There is a electron microscope photo of some surface:
What I want to do is to use OpenCV to detect edges of pyramid in this image and measure their length. Currently I have to label them manually (the red lines in image).
I try to use Canny with Hough transform but the result turn out not very well.
import cv2
from matplotlib import pyplot as plt
import numpy as np
img = cv2.imread('../data/sample.png')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2BGRA)
kernel_size = 9
blur_gray = cv2.GaussianBlur(gray, (kernel_size, kernel_size), 0)
lo, hi = 15, 27
edges = cv2.Canny(blur_gray, lo, hi)
plt.figure()
plt.imshow(blur_gray)
plt.figure()
plt.imshow(edges)
lines
= cv2.HoughLinesP(edges, 1, np.pi / 180, 50, None, 50, 10)
It looks to me like there is too much noise in the original image. What I want to detect is the lateral edges of pyramid, but the base edges are also included with my code. I don't know how to remove them before using Canny and Hough transform. And there are some glitches in the result of edge detection which I don't know how to eliminate them. I though Gaussian blur should be enough but it turns out not working very well.
I have to confess that I have little knowledge of computer vision so I am not sure what's the right tool for this. It would be really appreciated to have someone shed some light on it. There is no need to find a 100% accuracy method as there are some abnormal structures in the image that have to be adjust manually, what I want is try to reduce the human effort as much as possible by using some computer vision technique.
Related
It may seem like a common OpenCV recipe for a circle yields really jagged edges to the circles being drawn. Here's minimally viable reproducible code:
import cv2
import numpy as np
image = np.zeros((512,512,3), np.uint8)
cv2.circle(image, (350, 350), 200, (15,75,50), cv2.FILLED)
cv2.imshow("Circle", image)
cv2.waitKey(0)
cv2.destroyAllWindows()
It's perhaps a little better in the case of a non-filled circle (when using lineType=cv2.LINE_AA):
But it's sometimes nice to draw circles which are both filled and have a good edge to their circumference at the same time.
I wonder if this outcome can be improved within the framework of using OpenCV, even though OpenCV probably does not try to be a pixel perfect drawing library.
Thanks in advance for your help as I narrow down my choice between OpenCV and Pyglet for a certain computer vision with graphics implementation. And also because it would be nice to have visually pleasing conventional shapes even for just your haphazard use of shape drawing in some OpenCV laden applications.
Use both thickness=cv.FILLED and lineType=cv.LINE_AA in the same call.
You should always consult the documentation of the technologies you use.
import cv2
import numpy as np
image = np.zeros((512,512,3), np.uint8)
cv2.circle(image, (350, 350), 200, (15,75,50), thickness=cv2.FILLED, lineType=cv2.LINE_AA)
cv2.imshow("Circle", image)
cv2.waitKey(0)
cv2.destroyAllWindows()
I want to extract stripes from this sample file sample file, and the result should look like this one similar result image. Then, I need to count the number of stripes on the right, and calculate the distance from the end of each left stripe to the end of each adjacent right stripe.
I tried with the following code, but my result my result fileis still a little bit away from my target. Here is what I do:
import numpy as np
import cv2
from matplotlib import pyplot as plt
gray = cv2.imread('input_file.png',cv2.IMREAD_UNCHANGED)
sobelY = cv2.Sobel(gray, cv2.CV_32F, 0, 1, ksize=3)
sobelY2 = cv2.Sobel(sobelY, cv2.CV_32F, 0, 1, ksize=3)
sobelY2[sobelY2<0]=0
mask = np.where(sobelY2==0,0,1)
sobelY2 = cv2.normalize(sobelY2, dst=None, alpha=0, beta=65535, norm_type=cv2.NORM_MINMAX).astype(np.uint16)
clahe=cv2.createCLAHE(clipLimit=6, tileGridSize=(8,8))
sobelY2_clahe = clahe.apply(sobelY2)
sobelY2_clahe = clahe.apply(sobelY2_clahe)
result = np.where(mask!=0,sobelY2_clahe,0)
fig = plt.figure(figsize=(10, 10))
ax = plt.subplot(121)
plt.imshow(gray, cmap='gray')
ax = plt.subplot(122)
plt.imshow(result, cmap='gray')
plt.show()
The input file is in 16 bits format, so I keep it unchanged for accuracy. I do second order Sobel operation in Y direction to high light those stripes, and then I do two times Clahe operations to balance the contrast. To keep the background pixels as 0, I use a mask to set the values back after the Clahe operations.
Any advice is appreciated!
For completeness, I am attaching another more challenged input file for referencemore challenged input file.
Edit:
The sobelY2 image pretty much reflects the stripes, but could we make it look better?
I just opened a new question about how to trim each of these stripes based on gray scale values.trim image based on grayscale values
I'm working on a project which involves determining the volume of a transparent liquid (or air if it proves easier) in a confined space.
The images I'm working with are a background image of the container without any liquid and a foreground image which may be also be empty in rare cases, but most times is partly filled with some amount of liquid.
While it may seem like a pretty straightforward smooth and threshold approach, it proves somewhat more difficult.
I'm working with a set with tons of these image pairs of background and foreground images, and I can't seem to find an approach that is robust enough to be applied to all images in the set.
My work so far involves smoothing and thresholding the image and applying closing to wrap it up.
bg_image = cv.imread("bg_image", 0)
fg_image = cv.imread("fg_image", 0)
blur_fg = cv.GaussianBlur(fg_image, (5, 5), sigmaX=0, sigmaY=0)
thresholded_image = cv.threshold(blur_fg, 186, 255, cv.THRESH_BINARY_INV)[1]
kernel = np.ones((4,2),np.uint8)
closing = cv.morphologyEx(thresholded_image, cv.MORPH_CLOSE, kernel)
The results vary, here is an example when it goes well:
In other examples, it doesn't go as well:
Aside from that, I have also tried:
Subtraction of the background and foreground images
Contrast stretching
Histogram equalization
Other thresholding techniques such as Otsu
The main issue is that the pixel intensities in air and liquid sometime overlap (and pretty low contrast in general), causing inaccurate estimations. I am leaning towards utilizing the edge that occurs between the liquid and air but I'm not really sure how..
I don't want to overflow with information here so I'm leaving it at that. I am grateful for any suggestions and can provide more information if necessary.
EDIT:
Here are some sample images to play around with.
Here is an approach whereby you calculate the mean of each column of pixels in your image, then calculate the gradient of the means:
#!/usr/bin/env python3
import cv2
import numpy as np
import matplotlib.pyplot as plt
filename = 'fg1.png'
# Load image as greyscale and calculate means of each column of pixels
im = cv2.imread(filename, cv2.IMREAD_GRAYSCALE)
means = np.mean(im, axis=0)
# Calculate the gradient of the means
y = np.gradient(means)
# Plot the gradient of the means
xdata = np.arange(0, y.shape[0])
plt.plot(xdata, y, 'bo') # blue circles
plt.title(f'Gradient of Column Means for "{filename}"')
plt.xlabel('x')
plt.ylabel('Gradient of Column Means')
plt.grid(True)
plt.show()
If you just plot the means of all columns, without taking the gradient, you get this:
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.