Получить координаты opencv python

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Image Resizing using OpenCV | Python
Python OpenCV | cv2.erode() method
Python | Image blurring using OpenCV
Python OpenCV | cv2.copyMakeBorder() method
Python | Grayscaling of Images using OpenCV
Image Processing in Python (Scaling, Rotating, Shifting and Edge Detection)
Erosion and Dilation of images using OpenCV in python
OpenCV Python Program to analyze an image using Histogram
Histograms Equalization in OpenCV
Python | Thresholding techniques using OpenCV | Set-1 (Simple Thresholding)
Python | Thresholding techniques using OpenCV | Set-2 (Adaptive Thresholding)
Python | Thresholding techniques using OpenCV | Set-3 (Otsu Thresholding)
OpenCV: Segmentation using Thresholding
Python OpenCV | cv2.cvtColor() method
Filter Color with OpenCV
Python | Denoising of colored images using opencv
Python | Visualizing image in different color spaces
Find Co-ordinates of Contours using OpenCV | Python
Python | Bilateral Filtering
Image Inpainting using OpenCV
Python | Intensity Transformation Operations on Images
Python | Image Registration using OpenCV
Python | Background subtraction using OpenCV
Background Subtraction in an Image using Concept of Running Average
Python | Foreground Extraction in an Image using Grabcut Algorithm
Python | Morphological Operations in Image Processing (Opening) | Set-1
Python | Morphological Operations in Image Processing (Closing) | Set-2
Python | Morphological Operations in Image Processing (Gradient) | Set-3
Image segmentation using Morphological operations in Python
Image Translation using OpenCV | Python
Image Pyramid using OpenCV | Python

Python | Program to extract frames using OpenCV
Displaying the coordinates of the points clicked on the image using Python-OpenCV
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Python | OpenCV BGR color palette with trackbars
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ML | Unsupervised Face Clustering Pipeline
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Detect and Recognize Car License Plate from a video in real time

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How to Find the X and Y Coordinates of an Object in an Image in Python using OpenCV

In this article, we show how to find the x and y coordinates of an object in an image in Python using the OpenCV module.

OpenCV is very dynamic in which we can first find all the objects (or contours) in an image using the cv2.findContours() function.

We can then find the x and y coordinates of the contour using a little bit of custom code.

In our code, we’ll find beginning and end x coordinates and the beginning and end y coordinates. We’ll also show how to find the center x and y coordinates of each contour.

In this example, we will be working with the following image shown below.

As you can see in the above image, there are 2 rectangles, which we will say represents boxes.

We will find the x and y coordinates of both of the figures in the image above.

Below we’ll see how this works in code.

The code to find the x and y coordinates of both figures is shown below.

Let’s now go over this code.

First, we import OpenCV using the line, import cv2

Next, we read in the image, which in this case is, Boxes.png

We create the variable, original_image, to store the original image that will undergo modification throughout the code.

We create a grayscale version of the image and then find the Canny edges, which helps simplify the image for cvs.findContours() function. The cv2.findContours() function finds all external contours in our image, which finds the unique objects in an image.

We then sort this list from largest to smallest using the sorted() function. We store this sorted list into the variable, sorted_contours. We sort a list from largest to smallest by the attribute, reverse=True. To sort a list from smallest to largest by the attribute, reverse= False, or by specifying nothing in the reverse attribute, as reverse=False, is implied if not specified.

Now that the list is sorted from largest to smallest, the first item in the list is the largest item, which represents the largest object in our image. We get the largest item with the code, largest_item= sorted_contours[0]. The other object then is the smallest, smallest_item= sorted_contours[1].

We first find the x and y coordinates of the largest item.

We first compute the moments of the larger item, which will then allow us to compute the center x and y coordinates.

We then create a tuple of variables, x,y,w,h, and set it equal to cv2.boundingRect(). The x stands for the first x coordinate. The y stands for the first y coordinate. The w stands for the width of the object. The h stands for the height of the object.

Since we know the first x coordinate and the width, we can calculate the end x coordinate by adding the width to the x coordinate.

Since we know the first y coordinate and the height, we can calculate the end y coordinate by adding the height to the y coordinate.

We then do the same for the smaller object in the image.

Once we run this program, we get the following output.

We show the image using matplotlib with grids so you can directly compare the coordinates.

You can see the x and y coordinates output by the program accurately lines up with each of the objects (contours) of the image.

And this is how we can find the x and y coordinates of an object in an image in Python using OpenCV.

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