opencv scalar example

Each image is given an equal weight in the beginning. We have designed this FREE crash course in collaboration with OpenCV.org to help you take your first steps into the fascinating world of Artificial Intelligence and Computer Vision. You can process both videos and images.\n", //get the frame number and write it on the current frame, //show the current frame and the fg masks, // get the frame number and write it on the current frame, // show the current frame and the fg masks, 'This program shows how to use background subtraction methods provided by \, OpenCV. Does not work very well under substantial occlusion. Be careful when saving as an image file with Pillow because the data type is cast automatically. Isn't it a little inefficient and time consuming? torch.devicetorch.Tensor. OpenCV has many Haar based models which can be found here. (The process is not as simple as this. OpenCV >= 4.5.4 : Introduction. Otherwise, we use the quantized tensorflow model. If it is not, discard it in a single shot, and don't process it again. We can get rid of this problem by upscaling the image, but then the speed advantage of dlib as compared to OpenCV-DNN goes away. In this tutorial we will learn how to perform BS by using OpenCV. They are located in opencv/data/haarcascades. Lets go over the code step by step to find out how can we use OpenCVs multi-object tracking API. The pretrained models are located in the data folder in the OpenCV installation or can be found here. A full working example is included in the create_board_charuco.cpp inside the modules/aruco/samples/. However, I found surprising results. Formulas used to convert from one colorspace to another colorspace using cv::cvtColor function are described in Color conversions, The tutorial code's is shown lines below. Light-weight model as compared to the other three. An histogram can keep count not only of color intensities, but of whatever image features that we want to measure (i.e. Does not detect small faces as it is trained for minimum face size of 8080. We could not see any major drawback for this method except that it is slower than the Dlib HoG based Face Detector discussed next. Each feature is a single value obtained by subtracting sum of pixels under the white rectangle from sum of pixels under the black rectangle. We run each method 10000 times on the given image and take 10 such iterations and average the time taken. For this, we apply each and every feature on all the training images. ', Perform basic thresholding operations using OpenCV. and we can keep count of the number of pixels that fall in the range of each \(bin_{i}\). After compiling this program, run it. As you can see that for the image of this size, all the methods perform in real-time, except MMOD. The program will open two windows. In our previous tutorial we Detect an object based on the range of pixel values in the HSV colorspace. args[0] : src = Imgcodecs.imread(imageName, Imgcodecs.IMREAD_COLOR); HighGui.namedWindow( window_name, HighGui.WINDOW_AUTOSIZE ); top = (int) (0.05*src.rows()); bottom = top; left = (int) (0.05*src.cols()); right = left; Core.copyMakeBorder( src, dst, top, bottom, left, right, borderType, value); System.loadLibrary(Core.NATIVE_LIBRARY_NAME); value = [randint(0, 255), randint(0, 255), randint(0, 255)]. Here, Hello OpenCV is printed on the screen. In this tutorial you will learn how to: Use the OpenCV function copyMakeBorder() to set the borders (extra padding to your image). Apply the classifier to the frame, "../../data/haarcascades/haarcascade_frontalface_alt.xml", "../../data/haarcascades/haarcascade_eye_tree_eyeglasses.xml", 'data/haarcascades/haarcascade_frontalface_alt.xml', 'data/haarcascades/haarcascade_eye_tree_eyeglasses.xml'. HoG based detector does detect faces for left or right looking faces ( since it was trained on them ) but not as accurately as the DNN based detectors of OpenCV and Dlib. Again, to be fair with dlib, we make sure the face size is more than 8080. You can however, train your own face detector for smaller sized faces. top = (int) (0.05*src.rows); bottom = top; left = (int) (0.05*src.cols); right = left; " Program Arguments: [image_name -- default lena.jpg] \n", " ** Press 'c' to set the border to a random constant value \n", " ** Press 'r' to set the border to be replicated \n", "Program Arguments: [image_name -- default ../data/lena.jpg] \n", @brief Sample code that shows the functionality of copyMakeBorder, 'Usage: copy_make_border.py [image_name -- default lena.jpg] \n', ' ** Press \'c\' to set the border to a random constant value \n', ' ** Press \'r\' to set the border to be replicated \n', # First we declare the variables we are going to use. According to my analysis, the reasons for lower numbers for dlib are as follows : Thus, the only relevant metric for a fair comparison between OpenCV and Dlib is AP_50 ( or even less than 50 since we are mostly comparing the number of detected faces ). You will find cpp and python files for each face detector along with a separate file which compares all the methods together ( run-all.py and run-all.cpp ). For each feature, it finds the best threshold which will classify the faces to positive and negative. No need for. In Image Processing. Given below are the Precision scores for the 4 methods. System.loadLibrary(Core.NATIVE_LIBRARY_NAME); parser = argparse.ArgumentParser(description=, "{ help h | | Print usage }", "{ input | vtest.avi | Path to a video or a sequence of image }", "{ algo | MOG2 | Background subtraction method (KNN, MOG2) }", "This program shows how to use background subtraction methods provided by ", " OpenCV. If you want only a specific kind of symbol to be detected, you need to change ZBAR_NONE to a different type listed here.We then convert the image to grayscale ( lines 11-13).We then convert the grayscale image to minDistance: Minimum possible Euclidean distance between the returned corners. The more you upscale, the better are the chances of detecting smaller faces. Finally, we call the function copyMakeBorder() to apply the respective padding: We display our output image in the image created previously. Nice, isn't it? Learn how to install and use OpenCV DNN Module with Nvidia GPU on Windows OS. In our newsletter, we share OpenCV tutorials and examples written in C++/Python, and Computer Vision and Machine Learning algorithms and news. cv::Mat::copyTo copy the src image onto dst.However, it will only copy the pixels in the locations where they have non-zero values. args[1] : detectAndDisplay(frame, faceCascade, eyesCascade); System.loadLibrary(Core.NATIVE_LIBRARY_NAME); faces = face_cascade.detectMultiScale(frame_gray), eyes = eyes_cascade.detectMultiScale(faceROI), eye_center = (x + x2 + w2//2, y + y2 + h2//2), parser = argparse.ArgumentParser(description=, "{face_cascade|data/haarcascades/haarcascade_frontalface_alt.xml|Path to face cascade. The results as well as the input data are shown on the screen. This is the result of using the file lbpcascade_frontalface.xml (LBP trained) for the face detection. ; It can be downloaded from here. New error rates are calculated. In this example, default parameters are used, but it is also possible to declare specific parameters in the Also, the coordinates are present inside a rect object. Instead of applying all 6000 features on a window, the features are grouped into different stages of classifiers and applied one-by-one. Even a 24x24 window results over 160000 features). The authors' detector had 6000+ features with 38 stages with 1, 10, 25, 25 and 50 features in the first five stages. Web#include Finds the camera intrinsic and extrinsic parameters from several views of a calibration pattern. So it is a better idea to have a simple method to check if a window is not a face region. pytorch/libtorch qq2302984355 pytorch/libtorch qq 1041467052 pytorchlibtorch If you liked this article and would Thus, it is better to use OpenCV DNN method as it is pretty fast and very accurate, even for small sized faces. To solve this, they introduced the integral image. Detects faces across various scales ( detects big as well as tiny faces ), Works very well for frontal and slightly non-frontal faces. By default, it begins with the border set to BORDER_CONSTANT. Variation of the saturation goes from unsaturated to represent shades of gray and fully saturated (no white component). Let us see how well the methods perform under occlusion. In the above code, we first load the face detector. The first feature selected seems to focus on the property that the region of the eyes is often darker than the region of the nose and cheeks. Each member of the list is again a list with 4 elements indicating the (x, y) coordinates of the top-left corner and the width and height of the detected face. We discuss the main parts of the code above: With the vtest.avi video, for the following frame: The output of the program will look as the following for MOG2 method (gray areas are detected shadows): The output of the program will look as the following for the KNN method (gray areas are detected shadows): How to Use Background Subtraction Methods. The samples now take input via commandline via the OpenCV Commandline Parser. The paper says even 200 features provide detection with 95% accuracy. I tried to evaluate the 4 models using the FDDB dataset using the script used for evaluating the OpenCV-DNN model. As the name suggests, BS calculates the foreground mask performing a subtraction between the current frame and a background model, containing the static part of the scene or, more in general, everything that can be considered as background given the characteristics of the observed scene. DNN Face Detector in OpenCV. The MultiTracker class in OpenCV provides an implementation of multi-object tracking. It makes things super-fast. The training process for this method is very simple and you dont need a large amount of data to train a custom object detector. First, a cv::CascadeClassifier is created and the necessary XML file is loaded using the cv::CascadeClassifier::load method. For example, detections[0,0,0,2] gives the confidence score for the first face, and detections[0,0,0,3:6] give the bounding box. WebRsidence officielle des rois de France, le chteau de Versailles et ses jardins comptent parmi les plus illustres monuments du patrimoine mondial et constituent la plus complte ralisation de lart franais du XVIIe sicle. The model comes embedded in the header file itself. Here is the result of running the code above and using as input the video stream of a built-in webcam: Be sure the program will find the path of files haarcascade_frontalface_alt.xml and haarcascade_eye_tree_eyeglasses.xml. We will see it in the code below! We will see the basics of face detection and eye detection using the Haar Feature-based Cascade Classifiers. So now you take an image. Variation of the saturation goes from unsaturated to represent shades of gray and fully saturated (no white component). In most applications, we wont know the size of the face in the image before-hand. By SharkDderivative work: SharkD [CC BY-SA 3.0 or GFDL], via Wikimedia Commons, By SharkD [GFDL or CC BY-SA 4.0], from Wikimedia Commons. So, if you know that your application will not be dealing with very small sized faces ( for example a selfie app ), then HoG based Face detector is a better option. If you press 'r', the border will become a replica of the edge pixels. Theory . The function cv::ellipse with more parameters draws an ellipse outline, a filled ellipse, an elliptic arc, or a filled ellipse sector. VideoCapture cap(argc > 1 ? Where,AP_50 = Precision when overlap between Ground Truth and predicted bounding box is at least 50% ( IoU = 50% )AP_75 = Precision when overlap between Ground Truth and predicted bounding box is at least 75% ( IoU = 75% )AP_Small = Average Precision for small size faces ( Average of IoU = 50% to 95% )AP_medium = Average Precision for medium size faces ( Average of IoU = 50% to 95% )AP_Large = Average Precision for large size faces ( Average of IoU = 50% to 95% )mAP = Average precision across different IoU ( Average of IoU = 50% to 95% ). Then we pass it the image through the detector. According to the authors, on average 10 features out of 6000+ are evaluated per sub-window. The same would apply for more features (of course it gets trickier). For example, if the best corner has the quality measure = 1500, and the qualityLevel=0.01 , then all the corners with the quality measure less than 15 are rejected. The above code snippet loads the haar cascade model file and applies it to a grayscale image. OpenCV >= 3.0 : Background subtraction (BS) is a common and widely used technique for generating a foreground mask (namely, a binary image containing the pixels belonging to moving objects in the scene) by using static cameras. A piecewise-linear curve is used to approximate the elliptic arc boundary. HSV (hue, saturation, value) colorspace is a model to represent the colorspace similar to the RGB color model. (Normally the first few stages will contain very many fewer features). 'Code for Thresholding Operations using inRange tutorial. Take each 24x24 window. Check if it is face or not. So this is a simple intuitive explanation of how Viola-Jones face detection works. Value channel describes the brightness or the intensity of the color. We have provided code snippets throughout the blog for better understanding. minDistance: Minimum possible Euclidean distance between the returned corners. [FDDB Comparison code][Dlib Blog][dlib mmod python example][dlib mmod cpp example][OpenCV DNN Face detector][Haar Based Face Detector], Filed Under: Application, Deep Learning, DLIB, Face Application, Face Detection, Machine Learning, Object Detection, OpenCV, OpenCV DNN. Using RANSAC is useful when you suspect that a few data points are extremely noisy. In this chapter, We will understand the concepts of optical flow and its estimation using Lucas-Kanade method. We also show the size of the detected face along with the bounding box. IEEE, 2002. If it were a 2D-histogram we would use something like: Using as input argument an image like the one shown below: String filename = args.length > 0 ? It is then used to detect objects in other images. 2002. Let's identify some parts of the histogram: What if you want to count two features? After compiling this program, run it. (i is the iterator over the number of faces). Formulas used to convert from one colorspace to another colorspace using cv::cvtColor function are described in Color conversions, The tutorial code's is shown lines below. Its detection pipeline is an anchor-less approach based on CenterNet.FairMOT is not as fast as the traditional OpenCV tracking algorithms, but it It is a machine learning based approach where a cascade function is trained from a lot of positive and negative images. gradients, directions, etc). It is based on Single-Shot-Multibox detector and uses ResNet-10 Architecture as backbone. This course is available for FREE only till 22. ', #get the frame number and write it on the current frame, Background Models Challenge (BMC) website. So, we evaluate the methods on CPU only and also report result for MMOD on GPU as well as CPU. OpenCV solvePnPRansac. However, this point should always be kept in mind while using the Dlib Face detectors. After giving a short intro of how to use the program, we create a window: Now we initialize the argument that defines the size of the borders (top, bottom, left and right). It also detects faces at various angles. Create the trackbars to set the range of HSV values, Until the user want the program to exit do the following. Prev Tutorial: Basic Thresholding Operations. (Just imagine how much computation it needs? An interesting interview regarding Face Detection by. We will learn how the Haar cascade object detection works. if you need double floating-point accuracy and using single floating-point input data (CV_32F input and CV_64F output depth combination), you can use Mat::convertTo to convert the input data to the desired precision. The model was trained using images available from the web, but the source is not disclosed. The model can be downloaded from the dlib-models repository.It uses a dataset manually labeled by its Author, Davis King, consisting of images from various datasets like ImageNet, PASCAL VOC, VGG, WIDER, Face Scrub. The following code example will use pretrained Haar cascade models to detect faces and eyes in an image. Read the paper for more details or check out the references in the Additional Resources section. A piecewise-linear curve is used to approximate the elliptic arc boundary. This way, the convolution can be performed over the needed pixels without problems (the extra padding is cut after the operation is done). The drawing code uses general parametric form. We have designed this Python course in collaboration with OpenCV.org for you to build a strong foundation in the essential elements of Python, Jupyter, NumPy and Matplotlib. Finally, we will use the function cv::Mat::copyTo to map only the areas of the image that are identified as edges (on a black background). The program finishes when the user presses 'ESC'. Let's see an example. This model was included in OpenCV from version 3.3. WebIn this section, the procedure to run the C++ code using OpenCV library is shown. Create a window to display the default frame and the threshold frame. args[0] : backSub = Video.createBackgroundSubtractorMOG2(); backSub = Video.createBackgroundSubtractorKNN(); String frameNumberString = String.format(. We don't consider the remaining features on it. We select the features with minimum error rate, which means they are the features that most accurately classify the face and non-face images. But it does not detect small sized faces ( < 7070 ). We share some tips to get started. Reference: By SharkDderivative work: SharkD [CC BY-SA 3.0 or GFDL], via Wikimedia Commons, By SharkD [GFDL or CC BY-SA 4.0], from Wikimedia Commons. This is mainly because the CNN features are much more robust than HoG or Haar features. Loading the Libraries. We want our bins to have the same size (uniform) and to clear the histograms in the beginning, so: We proceed to calculate the histograms by using the OpenCV function. ', To calculate histograms of arrays of images by using the OpenCV function, To normalize an array by using the function. The output is in the form of a list of faces with the (x, y) coordinates of the diagonal corners. We used a 300300 image for the comparison of the methods. For this file the Proceedings. Next image shows the HSV cylinder. ', Perform basic thresholding operations using OpenCV. Websift128 If you want to change the learning rate used for updating the background model, it is possible to set a specific learning rate by passing a parameter to the, The current frame number can be extracted from the. Given below are the results. Wow.. For example, two faces have same identity if the cosine distance is greater than or equal to 0.363, or the normL2 distance is less than or equal to 1.128. Enumeration Type Documentation First, a cv::CascadeClassifier is created and the necessary XML file is loaded using the cv::CascadeClassifier::load method. If you press 'ESC' the program will exit. The drawing code uses general parametric form. If a window fails the first stage, discard it. So how do we select the best features out of 160000+ features? Imgproc.putText(frame, frameNumberString. In this section, we will see an example of end-to-end linear regression with the Sklearn library with a proper dataset. No need for. The corners with the quality measure less than the product are rejected. This value is a set of three numbers picked randomly in the range \([0,255]\). Capture the video stream from default or supplied capturing device. For example, we can look at the information captured by the pixels and flag an image as noisy or blurry. OpenCV provides 2 models for this face detector. We are ready to show the current input frame and the results. In this tutorial we will learn how to perform BS by using OpenCV. For example, if the best corner has the quality measure = 1500, and the qualityLevel=0.01 , then all the corners with the quality measure less than 15 are rejected. This function is an extension of calibrateCamera with the method of releasing object which was proposed in .In many common cases with inaccurate, unmeasured, roughly planar targets (calibration plates), this method can The course will be delivered straight into your mailbox. (The two features in the above image are actually obtained as the best two features from Adaboost). In an image, most of the image is non-face region. Create the trackbars to set the range of HSV values, Until the user want the program to exit do the following. Note when ddepth=-1, the output image will have the same depth as the source. We will use functions like cv.calcOpticalFlowPyrLK() to track feature points in a video. Apart from accuracy and speed, there are some other factors which help us decide which one to use. In the previous tutorial, we learnt how to perform thresholding using cv::threshold function. The process is continued until the required accuracy or error rate is achieved or the required number of features are found). We recommend to use OpenCV-DNN in most. WebThe resultant image can therefore be saved in a new matrix or by updating the existing matrix. If you press 'c', the random colored borders will appear again. As expected, Haar based detector fails totally. cameraDevice = Integer.parseInt(args[0]); frame.setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE); Image img = HighGui.toBufferedImage(matFrame); addComponentsToPane(frame.getContentPane(), img); Mat frame = frames.get(frames.size() - 1); Imgproc.cvtColor(frame, frameHSV, Imgproc.COLOR_BGR2HSV); JSlider source = (JSlider) e.getSource(); pane.add(sliderPanel, BorderLayout.PAGE_START); pane.add(framePanel, BorderLayout.CENTER); System.loadLibrary(Core.NATIVE_LIBRARY_NAME); parser = argparse.ArgumentParser(description=. Object Detection using Haar feature-based cascade classifiers is an effective object detection method proposed by Paul Viola and Michael Jones in their paper, "Rapid Object Detection using a Value channel describes the brightness or the intensity of the color. For simple purposes, OpenCV implements the function cv::calcHist , which calculates the histogram of a set of arrays (usually images or image planes). We will use cv::BackgroundSubtractorMOG2 in this sample, to generate the foreground mask. This way, we spend more time checking possible face regions. Prev Tutorial: Meanshift and Camshift Goal . The function cv::ellipse with more parameters draws an ellipse outline, a filled ellipse, an elliptic arc, or a filled ellipse sector. Dlib HoG is the fastest method on CPU. We use it to generate the random border color, as we will see soon. Since the output of the Canny detector is the edge contours on a black We had discussed the pros and cons of each method in the respective sections. Here we will work with face detection. Create an image to display the histograms: Observe that to access the bin (in this case in this 1D-Histogram): Finally we display our histograms and wait for the user to exit. You can also download it from here. Let's check the general structure of the program: As you set the range values from the trackbar, the resulting frame will be visible in the other window. Since the hue channel models the color type, it is very useful in image processing tasks that need to segment objects based on its color. The Ultimate Guide, Anti-Spoofing Face Recognition System using OAK-D and DepthAI, Face Recognition: An Introduction for Beginners, Deep Learning based Face Detector in OpenCV, Deep Learning based Face Detector in Dlib. The scalability, and robustness of our computer vision and machine learning algorithms have been put to rigorous test by more than 100M users who have tried our products. (optional) color we want to draw the corners with, of type cv::Scalar. If you continue to use this site we will assume that you are happy with it. We give them a value of 5% the size of src. Instead, focus on regions where there can be a face. In the following you can find the source code. Next Tutorial: Cascade Classifier Training. Since colors in the RGB colorspace are coded using the three channels, it is more difficult to segment an object in the image based on its color. Basically, this method works under most cases except a few as discussed below. We also discussed how to divide an image into smaller patches and some applications around it. This was just a simple example of how an histogram works and why it is useful. The top row shows two good features. Now, all possible sizes and locations of each kernel are used to calculate lots of features. Thus the coordinates should be multiplied by the height and width of the original image to get the correct bounding box on the image. We have included both the models along with the code. We also share all the models required for running the code. See the README. It can be seen that dlib based methods are able to detect faces of size upto ~(7070) after which they fail to detect. The code is similar to the HoG detector except that in this case, we load the cnn face detection model. We load the required model using the above code. OpenCV provides 2 models for this face detector. If you want to use some OpenCV features that are not provided by default in OpenCvSharp (e.g. Please download the code from the link below. where \(i\) indicates the dimension. An extended set of haar-like features for rapid object detection. The concept remains the same, but now we add a range of pixel values we need. Prev Tutorial: Basic Thresholding Operations. In the above code, the image is converted to a blob and passed through the network using the forward() function. It should also be noted that these numbers can be different on different systems. with the following arguments: dst: Output of the edge detector.It should be a grayscale image (although in fact it is a binary one) lines: A vector that will store the parameters \((r,\theta)\) of the detected lines; rho: The resolution of the parameter \(r\) in pixels.We use 1 pixel. Floating point 16 version of the original caffe implementation ( 5.4 MB ), 8 bit quantized version using Tensorflow ( 2.7 MB ), The 3rd dimension iterates over the detected faces. Goals . You can also download it from here. One problem that naturally arises is how to handle the boundaries. // Schedule a job for the event dispatch thread: // creating and showing this application's GUI. It can operate with up to 32 dimensions. The second argument is the number of times we want to upscale the image. This method uses a Maximum-Margin Object Detector ( MMOD ) with CNN based features. WebThe latest Lifestyle | Daily Life news, tips, opinion and advice from The Sydney Morning Herald covering life and relationships, beauty, fashion, health & wellbeing You can read more about HoG in our post. Let's check the general structure of the program: As you set the range values from the trackbar, the resulting frame will be visible in the other window. Applying this to the example above we get the image below ( axis x represents the bins and axis y the number of pixels in each of them). The final classifier is a weighted sum of these weak classifiers. WebOur research ranges from fundamental advances in algorithms and our understanding of computation, through to highly applied research into new display technologies for clinical diagnosis, energy-efficient data centres, and profound insight into data through visualisation. The MMOD detector can be run on a GPU, but the support for NVIDIA GPUs in OpenCV is still not there. Detect an object based on the range of pixel values in the HSV colorspace. After each classification, weights of misclassified images are increased. Yes, it is. It would be safe to say that it is time to bid farewell to Haar-based face detector and DNN based Face Detector should be the preferred choice in OpenCV. The authors have a good solution for that. Works for different face orientations up, down, left, right, side-face etc. It can be used to store real or complex-valued vectors and matrices, grayscale or color images, voxel volumes, vector fields, point clouds, tensors, histograms (though, very high-dimensional histograms may be better Next Tutorial: Sobel Derivatives Goal . What most of OpenCV functions do is to copy a given image onto another slightly larger image and then automatically pads the boundary (by any of the methods explained in the sample code just below). Since feeding high resolution images is not possible to these algorithms ( for computation speed ), HoG / MMOD detectors might fail when you scale down the image. It is achieved by Adaboost. In the previous tutorial, we learnt how to perform thresholding using cv::threshold function. How can we convolve them if the evaluated points are at the edge of the image? Next Tutorial: Making your own linear filters! our input is the image to be divided (this case with three channels) and the output is a vector of Mat ). 'Code for Thresholding Operations using inRange tutorial. ; theta: The resolution of the parameter \(\theta\) in radians.We use The following code example will use pretrained Haar cascade models to detect faces and eyes in an image. args[0] : String filenameEyesCascade = args.length > 2 ? However, upscaling the image will have substantial impact on the computation speed. MMOD detector is very fast on a GPU but is very slow on a CPU. You can process both videos and images. The tutorial code's is shown lines below. Dlib had worse numbers than Haar, although visually dlib outputs look much better. We will let the user choose to process either a video file or a sequence of images. cameraDevice = Integer.parseInt(args[0]); frame.setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE); Image img = HighGui.toBufferedImage(matFrame); addComponentsToPane(frame.getContentPane(), img); Mat frame = frames.get(frames.size() - 1); Imgproc.cvtColor(frame, frameHSV, Imgproc.COLOR_BGR2HSV); JSlider source = (JSlider) e.getSource(); pane.add(sliderPanel, BorderLayout.PAGE_START); pane.add(framePanel, BorderLayout.CENTER); System.loadLibrary(Core.NATIVE_LIBRARY_NAME); parser = argparse.ArgumentParser(description=. For this image: Image with Charuco board. Have any other suggestions? 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