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Fundamentals Erode, Dilate, Open and Close a Mask in OpenCV
Clean a colour mask with morphology: erosion shrinks white areas, dilation grows them, opening removes specks and closing fills holes. On the pool table the mask goes from 59 blobs to 14.
Fundamentals Open an Image and Read Its Pixels in OpenCV
Load a photo with cv2.imread, see that it is a grid of numbers with a shape and a type, and read one pixel and the mean brightness of a photo taken two stops too dark.
Fundamentals Rotate, Scale, Shear: Images as 2×2 Matrices
Lesson 1 of Image Alignment. A 2×2 matrix is where it sends the two axes; its determinant is how much it scales area and its SVD is rotate, stretch, rotate. The one thing it cannot do is make parallel lines meet. On a pickleball court, the sidelines are 20.35° apart in the photograph, so the best 2×2 misses every corner by 60 px.
Fundamentals Affine vs Perspective Transform
Lesson 2 of Image Alignment. Written on (x, y, 1), a 3×3 matrix can translate (affine, six numbers) and, once its last row is allowed to vary, make parallel lines meet (projective, eight numbers). On a pickleball court, an affine map fixed by three corners misses the fourth by 3.50 m; a homography through four lands the held-out centre-line corner 0.47 cm from the rulebook.
Fundamentals How to Compute a Homography from Four Points
Lesson 3 of Image Alignment. Each point pair gives two linear equations in the nine entries of H, and the answer is the singular vector of the smallest singular value. Normalising the coordinates drops the condition number from 53,466 to 6.4, and on this court changes the answer by under 0.2 cm. What decides the error is where the pixels are: one pixel is 0.6 cm of court at the near baseline and 8.1 cm at the far one.
Fundamentals Homogeneous Coordinates: Why Vision Adds a 1
Lesson 1 of Camera Geometry, and the prerequisite of Image Alignment. Add a third coordinate and a point becomes a ray, a line becomes a 3-vector, and both the line through two points and the point where two lines cross are one cross product. Measured on a pickleball court: a corner 18.5 px outside the frame, found anyway, and two sidelines that are parallel on the ground meeting 2,252 px to the right.
Fundamentals Bird's-Eye View and Panoramas: Warping with a Homography
Lesson 5 of Image Alignment. Pushing each pixel forward through H leaves holes in 56% of the far court's bird's-eye view; pulling each output pixel back fills every one exactly once, and matches OpenCV's warpPerspective to one grey level. Interpolation decides what the pulled-back value is. Then a median of 31 warped frames removes the players, who cover 7% of the court in one frame.
Evaluation How many good parts does it take to beat a System One AI model? Between one and sixteen, on VisA
System One models, the idea TypeSafe launched with Jev in September 2026, answer a question with a decision in a single pass. Jev-Omni, an open one, and the untouched Gemma 4 12B it was built from inspect all 2,162 VisA test images without seeing a good part. PatchCore, the standard industrial anomaly detector, is given k good parts per product: at 256 pixels it passes Jev-Omni at k = 8 and Gemma 4 at k = 16, at 512 pixels at k = 1 and k = 4. Also: the base model beats its System One fine-tune, and a day-0 demo on a lime line.
Fundamentals What Is a Homography?
How to take a photo of anything flat, a court, a page, a wall, and work out where on it every pixel lies. One pipeline, from painted lines to corners to a 3×3 map to a bird's-eye view, and why a simpler map cannot do it: measured on one frame of a pickleball final, an affine map misses the far sideline by 3.97 m where a homography misses it by 6 cm.