OpenCV C++/Obj-C: Advanced square detection

You might try using HoughLines to detect the four sides of the square. Next, locate the four resulting line intersections to detect the corners. The Hough transform is fairly robust to noise and occlusions, so it could be useful here. Also, here is an interactive demo showing how the Hough transform works (I thought it … Read more

OpenCV Object Detection – Center Point

There’s already an example of how to do rectangle detection in OpenCV (look in samples/squares.c), and it’s quite simple, actually. Here’s the rough algorithm they use: 0. rectangles <- {} 1. image <- load image 2. for every channel: 2.1 image_canny <- apply canny edge detector to this channel 2.2 for threshold in bunch_of_increasing_thresholds: 2.2.1 … Read more

Tensorflow serving No versions of servable found under base path

I had same problem, the reason is because object detection api does not assign version of your model when exporting your detection model. However, tensorflow serving requires you to assign a version number of your detection model, so that you could choose different versions of your models to serve. In your case, you should put … Read more

What are good algorithms for vehicle license plate detection? [closed]

There are a number of approaches you can take but the first strategy that pops into mind is to: Discovery/research: Identify the set of colors and fonts that you may need to identify. If your sample picture is representative of most British plates then your job is made easier. E.g. Simple, singular font and black … Read more

Calculating percentage of Bounding box overlap, for image detector evaluation

For axis-aligned bounding boxes it is relatively simple. “Axis-aligned” means that the bounding box isn’t rotated; or in other words that the boxes lines are parallel to the axes. Here’s how to calculate the IoU of two axis-aligned bounding boxes. def get_iou(bb1, bb2): “”” Calculate the Intersection over Union (IoU) of two bounding boxes. Parameters … Read more

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