Detect semi-transparent rectangular overlays on images
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I have images that contain transparent rectangular overlay similar to the following images: Image1 Image2. My goal is to detect if these rectangular boxes exist (location doesn't matter). These boxes will always have edges parallel to the sides of the image.
Assumptions:
- The transfer function of how the transparent rectangles are drawn is now known
- The sides of the rectangles will also be parallel to the image
Attempted Solution 1: Color Detection
So far, I've tried color detection via cv.threshold as well as using band-pass filters with the cv2.inRange() on multiple color spaces (HSV, LUV, XYZ etc). The issue with color detection is that I am also capturing too much noise to effectively just tune for the pixels for the transparent area. I tried laying the masks using cv2.bitwiseAnd but still can't tune the noise down to a negligible state. I tried only isolating for large groups of pixels using morphological transformations but this still fails.
Attempted Solution 2: Edge Detection + Edge Validation
My second try at detecting the box involved applying cv2.bilateralFilter and then generating hough lines via cv2.Canny,cv2.HoughLinesP. Although I am detecting a significant number of edges related to the transparent box, I also get many miscellaneous edges.
To filter out false edges, I take each line segment and check a few sample pixels to the left and right sides. By applying something something similar to what I believe the transfer function is (cv2.addWeighted) I checked to see if if I can reproduce the similar values. Unfortunately, this also doesn't work well enough to tell the difference between edges from the transparent box vs "real edges." Result From Edge Detection
Any thoughts on how I might detect these boxes is highly appreciated!
image-processing computer-vision object-detection cv2
asked Nov 8 at 23:24
imptr
61
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up vote
0
down vote
favorite
I have images that contain transparent rectangular overlay similar to the following images: Image1 Image2. My goal is to detect if these rectangular boxes exist (location doesn't matter). These boxes will always have edges parallel to the sides of the image.
Assumptions:
- The transfer function of how the transparent rectangles are drawn is now known
- The sides of the rectangles will also be parallel to the image
Attempted Solution 1: Color Detection
So far, I've tried color detection via cv.threshold as well as using band-pass filters with the cv2.inRange() on multiple color spaces (HSV, LUV, XYZ etc). The issue with color detection is that I am also capturing too much noise to effectively just tune for the pixels for the transparent area. I tried laying the masks using cv2.bitwiseAnd but still can't tune the noise down to a negligible state. I tried only isolating for large groups of pixels using morphological transformations but this still fails.
Attempted Solution 2: Edge Detection + Edge Validation
My second try at detecting the box involved applying cv2.bilateralFilter and then generating hough lines via cv2.Canny,cv2.HoughLinesP. Although I am detecting a significant number of edges related to the transparent box, I also get many miscellaneous edges.
To filter out false edges, I take each line segment and check a few sample pixels to the left and right sides. By applying something something similar to what I believe the transfer function is (cv2.addWeighted) I checked to see if if I can reproduce the similar values. Unfortunately, this also doesn't work well enough to tell the difference between edges from the transparent box vs "real edges." Result From Edge Detection
Any thoughts on how I might detect these boxes is highly appreciated!
image-processing computer-vision object-detection cv2
asked Nov 8 at 23:24
imptr
61
Are these images with alpha channels?
– Dave W. Smith
Nov 9 at 0:09
@Dave W. Smith No, they should be just RGB images
– imptr
Nov 9 at 1:31
If I were trying to tackle this, I'd be tempted to start by converting to HSV, isolating the V channel, and looking for first for potential corners, observing that there's a very high likelyhood of 'lighter' pixels with the corner, and darker ones on the outside. Then I'd try matching up the corners and sanity checking along the edges. It wouldn't be perfect. A piece of paper properly aligned would give a false positive.
– Dave W. Smith
Nov 9 at 3:42
add a comment |
up vote
0
down vote
favorite
up vote
0
down vote
favorite
I have images that contain transparent rectangular overlay similar to the following images: Image1 Image2. My goal is to detect if these rectangular boxes exist (location doesn't matter). These boxes will always have edges parallel to the sides of the image.
Assumptions:
- The transfer function of how the transparent rectangles are drawn is now known
- The sides of the rectangles will also be parallel to the image
Attempted Solution 1: Color Detection
So far, I've tried color detection via cv.threshold as well as using band-pass filters with the cv2.inRange() on multiple color spaces (HSV, LUV, XYZ etc). The issue with color detection is that I am also capturing too much noise to effectively just tune for the pixels for the transparent area. I tried laying the masks using cv2.bitwiseAnd but still can't tune the noise down to a negligible state. I tried only isolating for large groups of pixels using morphological transformations but this still fails.
Attempted Solution 2: Edge Detection + Edge Validation
My second try at detecting the box involved applying cv2.bilateralFilter and then generating hough lines via cv2.Canny,cv2.HoughLinesP. Although I am detecting a significant number of edges related to the transparent box, I also get many miscellaneous edges.
To filter out false edges, I take each line segment and check a few sample pixels to the left and right sides. By applying something something similar to what I believe the transfer function is (cv2.addWeighted) I checked to see if if I can reproduce the similar values. Unfortunately, this also doesn't work well enough to tell the difference between edges from the transparent box vs "real edges." Result From Edge Detection
Any thoughts on how I might detect these boxes is highly appreciated!
image-processing computer-vision object-detection cv2
asked Nov 8 at 23:24
imptr
61
I have images that contain transparent rectangular overlay similar to the following images: Image1 Image2. My goal is to detect if these rectangular boxes exist (location doesn't matter). These boxes will always have edges parallel to the sides of the image.
Assumptions:
- The transfer function of how the transparent rectangles are drawn is now known
- The sides of the rectangles will also be parallel to the image
Attempted Solution 1: Color Detection
So far, I've tried color detection via cv.threshold as well as using band-pass filters with the cv2.inRange() on multiple color spaces (HSV, LUV, XYZ etc). The issue with color detection is that I am also capturing too much noise to effectively just tune for the pixels for the transparent area. I tried laying the masks using cv2.bitwiseAnd but still can't tune the noise down to a negligible state. I tried only isolating for large groups of pixels using morphological transformations but this still fails.
Attempted Solution 2: Edge Detection + Edge Validation
My second try at detecting the box involved applying cv2.bilateralFilter and then generating hough lines via cv2.Canny,cv2.HoughLinesP. Although I am detecting a significant number of edges related to the transparent box, I also get many miscellaneous edges.
To filter out false edges, I take each line segment and check a few sample pixels to the left and right sides. By applying something something similar to what I believe the transfer function is (cv2.addWeighted) I checked to see if if I can reproduce the similar values. Unfortunately, this also doesn't work well enough to tell the difference between edges from the transparent box vs "real edges." Result From Edge Detection
Any thoughts on how I might detect these boxes is highly appreciated!
image-processing computer-vision object-detection cv2
image-processing computer-vision object-detection cv2
asked Nov 8 at 23:24
imptr
61
asked Nov 8 at 23:24
imptr
61
asked Nov 8 at 23:24
imptr
61
asked Nov 8 at 23:24
imptr
61
asked Nov 8 at 23:24
imptr
61
61
Are these images with alpha channels?
– Dave W. Smith
Nov 9 at 0:09
@Dave W. Smith No, they should be just RGB images
– imptr
Nov 9 at 1:31
If I were trying to tackle this, I'd be tempted to start by converting to HSV, isolating the V channel, and looking for first for potential corners, observing that there's a very high likelyhood of 'lighter' pixels with the corner, and darker ones on the outside. Then I'd try matching up the corners and sanity checking along the edges. It wouldn't be perfect. A piece of paper properly aligned would give a false positive.
– Dave W. Smith
Nov 9 at 3:42
add a comment |
Are these images with alpha channels?
– Dave W. Smith
Nov 9 at 0:09
@Dave W. Smith No, they should be just RGB images
– imptr
Nov 9 at 1:31
If I were trying to tackle this, I'd be tempted to start by converting to HSV, isolating the V channel, and looking for first for potential corners, observing that there's a very high likelyhood of 'lighter' pixels with the corner, and darker ones on the outside. Then I'd try matching up the corners and sanity checking along the edges. It wouldn't be perfect. A piece of paper properly aligned would give a false positive.
– Dave W. Smith
Nov 9 at 3:42
Are these images with alpha channels?
– Dave W. Smith
Nov 9 at 0:09
Are these images with alpha channels?
– Dave W. Smith
Nov 9 at 0:09
@Dave W. Smith No, they should be just RGB images
– imptr
Nov 9 at 1:31
@Dave W. Smith No, they should be just RGB images
– imptr
Nov 9 at 1:31
If I were trying to tackle this, I'd be tempted to start by converting to HSV, isolating the V channel, and looking for first for potential corners, observing that there's a very high likelyhood of 'lighter' pixels with the corner, and darker ones on the outside. Then I'd try matching up the corners and sanity checking along the edges. It wouldn't be perfect. A piece of paper properly aligned would give a false positive.
– Dave W. Smith
Nov 9 at 3:42
If I were trying to tackle this, I'd be tempted to start by converting to HSV, isolating the V channel, and looking for first for potential corners, observing that there's a very high likelyhood of 'lighter' pixels with the corner, and darker ones on the outside. Then I'd try matching up the corners and sanity checking along the edges. It wouldn't be perfect. A piece of paper properly aligned would give a false positive.
– Dave W. Smith
Nov 9 at 3:42
add a comment |
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Are these images with alpha channels?
– Dave W. Smith
Nov 9 at 0:09
@Dave W. Smith No, they should be just RGB images
– imptr
Nov 9 at 1:31
If I were trying to tackle this, I'd be tempted to start by converting to HSV, isolating the V channel, and looking for first for potential corners, observing that there's a very high likelyhood of 'lighter' pixels with the corner, and darker ones on the outside. Then I'd try matching up the corners and sanity checking along the edges. It wouldn't be perfect. A piece of paper properly aligned would give a false positive.
– Dave W. Smith
Nov 9 at 3:42