Contour detection with OpenCV/Python fails if a pixel is in diagonal proximity
0
I want to detect contours in images and fill them with a arbitrary values.
Unfortunately, I have problems finding the contours if two patches a close to each orther.
E.g. in the following image I expect to find three contours, but just one diagonal pixel in proximity is enough to fail in detecting three separate contours. Is there a way to make contour detection more robust?.
This is the current code
# find contours
cv_img = cv2.imread(f_name+".png")
imgray = cv2.cvtColor(cv_img, cv2.COLOR_BGR2GRAY)
inverted = cv2.bitwise_not(imgray)
#cv2.imwrite(f_name+"_inverted.png", inverted)
im2, contours, hierarchy = cv2.findContours(inverted, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
print ("Found ", len(contours), " contours")
contours = sorted(contours, key=cv2.contourArea)
if len(contours) == 3:
blank_image = np.zeros((int(model_size[1]/4), int(model_size[0]/4), 1), np.uint8)
cv2.drawContours(blank_image, contours, 0, 255, thickness=cv2.FILLED)
cv2.drawContours(blank_image, contours, 1, 255, thickness=cv2.FILLED)
cv2.imwrite(f_name+"_contours.png", blank_image)
python opencv
asked Nov 15 '18 at 15:22
dgratdgrat
1,07621233
add a comment |
0
I want to detect contours in images and fill them with a arbitrary values.
Unfortunately, I have problems finding the contours if two patches a close to each orther.
E.g. in the following image I expect to find three contours, but just one diagonal pixel in proximity is enough to fail in detecting three separate contours. Is there a way to make contour detection more robust?.
This is the current code
# find contours
cv_img = cv2.imread(f_name+".png")
imgray = cv2.cvtColor(cv_img, cv2.COLOR_BGR2GRAY)
inverted = cv2.bitwise_not(imgray)
#cv2.imwrite(f_name+"_inverted.png", inverted)
im2, contours, hierarchy = cv2.findContours(inverted, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
print ("Found ", len(contours), " contours")
contours = sorted(contours, key=cv2.contourArea)
if len(contours) == 3:
blank_image = np.zeros((int(model_size[1]/4), int(model_size[0]/4), 1), np.uint8)
cv2.drawContours(blank_image, contours, 0, 255, thickness=cv2.FILLED)
cv2.drawContours(blank_image, contours, 1, 255, thickness=cv2.FILLED)
cv2.imwrite(f_name+"_contours.png", blank_image)
python opencv
asked Nov 15 '18 at 15:22
dgratdgrat
1,07621233
Have you tried using morphological operations on the image? Additionally, I have worked with lung-segmentation before, and there was this very intuitive method called explosion based thresholding mentioned in this research paper.
– Rick M.
Nov 15 '18 at 15:39
No I haven't, but I fear the current image set is rather binary like. No true gray scale gradient.
– dgrat
Nov 15 '18 at 15:42
Maybe there is a way to split the contour based on concavity
– dgrat
Nov 15 '18 at 15:50
add a comment |
0
0
0
I want to detect contours in images and fill them with a arbitrary values.
Unfortunately, I have problems finding the contours if two patches a close to each orther.
E.g. in the following image I expect to find three contours, but just one diagonal pixel in proximity is enough to fail in detecting three separate contours. Is there a way to make contour detection more robust?.
This is the current code
# find contours
cv_img = cv2.imread(f_name+".png")
imgray = cv2.cvtColor(cv_img, cv2.COLOR_BGR2GRAY)
inverted = cv2.bitwise_not(imgray)
#cv2.imwrite(f_name+"_inverted.png", inverted)
im2, contours, hierarchy = cv2.findContours(inverted, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
print ("Found ", len(contours), " contours")
contours = sorted(contours, key=cv2.contourArea)
if len(contours) == 3:
blank_image = np.zeros((int(model_size[1]/4), int(model_size[0]/4), 1), np.uint8)
cv2.drawContours(blank_image, contours, 0, 255, thickness=cv2.FILLED)
cv2.drawContours(blank_image, contours, 1, 255, thickness=cv2.FILLED)
cv2.imwrite(f_name+"_contours.png", blank_image)
python opencv
asked Nov 15 '18 at 15:22
dgratdgrat
1,07621233
I want to detect contours in images and fill them with a arbitrary values.
Unfortunately, I have problems finding the contours if two patches a close to each orther.
E.g. in the following image I expect to find three contours, but just one diagonal pixel in proximity is enough to fail in detecting three separate contours. Is there a way to make contour detection more robust?.
This is the current code
# find contours
cv_img = cv2.imread(f_name+".png")
imgray = cv2.cvtColor(cv_img, cv2.COLOR_BGR2GRAY)
inverted = cv2.bitwise_not(imgray)
#cv2.imwrite(f_name+"_inverted.png", inverted)
im2, contours, hierarchy = cv2.findContours(inverted, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
print ("Found ", len(contours), " contours")
contours = sorted(contours, key=cv2.contourArea)
if len(contours) == 3:
blank_image = np.zeros((int(model_size[1]/4), int(model_size[0]/4), 1), np.uint8)
cv2.drawContours(blank_image, contours, 0, 255, thickness=cv2.FILLED)
cv2.drawContours(blank_image, contours, 1, 255, thickness=cv2.FILLED)
cv2.imwrite(f_name+"_contours.png", blank_image)
python opencv
python opencv
asked Nov 15 '18 at 15:22
dgratdgrat
1,07621233
asked Nov 15 '18 at 15:22
dgratdgrat
1,07621233
edited Nov 15 '18 at 15:29
dgrat
asked Nov 15 '18 at 15:22
dgratdgrat
1,07621233
asked Nov 15 '18 at 15:22
dgratdgrat
1,07621233
asked Nov 15 '18 at 15:22
dgratdgrat
1,07621233
1,07621233
Have you tried using morphological operations on the image? Additionally, I have worked with lung-segmentation before, and there was this very intuitive method called explosion based thresholding mentioned in this research paper.
– Rick M.
Nov 15 '18 at 15:39
No I haven't, but I fear the current image set is rather binary like. No true gray scale gradient.
– dgrat
Nov 15 '18 at 15:42
Maybe there is a way to split the contour based on concavity
– dgrat
Nov 15 '18 at 15:50
add a comment |
Have you tried using morphological operations on the image? Additionally, I have worked with lung-segmentation before, and there was this very intuitive method called explosion based thresholding mentioned in this research paper.
– Rick M.
Nov 15 '18 at 15:39
No I haven't, but I fear the current image set is rather binary like. No true gray scale gradient.
– dgrat
Nov 15 '18 at 15:42
Maybe there is a way to split the contour based on concavity
– dgrat
Nov 15 '18 at 15:50
Have you tried using morphological operations on the image? Additionally, I have worked with lung-segmentation before, and there was this very intuitive method called explosion based thresholding mentioned in this research paper.
– Rick M.
Nov 15 '18 at 15:39
Have you tried using morphological operations on the image? Additionally, I have worked with lung-segmentation before, and there was this very intuitive method called explosion based thresholding mentioned in this research paper.
– Rick M.
Nov 15 '18 at 15:39
No I haven't, but I fear the current image set is rather binary like. No true gray scale gradient.
– dgrat
Nov 15 '18 at 15:42
No I haven't, but I fear the current image set is rather binary like. No true gray scale gradient.
– dgrat
Nov 15 '18 at 15:42
Maybe there is a way to split the contour based on concavity
– dgrat
Nov 15 '18 at 15:50
Maybe there is a way to split the contour based on concavity
– dgrat
Nov 15 '18 at 15:50
add a comment |
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Have you tried using morphological operations on the image? Additionally, I have worked with lung-segmentation before, and there was this very intuitive method called explosion based thresholding mentioned in this research paper.
– Rick M.
Nov 15 '18 at 15:39
No I haven't, but I fear the current image set is rather binary like. No true gray scale gradient.
– dgrat
Nov 15 '18 at 15:42
Maybe there is a way to split the contour based on concavity
– dgrat
Nov 15 '18 at 15:50