5+FI3p+rQc6etaznvWsZz17HqzZbHJJJkPtWdjW2NgYx48ff9EBjx7o6FnPetaznvXsebBWq0UN+H+B4BlsJwT+y6lTtFqtHujoWc961rOe9axnT24B8OKCCs+e9YikPetZz3rWs549j9b/LHyejn3oQx/i5S9/OYVCgZGREe644w4OHz7csczb3/52+vr6Oj7XXHNNxzJhGPKe97yH4eFhcrkct99+OydOnHhax9IDHT3rWc961rOePY/mPwufp2P33HMP73rXu7j//vv5+te/ThRFvPa1r2VpaaljuVtvvZWTJ0/az5e//OWO+e9973v57Gc/yyc/+Unuu+8+arUat912G3EcP61z71nPetaznvWsZxeofeUrX+n4/7GPfYyRkRH279/PK17xCjs9CALGxsbOu43FxUX+7M/+jL/8y7/k5ptvBuDOO+9kYmKCu+66i1tuueUpHUvP09GznvWsZz3r2fNoPs8stKLegrNnz3Z8wjB8SvtfXFwEYGhoqGP63XffzcjICJdffjn/+l//a+bm5uy8/fv30263ee1rX2unjY+Ps23bNvbs2fOUz70HOnrWs571rGc9ex7t2QqvTExMMDg4aD8f+tCHfuS+V1ZWeN/73sf111/Ptm3b7PSf+Zmf4a/+6q/4xje+wX/9r/+Vf/zHf+RVr3qVBTKnTp0ilUqxevXqju2Njo5y6tSpp3XuPetZz3rWs5717EVm09PTDAwM2P9B8KMTcd/97nfz/e9/n/vuu69j+lvf+lb7e9u2bezatYv169fzd3/3d7zxjW980u2trKzQ19f3lI+5Bzp61rOe9axnPXse7cfJQHEtMt8DAwMdoONH2Xve8x6+8IUv8K1vfYt169b90GXXrFnD+vXrOXLkCCBiZK1WizNnznR4O+bm5rj22muf8jH0wis961nPetaznj2P9nxnr6ysrPDud7+bz3zmM3zjG99gw4YNP3KdcrnM9PQ0a9asAWDnzp309/fz9a9/3S5z8uRJHnzwwacFOnqejp71rGc961nPnkdTIumPa+2nufy73vUuPvGJT/D5z3+eQqFgORiDg4NkMhlqtRof/OAHedOb3sSaNWuYmpri3//7f8/w8DBveMMb7LK/9Eu/xK/92q9RKpUYGhri/e9/P9u3b7fZLE/FeqCjZz3rWc961rML2P74j/8YgJtuuqlj+sc+9jHe/va343keBw8e5C/+4i+oVCqsWbOGV77ylXzqU5+iUCjY5X//938f3/d5y1veQqPR4NWvfjUf//jH8TzvKR9L38rKysqzclY961nPetaznvXsSe3s2bMMDg7yv4HsM9hOHfhXSOrr0+F0vBCs5+noWc961rOe9ex5tGdKJH0m6/6krUck7VnPetaznvWsZ8+L9TwdPetZz3rWs549j3Yxezp6oKNnPetZz3rWs+fRfpy01+71X6zWC6/0rGc961nPetaz58VezICpZz3rWc961rMXnT1TnY4Xc8f9Yj72nvWsZz3rWc9edNYLr/SsZz3rWc961rOePcf2ggcdf/RHf8SGDRtIp9Ps3LmTe++99yd9SD3rWc961rOe/djW/yx8Xqz2ggYdn/rUp3jve9/LBz7wAb73ve9xww038DM/8zM8/vjjP+lD61nPetaznvXsx7Lnu+DbC8le0DLou3fv5mUve5nVjQd4yUtewh133MGHPvShn+CR9axnPetZz3r29Exl0O8B8s9gOzXgRnoy6M+qtVot9u/fz7/7d/+uY/prX/ta9uzZc87yYRgShqH9v7y8zMLCAqVSib6+vuf8eHvWs571rGcvXltZWaFarTI+Ps6qVS/oIMCL2l6woGN+fp44jhkdHe2YPjo6asvyuvahD32I3/iN33i+Dq9nPetZz3p2Adr09DTr1q17TvdxMWevvOCPvdtLsbKycl7Pxa//+q/zvve9z/5fXFzk0ksvhXXTsHpAzlSr73aftQ+kgRzJcrEz3zPTIzPdc5bPm+lR13YjZ9s+EJh1dJnArO8uo+u560ZA6KyTBgbNN0CzaxvuvoI2+O6JQJCrMzI4T0g/zTDL2fIgLKXBW6EvX2cl8iAyFyryIe4DbwX8CML+5HdkDtiPyJUW8fyYOPIIwxSeF5HNy4H1eyEBbTLUKVCjToYYjwZZ+gnxifGI8VgmT5UsDarkWcZjFTHLePTTsuvErCJLgxw1GmRYxiNNA4/YbglgkEU8InyWCUkR45EyF7JFAMAaTnIJ8ywwRI08q4gpUSZFizku4XHWm9ufXMMMDbvdOhkK1Oy8flqcYozHWc9iPEgceeSDGkMsMEiFFil7vBkaDLHACHOkaJMixCOmRp5FigDkqRLjUaZEkwxpGhSoscggVfIEtMwZy7l7xKxyjnXZXIsWKXsNlvGI8EjRIkuDiFXUKBDjEdBilb2Kkd3OIkVapPCIaZOiahzDnrNFgBQtCtTIUyMkhUdEQBuAkH5ifNqkOMmYuR9tcy1j6mQISbGMzyoiAnPPIzz7jOgz4BGTp2avz2lGqJOW7YcpwmY/4WJBntF0iJ9uEQQtwjBFHHmsRB59fkw21yCda7AwNyTPPUAzkGe+SfK+B8Bgm750S87bj/G8iHApC4tpmDXvaM68m2VgieT9Ds029Lf7fmtbo9OazvoHgb+dAv7GWeDpmA9cAowAq5FGQX9nzDbngMPAI0D1x9zP0z2mjPndJmnAXggWAr/fUcr9ubKeDPoL0IaHh/E87xyvxtzc3DneD4AgCAiC4JzpjA1A3ol5KcAAbH+h0zIkwKBJZ0cekTREPpAiAQ5N87u7MdHtup/zMYJcABHRCTxSJHTfPAnocAOCUdd3Hsiv0F+sAv3EkceyaVSX02n6B5ZYhUcUZ+lflaadkUYgN9yiXsuyHHms8mOWmylpuKM+SLchY36DPPVpATWNVWlSqZBgoEU2aOGxjG86jCwxWVqUqOOxTEge8MzhZ2ynGRCSxccni0/OdJIQ45OiTh8eKdOJeqyiTdo+vL65QH14LJMiwmOFPgYpU6RKixTzlGgwLNfAbOcMAWdZR4uAuik0XTHApErBTovxCEnRbbE5S7UWAdOzEyw/nJNnK4Kzw9C+7nHqVG0HG9GiBixRp0LVrq/XQm3e7FttmZgUFXJEtE0X3zbzClQZYZbYdNEhKVoEBugEtpGL8fDx8PDpB5YJSJtrFtBnAYdv9hsSkMMnoI+YNH2kSBngKMdsOmIifPrxWSZNi9XUifFpkCckIDL7aBFQYBURq4CAmBweIUVi6gY2eXgGagZ4dj8xKWJSrOCxQpE2Hj4VijS5hIw9jhYtYCaVZjny6E/3AWmW5k0b4K9A3MdKCEshLLXacmH6fAHThVhA93x/Z/9baLPix/SnW2TzdTy/j+VKmvbSAKwleYd95P3TvrRpPvq+6rtdMf8DZ90IeW5a5mb9NHDJS+FPRoGPgr3bT8X6gfXAZqBA0tGDgIsnEMBxwny3kUbL47m1fpIT9oAXHqWwF45/bu0
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 2 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plt.imshow(median_frame, cmap='jet')\n",
|
||
|
|
"plt.colorbar();"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {
|
||
|
|
"nbgrader": {
|
||
|
|
"grade": false,
|
||
|
|
"grade_id": "cell-5faa20bd7e208895",
|
||
|
|
"locked": true,
|
||
|
|
"schema_version": 3,
|
||
|
|
"solution": false,
|
||
|
|
"task": false
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"Now that we have the \"background\", we can find the difference from background, which should emphasize anything that is moving."
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 11,
|
||
|
|
"metadata": {
|
||
|
|
"nbgrader": {
|
||
|
|
"grade": false,
|
||
|
|
"grade_id": "cell-074247293d712cac",
|
||
|
|
"locked": true,
|
||
|
|
"schema_version": 3,
|
||
|
|
"solution": false,
|
||
|
|
"task": false
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAh0AAAGOCAYAAAAzerGXAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAABbdUlEQVR4nO3dfXgU9b03/nfIbpaQJisJJcvWoPG+06oNKgZLRY7g4UnLg8rVgoKId/n14EHQCMrDoVb00kRoBc4xRyxeVqiU4tW7Ym1rlWAVyg0KBqOAFuppCkGJsRp3E4zJJpnfH8t3MzvZ3ezuPOw8vF/XNRdkd3b2O7Mz3/nM9zFLkiQJRERERDobkOkEEBERkTMw6CAiIiJDMOggIiIiQzDoICIiIkMw6CAiIiJDMOggIiIiQzDoICIiIkMw6CAiIiJDuDKdACIiIqf46quv0NnZqXo7OTk5GDhwoAYpMhaDDiIiIgN89dVX+HpuLto02JbP50NDQ4PlAg8GHURERAbo7OxEG4D7AXhUbKcDwE+bmtDZ2cmgg4iIiOLzALBWqKAdBh1EREQGcp9b0tWtVUIygEEHERGRgVxQd/O18o2bXWaJiIjIEFYOmIiIiCzHBXXVK11aJSQDGHQQEREZiNUrRERERDqzcsBERERkOWp7r7B6hYiIiJLi5OoVK6ediIjIctQ2JA1plZAMYJsOIiIiMgRLOoiIiAzE6hUiIiIyhNqGpGo+m2msXiEiIrK5vXv3Yvr06fD7/cjKysKLL77YZ50PPvgAM2bMgNfrRX5+Pr773e/i1KlTkfc7OjqwZMkSDBkyBHl5eZgxYwZOnz6dUjoYdBARERnIrcGSqrNnz+Lyyy9HTU1NzPf/53/+B2PHjsXFF1+MN954A++++y4eeOABDBzYOx9uZWUldu7ciR07dmDfvn1oa2vDtGnT0N2d/BR0WZIkSWmkn4iIiFIQDAbh9XrxfwEMUrGdLwF8H0AgEEBBQUHKn8/KysLOnTtx0003RV675ZZb4Ha78dxzz8X8TCAQwNe//nU899xzmD17NgDg448/RklJCV5++WVMmTIlqe9mSQcREZGD9fT04I9//CO++c1vYsqUKRg6dChGjx4dVQVTV1eHUCiEyZMnR17z+/0oLy/H/v37k/4uBh1EREQGEuN0pLuIHiDBYDBq6ejoSCs9zc3NaGtrw2OPPYbrr78eu3btws0334yZM2diz549AICmpibk5ORg8ODBUZ8tLi5GU1NTSvtOREREBtGqy2xJSUnU6w8++CDWrFmT8vZ6enoAADfeeCPuvfdeAMAVV1yB/fv346mnnsK4cePiflaSJGRlZSX9XQw6iIiILKixsTGqTYfH40lrO0OGDIHL5cKll14a9foll1yCffv2AQB8Ph86OzvR0tISVdrR3NyMMWPGJP1dpq9eefLJJ1FaWoqBAweioqICf/nLXzKdJCIiorRp1XuloKAgakk36MjJycFVV12F48ePR71+4sQJXHDBBQCAiooKuN1u1NbWRt4/c+YMjh49mlLQYeqSjueffx6VlZV48skncc011+DnP/85brjhBrz//vsYPnx4ppNHRESUskyMSNrW1oYPP/ww8ndDQwPq6+tRWFiI4cOH4/7778fs2bNx7bXX4rrrrsMrr7yC3//+93jjjTcAAF6vFwsWLMCyZctQVFSEwsJC3HfffRgxYgQmTpyYdDpM3WV29OjRuPLKK7Fp06bIa5dccgluuukmVFdXZzBlREREqRFdZvcA+JqK7bQBGIfUusy+8cYbuO666/q8Pn/+fGzZsgUA8Itf/ALV1dU4ffo0vvWtb+Ghhx7CjTfeGFn3q6++wv3334/t27ejvb0dEyZMwJNPPtmnbUkipg06Ojs7MWjQIPzmN7/BzTffHHn9nnvuQX19faRFrdDR0RHVcrenpweff/45ioqKUmrkQkREziNJElpbW+H3+zFggD4tDzIZdJiFaatX/vnPf6K7uxvFxcVRr8frnlNdXY2HHnrIqOQREZENNTY24vzzz9f1Ozjhm4kpSynidc9ZtWoVli5dGvk7EAica/exEuHd7NI3oZahx7Hg8bUG/k7mMhDh36MLvVkxf5/M6QCwAfn5+bp/k5MnfDNt0DFkyBBkZ2f3KdVobm7uU/oBhLsKxW656wKQfW4Bwj9XqJ9vT2YdMxOnZLx9GJjgvXRlx3nd6sfSLsTvoMdvT+mREJ03Afx9Mo/V8foybZfZnJwcVFRURHXPAYDa2tqUuuf0fXKIdUEr40arXPTx4t1QgveNjJEZcJhHCEBuphNBROitXlGzWJWp07506VLMmzcPo0aNwtVXX43Nmzfj1KlTuPPOOzX8FrfsX6vdIOOlVwyU257CZ/RgteNpd13gb2KkdPIU/j5OIIZBV/N5qzJ12mfPno3PPvsMDz/8MM6cOYPy8nK8/PLLkcFKtBGCtWvI5ESwYZZ6YSsGcnbG38JY8UpV+TuQc5k66ACARYsWYdGiRTp/i90yAbPsj1nSQZQp/bWvIidiQ1KyAXEailIOvTM5PrFRf3jDDbejEdWc8lsFrx8nc3KXWdM2JKVUyCc7lmdk/TUc7C9eThSPp5JhWjkup/SFwBtrO3qPgTgeDDjIuawcMFFEvMw9VkNS5efUvJ8sM2ewfBonPcU6r3iuOZ0rG3Cr6JnrkgB0a5YcQzHoMByfcszDjejibyIi/blcgItBB+lLBBsMOMwjBF4CRGQ0t8qSDrcpZ0xLDtt0GCoXbN9gNvIhqImISE/MbQ0jnqpZ0mEOrOYioszQpHrFohh0GIptB8xD2aOArCkXHGmVrMadDbhV1DO4e7RLi9FYvUJEFmaW0XeJKBks6SAiIjJSNtQ98lt4IlwGHUmxU/2/fH4Wu+wTORfPYbIgF9QFHRauXmHQkRSzZWzyHjDppE0+0qjZ9o2IiOzKoUGH1Z/25T9bOlNnq91nvY+fsluxFX8jIqI4WNLhNCHFv1bTjsxV+YjeAmJ8C63ToLYUh4jI5Bh0OJHVJ17KRLqVE8Dp0XPAqr8HERH1x8FBh2DlwMNo4nRxI3r2TCKnYz5CKRiAcA8WB3Jg0KHMHJhRJE9essHjRk7AYIJ04IK6oINdZq1EZCCcAyV1HL2TnCbW+S4PRDiRI6XBwUGHg0ckZUahPwZ2ZHfMQ4hS4cCSDjIOM2Rnsvt8KFZvhE4Zlw226SAi0kYu7N3QmCV4pBKrV4j0xEzaWcQ4LnZm14CKSF8s6eiX3YpRxWii4v8AENTxu/TapvI3sdvvZGXifJL/Vm7Zv3KZ+s3UnC/9fY7nIvUjG469+zp0t1Nh18xDZIztOm1bEKeYFsdRBEwsObGOWL+7CD70GNE2WXp+r13zDNKM2jYdklYJMR6DDkcyYnAv+amlVVG7SK/Ynkv2NzN684k1Ho68tCOV88LujVOJnIFBhyPJAw49ioL1DAZibU/L0hQ9OanYPZlZjFP93VznFr1K59L9bWKN20GUgDiVHYgNSU1P66oE+fgkelVTtMOYp1J5yYeVMnq3bHEK5b7mnluSJSY51Es654/4DTnCMaXIpcGSor1792L69Onw+/3IysrCiy++GHfdhQsXIisrCxs3box6vaOjA0uWLMGQIUOQl5eHGTNm4PTp0ymlg0GH6eld96zX9lPdbro3YSsN8iavYpCnW9yA3UjtRmxmYv9i/a4hhBubfo7Ufrsgki/lMCKg6+/cc8OZASaZ0dmzZ3H55ZejpqYm4Xovvvgi3nrrLfj9/j7vVVZWYufOndixYwf27duHtrY2TJs2Dd3d3Umnw6EFPGQ+Vgkc9OKGdbqZJlN1IqRSqqZF1YSZxggxQxrIlDJQvXLDDTfghhtuSLjORx99hMWLF+PVV1/F1KlTo94LBAJ45pln8Nxzz2HixIkAgG3btqGkpAS7d+/GlClTkkoHSzoow5z+NCiCDZED6dFeQUu5AArRWzIj/80S/YapBChqdAEoSJAOJ+ExMC0xy2y6y7k7dzAYjFo6OjrSTlJPTw/mzZuH+++/H9/+9rf7vF9XV4dQKITJkydHXvP7/SgvL8f+/fuT/h4GHZR
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 2 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"frame0_absdiff = abs(frame0 - median_frame)\n",
|
||
|
|
"plt.imshow(frame0_absdiff, cmap='jet')\n",
|
||
|
|
"plt.colorbar();"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {
|
||
|
|
"nbgrader": {
|
||
|
|
"grade": false,
|
||
|
|
"grade_id": "cell-798add9cb796580b",
|
||
|
|
"locked": true,
|
||
|
|
"schema_version": 3,
|
||
|
|
"solution": false,
|
||
|
|
"task": false
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"Now lets make a histogram of the `frame0_absdiff` image."
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 12,
|
||
|
|
"metadata": {
|
||
|
|
"nbgrader": {
|
||
|
|
"grade": false,
|
||
|
|
"grade_id": "cell-c71b6c10e0ee3018",
|
||
|
|
"locked": true,
|
||
|
|
"schema_version": 3,
|
||
|
|
"solution": false,
|
||
|
|
"task": false
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plt.hist(frame0_absdiff.flat, bins=30);"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {
|
||
|
|
"nbgrader": {
|
||
|
|
"grade": false,
|
||
|
|
"grade_id": "cell-2e5d36068fff4d1d",
|
||
|
|
"locked": true,
|
||
|
|
"schema_version": 3,
|
||
|
|
"solution": false,
|
||
|
|
"task": false
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"We want to find a threshold for this \"absolute difference image\" which will separate the fly from the background. From frame to frame the background changes very little - only noise in the light levels, camera sensor and caused by lossy compression to a movie format cause changes. But when the fly moves over the background, there is a large change in luminance values. This happens only in very few pixels. So few that they do not show on the histogram. However, matplotlib will automatically scale the axes, so we know there must be few pixels with values at 150 or above."
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {
|
||
|
|
"nbgrader": {
|
||
|
|
"grade": false,
|
||
|
|
"grade_id": "cell-d05a54e4c069f9aa",
|
||
|
|
"locked": true,
|
||
|
|
"schema_version": 3,
|
||
|
|
"solution": false,
|
||
|
|
"task": false
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"## Questions Part A\n",
|
||
|
|
"\n",
|
||
|
|
"Enter a threshold below and describe why you chose this value. Put your answer in the variable `threshold`."
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 13,
|
||
|
|
"metadata": {
|
||
|
|
"nbgrader": {
|
||
|
|
"grade": false,
|
||
|
|
"grade_id": "cell-1ef3868b1b1b045c",
|
||
|
|
"locked": false,
|
||
|
|
"schema_version": 3,
|
||
|
|
"solution": true
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"# Type your answer here and then run this and the following cell.\n",
|
||
|
|
"threshold = 100"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {
|
||
|
|
"nbgrader": {
|
||
|
|
"grade": true,
|
||
|
|
"grade_id": "cell-c848bbfece87e521",
|
||
|
|
"locked": false,
|
||
|
|
"points": 1,
|
||
|
|
"schema_version": 3,
|
||
|
|
"solution": true
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"(Here you should describe why you chose your particular threshold value.)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 14,
|
||
|
|
"metadata": {
|
||
|
|
"nbgrader": {
|
||
|
|
"grade": true,
|
||
|
|
"grade_id": "cell-edadac5626867908",
|
||
|
|
"locked": true,
|
||
|
|
"points": 1,
|
||
|
|
"schema_version": 3,
|
||
|
|
"solution": false
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"# If this runs without error, it means the answer in your previous cell was a number.\n",
|
||
|
|
"assert(threshold+0==threshold)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {
|
||
|
|
"nbgrader": {
|
||
|
|
"grade": false,
|
||
|
|
"grade_id": "cell-f6fc50107a0d129a",
|
||
|
|
"locked": true,
|
||
|
|
"schema_version": 3,
|
||
|
|
"solution": false,
|
||
|
|
"task": false
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"Now we want to \"binarize\" or \"threshold\" the image so that it is all zeros and ones (or true and false). This will let use use analyses like connected components labeling later."
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 15,
|
||
|
|
"metadata": {
|
||
|
|
"nbgrader": {
|
||
|
|
"grade": false,
|
||
|
|
"grade_id": "cell-fcef9de824e56bd5",
|
||
|
|
"locked": true,
|
||
|
|
"schema_version": 3,
|
||
|
|
"solution": false,
|
||
|
|
"task": false
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAhkAAAGTCAYAAACF2QCoAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAAA0RUlEQVR4nO3de3TU9Z3/8deQyyRgMkqyZEiJGLZppQathorcCgqEcpWlLSgWsEWXLDdjwAvSKnLWpNIVWaWgWBQsUjhtodI9KSW0GmTBCiGx3Fa62ywJNDGFjZNwMZeZz+8PZH4OCUgyM99kZp6Pcz7nNJ/5fOf7+WA1b96fm80YYwQAABBgXTq6AwAAIDwRZAAAgKAgyAAAAEFBkAEAAIKCIAMAAAQFQQYAAAgKggwAABAUBBkAACAoCDIAAEBQEGQAAICgIMgAACDM7d69WxMmTFBqaqpsNpt+85vffOEzxcXFysrKUlxcnPr06aNXXnmlze8lyAAAIMydO3dOt912m1atWnVN7cvLyzV27FgNHTpUpaWleuqpp7RgwQL9+te/btN7bVyQBgBA5LDZbNq2bZsmTZp0xTZPPPGEtm/frmPHjnnrcnJy9OGHH2rfvn3X/K5ofzoKAACu3aeffqrGxka/v8cYI5vN5lNnt9tlt9v9/m5J2rdvn7Kzs33qRo8erXXr1qmpqUkxMTHX9D0EGQAAWODTTz9Veu/rVF3j9vu7rrvuOp09e9an7plnntHSpUv9/m5Jqq6uVkpKik9dSkqKmpubdfr0afXs2fOavocgAwAACzQ2Nqq6xq3ykt5KTGj/ksi6eo/Ss06osrJSiYmJ3vpAZTEuuTxTcml1xeX1V0OQAQCAhRITuvgVZHi/JzHRJ8gIJKfTqerqap+6mpoaRUdHKykp6Zq/hyADAAALuY1Hbj+2XLiNJ3CduYKBAwfqt7/9rU/dzp071b9//2tejyGxhRUAAEt5ZPwubXX27FmVlZWprKxM0sUtqmVlZaqoqJAkLV68WDNmzPC2z8nJ0YkTJ5SXl6djx47p9ddf17p167Ro0aI2vZdMBgAAYe7AgQO6++67vT/n5eVJkmbOnKn169erqqrKG3BIUnp6ugoLC/Xoo4/qpz/9qVJTU/XSSy/p29/+dpveyzkZAABYoK6uTg6HQ3/7qJffCz9Tv3pSLpcraGsyAoVMBgAAFnIbI7cff7/351mrsSYDAAAEBZkMAAAs1N7Fm59/PlQQZAAAYCGPjNwEGQAAINAiKZPBmgwAABAUZDIAALBQJO0uIcgAAMBCns+KP8+HCqZLAABAUJDJAADAQm4/d5f486zVCDIAALCQ28jPW1gD15dgY7oEAAAEBZkMAAAsFEkLPwkyAACwkEc2uWXz6/lQwXQJAAAIik4fZKxevVrp6emKi4tTVlaW3nvvvY7uEgAA7eYx/pdQ0amDjC1btig3N1dLlixRaWmphg4dqjFjxqiioqKjuwYAQLu4P5su8aeECpsxnfd80gEDBuiOO+7QmjVrvHV9+/bVpEmTVFBQ0IE9AwCgberq6uRwOLT3SE9dl9D+v+Ofrfdo0C1VcrlcSkxMDGAPA6/TLvxsbGxUSUmJnnzySZ/67Oxs7d27t0X7hoYGNTQ0eH/2eDz6v//7PyUlJclmC52oDwBgPWOM6uvrlZqaqi5dOnWSP6R02iDj9OnTcrvdSklJ8alPSUlRdXV1i/YFBQV69tlnreoeACAMVVZWqlevXkF9h8fY5DF+7C7x41mrddog45LLsxDGmFYzE4sXL1ZeXp73Z5fLpRtvvFFDNFbRigl6PwEAoatZTdqjQiUkJAT9Xf6uqwilNRmdNshITk5WVFRUi6xFTU1Ni+yGJNntdtnt9hb10YpRtI0gAwBwFZ+tTmR6PbA67cRTbGyssrKyVFRU5FNfVFSkQYMGdVCvAADwj1td/C6hotNmMiQpLy9P06dPV//+/TVw4ECtXbtWFRUVysnJ6eiuAQDQLsbPNRmGNRmBMXXqVJ05c0bLli1TVVWVMjMzVVhYqN69e3d01wAAwBfo1EGGJM2ZM0dz5szp6G4AABAQLPwEAABB4TZd5DbtX1fh7rRHaLYUOqtHAABASCGTAQCAhTyyyePH3/E9Cp1UBkEGAAAWYk0GAAAICv/XZIROJoM1GQAAICjIZAAAYKGLazL8uCCN6RIAANAaj59Hg4fSwk+mSwAAQFCQyQAAwEKRtPCTIAMAAAt51CVizslgugQAAAQFmQwAACzkNja5/biu3Z9nrUaQAQCAhdx+7i5xM10CAAAiHZkMAAAs5DFd5PFjd4mH3SUAAKA1kTRdQpABAICFPPJv8aYncF0JOtZkAACAoCCTAQCAhfw/jCt08gMEGQAAWMj/Y8VDJ8gInZ4CAICQQiYDAAALeWSTR/4s/OTETwAA0AqmSwAAAPxEJgMAAAv5fxhX6OQHCDIAALCQx9jk8ecwrhC6hTV0wiEAABBSyGQAAGAhj5/TJRzGBQAAWuX/LawEGQAAoBVu2eT246wLf561WuiEQwAAIKSQyQAAwEJMlwAAgKBwy78pD3fguhJ0oRMOAQCAkEImAwAACzFdAgAAgoIL0gAAQFhZvXq10tPTFRcXp6ysLL333ntXbf/WW2/ptttuU9euXdWzZ099//vf15kzZ9r0ToIMAAAsZGSTx49i2rFodMuWLcrNzdWSJUtUWlqqoUOHasyYMaqoqGi1/Z49ezRjxgzNmjVLR44c0S9/+Uvt379fDz30UJveS5ABAICFLk2X+FPaasWKFZo1a5Yeeugh9e3bVytXrlRaWprWrFnTavv3339fN910kxYsWKD09HQNGTJEs2fP1oEDB9r0XoIMAABCUF1dnU9paGhotV1jY6NKSkqUnZ3tU5+dna29e/e2+sygQYN08uRJFRYWyhijjz/+WL/61a80bty4NvWRIAMAAAtduurdnyJJaWlpcjgc3lJQUNDq+06fPi23262UlBSf+pSUFFVXV7f6zKBBg/TWW29p6tSpio2NldPp1PXXX6+XX365TWNldwkAABZy+3kL66VnKysrlZiY6K232+1Xfc5m813LYYxpUXfJ0aNHtWDBAj399NMaPXq0qqqq9NhjjyknJ0fr1q275r4SZAAAYKHPZyPa+7wkJSYm+gQZV5KcnKyoqKgWWYuampoW2Y1LCgoKNHjwYD322GOSpFtvvVXdunXT0KFD9a//+q/q2bPnNfWV6RIAAMJYbGyssrKyVFRU5FNfVFSkQYMGtfrM+fPn1aWLb4gQFRUl6WIG5FqRyQAAwEIedZHHj7/jt+fZvLw8TZ8+Xf3799fAgQO1du1aVVRUKCcnR5K0ePFinTp1Sm+++aYkacKECXr44Ye1Zs0a73RJbm6u7rzzTqWmpl7zewkyAACwkNvY5PZjuqQ9z06dOlVnzpzRsmXLVFVVpczMTBUWFqp3796SpKqqKp8zMx588EHV19dr1apVWrhwoa6//nrdc889ev7559v0XptpS94jhNTV1cnhcGi47lW0LaajuwMA6MSaTZPe1dtyuVzXtM6hPS79XvqX9ybLfl37fy81nG3SmqFbg9rXQCGTAQCAhQK18DMUEGQAAGAh4+ctrIYL0gAAQKQLeJBRUFCgb3zjG0pISFCPHj00adIkffTRRz5tjDFaunSpUlNTFR8fr+HDh+vIkSM+bRoaGjR//nwlJyerW7dumjhxok6ePBno7gIAYCm3bH6XUBHwIKO4uFhz587V+++/r6KiIjU3Nys7O1vnzp3ztlm+fLlWrFihVatWaf/+/XI6nRo1apTq6+u9bXJzc7Vt2zZt3rxZe/bs0dmzZzV+/Hi53e5AdxkAAMt4jL9Hi3f0CK5dwNdk7Nixw+fnN954Qz169FBJSYm++c1vyhijlStXasmSJZo8ebIkacOGDUpJSdGmTZs0e/ZsuVwurVu3Tj//+c81cuRISdLGjRuVlpamXbt2afTo0YHuNgAACLCgr8lwuVySpO7du0uSysvLVV1d7XMbnN1u17Bhw7y3wZWUlKipqcmnTWpqqjIzM694Y1xDQ0OLG+kAAOhsPJ8t/PS
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 2 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"binarized = ensure_dtype_uint8(frame0_absdiff>threshold)\n",
|
||
|
|
"plt.imshow(binarized);\n",
|
||
|
|
"plt.colorbar();"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {
|
||
|
|
"nbgrader": {
|
||
|
|
"grade": false,
|
||
|
|
"grade_id": "cell-0338555f51c06105",
|
||
|
|
"locked": true,
|
||
|
|
"schema_version": 3,
|
||
|
|
"solution": false,
|
||
|
|
"task": false
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"Now we will use **connected components labeling** via the `ndimage.label()` function.\n",
|
||
|
|
"\n",
|
||
|
|
"We will do more with **connected components labeling** later. For now, you can [read about connected components labeling](https://en.wikipedia.org/wiki/Connected-component_labeling).\n",
|
||
|
|
"\n",
|
||
|
|
"We will use these labels to perform object detection. We will not bother to detect the fly on every single frame. Only on the frames where we have exactly one label do we say that we detected the fly. We skip the other frames, ignoring them."
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 16,
|
||
|
|
"metadata": {
|
||
|
|
"nbgrader": {
|
||
|
|
"grade": false,
|
||
|
|
"grade_id": "cell-9dae893dac3022a4",
|
||
|
|
"locked": true,
|
||
|
|
"schema_version": 3,
|
||
|
|
"solution": false,
|
||
|
|
"task": false
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 2 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"labels, num_labels = ndimage.label(binarized)\n",
|
||
|
|
"plt.imshow(labels, cmap='jet')\n",
|
||
|
|
"plt.colorbar();"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {
|
||
|
|
"nbgrader": {
|
||
|
|
"grade": false,
|
||
|
|
"grade_id": "cell-bf4c1b87ad01a8bb",
|
||
|
|
"locked": true,
|
||
|
|
"schema_version": 3,
|
||
|
|
"solution": false,
|
||
|
|
"task": false
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"## Questions Part B\n",
|
||
|
|
"\n",
|
||
|
|
"Run the object detection algorithm for each frame and plot the result. Here, we are going to make lists `frames`, `xs`, `ys` to save the results of our object detection step.\n",
|
||
|
|
"\n",
|
||
|
|
"Try re-running the above cells (from \"Questions Part A\") to the cell below with varying values of the `threshold` value until you get a good tracking result. You should ideally have zero warnings printed, but one or two are OK. "
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 17,
|
||
|
|
"metadata": {
|
||
|
|
"nbgrader": {
|
||
|
|
"grade": false,
|
||
|
|
"grade_id": "cell-cb7ec6b50e3d024a",
|
||
|
|
"locked": true,
|
||
|
|
"schema_version": 3,
|
||
|
|
"solution": false,
|
||
|
|
"task": false
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"WARNING: num_labels 2 on frame 93, skipping\n",
|
||
|
|
"WARNING: num_labels 2 on frame 94, skipping\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"frames = []\n",
|
||
|
|
"xs = []\n",
|
||
|
|
"ys = []\n",
|
||
|
|
"for i in range(n_frames):\n",
|
||
|
|
" frame = all_frames[i,:,:]\n",
|
||
|
|
" frame_absdiff = abs(frame - median_frame)\n",
|
||
|
|
" binarized = ensure_dtype_uint8(frame_absdiff>threshold)\n",
|
||
|
|
" labels, num_labels = ndimage.label(binarized)\n",
|
||
|
|
" if num_labels!=1:\n",
|
||
|
|
" print('WARNING: num_labels %d on frame %d, skipping' % (num_labels,i))\n",
|
||
|
|
" continue\n",
|
||
|
|
" y,x = np.mean(np.nonzero(labels==1),axis=1)\n",
|
||
|
|
" frames.append(i)\n",
|
||
|
|
" xs.append(x)\n",
|
||
|
|
" ys.append(y)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {
|
||
|
|
"nbgrader": {
|
||
|
|
"grade": false,
|
||
|
|
"grade_id": "cell-c1e4e9b97c4899cb",
|
||
|
|
"locked": true,
|
||
|
|
"schema_version": 3,
|
||
|
|
"solution": false,
|
||
|
|
"task": false
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"Make with an image of the first frame (saved in the variable `frame0` above) in the background and then the fly trajectory overlaid on top of the image.\n",
|
||
|
|
"\n",
|
||
|
|
"You can do this by first calling `plt.imshow()` and then `plt.plot()`. Your result should look like this:\n",
|
||
|
|
"\n",
|
||
|
|
"<img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAbAAAAEgCAYAAADVKCZpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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%0AC5OTkygWiyiVSvB6vTh8+LBNaYXD
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 18,
|
||
|
|
"metadata": {
|
||
|
|
"nbgrader": {
|
||
|
|
"grade": true,
|
||
|
|
"grade_id": "cell-7250d526d95bf828",
|
||
|
|
"locked": false,
|
||
|
|
"points": 1,
|
||
|
|
"schema_version": 3,
|
||
|
|
"solution": true
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAg8AAAGiCAYAAABgTyUPAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOz9eZBs23XeB345VGVlZWZl1lx3eg8PEyU2yA4KVFCEZJEiCMhs0RoY3ZRMtmyFZYtqUaBgSOEwg/+QjmgizAiTDJKWWmTTkkJsBOWOaFp2t5rNR8qCRENwo2HJIqkIAATecO+7Q81VOVdO/Ufht+s7q069AXgPuFUvV8SNW5V18px99rDWt7619tqF2Ww201zmMpe5zGUuc5nL65Ti17sBc5nLXOYyl7nM5XrJHDzMZS5zmctc5jKXNyRz8DCXucxlLnOZy1zekMzBw1zmMpe5zGUuc3lDMgcPc5nLXOYyl7nM5Q3JHDzMZS5zmctc5jKXNyRz8DCXucxlLnOZy1zekMzBw1zmMpe5zGUuc3lDMgcPc5nLXOYyl7nM5Q3JHDzMZS5zmctc5jKXNyRPPXj423/7b+u5557T0tKS3v/+9+tf/It/8fVu0lzmMpe5zGUub2t5qsHDP/pH/0gf/ehH9WM/9mP6V//qX+nf+Xf+HX3P93yPXn755a930+Yyl7nMZS5zedtK4Wk+GOvbvu3b9If+0B/S3/k7fyd99gf/4B/Un/2zf1Yf//jHv44tm8tc5jKXuczl7Svlr3cDrpKzszN99rOf1X/+n//nmc8//OEP61Of+tSl64fDoYbDYfp9Op3q8PBQ6+vrKhQKb3l75zKXucxlLnO5zjKbzdRut3X79m0Vi68emHhqwcP+/r4mk4m2t7czn29vb+vx48eXrv/4xz+un/iJn/haNW8uc5nLXOYylxsp9+/f1927d1/1mqcWPCCRNZjNZrlMwo/+6I/qYx/7WPr95OREzzzzjG7fvq3l5WWVSqUMkioWi5fuUygUVCwW03X8P51OM7/nCfcvFouveb3/nXZwbaFQUKlUUqFQuPJdX0uKxaLK5bKKxaJms1n6VygU0j/azO95bS0UClpYWEj3mU6nGo/HGo1Gmb7i/og/i/fld67j91KplL7DtcViMX3u7aOPZ7OZJpOJJpNJpg/9Op41mUxUKpUyz51MJqnNxWJRi4uLks7ZLh+Xq97HP4vXzWYzVSoVVSoVFYtFnZ2dpfFeWFhI79XpdFI/upTL5cy4jMfj9NlkMkl9NhqN1O/3NR6P03dLpVL6Lu/IfcrlsqrVqhYWFjJjNx6PNR6PNRwOVS6XVS6XNZvNdHZ2ptFopOl0qqWlpdQ/tKFcLmfGl76N84d5wz+fJz723IP+517ev9PpVKPRKDOOvL/Pab+/zzHmAvf3OTeZTBJz6WuRv9FOb5u/C5/RPtbgwsJC6s/xeJz6IPZHbL+3jXmxsLCgyWSS5inzO44D4x51HsLzh8NhejfWOW3kPfk79/G+oN2+1tB/3qe7u7t68OBBavdXK6wH1pP/zPwdj8caDAbq9Xoaj8ep3V9LcZ0RdeTTLo1G4zWveWrBw8bGhkql0iWWYXd39xIbISkp7CiNRiMtDOlikeYZAunCcLiCiYbMvxcXarlcTouHheSAQlJSvNHw8QyeFwFOVLLeBldsLKKr3hHh+SjZPFlcXFS5XNZkMkkgwAFPuVxOCtzb4v3lxiWCGDeUiCsk2lcqlVSpVDQajTSZTJJS9n7jfozFaDRKP7vhdQXo/Y2R5P1c0QMyUFwoR1fYvDfzLYI1fo7gS7pQ/IAiFwdfhUJBw+FQ7XZbg8FAo9EozdF6va5KpZLpU35mDvLsON6Aivge/j4LCwu5BmBhYUHl8oUqoc+4VwQNPj+YWw52fK362mE+8DeAld8vOggYlmhEGT/6Ln7Pxd8hGgQfV+l8vcxmswTEGG/mkN9zMplkwFCcC7SF57DO6GsfLzfcfI8x5Xfm5Gg00mg0SvqS9nKdr5XxeJzRQz6ucbx8jFmn5XJZzz77rJaXl/XFL35R/X5fX6kUCgUtLi6m+eb6jrYNh8Pk4AyHw9T3X2uJa1/K9s/TLq/HaX1qwcPi4qLe//736/nnn9ef+3N/Ln3+/PPP68/8mT/zuu8TFZuU9SzH4/Elw+UAQrrwWFxpoODz0GVUejxzOp1mgIF709wrCvfPM1R5Bjd64XmGkHu4hxG9QO4/m820sLCQvueeXRSMap6yzQMV9K0DGGdvCoVCUoB5BsCNsvehgxRJGePl/3sbfIz8Xu4Bch8ACh5O9CqGw6EGg0Hm3pLSZ9GgMp/imOYxOpKS8ofV8GdE79jn8Wg0utSHeePp7aANPk/jvMZA00eRcfCx8n5lXkdA6fPYx8Xby1qKgDV68lwL0MVg+1pgLAD+3gfOWsQ+imOUB4q5v681B5EAAv7GmEZdQ/t9LXk/xXkQWQ1n86KT4X2xsLCghYWFDKPj69XnvYuvO+9DnrexsSFJevHFF9XpdPRGBfC+tLSU1p+/K+/P+H69QMPbSZ5a8CBJH/vYx/QX/+Jf1Ld+67fq27/92/WLv/iLevnll/VX/+pffd33wAO4SpzS9nBFpK594YLM84BC9H5cokKMiyCKexF5jAPPiwo3em6uYPy+3MeVWnyOK8GorPz7XAv1eVUbvZ9RqFeBAqhIvK/hcJgMWVTabtz43JUjHnP0grnexzUqwmhM+Qzl76xBpKXjM6bTaaKw/Tr/rrfH54Ib2XK5rEqlkgzi2dmZBoOBjo+PLxkpxg3mJO8zxiXPY4rep49T9ErjGOaBaAcAEYQ7oKYPrqL3nbL3Pvb1DlDg2fzv32UcaBfz142tt9kN51WMiv8OC4Bx8zXvgAUgFfvS7/lqHmEEn+Px+JJzwtqhX33+eUjHgXReH+cBvjiPI4jc3NxUpVLRyy+/rP39/dfliftcX1xczOhM2gFYIAT3NIAGH4vX48VfR3mqwcOf//N/XgcHB/ov/ov/Qo8ePdL73vc+/ZN/8k/07LPPvu57vNbAuacATevIX1JGoTlTEa+Jnq7/Hg2bK6u46PO+/1rvEIFDZDXivePkzmMH+K7nAjhj4cox0r55hsSvjaCFfohG04210/dcyz0AGpFWd/YAo+H39j7hGm+L/2NHT7fbTd6XG5Kzs7OUF+DKjJgrShTw4AA0ApU8Y837LC4uqlqtamlpKfUnbIf3u4NP/74bYJQzMfVSqZQ8POYQFLHnVLBmUNYAOxgi3hVjFeeojwvtYg3CYpB34YbN5wQSc1rifAL8+H18HcT5EL/v34nzGqAS16g/y1nHyBDEvgAUebvywLUzH3m/0yZfq+5MuETWhmfGd6IP43URRPg4+DyuVCra3NxMc2tvb+9VdRuga2lpKTHIOAPMAV9jDhSfRolz/rrLU13n4auR09NTNZtNfcu3fMulJKSolEulkpaXl1WpVDJJaH4dShEjkEcz49VFSk+6rCTw+lzx0cYIHvIWfDQMkfaPzAZKmO86uKhUKpkF6eJJfig2VyBRWRcKhdy4vb8H10TlEo0J+QUoP/d2+HlhYSHTj8Ph8JIy9b7K82bzxsnDFNwHEAA4ODs7U6/XS0bbGYVoIKCd/Wc3iNEw85n3GQIwQaGS+1AsFlPeRgwfOevEOzCOKOG4PmhnuVzOKPBKpZKMPO32ORKNeWSYnM1yLx9jUS6XU1/iVUbwcJUHHIEB40h7uc7DTZ58G0FFuVxOCbW0dzQapX6I3rYbbPpuPB7r7OwsPTcaYJ9jrFMHytEgex9Ejz/P6DOW5Av49ZFJ8URrBzr0fWx/ZAKm04ukVn+/uNYHg4E+97nP6dGjR7khEMCsJx97f9Cnw+EwgcynzZRF5yiO+9MsJycnWllZedVrnmrm4c0QFqEbkDz6lUUADRwlGtZI7frzokTWISoBDwFEijcaS39+nIixTXn5BD6BHdB4EqErZN4JxRYVNYAieoKe9extc3DjXhN/iwbMWaD4fq6oi8Vipn2eke4sCv2St4h5RwcYKKl+v5+AJUlZDhQwFBjWpaWlBBB8bkXgkDeHfPzz5hO
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"# Type your answer here\n",
|
||
|
|
"plt.imshow(frame0,cmap='gray');\n",
|
||
|
|
"plt.plot(xs,ys,'x');"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {
|
||
|
|
"nbgrader": {
|
||
|
|
"grade": false,
|
||
|
|
"grade_id": "cell-06fde843bcd5b77e",
|
||
|
|
"locked": true,
|
||
|
|
"schema_version": 3,
|
||
|
|
"solution": false,
|
||
|
|
"task": false
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"Make a plot of the timeseries of the x and y values.\n",
|
||
|
|
"\n",
|
||
|
|
"We want a plot that looks like the following. You need to fix the following code to do this:\n",
|
||
|
|
"\n",
|
||
|
|
"```\n",
|
||
|
|
"fig, axes = plt.subplots(nrows=2, ncols=1, sharex=True)\n",
|
||
|
|
"axes[0].plot(time_values,x_values,'x')\n",
|
||
|
|
"axes[0].set_ylabel('x (px)')\n",
|
||
|
|
"axes[1].plot(time_values,y_values,'x')\n",
|
||
|
|
"axes[1].set_ylabel('y (px)')\n",
|
||
|
|
"axes[1].set_xlabel('time (frames)')\n",
|
||
|
|
"```\n",
|
||
|
|
"\n",
|
||
|
|
"As a side note, the `sharedx` links the two panels together so they have the same X axis. When using matplotlib interactively (highly recommended!!!), panning and zooming in one panel also pans and zooms the other panel. This is incredibly useful.\n",
|
||
|
|
"\n",
|
||
|
|
"<img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAoAAAAHgCAYAAAA10dzkAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90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%0AaCT3eevSesYpv/i4UudutOaxc36W
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 19,
|
||
|
|
"metadata": {
|
||
|
|
"nbgrader": {
|
||
|
|
"grade": true,
|
||
|
|
"grade_id": "cell-8680c2c7c9c5cb22",
|
||
|
|
"locked": false,
|
||
|
|
"points": 1,
|
||
|
|
"schema_version": 3,
|
||
|
|
"solution": true,
|
||
|
|
"task": false
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"Text(0.5, 0, 'time (frames)')"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 19,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 2 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"fig, axes = plt.subplots(nrows=2, ncols=1, sharex=True)\n",
|
||
|
|
"axes[0].plot(frames,xs,'x')\n",
|
||
|
|
"axes[0].set_ylabel('x (px)')\n",
|
||
|
|
"axes[1].plot(frames,ys,'x')\n",
|
||
|
|
"axes[1].set_ylabel('y (px)')\n",
|
||
|
|
"axes[1].set_xlabel('time (frames)')\n",
|
||
|
|
"# plt.savefig('fly-tracking2.png')"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"## Questions Part C\n",
|
||
|
|
"\n",
|
||
|
|
"Describe: How did we get the background image? How did we compute a binary image? What did we do with the binary image?"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {
|
||
|
|
"nbgrader": {
|
||
|
|
"grade": true,
|
||
|
|
"grade_id": "cell-0a341a483941e5cd",
|
||
|
|
"locked": false,
|
||
|
|
"points": 2,
|
||
|
|
"schema_version": 3,
|
||
|
|
"solution": true
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"source": [
|
||
|
|
"(Answer here.)"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"celltoolbar": "Create Assignment",
|
||
|
|
"kernelspec": {
|
||
|
|
"display_name": "Python 3 (ipykernel)",
|
||
|
|
"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": "3.11.10"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 4
|
||
|
|
}
|