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Can't find info on how to just draw a border around a circle. Simply looking to draw a circle with a black border just like the BorderedRectangle function.
import pyglet
from pyglet import shapes
def display_get_width():
display = pyglet.canvas.get_display()
screen = display.get_default_screen()
return screen.width
def display_get_height():
display = pyglet.canvas.get_display()
screen = display.get_default_screen()
return screen.height
screen_width = display_get_width()
screen_height = display_get_height()
print(screen_width, screen_height)
window = pyglet.window.Window(screen_width//2,screen_height//2)
batch = pyglet.graphics.Batch()
rectangle = shapes.BorderedRectangle(250, 300, 600, 300, border=1, color=(255, 255, 255), border_color=(100, 100, 100), batch=batch)
rectangle2 = shapes.BorderedRectangle(300, 350, 300, 150, border=1, color=(255, 255, 255), border_color=(100, 100, 100), batch=batch)
circle = shapes.Circle(550, 450, 100, color=(50, 225, 30), batch=batch)
#window.event
def on_draw():
window.clear()
batch.draw()
pyglet.app.run()
It's been a year and half, but I had the same question a moment ago. I solved it by drawing shapes.ARC and setting width with a GL function:
pyglet.gl.glLineWidth(5)
arc = shapes.Arc(200, 200, 150, color=(255, 0, 0), batch = batch)
Omitting "angle" parameter while creating an Arc instance assumes angle is 2π - a full circle.
You can make a circle inside another circle:
circle = shapes.Circle(550, 450, 100, color=(50, 225, 30), batch=batch)
insideCircle = shapes.Circle(550, 450, 80, color=(30, 205, 10), batch=batch)
I need to plot two features of a dataframe where df['DEPTH'] should be inverted and at y-axis and df['SPECIES'] should be at x-axis. Imagining that the plot would be a variant line, I would like to fill with color the area near the y-axis (left side of the line). So I wrote some code:
df = pd.DataFrame({'DEPTH': [100, 150, 200, 250, 300, 350, 400, 450, 500, 550],
'SPECIES':[12, 8, 9, 6, 10, 7, 4, 3, 1, 2]})
plt.plot(df['SPECIES'], df['DEPTH'])
plt.fill_between(df['SPECIES'], df['DEPTH'])
plt.ylabel('DEPTH')
plt.xlabel('SPECIES')
plt.ylim(np.max(df['DEPTH']), np.min(df['DEPTH']))
I tried 'plt.fill_between', but then the left part of the plot doesn't get all filled.
Anyone knows how can the filled part (blue color) reach the y-axis?
Instead of fill_between, you can use fill_betweenx. It will start filling from 0 by default, thus you need to set your x limit to be 0 too.
plt.plot(df['SPECIES'], df['DEPTH'])
# changing fill_between to fill_betweenx -- the order also changes
plt.fill_betweenx(df['DEPTH'], df['SPECIES'])
plt.ylabel('DEPTH')
plt.xlabel('SPECIES')
plt.ylim(np.max(df['DEPTH']), np.min(df['DEPTH']))
# setting the lower limit to 0 for the filled area to reach y axis.
plt.xlim(0,np.max(df['SPECIES']))
plt.show()
The result is below.
The problem I have at hand is to draw boundaries around a white ball. But the ball is present in different illuminations. Using canny edge detections and Hough transform for circles, I am able to detect the ball in bright light/partial bright light but not in low illumination.
So can anyone help with this problem.
The code that I have tried is below.
img=cv2.imread('14_04_2018_10_38_51_.8242_P_B_142_17197493.png.png')
cimg=img.copy()
img = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
edges = cv2.medianBlur(img,5)
edges=cv2.Canny(edges,200,200)
circles = cv2.HoughCircles(edges,cv2.HOUGH_GRADIENT,1,20,
param1=25,param2=10,minRadius=0,maxRadius=0)
if circles is not None:
circles = np.uint16(np.around(circles))
for i in circles[0,:]:
# draw the outer circle
cv2.circle(cimg,(i[0],i[1]),i[2],(255,255,255),2)
# draw the center of the circle
cv2.circle(cimg,(i[0],i[1]),2,(0,0,255),3)
cv2.imwrite('segmented_out.png',cimg)
else:
print("no circles")
cv2.imwrite('edges_out.png',edges)
In the image below we need to segment if the ball is in the shadow region as well.
The output should be something like below images..
Well I am not very experienced in OpenCV or Python but I am learning as well. Probably not very pythonic piece of code but you could try this:
import cv2
import math
circ=0
n = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220]
img = cv2.imread("ball1.jpg")
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
for i in n:
ret, threshold = cv2.threshold(gray,i,255,cv2.THRESH_BINARY)
im, contours, hierarchy = cv2.findContours(threshold,cv2.RETR_TREE,cv2.CHAIN_APPROX_NONE)
for j in range(0, len(contours)):
size = cv2.contourArea(contours[j])
if 500 < size < 5000:
if circ > 0:
(x,y),radius = cv2.minEnclosingCircle(contours[j])
radius = int(radius)
area = cv2.contourArea(contours[j])
circif = 4*area/(math.pi*(radius*2)**2)
if circif > circ:
circ = float(circif)
radiusx = radius
center = (int(x),int(y))
elif circ == 0:
(x,y),radius = cv2.minEnclosingCircle(contours[j])
radius = int(radius)
area = cv2.contourArea(contours[j])
circ = 4*area/(math.pi*(radius*2)**2)
else:
pass
cv2.circle(img,center,radiusx,(0,255,0),2)
cv2.imshow('image', img)
cv2.waitKey(0)
cv2.detroyAllWindows()
What it does is acctually you convert your picture to grayscale and apply different threshold settings to it. Then you eliminate noises with adding size to your specific contour. When you find it, you check its circularity (NOTE: it is not a scientific formula) and compare it to the next circularity. Perfect circle should return the result 1, so the highest number that will get in a contour (of all the contours) will be your ball.
Result:
NOTE: I haven't tried increasing the limit of size so maybe higher limit could return better result if you have a high resolution picture
Working with grayscale image will make you subject to different light conditions.
To be free from this I suggest to work in HSV color space, then use the Hue component instead of the grayscale image.
Hue is independent from the light condition, since it gives you information about the color, regardless of its Saturation or Value (a value bound to the brightness of the image).
This might bring you some clarity about color spaces and which is best to use for image segmentation.
In your case here. We have a white ball.White is not a color by itself.The main factor here is, what kind light actually falls on the white ballAs the kind of light that falls on it has a direct influence on the kind of extraction you might plan to do using a color space like HSV as mentioned above by #magicleon
HSV is your best bet for segmentation here.Using
whiteObject = cv2.inRange(hsvImage,lowerHSVLimit,upperHSVLimit)
lowerHSVLimit and upperHSVLimit HSV color range
Keeping in mind that the conditions
1) The image have similar conditions while they were clicked
2) You cover all the ranges of HSV before extraction
Hope you get an idea
Consider this example
Selecting a particular hue range from 45 to 60
Code
image = cv2.imread('allcolors.png')
hsvImg = cv2.cvtColor(image,cv2.COLOR_BGR2HSV)
lowerHSVLimit = np.array([45,0,0])
upperHSVLimit = np.array([60,255,255])
colour = cv2.inRange(hsvImg,lowerHSVLimit,upperHSVLimit)
plt.subplot(111), plt.imshow(colour,cmap="gray")
plt.title('hue range from 45 to 60'), plt.xticks([]), plt.yticks([])
plt.show()
Here the hue selected from 45 to 60
my data set is
var data = [
[2010,"Internet",19],
[2010,"Water",20],
[2011,"Internet",36],
[2011,"Water",44],
[2012,"Internet",51],
[2012,"Water",53],
[2013,"Internet",39],
[2013,"Water",30]
]
var allHeights = [-250,-215,-185,-140, -85, 40, 85, 140, 185, 215, 250];
var allColors = [
d3.rgb(203,23,23),d3.rgb(108,74,246),d3.rgb(9,235,175),d3.rgb(159,172,38),d3.rgb(236,105,18),
d3.rgb(137,195,95),d3.rgb(241,137,108),d3.rgb(253,233,127),d3.rgb(109,151,216),d3.rgb(15,199,58)
];
I want to draw a y-axis on the extreme right of the svg. The axis should be divided into points corresponding to "allHeights" and each point should be labelled with the
year (i.e. data[i][0]).
I want to draw a legend for data[i][2] and fill each legend with allColors[i].
Please help
how do I plot an area around a set of points on a map in R? e.g.
map('world')
map.axes()
p <- matrix(c(50, 50, 80, 100, 70, 40, 25, 60), ncol=2) # make some points
points(p, pch=19, col="red")
polygon(p, col="blue")
... which gives me a polygon with a vertex at each of the points, but it looks rather crappy. Is there any way to "smooth" the polygon into some sort of curve?
One option is to make a polygon bounded by a Bézier curve, using the bezier function in the Hmisc package. However I cannot get the start/end point to join up neatly. For example:
## make some points
p <- matrix(c(50, 50, 80, 100, 70, 40, 25, 60), ncol=2)
## add the starting point to the end
p2 <- cbind(1:5,p[c(1:4,1),])
## linear interpolation between these points
t.coarse <- seq(1,5,0.05)
x.coarse <- approx(p2[,1],p2[,2],xout=t.coarse)$y
y.coarse <- approx(p2[,1],p2[,3],xout=t.coarse)$y
## create a Bezier curve
library(Hmisc)
bz <- bezier(x.coarse,y.coarse)
library(maps)
map('world')
map.axes()
polygon(bz$x,bz$y, col=rgb(0,0,1,0.5),border=NA)
Here's one way, draw the polygon and make it as pretty as you like. This really has nothing to do with areas on maps, more about how you generate the vertices of your polygon.
library(maps)
p <- matrix(c(50, 50, 80, 100, 70, 40, 25, 60), ncol=2)
plot(p, pch = 16, col = "red", cex = 3, xlim = range(p[,1]) + c(-10,10), ylim = range(p[,2]) + c(-5, 5))
map(add = TRUE)
#click until happy, right-click "stop" to finish
p <- locator(type = "l")
map()
polygon(cbind(p$x, p$y), col = "blue")
Otherwise you could interpolate intermediate vertices and smooth them somehow, and in the context of a lon/lat map maybe with use reprojection to get more realistic line segments - but depends on your purpose.