In this tutorial, we will learn how to select a bounding box or a rectangular region of interest ROI in an image in OpenCV. In the past, we had to write our own bounding box selector by handling mouse events. I am always amazed by the weird choices made in the OpenCV library. You would think that selectROI would be part of highgui that has functions for displaying images, drawing on images etc.
As you will notice later in the post, the choices made while writing selectROI are a bit odd. But, before we criticize we gotta be thankful that someone produced something useful even though it is not perfect. It allows you to select a rectangle in an image, crop the rectangular region and finally display the cropped image. If you are like me, you would prefer to drag a rectangle from the top left corner to the bottom right corner instead of the dragging it from the center.
Well, we can easily fix that by replacing the highlighted line with the following line. Well here you go. Now, suppose you do not like the crosshair and would like to see the rectangle without it. You can modify the code to not show the crosshair.
The function selectROI also allows you to select multiple regions of interest, but there appear to be two bugs. However, there appears to be a bug in the implementation in OpenCV 3.
Bug Alert 2 : I could not get the python version to work and there is no documentation. The following code runs, but the variable rects is not populated. The function also does not return anything. If you find a fix, please let me know in the comments below.
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Skip to primary navigation Skip to main content Skip to primary sidebar Skip to footer In this tutorial, we will learn how to select a bounding box or a rectangular region of interest ROI in an image in OpenCV. We will modify the highlighted line to try different options. Python Note this code does not work.
The image does not contain the box itself. And as you can see my thinner blue line lies exactly inside your original red line and it stays there whatever targetSize you chose so the scaling actually works correctly. Learn more. Resizing image and its bounding box Ask Question.
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Asked 2 years, 7 months ago. Active 1 year, 8 months ago. Viewed 11k times. I have an image with bounding box in it, and I want to resize the image. PinkClimbingApple 1 1 silver badge 9 9 bronze badges. Welcome to the site! Check out the tour and the how-to-ask page for more about asking questions that will attract quality answers. You can edit your question to include more information.
Where is Box. I don't see it in the numpy or opencv docs.Object Detection using Python, OpenCV and Deep Learning
I'm not sure I understand. Would you please adapt the code to an MCVE and post it? Is the bounding box part of the image data of your original image, or is it something you are drawing separately? We can talk about consistency only if you show code for how both boxes are drawn.
Particularly on your "original" image the frame is not a rect ,25 -Active Oldest Votes. On your original image the rectangle is35 -rather than ,25 -that you use in the code.
If I use the following code: import cv2 import numpy as np def drawBox boxes, image : for i in range 0, len boxes : changed color and width to make it visible cv2. SergGr SergGr Sign up or log in Sign up using Google.
So far, from this site I've gotten this answer:. It works, however, it only gets one. How should I modify the code? I tried putting it in the loop for contours but it still spews out one image instead of multiple ones.
Multi-class object detection and bounding box regression with Keras, TensorFlow, and Deep Learning
A simple approach is to find all contours, obtain the bounding rectangle coordinates using cv2. We can keep a counter to save each ROI then save it with cv2. Here's a working example:. Learn more. Asked 6 years, 9 months ago. Active 8 months ago. Viewed 37k times. Thank you so much in advance. Active Oldest Votes. Mukul 89 3 3 silver badges 12 12 bronze badges. Zaw Lin Zaw Lin 5, 1 1 gold badge 19 19 silver badges 38 38 bronze badges.
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Today, we are going to extend our bounding box regression method to work with multiple classes. The order of operations will be to:. This is a more advanced tutorial, and I would consider the following tutorials a prerequisite and required reading for this guide:. Multi-class object detection, as the name suggests, implies that we are trying to 1 detect where an object is in an input image and 2 predict what the detected object is.
Unlike single-class object detectors, which require only a regression layer head to predict bounding boxes, a multi-class object detector needs a fully-connected layer head with two branches:. Used together, a single forward pass of our multi-class object detector will result in:.
The example dataset we are using here today is a subset of the CALTECH datasetwhich can be used to train object detection models. In total, our dataset consists of 2, images and their corresponding bounding box x, y -coordinates.
Our goal is to train an object detector capable of accurately predicting the bounding box coordinates of the airplanesfacesand motorcycles in the input images. Either tutorial will help you configure your system with all the necessary software for this blog post in a convenient Python virtual environment. Then join PyImageSearch Plus today! Go ahead and grab the. Inside the dataset directory, we have two subdirectories, annotations and images. A sample of the face.
The images subdirectory then contains all images in our dataset, with a corresponding subdirectory for the name of the label. The output directory is populated by the train.
How to select a bounding box ( ROI ) in OpenCV (C++/Python) ?
It includes two plots of training history for both the accuracies accs. The rest of our output directory contains:. Open up the config.
Our learning rate, number of training epochs, and batch size were determined experimentally. Open up the train. Lines initialize four empty lists associated with our data; these lists will soon be populated to include:.
Looping over our CSV annotation files Line 31we grab all rows in the file Line 33 and proceed to loop over each of them.
For reference, here are the first five lines rows of each of our CSV annotation files:. Inside our loop, we unpack the comma-delimited row Lines 39 and 40 giving us our filenamex, y -coordinates, and class label for the particular line in the CSV.
Using the imagePath derived from our configclass labeland filename Lines 44 and 45we load the image and extract its spatial dimensions Lines 46 and To close out our data preparation loop, we update each of our lists — datalabelsbboxesand imagePathsrespectively. Despite our data prep loop being finished, we still have a few more preprocessing tasks to take care of:. The split data is further unpacked via Lines via list slicing.
Now its time to shift gears to preparing our multi-output two-branch model for multi-class bounding box regression. Line freezes the body of the VGG16 network such that the weights will not be updated during the fine-tuning process. We then flatten the output of the network so we can construct our new layer had and add it to the body of the network Lines and The first branch, bboxHeadis responsible for predicting the bounding box x, y -coordinates of the object in the image.
This branch is a simple fully-connected subnetwork, consisting of64, 32, and 4 nodes, respectively. Our second branch, softmaxHeadis responsible for predicting the class label of the detected object.In computer vision tasks, we need to crop a rotated rectangle from the original image sometimes, for example, to crop a rotated text box.
In this post, I would like to introduce how to do this in OpenCV. If you search on the internet about cropping rotated rectangle, there are several answers in the Stack Overflow which suggest using minAreaRect to find the minimum bounding rectangle, rotating the original image and finally cropping the rectangle from the image. You can find these questions here and here. While some of the answers work, they only work in certain conditions. But if the rotated rectangle is near the edge of the original image, some part of the cropped rectangle is cut out in the output.
The corner points 1 are in top left, top right, bottom right, bottom left order :. If you crop the rectangle using the following script based on this answer :. In the above code, we first find the rectangle enclosing the text area based on the four points we provide using the cv2.
Finally, the rectangle text area is cropped from the rotated image using cv2. We can see clearly that some parts of the text are cut out in the final result. Of course, you can pad the image beforehand, and crop the rectangle 2 from the padded image, which will prevent the cutting-out effect. In the above code, when we want to draw the rectangle area in the image, we use cv2.
We also know the width and height of rectangle from rect. Then we can directly warp the rectangle from the image using cv2. The following script shows an example:. If you check carefully, you will notice that there are some black area in the cropped image. That is because a small part of the detected rectangle is out of the bound of the image. To remedy this, you may pad the image a bit and do the crop after that.
If this is the case, please explain how I can correctly use this function. If we consider 0,0 as top left corner of image called im with left-to-right as x direction and top-to-bottom as y direction.
Note that, image slicing is not creating a copy of the cropped image but creating a pointer to the roi. If you are loading so many images, cropping the relevant parts of the images with slicing, then appending into a list, this might be a huge memory waste.
Alternatively, you can copy the relevant part by. After finding out this, I realized one of the comments by user mentioned that but it took me quite some time to find out i.
So, I think it worths mentioning.
Cropping Rotated Rectangles from Image with OpenCV
Alternatively, you could use tensorflow for the cropping and openCV for making an array from the image. Now img is a imageheight, imagewidth, 3 shape array. Crop the array with tensorflow:. Learn more.
Asked 7 years, 7 months ago. Active 3 months ago. Viewed k times. Seanny 5, 9 9 gold badges 50 50 silver badges bronze badges. Nolik Nolik 2, 3 3 gold badges 13 13 silver badges 12 12 bronze badges. Active Oldest Votes. It's very simple. Use numpy slicing. Froyo Froyo But how i can save crop image into variable? I missed this. Hugo Because numpy uses excluded region to slice. Because they are still used. In mean of memory space occupied, I understand that copying the region of interest is the best thing to do, but what about time consuming?We beg to differ.
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