{ "cells": [ { "cell_type": "markdown", "id": "e18b6699", "metadata": {}, "source": [ "# Radar Image Data Exploration\n", "\n", "Eric Martin \n", "MBARI 2021\n", "\n", "This assumes you've built the binaries in this directory, the goal of this notebook is to demonstrate the following:\n", "\n", "1. Processing a Radar PCAP file into three CSV files: (1) Radar image data (2) radar configuration messages, and (3) the AHRS data. \n", "2. Plotting a single scan of radar data." ] }, { "cell_type": "markdown", "id": "ffc58746", "metadata": {}, "source": [ "## Process the PCAP File\n", "To date the project has been collecting the data through a raw packet capture tool, `tshark`. We have written C tools to go through and parse these files into CSV files of interest for broader accessibility. Some sample data is present in the repository. " ] }, { "cell_type": "markdown", "id": "97fb8719", "metadata": {}, "source": [ "### Radar Settings Reports" ] }, { "cell_type": "code", "execution_count": 1, "id": "335e9399", "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "total 1635120\r\n", "-rw-r--r-- 1 emartin emartin 737B Oct 4 11:43 mbariAtSeaWecRadar_00315_202109\r\n", "-rwxrwxrwx 1 emartin emartin 200M Sep 28 17:20 \u001b[31mmbariAtSeaWecRadar_00315_20210928171518.pcapng\u001b[m\u001b[m\r\n", "-rwxrwxrwx@ 1 emartin emartin 200M Sep 28 17:25 \u001b[31mmbariAtSeaWecRadar_00316_20210928172018.pcapng\u001b[m\u001b[m\r\n", "-rwxrwxrwx@ 1 emartin emartin 200M Sep 30 14:20 \u001b[31mmbariAtSeaWecRadar_00684_20210930000030.pcapng\u001b[m\u001b[m\r\n", "-rw-r--r-- 1 emartin emartin 200M Oct 5 21:07 mbariAtSeaWecRadar_02325_20211005164626.pcapng\r\n" ] } ], "source": [ "!ls -lh ../data/" ] }, { "cell_type": "markdown", "id": "555dfc75", "metadata": {}, "source": [ "We are pretty interested in the biggest day observed yet, 9/28/2021. Let's process that one. First let's process the radar data. The program, if built and installed should print out help with no inputs:" ] }, { "cell_type": "code", "execution_count": 2, "id": "a1c32660", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "radar packet parser, usage pcaptoradar \r", "\r\n", " flags: -a | -b : print report data for channels A or B\r", "\r\n", " -h : add csv header line for reports, use with -a/-b\r", "\r\n", " -A | -B : print scanline data for channels A or B\r", "\r\n", " -H : add csv header line for scan data, use with -A/-B\r", "\r\n", " -s : print statistics for information decodable in the file\r", "\r\n", " -q : quiet mode, suppress output\r", "\r\n" ] } ], "source": [ "!pcaptoradar" ] }, { "cell_type": "markdown", "id": "4fdc69ac", "metadata": {}, "source": [ "Let's make sure there are data in the file: " ] }, { "cell_type": "code", "execution_count": 3, "id": "6c6a7d0d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Opening file: ../data/mbariAtSeaWecRadar_02325_20211005164626.pcapng\n", "----------------\n", "REPORTS_A: 300\n", "REPORTS_B: 300\n", "FRAMES_A: 11462\n", " SCANS: 366784\n", " REVS_A: 89\n", "AHRS: 140817\n", "----------------\n" ] } ], "source": [ "!pcaptoradar -s ../data/mbariAtSeaWecRadar_02325_20211005164626.pcapng" ] }, { "cell_type": "markdown", "id": "64b12aa9", "metadata": {}, "source": [ "Yes, there are reports data for both channels on the radar, and many Frames of sonar data, covering 367K radar scan lines, which add up to 89 full revolutions of the radar for the entire file.\n", "\n", "Next, let's convert the reports, and just print the header:" ] }, { "cell_type": "code", "execution_count": 4, "id": "c39f2c53", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Opening file: ../data/mbariAtSeaWecRadar_02325_20211005164626.pcapng\n", "time,channel,range,gain,sea_auto,sea,rain,interference_rejection,target_expansion,target_boost\n", "1633477586.071070,3,5000,60,0,25,0,0,1,1\n", "1633477587.070790,3,5000,60,0,25,0,0,1,1\n", "1633477588.070737,3,5000,60,0,25,0,0,1,1\n" ] } ], "source": [ "!pcaptoradar -ah ../data/mbariAtSeaWecRadar_02325_20211005164626.pcapng | head -n4" ] }, { "cell_type": "markdown", "id": "4afa729d", "metadata": {}, "source": [ "We can see that this isn't changing, which is good, at the time of this analysis, there is little change happening on the radar setting, and all of these values make sense (but range is 500.0meters, so something is a bit off). If we wanted to make a file for all the reports we simply redirecto the stdout to a file, as so:" ] }, { "cell_type": "code", "execution_count": 5, "id": "4094ea20", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Opening file: ../data/mbariAtSeaWecRadar_02325_20211005164626.pcapng\r\n" ] } ], "source": [ "!pcaptoradar -ah ../data/mbariAtSeaWecRadar_02325_20211005164626.pcapng > reports_a.csv" ] }, { "cell_type": "code", "execution_count": 6, "id": "00d53c0d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-rw-r--r-- 1 emartin emartin 633K Oct 6 05:55 ahrs.csv\r\n", "-rw-r--r-- 1 emartin emartin 12K Oct 8 10:59 reports_a.csv\r\n", "-rw-r--r-- 1 emartin emartin 738M Oct 6 15:19 scans.csv\r\n" ] } ], "source": [ "!ls -lh *.csv" ] }, { "cell_type": "code", "execution_count": 7, "id": "2251ca2e", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " time channel range gain sea_auto sea rain \\\n", "0 1.633478e+09 3 5000 60 0 25 0 \n", "1 1.633478e+09 3 5000 60 0 25 0 \n", "2 1.633478e+09 3 5000 60 0 25 0 \n", "3 1.633478e+09 3 5000 60 0 25 0 \n", "4 1.633478e+09 3 5000 60 0 25 0 \n", "\n", " interference_rejection target_expansion target_boost \n", "0 0 1 1 \n", "1 0 1 1 \n", "2 0 1 1 \n", "3 0 1 1 \n", "4 0 1 1 " ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "reports_df = pd.read_csv('reports_a.csv')\n", "reports_df.head()" ] }, { "cell_type": "code", "execution_count": 24, "id": "f9f63792", "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "reports_df['time'].diff().plot(figsize=(12,4))" ] }, { "cell_type": "markdown", "id": "ca65d411", "metadata": {}, "source": [ "### AHRS Data Processing\n", "Much like the reports data, we can generate csv files using the `pcaptoahrs` tool." ] }, { "cell_type": "code", "execution_count": 10, "id": "4346b735", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ahrs lcm parser, usage pcaptoahrs \r\n", "\toptions: -h (enable csv header)\r\n" ] } ], "source": [ "!pcaptoahrs" ] }, { "cell_type": "code", "execution_count": 11, "id": "d6b28bc9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Opening file: ../data/mbariAtSeaWecRadar_02325_20211005164626.pcapng\r\n", "pcap_time,lcm_time,latitude,longitude,yaw_true,pitch,roll,x_accel,y_accel,z_accel,n_velocity,e_velocity,d_velocity,altitude,x_rate,y_rate,z_rate, x_rate_temp,\r\n", "1633477586.057278,1633477586.057142,36.743225097656250,-121.881103515625000,123.420410,-1.235962,-2.949829,-0.060730,0.033875,-1.022949,0.007812,-0.070312,-0.039062,-32.750000,-3.729858,-10.997314,-0.307617,29.324341,\r\n" ] } ], "source": [ "!pcaptoahrs -h ../data/mbariAtSeaWecRadar_02325_20211005164626.pcapng | head -n2" ] }, { "cell_type": "code", "execution_count": 12, "id": "70049579", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Opening file: ../data/mbariAtSeaWecRadar_02325_20211005164626.pcapng\r\n" ] } ], "source": [ "!pcaptoahrs -h ../data/mbariAtSeaWecRadar_02325_20211005164626.pcapng > ahrs.csv" ] }, { "cell_type": "code", "execution_count": 13, "id": "c1287840", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-rw-r--r-- 1 emartin emartin 633K Oct 8 11:00 ahrs.csv\r\n", "-rw-r--r-- 1 emartin emartin 12K Oct 8 10:59 reports_a.csv\r\n", "-rw-r--r-- 1 emartin emartin 738M Oct 6 15:19 scans.csv\r\n" ] } ], "source": [ "!ls -lh *.csv" ] }, { "cell_type": "code", "execution_count": 14, "id": "b24eee91", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " pcap_time lcm_time latitude longitude yaw_true pitch \\\n", "0 1.633478e+09 1.633478e+09 36.743225 -121.881104 123.420410 -1.235962 \n", "1 1.633478e+09 1.633478e+09 36.743225 -121.881104 123.431396 -2.169800 \n", "2 1.633478e+09 1.633478e+09 36.743225 -121.881104 123.436890 -2.609253 \n", "3 1.633478e+09 1.633478e+09 36.743225 -121.881104 123.425903 -2.466431 \n", "4 1.633478e+09 1.633478e+09 36.743225 -121.881104 123.414917 -1.823730 \n", "\n", " roll x_accel y_accel z_accel n_velocity e_velocity d_velocity \\\n", "0 -2.949829 -0.060730 0.033875 -1.022949 0.007812 -0.070312 -0.039062 \n", "1 -3.180542 -0.114746 0.066528 -1.022339 0.007812 -0.078125 -0.023438 \n", "2 -3.147583 -0.144958 0.088501 -1.024170 0.031250 -0.164062 -0.054688 \n", "3 -2.834473 -0.145264 0.095215 -1.020813 0.054688 -0.265625 -0.078125 \n", "4 -2.296143 -0.108032 0.087585 -1.022949 0.046875 -0.273438 -0.039062 \n", "\n", " altitude x_rate y_rate z_rate x_rate_temp Unnamed: 18 \n", "0 -32.75 -3.729858 -10.997314 -0.307617 29.324341 NaN \n", "1 -32.75 -0.961304 -7.305908 -0.269165 29.321289 NaN \n", "2 -32.75 1.807251 -1.730347 0.000000 29.318237 NaN \n", "3 -32.75 4.441223 3.864441 0.192261 29.318237 NaN \n", "4 -32.75 6.152344 8.555603 0.076904 29.315186 NaN " ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "ahrs_df = pd.read_csv('ahrs.csv')\n", "ahrs_df.head()" ] }, { "cell_type": "code", "execution_count": 27, "id": "c9397e5a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1633477586.057278" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.to_datetime(ahrs_df['pcap_time'][0], " ] }, { "cell_type": "markdown", "id": "44e9affb", "metadata": {}, "source": [ "### Radar Scan Line Processing\n", "Our last pcap file process is to take the file, restructure the packets, and turn it into something we can parse in in a higher level language, like python:" ] }, { "cell_type": "code", "execution_count": 41, "id": "0761b147", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Opening file: ../data/mbariAtSeaWecRadar_02325_20211005164626.pcapng\r\n" ] } ], "source": [ "!pcaptoradar -AH ../data/mbariAtSeaWecRadar_02325_20211005164626.pcapng > scans.csv" ] }, { "cell_type": "markdown", "id": "00f6802d", "metadata": {}, "source": [ "That took a while, but now we have the file in CSV form, let's take a look at how large it ended up." ] }, { "cell_type": "code", "execution_count": 42, "id": "049a17cd", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-rw-r--r-- 1 emartin emartin 633K Oct 6 05:55 ahrs.csv\r\n", "-rw-r--r-- 1 emartin emartin 12K Oct 6 05:55 reports_a.csv\r\n", "-rw-r--r-- 1 emartin emartin 738M Oct 6 15:19 scans.csv\r\n" ] } ], "source": [ "!ls -lh *.csv" ] }, { "cell_type": "markdown", "id": "017014b1", "metadata": {}, "source": [ "So in the end, from a 200MB file, we are generating another file that is 745MB. Let's see if we can parse the file with pandas, and then the fun can begin. " ] }, { "cell_type": "markdown", "id": "5405a7b3", "metadata": {}, "source": [ "### CSV Reading" ] }, { "cell_type": "code", "execution_count": 43, "id": "b03b174a", "metadata": {}, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 44, "id": "0621b5e6", "metadata": {}, "outputs": [], "source": [ "#Set the dtypes to improve memory performance on import\n", "dtypes_scans = {'time':'float',\n", " 'channel':'string',\n", " 'scan_line':'uint16',\n", " 'heading':'int32',\n", " 'angle':'uint16',\n", " 'angle_d':'float',\n", " 'range':'uint16',\n", " } \n", "for i in range(1024):\n", " colname = f'r{i:04}'\n", " dtypes_scans[colname]='uint8'" ] }, { "cell_type": "code", "execution_count": 45, "id": "42964b83", "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 22.5 s, sys: 961 ms, total: 23.5 s\n", "Wall time: 23.5 s\n" ] } ], "source": [ "%%time\n", "df_scans = pd.read_csv('scans.csv', dtype=dtypes_scans)" ] }, { "cell_type": "code", "execution_count": 46, "id": "e8656712", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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timechannelscan_lineheadingangleangle_dranger0000r0001r0002...r1015r1016r1017r1018r1019r1020r1021r1022r1023Unnamed: 1031
3667791.633478e+09A2748-3276860152.822266808151515...000000000NaN
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3667831.633478e+09A2752-3276860953.525391808151515...000000000NaN
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5 rows × 1032 columns

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" ], "text/plain": [ " time channel scan_line heading angle angle_d range \\\n", "366779 1.633478e+09 A 2748 -32768 601 52.822266 808 \n", "366780 1.633478e+09 A 2749 -32768 603 52.998047 808 \n", "366781 1.633478e+09 A 2750 -32768 605 53.173828 808 \n", "366782 1.633478e+09 A 2751 -32768 607 53.349609 808 \n", "366783 1.633478e+09 A 2752 -32768 609 53.525391 808 \n", "\n", " r0000 r0001 r0002 ... r1015 r1016 r1017 r1018 r1019 r1020 \\\n", "366779 15 15 15 ... 0 0 0 0 0 0 \n", "366780 15 15 15 ... 0 0 0 0 0 0 \n", "366781 15 15 15 ... 0 0 0 0 0 0 \n", "366782 15 15 15 ... 0 0 0 0 0 0 \n", "366783 15 15 15 ... 0 0 0 0 0 0 \n", "\n", " r1021 r1022 r1023 Unnamed: 1031 \n", "366779 0 0 0 NaN \n", "366780 0 0 0 NaN \n", "366781 0 0 0 NaN \n", "366782 0 0 0 NaN \n", "366783 0 0 0 NaN \n", "\n", "[5 rows x 1032 columns]" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_scans.tail()" ] }, { "cell_type": "code", "execution_count": 47, "id": "d7389d2d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 366784 entries, 0 to 366783\n", "Columns: 1032 entries, time to Unnamed: 1031\n", "dtypes: float64(3), int32(1), string(1), uint16(3), uint8(1024)\n", "memory usage: 372.9 MB\n" ] } ], "source": [ "df_scans.info()" ] }, { "cell_type": "markdown", "id": "79b1a565", "metadata": {}, "source": [ "### Working with one scan" ] }, { "cell_type": "markdown", "id": "a11f5b6c", "metadata": {}, "source": [ "Let's find the first full revolution, `scan_line` set to 0." ] }, { "cell_type": "code", "execution_count": 20, "id": "fd9f31a4", "metadata": {}, "outputs": [], "source": [ "# Get the first full scan\n", "idx = df_scans.index[df_scans['angle'] == 1].to_list()" ] }, { "cell_type": "code", "execution_count": 21, "id": "162cb013", "metadata": {}, "outputs": [], "source": [ "\n", "scan = df_scans.loc[idx[40]:idx[41]-1]" ] }, { "cell_type": "code", "execution_count": 39, "id": "dfec7444", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "count 2048.000000\n", "mean 2048.000000\n", "std 1182.701991\n", "min 1.000000\n", "25% 1024.500000\n", "50% 2048.000000\n", "75% 3071.500000\n", "max 4095.000000\n", "Name: angle, dtype: float64" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "scan.angle.describe()" ] }, { "cell_type": "markdown", "id": "2e61541b", "metadata": {}, "source": [ "Good! there are 2048 scans in one full revolution, we have something to look at." ] }, { "cell_type": "code", "execution_count": 23, "id": "59ed37c4", "metadata": {}, "outputs": [], "source": [ "from PIL import Image as im\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 24, "id": "1b7b11e8", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "240\n" ] } ], "source": [ "# Get an uint8 array from the scans\n", "scan_data = scan.iloc[:, 7:-1].to_numpy()*16\n", "max_scan = np.amax(scan_data)\n", "print(max_scan)" ] }, { "cell_type": "code", "execution_count": 25, "id": "bdb826fd", "metadata": {}, "outputs": [], "source": [ "image_ = im.fromarray(scan_data)\n", "image_.save('scan_nonpolar.jpg')" ] }, { "cell_type": "markdown", "id": "5d37a1d4", "metadata": {}, "source": [ "Ok, let's see the result: " ] }, { "cell_type": "markdown", "id": "e0c87d67", "metadata": {}, "source": [ "### Polar Graphic Generation\n", "The final step in creating a monochrome image is to reshape the array into a cartesian one. At the end of the day, we should have a square bound by the radius of the spokes, or $2*(1024)=2048$. Let's take a brute force attempt at this. " ] }, { "cell_type": "code", "execution_count": 26, "id": "2ae95737", "metadata": {}, "outputs": [], "source": [ "#create an array to pack\n", "cart = np.zeros((2048,2048),dtype='uint8')" ] }, { "cell_type": "code", "execution_count": 27, "id": "4009066e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(0, 1024)" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# def a remapping function \n", "def map_polar(angle,length, max_angle=2048, max_length=1024):\n", " '''\n", " angle in decimal degrees\n", " length of array to output\n", " \n", " returns -- x and y arrays for index mapping\n", " '''\n", " ANG2RAD = 2*np.pi/max_angle\n", " x = length * np.cos(angle*ANG2RAD)\n", " y = length * np.sin(angle*ANG2RAD)\n", " \n", " ## shift into positive spaces\n", " x = np.round(x)\n", " y = np.round(y)\n", " x += max_length\n", " y += max_length\n", " return (int(x),int(y))\n", "\n", "map_polar(1024,1024)" ] }, { "cell_type": "code", "execution_count": 28, "id": "6f561dee", "metadata": {}, "outputs": [], "source": [ "# Super stupidly, let's go line by line and write over the data for the one before\n", "it = np.nditer(scan_data, flags=['multi_index'])\n", "for x in it:\n", " if x == 0:\n", " continue\n", " r,c = it.multi_index\n", " crow,ccol = map_polar(r,c)\n", " cart[crow,ccol] = x\n", " " ] }, { "cell_type": "code", "execution_count": 29, "id": "598f6727", "metadata": {}, "outputs": [], "source": [ "# make an image from the cartesian converted array\n", "cart_img = im.fromarray(cart)\n", "cart_img.save('scan_polar.jpg')" ] }, { "cell_type": "markdown", "id": "8f09ee58", "metadata": {}, "source": [ "Here is the result " ] }, { "cell_type": "code", "execution_count": 30, "id": "21ad7647", "metadata": {}, "outputs": [], "source": [ "import cv2" ] }, { "cell_type": "code", "execution_count": 38, "id": "f44305b6", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cart_color = cv2.applyColorMap(cart, cv2.COLORMAP_HOT)\n", "cv2.imwrite('scan_polar_hot.jpg',cart_color)" ] }, { "cell_type": "markdown", "id": "84d47c7a", "metadata": {}, "source": [ "Here is the result " ] }, { "cell_type": "markdown", "id": "28c57cf0", "metadata": {}, "source": [ "### Adding in a yaw offset\n", "\n", "Just a little sandbox area for me to sort out the best way to get a single yaw offset for the whole revolution." ] }, { "cell_type": "code", "execution_count": 44, "id": "f24174e1", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1632874586.014958 1632874587.689166 1.674208164215088\n" ] } ], "source": [ "start_t = scan.time.iloc[0]\n", "end_t = scan.time.iloc[-1]\n", "print(start_t, end_t, end_t-start_t)" ] }, { "cell_type": "code", "execution_count": 59, "id": "de42676a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 3005 entries, 0 to 3004\n", "Data columns (total 19 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 pcap_time 3005 non-null float64\n", " 1 lcm_time 3005 non-null float64\n", " 2 latitude 3005 non-null float64\n", " 3 longitude 3005 non-null float64\n", " 4 yaw_true 3005 non-null float64\n", " 5 pitch 3005 non-null float64\n", " 6 roll 3005 non-null float64\n", " 7 x_accel 3005 non-null float64\n", " 8 y_accel 3005 non-null float64\n", " 9 z_accel 3005 non-null float64\n", " 10 n_velocity 3005 non-null float64\n", " 11 e_velocity 3005 non-null float64\n", " 12 d_velocity 3005 non-null float64\n", " 13 altitude 3005 non-null float64\n", " 14 x_rate 3005 non-null float64\n", " 15 y_rate 3005 non-null float64\n", " 16 z_rate 3005 non-null float64\n", " 17 x_rate_temp 3005 non-null float64\n", " 18 Unnamed: 18 0 non-null float64\n", "dtypes: float64(19)\n", "memory usage: 446.2 KB\n" ] } ], "source": [ "ahrs_df.info()" ] }, { "cell_type": "code", "execution_count": 72, "id": "09283a73", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "numpy.float64" ] }, "execution_count": 72, "metadata": {}, "output_type": "execute_result" } ], "source": [ "scan_angles = ahrs_df.loc[(ahrs_df.lcm_time >= start_t) & (ahrs_df.lcm_time <=end_t)]\n", "scan_angles.yaw_true.describe()\n", "type(scan_angles.yaw_true.mean())" ] }, { "cell_type": "code", "execution_count": 71, "id": "70ca2922", "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "unsupported operand type(s) for +: 'int' and 'NoneType'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;36m10\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for +: 'int' and 'NoneType'" ] } ], "source": [ "10+None" ] }, { "cell_type": "markdown", "id": "a72146dd", "metadata": {}, "source": [ "## Processing the whole file (VERY INEFFICIENTLY)" ] }, { "cell_type": "markdown", "id": "02321f7c", "metadata": {}, "source": [ "Let's turn this into and full script, here are the contents of [wec_radar_movie.py](wec_radar_movie.py) file subject to change: " ] }, { "cell_type": "code", "execution_count": 73, "id": "766f9520", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "import numpy as np\r\n", "import pandas as pd\r\n", "from pandas.core.frame import DataFrame\r\n", "\r\n", "\r\n", "class RadarScans():\r\n", "\r\n", " def __init__(self, csvfile:str, ahrs_csv:str = 'None', mode='cartesian'):\r\n", " print(f\"opening file: {csvfile}\")\r\n", " self.scan_df = RadarScans.open_csv_file(csvfile)\r\n", " self.ahrs_df = RadarScans.open_ahrs_file(ahrs_csv)\r\n", " self.initialize_scan_indexes()\r\n", " self.mode=mode\r\n", "\r\n", " def open_csv_file(csvfile:str):\r\n", " dtypes_scans = {'time':'float',\r\n", " 'channel':'string',\r\n", " 'scan_line':'uint16',\r\n", " 'heading':'int32',\r\n", " 'angle':'uint16',\r\n", " 'angle_d':'float',\r\n", " 'range':'uint16',\r\n", " } \r\n", " for i in range(1024):\r\n", " colname = f'r{i:04}'\r\n", " dtypes_scans[colname]='uint8'\r\n", "\r\n", " scan_df = pd.read_csv(csvfile, dtype=dtypes_scans)\r\n", " return scan_df\r\n", "\r\n", " def open_ahrs_file(csvfile):\r\n", " ahrs_df = pd.read_csv(csvfile)\r\n", " return ahrs_df\r\n", "\r\n", " def initialize_scan_indexes(self):\r\n", " self.indexes = self.scan_df.index[self.scan_df['angle']==1].to_list()\r\n", " self.num_scans = len(self.indexes)-1\r\n", " print(f'found {self.num_scans} full revolutions in file.')\r\n", "\r\n", "\r\n", " def select_scan(self, n:int):\r\n", " if (n >= self.num_scans):\r\n", " return None;\r\n", "\r\n", " idx = self.indexes\r\n", " scan = self.scan_df.loc[idx[n]:idx[n+1]-1]\r\n", " return scan\r\n", "\r\n", " def scan_angle_offset(self, scan:DataFrame):\r\n", "\r\n", " # Guard against uninitialized access\r\n", " if self.ahrs_df is None:\r\n", " raise Exception('No AHRS data present')\r\n", " \r\n", " # Find the bounds of the time window\r\n", " start_t = scan.time.iloc[0]\r\n", " end_t = scan.time.iloc[-1]\r\n", "\r\n", " yaw_true_mean = self.ahrs_df.loc[(self.ahrs_df.lcm_time >= start_t) & (self.ahrs_df.lcm_time <=end_t)].yaw_true.mean();\r\n", " return yaw_true_mean\r\n", "\r\n", "\r\n", "\r\n", "\r\n", " def convert_to_polar(self, scan, scan_df, returns_per_scanline=1024):\r\n", "\r\n", " #create an array to pack\r\n", " out = np.zeros((2*returns_per_scanline,2*returns_per_scanline),dtype='uint8')\r\n", " it = np.nditer(scan, flags=['multi_index'])\r\n", " if self.ahrs_df is None: \r\n", " yaw_angle=0 \r\n", " else: \r\n", " yaw_angle = self.scan_angle_offset(scan_df)\r\n", " for x in it:\r\n", " r,c = it.multi_index\r\n", " if x == 0:\r\n", " continue\r\n", " crow,ccol = RadarScans.map_polar(r,c, yaw=yaw_angle)\r\n", " out[crow,ccol] = x\r\n", " return out\r\n", "\r\n", " def map_polar(angle,length, yaw=0, max_angle=2048, max_length=1024):\r\n", " '''\r\n", " angle in integer representaion, scaled by Max_angle (default 0-2048)\r\n", " length of array to output\r\n", "\r\n", " returns -- x and y arrays for index mapping\r\n", " '''\r\n", " DEG2RAD = np.pi/180\r\n", " ANG2RAD = 2*np.pi/max_angle\r\n", " f_angle = ANG2RAD*angle\r\n", " f_angle += yaw*DEG2RAD\r\n", " x = length * np.cos(f_angle)\r\n", " y = length * np.sin(f_angle)\r\n", " \r\n", " ## shift into positive spaces\r\n", " x = np.round(x)\r\n", " y = np.round(y)\r\n", " x += max_length\r\n", " y += max_length\r\n", " return (int(x),int(y)) \r\n", "\r\n", " def __iter__(self):\r\n", " self.current_scan = 0\r\n", " return self\r\n", "\r\n", " def __next__(self):\r\n", " \"\"\"Returns a ndarray of uint8 that can be used for image/movie conversion\r\n", " \"\"\"\r\n", " scan =self.select_scan(self.current_scan)\r\n", " if scan is None:\r\n", " raise StopIteration\r\n", "\r\n", " out = scan.iloc[:, 7:-1].to_numpy()*16\r\n", " if self.mode == 'polar':\r\n", " out = self.convert_to_polar(out, scan)\r\n", "\r\n", " \r\n", "\r\n", " self.current_scan += 1\r\n", " return out\r\n", " \r\n", "if __name__ == '__main__':\r\n", " from PIL import Image\r\n", "\r\n", " fname = 'scans.csv'\r\n", " scanner = RadarScans(fname, ahrs_csv='ahrs.csv', mode='polar')\r\n", "\r\n", " cnt = 0\r\n", "\r\n", " for scan in scanner:\r\n", " image = Image.fromarray(scan)\r\n", " im_name = f'./img/scan_pa_{cnt:04}.jpg'\r\n", " print(f'Writing image: {im_name}...')\r\n", " image.save(im_name) \r\n", " cnt+=1\r\n", "\r\n", "\r\n" ] } ], "source": [ "!cat wec_radar_movie.py" ] }, { "cell_type": "markdown", "id": "a418edf1", "metadata": {}, "source": [ "Now we have a set of images that we can turn into a movie. There are many ways to achieve this task, for now we will use [ffmpeg](https://ffmpeg.org/). " ] }, { "cell_type": "code", "execution_count": 2, "id": "5f4cd986", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ffmpeg version 4.4 Copyright (c) 2000-2021 the FFmpeg developers\n", " built with Apple clang version 12.0.0 (clang-1200.0.32.29)\n", " configuration: --prefix=/usr/local/Cellar/ffmpeg/4.4_2 --enable-shared --enable-pthreads --enable-version3 --cc=clang --host-cflags= --host-ldflags= --enable-ffplay --enable-gnutls --enable-gpl --enable-libaom --enable-libbluray --enable-libdav1d --enable-libmp3lame --enable-libopus --enable-librav1e --enable-librubberband --enable-libsnappy --enable-libsrt --enable-libtesseract --enable-libtheora --enable-libvidstab --enable-libvorbis --enable-libvpx --enable-libwebp --enable-libx264 --enable-libx265 --enable-libxml2 --enable-libxvid --enable-lzma --enable-libfontconfig --enable-libfreetype --enable-frei0r --enable-libass --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libopenjpeg --enable-libspeex --enable-libsoxr --enable-libzmq --enable-libzimg --disable-libjack --disable-indev=jack --enable-avresample --enable-videotoolbox\n", " libavutil 56. 70.100 / 56. 70.100\n", " libavcodec 58.134.100 / 58.134.100\n", " libavformat 58. 76.100 / 58. 76.100\n", " libavdevice 58. 13.100 / 58. 13.100\n", " libavfilter 7.110.100 / 7.110.100\n", " libavresample 4. 0. 0 / 4. 0. 0\n", " libswscale 5. 9.100 / 5. 9.100\n", " libswresample 3. 9.100 / 3. 9.100\n", " libpostproc 55. 9.100 / 55. 9.100\n", "Input #0, image2, from './img/scan_p_%04d.jpg':\n", " Duration: 00:00:14.92, start: 0.000000, bitrate: N/A\n", " Stream #0:0: Video: mjpeg (Baseline), gray(bt470bg/unknown/unknown), 2048x2048 [SAR 1:1 DAR 1:1], 12 fps, 12 tbr, 12 tbn, 12 tbc\n", "Stream mapping:\n", " Stream #0:0 -> #0:0 (mjpeg (native) -> h264 (libx264))\n", "Press [q] to stop, [?] for help\n", "\u001b[1;36m[libx264 @ 0x7fcc0c008e00] \u001b[0musing SAR=1/1\n", "\u001b[1;36m[libx264 @ 0x7fcc0c008e00] \u001b[0musing cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 BMI2 AVX2\n", "\u001b[1;36m[libx264 @ 0x7fcc0c008e00] \u001b[0mprofile High, level 5.0, 4:2:0, 8-bit\n", "\u001b[1;36m[libx264 @ 0x7fcc0c008e00] \u001b[0m264 - core 163 r3060 5db6aa6 - H.264/MPEG-4 AVC codec - Copyleft 2003-2021 - http://www.videolan.org/x264.html - options: cabac=1 ref=3 deblock=1:0:0 analyse=0x3:0x113 me=hex subme=7 psy=1 psy_rd=1.00:0.00 mixed_ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=1 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=-2 threads=24 lookahead_threads=4 sliced_threads=0 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=3 b_pyramid=2 b_adapt=1 b_bias=0 direct=1 weightb=1 open_gop=0 weightp=2 keyint=250 keyint_min=12 scenecut=40 intra_refresh=0 rc_lookahead=40 rc=crf mbtree=1 crf=23.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=1:1.00\n", "Output #0, mp4, to 'output.mp4':\n", " Metadata:\n", " encoder : Lavf58.76.100\n", " Stream #0:0: Video: h264 (avc1 / 0x31637661), yuv420p(tv, bt470bg/unknown/unknown, progressive), 2048x2048 [SAR 1:1 DAR 1:1], q=2-31, 12 fps, 12288 tbn\n", " Metadata:\n", " encoder : Lavc58.134.100 libx264\n", " Side data:\n", " cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: N/A\n", "frame= 179 fps= 44 q=-1.0 Lsize= 9146kB time=00:00:14.66 bitrate=5108.4kbits/s speed=3.63x \n", "video:9143kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.032609%\n", "\u001b[1;36m[libx264 @ 0x7fcc0c008e00] \u001b[0mframe I:16 Avg QP:17.88 size: 88040\n", "\u001b[1;36m[libx264 @ 0x7fcc0c008e00] \u001b[0mframe P:55 Avg QP:24.82 size: 46904\n", "\u001b[1;36m[libx264 @ 0x7fcc0c008e00] \u001b[0mframe B:108 Avg QP:25.82 size: 49754\n", "\u001b[1;36m[libx264 @ 0x7fcc0c008e00] \u001b[0mconsecutive B-frames: 15.6% 6.7% 15.1% 62.6%\n", "\u001b[1;36m[libx264 @ 0x7fcc0c008e00] \u001b[0mmb I I16..4: 18.3% 75.9% 5.8%\n", "\u001b[1;36m[libx264 @ 0x7fcc0c008e00] \u001b[0mmb P I16..4: 0.3% 10.4% 3.7% P16..4: 1.4% 0.3% 0.1% 0.0% 0.0% skip:83.8%\n", "\u001b[1;36m[libx264 @ 0x7fcc0c008e00] \u001b[0mmb B I16..4: 0.1% 1.4% 3.0% B16..8: 14.1% 0.9% 0.3% direct: 0.3% skip:79.8% L0:50.8% L1:48.1% BI: 1.1%\n", "\u001b[1;36m[libx264 @ 0x7fcc0c008e00] \u001b[0m8x8 transform intra:67.2% inter:33.8%\n", "\u001b[1;36m[libx264 @ 0x7fcc0c008e00] \u001b[0mcoded y,uvDC,uvAC intra: 19.6% 0.0% 0.0% inter: 1.4% 0.0% 0.0%\n", "\u001b[1;36m[libx264 @ 0x7fcc0c008e00] \u001b[0mi16 v,h,dc,p: 88% 7% 5% 0%\n", "\u001b[1;36m[libx264 @ 0x7fcc0c008e00] \u001b[0mi8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 51% 6% 39% 1% 1% 1% 1% 1% 1%\n", "\u001b[1;36m[libx264 @ 0x7fcc0c008e00] \u001b[0mi4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 19% 13% 38% 6% 5% 6% 5% 5% 4%\n", "\u001b[1;36m[libx264 @ 0x7fcc0c008e00] \u001b[0mi8c dc,h,v,p: 100% 0% 0% 0%\n", "\u001b[1;36m[libx264 @ 0x7fcc0c008e00] \u001b[0mWeighted P-Frames: Y:0.0% UV:0.0%\n", "\u001b[1;36m[libx264 @ 0x7fcc0c008e00] \u001b[0mref P L0: 68.6% 1.4% 21.7% 8.3%\n", "\u001b[1;36m[libx264 @ 0x7fcc0c008e00] \u001b[0mref B L0: 82.0% 15.8% 2.2%\n", "\u001b[1;36m[libx264 @ 0x7fcc0c008e00] \u001b[0mref B L1: 95.1% 4.9%\n", "\u001b[1;36m[libx264 @ 0x7fcc0c008e00] \u001b[0mkb/s:5020.83\n" ] } ], "source": [ "!ffmpeg -framerate 12 -i ./img/scan_p_%04d.jpg -vf format=yuv420p output.mp4" ] }, { "cell_type": "code", "execution_count": 28, "id": "25c0a17e", "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import Video\n", "Video('output.mp4', width=400)" ] }, { "cell_type": "code", "execution_count": 83, "id": "cced6e55", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2.4" ] }, "execution_count": 83, "metadata": {}, "output_type": "execute_result" } ], "source": [ "frame_period = 300/180\n", "1/frame_period*4" ] }, { "cell_type": "code", "execution_count": 84, "id": "898a419f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ffmpeg version 4.4 Copyright (c) 2000-2021 the FFmpeg developers\n", " built with Apple clang version 12.0.0 (clang-1200.0.32.29)\n", " configuration: --prefix=/usr/local/Cellar/ffmpeg/4.4_2 --enable-shared --enable-pthreads --enable-version3 --cc=clang --host-cflags= --host-ldflags= --enable-ffplay --enable-gnutls --enable-gpl --enable-libaom --enable-libbluray --enable-libdav1d --enable-libmp3lame --enable-libopus --enable-librav1e --enable-librubberband --enable-libsnappy --enable-libsrt --enable-libtesseract --enable-libtheora --enable-libvidstab --enable-libvorbis --enable-libvpx --enable-libwebp --enable-libx264 --enable-libx265 --enable-libxml2 --enable-libxvid --enable-lzma --enable-libfontconfig --enable-libfreetype --enable-frei0r --enable-libass --enable-libopencore-amrnb --enable-libopencore-amrwb --enable-libopenjpeg --enable-libspeex --enable-libsoxr --enable-libzmq --enable-libzimg --disable-libjack --disable-indev=jack --enable-avresample --enable-videotoolbox\n", " libavutil 56. 70.100 / 56. 70.100\n", " libavcodec 58.134.100 / 58.134.100\n", " libavformat 58. 76.100 / 58. 76.100\n", " libavdevice 58. 13.100 / 58. 13.100\n", " libavfilter 7.110.100 / 7.110.100\n", " libavresample 4. 0. 0 / 4. 0. 0\n", " libswscale 5. 9.100 / 5. 9.100\n", " libswresample 3. 9.100 / 3. 9.100\n", " libpostproc 55. 9.100 / 55. 9.100\n", "Input #0, image2, from './img/scan_pa_%04d.jpg':\n", " Duration: 00:01:14.58, start: 0.000000, bitrate: N/A\n", " Stream #0:0: Video: mjpeg (Baseline), gray(bt470bg/unknown/unknown), 2048x2048 [SAR 1:1 DAR 1:1], 2.40 fps, 2.40 tbr, 2.40 tbn, 2.40 tbc\n", "Stream mapping:\n", " Stream #0:0 -> #0:0 (mjpeg (native) -> h264 (libx264))\n", "Press [q] to stop, [?] for help\n", "\u001b[1;36m[libx264 @ 0x7fa41a008e00] \u001b[0musing SAR=1/1\n", "\u001b[1;36m[libx264 @ 0x7fa41a008e00] \u001b[0musing cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 BMI2 AVX2\n", "\u001b[1;36m[libx264 @ 0x7fa41a008e00] \u001b[0mprofile High, level 5.0, 4:2:0, 8-bit\n", "\u001b[1;36m[libx264 @ 0x7fa41a008e00] \u001b[0m264 - core 163 r3060 5db6aa6 - H.264/MPEG-4 AVC codec - Copyleft 2003-2021 - http://www.videolan.org/x264.html - options: cabac=1 ref=3 deblock=1:0:0 analyse=0x3:0x113 me=hex subme=7 psy=1 psy_rd=1.00:0.00 mixed_ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=1 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=-2 threads=24 lookahead_threads=4 sliced_threads=0 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=3 b_pyramid=2 b_adapt=1 b_bias=0 direct=1 weightb=1 open_gop=0 weightp=2 keyint=250 keyint_min=2 scenecut=40 intra_refresh=0 rc_lookahead=40 rc=crf mbtree=1 crf=23.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=1:1.00\n", "Output #0, mp4, to 'output_corrected_4x.mp4':\n", " Metadata:\n", " encoder : Lavf58.76.100\n", " Stream #0:0: Video: h264 (avc1 / 0x31637661), yuv420p(tv, bt470bg/unknown/unknown, progressive), 2048x2048 [SAR 1:1 DAR 1:1], q=2-31, 2.40 fps, 12288 tbn\n", " Metadata:\n", " encoder : Lavc58.134.100 libx264\n", " Side data:\n", " cpb: bitrate max/min/avg: 0/0/0 buffer size: 0 vbv_delay: N/A\n", "frame= 179 fps= 46 q=-1.0 Lsize= 13055kB time=00:01:13.33 bitrate=1458.4kbits/s speed=18.8x \n", "video:13052kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 0.022872%\n", "\u001b[1;36m[libx264 @ 0x7fa41a008e00] \u001b[0mframe I:28 Avg QP: 9.11 size:121326\n", "\u001b[1;36m[libx264 @ 0x7fa41a008e00] \u001b[0mframe P:61 Avg QP:18.43 size: 59969\n", "\u001b[1;36m[libx264 @ 0x7fa41a008e00] \u001b[0mframe B:90 Avg QP:19.67 size: 70108\n", "\u001b[1;36m[libx264 @ 0x7fa41a008e00] \u001b[0mconsecutive B-frames: 26.8% 14.5% 11.7% 46.9%\n", "\u001b[1;36m[libx264 @ 0x7fa41a008e00] \u001b[0mmb I I16..4: 48.4% 45.6% 5.9%\n", "\u001b[1;36m[libx264 @ 0x7fa41a008e00] \u001b[0mmb P I16..4: 0.8% 9.9% 3.2% P16..4: 1.2% 0.3% 0.1% 0.0% 0.0% skip:84.5%\n", "\u001b[1;36m[libx264 @ 0x7fa41a008e00] \u001b[0mmb B I16..4: 0.1% 1.6% 3.2% B16..8: 12.2% 1.0% 0.4% direct: 0.3% skip:81.3% L0:45.6% L1:52.5% BI: 1.9%\n", "\u001b[1;36m[libx264 @ 0x7fa41a008e00] \u001b[0m8x8 transform intra:49.6% inter:38.8%\n", "\u001b[1;36m[libx264 @ 0x7fa41a008e00] \u001b[0mcoded y,uvDC,uvAC intra: 16.0% 0.0% 0.0% inter: 1.3% 0.0% 0.0%\n", "\u001b[1;36m[libx264 @ 0x7fa41a008e00] \u001b[0mi16 v,h,dc,p: 95% 3% 2% 0%\n", "\u001b[1;36m[libx264 @ 0x7fa41a008e00] \u001b[0mi8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 44% 7% 43% 1% 1% 1% 1% 1% 1%\n", "\u001b[1;36m[libx264 @ 0x7fa41a008e00] \u001b[0mi4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 20% 13% 35% 6% 5% 5% 6% 5% 5%\n", "\u001b[1;36m[libx264 @ 0x7fa41a008e00] \u001b[0mi8c dc,h,v,p: 100% 0% 0% 0%\n", "\u001b[1;36m[libx264 @ 0x7fa41a008e00] \u001b[0mWeighted P-Frames: Y:0.0% UV:0.0%\n", "\u001b[1;36m[libx264 @ 0x7fa41a008e00] \u001b[0mref P L0: 76.8% 1.4% 17.6% 4.3%\n", "\u001b[1;36m[libx264 @ 0x7fa41a008e00] \u001b[0mref B L0: 89.6% 9.8% 0.6%\n", "\u001b[1;36m[libx264 @ 0x7fa41a008e00] \u001b[0mref B L1: 95.9% 4.1%\n", "\u001b[1;36m[libx264 @ 0x7fa41a008e00] \u001b[0mkb/s:1433.56\n" ] } ], "source": [ "!ffmpeg -framerate 2.4 -i ./img/scan_pa_%04d.jpg -vf format=yuv420p output_corrected_4x.mp4" ] }, { "cell_type": "code", "execution_count": 32, "id": "3e6fd527", "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import Video\n", "Video('output_corrected.mp4', width=400)" ] }, { "cell_type": "code", "execution_count": null, "id": "3a68d6b6", "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": "3.8.6" } }, "nbformat": 4, "nbformat_minor": 5 }