I am trying to constitute a numpy array containing the color hue of each pixel within a contour, using opencv 2.4. I have extracted the coordinates of all point included inside the contour using pixelpoints = np.transpose(np.nonzero(mask)) (format: N x 2, where N is the number of pixels inside the contour) just as here, and I extracted the hue of all pixels within the image using cv2.split(image) (format: 480 , 640). So I need to obtain an array containing elements (xcoord, ycoord, hue) to 3D plot the hue colormap. Unfortunately, I am not a numpy expert, and do not find what I need in the documentation. Could someone please help? Please find below an example of what I wish to achieve.
import numpy as np
a=range(35,135)
hue=np.reshape(a,(10,10))
pixelpoints=np.array([[5,5],[5,6],[5,7],[6,5],[6,6],[6,7],[7,5],[7,6],[7,7]])
print hue
print pixelpoints
result=np.array([[5,5,90],[5,6,91],[5,7,92],[6,5,100],[6,6,101],[6,7,102],[7,5,110],[7,6,111],[7,7,112]])
print result
For all who wonder, the answer has been given here.
np.c_[pixelpoints, hue[tuple(pixelpoints.T)]]
Related
I have an image that is with the spikes/small triangles on the outline border, like this:
I would like to remove the un-wanted spikes/small triangles:
And output the image like this:
I have searched many posts on the web using OpenCV/Emgu CV but no luck.
The problem is the contour is not in equal spacing and I can not use any find peak functions to find them and remove them.
I have also used cubic spline to smooth the image, but it just destroyed the original image shape (too smooth) or just got a little effect on the spikes.
Could anyone who have ideas help me with this issue?
As suggested by Cris, a morphological closing is a good starting point.
In the picture below, I performed closing with an octognal kernel 49x49 (circular would be better), and took the difference with the original.
If you filter out the blobs by size (and possibly by shape), you will get the true spikes that you can subtract. The rest of the shape remains unchanged.
Something like this will also help.
Where:
#contours is your list of contours after findContrours()
#idx is the index of your contour
#eps regulates how much the contour is approximated.
cv::Mat approx;
double eps = cv::arcLength(contours[idx], true) * 0.05;
cv::approxPolyDP(contours[idx], approx, eps, true);
approx.copyTo(contours[idx]);
Maybe this is what you want (its not accurate at all)
OpenCV + Python
# Import preprocessors
import os
import cv2
import numpy as np
# Read image
dir = os.path.abspath(os.path.dirname(__file__))
im = cv2.imread(dir+'/im.png')
# Remove triangles
kernel = np.ones((5,5), np.uint8)
factor=11
im = cv2.dilate(im, kernel, iterations=factor)
im = cv2.erode(im, kernel, iterations=factor)
# Save the processed image
cv2.imwrite(dir+'/spike_res.png', im)
Update:
Maybe not related to OpenCV tag; but with .NET you can also use Erosion and Dialation of AForge.
I have a texture image and some uv coordinators (some float 2D vectors).
Is there any method in OpenCV can automatically interpolate the image and then I can directly use these float number as the pixel coordinator and get the correct pixel value?
I think it should be possible because in some computer vision algorithms like optic flow we will always have some sub pixel value...
Remap is what you want, here is an example:
import cv2
import numpy as np
img = cv2.imread('myimage.png')
interpolated_pixel = cv2.remap(img, np.array([[2.4]], np.float32), np.array([[5.4]], np.float32), cv2.INTER_LINEAR)
print(interpolated_pixel)
You can play with different interpolation schemes, see interpolation flags
Of course you can also batch your request by providing multiple uv coordinates.
I've been recently working at a segmentation process for corneal
endothelial cells, and I've found a pretty decent paper that describes ways to perform it with nice results. I have been trying to follow that paper and implement it all using scikit-image and openCV, but I've gotten stucked at the watershed segmentation.
I will briefly describe how is the process supposed to be:
First of all, you have the original endothelial cells image
original image
Then, they instruct you to perform a morphological grayscale reconstruction, in order to level a little bit the grayscale of the image (however, they do not explain how to get the markers for the grayscale, so I've been fooling around and tried to get some on my own way)
This is what the reconstructed image was supposed to look like:
desired reconstruction
This is what my reconstructed image (lets label it as r) looks like:
my reconstruction
The purpose is to use the reconstructed image to get the markers for the watershed segmentation, how do we do that?! We get the original image (lets label it as f), and perform a threshold in (f - r) to extract the h-domes of the cell, i.e., our markers.
This is what the hdomes image was supposed to look like:
desired hdomes
This is what my hdomes image looks like:
my hdomes
I believe that the hdomes I've got are as good as theirs, so, the final step is to finally perform the watershed segmentation on the original image, using the hdomes we've been working so hard to get!
As input image, we will use the inverted original image, and as markers, our markers.
This is the derised output:
desired output
However, I am only getting a black image, EVERY PIXEL IS BLACK and I have no idea of what's happening... I've also tried using their markers and inverted image, however, also getting black image. The paper I've been using is Luc M. Vincent, Barry R. Masters, "Morphological image processing and network analysis of cornea endothelial cell images", Proc. SPIE 1769
I apologize for the long text, however I really wanted to explain everything in detail of what is my understanding so far, btw, I've tried watershed segmentation from both scikit-image and opencv, both gave me the black image.
Here is the following code that I have been using
img = cv2.imread('input.png',0)
mask = img
marker = cv2.erode(mask, cv2.getStructuringElement(cv2.MORPH_ERODE,(3,3)), iterations = 3)
reconstructedImage = reconstruction(marker, mask)
hdomes = img - reconstructedImage
cell_markers = cv2.threshold(hdomes, 0, 255, cv2.THRESH_BINARY)[1]
inverted = (255 - img)
labels = watershed(inverted, cell_markers)
cv2.imwrite('test.png', labels)
plt.figure()
plt.imshow(labels)
plt.show()
Thank you!
Here's a rough example for the watershed segmentation of your image with scikit-image.
What is missing in your script is calculating the Euclidean distance (see here and here) and extracting the local maxima from it.
Note that the watershed algorithm outputs a piece-wise constant image where pixels in the same regions are assigned the same value. What is shown in your 'desired output' panel (e) are the edges between the regions instead.
import numpy as np
import cv2
import matplotlib.pyplot as plt
from skimage.morphology import watershed
from scipy import ndimage as ndi
from skimage.feature import peak_local_max
from skimage.filters import threshold_local
img = cv2.imread('input.jpg',0)
'''Adaptive thersholding
calculates thresholds in regions of size block_size surrounding each pixel
to handle the non-uniform background'''
block_size = 41
adaptive_thresh = threshold_local(img, block_size)#, offset=10)
binary_adaptive = img > adaptive_thresh
# Calculate Euclidean distance
distance = ndi.distance_transform_edt(binary_adaptive)
# Find local maxima of the distance map
local_maxi = peak_local_max(distance, labels=binary_adaptive, footprint=np.ones((3, 3)), indices=False)
# Label the maxima
markers = ndi.label(local_maxi)[0]
''' Watershed algorithm
The option watershed_line=True leave a one-pixel wide line
with label 0 separating the regions obtained by the watershed algorithm '''
labels = watershed(-distance, markers, watershed_line=True)
# Plot the result
plt.imshow(img, cmap='gray')
plt.imshow(labels==0,alpha=.3, cmap='Reds')
plt.show()
I want to find the orientation of the bright object in the images attached. For this purpose, I used Principal Component Analysis(PCA).
In case of image 1, PCA finds correct orientation as the first principal component is alligned in that direction. However, in case of image 2, the principal components are disoriented.
Can anyone please explain why the PCA is showing different results in the two images? Also, please suggest if there is some other method to find the orientation of the object.
import os
import gdal
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import skimage
from skimage.filters import threshold_otsu
from skimage.filters import try_all_threshold
import cv2
import math
from skimage import img_as_ubyte
from skimage.morphology import convex_hull_image
import pandas as pd
file="path to image file"
(fileRoot, fileExt)= os.path.splitext(file)
ds = gdal.Open(file)
band = ds.GetRasterBand(1)
arr = band.ReadAsArray()
geotransform = ds.GetGeoTransform()
[cols, rows] = arr.shape
thresh = threshold_otsu(arr)
binary = arr > thresh
points = binary>0
y,x = np.nonzero(points)
x = x - np.mean(x)
y = y - np.mean(y)
coords = np.vstack([x, y])
cov = np.cov(coords)
evals, evecs = np.linalg.eig(cov)
sort_indices = np.argsort(evals)[::-1]
evec1, evec2 = evecs[:, sort_indices]
x_v1, y_v1 = evec1
x_v2, y_v2 = evec2
scale = 40
plt.plot([x_v1*-scale*2, x_v1*scale*2],
[y_v1*-scale*2, y_v1*scale*2], color='red')
plt.plot([x_v2*-scale, x_v2*scale],
[y_v2*-scale, y_v2*scale], color='blue')
plt.plot(x,y, 'k.')
plt.axis('equal')
plt.gca().invert_yaxis()
plt.show()
theta = np.tanh((x_v1)/(y_v1)) * 180 /(math.pi)
You claim you are using just white pixels. Did you check which ones are selected by some overlay render? Anyway I do not think it is enough especially for your second image as it does not contain any fully saturated white pixels. I would use more processing before the PCA.
enhance dynamic range
your current images does not need this step as they contain both black and almost fully saturated white. This step allow to unify threshold values among more sample input images. For more info see:
Enhancing dynamic range and normalizing illumination
smooth a bit
this step will significantly lover the intensity of noise points and smooth the edges of bigger objects (but shrink them a bit). This can be done by any FIR filter or convolution or Gaussian filtering. Some also use morphology operators for this.
threshold by intensity
this will remove darker pixels (clear to black) so noise is fully removed
enlarge remaining objects by morphology operators back to former size
You can avoid this by enlarging the resulting OBB by few pixels (number is bound to smooth strength from #2).
now apply OBB search
You are using PCA so use it. I am using this instead:
How to Compute OBB of Multiple Curves?
When I tried your images with above approach (without the #4) I got these results:
Another problem I noticed with your second image is that there are not many white pixels in it. That may bias the PCA significantly especially without preprocessing. I would try to enlarge the image by bicubic filtering and use that as input. May be that is the only problem you got with it.
I just wanted to know if this possible. For example, if I was to find contours in a specific image (http://docs.opencv.org/modules/imgproc/doc/structural_analysis_and_shape_descriptors.html), could I store the data that represents the contours in the specific image? Then could I have another image and detect the contours and store them and then compare the contour data of each image to each other to see if there are objects with related geometric features?
Your question is not clear enough, so I apologize for my poor answer in advance. Anyway, let me try to answer them:
could I store the data that represents the contours in the specific image?
If you take a look at those docs, you might notice that findContours() uses one argument as input, and another as output, so you can't pass the input image to this method and also used it to store the output contours because the method will throw an exception (I've tried this in the past).
could I have another image and detect the contours and store them and then compare the contour data of each image to each other to see if there are objects with related geometric features?
It is possible to analyse 2 contours and compare them to each other. In fact, section 3. Match Shapes of this tutorial shares Python code that uses hu-moments to demonstrate how this can be achieved (invariant to translation, rotation and scale):
import cv2
import numpy as np
img1 = cv2.imread('star.jpg',0)
img2 = cv2.imread('star2.jpg',0)
ret, thresh = cv2.threshold(img1, 127, 255,0)
ret, thresh2 = cv2.threshold(img2, 127, 255,0)
contours,hierarchy = cv2.findContours(thresh,2,1)
cnt1 = contours[0]
contours,hierarchy = cv2.findContours(thresh2,2,1)
cnt2 = contours[0]
ret = cv2.matchShapes(cnt1,cnt2,1,0.0)
print ret