I have the following code:
import cv2 as cv
import numpy as np
image = cv.imread("input1.jpg")
img_gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
img_denoised = cv.GaussianBlur(img_gray,(5,5),2)
ret, thresh = cv.threshold(img_denoised, 216, 255, cv.THRESH_BINARY)
kernel = np.ones((1,1),np.uint8)
opening = cv.dilate(thresh, kernel)
opening = cv.erode(opening, kernel)
# detect the contours on the binary image using cv.CHAIN_APPROX_NONE
contours, hierarchy = cv.findContours(image=opening, mode=cv.RETR_TREE, method=cv.CHAIN_APPROX_NONE)
for i in contours:
x, y, w, h = cv.boundingRect(i)
cv.drawContours(image, [i], -1, (0, 0, 255), 2)
cv.imshow("A.jpg", image)
cv.waitKey(0)
cv.destroyAllWindows()
Output:
enter image description here
It only shows the stars with a red contours but I want all the text to have a red contours, including the background. Here is the original file:
enter image description here
Many thanks in advance!
I messed with this a bit and the best outcome I could get was the following, I think with some tweaking you could ignore the shading, as I'm converting it to grayscale it seems to be dropping the correct contour on the shapes, but the text is working as expected;
import cv2
import numpy as np
src = cv2.imread('c:\\input1.jpg')
gray = cv2.cvtColor(src, cv2.COLOR_BGR2GRAY)
# blur
blur = cv2.GaussianBlur(gray, (3, 3), 0)
# canny edge
canny = cv2.Canny(blur, 100, 200)
# dilate
kernel = np.ones((5, 5))
dilate = cv2.dilate(canny, kernel, iterations=1)
# find contours
contours, hierarchy = cv2.findContours(
dilate, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
# draw contours
cv2.drawContours(src, contours, -1, (0, 255, 0), 3)
cv2.imshow("a.jpg", src)
cv2.waitKey()
I have a mammography image dataset (mini DDSM). These images show letter artifacts indicating left or right mamma and other useless information for my ML model, so I want to curate this dataset before training the model.
In this paper, Preprocessing of Digital Mammogram Image Based on
Otsu’s Threshold, they use Otsu's binarization and opening on the mammography to clean the image (page 5 of 10):
Their results
So far, I have coded this:
im = io.imread('/content/drive/MyDrive/TFM/DDSMPNG/ALL2/0.jpg')
# thresholding
thresh = im > filters.threshold_otsu(im)
# opening with a disk structure
disk = morphology.disk(5)
opened = morphology.binary_opening(thresh,disk)
# plotting
plt.figure(figsize=(10, 10))
plt.subplot(131)
plt.imshow(im,cmap='gray')
plt.subplot(132)
plt.imshow(opened,cmap='gray')
plt.imsave('/content/drive/MyDrive/TFM/DDSMPNG/Blackened/0.jpg',opened)
And these are the plots:
Results
I have also tried with a higher disk shape to do the opening, it seems to remove more white of the small letter artifact, but also crops a bit the mammography:
disk = morphology.disk(45)
opened = morphology.binary_opening(thresh,disk)
The result:
Result with disk shape (45,45)
I guess I will have to create some kind of mask with the binarization and apply it to the original image, but I am new to image processing libraries and I'm not sure how to achieve the results
EDIT 1: I tried #fmw42 suggestion and I have some issues with it (I work on Google Colab, dont know If it has something to do...):
First, with the image used as example on your code, it doesn't seem to work propperly, don't know why, I copied your code and just modified the path to the image as well as some subplots to see the results:
# read image
img = cv2.imread('/content/drive/MyDrive/TFM/DDSMPNG/ALL2/0.jpg')
hh, ww = img.shape[:2]
# convert to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# apply otsu thresholding
thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_OTSU )[1]
# apply morphology close to remove small regions
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5,5))
morph = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)
# apply morphology open to separate breast from other regions
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5,5))
morph = cv2.morphologyEx(morph, cv2.MORPH_OPEN, kernel)
# get largest contour
contours = cv2.findContours(morph, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
contours = contours[0] if len(contours) == 2 else contours[1]
big_contour = max(contours, key=cv2.contourArea)
# draw largest contour as white filled on black background as mask
mask = np.zeros((hh,ww), dtype=np.uint8)
cv2.drawContours(mask, [big_contour], 0, 255, cv2.FILLED)
# dilate mask
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (55,55))
mask = cv2.morphologyEx(mask, cv2.MORPH_DILATE, kernel)
# apply mask to image
result = cv2.bitwise_and(img, img, mask=mask)
# save results
cv2.imwrite('/content/drive/MyDrive/TFM/DDSMPNG/Blackened/0.jpg', result)
# show resultls
plt.figure(figsize=(10, 10))
plt.subplot(141)
plt.imshow(thresh,cmap='gray')
plt.subplot(142)
plt.imshow(morph,cmap='gray')
plt.subplot(143)
plt.imshow(mask,cmap='gray')
plt.subplot(144)
plt.imshow(result,cmap='gray')
Results:
Second, for the rest of the images, it seems to work well for most of them, but it crops a bit the breast surface:
In your result image, it seems to be much more smooth, how can I achieve that?
Thanks in advance!
EDIT 2: #fmw42 solution works fine, if someone has the same issue, you only need to play with the kernel sizes of the morphological filters until the image behaves like his results on the answer.
Thank you so much!
Here is one way to process your image in Python/OpenCV.
- Read the input
- Convert to grayscale
- Otsu threshold
- Morphology processing
- Get largest contour from external contours
- Draw all contours as white filled on a black background except the largest as a mask and invert mask
- Apply the mask to the input image
- Save the results
Input:
import cv2
import numpy as np
# read image
img = cv2.imread("mammogram.png")
hh, ww = img.shape[:2]
# convert to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# apply otsu thresholding
thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_OTSU )[1]
# apply morphology close to remove small regions
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5,5))
morph = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)
# apply morphology open to separate breast from other regions
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5,5))
morph = cv2.morphologyEx(morph, cv2.MORPH_OPEN, kernel)
# apply morphology dilate to compensate for otsu threshold not getting some areas
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (29,29))
morph = cv2.morphologyEx(morph, cv2.MORPH_DILATE, kernel)
# get largest contour
contours = cv2.findContours(morph, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
contours = contours[0] if len(contours) == 2 else contours[1]
big_contour = max(contours, key=cv2.contourArea)
big_contour_area = cv2.contourArea(big_contour)
# draw all contours but the largest as white filled on black background as mask
mask = np.zeros((hh,ww), dtype=np.uint8)
for cntr in contours:
area = cv2.contourArea(cntr)
if area != big_contour_area:
cv2.drawContours(mask, [cntr], 0, 255, cv2.FILLED)
# invert mask
mask = 255 - mask
# apply mask to image
result = cv2.bitwise_and(img, img, mask=mask)
# save results
cv2.imwrite('mammogram_thresh.jpg', thresh)
cv2.imwrite('mammogram_morph.jpg', morph)
cv2.imwrite('mammogram_mask.jpg', mask)
cv2.imwrite('mammogram_result.jpg', result)
# show resultls
cv2.imshow('thresh', thresh)
cv2.imshow('morph', morph)
cv2.imshow('mask', mask)
cv2.imshow('result', result)
cv2.waitKey(0)
cv2.destroyAllWindows()
Thresholded Image:
Morphology Processed Image:
Mask Image From Contours:
Result Image:
Alternate
- Read the input
- Convert to grayscale
- Otsu threshold
- Morphology processing
- Get largest contour from external contours
- Draw largest as white filled on black background as a mask
- Dilate mask
- Apply the mask to the input image
- Save the results
Input:
import cv2
import numpy as np
# read image
img = cv2.imread("mammogram.png")
hh, ww = img.shape[:2]
# convert to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# apply otsu thresholding
thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_OTSU )[1]
# apply morphology close to remove small regions
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5,5))
morph = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)
# apply morphology open to separate breast from other regions
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5,5))
morph = cv2.morphologyEx(morph, cv2.MORPH_OPEN, kernel)
# get largest contour
contours = cv2.findContours(morph, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
contours = contours[0] if len(contours) == 2 else contours[1]
big_contour = max(contours, key=cv2.contourArea)
# draw largest contour as white filled on black background as mask
mask = np.zeros((hh,ww), dtype=np.uint8)
cv2.drawContours(mask, [big_contour], 0, 255, cv2.FILLED)
# dilate mask
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (55,55))
mask = cv2.morphologyEx(mask, cv2.MORPH_DILATE, kernel)
# apply mask to image
result = cv2.bitwise_and(img, img, mask=mask)
# save results
cv2.imwrite('mammogram_thresh.jpg', thresh)
cv2.imwrite('mammogram_morph2.jpg', morph)
cv2.imwrite('mammogram_mask2.jpg', mask)
cv2.imwrite('mammogram_result2.jpg', result)
# show resultls
cv2.imshow('thresh', thresh)
cv2.imshow('morph', morph)
cv2.imshow('mask', mask)
cv2.imshow('result', result)
cv2.waitKey(0)
cv2.destroyAllWindows()
Threshold Image:
Morphology Processed Image:
Mask Image:
Result:
ADDITION
Here is the second method of processing applied to your larger JPG image. I noted that it was about 6x in width and height. So I increased the morphology kernels by about 6x from 5 to 31. I also trimmed the image borders 40 pixels all around and then added back a black border of the same amounts.
Input:
import cv2
import numpy as np
# read image
img = cv2.imread("mammogram.jpg")
hh, ww = img.shape[:2]
# convert to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# shave 40 pixels all around
gray = gray[40:hh-40, 40:ww-40]
# add 40 pixel black border all around
gray = cv2.copyMakeBorder(gray, 40,40,40,40, cv2.BORDER_CONSTANT, value=0)
# apply otsu thresholding
thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_OTSU )[1]
# apply morphology close to remove small regions
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (31,31))
morph = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)
# apply morphology open to separate breast from other regions
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (31,31))
morph = cv2.morphologyEx(morph, cv2.MORPH_OPEN, kernel)
# get largest contour
contours = cv2.findContours(morph, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
contours = contours[0] if len(contours) == 2 else contours[1]
big_contour = max(contours, key=cv2.contourArea)
# draw largest contour as white filled on black background as mask
mask = np.zeros((hh,ww), dtype=np.uint8)
cv2.drawContours(mask, [big_contour], 0, 255, cv2.FILLED)
# dilate mask
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (305,305))
mask = cv2.morphologyEx(mask, cv2.MORPH_DILATE, kernel)
# apply mask to image
result = cv2.bitwise_and(img, img, mask=mask)
# save results
cv2.imwrite('mammogram_thresh.jpg', thresh)
cv2.imwrite('mammogram_morph2.jpg', morph)
cv2.imwrite('mammogram_mask2.jpg', mask)
cv2.imwrite('mammogram_result2.jpg', result)
# show resultls
cv2.imshow('thresh', thresh)
cv2.imshow('morph', morph)
cv2.imshow('mask', mask)
cv2.imshow('result', result)
cv2.waitKey(0)
cv2.destroyAllWindows()
Threshold Image:
Morphology Image:
Mask Image:
Result:
I'm trying to detect the black spots on the following image.
I use adaptive thresholding and use find contours in opencv. This method is successful for detecting most of the black spots inside the gray background. However, it's not able to detect the spots on the edges, simply because contour detection thinks the spots are part of the black background, see here:
Here is the code I used to get these contours:
import cv2
image_path = "cropped.png"
img = cv2.imread(image_path)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# do adaptive threshold on gray image
thresh = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 101, 3)
# apply morphology open then close
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (1, 1))
blob = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel)
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (10, 10))
blob = cv2.morphologyEx(blob, cv2.MORPH_CLOSE, kernel)
# invert blob
blob = (255 - blob)
# Get contours
cnts, hierarchy = cv2.findContours(blob, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
result1 = img.copy()
cv2.drawContours(result1, cnts, -1, (0, 0, 255), 3)
cv2.imwrite("_Fail_Blob.png", result1)
Any suggestions on how to detect the black spots on the edges? Eventually looking for an algorithm to be able to output sth like the following:
You can use morphological operations for select spot:
By example:
import cv2
fn = 'IdTPp.jpg'
img = cv2.imread(fn)
img=cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
se=cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (45,45))
img2=cv2.morphologyEx(img, cv2.MORPH_CLOSE, se)
img3=cv2.absdiff(img, img2)
cv2.imshow("detected circles", img3)
i have a problem with my python code. I want to make image processing with chest X-rays in order to obtain a lung pattern. but my code results still have little stains. how to get rid of these small objects
and this is my code
import cv2
import numpy as np
from skimage import morphology
im = cv2.imread('image.jpg')
ret, thresh = cv2.threshold(im, 150, 255, cv2.THRESH_BINARY)
kernel = np.ones((5, 5), np.uint8)
opening = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel)
cleaned = morphology.remove_small_objects(opening, min_size=62, connectivity=2)
cv2.imshow("cleaned", cleaned)
cv2.waitKey(0)
P.S :
when i try with the matlab code, the small object can be removed with this code
K=bwareaopen(~K,1500); %Remove small object (area) pixels less than 1500 pixels
and that code can remove small object well:
You can filter using contour area then apply morpholgical closing to fill the small holes in the image. Here's the result:
import cv2
# Load image, convert to grayscale, Gaussian blur, Otsu's threshold
image = cv2.imread('1.jpg')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray, (3,3), 0)
thresh = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]
# Filter using contour area and remove small noise
cnts = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
for c in cnts:
area = cv2.contourArea(c)
if area < 5500:
cv2.drawContours(thresh, [c], -1, (0,0,0), -1)
# Morph close and invert image
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5,5))
close = 255 - cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel, iterations=2)
cv2.imshow('thresh', thresh)
cv2.imshow('close', close)
cv2.waitKey()
From the Documentation for bwareaopen you can find the algorithm that is used in the method, which is:
Determine the connected components:
CC = bwconncomp(BW, conn);
Compute the area of each component:
S = regionprops(CC, 'Area');
Remove small objects:
L = labelmatrix(CC);
BW2 = ismember(L, find([S.Area] >= P));
You could simply follow these steps to get to the result.
is it possible to smooth edges using openCV(python). mask.
Try this code:
import cv2
print(cv2.__version__)
img = cv2.imread('iep43.jpg', 0)
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (11, 11))
(thresh, binRed) = cv2.threshold(img, 128, 255, cv2.THRESH_BINARY)
opening = cv2.morphologyEx(img, cv2.MORPH_OPEN, kernel, iterations=3)
cv2.imwrite("cleaned.jpg", opening)
It uses the morphological operation of opening