I've to plot multiple scatter and table in a grid space and I'm having a couple of issues with the relative position but most important with defining and maintaining the aspect ratio of the scatter plot.
I've written a script with "fake" data on it to describe my problem and a minimum "not working" example below.
What I have is a dataframe with x, and y positions of objects, and what I want to do is to put the corresponding image below.
Since the image can have an arbitrary aspect ratio I need to read the aspect ratio and construct the scatter plot in that way but I'm unable to make it work.
Another problem is connected with the invert_xaxis and invert_yaxis that don't work (I need that command since the scatter data are inverted.
I've used the following commands, and as far as I've understood each of them should block the aspect ratio of the scatter plot to the same ratio of the figure but they do not work.
The aspect ratio becomes corrected only when the figure is plotted but that eliminates the effect of axis inversion.
I've had a similar problem with setting the aspect ratio of plots without the addition of a figure, sometimes it worked but not with tight_layout.
It is obvious that I'm missing something important....but I'm unable to figure it out.
This is the fake data code:
###############################################################################
# fake data
#general data aspect ratio
image_height= 5 #4270
image_width = 10 # 8192
pix2scale = 0.3125
data_AR = image_height / image_width
#random data generation
data_width = image_width* pix2scale
data_height = image_height * pix2scale
data1x = np.random.uniform(-data_width/2, data_width/2, size=(40))
data1y = np.random.uniform(-data_height/2, data_height/2, size=(40))
data2x = np.random.uniform(-data_width/2, data_width/2, size=(40))
data2y = np.random.uniform(-data_height/2,data_height/2, size=(40))
temp_df1 = pd.DataFrame([data1x,data1y,['random1']*40],index = ['x','y','label']).T
temp_df2 = pd.DataFrame([data2x,data2y,['random2']*40],index = ['x','y','label']).T
df = pd.concat([temp_df1,temp_df2],axis = 0, ignore_index = True)
del temp_df1, temp_df2
#sample image generation of variable aspect ratio
img_size = (image_height, image_width)
R_layer = np.ones(shape= img_size)*0.50
G_layer = np.ones(shape= img_size)*0.50
B_layer = np.ones(shape= img_size)*0.50
A_layer = np.ones(shape= img_size)
img = np.dstack([R_layer,G_layer,B_layer,A_layer])
#add a mark at the top left of the image
for k in range(0,3):
for i in range(0,int(image_width*0.2*data_AR)):
for j in range(0,int(image_width*0.2)):
img[i,j,k] = 0
#add a mark at the center of the image
# get center coordinates of the image
center = [[2, 4], [2, 5]]
for k in range(0,3):
for point in center:
if k == 0:
img[point[0],point[1],k] = 1
else:
img[point[0],point[1],k] = 0
#show image
fig, ax = plt.subplots()
ax.imshow(img)
###############################################################################
this is the code that generates the image:
#%%
# sample code
# at this point Iìve already loaded the image, the pix2scale value
# and the df containing data points
#read image aspect ratio
img_AR = img.shape[0]/img.shape[1]
pixel_width = img.shape[1]
pixel_height = img.shape[0]
# each pixel correspond to 0.3125 unit (mm)
pix2scale = 0.3125
#define image position
#the center of the image need to be placed at (0,0)
#bottom left corner
left = - (pixel_width * pix2scale)/2
bottom = - (pixel_height * pix2scale)/2
right = left + (pixel_width * pix2scale)
top = bottom + (pixel_height * pix2scale)
extent = [left,right,bottom,top]
#figure definition
figure_width = 15 #inch
figure_AR = 1
scatter_AR = img_AR
#initialize figure
fig_s= plt.figure(figsize = (figure_width,figure_width*figure_AR))
gs = plt.GridSpec (3,3)
#scatter plot in ax1
ax1 = fig_s.add_subplot(gs[:2,:2])
g = sns.scatterplot( data = df,
x = 'x',
y = 'y',
hue = 'label',
ax =ax1
)
ax1.invert_xaxis()
ax1.invert_yaxis()
#resize the figure box
box = ax1.get_position()
ax1.set_position([box.x0,box.y0,box.width*0.4,box.width*0.4*scatter_AR])
ax1.legend(loc = 'center left', bbox_to_anchor = (1,0.5))
ax1.set_title('Inclusions Scatter Plot')
ax1.set_aspect(scatter_AR)
#plt image
ax1.imshow(img,extent = extent)
#scatter plot
ax2 = fig_s.add_subplot(gs[2,:2])
g = sns.scatterplot( data = df,
x = 'x',
y = 'y',
hue = 'label',
ax =ax2
)
#resize the figure box
box = ax2.get_position()
ax2.set_position([box.x0,box.y0,box.width*0.4,box.width*0.4*scatter_AR])
ax2.legend(loc = 'center left', bbox_to_anchor = (1,0.5))
ax2.set_title('Inclusions Scatter Plot')
ax2.set_aspect(scatter_AR)
ax2.imshow(img,extent = extent)
#scatter plot
ax3 = fig_s.add_subplot(gs[1,2])
g = sns.scatterplot( data = df,
x = 'x',
y = 'y',
hue = 'label',
ax =ax3
)
#resize the figure box
box = ax3.get_position()
ax3.set_position([box.x0,box.y0,box.width*0.4,box.width*0.4*scatter_AR])
ax3.legend(loc = 'center left', bbox_to_anchor = (1,0.5))
ax3.set_title('Inclusions Scatter Plot')
ax3.set_aspect(scatter_AR)
ax3.imshow(img,extent = extent)
#add suptitle to figure
fig_s.suptitle('my title',fontsize= 22)
fig_s.subplots_adjust(top=0.85)
# #make it fancy
for i in range(3):
fig_s.tight_layout()
plt.pause(0.2)
I've plotted multiple grid because I wanted to test the tight_layout().
[enter image description here][2]
Related
I am trying to implement FRST on python to detect centroids of elliptical objects (e.g. cells in microscopy images), but my implementation does not find seed points (more or less center points) of elliptical objects. This effort comes from duplicating FRST from Segmentation of Overlapping Elliptical Objects in Silhouette Images (https://ieeexplore.ieee.org/document/7300433). I don't know why I have these artifacts. An interesting thing is that I see these patterns (crosses) all in the same direction per object. Any point in the right direction to generate the same result as in the paper (just to find the seed points) will be most welcome.
Original Paper: A Fast Radial Symmetry Transform for Detecting Points of Interest by Loy and Zelinsky (ECCV 2002)
I have also tried the pre-existing python package for FRST: https://pypi.org/project/frst/. This somehow results in the same artifacts. Weird.
First image: Original Image
Second image: Sobel-operated Image
Third image: Magnitude Projection Image
Fourth image: Magnitude Projection Image with positively affected pixels only
Fifth image: FRST'd image: end-product with original image overlaid (shadowed)
Sixth image: FRST'd image by the pre-existing python package with original image overlaid (shadowed).
from scipy.ndimage import gaussian_filter
import numpy as np
from scipy.signal import convolve
# Get orientation projection image
def get_proj_img(image, radius):
workingDims = tuple((e + 2*radius) for e in image.shape)
h,w = image.shape
ori_img = np.zeros(workingDims) # Orientation Projection Image
mag_img = np.zeros(workingDims) # Magnitutde Projection Image
# Kenels for the sobel operator
a1 = np.matrix([1, 2, 1])
a2 = np.matrix([-1, 0, 1])
Kx = a1.T * a2
Ky = a2.T * a1
# Apply the Sobel operator
sobel_x = convolve(image, Kx)
sobel_y = convolve(image, Ky)
sobel_norms = np.hypot(sobel_x, sobel_y)
# Distances to afpx, afpy (affected pixels)
dist_afpx = np.multiply(np.divide(sobel_x, sobel_norms, out = np.zeros(sobel_x.shape), where = sobel_norms!=0), radius)
dist_afpx = np.round(dist_afpx).astype(int)
dist_afpy = np.multiply(np.divide(sobel_y, sobel_norms, out = np.zeros(sobel_y.shape), where = sobel_norms!=0), radius)
dist_afpy = np.round(dist_afpy).astype(int)
for cords, sobel_norm in np.ndenumerate(sobel_norms):
i, j = cords
pos_aff_pix = (i+dist_afpx[i,j], j+dist_afpy[i,j])
neg_aff_pix = (i-dist_afpx[i,j], j-dist_afpy[i,j])
ori_img[pos_aff_pix] += 1
ori_img[neg_aff_pix] -= 1
mag_img[pos_aff_pix] += sobel_norm
mag_img[neg_aff_pix] -= sobel_norm
ori_img = ori_img[:h, :w]
mag_img = mag_img[:h, :w]
print ("Did it go back to the original image size? ")
print (ori_img.shape == image.shape)
# try normalizing ori and mag img
return ori_img, mag_img
def get_sn(ori_img, mag_img, radius, kn, alpha):
ori_img_limited = np.minimum(ori_img, kn)
fn = np.multiply(np.divide(mag_img,kn), np.power((np.absolute(ori_img_limited)/kn), alpha))
# convolute fn with gaussian filter.
sn = gaussian_filter(fn, 0.25*radius)
return sn
def do_frst(image, radius, kn, alpha, ksize = 3):
ori_img, mag_img = get_proj_img(image, radius)
sn = get_sn(ori_img, mag_img, radius, kn, alpha)
return sn
Parameters:
radius = 50
kn = 10
alpha = 2
beta = 0
stdfactor = 0.25
I am using MATLAB in order to analyse cell images. At the moment, I am able to detected and measure circular cells only (image attached). Is there a way, I can detect the other cells and measure them? Perhaps, by adding an outline to all the cells and then measuring?
Code so far:
tic
close all
clear all
dir = 'C:\Users\CBE user 70\Google Drive\Gloria - PhD\PhD Thesis\Chapter 4 - DS coculture\Pictures\DSHALO MBB\16MARCH\';
FileName = 'DH1L116march(2).JPG';
Pixel2MicroMeterRatio = 6;
HistBinEdges = [5:1:25 ];
vi = 0.05; % vertical increase in sublpot size (normalized)
hi = 0.08; % horizontal increase in sublpot size (normalized)
i = imread([dir,FileName]);
i2 = i(:,:,3);
i2= imadjust(i2);
% increase imfindcircles from [11 50] to [5 99], as the cells here are
% larger here
[CircCenter, CircRadii] = imfindcircles(i2,[5 99],'ObjectPolarity','dark');
NumOfCells = length(CircRadii);
sp(1) = subplot(2,2,1);
imshow(i)
sp(2) = subplot(2,2,2);
imshow(i2)
hold on
c=viscircles(CircCenter, CircRadii,'EdgeColor','r','linewidth',1,'linestyle',':');
c.Children(2).Visible = 'off';
for i=1:NumOfCells
text(CircCenter(i,1),CircCenter(i,2),num2str(i),'fontsize',6,'color','c','HorizontalAlignment','center','VerticalAlignment','middle')
end
I'm trying to locate an object (here a PWB) on a picture.
First I do this by finding the largest contour. Then I want to rewrite solely this object into a new picture so that in the future I can work on smaller pictures.
The problem however is that when I rewrite this ROI, the picture gets of a lighter color than the original one.
CODE:
Original = cv2.imread(picture_location)
image = cv2.imread(mask_location)
img = cv2.medianBlur(image,29)
imgray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
dst = cv2.bitwise_and(Original, image)
roi = cv2.add(dst, Original)
ret,thresh = cv2.threshold(imgray,127,255,0)
im2, contours, hierarchy = cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
area = 0
max_x = 0
max_y = 0
min_x = Original.shape[1]
min_y = Original.shape[0]
for i in contours:
new_area = cv2.contourArea(i)
if new_area > area:
area = new_area
cnt = i
x,y,w,h = cv2.boundingRect(cnt)
min_x = min(x, min_x)
min_y = min(y, min_y)
max_x = max(x+w, max_x)
max_y = max(y+h, max_y)
roi = roi[min_y-10:max_y+10, min_x-10:max_x+10]
Original = cv2.rectangle(Original,(x-10,y-10),(x+w+10,y+h+10),(0,255,0),2)
#Writing down the images
cv2.imwrite('Pictures/PCB1/LocatedPCB.jpg', roi)
cv2.imwrite('Pictures/PCB1/LocatedPCBContour.jpg',Original)
Since I don't have 10 reputation yet I cannot post the pictures. I can however provide the links:
Original
Region of Interest
The main question is how do I get the software to write down the ROI in the exact same colour as the original picture?
I'm a elektromechanical engineer however, so I'm fairly new to this, remarks on the way I wrote my code would also be appreciated if possible.
The problem is that you first let roi = cv2.add(dst, Original)
and finally cut from the lighten picture in here:
roi = roi[min_y-10:max_y+10, min_x-10:max_x+10]
If you want to crop the original image, you should do:
roi = Original[min_y-10:max_y+10, min_x-10:max_x+10]
You can perhaps perform an edge detection after blurring your image.
How to select best parameters for Canny edge? SEE HERE
lower = 46
upper = 93
edged = cv2.Canny(img, lower, upper) #--- Perform canny edge on the blurred image
kernel = np.ones((5,5),np.uint8)
dilate = cv2.morphologyEx(edged, cv2.MORPH_DILATE, kernel, 3) #---Morphological dilation
_, contours , _= cv2.findContours(dilate, cv2.RETR_EXTERNAL, 1) #---Finds all parent contours, does not find child contours(i.e; does not consider contours within another contour)
max = 0
cc = 0
for i in range(len(contours)): #---For loop for finding contour with maximum area
if (cv2.contourArea(contours[i]) > max):
max = cv2.contourArea(contours[i])
cc = i
cv2.drawContours(img, contours[cc], -1, (0,255,0), 2) #---Draw contour having the maximum area
cv2.imshow(Contour of PCB.',img)
x,y,w,h = cv2.boundingRect(cnt[cc]) #---Calibrates a straight rectangle for the contour of max. area
crop_img = img1[y:y+h, x:x+w] #--- Cropping the ROI having the coordinates of the bounding rectangle
cv2.imshow('cropped PCB.jpg',crop_img)
What I would like to do is plot an image of a graph (from say a pdf file or a scanned image). Next, I would like to overlay an axis on the graph in the image, and then plot data on that axis (over the image).
Using imtool, I know the coordinates of the graph in the image (x range = ~52-355 pixels, and y range = 23(top) - 262(bottom) pixels in this case).
This is what I have tried:
I = imread('C:\MATLAB\R2014a\help\images\ref\ftrans2_fig.png');
I = squeeze(uint8(mean(I,3)));
figure, imshow(I)
[rows, cols] = size(I);
x_data = (-1 : .01 : +1)';
y_data = 1 - x_data.^2;
h1 = axes('Position',([52, 23, 355-52, 262-23] ./ [cols, rows, cols, rows] ));
set(h1, 'Color', 'none')
hold on
plot(x_data, y_data, '-rx')
Question: Knowing the pixel coordinates of the graph in the image, how do I determine the proper position of the axis in the figure, (my code fails to account for the actual size of the figure box, the gray border around the image). I have to do this for several images and sets of data, so I would like an automated method, assuming I find the coordinates of the graphs in the image ahead of time.
Thanks for your reply! (1st time posting, please be kind)
You may be able to solve your problem by forcing the image onto the same axis as the plot. Try this:
I = imread('C:\MATLAB\R2014a\help\images\ref\ftrans2_fig.png');
I = squeeze(uint8(mean(I,3)));
[rows, cols] = size(I);
x_data = (-1 : .01 : +1)';
y_data = 1 - x_data.^2;
h1 = axes('Position',([52, 23, 355-52, 262-23] ./ [cols, rows, cols, rows] ));
set(h1, 'Color', 'none')
hold on
image(I, 'Parent', h1);
plot(h1, x_data, y_data, '-rx')
That should at ensure that the plot axis and the image axis have the same origin, as they will be one and the same. You may need to adjust your sizing code. Let me know if that doesn't do it for you.
Good Luck!
I think I have it figured out.
It would have been easier if I could use:
figure, h1=imshow(I)
get(h1,'Position')
but that results in "The name 'Position' is not an accessible property for an instance of class 'image'."
Instead, this appears to work:
I = imread('C:\MATLAB\R2014a\help\images\ref\ftrans2_fig.png');
I = squeeze(uint8(mean(I,3)));
in_mag = 300;
figure, imshow(I, 'Border', 'tight', 'InitialMagnification', in_mag)
[rows, cols] = size(I);
x_data = (-1 : .01 : +1)';
y_data = 1 - x_data.^2;
% Coord of graph in image pixels
x_0 = 50; x_max = 354; y_0 = 262; y_max = 23;
h1 = axes('Position',([x_0, rows-y_0, x_max-x_0, y_0-y_max] ...
./ [cols, rows, cols, rows] ));
set(h1,'Color','none')
hold on
plot(x_data, y_data, '-rx')
ylim([0,1.4])
set(gca,'YColor', [0 0 1], 'XColor', [0 0 1])
However, if anybody has a better idea, I would be very happy to explore it!
Thanks
I am using the following code in MATLAB to find the rectangle containing a car's license plate:
clc
clear
close all
%Open Image
I = imread('plate_1.jpg');
figure, imshow(I);
%Gray Image
Ib = rgb2gray(I);
figure,
subplot(1,2,1), imshow(Ib);
%Enhancement
Ih = histeq(Ib);
subplot(1,2,2), imshow(Ih);
figure,
subplot(1,2,1), imhist(Ib);
subplot(1,2,2), imhist(Ih);
%Edge Detection
Ie = edge(Ih, 'sobel');
figure,
subplot(1,2,1), imshow(Ie);
%Dilation
Id = imdilate(Ie, strel('diamond', 1));
subplot(1,2,2), imshow(Id);
%Fill
If = imfill(Id, 'holes');
figure, imshow(If);
%Find Plate
[lab, n] = bwlabel(If);
regions = regionprops(lab, 'All');
regionsCount = size(regions, 1) ;
for i = 1:regionsCount
region = regions(i);
RectangleOfChoice = region.BoundingBox;
PlateExtent = region.Extent;
PlateStartX = fix(RectangleOfChoice(1));
PlateStartY = fix(RectangleOfChoice(2));
PlateWidth = fix(RectangleOfChoice(3));
PlateHeight = fix(RectangleOfChoice(4));
if PlateWidth >= PlateHeight*3 && PlateExtent >= 0.7
im2 = imcrop(I, RectangleOfChoice);
figure, imshow(im2);
end
end
Plates all have white backgrounds. Currently,I use the rectangles' ratio of width to height to select candidate regions for output. This gives the plate rectangle in addition to several other irrelevant ones in the case of a white car. What method can I use to get only one output: the license plate?
Also, I don't find a plate at all when I run the code on a black car. Maybe that's because the car's color is the same as the plate edge.
Are there any alternatives to edge detection to avoid this problem?
Try this !!!
I = imread('http://8pic.ir/images/88146564605446812704.jpg');
im=rgb2gray(I);
sigma=1;
f=zeros(128,128);
f(32:96,32:96)=255;
[g3, t3]=edge(im, 'canny', [0.04 0.10], sigma);
se=strel('rectangle', [1 1]);
BWimage=imerode(g3,se);
gg = imclearborder(BWimage,8);
bw = bwareaopen(gg,200);
gg1 = imclearborder(bw,26);
imshow(gg1);
%Dilation
Id = imdilate(gg1, strel('diamond', 1));
imshow(Id);
%Fill
If = imfill(Id, 'holes');
imshow(If);
%Find Plate
[lab, n] = bwlabel(If);
regions = regionprops(lab, 'All');
regionsCount = size(regions, 1) ;
for i = 1:regionsCount
region = regions(i);
RectangleOfChoice = region.BoundingBox;
PlateExtent = region.Extent;
PlateStartX = fix(RectangleOfChoice(1));
PlateStartY = fix(RectangleOfChoice(2));
PlateWidth = fix(RectangleOfChoice(3));
PlateHeight = fix(RectangleOfChoice(4));
if PlateWidth >= PlateHeight*1 && PlateExtent >= 0.7
im2 = imcrop(I, RectangleOfChoice);
%figure, imshow(I);
figure, imshow(im2);
end
end