#Screen grabber detection how to#
#Screen grabber detection install#
#Screen grabber detection code#

We load our serialized model, providing the references to our prototxt and model files on Line 30 - notice how easy this is in OpenCV 3.3. # initialize the video stream, allow the cammera sensor to warmup, Now, let’s load our model and set up our video stream: # load our serialized model from disk For more information on these classes (and how the network was trained), please refer to last week’s blog post.

On Lines 22-26 we initialize CLASS labels and corresponding random COLORS. 'dog', 'horse', 'motorbike', 'person', 'pottedplant', 'sheep',ĬOLORS = np.random.uniform(0, 255, size=(len(CLASSES), 3)) # detect, then generate a set of bounding box colors for each classĬLASSES = ['background', 'aeroplane', 'bicycle', 'bird', 'boat', We then initialize a class list and a color set: # initialize the list of class labels MobileNet SSD was trained to

-confidence : The minimum probability threshold to filter weak detections.

-model : The path to the pre-trained model.

-prototxt : The path to the Caffe prototxt file.

Help='minimum probability to filter weak detections')Ĭompared to last week, we don’t need the image argument since we’re working with streams and videos - other than that the following arguments remain the same: Help='path to Caffe 'deploy' prototxt file')Īp.add_argument('-m', '-model', required=True,Īp.add_argument('-c', '-confidence', type=float, default=0.2, Next, we’ll parse our command line arguments: # construct the argument parse and parse the argumentsĪp.add_argument('-p', '-prototxt', required=True,

#Screen grabber detection code#

You must have OpenCV 3.3 (or newer) to run the code in this tutorial. This will ensure that the deep neural network ( dnn) module is installed.

#Screen grabber detection install#

Note: Make sure to download and install opencv and and opencv-contrib releases for OpenCV 3.3. To get your system set up, simply install OpenCV using the relevant instructions for your system (while ensuring you’re following any Python virtualenv commands).

For this tutorial, you will need imutils and OpenCV 3.3.

We begin by importing packages on Lines 2-8. To see how this is done, open up a new file, name it real_time_object_detection.py and insert the following code: # import the necessary packages To build our deep learning-based real-time object detector with OpenCV we’ll need to (1) access our webcam/video stream in an efficient manner and (2) apply object detection to each frame. Object detection in video with deep learning and OpenCV This will be accomplished using the highly efficient VideoStream class discussed in this tutorial.įrom there, we’ll apply our deep learning + object detection code to actual video streams and measure the FPS processing rate.

#Screen grabber detection how to#

In the first part we’ll learn how to extend last week’s tutorial to apply real-time object detection using deep learning and OpenCV to work with video streams and video files. Today’s blog post is broken into two parts. Looking for the source code to this post? Jump Right To The Downloads Section Real-time object detection with deep learning and OpenCV