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
I have a method to do this. I don’t know whether this method applicable to all, but it works good here. Below is code ( in Python ): First convert image to grayscale, resize image, apply threshold, and make a mask image of same size and type of that of resized grayscale image. (Mask image … Read more
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
“segmentation” is a partition of an image into several “coherent” parts, but without any attempt at understanding what these parts represent. One of the most famous works (but definitely not the first) is Shi and Malik “Normalized Cuts and Image Segmentation” PAMI 2000. These works attempt to define “coherence” in terms of low-level cues such … Read more
This assumes the module is already installed. Please refer to their documentation if not. Disclaimer This answer is not meant to be the right or only way of training the object detection module. This is simply I sharing my experience and what has worked for me. I’m open to suggestions and learning more about this … Read more
For easy and simple way using COCO dataset, follow these steps : Modify (or copy for backup) the coco.names file in darknet\data\coco.names Delete all other classes except car Modify your cfg file (e.g. yolov3.cfg), change the 3 classes on line 610, 696, 783 from 80 to 1 Change the 3 filters in cfg file on … Read more
The source code below presents a small variation of the Square Detector program. It’s not perfect, but it illustrates one way to approach your problem. You can diff this code to the original and check all the changes that were made, but the main ones are: Decrease the number of threshold levels to 2. In … Read more
Well, basically you need to detect circles. Have you seen cvHoughCircles()? Are you allowed to use it? This page has good info on how detecting stuff with OpenCV. You might be more interested on section 2.5. This is a small demo I just wrote to detect coins in this picture. Hopefully you can use some … Read more
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
A simple way to achieve what you want: import cv2 s_img = cv2.imread(“smaller_image.png”) l_img = cv2.imread(“larger_image.jpg”) x_offset=y_offset=50 l_img[y_offset:y_offset+s_img.shape[0], x_offset:x_offset+s_img.shape[1]] = s_img Update I suppose you want to take care of the alpha channel too. Here is a quick and dirty way of doing so: s_img = cv2.imread(“smaller_image.png”, -1) y1, y2 = y_offset, y_offset + s_img.shape[0] … Read more