OpenCV Computer Vision with Python

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Prateek Joshi is a computer vision researcher with a primary focus on content-based analysis. He is particularly interested in intelligent algorithms that can understand images to produce scene descriptions in terms of constituent objects. He has a master's degree from the University of Southern California, specializing in computer vision. He was elected to become a member of the Honor Society for academic excellence and an ambassador for the School of Engineering.

Over the course of his career, he has worked for companies such as Nvidia, Microsoft Research, Qualcomm, and a couple of early stage start-ups in Silicon Valley. His work in this field has resulted in multiple patents, tech demos, and research papers at major IEEE conferences. He has won many hackathons using a wide variety of technologies related to image recognition.

He enjoys blogging about topics such as artificial intelligence, abstract mathematics, and cryptography. His blog has been visited by users in more than countries, and he has been featured as a guest author in prominent tech magazines. Michael Beyeler is a PhD candidate in the department of computer science at the University of California, Irvine, where he is working on computational models of the brain as well as their integration into autonomous brain-inspired robots. His work on vision-based navigation, learning, and cognition has been presented at IEEE conferences and published in international journals.

This is his first technical book that, in contrast to his or any dissertation, might actually be read. Go back. Launching Xcode Launching Visual Studio Fetching latest commit…. About the Book OpenCV 3 is a native cross-platform library for computer vision, machine learning, and image processing.

Instructions and Navigation All of the code is organized into folders. ArgumentParser parser. You signed in with another tab or window. Reload to refresh your session.

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OpenCV Python Tutorial - Creating Face Detection System And Motion Detector Using OpenCV - Edureka

Mar 20, This is the array with the face rectangle coordinates. Step 3: This final step involves displaying the image with the rectangular face box. Check out the following image, here I have summarized the 3 steps in the form of an image for easier readability:. First, we create a CascadeClassifier object to extract the features of the face as explained earlier. The path to the XML file which contains the face features is the parameter here. Followed by this, we search for the coordinates for the image. This is done using detectMultiScale. What coordinates, you ask?

OpenCV Computer Vision with Python

So, on the whole — Smaller the value, greater is the accuracy. This logic is very simple — As simple as making use of a for loop statement. Check out the following image.

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We define the method to create a rectangle using cv2. Capturing videos using OpenCV is pretty simple as well. Check it out:. The images are read one-by-one and hence videos are produced due to fast processing of frames which makes the individual images move. First, we import the OpenCV library as usual. The parameter to this function denotes if the program should make use of the built-in camera or an add-on camera.

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And lastly, the release method is used to release the camera in a few milliseconds. When you go ahead and type in and try to execute the above code, you will notice that the camera light switches on for a split second and turns off later. Why does this happen? This happens because there is no time delay to keep the camera functional. Looking at the above code, we have a new line called time. Do note that the parameter passed is the time in seconds.

So, when the code is executed, the webcam will be turned on for 3 seconds. Adding a window to show the video output is pretty simple and can be compared to the same methods used for images. However, there is a slight change. Check out the following code:. I am pretty sure you can make the most sense from the above code apart from one or two lines. Here, we have defined a NumPy array which we use to represent the first image that the video captures — This is stored in the frame array.

We also have check — This is a boolean datatype which returns True if Python is able to access and read the VideoCapture object. To do exactly that, we need to first create a frame object which will read the images of the VideoCapture object. As seen above, the imshow method is used to capture the first frame of the video. So how do we go about capturing the video instead of the first image in OpenCV?

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We make use of the cvtColor function to convert each frame into a grey-scale image as explained earlier. There is a user event trigger here as well. OpenCV is pretty easy to grasp, right? I personally love how good the readability is and how quickly a beginner can get started working with OpenCV.

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You have been approached by a company that is studying human behavior. Your task is to give them a webcam, that can detect the motion or any movement in front of it. So, now that we have defined our problem statement, we need to build a solution logic to approach the problem in a structured way. The next step involves converting the image to a Gaussian blur image. This is done so as to ensure we calculate a palpable difference between the blurred image and the actual image. At this point, the image is still not an object.

We define a threshold to remove blemishes such as shadows and other noises in the image.

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Borders for the object are defined later and we add a rectangular box around the object as we discussed earlier on the blog. Lastly, we calculate the time at which the object appears and exits the frame. The same principle follows through here as well. We first import the package and create the VideoCapture object to ensure we capture video using the webcam. The while loop iterates through the individual frames of the video.

We convert the color frame to a grey-scale image and later we convert this grey-scale image to Gaussian blur. We make use of the absdiff function to calculate the difference between the first occurring frame and all the other frames. The threshold function provides a threshold value, such that it will convert the difference value with less than 30 to black. If the difference is greater than 30 it will convert those pixels to white color. Later, we make use of the findContours function to define the contour area for our image. And we add in the borders at this stage as well.

Later, we create a rectangular box around our object in the working frame. One thing that remains with our use-case is that we need to calculate the time for which the object was in front of the camera. We make use of DataFrame to store the time values during which object detection and movement appear in the frame.