I have an elevation raster layer in my GeoServer with a single channel ("gray").
The "gray" values is elevations values (signed int16).
I have 2 clients:
The first one is using that elevation values as is.
The second one expect to get [Mapbox Terrain-RGB format][1]
I do not want to convert the "gray scale" format to Mapbox Terrain-RGB format and hold duplicate data in the GeoServer.
I was thinking to use the SLD style and elements to map the elevation value to the appropriate RGB value (with gradient interpolation between discrete values).
For example:
<ColorMap>
<ColorMapEntry color="#000000" quantity="-10000" />
<ColorMapEntry color="#FFFFFF" quantity="1667721.5" />
</ColorMap>
It turns out that the above example does not span the full range of colors but rather creates gray values only.
That is, it seems that it interpolate each color (red, green, blue) independent of each other.
Any idea how to make it interpolate values like that: #000000, #000001, #000002, ... , #0000FF, #000100, ..., #0001FF, ..., #FFFFFF?
Tx.
[1]: https://docs.mapbox.com/data/tilesets/reference/mapbox-terrain-rgb-v1/
I'm trying to do the same with no luck, and i think it can't be done... Check this example. It's a "gradient" [-10000, -5000, -1000, -500 ... 100000000000000000, 5000000000000000000, 1000000000000000000] with the Mapbox color codification. The color progression/interpolation is anything but linear, so i think it can't be emulated in an SLD.
If you have the elevation data in the format you desire then that is the easiest option: it just works. However, if you want a more customized solution, here's what I've done for a project using the Mapbox Terrain-RGB format:
I have a scale of colors from dark blue to light blue to white.
I want to be able to specify how many steps are used from light blue to white (default is 10).
This code uses GDAL Python bindings. The following code snippet was used for testing.
It just outputs the color mapping to a GeoTIFF file.
To get values between 0 and 1, simply use value *= 1/num_steps.
You can use that value in the lookup table to get an RGB value.
If you're only interested in outputting the colors, you can ignore everything involving gdal_translate. The colors will automatically be stored in a single-band GeoTIFF. If you do want to re-use those colors, note that this version ignores alpha values (if present). You can use gdal_translate to add those. That code snippet is also available at my gist here.
import numpy as np
import gdal
from osgeo import gdal, osr
def get_color_map(num_steps):
colors = np.zeros((num_steps, 3), dtype=np.uint8)
colors[:, 0] = np.linspace(0, 255, num_steps, dtype=np.uint8)
colors[:, 1] = colors[::-1, 0]
return colors
ds = gdal.Open('/Users/myusername/Desktop/raster.tif')
band = ds.GetRasterBand(1) # Assuming single band raster
arr = band.ReadAsArray()
arr = arr.astype(np.float32)
arr *= 1/num_steps # Ensure values are between 0 and 1 (or use arr -= arr.min() / (arr.max() - arr.min()) to normalize to 0 to 1)
colors = get_color_map(num_steps) # Create color lookup table
colors[0] = [0, 0, 0] # Set black for no data so it doesn't show up as gray in the final product.
# Create new GeoTIFF with colors included (transparent alpha channel if possible). If you don't care about including the colors in the GeoTIFF, skip this.
cols = ds.RasterXSize
rows = ds.RasterYSize
out_ds = gdal.GetDriverByName('GTiff').Create('/Users/myusername/Desktop/raster_color.tif', cols, rows, 4)
out_ds.SetGeoTransform(ds.GetGeoTransform())
out_ds.SetProjection(ds.GetProjection())
band = out_ds.GetRasterBand(1)
band.WriteArray(colors[arr]) # This can be removed if you don't care about including the colors in the GeoTIFF
band = out_ds.GetRasterBand(2)
band.WriteArray(colors[arr]) # This can be removed if you don't care about including the colors in the GeoTIFF
band = out_ds.GetRasterBand(3)
band.WriteArray(colors[arr]) # This can be removed if you don't care about including the colors in the GeoTIFF
band = out_ds.GetRasterBand(4)
alpha = np.zeros((rows, cols), dtype=np.uint8) # Create alpha channel to simulate transparency of no data pixels (assuming 0 is "no data" and non-zero is data). You can remove this if your elevation values are not 0.
alpha[arr == 0] = 255
band.WriteArray(alpha) # This can be removed if you don't care about including the colors in the GeoTIFF
out_ds.FlushCache()
This issue is also present in Rasterio when using a palette with multiple values. Here is an example.
However, if your raster has n-dimensions or is a masked array, the flip operation can be tricky. Here's a solution based on one of the answers in this stackoverflow question: How to vertically flip a 2D NumPy array?.
Related
Following a frequent issue in Altair:
merging legends 1
merging legends 2
combining color and shape
I want to plot several point series with line plots and point marks visualized both with different colors, shapes, and stroke dashes:
This works as expected when using resolve_scale
x = np.arange(0, 5, 0.1)
mask = np.ones_like(x)
mask[::2] = 0
df = pd.DataFrame({
"x": x,
"y": np.sin(x)*mask + np.cos(x)*(1-mask),
"y2": np.sin(2*x)*mask + np.cos(2*x)*(1-mask) ,
"col": mask
})
base= alt.Chart(df).mark_line(point=True, size=1).encode(
alt.X("x:Q"),
color = alt.Color("col:N"),
shape = alt.Shape("col:N"),
strokeDash = alt.StrokeDash("col:N")
).resolve_scale(color="independent", shape="independent", strokeDash="independent")
base.encode(alt.Y("y:Q"))
But when concatenated with other charts with a different y-value multiple identical legends appear:
base.encode(alt.Y("y:Q")) | base.encode(alt.Y("y2:Q"))
I understand this is the purpose of "resolve_scale", would really appreciate a workaround.
not using the resolve_scale method or using it on the concatenated chart would get me a legend with every visualized property (color, shape, etc) set apart.
You have set the color, shape, and strokeDash to one thing: "col:N". If you want them to be independent, then define them as different things.
base= alt.Chart(df).mark_line(point=True, size=1).encode(
alt.X("x:Q"),
color = alt.Color("col:N"),
shape = alt.Shape("col:N"),
strokeDash = alt.StrokeDash("col:N")
)
h = base.encode(alt.Y("y:Q"), color=alt.value('red')) | base.encode(alt.Y("y2:Q"), color=alt.value('blue')).resolve_scale(color="independent", shape="independent", strokeDash="independent")
as for a workaround, you could go into the h.hconcat[0].encoding and h.hconcat[1].encoding and change the map to be whatever you want for vega-lite to read. At that point I'd just use a different library.
Hopefully this helps.
I've tried an image-editing-effect which should recolor a picture with little black dots, however it only works for certain images and I honestly don't know why. Any ideas?
#url = member.avatar_url
#print(url)
#response = requests.get(url=url, stream=True).raw
#imag = Image.open(response)
imag = Image.open("unknown.png")
#out = Image.new('I', imag.size)
i = 0
width, height = imag.size
for x in range(width):
i+=1
for y in range(height):
if i ==5:
# changes every 5th pixel to a certain brightness value
r,g,b,a = imag.getpixel((x,y))
print(imag.getpixel((x,y)))
brightness = int(sum([r,g,b])/3)
print(brightness)
imag.putpixel((x, y), (brightness,brightness,brightness,255))
i= 0
else:
i += 1
imag.putpixel((x,y),(255,255,255,255))
imag.save("test.png")
The comments are what I would've used if my tests had worked. Using local pngs also don't work all the time.
Your image that doesn't work doesn't have an alpha channel but your code assumes it does. Try forcing in an alpha channel on opening like this:
imag = Image.open("unknown.png").convert('RGBA')
See also What's the difference between a "P" and "L" mode image in PIL?
A couple of other ideas too:
looping over images with Python for loops is slow and inefficient - in general, try to find a vectorised Numpy alternative
you have an alpha channel but set it to 255 (i.e. opaque) everywhere, so in reality, you may as well not have it and save roughly 1/4 of the file size
your output image is RGB with all 3 components set identically - that is really a greyscale image, so you could create it as such and your output file will be 1/3 the size
So, here is an alternative rendition:
#!/usr/bin/env python3
from PIL import Image
import numpy as np
# Load image and ensure neither palette nor alpha
im = Image.open('paddington.png').convert('RGB')
# Make into Numpy array
na = np.array(im)
# Calculate greyscale image as mean of R, G and B channels
grey = np.mean(na, axis=-1).astype(np.uint8)
# Make white output image
out = np.full(grey.shape, 255, dtype=np.uint8)
# Copy across selected pixels
out[1::6, 1::4] = grey[1::6, 1::4]
out[3::6, 0::4] = grey[3::6, 0::4]
out[5::6, 2::4] = grey[5::6, 2::4]
# Revert to PIL Image
Image.fromarray(out).save('result.png')
That transforms this:
into this:
If you accept calculating the greyscale with the normal method, rather than averaging R, G and B, you could change to this:
im = Image.open('paddington.png').convert('L')
and remove the line that does the averaging:
grey = np.mean(na, axis=-1).astype(np.uint8)
I'm trying to model voting dynamics on networks, and would like to be able to create a graph in NetworkX where I can iterate the voter process on nodes, having their colour change corresponding to their vote 'labels'.
I've managed to get this code to let me see the attributes for each node, but how do I go about using those in a for loop to designate colour?
H = nx.Graph()
H.add_node(1,vote='labour')
H.add_node(2,vote='labour')
H.add_node(3,vote='conservative')
h=nx.get_node_attributes(H,'vote')
h.items()
Gives me the result:
[(1, 'labour'), (2, 'labour'), (3, 'conservative')]
I've got a for loop to do this type of colour coding based on the node number as follows, but haven't managed to make it work for my 'vote' status.
S=nx.star_graph(10)
colour_map=[]
for node in S:
if node % 2 ==0:
colour_map.append('blue')
else: colour_map.append('yellow')
nx.draw(S, node_color = colour_map,with_labels = True)
plt.show()
You can iterate the node attributes with H.nodes(data=True) which returns the node name and the node attributes in a dictionary. Here's a full example using your graph.
import networkx as nx
import matplotlib.pyplot as plt
H = nx.Graph()
H.add_node(1, vote='labour')
H.add_node(2, vote='labour')
H.add_node(3, vote='conservative')
color_map = []
for node, data in H.nodes(data=True):
if data['vote'] == 'labour':
color_map.append(0.25) # blue color
elif data['vote'] == 'conservative':
color_map.append(0.7) # yellow color
nx.draw(H, vmin=0, vmax=1, cmap=plt.cm.jet, node_color=color_map, with_labels=True)
plt.show()
This code will draw a different layout of nodes each time you run it (some layouts, as e.g. draw_spring, are available here).
Regarding colors, I use 0.25 for blue and 0.7 for yellow. Note that I use the jet matplotlib colormap and that I set vmin=0 and vmax=1 so that the color values are absolute (and not relative to eachother).
Output of the code above:
UPDATE:
I wasn't aware that you could simply use color names in matplotlib. Here's the updated for loop:
for node, data in H.nodes(data=True):
if data['vote'] == 'labour':
color_map.append("blue")
elif data['vote'] == 'conservative':
color_map.append("yellow")
And the updated draw command:
nx.draw(H, node_color=color_map, with_labels=True)
Note that this way you get different shades of blue and yellow than in the image above.
I am doing a research for my higher studies in automation. I have done the automation part of the microscope but I need help in MATLAB. An example of what I would like to segment is shown here:
I need to extract the dark purple pixels from this image and only display that in a figure. It is almost like colour based segmentation but I just want to only take the dark purple pixel from the whole image.
What would I do in this case?
Here's something to get you started. Let's go with the theme of colour segmentation where you only want to extract pixels that are of a deep purple. I would like to point you to the HSV colour space before we get started. The HSV colour space is ideal for representing colours in a way that is most intuitive to humans. We tend to describe colours by their dominant colour, followed by attributes such as how washed out or how pure the colour is, and how bright or dark the colour is. The dominant colour is represented by the Hue, the appearance of how washed out or how pure the colour is is represented by the Saturation and the intensity of the colour is represented by the Value, and hence Hue-Saturation-Value, or the HSV colour space.
We can transform a RGB image so that it becomes HSV by rgb2hsv. This will return a 3D matrix that has the hue, saturation and value as 2D slices in a 3D matrix, much like a RGB image where each slices represents the red, green and blue channels. Let's see what each component looks like once we transform the image into HSV:
im = imread('https://www.cdc.gov/dpdx/images/malaria/ovale/Po_gametocyte_thickB.jpg');
hsv = rgb2hsv(im2double(im));
figure;
for idx = 1 : 3
subplot(1,3,idx);
imshow(hsv(:,:,idx));
end
The first line of code reads in an image from a URL. I'm going to use the one that Hoki referred you to, as it's the most simplest one to deal with. For self-containment, this is what the original image looks like:
Once we do this, we convert the image into the HSV colour space. It is important that you convert the image to double precision and you normalize each component to [0,1], and that is performed by im2double. Next, we spawn a new figure, and place each component in a single row over three columns. The first column represents the hue, next column the saturation and finally the last column being the value. This is the figure that we see:
With the first figure, it looks like the dominant colour is purple, whether it's a light shade or a dark shade of the colour, so the hue won't help us here. If you look at a HSV colour wheel:
(source: hobbitsandhobos.com)
Normalize the wheel so that it falls between [0,1] instead of 0 to 360 degrees. The hue is actually represented as degrees due to the nature of the colour space, but MATLAB normalizes this to [0,1]. You can see that purple falls within a hue of [0.6,0.8], which corresponds to the first figure I showed you that displays the hue for our image. If you examine the pixels around the image, they fluctuate between this range. Therefore, the hue won't help us much here.
What will certainly help us are the saturation and value components. If you take a look, the deep purple pixels have a higher saturation than the rest of the background, which makes sense because the deep purple has a much more pure version of purple than the rest of the background. For the value, you can see that the brightness of the dark purple is darker than the background.
We can use these two points as an exploit to segment out the purple colour in the image. The easiest thing to do would be to threshold the saturation and value planes so that any values that are within a certain range you keep while those that are outside you throw away. Therefore, you can do something like this:
sThresh = hsv(:,:,2) > 0.6 & hsv(:,:,2) < 0.9;
vThresh = hsv(:,:,3) > 0.4 & hsv(:,:,3) < 0.65;
I used impixelinfo and I hovered my mouse over the saturation and value components to examine what the values were for the deep purple regions. It looks like those pixels that are deep purple have a saturation value between 0.6 and 0.9, while the value component has values between 0.4 and 0.65. The above code will create two binary masks where true means that the pixel satisfies our criteria while false means it doesn't. Because I want to combine both things together and not leave any stone unturned, let's logical OR the masks together for the final result:
figure;
result = sThresh | vThresh;
imshow(result);
We will also show the result too. This is what we get:
As you can see, this does a pretty good job, but we have remnants of the red arrow that we don't want in the final result. To do a bit of cleanup, we can use morphology - specifically an opening filter of a small window so that we don't affect the pixels that we want as much. We can use imopen to perform our opening operation for us. A morphological opening removes isolated pixels that appear around your image. You use what is called a structuring element that is used to look at local neighbourhoods of your image. For the basics, any pixel regions that are as small as the shape that is contained within the structuring element get removed. Because we want to preserve the shape of the other objects, we can try using a 5 x 5 disk structuring element to clean these pixels up:
figure;
se = strel('disk', 2, 0);
final = imopen(result, se);
imshow(final);
This is what we get:
Not bad! There are some holes that we need to patch up, so let's fill in those holes with imfill:
figure;
final_noholes = imfill(final, 'holes');
imshow(final_noholes);
This is what we get:
OK! So we have our mask. The last thing we need to do is present the image so that you only show the deep purple colours from the original image, and nothing else. That can easily be achieved with bsxfun:
figure;
out = bsxfun(#times, im, uint8(final_noholes));
imshow(out);
The above operation takes your mask, and multiplies every pixel in your image by this mask. One small thing I'd like to point out is that the mask we found in the previous step needs to be cast to uint8, because bsxfun requires that the multiplication (or whatever operation you perform) need to be the same type. We replicate this mask in 3D so that you mask out the unwanted RGB pixels and only keep the ones you are looking for.
This is what we finally get:
As you can see, it isn't perfect, but it's certainly enough to get you started. Those thresholds are what are important, but with some very simple thresholding, I extracted most of the purple pixels out.
To make it easier for you, here's the code that I wrote above that can easily be copied and pasted into MATLAB for you to run:
clear all; close all; clc;
im = imread('https://www.cdc.gov/dpdx/images/malaria/ovale/Po_gametocyte_thickB.jpg');
hsv = rgb2hsv(im2double(im));
figure;
for idx = 1 : 3
subplot(1,3,idx);
imshow(hsv(:,:,idx));
end
sThresh = hsv(:,:,2) > 0.6 & hsv(:,:,2) < 0.9;
vThresh = hsv(:,:,3) > 0.4 & hsv(:,:,3) < 0.65;
figure;
result = sThresh | vThresh;
imshow(result);
figure;
se = strel('disk', 2, 0);
final = imopen(result, se);
imshow(final);
figure;
final_noholes = imfill(final, 'holes');
imshow(final_noholes);
figure;
out = bsxfun(#times, im, uint8(final_noholes));
imshow(out);
Good luck!
Try this:
function main
clc,clear
A = imread('https://www.cdc.gov/dpdx/images/malaria/ovale/Po_gametocyte_thickB.jpg');
subplot(1,2,1)
imshow(A)
RGB = [230 210 200]; % color you want
e = 40; % color shift
B = pix_in(A,RGB,e);
B = B + 255.*uint8(~B); % choosing white background
subplot(1,2,2)
imshow(B)
end
function B = pix_in(A,RGB,e)
% select specific pixels in image
% A - color image (3D matrix uint8)
% RGB - [R G B] - color to select
% e - color shift/deviation
A = double(A); % for same class operations (RGB - double)
[m, n, ~] = size(A);
RGB = reshape(RGB,1,1,3);
RGB = repmat(RGB,m,n,1); % creating 3D matrix
b = abs(A-RGB) < e; % logical 3D
b = sum(b,3) == 3; % if [R,G,B] of a pixel in range
B = A.*repmat(b,1,1,3); % selecting pixels those in range
B = uint8(B);
end
I generate a transparent chart that lets the background of a web page be seen through it.
So far I've done this (omited the populating of dataset for brevity):
lineChartObject=ChartFactory.createLineChart("Title","Legend","Amount",line_chart_dataset,PlotOrientation.VERTICAL,true,true,false);
CategoryPlot p = lineChartObject.getCategoryPlot();
Color trans = new Color(0xFF, 0xFF, 0xFF, 0);
lineChartObject.setBackgroundPaint(trans);
p.setBackgroundPaint(trans);
for (int i=0;i<=3;i++){
lineChartObject.getCategoryPlot().getRenderer().setSeriesStroke(i, new BasicStroke(3.0f));
lineChartObject.getCategoryPlot().getRenderer().setBaseItemLabelsVisible(false);
}
Which renders this:
I cannot find a way of:
Removing border of plot (1)
Removing border of leyend as well as making it transparent (3)
Making the labels on the X axis (2) to behave intelligently as the labels of Y axis do (A). Labels of Y axis space themselves so as to not clutter the graph, for example if I rendered the graph smaller, it would show fewer labels, like this:
Edit: X label domain is dates.
For (1) try:
plot.setOutlineVisible(false);
For (2), a common reason for having too many categories along the x-axis is that the data is actually numerical, in which case you should be using XYPlot rather than CategoryPlot. With XYPlot, the x-axis scale adjusts in the same way that the y-axis does.
Edit from OP: Using a TimeSeriesChart with a TimeSeriesCollection as XYDataSet did the work! (fotgot to say X domain is dates)
For (3) try:
LegendTitle legend = chart.getLegend();
legend.setFrame(BlockBorder.NONE);
legend.setBackgroundPaint(new Color(0, 0, 0, 0));