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I created my graph, everything looks great so far, but I want to update color of my nodes after creation.
My goal is to visualize DFS, I will first show the initial graph and then color nodes step by step as DFS solves the problem.
If anyone is interested, sample code is available on Github
All you need is to specify a color map which maps a color to each node and send it to nx.draw function. To clarify, for a 20 node I want to color the first 10 in blue and the rest in green. The code will be as follows:
G = nx.erdos_renyi_graph(20, 0.1)
color_map = []
for node in G:
if node < 10:
color_map.append('blue')
else:
color_map.append('green')
nx.draw(G, node_color=color_map, with_labels=True)
plt.show()
You will find the graph in the attached image.
Refer to node_color parameter:
nx.draw_networkx_nodes(G, pos, node_size=200, node_color='#00b4d9')
has been answered before, but u can do this as well:
# define color map. user_node = red, book_nodes = green
color_map = ['red' if node == user_id else 'green' for node in G]
graph = nx.draw_networkx(G,pos, node_color=color_map) # node lables
In my case, I had 2 groups of nodes (from sklearn.model_selection import train_test_split). I wanted to change the color of each group (default color are awful!). It took me while to figure it out how to change it but, Tensor is numpy based and Matplotlib is the core of networkx library. Therefore ...
test=data.y
test=test.numpy()
test=test.astype(np.str_)
test[test == '0'] = '#C6442A'
test[test == '1'] = '#9E2AC6'
nx.draw(G, with_labels=True, node_color=test, node_size=400, font_color='whitesmoke')
Long story short: convert the Tensor in numpy array with string type, check your best Hex color codes for HTML (https://htmlcolorcodes.com/) and you are ready to go!
I am trying to do a basic colour conversion in python however I can't seem to get past the below error. I have re-installed python, opencv and tried on both python 3.4.3 (latest) and python 2.7 (which is on my Mac).
I installed opencv using python's package manager opencv-python.
Here is the code that fails:
frame = cv2.imread('frames/frame%d.tiff' % count)
frame_HSV= cv2.cvtColor(frame,cv2.COLOR_RGB2HSV)
This is the error message:
cv2.error: OpenCV(3.4.3) /Users/travis/build/skvark/opencv-python/opencv/modules/imgproc/src/color.cpp:181: error: (-215:Assertion failed) !_src.empty() in function 'cvtColor'
This error happened because the image didn't load properly. So you have a problem with the previous line cv2.imread. My suggestion is :
check if the image exists in the path you give
check if the count variable has a valid number
If anyone is experiencing this same problem when reading a frame from a webcam:
Verify if your webcam is being used on another task and close it. This wil solve the problem.
I spent some time with this error when I realized my camera was online in a google hangouts group. Also, Make sure your webcam drivers are up to date
I kept getting this error too:
Traceback (most recent call last):
File "face_detector.py", line 6, in <module>
gray_img=cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
cv2.error: OpenCV(4.1.0) C:\projects\opencv-python\opencv\modules\imgproc\src\color.cpp:182: error: (-215:Assertion failed) !_src.empty() in function 'cv::cvtColor
My cv2.cvtColor(...) was working fine with \photo.jpg but not with \news.jpg. For me, I finally realized that when working on Windows with python, those escape characters will get you every time!! So my "bad" photo was being escaped because of the file name beginning with "n". Python took the \n as an escape character and OpenCV couldn't find the file!
Solution:
Preface file names in Windows python with r"...\...\" as in
cv2.imread(r".\images\news.jpg")
If the path is correct and the name of the image is OK, but you are still getting the error
use:
from skimage import io
img = io.imread(file_path)
instead of:
cv2.imread(file_path)
The function imread loads an image from the specified file and returns
it. If the image cannot be read (because of missing file, improper permissions, unsupported or invalid format), the function returns an empty matrix ( Mat::data==NULL ).
check if the image exists in the path and verify the image extension (.jpg or .png)
Check whether its the jpg, png, bmp file that you are providing and write the extension accordingly.
Another thing which might be causing this is a 'weird' symbol in your file and directory names. All umlaut (äöå) and other (éóâ etc) characters should be removed from the file and folder names. I've had this same issue sometimes because of these characters.
Most probably there is an error in loading the image, try checking directory again.
Print the image to confirm if it actually loaded or not
In my case, the image was incorrectly named. Check if the image exists and try
import numpy as np
import cv2
img = cv2.imread('image.png', 0)
cv2.imshow('image', img)
I've been in same situation as well, and My case was because of the Korean letter in the path...
After I remove Korean letters from the folder name, it works.
OR put
[#-*- coding:utf-8 -*-]
(except [ ] at the edge)
or something like that in the first line to make python understand Korean or your language or etc.
then it will work even if there is some Koreans in the path in my case.
So the things is, it seems like there is something about path or the letter.
People who answered are saying similar things. Hope you guys solve it!
I had the same problem and it turned out that my image names included special characters (e.g. château.jpg), which could not bet handled by cv2.imread. My solution was to make a temporary copy of the file, renaming it e.g. temp.jpg, which could be loaded by cv2.imread without any problems.
Note: I did not check the performance of shutil.copy2 vice versa other options. So probably there is a better/faster solution to make a temporary copy.
import shutil, sys, os, dlib, glob, cv2
for f in glob.glob(os.path.join(myfolder_path, "*.jpg")):
shutil.copy2(f, myfolder_path + 'temp.jpg')
img = cv2.imread(myfolder_path + 'temp.jpg')
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
os.remove(myfolder_path + 'temp.jpg')
If there are only few files with special characters, renaming can also be done as an exeption, e.g.
for f in glob.glob(os.path.join(myfolder_path, "*.jpg")):
try:
img = cv2.imread(f)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
except:
shutil.copy2(f, myfolder_path + 'temp.jpg')
img = cv2.imread(myfolder_path + 'temp.jpg')
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
os.remove(myfolder_path + 'temp.jpg')
In my case it was a permission issue. I had to:
chmod a+wrx the image,
then it worked.
must please see guys that the error is in the cv2.imread() .Give the right path of the image. and firstly, see if your system loads the image or not. this can be checked first by simple load of image using cv2.imread().
after that ,see this code for the face detection
import numpy as np
import cv2
cascPath = "/Users/mayurgupta/opt/anaconda3/lib/python3.7/site- packages/cv2/data/haarcascade_frontalface_default.xml"
eyePath = "/Users/mayurgupta/opt/anaconda3/lib/python3.7/site-packages/cv2/data/haarcascade_eye.xml"
smilePath = "/Users/mayurgupta/opt/anaconda3/lib/python3.7/site-packages/cv2/data/haarcascade_smile.xml"
face_cascade = cv2.CascadeClassifier(cascPath)
eye_cascade = cv2.CascadeClassifier(eyePath)
smile_cascade = cv2.CascadeClassifier(smilePath)
img = cv2.imread('WhatsApp Image 2020-04-04 at 8.43.18 PM.jpeg')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, 1.3, 5)
for (x,y,w,h) in faces:
img = cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)
roi_gray = gray[y:y+h, x:x+w]
roi_color = img[y:y+h, x:x+w]
eyes = eye_cascade.detectMultiScale(roi_gray)
for (ex,ey,ew,eh) in eyes:
cv2.rectangle(roi_color,(ex,ey),(ex+ew,ey+eh),(0,255,0),2)
cv2.imshow('img',img)
cv2.waitKey(0)
cv2.destroyAllWindows()
Here, cascPath ,eyePath ,smilePath should have the right actual path that's picked up from lib/python3.7/site-packages/cv2/data here this path should be to picked up the haarcascade files
Your code can't find the figure or the name of your figure named the by error message.
Solution:
import cv2
import numpy as np
import matplotlib.pyplot as plt
img=cv2.imread('哈哈.jpg')#solution:img=cv2.imread('haha.jpg')
print(img)
If anyone is experiencing this same problem when reading a frame from a webcam [with code similar to "frame = cv2.VideoCapture(0)"] and work in Jupyter Notebook, you may try:
ensure previously tried code is not running already and restart Jupyter Notebook kernel
SEPARATE code "frame = cv2.VideoCapture(0)" in separate cell on place where it is [previous code put in cell above, code under put to cell down]
then run all the code above cell where is "frame = cv2.VideoCapture(0)"
then try run next cell with its only code "frame = cv2.VideoCapture(0)" - AND - till you will continue in executing other cells - ENSURE - that ASTERIX on the left side of this particular cell DISAPEAR and command order number appear instead - only then continue
now you can try execute the rest of your code as your camera input should not be empty anymore :-)
After end, ensure you close all your program and restart kernel to prepare it for another run
As #shaked litbak , this error arised with my initial use with the ASCII-generator , as i naively thought i just had to add to the ./data directory , with its load automatically .
I had to append the --input option with the desired file path .
I checked my image file path and it was correct. I made sure there was no corrupt images.The problem was with my mac. It sometimes have a hidden file called .DS_Store which was saved together with the image file path. Therefore cv2 was having a problem with that file.So I solved the problem by deleting .DS_Store
I also encountered this type of error:
error: OpenCV(4.1.2) /io/opencv/modules/imgproc/src/color.cpp:182: error: (-215:Assertion failed) !_src.empty() in function 'cvtColor'
The solution was to load the image properly. Since the file mentioned was wrong, images were not loaded and hence it threw this error. You can check the path of the image or if uploading an image through colab or drive, make sure that the image is present in the drive.
I encounter the problem when I try to load the image from non-ASCII path.
If I simply use imread to load the image, I am only able to get None.
Here is my solution:
import cv2
import numpy as np
path = r'D:\map\上海地图\abc.png'
image = cv2.imdecode(np.fromfile(path, dtype=np.uint8), cv2.IMREAD_UNCHANGED)
Similar thing will happen when I save the image in a non-ASCII path. It will not be successfully saved without any warnings. And here is what I did.
import cv2
import numpy as np
path = r'D:\map\上海地图\abc.png'
cv2.imencode('.png', image)[1].tofile(path)
path = os.path.join(raw_folder, folder, file)
print('[DEBUG] path:', path)
img = cv2.imread(path) #read path Image
if img is None: # check if the image exists in the path you give
print('Wrong path:', path)
else: # It completes the steps
img = cv2.resize(img, dsize=(128,128))
pixels.append(img)
The solution os to ad './' before the name of image before reading it...
Just Try Degrading the OpenCV
in python Shell (in cmd)
>>> import cv2
>>> cv2.__version__
after Checking in cmd
pip uninstall opencv-python
after uninstalling the version of opencv install
pip install opencv-python==3.4.8.29
I am trying to make a Choropleth map using matplotlib and cartopy for which I obviously need to plot a shapefile first. However, I did not manage to do so, even though a similar question has been asked here and here. I suspect either the projection or the bounds to be misspecified.
My shapefile has the projection
PROJCS["WGS_1984_UTM_Zone_32Nz",
GEOGCS["GCS_WGS_1984",
DATUM["WGS_1984",
SPHEROID["WGS_84",6378137,298.257223563]],
PRIMEM["Greenwich",0],
UNIT["Degree",0.017453292519943295]],
PROJECTION["Transverse_Mercator"],
PARAMETER["False_Easting",32500000],
PARAMETER["False_Northing",0],
PARAMETER["Central_Meridian",9],
PARAMETER["Scale_Factor",0.9996],
PARAMETER["Latitude_Of_Origin",0],
UNIT["Meter",1]]
and can be downloaded here where I am talking about vg250_2010-01-01.utm32w.shape.ebenen/vg250_ebenen-historisch/de1001/vg250_gem.shp
My code is
#!/usr/local/bin/python
# -*- coding: utf8 -*-
import cartopy.crs as ccrs
import cartopy.io.shapereader as shpreader
import matplotlib.pyplot as plt
fname = 'path/vg250_gem.shp'
proj = ccrs.TransverseMercator(central_longitude=0.0,central_latitude=0.0,
false_easting=32500000.0,false_northing=0.0,
scale_factor=0.9996)
municipalities = list(shpreader.Reader(fname).geometries())
ax = plt.axes(projection=proj)
plt.title('Deutschland')
ax.add_geometries(municipalities,proj,edgecolor='black',facecolor='gray',alpha=0.5)
ax.set_extent([32458044.649189778*0.9, 5556418.748046352*1.1, 32465287.307457082*0.9, 5564153.5456742775*1.1],proj)
plt.show()
where I obtained the bounds using the corresponding method from fiona. Python throws an error
Traceback (most recent call last):
File "***/src/analysis/test.py", line 16, in <module>
ax.set_extent([32458044.649189778, 5556418.748046352, 32465287.307457082, 5564153.5456742775],proj)
File "/usr/local/lib/python2.7/site-packages/cartopy/mpl/geoaxes.py", line 652, in set_extent
ylim=self.projection.y_limits))
ValueError: Failed to determine the required bounds in projection coordinates. Check that the values provided are within the valid range (x_limits=[-20000000.0, 20000000.0], y_limits=[-10000000.0, 10000000.0]).
[Finished in 53.9s with exit code 1]
This doesn't make sense to me. Also, experimenting with ccrs.UTM() gives me a plot showing a white area. I'd appreciate it if anyone can tell me how to fix this. Thank you!
I have found two issues. One is an incorrect specification of limits in your call to set_extent, the documentation specifies [x0,x1,y0,y1] should be the input, you seem to have given [x0,y0,x1,y1].
The other issue seems to be a limitation in cartopy, as best I can tell. It looks like projections outside the limits listed in the error message will always fail, and those limits are hardcoded. You can just edit the source (this line in their latest release), changing -2e7 to -4e7, likewise for the upper bound. After these fixes, your plot is generated without issue:
The new set_extent line:
ax.set_extent([32458044.649189778*0.975, 32465287.307457082*1.025,5556418.748046352*0.9, 556415,3.5456742775*1.1],proj)
You may also want to set central_longitude=9.0 in your TransverseMercator, that seems to be what's specified in your shapefile.
I would recommend contacting the developers about this, they might have a good reason for setting those bounds, or they might have a better workaround, or maybe they'll widen the bounds in a later release!
Update
Your bounds also seem to have been set based on only the first of the municipalities:
In [34]: municipalities[0].bounds
Out[34]: (32458044.649189778, 5556418.748046352, 32465287.307457082, 5564153.5456742775)
But the other elements have different bounds. You can get limits flushed to the actual drawing based on min/max values of the bounds of all municipalities.
bnd = np.array([i.bounds for i in municipalities])
x0,x1 = np.min(bnd[:,0]),np.max(bnd[:,2])
y0,y1 = np.min(bnd[:,1]),np.max(bnd[:,3])
ax.set_extent([x0,x1,y0,y1],proj)
I have a bunch of points in a 2D space which all reside on a line (polygon). How can I compute the mean coordinate of these points on the line?
I don't mean the centroid of the points in the 2D space (as #rth initially proposed in his answer), but the mean location of the points along the line on which they reside. So basically, I could transform the line to a 1D axis, compute the mean location in 1D, and transform the location of the mean back into the 2D space.
Maybe these are exactly the necessary steps, but I think (or hope) that there is a function in numpy/scipy which allows me to do this in one step.
Edit: The approach you describe in the question is indeed probably the simplest way for solving this problem.
Here is an implementation that calculates the positions of vertices along the line in 1D, takes their mean, and finally calculates the corresponding 2D position with parametric interpolation,
import numpy as np
from scipy.interpolate import splprep, splev
vert = np.random.randn(1000, 2) # vertices definition here
# calculate the Euclidean distances between consecutive vertices
# equivalent to a for loop with
# dl[i] = ((vert[i+1, 0] - vert[i, 0])**2 + (vert[i+1,1] - vert[i,1])**2)**0.5
dl = (np.diff(vert, axis=0)**2).sum(axis=1)**0.5
# pad with 0, so dl.shape[0] == vert.shape[0] for convenience
dl = np.insert(dl, 0, 0.0)
l = np.cumsum(dl) # 1D coordinates along the line
l_mean = np.mean(l) # mean in the line coordinates
# calculate the coordinate of l_mean in 2D space
# with parametric B-spline interpolation
tck, _ = splprep(x=vert.T, u=l, k=3)
res = splev(l_mean, tck)
print(res)
Edit2: Assuming now that you have a high resolution set of points for your path vert_full and some approximate measurements vert_1, vert_2, etc, what you could do is the following.
Project each points of vert_1, etc. onto the exact path. Assuming that vert_full has much more datapoints than vert_1, we can simply look for the nearest neighbours of vert_1 in vert_full:
from scipy.spatial import cKDTree
tr = cKDTree(vert_full)
d, idx = tr.query(vert_1, k=1)
vert_1_proj = vert_full[idx] # this gives the projected corrdinates onto vert_full
# I have not actually run this, so it might require minor changes
Use the above mean calculation with the new vert_1_proj vector.
Meanwhile I've found the answer to my question, although using Shapely instead of Numpy.
from shapely.geometry import LineString, Point
# lists of points as (x,y) tuples
path_xy = [...]
points_xy = [...] # should be on or near path
path = LineString(path_xy) # create path object
pts = [Point(p) for p in points_xy] # create point objects
dist = [path.project(p) for p in pts] # distances along path
mean_dist = np.mean(dist) # mean distance along path
mean = path.interpolate(mean_dist) # mean point
mean_xy = (mean.x,mean.y)
This works perfectly!
(That's is also why I have to accept it as the answer, though I highly appreciate #rth's help!)
I am trying to make some plots of sea ice data. The data is delivered in the EASE-North grid, an example file (HDF4) can be downloaded at:
ftp://n4ftl01u.ecs.nasa.gov/SAN/OTHR/NISE.004/2013.09.30/
I created a custom projection class for the EASE-Grid, it seems to be working (the coastlines align well with the data).
When i try to add a Natural Earth feature, it returns an empty Matplotlib figure.
import gdal
import cartopy
# projection class
class EASE_North(cartopy.crs.Projection):
def __init__(self):
# see: http://www.spatialreference.org/ref/epsg/3408/
proj4_params = {'proj': 'laea',
'lat_0': 90.,
'lon_0': 0,
'x_0': 0,
'y_0': 0,
'a': 6371228,
'b': 6371228,
'units': 'm',
'no_defs': ''}
super(EASE_North, self).__init__(proj4_params)
#property
def boundary(self):
coords = ((self.x_limits[0], self.y_limits[0]),(self.x_limits[1], self.y_limits[0]),
(self.x_limits[1], self.y_limits[1]),(self.x_limits[0], self.y_limits[1]),
(self.x_limits[0], self.y_limits[0]))
return cartopy.crs.sgeom.Polygon(coords).exterior
#property
def threshold(self):
return 1e5
#property
def x_limits(self):
return (-9000000, 9000000)
#property
def y_limits(self):
return (-9000000, 9000000)
# read the data
ds = gdal.Open('D:/NISE_SSMISF17_20130930.HDFEOS')
# this loads the layers for both hemispheres
data = np.array([gdal.Open(name, gdal.GA_ReadOnly).ReadAsArray()
for name, descr in ds.GetSubDatasets() if 'Extent' in name])
ds = None
# mask anything other then sea ice
sea_ice_concentration = np.ma.masked_where((data < 1) | (data > 100), data, 0)
# plot
lim = 3000000
fig, ax = plt.subplots(figsize=(8,8),subplot_kw={'projection': EASE_North(), 'xlim': [-lim,lim], 'ylim': [-lim,lim]})
land = cartopy.feature.NaturalEarthFeature(
category='physical',
name='land',
scale='50m',
facecolor='#dddddd',
edgecolor='none')
#ax.add_feature(land)
ax.coastlines()
# from the metadata in the HDF
extent = [-9036842.762500, 9036842.762500, -9036842.762500, 9036842.762500]
ax.imshow(sea_ice_concentration[0,:,:], cmap=plt.cm.Blues, vmin=1,vmax=100,
interpolation='none', origin='upper', extent=extent, transform=EASE_North())
The script above works fine and produces this result:
But when i uncomment the ax.add_feature(land) it fails without any error, only returning the empty figure. Am i missing something obvious?
Here is the IPython Notebook:
http://nbviewer.ipython.org/6779935
My Cartopy build is version 0.9 from Christoph Gohlke's website (thanks!).
edit:
Trying to save the figure does throw an exception:
fig.savefig(r'D:\test.png')
C:\Python27\Lib\site-packages\shapely\speedups\_speedups.pyd in shapely.speedups._speedups.geos_linearring_from_py (shapely/speedups/_speedups.c:2270)()
ValueError: A LinearRing must have at least 3 coordinate tuples
Examining the 'land' cartopy.feature reveals no issues, all polygons pass the .isvalid() and all rings (ext en int) are of 4 or more tuples. So the input shape doesnt seem to be the problem (and works fine in PlateCaree()).
Maybe some rings (like on the southern hemisphere) get 'corrupt' after transforming to EASE_North?
edit2:
When i remove the build-in NE features and load the same shapefile (but with anything below 40N clipped) it works. So it seems like some sort of reprojection issue.
for state in shpreader.Reader(r'D:\ne_50m_land_clipped.shp').geometries():
ax.add_geometries([state], cartopy.crs.PlateCarree(),facecolor='#cccccc', edgecolor='#cccccc')
I'd have said that this was a bug. I'm guessing add_feature updates the matplotlib viewLim and the result is that the picture zooms in to a tiny area (which appears white unless you zoom out a lot).
From the top of my head, I think the underlying behaviour has been improved in matplotlib, but cartopy is not yet making use of the new viewLim calculation. In the meantime I'd suggest setting the extents of your map manually with:
ax.set_extent(extent, transform=EASE_North())
HTH