Add code and explaination to blog post
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@ -31,3 +31,7 @@
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- The test images were generated using
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- https://moonlit.technology/cqql/frost_patterns
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# Build Blog
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- pandoc --standalone blog.md -o shape_matching.html --metadata title="Shape Matching"
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78
blog/blog.md
78
blog/blog.md
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@ -1,6 +1,6 @@
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# Acknowledgements
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I blame [cqql](https://tech.lgbt/@cqql) for this existing.
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I blame [cqql](https://cqql.site) for this existing.
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# Disclaimer
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@ -9,10 +9,10 @@ I largely know nothing about this topic and everything you see here is knowledge
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# The Idea
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This is the post that threw me down this rabbit hole and started it all.
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After doing a bit of searching around, for ways to maybe solve this problem I stumbled on opencv, which also happens to have a python port. Perfect for quickly hacking something together.
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After doing a bit of searching around, for ways to maybe solve this problem I stumbled on OpenCV, which also happens to have a python port. Perfect for quickly hacking something together.
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# The Process
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@ -22,6 +22,19 @@ First I imported the image as grayscale and let OpenCV find the contours for me.
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Which we can trace to form the complete outline as seen here:
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Actually Implementing this was as simple as
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```py
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def GetContoursFromFiles():
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cnt = []
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for i in range (0, MAX, 1):
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img = cv2.imread(str(i)+'.png',0)
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ret, thresh1 = cv2.threshold(img, GrayscaleThreshhold, 255,cv2.THRESH_BINARY_INV)
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contours,hierarchy = cv2.findContours(thresh1,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
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print("Number of Shapes detected in Image "+str(i)+":",len(contours))
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cnt.append(contours[0])
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return cnt
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```
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Next, I had OpenCV compare the different outlines with each other, which I used to build a NxN Matrix of weights.
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(I love when libraries already do everything for me, especially all the math I wouldn't even know where to start with learning it).
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Then I just had to pretty print the matrix and that could be considered Task Complete:
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@ -36,6 +49,54 @@ So that's exactly what I did. I stitched together all the source images to form
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But the result was well worth the effort:
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So, what actually took all the effort?
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This:
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```py
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def CreateMatchingColorMatrix(mat):
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im_res = cv2.imread(str(0)+'.png',cv2.IMREAD_COLOR)
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height, width, channels = im_res.shape
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# (Coordinate 0/0) Color (white)
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im_temp = np.full((height, width, 3), 255, np.uint8)
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norm = matplotlib.colors.Normalize(vmin=0.0, vmax=NormUpperBound)
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# Build Topmost row (just iterate through all images and concat them sequentially)
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for i in range(0, MAX, 1):
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img = cv2.imread(str(i)+'.png',cv2.IMREAD_COLOR)
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imgg = cv2.imread(str(i)+'.png',0)
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ret, thresh1 = cv2.threshold(imgg, GrayscaleThreshhold, 255,cv2.THRESH_BINARY_INV)
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# find the contours in the binary image
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contours,hierarchy = cv2.findContours(thresh1,cv2.RETR_TREE,cv2.CHAIN_APPROX_NONE)
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img = cv2.drawContours(img,contours[0] , -1, (255, 0,0), 3)
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im_temp = cv2.hconcat([im_temp, img])
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# This top row is now our first row
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im_res = im_temp
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# Build The matrix row by row
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for i in range(0, MAX, 1):
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im_temp = cv2.imread(str(i)+'.png',cv2.IMREAD_COLOR)
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img = np.full((height, width, 3), 255, np.uint8)
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img[:] = (0, 0, 255)
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# Individual row here, current sequential image gets chosen above, so here, we can do square coloring
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for j in range(0, MAX, 1):
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cmap = matplotlib.cm.get_cmap('brg_r')
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cmap.set_over((0.0,0.0,0.0))
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# Gets current weight, normalises it, looks it up in color map, converts it to full scale, colors
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img[:] = NtoF(cmap(norm(mat[i*MAX+j]))[:-1])
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# build up row
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im_temp = cv2.hconcat([im_temp, img])
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#build up matrix
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im_res = cv2.vconcat([im_res, im_temp])
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DebugDrawImage(im_res)
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```
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That's because I'm assembling the Matrix piece by piece and since I was learning as I went, I incrementally built it in such a way, that one part completely worked before moving on to the rest.
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Specifically, I first tried to build the row and column headers, by filling all the other spaces with a dummy image 0.png. Then I had to figure out how to color them white. Afterwards, I tried to color code them, at which point I pulled in matplotlib for the colormaps. But because it uses different value ranged, I needed to do a bit of back and forth conversion, which led to this fun line which does all the heavy lifting for the coloring right here:
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```py
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# Gets current weight, normalises it, looks it up in color map, converts it to full scale, colors
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img[:] = NtoF(cmap(norm(mat[i*MAX+j]))[:-1])
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```
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# Rotation
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@ -56,7 +117,7 @@ To Demonstrate, here we have 45° Rotations of the same object, performed with d
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The second algorithm seemed to perform more consistantly when using 45° rotations. Sadly, it completely failed with 22.5° rotations (which may be something I could fix, but I've already spent enough time on a "let's just do 10-15 minutes of research" project.)
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The second algorithm seemed to perform more consistently when using 45° rotations. Sadly, it completely failed with 22.5° rotations (which may be something I could fix, but I've already spent enough time on a "let's just do 10-15 minutes of research" project.)
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# Why write this post in the first place?
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@ -76,4 +137,11 @@ I used the Frost Pattern Generator written by cqql for my sample images, as they
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Now, keep in mind, this was quickly hacked together while I was learning how to even do this thing. With this disclaimer, the code can be found here:
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[https://moonlit.technology/NixLynx/shape_matching](https://moonlit.technology/NixLynx/shape_matching)
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(Make sure to follow the license)
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(Make sure to follow the license :3)
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``` {=html}
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<style>
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body { min-width: 40% !important; }
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</style>
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```
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