When using Octave's mesh function, the color of the lines can be set with edgecolor. But this property isn't available when you create a contour plot with meshc.
Is there any easy way to set the lines of a contour plot to a constant color?
meshc plots a contour graph under a mesh graph. Use h=meshc(...) to get the handle h to the mesh (h(1)) and the contour plot (h(2)).
Now to change the color of the lines, the relevant property for mesh is EdgeColor while for the contour plot, it is LineColor. So you need to modify these properties to get the desired output.
Example:
[X,Y] = meshgrid(-3:.125:3);
Z = peaks(X,Y);
h=meshc(Z);
set(h(1),'EdgeColor','k');
set(h(2),'LineColor','k');
which gives:
Related
I am making a face mask detection project and I trained my model using ultralytics/yolov5.I saved the trained model as an onnx file, you can find the model file here model.onnx. Now I want you use this model.onnx with opencv to detect real time face mask. The input image size during training was 320*320. You can visualize this model using netron.
I have written this code to capture the image using webcam and pass it to model.onnx to predict my bounding boxes. The code is as follows:
def predict(img):
session = onnxruntime.InferenceSession(model_path)
input_name = session.get_inputs()[0].name
output_name = session.get_outputs()[0].name
img = img.reshape((1,3,320,320))
data = json.dumps({'data':img.tolist()})
data = np.array(json.loads(data)['data']).astype('float32')
result = session.run([output_name],{input_name:data})
result = np.array(result)
print(result.shape)
The output of result.shape is (1, 1, 3, 40, 40, 85)
Can anyone help me in interpreting this shape and how can i use this result array to predict my class, bounding box and confidence.
I've never worked with a pure yolov5 model, but here's the output format for yolov5s. It looks like it should be similar.
ouput tensor structure (yolov5s):
output_tensor[a, b, c, d]
a -> image index (If you're input is a batch of images, this tells you which image's output you're looking at. If your input is just one image, leave this as 0.)
b -> index of image in batch
c -> information about bounding box
0, 1 -> x and y coordinate of bounding box center
2, 3 -> width and height of bounding box
4 -> bounding box confidence
5 - 85 -> single class confidences
d -> index of proposed bounding boxes
I am having trouble mapping Nebraska school districts in D3 (v4). (See bl.ock here.) I can map Nebraska counties no problem, but the same code modified for school districts--and pointing to a school district TopoJSON file--gives me a blank page.
Here's how I created the JSON, based on Mike Bostock's excellent instructions :
curl "https://www2.census.gov/geo/tiger/GENZ2017/shp/cb_2017_31_unsd_500k.zip" -o cb_2017_31_unsd_500k.zip
unzip -o cb_2017_31_unsd_500k.zip
shp2json cb_2017_31_unsd_500k.shp -o ne_district.json
ndjson-split "d.features" < ne_district.json > ne_district.ndjson
ndjson-map "d.id = d.properties.GEOID, d" < ne_district.ndjson > ne_district-id.ndjson
geo2topo -n districts=ne_district-id.ndjson > ne_district-id-topo.json
And here's my projection:
var projection = d3.geoConicConformal()
.parallels([40, 43])
.rotate([100, 0])
.scale(8000);
Thanks for your help and apologies in advance for anything important I left out!
The issue is you haven't finished setting your projection parameters. You have rotate the map, which is how you should center a conic projection along the x axis. But you haven't centered the map on the y axis, it is centered on the equator. You
For a conical projection, you can do this one of three ways:
Center the map on a central latitude : projection.center([0,y])
You don't need to use .center with an x value because the map is already centered on the x by rotation, rotation and centering are cumulative
Rotate the map to a central latitude and longitude: projection.rotate([-x,-y])
On a conical projection the rotation on the meridian does not warp the map (generally), we rotate by the negative as we move the earth under us. This option does slightly distort the map relative to the other options - this may be preferrable.
Use the projection translation to center the map
The easiest way is to translate the result while automatically scaling (though you can do this manually too) with projection.fitSize or projection.fitExtent. These methods modify projection.scale and projection.translate. As with centering with .center, you need to keep your rotation - otherwise you'll get an odd tilt to the map.
These methods set translate and scale to appropriate values so that your map area contains the desired features:
var featureCollection = topojson.feature(ne, ne.objects.districts);
projection.fitSize([width,height],featureCollection);
These methods must take objects, not arrays, so we use the featureCollection, not the features as an array
Both methods take an array specifying the size to stretch a provided geojson object over:
projection.fitSize([mapwidth,mapheight],geojsonObject)
projection.fitExtent([[left,top],[right,bottom]],geojsonObject)
Here's an updated gist using fitSize.
I am interested in generating weighted, directed random graphs with node constraints. Is there a graph generator in R or Python that is customizable? The only one I am aware of is igraph's erdos.renyi.game() but I am unsure if one can customize it.
Edit: the customizations I want to make are 1) drawing a weighted graph and 2) constraining some nodes from drawing edges.
In igraph python, you can use link the Erdos_Renyi class.
For constraining some nodes from drawing edges, this is controlled by the p value.
Erdos_Renyi(n, p, m, directed=False, loops=False) #these are the defaults
Example:
from igraph import *
g = Graph.Erdos_Renyi(10,0.1,directed=True)
plot(g)
By setting the p=0.1 you can see that some nodes do not have edges.
For the weights you can do something like:
g.ecount() # to find the number of edges
g.es["weights"] = range(1, g.ecount())
g.es["label"] = weights
plot(g)
Result:
I have a csv file with the following format.
Label 1, 20
Label 2, 10
Label 3, 30
.
.
.
LabelN, 5
How do I plot the second column using the labels given in the csv file as labels on the x-axis?
(Something like this, where 1891-1900 is a label)
EDIT:
Found these questions which are quite similar to mine,
Plotting word frequency histogram using gnuplot
Gnuplot xticlabels with several lines
After trying the commands given in answer 1.
set xtics border in scale 1,0.5 nomirror rotate by -90 offset character 0, 0, 0
plot "data.txt" using 2:xticlabels(1) with histogram
I'm getting a not so clean histogram because the number of labels is quite large. I've tried the formatting given in answer 2. Can anyone suggest a way to get a cleaner histogram?
You have several options:
Plot only the important labels (extremes, mean etc. for example)
Skip every 5th label or so if labels form a series
Split your graph if you must plot every single label.
Seems like case 2) applies here, and thus skipping some of the labels before plotting will make the plot look better.
You can pre-process the file to skip every 5th label (say) using something like the following script:
line_number = 0
for line in open("d1.txt", "r"):
line_split = line.split(",")
if(line_number % 5 == 0):
print line,
else:
print ",",line_split[1],
line_number += 1
You can now plot with appropriate font size
set xtics border in scale 1,0.5 nomirror rotate by -90 offset character 0, 0, 0
set xtics font ",9"
plot "d2.txt" using 2:xticlabels(1) with histogram title "legend_here"
I have plots of 3-axis accelerometer time-series data (t,x,y,z) in separate subplots I'd like to zoom together. That is, when I use the "Zoom to Rectangle" tool on one plot, when I release the mouse all 3 plots zoom together.
Previously, I simply plotted all 3 axes on a single plot using different colors. But this is useful only with small amounts of data: I have over 2 million data points, so the last axis plotted obscures the other two. Hence the need for separate subplots.
I know I can capture matplotlib/pyplot mouse events (http://matplotlib.sourceforge.net/users/event_handling.html), and I know I can catch other events (http://matplotlib.sourceforge.net/api/backend_bases_api.html#matplotlib.backend_bases.ResizeEvent), but I don't know how to tell what zoom has been requested on any one subplot, and how to replicate it on the other two subplots.
I suspect I have the all the pieces, and need only that one last precious clue...
-BobC
The easiest way to do this is by using the sharex and/or sharey keywords when creating the axes:
from matplotlib import pyplot as plt
ax1 = plt.subplot(2,1,1)
ax1.plot(...)
ax2 = plt.subplot(2,1,2, sharex=ax1)
ax2.plot(...)
You can also do this with plt.subplots, if that's your style.
fig, ax = plt.subplots(3, 1, sharex=True, sharey=True)
Interactively this works on separate axes
for ax in fig.axes:
ax.set_xlim(0, 50)
fig.draw()