![]() ![]() # if our frame dimensions are None, we still need to compute the # resize the frame, maintaining the aspect ratioįrame = imutils.resize(frame, width=1000) # check to see if we have reached the end of the stream # grab the current frame, then handle if we are using aįrame = frame if args.get("video", False) else frame # otherwise, grab a reference to the video file # if a video path was not supplied, grab the reference to the web cam # load the pre-trained EAST text detector # second can be used to derive the bounding box coordinates of text # we are interested - the first is the output probabilities and the ![]() # define the two output layer names for the EAST detector model that # initialize the original frame dimensions, new frame dimensions, Help="resized image height (should be multiple of 32)") Help="resized image width (should be multiple of 32)")Īp.add_argument("-e", "-height", type=int, default=320, Help="minimum probability required to inspect a region")Īp.add_argument("-w", "-width", type=int, default=320, # construct the argument parser and parse the argumentsĪp.add_argument("-east", "-east", type=str, required=True,Īp.add_argument("-v", "-video", type=str,Īp.add_argument("-c", "-min-confidence", type=float, default=0.5, # return a tuple of the bounding boxes and associated confidences Rects.append((startX, startY, endX, endY)) # add the bounding box coordinates and probability score # compute both the starting and ending (x, y)-coordinatesĮndX = int(offsetX + (cos * xData1) + (sin * xData2))ĮndY = int(offsetY - (sin * xData1) + (cos * xData2)) # use the geometry volume to derive the width and height # extract the rotation angle for the prediction and # maps will be 4x smaller than the input image ![]() # compute the offset factor as our resulting feature # if our score does not have sufficient probability, # geometrical data used to derive potential bounding box # extract the scores (probabilities), followed by the # initialize our set of bounding box rectangles and corresponding # grab the number of rows and columns from the scores volume, then # python text_detection_video.py -east frozen_east_text_detection.pbĭef decode_predictions(scores, geometry): Installed Libraries from import VideoStreamįrom imutils.object_detection import non_max_suppression Last, run the project with the command “ py main.py” Next, import the source code you’ve download to your P圜harm IDE. Step 2: Import the project to your P圜harm IDE.Step 1: Download the given source code below.įirst, download the given source code below and unzip the source code.These are the steps on how to run Real-Time Text Detection OpenCV Python With Source Code In this Python OpenCV Project also includes a downloadable Python Project With Source Code for free, just find the downloadable source code below and click to start downloading. The EAST text detector requires that we are running OpenCV on our systems - if you do not already have OpenCV or better installed, please refer to my OpenCV install guides and follow the one for your respective operating system Project Information’s Project Name: OpenCV Python Text Detection With Source Code Language/s Used: Python (OpenCV) Python version (Recommended): 2.x or 3.x Database: None Type: Deep Learning Project Developer: IT SOURCECODE Updates: 0 OpenCV East Text Detection Python with Source Code – Project Information About The Project In this OpenCV Text Detection Python you will learn how to use OpenCV’s EAST detector to automatically detect text in both images and video streams.Īlso, you will learn how to use OpenCV to detect text in images using the EAST text detector. OpenCV Python is a deep learning model, based on a novel architecture and training pattern. The OpenCV Python Text Detection was developed using Python OpenCV, In this tutorial you will learn how to use OpenCV to detect text in real-time using web-camera.
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