title
OpenCV Python Tutorial For Beginners 13 - Object Detection and Object Tracking Using HSV Color Space

description
In this video on OpenCV Python Tutorial For Beginners, I am going to show How to do Object Detection and Object Tracking Using HSV Color Space. So we will be Implementing color and shape-based object detection and tracking using hue-saturation-value (HSV) color model. For Choosing the correct upper and lower HSV boundaries for color detection with`cv::inRange` (OpenCV) we will use trackbar. Gist of code I used in this video (Python | Simple object tracking with OpenCV ) - https://gist.github.com/pknowledge/aa1469b7ba8cd652adb652d4359ef4f0 OpenCV is an image processing library created by Intel and later supported by Willow Garage and now maintained by Itseez. opencv is available on Mac, Windows, Linux. Works in C, C++, and Python. it is Open Source and free. opencv is easy to use and install. Starting with an overview of what the course will be covering, we move on to discussing morphological operations and practically learn how they work on images. We will then learn contrast enhancement using equalization and contrast limiting. Finally we will learn 3 methods to subtract the background from the video and implement them using OpenCV. At the end of this course, you will have a firm grasp of Computer Vision techniques using OpenCV libraries. This course will be your gateway to the world of data science. Feel the real power of Python and programming! The course offers you a unique approach of learning how to code by solving real world problems. #ProgrammingKnowledge #ComputerVision #OpenCV ★★★Top Online Courses From ProgrammingKnowledge ★★★ Python Programming Course ➡️ http://bit.ly/2vsuMaS ⚫️ http://bit.ly/2GOaeQB Java Programming Course ➡️ http://bit.ly/2GEfQMf ⚫️ http://bit.ly/2Vvjy4a Bash Shell Scripting Course ➡️ http://bit.ly/2DBVF0C ⚫️ http://bit.ly/2UM06vF Linux Command Line Tutorials ➡️ http://bit.ly/2IXuil0 ⚫️ http://bit.ly/2IXukt8 C Programming Course ➡️ http://bit.ly/2GQCiD1 ⚫️ http://bit.ly/2ZGN6ej C++ Programming Course ➡️ http://bit.ly/2V4oEVJ ⚫️ http://bit.ly/2XMvqMs PHP Programming Course ➡️ http://bit.ly/2XP71WH ⚫️ http://bit.ly/2vs3od6 Android Development Course ➡️ http://bit.ly/2UHih5H ⚫️ http://bit.ly/2IMhVci C# Programming Course ➡️ http://bit.ly/2Vr7HEl ⚫️ http://bit.ly/2W6RXTU JavaFx Programming Course ➡️ http://bit.ly/2XMvZWA ⚫️ http://bit.ly/2V2CoAi NodeJs Programming Course ➡️ http://bit.ly/2GPg7gA ⚫️ http://bit.ly/2GQYTQ2 Jenkins Course For Developers and DevOps ➡️ http://bit.ly/2Wd4l4W ⚫️ http://bit.ly/2J1B1ug Scala Programming Tutorial Course ➡️ http://bit.ly/2PysyA4 ⚫️ http://bit.ly/2PCaVj2 Bootstrap Responsive Web Design Tutorial ➡️ http://bit.ly/2DFQ2yC ⚫️ http://bit.ly/2VoJWwH MongoDB Tutorial Course ➡️ http://bit.ly/2LaCJfP ⚫️ http://bit.ly/2WaI7Ap QT C++ GUI Tutorial For Beginners ➡️ http://bit.ly/2vwqHSZ ★★★ Online Courses to learn ★★★ Get 2 FREE Months of Unlimited Classes from skillshare - https://skillshare.eqcm.net/r1KEj Data Science - http://bit.ly/2lD9h5L | http://bit.ly/2lI8wIl Machine Learning - http://bit.ly/2WGGQpb | http://bit.ly/2GghLXX Artificial Intelligence - http://bit.ly/2lYqaYx | http://bit.ly/2NmaPya MERN Stack E-Degree Program - http://bit.ly/2kx2NFe | http://bit.ly/2lWj4no DevOps E-degree - http://bit.ly/2k1PwUQ | http://bit.ly/2k8Ypfy Data Analytics with R - http://bit.ly/2lBKqz8 | http://bit.ly/2lAjos3 AWS Certification Training - http://bit.ly/2kmLtTu | http://bit.ly/2lAkQL1 Projects in Java - http://bit.ly/2kzn25d | http://bit.ly/2lBMffs Machine Learning With TensorFlow - http://bit.ly/2m1z3AF | http://bit.ly/2lBMhnA Angular 8 - Complete Essential Guide - http://bit.ly/2lYvYRP Kotlin Android Development Masterclass - http://bit.ly/2GcblsI Learn iOS Programming Building Advance Projects - http://bit.ly/2kyX7ue ★★★ Follow ★★★ My Website - http://www.codebind.com DISCLAIMER: This video and description contains affiliate links, which means that if you click on one of the product links, I’ll receive a small commission. This help support the channel and allows us to continue to make videos like this. Thank you for the support!

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{'title': 'OpenCV Python Tutorial For Beginners 13 - Object Detection and Object Tracking Using HSV Color Space', 'heatmap': [{'end': 254.872, 'start': 230.43, 'weight': 0.724}, {'end': 606.215, 'start': 569.655, 'weight': 0.784}, {'end': 908.089, 'start': 881.214, 'weight': 0.936}, {'end': 1140.29, 'start': 1083.94, 'weight': 1}], 'summary': 'The tutorial series discusses object detection using hsv color space in opencv, covering over 150 color space conversion methods, importance of hsv in separating image luminance from color information, demonstrating hsv color space components, and showcasing object detection and tracking with track bars for adjusting hsv values.', 'chapters': [{'end': 254.872, 'segs': [{'end': 137.43, 'src': 'embed', 'start': 28.155, 'weight': 0, 'content': [{'end': 34.899, 'text': 'So there are more than 150 color space conversion methods in OpenCV.', 'start': 28.155, 'duration': 6.744}, {'end': 39.101, 'text': 'And one of them is colored image to HSV image.', 'start': 34.919, 'duration': 4.182}, {'end': 47.404, 'text': 'Now what is HSV color space? So HSV stands for hue, saturation, value.', 'start': 39.481, 'duration': 7.923}, {'end': 52.925, 'text': 'So H stands for hue, S for saturation, and V for the value.', 'start': 47.464, 'duration': 5.461}, {'end': 62.128, 'text': 'Now generally, RGB in RGB color space are all correlated to the color luminance.', 'start': 53.746, 'duration': 8.382}, {'end': 65.909, 'text': 'That is what we loosely call intensity.', 'start': 62.668, 'duration': 3.241}, {'end': 71.63, 'text': 'In other words, we cannot separate color information from luminance.', 'start': 66.549, 'duration': 5.081}, {'end': 81.68, 'text': 'So HSV or hue saturation value is used to separate image luminance from color information.', 'start': 72.33, 'duration': 9.35}, {'end': 90.991, 'text': 'So this makes it easier when we are working on or we need luminance in our images.', 'start': 82.381, 'duration': 8.61}, {'end': 99.436, 'text': 'That is why generally we use HSV in the situation where color description plays a very important role.', 'start': 91.511, 'duration': 7.925}, {'end': 103.658, 'text': 'Now, as I said, HSV stands for hue, saturation and value.', 'start': 99.456, 'duration': 4.202}, {'end': 115.105, 'text': 'But what is the meaning of each and every single word in HSV? Now, HSV is also known as the hexagon color model.', 'start': 104.178, 'duration': 10.927}, {'end': 132.989, 'text': 'So this color space can be described in this kind of cylindrical cone model, where hue is this circular angle which varies from 0 to 360, and hence,', 'start': 115.905, 'duration': 17.084}, {'end': 137.43, 'text': 'just by selecting the range of hue, you can select any color.', 'start': 132.989, 'duration': 4.441}], 'summary': 'Opencv offers 150+ color space methods, including hsv for separating luminance from color; hue ranges 0-360 for color selection.', 'duration': 109.275, 'max_score': 28.155, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/3D7O_kZi8-o/pics/3D7O_kZi8-o28155.jpg'}, {'end': 198.731, 'src': 'embed', 'start': 167.661, 'weight': 5, 'content': [{'end': 178.404, 'text': 'And this value is described from the center towards the outer layer of this cylindrical cone.', 'start': 167.661, 'duration': 10.743}, {'end': 189.047, 'text': 'So here you can see at the center this saturation start at 0 and it can go up to 1 at the end of this cylindrical cone.', 'start': 178.724, 'duration': 10.323}, {'end': 193.968, 'text': 'And this saturation can be increased from 0 to 100%.', 'start': 189.367, 'duration': 4.601}, {'end': 198.731, 'text': 'similarly, the value is basically the brightness of the color.', 'start': 193.968, 'duration': 4.763}], 'summary': 'Saturation ranges from 0 to 1, or 0% to 100%, representing color brightness.', 'duration': 31.07, 'max_score': 167.661, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/3D7O_kZi8-o/pics/3D7O_kZi8-o167661.jpg'}, {'end': 254.872, 'src': 'heatmap', 'start': 230.43, 'weight': 3, 'content': [{'end': 238.495, 'text': 'So here I have this simple code to load an image using IAM read method and show it inside a window.', 'start': 230.43, 'duration': 8.065}, {'end': 242.398, 'text': 'So by now you might already know how this code works.', 'start': 238.575, 'duration': 3.823}, {'end': 247.003, 'text': "So let's run this code and let's see what does this code do.", 'start': 242.718, 'duration': 4.285}, {'end': 254.872, 'text': 'So I have this image which is called smarties.png and here are some circles in different colors.', 'start': 247.584, 'duration': 7.288}], 'summary': "Code loads and displays 'smarties.png' image with circles in different colors.", 'duration': 24.442, 'max_score': 230.43, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/3D7O_kZi8-o/pics/3D7O_kZi8-o230430.jpg'}], 'start': 0.851, 'title': 'Hsv color space for object detection', 'summary': 'Discusses object detection using hsv color space in opencv, featuring over 150 color space conversion methods, the importance of hsv in separating image luminance from color information, and the description of hue, saturation, and value components. it also introduces the hsv color space and its components, which can range from 0 to 1, and demonstrates its usage for object detection.', 'chapters': [{'end': 166.581, 'start': 0.851, 'title': 'Object detection using hsv color space', 'summary': 'Discusses how to perform object detection using hsv color space in opencv, highlighting over 150 color space conversion methods, the significance of hsv in separating image luminance from color information, and the description of hue, saturation, and value components in the hsv color space.', 'duration': 165.73, 'highlights': ['HSV color space has over 150 conversion methods in OpenCV.', 'HSV separates image luminance from color information, making it easier to work with luminance in images.', 'Hue, saturation, and value are the components of the HSV color space, with hue varying from 0 to 360, representing different colors.']}, {'end': 254.872, 'start': 167.661, 'title': 'Introduction to hsv color space', 'summary': 'Introduces the hsv color space, explaining the components of hue, saturation, and value, which can range from 0 to 1, and demonstrates the usage of hsv color space to detect objects in an image.', 'duration': 87.211, 'highlights': ['The HSV color space components include hue, saturation, and value, which can range from 0 to 1, allowing the selection of any color.', 'Saturation ranges from 0 to 100%, while brightness (value) increases from 0 to 1 from the bottom to the top of the cone.', 'The chapter provides a simple code to load an image using the IAM read method and demonstrates the usage of HSV color space to detect colored circles in the image.']}], 'duration': 254.021, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/3D7O_kZi8-o/pics/3D7O_kZi8-o851.jpg', 'highlights': ['HSV color space has over 150 conversion methods in OpenCV.', 'HSV separates image luminance from color information, making it easier to work with luminance in images.', 'The HSV color space components include hue, saturation, and value, which can range from 0 to 1, allowing the selection of any color.', 'The chapter provides a simple code to load an image using the IAM read method and demonstrates the usage of HSV color space to detect colored circles in the image.', 'Hue, saturation, and value are the components of the HSV color space, with hue varying from 0 to 360, representing different colors.', 'Saturation ranges from 0 to 100%, while brightness (value) increases from 0 to 1 from the bottom to the top of the cone.']}, {'end': 742.428, 'segs': [{'end': 287.145, 'src': 'embed', 'start': 254.952, 'weight': 0, 'content': [{'end': 261.439, 'text': 'So we have blue circles or green or red, orange and brown circles here inside this image.', 'start': 254.952, 'duration': 6.487}, {'end': 269.862, 'text': "So let's say we somehow want to detect only the blue circles or balls or green circles or balls.", 'start': 262.28, 'duration': 7.582}, {'end': 278.823, 'text': "How can we just detect only these balls? Let's say we just want to detect the green balls.", 'start': 270.422, 'duration': 8.401}, {'end': 287.145, 'text': 'How can we achieve this using OpenCV? We are going to see this using this HSV object detection.', 'start': 279.143, 'duration': 8.002}], 'summary': "Detect specific colored balls using opencv's hsv object detection", 'duration': 32.193, 'max_score': 254.952, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/3D7O_kZi8-o/pics/3D7O_kZi8-o254952.jpg'}, {'end': 343.758, 'src': 'embed', 'start': 314.973, 'weight': 1, 'content': [{'end': 325.303, 'text': 'So first of all, after this image is read, what we want to do is we want to convert our colored image into our HSV image.', 'start': 314.973, 'duration': 10.33}, {'end': 329.847, 'text': 'And by now you might already guess how to convert an image.', 'start': 325.743, 'duration': 4.104}, {'end': 336.193, 'text': 'You can just write HSV is equal to CV2 dot CVT color.', 'start': 330.027, 'duration': 6.166}, {'end': 343.758, 'text': 'and then your frame name, which is frame in this case, and then cv to dot,', 'start': 336.853, 'duration': 6.905}], 'summary': 'Convert colored image to hsv using cv2.cvtcolor.', 'duration': 28.785, 'max_score': 314.973, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/3D7O_kZi8-o/pics/3D7O_kZi8-o314973.jpg'}, {'end': 509.082, 'src': 'embed', 'start': 478.933, 'weight': 4, 'content': [{'end': 487.575, 'text': "so i'm going to just say l underscore b is my lower range and u underscore b is my upper range.", 'start': 478.933, 'duration': 8.642}, {'end': 493.797, 'text': 'Now we have already seen how we can use bitwise and or bitwise operations on images.', 'start': 488.015, 'duration': 5.782}, {'end': 509.082, 'text': 'So what we are going to do next is we are going to define a variable called res and then we will just call cv2.bitwise and to mask the original image.', 'start': 494.337, 'duration': 14.745}], 'summary': 'Using bitwise operations to mask original image with lower and upper ranges.', 'duration': 30.149, 'max_score': 478.933, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/3D7O_kZi8-o/pics/3D7O_kZi8-o478933.jpg'}, {'end': 606.215, 'src': 'heatmap', 'start': 569.655, 'weight': 0.784, 'content': [{'end': 576.297, 'text': 'we are going to show the mask and we are going to show the result using res variable.', 'start': 569.655, 'duration': 6.642}, {'end': 582.72, 'text': "so this is going to open three windows, and let's see what happens when we run this code.", 'start': 576.297, 'duration': 6.423}, {'end': 591.064, 'text': 'so we are going to run this code and this opens three windows here And now you can see the mask first of all.', 'start': 582.72, 'duration': 8.344}, {'end': 598.31, 'text': 'So we are just detecting the blue colored balls using this mask.', 'start': 591.104, 'duration': 7.206}, {'end': 606.215, 'text': "That's why we have defined the lower boundary of the blue color and the upper boundary of the blue color.", 'start': 598.35, 'duration': 7.865}], 'summary': 'Code opens three windows to detect blue colored balls using a defined color boundary.', 'duration': 36.56, 'max_score': 569.655, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/3D7O_kZi8-o/pics/3D7O_kZi8-o569655.jpg'}, {'end': 686.556, 'src': 'embed', 'start': 653.145, 'weight': 3, 'content': [{'end': 661.554, 'text': "So that's why you can use the track bar for adjusting these lower and upper boundary of any color.", 'start': 653.145, 'duration': 8.409}, {'end': 665.338, 'text': 'So for that, what we are going to do is, first of all,', 'start': 662.274, 'duration': 3.064}, {'end': 677.55, 'text': 'we will create a named window and then we are going to create a new window which we will use to adjust the lower and upper foundation of HSV values.', 'start': 665.338, 'duration': 12.212}, {'end': 681.713, 'text': "So now I'm going to just use CV2.", 'start': 678.351, 'duration': 3.362}, {'end': 686.556, 'text': 'We have already seen how to create a track bar.', 'start': 683.714, 'duration': 2.842}], 'summary': 'Using track bar to adjust lower and upper boundary of any color in opencv.', 'duration': 33.411, 'max_score': 653.145, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/3D7O_kZi8-o/pics/3D7O_kZi8-o653145.jpg'}], 'start': 254.952, 'title': 'Color detection with opencv and hsv image thresholding', 'summary': 'Explores using opencv to detect colored balls, particularly blue or green, introducing hsv object detection method. it also demonstrates converting an image into an hsv image, thresholding for blue color, and using a track bar to adjust hsv values for color detection.', 'chapters': [{'end': 314.173, 'start': 254.952, 'title': 'Object detection using opencv', 'summary': 'Explores using opencv to detect colored balls in an image, focusing on how to detect specific colors like blue or green balls and introducing the hsv object detection method.', 'duration': 59.221, 'highlights': ['The chapter explores using OpenCV to detect colored balls in an image, focusing on how to detect specific colors like blue or green balls and introducing the HSV object detection method.', 'Introduces the concept of using HSV object detection to achieve color-based ball detection using OpenCV.', 'Describes the presence of colored circles (blue, green, red, orange, and brown) in the image as the target for detection.']}, {'end': 742.428, 'start': 314.973, 'title': 'Hsv image thresholding for color detection', 'summary': 'Demonstrates the process of converting a colored image into an hsv image, thresholding the hsv image for a range of blue color, and using a track bar to adjust the lower and upper boundaries of hsv values for color detection.', 'duration': 427.455, 'highlights': ["The process begins with converting the colored image into an HSV image using the 'CV2.CVTcolor' method, followed by thresholding the HSV image for a range of blue color by defining lower and upper boundaries of blue color (110, 50, 50 and 130, 255, 255 respectively).", "The next step involves using 'cv2.bitwise_and' to create a mask for the original image based on the defined lower and upper blue color values, resulting in the detection of blue colored balls and the ability to detect other colored balls from the image using the same method.", 'The tutorial also demonstrates the use of a track bar to adjust the lower and upper boundaries of HSV values for color detection, creating a named window and track bar for adjusting the lower and upper boundary of HSV values, and providing a callback function to handle the track bar adjustments.']}], 'duration': 487.476, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/3D7O_kZi8-o/pics/3D7O_kZi8-o254952.jpg', 'highlights': ['Introduces the concept of using HSV object detection for color-based ball detection using OpenCV.', "The process involves converting the colored image into an HSV image using 'CV2.CVTcolor' method.", 'Describes the presence of colored circles (blue, green, red, orange, and brown) in the image as the target for detection.', 'The tutorial demonstrates the use of a track bar to adjust the lower and upper boundaries of HSV values for color detection.', "Using 'cv2.bitwise_and' to create a mask for the original image based on the defined lower and upper blue color values."]}, {'end': 1174.754, 'segs': [{'end': 881.034, 'src': 'embed', 'start': 835.88, 'weight': 2, 'content': [{'end': 846.543, 'text': 'so here is the second argument, and similarly what we are going to do is we are going to define the other lower values and upper values, so,', 'start': 835.88, 'duration': 10.663}, {'end': 856.548, 'text': 'and also the name of your trackbars.', 'start': 853.369, 'duration': 3.179}, {'end': 870.252, 'text': 'So once you have the values of lower HSV and upper HSV, you can provide these values here in place of these static values.', 'start': 860.27, 'duration': 9.982}, {'end': 881.034, 'text': 'So first element of this array will be LH, and then the LS variable, and then the LV variable.', 'start': 870.772, 'duration': 10.262}], 'summary': 'Defining lower and upper hsv values for trackbars lh, ls, and lv.', 'duration': 45.154, 'max_score': 835.88, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/3D7O_kZi8-o/pics/3D7O_kZi8-o835880.jpg'}, {'end': 908.089, 'src': 'heatmap', 'start': 881.214, 'weight': 0.936, 'content': [{'end': 887.896, 'text': 'Similarly, for the upper-boundation, we will provide these three upper-boundation variables.', 'start': 881.214, 'duration': 6.682}, {'end': 893.98, 'text': "and now, when we will run our code, let's see what happens.", 'start': 889.076, 'duration': 4.904}, {'end': 901.766, 'text': 'so we are running our code and you can see these windows, these three windows.', 'start': 893.98, 'duration': 7.786}, {'end': 908.089, 'text': 'one is the mask, other is the result and the third one is the frame,', 'start': 901.766, 'duration': 6.323}], 'summary': 'Providing three upper-boundary variables, running code displays three windows: mask, result, and frame.', 'duration': 26.875, 'max_score': 881.214, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/3D7O_kZi8-o/pics/3D7O_kZi8-o881214.jpg'}, {'end': 972.096, 'src': 'embed', 'start': 945.968, 'weight': 1, 'content': [{'end': 954.217, 'text': "it's easier to adjust these lower and upper boundary and now you can see all the three blue colored balls.", 'start': 945.968, 'duration': 8.249}, {'end': 962.566, 'text': 'So you can refine this object detection by moving these trackbars little bit left or little bit right.', 'start': 954.637, 'duration': 7.929}, {'end': 963.627, 'text': 'You can see here.', 'start': 962.866, 'duration': 0.761}, {'end': 968.552, 'text': "Now let's adjust this value to detect some other balls.", 'start': 964.067, 'duration': 4.485}, {'end': 972.096, 'text': "So let's say we want to detect the green balls.", 'start': 968.592, 'duration': 3.504}], 'summary': 'Adjust boundary to detect 3 blue balls, refine detection with trackbars.', 'duration': 26.128, 'max_score': 945.968, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/3D7O_kZi8-o/pics/3D7O_kZi8-o945968.jpg'}, {'end': 1025.248, 'src': 'embed', 'start': 997.823, 'weight': 0, 'content': [{'end': 1007.871, 'text': 'So you just need to play with this track bar for the lower HSV values and the upper HSV values and you will be able to detect the object,', 'start': 997.823, 'duration': 10.048}, {'end': 1011.494, 'text': 'whatever colored object you want to detect from the image.', 'start': 1007.871, 'duration': 3.623}, {'end': 1015.498, 'text': 'Now this is the object detection from the image.', 'start': 1012.435, 'duration': 3.063}, {'end': 1025.248, 'text': 'similarly, we can use the same method in order to track an object from a live video.', 'start': 1016.238, 'duration': 9.01}], 'summary': 'Adjust track bar to detect colored objects in image and live video.', 'duration': 27.425, 'max_score': 997.823, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/3D7O_kZi8-o/pics/3D7O_kZi8-o997823.jpg'}, {'end': 1140.29, 'src': 'heatmap', 'start': 1083.94, 'weight': 1, 'content': [{'end': 1093.847, 'text': 'which is going to read the frames from your default camera and at the end, when you are done playing with your images,', 'start': 1083.94, 'duration': 9.907}, {'end': 1099.01, 'text': 'you can just destroy this cap using the release method.', 'start': 1093.847, 'duration': 5.163}, {'end': 1106.816, 'text': 'so you can just write cap dot release, just going to release all the cameras you are just capturing right.', 'start': 1099.01, 'duration': 7.806}, {'end': 1119.36, 'text': 'So now this is the three line code you need to use in order to capture the camera input and then track any object of any color.', 'start': 1107.236, 'duration': 12.124}, {'end': 1140.29, 'text': "So I'm going to run this code now and you can see I'm just holding a blue colored object here and I'm moving this object on the left and right And you can see only blue colored object is detected and every other frame value is masked.", 'start': 1119.88, 'duration': 20.41}], 'summary': 'Code captures camera input, tracks any color object, detects only blue objects, and masks other frames.', 'duration': 56.35, 'max_score': 1083.94, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/3D7O_kZi8-o/pics/3D7O_kZi8-o1083940.jpg'}], 'start': 742.748, 'title': 'Hsv color space for object detection', 'summary': 'Covers implementing track bars to set lower and upper hsv values for object detection and tracking, demonstrating flexibility of adjusting color boundaries using track bars, and showcasing object detection and tracking in both images and live video.', 'chapters': [{'end': 1174.754, 'start': 742.748, 'title': 'Hsv color space for object detection', 'summary': 'Covers the implementation of track bars to set lower and upper hsv values for object detection and tracking, showcasing the flexibility of adjusting color boundaries using track bars and demonstrating object detection and tracking in both images and live video.', 'duration': 432.006, 'highlights': ['Demonstrating object detection and tracking in both images and live video', 'Flexibility of adjusting color boundaries using track bars', 'Setting lower and upper HSV values using track bars']}], 'duration': 432.006, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/3D7O_kZi8-o/pics/3D7O_kZi8-o742748.jpg', 'highlights': ['Demonstrating object detection and tracking in both images and live video', 'Flexibility of adjusting color boundaries using track bars', 'Setting lower and upper HSV values using track bars']}], 'highlights': ['HSV color space has over 150 conversion methods in OpenCV.', 'The tutorial series discusses object detection using hsv color space in opencv.', 'HSV separates image luminance from color information, making it easier to work with luminance in images.', 'The HSV color space components include hue, saturation, and value, which can range from 0 to 1, allowing the selection of any color.', 'Introduces the concept of using HSV object detection for color-based ball detection using OpenCV.', 'Demonstrating object detection and tracking in both images and live video', "The process involves converting the colored image into an HSV image using 'CV2.CVTcolor' method.", 'Describes the presence of colored circles (blue, green, red, orange, and brown) in the image as the target for detection.', 'The tutorial demonstrates the use of a track bar to adjust the lower and upper boundaries of HSV values for color detection.', 'Flexibility of adjusting color boundaries using track bars', "Using 'cv2.bitwise_and' to create a mask for the original image based on the defined lower and upper blue color values.", 'The chapter provides a simple code to load an image using the IAM read method and demonstrates the usage of HSV color space to detect colored circles in the image.']}