remove rectangle from image opencv python

How to crop images to remove excess background using image mask? Lets look at another example, but this time using a non-rectangular mask: On Line 32, we re-initialize our mask to be filled with zeros and the same dimensions as our original image. How to delete drawn objects with OpenCV in Python? Line 21 draws a white rectangle on our mask, which corresponds to the region we want to extract from our original image. How to delete drawn objects with OpenCV in Python ? But I do not know how to implement this in code. Step 4: Remove the background of the image using the remove () function. From there, open a shell and execute the following command: $ python opencv_crop.py. - Yunus Temurlenk Feb 09 '20 at 12:14 . And thats exactly what Lines 7-11 do. Awhile back I was going through /r/computervision when I stumbled across a question asking how to remove contours from an image using OpenCV. To draw a rectangle, you need top-left corner and bottom-right corner of rectangle. How do I concatenate two lists in Python? x,y,w,h = cv2.boundingRect (mask) The area of the label is simply the count of the pixels with given label (i.e. (X coordinate value, Y coordinate value).end_point: It is the ending coordinates of rectangle. OpenCV 3.x with Python By ExampleCC BY-NC-SA 4.0ApacheCN MTPE . Identify text in the image and obtain the bounding box coordinates of each text, using Keras-ocr. Hu moments are built into the OpenCV library via the cv2.HuMoments function. 4OpenCV44 . At sometimes if object is going out of frame i want to clear the rectangle which i have drawn. When applying an inpainting algorithm using OpenCV we need to provide two images: Cv2 features two possible inpainting algorithms and allows to apply rectangular, circular or line masks (see: https://opencv24-python-tutorials.readthedocs.io/en/latest/py_tutorials/py_photo/py_inpainting/py_inpainting.html). Could a subterranean river or aquifer generate enough continuous momentum to power a waterwheel for the purpose of producing electricity? After using findContours function, contourArea() function has been used to remove the most of the contours but still I am not able retain the required contour and eliminate other contours. We then parse our command line arguments on Lines 7-10. Could you please share your code ? By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. 1. Finding the actual contours happens on Line 23 by making a call to cv2.findContours . Intrigued, I posted a reply. Put simply; a mask allows us to focus only on the portions of the image that interests us. How do I change the size of figures drawn with Matplotlib? Built on Forem the open source software that powers DEV and other inclusive communities. Step 2: Loop over contours individually. acknowledge that you have read and understood our, Data Structure & Algorithm Classes (Live), Data Structures & Algorithms in JavaScript, Data Structure & Algorithm-Self Paced(C++/JAVA), Full Stack Development with React & Node JS(Live), Android App Development with Kotlin(Live), Python Backend Development with Django(Live), DevOps Engineering - Planning to Production, GATE CS Original Papers and Official Keys, ISRO CS Original Papers and Official Keys, ISRO CS Syllabus for Scientist/Engineer Exam, Perspective Transformation Python OpenCV, Top 50+ Python Interview Questions & Answers (Latest 2023), Face Detection using Python and OpenCV with webcam, Adding new column to existing DataFrame in Pandas, How to get column names in Pandas dataframe, Reading and Writing to text files in Python, Python program to convert a list to string. With you every step of your journey. When you execute the above code, it will produce the following output. Then I drew the contour interior mask. From here, youll be able to take this code and modify the contour removal criterion according to your own needs. org/- python-and-opencv-/ OpenCV python OpenCV :-Python 2.7; OpenCV; Is't possible to find depth of a 2D image with opencv? Here youll learn how to successfully and confidently apply computer vision to your work, research, and projects. ). Why in the Sierpiski Triangle is this set being used as the example for the OSC and not a more "natural"? What does the power set mean in the construction of Von Neumann universe? OCR. This time we are interested in only those contours which resemble a circle and are of a given size. and a yellow rectangle with gray triangles inside the white area. C++ Programming - Beginner to Advanced; Java Programming - Beginner to Advanced; C Programming - Beginner to Advanced; Web Development. Connect and share knowledge within a single location that is structured and easy to search. The first time you run labelme, it will create a config file in ~/.labelmerc. cv2.minAreaRect . Character and Noise Removal (Connected Component Analysis) 3. This is precisely what makes Computer Vision such an interesting and challenging field. How can i remove the orange boxes/rectangle from the original images ? But I do not know how to implement this in code. How do I stop the Flickering on Mode 13h? Furthermore, we can use this approach to extract regions from an image of arbitrary shape (rectangles, circles, lines, polygons, etc.). 4.84 (128 Ratings) 15,900+ Students Enrolled. Removing contours from an image is extremely straightforward and can be accomplished using the following 5 steps: To apply this algorithm to your own images youll need to take a second and considerStep 3 and determine the criterion you are using to remove contours. We set it [0.9, 1.1]. Access to centralized code repos for all 500+ tutorials on PyImageSearch then we return original image if no need to resize: Load template, convert to grayscale, perform canny edge detection, Load original image, convert to grayscale, Dynamically rescale image for better template matching, When we run the script, we get this result. Machine Learning Engineer and 2x Kaggle Master, Click here to download the source code to this post, I suggest you refer to my full catalog of books and courses, Thermal Vision: Night Object Detection with PyTorch and YOLOv5 (real project), Thermal Vision: Fever Detector with Python and OpenCV (starter project), Thermal Vision: Measuring Your First Temperature from an Image with Python and OpenCV, Image Gradients with OpenCV (Sobel and Scharr), Deep Learning for Computer Vision with Python. Have a method or something that when it's executed, will replace the image with stuff drawn on it with an original unaltered image. In this article, we are going to see how to draw the minimum enclosing rectangle covering the object using OpenCV Python. Why xargs does not process the last argument? OpenCV Image Masking is a great way to easily create stunning visuals and might help you with: I strongly believe that if you had the right teacher you could master computer vision and deep learning. Syntax: cv2.rectangle(image, start_point, end_point, color, thickness). Find centralized, trusted content and collaborate around the technologies you use most. If the ratio is between 0.9 and 1.1, the detected contour is a square else it is a rectangle. Using OpenCV in Python to Cartoonize an Image. Adjust the second parameter to get a better contour detection. Step 3: Determine if the contour is "bad" and should be removed according to some criterion. import numpy as np import cv2 image = cv2.imread('download.jpg') y=0 x=0 h=100 w=200 crop = image[y:y+h, x:x+w] cv2.imshow('Image', crop) cv2.waitKey(0) Note that, image slicing is not creating a copy of the cropped image but creating a pointer to the roi. A Medium publication sharing concepts, ideas and codes. In this tutorial, you learned the basics of masking using OpenCV. Once we have the HSV color map for the table top, we can use the OpenCV inRange() function to obtain a visualization of the extracted mask as below. Welcome to the first post in this series of blogs on extracting features from images using OpenCV and Python. This is a two-step approach since the table has both an outer and inner edge and we are interested in only the latter. The final subtraction result is shown on the image below. In this case, the contour will be kept if the approximation has 4 points (vertices), indicating that the contour is a rectangle. For example, prediction_groups[0][10] would look like: The first element of the array corresponds to the coordinates of the top-left corner, the second element corresponds to the bottom-right corner, the third elements is the top-right corner, while the fourth element is the bottom-left corner. Read the input image using cv2.imread() and convert it to grayscale. In this article, we discussed how to implement an algorithm to automatically remove text from images with a pre-trained OCR model using Keras and an inpainting algorithm using cv2. Anyway, lets go ahead and get this example started. Some of these functions include rectangle(), circle(), line(), and polylines(). Then I would like to delete only drawn objects. If you can assume that orange box size will always be the same, just check the box size instead of standard deviation of the signal in the last loop of the algorithm: Warning: actual area of rectangles is around 600Px^2, but I took into account the Gaussian Blurring, which caused the contour to expand. Updated: December 30th, 2022 with updated links and content. I have your support it will better. As you can see the work Tuesday was removed from the image. Filling 4. If the number of vertex points in the approximate contour is 4 then we compute the aspect ratio to make a difference between the rectangle and square. Finally, all we have to do is apply a bitwise and to the image using the accumulated mask to remove the contours on Line 34. use that mask to remove the background image[mask == 0] *= 0 for BGR, pass it as a tuple, eg: (255,0,0) for blue. Load the image, convert to grayscale, apply a large Gaussian blur, and then Otsu's threshold. To execute our script, just issue the following command: First, youll see our mask of accumulated contours that will be removed: Notice how the contours appear as black shapes on awhite background. OCR. Please also note that if you use this approach you no longer need to perform blurring or laplace operations on blue channel image. In all the following Python examples, the required Python library is OpenCV. Performing image masking with OpenCV is easier than you think. I must delete with eraser, sometimes i do not need everything to erase. Thanks for your reply, But I need the different requirements. How to detect eyes in an image using OpenCV Python? All you need to master computer vision and deep learning is for someone to explain things to you in simple, intuitive terms. The code is given below: import cv2 import numpy as np from matplotlib import pyplot as plt img = cv2.imread('frame0 . Make sure you have already installed it. erasing the rectangle drawn in image [closed] edit object tracking asked Aug 21 '18 saniket123 11 2 3 updated Aug 22 '18 berak 32993 7 81 312 I am doing object tracking. How do I stop the Flickering on Mode 13h? We can also check the masked area to make sure it is working properly. Image 3 shows the desired capture area in red. 86+ hours of on-demand video Keras-ocr would automatically download the pre-trained weights for the detector and recognizer. Find the contours in the image using cv2.findContours() function. At sometimes if object is going out of frame i want to clear the rectangle which i have drawn. For information , the mask contains exactly all the boxes/rectangle that i want to remove. Standard deviation was high inside the contours that surrounded numbers; and it was low inside the two contours that surrounded the dog's head and the letters on top of the stamp. 10/10 would recommend. you should get a fresh image every time, no ? We'll then use masking to extract both the body and face from the image using rectangular and circular masks, respectively. Then I changed all pixel values under the "box edge mask" to those values on every channel. is it possible to clear rectangle after it is drawn? NumPy works to make some the number-crunching more efficient. 3) Eventually discard contours according to area / aspect ratio / size 4) For each rectangle, draw a filled white rectangle on a new black initialized mask 5) use setTo with the new mask, setting al pixels under the mask to a color of your choice - Miki Feb 13, 2017 at 21:43 I have tried this approach. multiple object tracking using kalman filter, Multi Object detection and tracking: application to rolling stones in rivers. . background, external objects etc. @berak every time i am getting fresh image. To crop images with OpenCV, be sure you have gone to the "Downloads" section of this tutorial to access the source code and example images. Instead, my goal is to do the most good for the computer vision, deep learning, and OpenCV community at large by focusing my time on authoring high-quality blog posts, tutorials, and books/courses. Asking for help, clarification, or responding to other answers. How do I remove the background from this kind of image? In this tutorial, you will learn how to mask images using OpenCV. First we will import a module, After that we do resize a image and maintain aspect ratio, then we grab the image size and initialize dimensions. It is often the first step for many interesting applications, such as image-foreground extraction, simple-image segmentation, detection and recognition. Busque trabalhos relacionados a Object detection using yolov3 and opencv ou contrate no maior mercado de freelancers do mundo com mais de 22 de trabalhos. startAngle and endAngle denotes the starting and ending of ellipse arc measured in clockwise direction from major axis. Why does Acts not mention the deaths of Peter and Paul? Make sure you have already installed it. Your cropping output should match mine from the previous section. Would you ever say "eat pig" instead of "eat pork"? . All too often I see developers, students, and researchers wasting their time, studying the wrong things, and generally struggling to get started with Computer Vision, Deep Learning, and OpenCV. The basic algorithm for removing contours from an image goes something like this: The algorithm itself is very straightforward, the main step that you need to pay attention to and consider is Step 3, determining if a contour should be removed. When supplied, the bitwise_and function is True when the pixel values of the input images are equal, and the mask is non-zero at each (x, y)-coordinate (in this case, only pixels that are part of the white rectangle). 3. The rectangles have different dimensions and orientations and sometimes they are interrupted by a black line (see image 2). And thats exactly what I do. How about saving the world? How to detect license plates using OpenCV Python? Firstly I wanted to isolate the signal that was specific for red channel. file_name = "#Image-Location" Step 3: Then, read the image in OpenCV. My next goal is to essentially "remove" the stars from the image. Lets now load this image from disk and perform masking: Lines 13 and 14 load the original image from disk and display it to our screen: We then construct a NumPy array, filled with zeros, with the same width and height as our original image on Line 20. And we get the following window, showing the output . We first morph open with a small kernel to remove noise then morph close with a large kernel to combine the contours. 23K views 2 years ago In this tutorial, we are going to learn how to remove duplicates from object detection when using the mobile net SSD that we ran in the previous tutorial. Step 3: Open the image using the Image.open () function. Draw bounding box on ROI to remove cv2.rectangle (original_image, (start_x, start_y), (end_x, end_y), (0,255,0), 2) cv2.imshow ('detected', original_image) Erase unwanted ROI (Fill ROI with white) cv2.rectangle (final, (start_x, start_y), (end_x, end_y), (255,255,255), -1) cv2.imwrite ('final.png', final) cv2.waitKey (0) Original image: In this case we will be using cv2.INPAINT_NS which refers to the inpainting algorithm described in the paper Navier-Stokes, Fluid Dynamics, and Image and Video Inpainting. In this section, you will modify your code to extract the detected faces from the image into their own files. Python Backend Development with Django(Live) Machine Learning and Data Science. To put texts in images, you need specify following things. Lead Data Scientist at Huge Inc, Passionate about Social Media Data and Miniature art Msc in Economics and Msc in Research Methods. We will use the OpenCV findContours() function for edge detection to extract all contours in the mask image. How will we quantify and classify just the flower we are interested in? Well be using this mask along with bitwise operations later on in the code to perform the actual removal of the contour. Again there are many ways to detect the ball contours, but one method which works best is to find the minimum bounding rectangle for each detected contour and chose the ones which best resemble a square and also lie within the desired range of area. Here youll learn how to successfully and confidently apply computer vision to your work, research, and projects. 75 courses on essential computer vision, deep learning, and OpenCV topics This allows us to extract regions from images that are of completely arbitrary shape. For better look, lineType =, The angles used in ellipse function is not our circular angles. Syntax cv2.rectangle (image, start_point, end_point, color, thickness) Parameters image: It is the actual image on which the rectangle is to be drawn. In order to apply the mask we need to provide the coordinates of the starting and the ending points of the line, and the thickness of the line: The start point will be the mid-point between the top-left corner and the bottom-left corner of the box while the end point will be the mid-point between the top-right corner and the bottom-right corner. The cv2.rectangle() function can be used to draw simple, thick, or filled rectangles depending on your needs. How can I control PNP and NPN transistors together from one pin? Hey, Adrian Rosebrock here, author and creator of PyImageSearch. Well then implement a Python script to mask images with OpenCV. A Medium publication sharing concepts, ideas and codes. In order to implement a smooth extraction of the table, we will find the bounding rectangle (OpenCV boundingRect() function) of the table contour and use its coordinates to extract the sub-image from the original image containing only the object of interest, in this case, the table surface and balls as shown in the image below. There are multiple options available such as Canny and Sobel functions and each has its merits and demerits. Can my creature spell be countered if I cast a split second spell after it? To draw a circle, you need its center coordinates and radius. hosh0425. I would like to remove the orange boxes/rectangle around numbers and keep the original image clean without any orange grid/rectangle. Is haartraining a good approach ? Inside you'll find my hand-picked tutorials, books, courses, and libraries to help you master CV and DL! but the rectangle which is previously drawn is at that place. Applying face blurring with OpenCV and computer vision is a four-step process. Start by using the Downloads section of this guide to access the source code and example image. Continuously rescale the image, apply template matching using edges, and keep track of the correlation coefficient (higher value means better match) The way I see it, one of these approaches is needed (or perhaps a mixture of both) to obtain a more "general" solution: I will give a trivial example of the first approach. The basic algorithm for removing contours from an image goes something like this: Step 1: Detect and find contours in your image. Thus, I tried first using OpenCV's filter2D function: 6 1 import cv2 2 3 img = cv2.imread(file_name) 4 If you would prefer to use a config file from another location, you can specify this file with the --config flag. Here is what you can do to flag stokry: stokry consistently posts content that violates DEV Community's This is because the black shapeswill be removed from the original image while the white regions will be retained once we apply the cv2.bitwise_andfunction. erasing the rectangle drawn in image [closed], Creative Commons Attribution Share Alike 3.0. If you're serious about learning computer vision, your next stop should be PyImageSearch University, the most comprehensive computer vision, deep learning, and OpenCV course online today. In this article I will discuss how to quickly remove text from images as a pre-processing step for an image classifier or a multi-modal text and image classifier involving images with text such as memes (for instance the Hateful Memes Challenge by Facebook). Python Program to detect the edges of an image using OpenCV. Removing text can be useful for a variety or reasons, for example we can use the text-free images for data augmentation as we can now pair the text-free image with a new text. import numpy as np import cv2 fn = 'letter-recognition.data' a = np.loadtxt (fn, np.float32, delimiter=',', converters= { 0 : lambda ch : ord (ch)-ord ('A') }) samples, responses = a [:,1:], a [:,0] model = cv2.KNearest () retval = model.train (samples,responses) retval, results, neigh_resp, dists = model.find_nearest (samples, k = 10) print Can I connect multiple USB 2.0 females to a MEAN WELL 5V 10A power supply? As you can see the photos are captured from book pages, and I wanna remove the convexity. I get in trouble by finding an algorithm to remove the convexity of my photos. However, the important part of this function is the mask keyword. I don't know how to use this mask to remove boxes/rectangle from the source (src) image as if they were not present. Using template matching I have got it to detect stars with a threshold (click the 2) 2 by drawing a rectangle around a star template. Below example draws a half ellipse at the center of the image. Then Loop over all contours. I do not know of any way to erase drawing on an image after the image pixels have been replaced by the drawing color. We'll use the cv2 module and NumPy. Well accomplish this by applying a test to every contour to determine if it should be removed or not. Now the remaining task is to extract the individual balls and identify the inner edges of the table. How can I control PNP and NPN transistors together from one pin? If you are loading so many images . Create a new folder on your desktop called rembg. At the time I was receiving 200+ emails per day and another 100+ blog post comments. Then I applied a threshold to obtain a binary image; finally I looked for external contours within that image. Figure 3: The first step for face blurring with OpenCV and Python is to detect all faces in an image/video ( image source ). Click to see red channel of the image, the result of convolution with Laplacian operator, drawn mask of the box edges and the final result. For each bounding box, apply a mask to tell the algorithm which part of the image we should inpaint. Applying a test of some sort to determine if the contour should be removed. An easy way to do this is to convert the RBG image into HSV format and then find out the range of H, S and V values corresponding to the object of interest. Inside youll find our hand-picked tutorials, books, courses, and libraries to help you master CV and DL. How to detect a triangle in an image using OpenCV Python? cv.rectangle (img, (384,0), (510,128), (0,255,0),3) Drawing Circle To draw a circle, you need its center coordinates and radius. What were the poems other than those by Donne in the Melford Hall manuscript? With this mask we can now extract the inner edges by locating the two horizontal and two vertical lines which are closest from the center of the image. Well use NumPy for numerical processing and cv2 for our OpenCV bindings. Looking for job perks? 75 Certificates of Completion Syntax: cv2.rectangle (image, start_point, end_point, color, thickness) Parameters: image: It is the image on which rectangle is to be drawn. After applying our mask, we display the output on Lines 27 and 28, which you can see in Figure 3: Using our rectangular mask, we could extract only the region of the image that contains the person and ignore the rest. All you need to master computer vision and deep learning is for someone to explain things to you in simple, intuitive terms. In the folder place an image that you wish to remove the background from. After that, I had to make an estimate whether the interior of each contour contained a number or something else. For details on this step refer to my blog (coming soon) on HSV based extraction. To achieve that you'll need to draw on transparent overlay image instead. Using thickness of -1 px to fill the rectangle by black color. You can edit this file and the changes will be applied the next time that you launch labelme. In the above image i want to retain the hand contour and remove all the other contours. CBSE Class 12 Computer Science; School . For the thickness we will calculate the length of the line between the top-left corner and the bottom-left corner. Learn more. How about saving the world? To do that, I smoothed the image a little bit with a Gaussian filter. In my next post, I will cover another interesting example of feature extraction so stay tuned. Content Discovery initiative April 13 update: Related questions using a Review our technical responses for the 2023 Developer Survey. Hi there, Im Adrian Rosebrock, PhD. In the first part of this tutorial, well configure our development environment and review our project structure. Next argument is axes lengths (major axis length, minor axis length). I used erosion and subtraction to obtain the "box edge mask". See also "inpaint" ;), Please post the code you used, the mask, the result you get and the result you want. Unlike the output from Figure 3, when we extracted a rectangular region, this time, we have extracted a circular region that corresponds to only my face in the image. To learn more, see our tips on writing great answers. Remember, in our toy example image above, our goal is to remove the circles/ellipses, while keeping the rectangles intact. When applying transparency to images with OpenCV, we need to tell OpenCV what parts of the image transparency should be applied to versus not masks allow us to make that distinction. In this post, we will consider the task of identifying balls and table edges on a pool table. Pythoncv2.bilateralFilter (). Enjoy unlimited access on 5500+ Hand Picked Quality Video Courses. Hi there, Im Adrian Rosebrock, PhD. Start by reopening the app.py file with your text editor: nano app.py Otherwise is hard to follow, and help you. Making statements based on opinion; back them up with references or personal experience. Well then use masking to extract both the body and face from the image using rectangular and circular masks, respectively. The obtained mask looks like below in which all four sides can be easily distinguished. How to detect faces in an image using Java OpenCV library? Already a member of PyImageSearch University? If. @ctbcorp Personally I appreciate the 'thank you' comment and I am glad I could help, but I would just like to warn you about the community rules about such types of comments: I altered the input image so that it contains different kinds of numbers (click to see the image). How to blur faces in an image using OpenCV Python? Inside PyImageSearch University you'll find: Click here to join PyImageSearch University. For more details, check the documentation of cv.ellipse(). Course information: My mission is to change education and how complex Artificial Intelligence topics are taught. Open up a new file, name it remove_contours.py , and lets get coding: The first thing well do is import our necessary packages. Position coordinates of where you want put it (i.e. Can I use my Coinbase address to receive bitcoin? is it possible to clear rectangle after it is drawn? I then subtracted the red channel from blue channel and the red from green channel. Do you think learning computer vision and deep learning has to be time-consuming, overwhelming, and complicated? Let's say we want to mark the positions of every ball in this image and also the four inner edges of the table. Or requires a degree in computer science? 1 in the structuring element corresponds to a pixel that you want to look at in this shape and 0 is one you want to ignore. Parameters:image: It is the image on which rectangle is to be drawn.start_point: It is the starting coordinates of rectangle. After that I subtracted both previous subtraction results from one another. The is_contour_bad function requires a single argument, c , which is the contour we are going to test to determine if it should be removed or not. Consider the following image as the Input File in the above program code. That is why I could appliy the standard deviation threshold. Do you think learning computer vision and deep learning has to be time-consuming, overwhelming, and complicated? For BGR, we pass a tuple. How to resize an image in OpenCV using Python? @Ziri is there any another way so that i could do it? We will draw a circle inside the rectangle drawn above. 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