How do i remove the background from this image, just want the lettuce from the picture?
0
Just want to retain the lettuce, I have hundreds of image like this and would be comparing the size of lettuce, so to began with I tried the canny edge detection but it doesn't seems to work, any idea how shall move ahead with this
python opencv image-processing scikit-image edge-detection
asked Nov 23 '18 at 21:32
Anurag ChaudharyAnurag Chaudhary
176
|
show 2 more comments
0
Just want to retain the lettuce, I have hundreds of image like this and would be comparing the size of lettuce, so to began with I tried the canny edge detection but it doesn't seems to work, any idea how shall move ahead with this
python opencv image-processing scikit-image edge-detection
asked Nov 23 '18 at 21:32
Anurag ChaudharyAnurag Chaudhary
176
can you post more sample images, like 5, just purely from this image, i think you can isolate the lettuce based on some custom intensity thresholding from the rgb channels.
– teng
Nov 23 '18 at 21:37
@teng all images are nearly same just the size of the leafs will increase, that too very minor increase in size. here's the google drive link you can have a look on sample images, drive.google.com/open?id=1HcaxCCKHEYSe4geIj5PjUiH6nf_AHsZZ
– Anurag Chaudhary
Nov 23 '18 at 21:46
1
Why do you think that a edge detecting algorythm would be any good at removing the lettuce from all those balls? there are so many edges in your image - that does not seem like a good idea. ... USe color thresholding - your lettuce is green the rest - rather not. the blue has probably some green in it - but marginally less then the lettuce...
– Patrick Artner
Nov 23 '18 at 21:47
3
Beside that .... SO is about fixing your Code - not implementing your ideas. Please go over how to ask and on-topic again and if you have questions provide your code as Minimal, Complete, and Verifiable example. Show what you tried and what did not work and we might be able to help you improve it - but dont use an edge detector for a color thresholding task ....
– Patrick Artner
Nov 23 '18 at 21:48
I would probably go with @PatrickArtner's approach, perhaps this is a good place to start: docs.opencv.org/3.4/da/d97/tutorial_threshold_inRange.html
– andersource
Nov 23 '18 at 22:19
|
show 2 more comments
0
0
0
Just want to retain the lettuce, I have hundreds of image like this and would be comparing the size of lettuce, so to began with I tried the canny edge detection but it doesn't seems to work, any idea how shall move ahead with this
python opencv image-processing scikit-image edge-detection
asked Nov 23 '18 at 21:32
Anurag ChaudharyAnurag Chaudhary
176
Just want to retain the lettuce, I have hundreds of image like this and would be comparing the size of lettuce, so to began with I tried the canny edge detection but it doesn't seems to work, any idea how shall move ahead with this
python opencv image-processing scikit-image edge-detection
python opencv image-processing scikit-image edge-detection
asked Nov 23 '18 at 21:32
Anurag ChaudharyAnurag Chaudhary
176
asked Nov 23 '18 at 21:32
Anurag ChaudharyAnurag Chaudhary
176
asked Nov 23 '18 at 21:32
Anurag ChaudharyAnurag Chaudhary
176
asked Nov 23 '18 at 21:32
Anurag ChaudharyAnurag Chaudhary
176
asked Nov 23 '18 at 21:32
Anurag ChaudharyAnurag Chaudhary
176
176
can you post more sample images, like 5, just purely from this image, i think you can isolate the lettuce based on some custom intensity thresholding from the rgb channels.
– teng
Nov 23 '18 at 21:37
@teng all images are nearly same just the size of the leafs will increase, that too very minor increase in size. here's the google drive link you can have a look on sample images, drive.google.com/open?id=1HcaxCCKHEYSe4geIj5PjUiH6nf_AHsZZ
– Anurag Chaudhary
Nov 23 '18 at 21:46
1
Why do you think that a edge detecting algorythm would be any good at removing the lettuce from all those balls? there are so many edges in your image - that does not seem like a good idea. ... USe color thresholding - your lettuce is green the rest - rather not. the blue has probably some green in it - but marginally less then the lettuce...
– Patrick Artner
Nov 23 '18 at 21:47
3
Beside that .... SO is about fixing your Code - not implementing your ideas. Please go over how to ask and on-topic again and if you have questions provide your code as Minimal, Complete, and Verifiable example. Show what you tried and what did not work and we might be able to help you improve it - but dont use an edge detector for a color thresholding task ....
– Patrick Artner
Nov 23 '18 at 21:48
I would probably go with @PatrickArtner's approach, perhaps this is a good place to start: docs.opencv.org/3.4/da/d97/tutorial_threshold_inRange.html
– andersource
Nov 23 '18 at 22:19
|
show 2 more comments
can you post more sample images, like 5, just purely from this image, i think you can isolate the lettuce based on some custom intensity thresholding from the rgb channels.
– teng
Nov 23 '18 at 21:37
@teng all images are nearly same just the size of the leafs will increase, that too very minor increase in size. here's the google drive link you can have a look on sample images, drive.google.com/open?id=1HcaxCCKHEYSe4geIj5PjUiH6nf_AHsZZ
– Anurag Chaudhary
Nov 23 '18 at 21:46
1
Why do you think that a edge detecting algorythm would be any good at removing the lettuce from all those balls? there are so many edges in your image - that does not seem like a good idea. ... USe color thresholding - your lettuce is green the rest - rather not. the blue has probably some green in it - but marginally less then the lettuce...
– Patrick Artner
Nov 23 '18 at 21:47
3
Beside that .... SO is about fixing your Code - not implementing your ideas. Please go over how to ask and on-topic again and if you have questions provide your code as Minimal, Complete, and Verifiable example. Show what you tried and what did not work and we might be able to help you improve it - but dont use an edge detector for a color thresholding task ....
– Patrick Artner
Nov 23 '18 at 21:48
I would probably go with @PatrickArtner's approach, perhaps this is a good place to start: docs.opencv.org/3.4/da/d97/tutorial_threshold_inRange.html
– andersource
Nov 23 '18 at 22:19
can you post more sample images, like 5, just purely from this image, i think you can isolate the lettuce based on some custom intensity thresholding from the rgb channels.
– teng
Nov 23 '18 at 21:37
can you post more sample images, like 5, just purely from this image, i think you can isolate the lettuce based on some custom intensity thresholding from the rgb channels.
– teng
Nov 23 '18 at 21:37
@teng all images are nearly same just the size of the leafs will increase, that too very minor increase in size. here's the google drive link you can have a look on sample images, drive.google.com/open?id=1HcaxCCKHEYSe4geIj5PjUiH6nf_AHsZZ
– Anurag Chaudhary
Nov 23 '18 at 21:46
@teng all images are nearly same just the size of the leafs will increase, that too very minor increase in size. here's the google drive link you can have a look on sample images, drive.google.com/open?id=1HcaxCCKHEYSe4geIj5PjUiH6nf_AHsZZ
– Anurag Chaudhary
Nov 23 '18 at 21:46
1
1
Why do you think that a edge detecting algorythm would be any good at removing the lettuce from all those balls? there are so many edges in your image - that does not seem like a good idea. ... USe color thresholding - your lettuce is green the rest - rather not. the blue has probably some green in it - but marginally less then the lettuce...
– Patrick Artner
Nov 23 '18 at 21:47
Why do you think that a edge detecting algorythm would be any good at removing the lettuce from all those balls? there are so many edges in your image - that does not seem like a good idea. ... USe color thresholding - your lettuce is green the rest - rather not. the blue has probably some green in it - but marginally less then the lettuce...
– Patrick Artner
Nov 23 '18 at 21:47
3
3
Beside that .... SO is about fixing your Code - not implementing your ideas. Please go over how to ask and on-topic again and if you have questions provide your code as Minimal, Complete, and Verifiable example. Show what you tried and what did not work and we might be able to help you improve it - but dont use an edge detector for a color thresholding task ....
– Patrick Artner
Nov 23 '18 at 21:48
Beside that .... SO is about fixing your Code - not implementing your ideas. Please go over how to ask and on-topic again and if you have questions provide your code as Minimal, Complete, and Verifiable example. Show what you tried and what did not work and we might be able to help you improve it - but dont use an edge detector for a color thresholding task ....
– Patrick Artner
Nov 23 '18 at 21:48
I would probably go with @PatrickArtner's approach, perhaps this is a good place to start: docs.opencv.org/3.4/da/d97/tutorial_threshold_inRange.html
– andersource
Nov 23 '18 at 22:19
I would probably go with @PatrickArtner's approach, perhaps this is a good place to start: docs.opencv.org/3.4/da/d97/tutorial_threshold_inRange.html
– andersource
Nov 23 '18 at 22:19
|
show 2 more comments
3 Answers
3
active
oldest
votes
0
You can convert the RGB image into HSV image and segment the Green color region.
import cv2
import numpy as np
frame=cv2.imread('a.png')
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
lower = np.array([50,50,50])
upper = np.array([70,255,255])
mask = cv2.inRange(hsv, lower, upper)
res = cv2.bitwise_and(frame,frame, mask= mask)
cv2.imshow('frame',frame)
cv2.imshow('res',res)
cv2.waitKey(0)
cv2.destroyAllWindows()
answered Nov 24 '18 at 0:18
Joy MazumderJoy Mazumder
58116
add a comment |
1
A possible approach is by using the Graph Segmentation method (cv::ximgproc::segmentation::GraphSegmentation), that you apply to the image converted to HSV or HSL, where you set the V or L plane to a constant to flatten illumination.
answered Nov 23 '18 at 22:35
Yves DaoustYves Daoust
37.7k72659
1
up voted for the illumination flattening suggestion.
– teng
Nov 23 '18 at 23:10
add a comment |
0
You may get away with thresholding as long as you fix your lighting (method 1 listed below), if not, you might need a simple classifier method (for example a clustering technique, method 2) in conjunction with connected components and assumption on the location of the plant or color to assign the detected class to the plant.
from scipy.misc import imread
import matplotlib.pyplot as plt
import matplotlib.patches as patches
%matplotlib inline
import matplotlib
import numpy as np
# read the image
img = imread('9v5wv.png')
# show the image
fig,ax = plt.subplots(1)
ax.imshow(img)
ax.grid('off')
# show the r,g,b channels separately.
for n,d in enumerate([('r',0),('g',1),('b',2)]):
k,v = d
plt.figure(n)
plt.subplot(131)
plt.imshow(arr[:,:,v],cmap='gray')
plt.grid('off')
plt.title(k)
plt.subplot(133)
_=plt.hist(arr[:,:,v].ravel(),bins=100)
# method 1, rgb thresholding will not work when lighting changes
arr = img
r_filter = lambda x: x[:,:,0] < 100
g_filter = lambda x: x[:,:,1] > 80
b_filter = lambda x: x[:,:,2] < 200
mask=np.logical_and(np.logical_and(r_filter(arr),g_filter(arr)),b_filter(arr))
plt.imshow(mask,cmap='gray')
plt.grid('off')
# method 2, kmeans clustering
from sklearn.cluster import KMeans
arr = matplotlib.colors.rgb_to_hsv(img[:,:,0:3])
# ignore v per Yves Daoust
data = np.array(arr[:,:,0:2])
x,y,z = data.shape
X = np.reshape(data,(x*y,z))
kmeans = KMeans(n_clusters=6, random_state=420).fit(X)
mask = np.reshape(kmeans.labels_,(x,y,))
plt.imshow(mask==0,cmap='gray')
plt.grid('off')
answered Nov 23 '18 at 23:09
tengteng
840721
add a comment |
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3 Answers
3
active
oldest
votes
3 Answers
3
active
oldest
votes
active
oldest
votes
active
oldest
votes
0
You can convert the RGB image into HSV image and segment the Green color region.
import cv2
import numpy as np
frame=cv2.imread('a.png')
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
lower = np.array([50,50,50])
upper = np.array([70,255,255])
mask = cv2.inRange(hsv, lower, upper)
res = cv2.bitwise_and(frame,frame, mask= mask)
cv2.imshow('frame',frame)
cv2.imshow('res',res)
cv2.waitKey(0)
cv2.destroyAllWindows()
answered Nov 24 '18 at 0:18
Joy MazumderJoy Mazumder
58116
add a comment |
0
You can convert the RGB image into HSV image and segment the Green color region.
import cv2
import numpy as np
frame=cv2.imread('a.png')
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
lower = np.array([50,50,50])
upper = np.array([70,255,255])
mask = cv2.inRange(hsv, lower, upper)
res = cv2.bitwise_and(frame,frame, mask= mask)
cv2.imshow('frame',frame)
cv2.imshow('res',res)
cv2.waitKey(0)
cv2.destroyAllWindows()
answered Nov 24 '18 at 0:18
Joy MazumderJoy Mazumder
58116
add a comment |
0
0
0
You can convert the RGB image into HSV image and segment the Green color region.
import cv2
import numpy as np
frame=cv2.imread('a.png')
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
lower = np.array([50,50,50])
upper = np.array([70,255,255])
mask = cv2.inRange(hsv, lower, upper)
res = cv2.bitwise_and(frame,frame, mask= mask)
cv2.imshow('frame',frame)
cv2.imshow('res',res)
cv2.waitKey(0)
cv2.destroyAllWindows()
answered Nov 24 '18 at 0:18
Joy MazumderJoy Mazumder
58116
You can convert the RGB image into HSV image and segment the Green color region.
import cv2
import numpy as np
frame=cv2.imread('a.png')
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
lower = np.array([50,50,50])
upper = np.array([70,255,255])
mask = cv2.inRange(hsv, lower, upper)
res = cv2.bitwise_and(frame,frame, mask= mask)
cv2.imshow('frame',frame)
cv2.imshow('res',res)
cv2.waitKey(0)
cv2.destroyAllWindows()
answered Nov 24 '18 at 0:18
Joy MazumderJoy Mazumder
58116
answered Nov 24 '18 at 0:18
Joy MazumderJoy Mazumder
58116
answered Nov 24 '18 at 0:18
Joy MazumderJoy Mazumder
58116
answered Nov 24 '18 at 0:18
Joy MazumderJoy Mazumder
58116
58116
add a comment |
add a comment |
1
A possible approach is by using the Graph Segmentation method (cv::ximgproc::segmentation::GraphSegmentation), that you apply to the image converted to HSV or HSL, where you set the V or L plane to a constant to flatten illumination.
answered Nov 23 '18 at 22:35
Yves DaoustYves Daoust
37.7k72659
1
up voted for the illumination flattening suggestion.
– teng
Nov 23 '18 at 23:10
add a comment |
1
A possible approach is by using the Graph Segmentation method (cv::ximgproc::segmentation::GraphSegmentation), that you apply to the image converted to HSV or HSL, where you set the V or L plane to a constant to flatten illumination.
answered Nov 23 '18 at 22:35
Yves DaoustYves Daoust
37.7k72659
1
up voted for the illumination flattening suggestion.
– teng
Nov 23 '18 at 23:10
add a comment |
1
1
1
A possible approach is by using the Graph Segmentation method (cv::ximgproc::segmentation::GraphSegmentation), that you apply to the image converted to HSV or HSL, where you set the V or L plane to a constant to flatten illumination.
answered Nov 23 '18 at 22:35
Yves DaoustYves Daoust
37.7k72659
A possible approach is by using the Graph Segmentation method (cv::ximgproc::segmentation::GraphSegmentation), that you apply to the image converted to HSV or HSL, where you set the V or L plane to a constant to flatten illumination.
answered Nov 23 '18 at 22:35
Yves DaoustYves Daoust
37.7k72659
answered Nov 23 '18 at 22:35
Yves DaoustYves Daoust
37.7k72659
answered Nov 23 '18 at 22:35
Yves DaoustYves Daoust
37.7k72659
answered Nov 23 '18 at 22:35
Yves DaoustYves Daoust
37.7k72659
37.7k72659
1
up voted for the illumination flattening suggestion.
– teng
Nov 23 '18 at 23:10
add a comment |
1
up voted for the illumination flattening suggestion.
– teng
Nov 23 '18 at 23:10
1
1
up voted for the illumination flattening suggestion.
– teng
Nov 23 '18 at 23:10
up voted for the illumination flattening suggestion.
– teng
Nov 23 '18 at 23:10
add a comment |
0
You may get away with thresholding as long as you fix your lighting (method 1 listed below), if not, you might need a simple classifier method (for example a clustering technique, method 2) in conjunction with connected components and assumption on the location of the plant or color to assign the detected class to the plant.
from scipy.misc import imread
import matplotlib.pyplot as plt
import matplotlib.patches as patches
%matplotlib inline
import matplotlib
import numpy as np
# read the image
img = imread('9v5wv.png')
# show the image
fig,ax = plt.subplots(1)
ax.imshow(img)
ax.grid('off')
# show the r,g,b channels separately.
for n,d in enumerate([('r',0),('g',1),('b',2)]):
k,v = d
plt.figure(n)
plt.subplot(131)
plt.imshow(arr[:,:,v],cmap='gray')
plt.grid('off')
plt.title(k)
plt.subplot(133)
_=plt.hist(arr[:,:,v].ravel(),bins=100)
# method 1, rgb thresholding will not work when lighting changes
arr = img
r_filter = lambda x: x[:,:,0] < 100
g_filter = lambda x: x[:,:,1] > 80
b_filter = lambda x: x[:,:,2] < 200
mask=np.logical_and(np.logical_and(r_filter(arr),g_filter(arr)),b_filter(arr))
plt.imshow(mask,cmap='gray')
plt.grid('off')
# method 2, kmeans clustering
from sklearn.cluster import KMeans
arr = matplotlib.colors.rgb_to_hsv(img[:,:,0:3])
# ignore v per Yves Daoust
data = np.array(arr[:,:,0:2])
x,y,z = data.shape
X = np.reshape(data,(x*y,z))
kmeans = KMeans(n_clusters=6, random_state=420).fit(X)
mask = np.reshape(kmeans.labels_,(x,y,))
plt.imshow(mask==0,cmap='gray')
plt.grid('off')
answered Nov 23 '18 at 23:09
tengteng
840721
add a comment |
0
You may get away with thresholding as long as you fix your lighting (method 1 listed below), if not, you might need a simple classifier method (for example a clustering technique, method 2) in conjunction with connected components and assumption on the location of the plant or color to assign the detected class to the plant.
from scipy.misc import imread
import matplotlib.pyplot as plt
import matplotlib.patches as patches
%matplotlib inline
import matplotlib
import numpy as np
# read the image
img = imread('9v5wv.png')
# show the image
fig,ax = plt.subplots(1)
ax.imshow(img)
ax.grid('off')
# show the r,g,b channels separately.
for n,d in enumerate([('r',0),('g',1),('b',2)]):
k,v = d
plt.figure(n)
plt.subplot(131)
plt.imshow(arr[:,:,v],cmap='gray')
plt.grid('off')
plt.title(k)
plt.subplot(133)
_=plt.hist(arr[:,:,v].ravel(),bins=100)
# method 1, rgb thresholding will not work when lighting changes
arr = img
r_filter = lambda x: x[:,:,0] < 100
g_filter = lambda x: x[:,:,1] > 80
b_filter = lambda x: x[:,:,2] < 200
mask=np.logical_and(np.logical_and(r_filter(arr),g_filter(arr)),b_filter(arr))
plt.imshow(mask,cmap='gray')
plt.grid('off')
# method 2, kmeans clustering
from sklearn.cluster import KMeans
arr = matplotlib.colors.rgb_to_hsv(img[:,:,0:3])
# ignore v per Yves Daoust
data = np.array(arr[:,:,0:2])
x,y,z = data.shape
X = np.reshape(data,(x*y,z))
kmeans = KMeans(n_clusters=6, random_state=420).fit(X)
mask = np.reshape(kmeans.labels_,(x,y,))
plt.imshow(mask==0,cmap='gray')
plt.grid('off')
answered Nov 23 '18 at 23:09
tengteng
840721
add a comment |
0
0
0
You may get away with thresholding as long as you fix your lighting (method 1 listed below), if not, you might need a simple classifier method (for example a clustering technique, method 2) in conjunction with connected components and assumption on the location of the plant or color to assign the detected class to the plant.
from scipy.misc import imread
import matplotlib.pyplot as plt
import matplotlib.patches as patches
%matplotlib inline
import matplotlib
import numpy as np
# read the image
img = imread('9v5wv.png')
# show the image
fig,ax = plt.subplots(1)
ax.imshow(img)
ax.grid('off')
# show the r,g,b channels separately.
for n,d in enumerate([('r',0),('g',1),('b',2)]):
k,v = d
plt.figure(n)
plt.subplot(131)
plt.imshow(arr[:,:,v],cmap='gray')
plt.grid('off')
plt.title(k)
plt.subplot(133)
_=plt.hist(arr[:,:,v].ravel(),bins=100)
# method 1, rgb thresholding will not work when lighting changes
arr = img
r_filter = lambda x: x[:,:,0] < 100
g_filter = lambda x: x[:,:,1] > 80
b_filter = lambda x: x[:,:,2] < 200
mask=np.logical_and(np.logical_and(r_filter(arr),g_filter(arr)),b_filter(arr))
plt.imshow(mask,cmap='gray')
plt.grid('off')
# method 2, kmeans clustering
from sklearn.cluster import KMeans
arr = matplotlib.colors.rgb_to_hsv(img[:,:,0:3])
# ignore v per Yves Daoust
data = np.array(arr[:,:,0:2])
x,y,z = data.shape
X = np.reshape(data,(x*y,z))
kmeans = KMeans(n_clusters=6, random_state=420).fit(X)
mask = np.reshape(kmeans.labels_,(x,y,))
plt.imshow(mask==0,cmap='gray')
plt.grid('off')
answered Nov 23 '18 at 23:09
tengteng
840721
You may get away with thresholding as long as you fix your lighting (method 1 listed below), if not, you might need a simple classifier method (for example a clustering technique, method 2) in conjunction with connected components and assumption on the location of the plant or color to assign the detected class to the plant.
from scipy.misc import imread
import matplotlib.pyplot as plt
import matplotlib.patches as patches
%matplotlib inline
import matplotlib
import numpy as np
# read the image
img = imread('9v5wv.png')
# show the image
fig,ax = plt.subplots(1)
ax.imshow(img)
ax.grid('off')
# show the r,g,b channels separately.
for n,d in enumerate([('r',0),('g',1),('b',2)]):
k,v = d
plt.figure(n)
plt.subplot(131)
plt.imshow(arr[:,:,v],cmap='gray')
plt.grid('off')
plt.title(k)
plt.subplot(133)
_=plt.hist(arr[:,:,v].ravel(),bins=100)
# method 1, rgb thresholding will not work when lighting changes
arr = img
r_filter = lambda x: x[:,:,0] < 100
g_filter = lambda x: x[:,:,1] > 80
b_filter = lambda x: x[:,:,2] < 200
mask=np.logical_and(np.logical_and(r_filter(arr),g_filter(arr)),b_filter(arr))
plt.imshow(mask,cmap='gray')
plt.grid('off')
# method 2, kmeans clustering
from sklearn.cluster import KMeans
arr = matplotlib.colors.rgb_to_hsv(img[:,:,0:3])
# ignore v per Yves Daoust
data = np.array(arr[:,:,0:2])
x,y,z = data.shape
X = np.reshape(data,(x*y,z))
kmeans = KMeans(n_clusters=6, random_state=420).fit(X)
mask = np.reshape(kmeans.labels_,(x,y,))
plt.imshow(mask==0,cmap='gray')
plt.grid('off')
answered Nov 23 '18 at 23:09
tengteng
840721
answered Nov 23 '18 at 23:09
tengteng
840721
answered Nov 23 '18 at 23:09
tengteng
840721
answered Nov 23 '18 at 23:09
tengteng
840721
840721
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can you post more sample images, like 5, just purely from this image, i think you can isolate the lettuce based on some custom intensity thresholding from the rgb channels.
– teng
Nov 23 '18 at 21:37
@teng all images are nearly same just the size of the leafs will increase, that too very minor increase in size. here's the google drive link you can have a look on sample images, drive.google.com/open?id=1HcaxCCKHEYSe4geIj5PjUiH6nf_AHsZZ
– Anurag Chaudhary
Nov 23 '18 at 21:46
1
Why do you think that a edge detecting algorythm would be any good at removing the lettuce from all those balls? there are so many edges in your image - that does not seem like a good idea. ... USe color thresholding - your lettuce is green the rest - rather not. the blue has probably some green in it - but marginally less then the lettuce...
– Patrick Artner
Nov 23 '18 at 21:47
3
Beside that .... SO is about fixing your Code - not implementing your ideas. Please go over how to ask and on-topic again and if you have questions provide your code as Minimal, Complete, and Verifiable example. Show what you tried and what did not work and we might be able to help you improve it - but dont use an edge detector for a color thresholding task ....
– Patrick Artner
Nov 23 '18 at 21:48
I would probably go with @PatrickArtner's approach, perhaps this is a good place to start: docs.opencv.org/3.4/da/d97/tutorial_threshold_inRange.html
– andersource
Nov 23 '18 at 22:19