I have the following code and I want to detect the circle.
img = cv2.imread("act_circle.png")
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
circles = cv2.HoughCircles(gray,cv2.CV_HOUGH_GRADIENT)
it looks like it does not have the attribute and the error is the following
'module' object has no attribute 'CV_HOUGH_GRADIENT'
Does anybody know where this hidden parameters is?
Thanks
CV_HOUGH_GRADIENT belongs to the cv module, so you'll need to import that:
import cv2.cv as cv
and change your function call to
circles = cv2.HoughCircles(gray,cv.CV_HOUGH_GRADIENT)
Now in current cv2 versions:
import cv2
cv2.HOUGH_GRADIENT
In my case, I am using opencv 3.0.0 and it worked the following way:
circles = cv2.HoughCircles(gray_im, cv2.HOUGH_GRADIENT, 2, 10, np.array([]), 20, 60, m/10)[0]
i.e. instead of cv2.cv.CV_HOUGH_GRADIENT, I have used just cv2.HOUGH_GRADIENT.
if you use OpenCV 3, then use this code :
img = cv2.imread("act_circle.png")
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
circles = cv2.HoughCircles(gray,cv2.HOUGH_GRADIENT) # change here
Related
I am new to python and I was playing around with background subtraction to visualize changes in pre and post change images.
I wrote a short and simple script using the cv2 library:
#!/usr/bin/env python
import cv2 as cv
import numpy as np
from matplotlib import pyplot as plt
#GRAYSCALE ONLY FOR TESTING
#Test with person appearing in image
img1 = cv.imread("images/1.jpg", 0)
img2 = cv.imread("images/2.jpg", 0)
img3 = cv.subtract(img1, img2)
ret,thresh1 = cv.threshold(img3,90,255,cv.THRESH_BINARY)
#Test with satelite image of japan landslide changes after earthquake
jl_before = cv.imread("images/japan_earthquake_before.jpg",0)
jl_after = cv.imread("images/japan_earthquake_after.jpg",0)
jl_subtraction = cv.subtract(jl_before, jl_after)
ret,thresh2 = cv.threshold(img3,20,255,cv.THRESH_BINARY)
images = [img1, img2, thresh1, jl_before, jl_after, thresh2]
titles = ["Image1", "Image2", "Changes", "Japan_Before", "Japan_After", "Japan_Changes" ]
for i in range(6):
plt.subplot(2,3,i+1),plt.imshow(images[i],'gray')
plt.title(titles[i])
plt.xticks([]),plt.yticks([])
plt.show()
The result looks like this:
Why is the mask with changes from the first set of images present in the mask of the second set of images?
I used different variables, thresh1 and thresh2.
Any help would be greatly appreciated as I can't seem to find the problem.
Because you missed a change when copy pasting:
ret,thresh2 = cv.threshold(img3,20,255,cv.THRESH_BINARY)
^^^^
So basically I'm trying to convert a set of RGB images to grayscale using cv2.cvtColor and python is throwing the following error:
Traceback (most recent call last):
File "MCG.py", line 53, in
gray = cv2.cvtColor(data, cv2.COLOR_BGR2GRAY)
TypeError: src is not a numpy array, neither a scalar.
This here is the code:
import numpy as np
import cv2
import dlib
import sys
import skimage
from PIL import Image
import os
import glob
folderpath = sys.argv[1]
cascPath = sys.argv[2]
imageformat = ".tif"
path = folderpath
imfilelist = [os.path.join(path,f) for f in os.listdir(path) if f.endswith(imageformat)]
data = []
for IMG in imfilelist:
print IMG
image = cv2.imread(IMG)
data.append(image)
cv2.imshow('Image', image)
cv2.waitKey(0)
faceCascade = cv2.CascadeClassifier(cascPath)
predictor = dlib.shape_predictor(PREDICTOR_PATH)
gray = cv2.cvtColor(data, cv2.COLOR_BGR2GRAY)
faces = faceCascade.detectMultiScale(
gray,
scaleFactor=1.05,
minNeighbors=5,
minSize=(100,100)
)
As you can see, I'm trying to append all these images to a list, which will then be converted using the cv2.cvtColor function. However, that error is thrown. What am I doing wrong? Thank you.
P.S if anyone is wondering why I imported modules that don't seem to be used in this code, this code is just a segment of the whole thing and all of those modules have are being utilized in one way or the other.
If you read the cv2.cvtColor documentation, you can see that the first parameter is the Src 8-bit single channel image. However, in your case you are giving an entire list of images.
So change the code as
gray = []
for j in range(0,len(data)):
gray.append(cv2.cvtColor(np.array(data[j]), cv2.COLOR_BGR2GRAY))
I guess this should work.
You are collecting the images into a list with
data = []
for IMG in imfilelist:
...
data.append(image)
....
and then trying to convert the list with
gray = cv2.cvtColor(data, cv2.COLOR_BGR2GRAY)
This is why you are getting the error - the error is telling you that data is not an image (numpy array) but is a list. You need to convert one image at a time with cv2.cvtColor().
You could try
gray = []
for img in data:
gray.append(cv2.cvtColor(img, cv2.COLOR_BGR2GRAY))
This would give you a list of greyscaled images, which is what I think you want to do.
My code:
import cv2
import numpy as np
imgL = cv2.imread('Blender_Suzanne1.jpg')
img1 = cv2.cvtColor(imgL, cv2.COLOR_BGR2GRAY)
imgR = cv2.imread('Blender_Suzanne2.jpg')
img2 = cv2.cvtColor(imgR, cv2.COLOR_BGR2GRAY)
stereo = cv2.StereoBM_create(numDisparities = 16, blockSize = 17)
disparity = stereo.compute(img2, img1)
cv2.imshow('DepthMap', disparity)
cv2.waitKey()
cv2.destroyAllWindows()
When I run it, I see a window which is all grey? I think it is wrong.
I used this code from the OpenCV docs website.
Can anyone help?
PS: First I had some error which did not allow the output window to pop up. So, I added the two lines namely img1 and img 2 in my code.
You can display the result disparity using cv2.imshow() as well after you normalize it.
norm_image = cv2.normalize(disparity, None, alpha = 0, beta = 1, norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_32F)
cv2.imshow('norm_image', norm_image)
Notice the change of data type after normalizing the image. Prior to normalization disparity was of type int16. After normalization it is float32 (mentioned within the function cv2.normalize())
Instead of using imshow use matplotlib to visualization as per the documentation. Also you can convert image into gray in the same line you read the image as follows.
import cv2
from matplotlib import pyplot as plt
imgL = cv2.imread('Blender_Suzanne1.jpg',0)
imgR = cv2.imread('Blender_Suzanne2.jpg',0)
stereo = cv2.StereoBM_create(numDisparities = 16, blockSize = 17)
disparity = stereo.compute(imgL, imgR)
plt.imshow(disparity,'gray')
plt.show()
I have this image of an eye where I want to get the center of the pupil:
Original Image
I applied adaptive threshold as well as laplacian to the image using this code:
import cv2
import numpy as np
from matplotlib import pyplot as plt
img = cv2.imread('C:\Users\User\Documents\module4\input\left.jpg',0)
image = cv2.medianBlur(img,5)
th = cv2.adaptiveThreshold(image,255,cv2.ADAPTIVE_THRESH_MEAN_C,
cv2.THRESH_BINARY,11,2)
laplacian = cv2.Laplacian(th,cv2.CV_64F)
cv2.imshow('output', laplacian)
cv2.imwrite('C:\Users\User\Documents\module4\output\output.jpg', laplacian)
cv2.waitKey(0)
cv2.destroyAllWindows
and the resulting image looks like this: Resulting image by applying adaptive threshold
I want to draw a circle around the smaller inner circle and get its center. I've tried using contours and circular hough transform but it does not correctly detect any circles in the image.
Here is my code for Circular Hough Transform:
import cv2
import numpy as np
from matplotlib import pyplot as plt
img = cv2.imread('C:\Users\User\Documents\module4\output\output.jpg',0)
circles = cv2.HoughCircles(img,cv2.HOUGH_GRADIENT,1,20,param1=50,param2=30,minRadius=0,maxRadius=0)
circles = np.uint16(np.around(circles))
for i in circles[0,:]:
# draw the outer circle
cv2.circle(img,(i[0],i[1]),i[2],(255,255,0),2)
# draw the center of the circle
cv2.circle(img,(i[0],i[1]),2,(255,0,255),3)
cv2.imshow('detected circles',img)
cv2.waitKey(0)
cv2.destroyAllWindows()
And here is the code for applying contour:
import cv2
import numpy as np
img = cv2.imread('C:\Users\User\Documents\module4\output\output.jpg',0)
_, contours,hierarchy = cv2.findContours(img, 1, 2)
cnt = contours[0]
(x,y),radius = cv2.minEnclosingCircle(cnt)
center = (int(x),int(y))
radius = int(radius)
img = cv2.circle(img,center,radius,(0,255,255),2)
cv2.imshow('contour', img)
cv2.waitKey(0)
cv2.destroyAllWindows()
The resulting image of this code exactly looks like the image wherein I applied adaptive threshold. I would really appreciate it if anyone can help me solve my problem. I've been stuck with this for a while now. Also, if any of you guys can suggest a better way to detect the center of the pupil besides this method, I would also really appreciate it.
try to apply edge detection instead of shareholding after filtering of original image and then apply hough circle
My thought would be to use the Hough transform like you're doing. But another method might be template matching like this. This assumes you know the approximate radius of the pupil in the image, you can try to build a template.
import skimage
import numpy as np
import matplotlib.pyplot as plt
img = skimage.io.imread('Wjioe.jpg')
#just use grayscale, but you could make separate template for each r,g,b channel
img = np.mean(img, axis=2)
(M,N) = img.shape
mm = M-20
nn = N-20
template = np.zeros([mm,nn])
## Create template ##
#darkest inner circle (pupil)
(rr,cc) = skimage.draw.circle(mm/2,nn/2,4.5, shape=template.shape)
template[rr,cc]=-2
#iris (circle surrounding pupil)
(rr,cc) = skimage.draw.circle(mm/2,nn/2,8, shape=template.shape)
template[rr,cc] = -1
#Optional - pupil reflective spot (if centered)
(rr,cc) = skimage.draw.circle(mm/2,nn/2,1.5, shape=template.shape)
template[rr,cc] = 1
plt.imshow(template)
normccf = skimage.feature.match_template(img, template,pad_input=True)
#center pixel
(i,j) = np.unravel_index( np.argmax(normccf), normccf.shape)
plt.imshow(img)
plt.plot(j,i,'r*')
You're defining a 3 channel color for a gray-scale image. Based on my test it will only read the first value in that tuple. Because the first value in your other colors (in the middle code) starts with 255, it draws a full white circle and because the first value in your last color (in your last code) starts with 0, it draws a full black circle which you can't see.
Just change your color values to a 1 channel color (an int between 0 and 255) and you'll be fine.
i am trying to write a code using opencv python that automatically get canny threshold values instead of doing them manually every time.
img= cv2.imread('micro.png',0)
output = np.zeros(img.shape, img.dtype)
# Otsu's thresholding
ret2,highthresh = cv2.threshold(img,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
lowthresh=0.1*highthres
edges = cv2.Canny(img,output,lowthresh,highthresh)
cv2.imshow('canny',edges)
i am getting this error
"File "test2.py", line 14, in
edges = cv2.Canny(img,output,lowthresh,highthresh)
TypeError: only length-1 arrays can be converted to Python scalars"
can anyone help me to sort out this error.thankx in advance
It seems like cv2.threshold returns the detected edges, and Canny applies them to the image. The code below worked for me and gave me some nice detected edges in my image.
import cv2
cv2.namedWindow('canny demo')
img= cv2.imread('micro.png',0)
ret2,detected_edges = cv2.threshold(img,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
edges = cv2.Canny(detected_edges,0.1,1.0)
dst = cv2.bitwise_and(img,img,mask = edges)
cv2.imshow('canny',dst)
if cv2.waitKey(0) == 27:
cv2.destroyAllWindows()
You are running:
cv2.Canny(img,output,lowthresh,highthresh)
It is looking for
cv2.Canny(img,lowthresh,highthresh,output)
I think the ordering changed in some version, because I have seen references to both.