How can I convert a point cloud saved in rosbag, in format sensor_msgs/PointCloud2, to .bin files in KITTI format?
I know that it is possible to convert to .pcd (http://wiki.ros.org/pcl_ros#pointcloud_to_pcd) so perhaps even a pcd to bin converter would be enough.
Is there any available tool to do this?
I've found this, but it needs ROS kinetic (legacy ROS version).
A python script to do it:
pc = pypcd.PointCloud.from_msg(msg)
x = pc.pc_data['x']
y = pc.pc_data['y']
z = pc.pc_data['z']
intensity = pc.pc_data['intensity']
arr = np.zeros(x.shape[0] + y.shape[0] + z.shape[0] + intensity.shape[0], dtype=np.float32)
arr[::4] = x
arr[1::4] = y
arr[2::4] = z
arr[3::4] = intensity
arr.astype('float32').tofile('filename.bin')
Where x,y,z and intensity are arrays for a single point cloud. It's not strictly needed to use pypcd. (Source)
Also this conversion tool can actually be used without ROS, using another tool for the conversion to pcd file.
I'm studying opencv and dlib, for a face detector to use on a university project, and I'm really new at this whole thing of machine learning and computer vision. How can I use the evaluation code from FDDB to evaluate my code for face detection? I'm using dlib's CNN method for detecting faces from images.
import cv2
import dlib
image = cv2.imread('..\\pessoas\\beatles.jpg')
detector = dlib.cnn_face_detection_model_v1("..\\mmods\\mmod_human_face_detector.dat")
detectedFaces = detector(image)
for face in detectedFaces:
l, t, r, b, c = (int(face.rect.left()), int(face.rect.top()), int(face.rect.right()), int(face.rect.bottom()),
face.confidence)
cv2.rectangle(image, (l, t), (r, b), (255, 0, 0), 2)
cv2.imshow("CNN Detector", image)
cv2.waitKey(0)
cv2.destroyAllWindows()
As you can see the code is pretty simple, but I have to calculate precision, recall, and F1-score to plot the ROC curves and I'm don't know yet how to do it, the readme on the project's github doesn't help.
As to me in ubuntu16, I have to done it by the following steps:
Download the fddb original images dataset which you detect face and get detection result.You can download it here.Here is my directory:
Join all the images file path to a txt file , and join all the fddb annotations to a txt file.
You can download all the files here
As to me I move all the FDDB-FOLD-%d.txt to the directory all_file_path, and then join them to one file by cat * > filePath.txt
Join all the FDDB-fold-%d-ellipseList.txt to one txt by cat *ellipse*.txt > annotFile.txt
Note you may no need to create it, because runEvaluate.pl have do it for you during the running process.
3.Create FDDB evalute exe, download the source code here here
And then compile it , you may change the makefile, see the reason here, add
INCS = -I/usr/local/include/opencv
LIBS = -L/usr/local/lib -lopencv_core -lopencv_imgproc -lopencv_highgui
-lopencv_ml -lopencv_video -lopencv_features2d -lopencv_calib3d
-lopencv_objdetect -lopencv_contrib -lopencv_legacy
to the make file.
Evaluate, you can use the runEvaluate.pl to evaluate it , but as to me(ubuntu16), I can’t run it directly.
4.1 change the GUNPLOT path(you should install gnuplot first using it to create ROC image )
4.2 I use rectangle detection model, so I change $detFormat to 0.
my $detFormat = 0; # 0: rectangle, 1: ellipse 2: pixels
4.3 All the images relative path:
my $listFile ="/home/xy/face_sample/evaluation/compareROC/FDDB-folds/filePath.txt";
4.4 All the images annotations
my $annotFile = "/home/xy/face_sample/evaluation/compareROC/FDDB-folds/annotFile.txt";
4.5 The roc file you want to generate(created by evaluate exe):
my $gpFile ="/home/xy/face_sample/evaluation/compareROC/createROC.p";
4.6 You detection file (I will give how to create it latter)
my $detFile ="/home/xy/face_sample/evaluation/compareROC/detDir/fddb_rect_ret1.txt";
It’s content like that:
The ‘runEvaluate.pl’ have some error, change the execute evaluation to the below:
system($evaluateBin, "-a", $annotFile, "-d", $detFile, "-f", $detFormat, "-i", $imDir, "-l", $listFile, "-r", $detDir, "-z", ".jpg");
You can also use command to check it:
xy#xy:~/face_sample/evaluation/compareROC$ ./evaluate \
> -a /home/xy/face_sample/evaluation/compareROC/FDDB-folds/annotFile.txt \
> -d /home/xy/face_sample/evaluation/compareROC/detDir/fddb_rect_ret1.txt \
> -f 0 \
> -i /home/xy/face_sample/evaluation/compareROC/originalPics/ \
> -l /home/xy/face_sample/evaluation/compareROC/FDDB-folds/filePath.txt \
> -r /home/xy/face_sample/evaluation/compareROC/detDir/ \
> -z .jpg
Use python to create fddb evaluation txt file:
def get_img_relative_path():
"""
:return: ['2002/08/11/big/img_344', '2002/08/02/big/img_473', ......]
"""
f_name = 'E:/face_rec/face__det_rec_code/face_det/FDDB-folds/all_img_files.txt'
lst_name = open(f_name).read().split('\n')
return lst_name
def write_lines_to_txt(lst):
# lst = ['line1', 'line2', 'line3']
f_path = 'fddb_rect_ret.txt'
with open(f_path, 'w') as fp:
for line in lst:
fp.write("%s\n" % line)
# For example use opencv to face detection
def detect_face_lst(img):
"""
:param img: opencv image
:return: face rectangles [[x, y, w, h], ..........]
"""
m_path = 'D:/opencv/sources/data/haarcascades/haarcascade_frontalface_default.xml'
face_cascade = cv2.CascadeClassifier(m_path)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, 1.3, 5)
return faces
def generate_fddb_ret():
# The directory from which we get the test images from FDDB
img_base_dir = 'E:/face_rec/face__det_rec_code/face_det/originalPics/'
# All the images relative path, like '['2002/08/11/big/img_344', '2002/08/02/big/img_473', ......]'
lst_img_name = get_img_relative_path()
# Store detect result, like:
# ['2002/08/11/big/img_344', '1', '10 10 50 50 1', .............]
lst_write2_fddb_ret = []
try:
for img_name in lst_img_name:
img_full_name = img_base_dir + img_name + '.jpg'
img = cv2.imread(img_full_name)
if img == None:
print 'error %s not exists, can not generate complete fddb evaluate file' % img_full_name
return -1
lst_face_rect = detect_face_lst(img)
# append img name like '2002/08/11/big/img_344'
lst_write2_fddb_ret.append(img_name)
face_num = len(lst_face_rect)
# append face num, note if no face 0 should be append
lst_write2_fddb_ret.append(str(face_num))
if face_num > 0:
# append each face rectangle x y w h score
for face_rect in lst_face_rect:
# append face rectangle x, y, w, h score
# note: opencv hava no confidence so use 1 here
s_rect = " ".join(str(item) for item in face_rect) + " 1"
lst_write2_fddb_ret.append(s_rect)
except Exception as e:
print 'error %s , can not generate complete fddb evaluate file' % e
return -1
# Write all the result to txt for FDDB evaluation
write_lines_to_txt(lst_write2_fddb_ret)
After run the above code you can create FDDB result:
Note: when you create the above txt in windows, if you test it in ubuntu you may get the following errorIncompatible annotation and detection files. See output specifications:
Just copy the content to a new txt file(created in ubuntu) then it solves.
Here is the result:
Some tips:
You can see the runEvaluate.pl it's not hard, the above changes may not be needed.You can also change some variable in runEvaluate.pl, like $GNUPLOT, $imDir and so on.
add "-z", ".jpg" to
system($evaluateBin, "-a", $annotFile, "-d", $detFile, "-f", $detFormat, "-i", $imDir, "-l", $listFile, "-r", $detDir);
system($evaluateBin, "-a", $annotFile, "-d", $detFile, "-f", $detFormat, "-i", $imDir, "-l", $listFile, "-r", $detDir, "-z", ".jpg");
You can also read the evaluate code(mainly the evaluate.cpp which is easy to understand ), so you will have a deep understand of how to evaluate it.
can you explain the step you are in?
You need to download the labelled data from:
http://vis-www.cs.umass.edu/fddb/ where it says: Download the database
After that you need to download the result source code:
http://vis-www.cs.umass.edu/fddb/results.html
Then you need to modify your program so that the output looks like this:
2002/08/11/big/img_591
1
191 88 164 163 0
2002/08/26/big/img_265
3
52 39 95 95 0
282 59 114 114 0
Where first is the name of the image,
then the number of faces in that image,
then the coordenates for each face and repeat...
I advice you to build the evaluation on linux since it's a lot easier (at least for me it was).
Hope it helps.
Is it possible to get the most left bin value after a peak as in the image.
I had tried the syntax below but it always return of coz the first bin (0,0).
cv::Point min_loc, max_loc;
cv::minMaxLoc(g_hist, &min, &max, &min_loc, &max_loc );
cout<<"\nMax Histo: "<<max;
cout<<"\nmax_loc: "<<max_loc;
l_hist: [0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0; 0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0; 0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;
0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;28.571428;400;166.96428;63.392857;0;33.92857;88.392853;18.75;3.5714285;0;0;0;0;0;0;0;0; 0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0; 0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;0;
0;0;0;0;0;0;0;0;0;0]
the bin is 165. I need to get the number of the bin.
I'm performing NDVI calculation on a Planet Scope 4 band image as per Planet's documentation
The following block of code is what I wrote:
Extract band data from original image in working directory
import rasterio import numpy
image_file = "20170430_194027_0c82_3B_AnalyticMS"
with rasterio.open(image_file) as src: band_red = src.read(3)
with rasterio.open(image_file) as src: band_nir = src.read(4)
from xml.dom import minidom
xmldoc = minidom.parse("20170430_194027_0c82_3B_AnalyticMS_metadata") nodes = xmldoc.getElementsByTagName("ps:bandSpecificMetadata")
Extract TOA correction coefficients from metadata file in directory
TOA_coeffs = {} for node in nodes: bn = node.getElementsByTagName("ps:bandNumber")[0].firstChild.data if bn in ['1', '2', '3', '4']:
i = int(bn)
value = node.getElementsByTagName("ps:ReflectanceCoefficient")[0].firstChild.data
TOA_coeffs[1] = float(value)
Calculate NDVI and save file
band_red = band_red * TOA_coeffs[3] band_nir = band_nir * TOA_coeffs[4]
numpy.seterr(divide = 'ignore', invalid = 'ignore')
NDVI = (band_nir.astype(float) - band_red.astype(float))/(band_nir + band_red) numpy.nanmin(NDVI), numpy.nanmax(NDVI)
kwargs = src.meta kwargs.update(dtype=rasterio.float32,
count = 1)
with rasterio.open('ndvi.tif', 'W', **kwargs) as dst: dst.write_band(1, NDVI.astype(rasterio.float32))
Add symbology and plot color bar
import matplotlib.pyplot as plt import matplotlib.colors as colors
class MidpointNormalize(colors.Normalize): def __init__(self, vmin=None, vmax=None, midpoint=None, clip=False):
self.midpoint = midpoint
colors.Normalize.__init__(self, vmin, vmax, clip)
def __call__(self, value, clip=None):
x, y = [self.vmin, self.midpoint, self.vmax], [0, 0.5, 1]
return numpy.ma.masked_array(numpy.interp(value, x, y), >numpy.isnan(value))
min = numpy.nanmin(NDVI) min = numpy.nanmax(NDVI) mid = 0.1
fig = plt.figure(figsize= (20,10)) ax = fig.add_subplot(111)
cmap = plt.cm.RdYlGn
cax = ax.imshow(NDVI, cmap=cmap, clim=(min,max),
>norm=MidpointNormalize(midpoint=mid, vmin=min, vmax=max))
ax.axis('off') ax.set_title('NDVI_test', fontsize= 18, fontweight='bold')
cbar = fig.colorbar(cax, orientation= 'horizontal', shrink=0.65)
fig.savefig("output/NDVI_test.png", dpi=200, bbox_inches='tight',
>pad_inches=0.7)
plt.show()
Plot histogram for NDVI pixel value distribution
fig2 = plt.figure(figsize=(10,10)) ax = fig2.add_subplot(111)
plt.title("NDVI Histogram", fontsize=18, fontweight='bold') plt.xlabel("NDVI values", fontsize=14) plt.ylabel("# pixels", fontsize=14)
x = NDVI[~numpy.isnan(NDVI)] numBins = 20 ax.hist(x,numBins,color='green',alpha=0.8)
fig2.savefig("output/ndvi-histogram.png", dpi=200, bbox_inches='tight', >pad_inches=0.7)
plt.show()
Alas, the execution of the script is cut short at the beginning of the code:
File "C:/Users/David/Desktop/ArcGIS files/Planet Labs/2017.6_Luis_Bedin_Bolivia/planet_order_58311/20170430_194027_0c82/TOA_correction_NDVI.py", line 8, in <module>
import rasterio
ModuleNotFoundError: No module named 'rasterio'
So I decide to install rasterio, that should solve the problem:
C:\Users\David\Desktop\ArcGIS files\Planet Labs\2017.6_Luis_Bedin_Bolivia\planet_order_58311\20170430_194027_0c82>pip install rasterio
Collecting rasterio
Using cached rasterio-0.36.0.tar.gz
Requirement already satisfied: affine in c:\users\david\anaconda3\lib\site-packages (from rasterio)
Requirement already satisfied: cligj in c:\users\david\anaconda3\lib\site-packages (from rasterio)
Requirement already satisfied: numpy in c:\users\david\anaconda3\lib\site-packages (from rasterio)
Requirement already satisfied: snuggs in c:\users\david\anaconda3\lib\site-packages (from rasterio)
Requirement already satisfied: click-plugins in c:\users\david\anaconda3\lib\site-packages (from rasterio)
What I interpret from this is that rasterio is already installed. How can this be if the Python console tells me there's no module named rasterio. The output from the console also says Microsoft Visual C++ is required. Upon further research I find this user's solution. I tried it but the console also tells me that rasterio is already installed:
(envpythonfs) C:\Users\David\Desktop\ArcGIS files\Planet Labs\2017.6_Luis_Bedin_Bolivia\planet_order_58311\20170430_194027_0c82>conda install rasterio gdal
Fetching package metadata .............
Solving package specifications: .
# All requested packages already installed.
# packages in environment at C:\Users\David\Anaconda3\envs\envpythonfs:
#
I'm creating the script using Spyder 3.1.2 with Python 3.6 on a Windows 10 64-bit machine.
I think pip is not the best way to go for making sure dependencies are handled appropriately. Since you're already using anaconda, I would suggest:
conda install rasterio -c conda-forge/label/dev
Note that installing from the dev labeled version is not the long term solution (see https://github.com/conda-forge/rasterio-feedstock/pull/36).
I have a large dataset of x,y coordinates in "NAD 1983 StatePlane Michigan South FIPS 2113 Feet" (aka ESRI 102690). I'd like to convert them to lat-lng points.
In theory, this is something proj is built to handle, but the documentation hasn't given me a clue -- it seems to describe much more complicated cases.
I've tried using a python interface, like so:
from pyproj import Proj
p = Proj(init='esri:102690')
sx = 13304147.06410000000 #sample points
sy = 288651.94040000000
x2, y2 = p(sx, sy, inverse=True)
But that gives wildly incorrect output.
There's a Javascript library, but I have ~50,000 points to handle, so that doesn't seem appropriate.
What worked for me:
I created a file called ptest with each pair on its own line, x and y coordinates separated by a space, like so:
13304147.06410000000 288651.94040000000
...
Then I fed that file into the command and piped the results to an output file:
$>cs2cs -f %.16f +proj=lcc +lat_1=42.1 +lat_2=43.66666666666666
+lat_0=41.5 +lon_0=-84.36666666666666 +x_0=4000000 +y_0=0 +ellps=GRS80
+datum=NAD83 +to_meter=0.3048006096012192 +no_defs +zone=20N +to
+proj=latlon ptest > out.txt
If you only need to reproject and can do some data-mining on your text files use whatever you like and use http://spatialreference.org/ref/esri/102690/ as reference.
For example use the Proj4 and store it in a shell/cmd file and call out your input file with proj4 (linux/windows version available) no problem with the size your dataset.
cs2cs +proj=latlong +datum=NAD83 +to +proj=utm +zone=10 +datum=NAD27 -r <<EOF
cs2cs -f %.16f +proj=utm +zone=20N +to +proj=latlon - | awk '{print $1 " " $2}
so in your case something like this:
cs2cs -f %.16f +proj=lcc +lat_1=42.1 +lat_2=43.66666666666666 +lat_0=41.5 +lon_0=-84.36666666666666 +x_0=4000000 +y_0=0 +ellps=GRS80 +datum=NAD83 +to_meter=0.3048006096012192 +no_defs +zone=20N +to +proj=latlon
http://trac.osgeo.org/proj/wiki/man_cs2cs
http://trac.osgeo.org/proj/
If you have coordinates in TXT, CSV or XLS file, you can do CTRL+C and insert them to http://cs2cs.mygeodata.eu where you can set appropriate input and desired output coordinate system. It is possible to insert thousands of coordinates in various formats...