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OpenCV functions and methods are accessed through the cv2 import. Our os import will allow us to build file paths regardless of operating system. In order to utilize the Holistically-Nested Edge Detection model with OpenCV, we need to define a custom layer cropping class � we appropriately name this class CropLayer.
In the constructor of this class, we store the starting and ending x, y -coordinates of where the crop will start and end, respectively Lines The next step when applying HED with OpenCV is to define the getMemoryShapes function, the method responsible for computing the volume size of the inputs :. Line 28 extracts the batch size and number of channels from the inputs as well. Finally, Line 29 extracts the height and width of the target shape, respectively.
Given these variables, we can compute the starting and ending crop x, y -coordinates on Lines The final method we need to define is the forward function. This function is responsible for performing the crop during the forward pass i. From there, both the protoPath and modelPath are used to load and initialize our Caffe model on Line Our original image is loaded and spatial dimensions width and height are extracted on Lines 58 and From there, open up a terminal and execute the following command:.
Notice how the Canny edge detector is not able to preserve the object boundary of the cat, mountains, or the rock the cat is sitting on. In Figure 3 above we can see an example image of myself playing guitar. Furthermore, HED does a better job of capturing the object boundaries of my shirt, my jeans including the hole in my jeans , and my guitar. Our CropLayer class is identical to the one we defined previously:. Whether we elect to use our webcam or a video file, the script will dynamically work for either Lines We begin looping over frames on Lines Lines 88 and 89 resize our frame so that it has a width of pixels.
We then grab the dimensions of the frame after resizing. Canny edge detection Lines and HED edge detection Lines are computed over the input frame. Our three output frames are displayed on Lines : 1 the original, resized frame, 2 the Canny edge detection result, and 3 the HED result. Keypresses are captured via Line Notice in particular how the boundary of the lamp in the background is completely lost when using the Canny edge detector; however, when using HED the boundary is preserved.
Unlike the Canny edge detector, which requires preprocessing steps, manual tuning of parameters, and often does not perform well on images captured using varying lighting conditions, Holistically-Nested Edge Detection seeks to create an end-to-end deep learning edge detector. As our results show, the output edge maps produced by HED do a better job of preserving object boundaries than the simple Canny edge detector. Holistically-Nested Edge Detection can potentially replace Canny edge detection in applications where the environment and lighting conditions are potentially unknown or simply not controllable.
The downside is that HED is significantly more computationally expensive than Canny. To download the source code to this guide, and be notified when future tutorials are published here on PyImageSearch, just enter your email address in the form below!
Enter your email address below to get a. Inside you'll find my hand-picked tutorials, books, courses, and libraries to help you master CV and DL! All too often I see developers, students, and researchers wasting their time, studying the wrong things, and generally struggling to get started with Computer Vision, Deep Learning, and OpenCV.
I created this website to show you what I believe is the best possible way to get your start. Cool stuff once again Adrian. I need to use something like this in the future to measure the size difference between one edge compared to another.
Luckily I can use an image rather than a video. Adrian, great demo as usual. I have few questions � 1. What are the usecases according to you? How would you do this using custom CNN i.
Building a document scanner is a great example of how edge detection can be applied. Are you asking how to train your own custom HED network? If so, refer to the paper publication. You would want to apply contour detection. How would you recommend cleaning the HED results up? Run Canny as a second step?
There are a number of steps you could do. Thresholding would be a super simple method to completely binarize the image. I would suggest starting there. Good day, Dr. Thanks for this cool tutorial! Just have a couple of quick questions: 1. Would it be likely to work if I pass each roi will save each roi in an individual image file to an object detection model like SSD trained on Coco? Please advise. Thanks in advance! You would apply contour detection. See this tutorial for an example. You would just apply the object detector to the entire image and skip edge detection.
Since the hed does not provide binary output, it would be more correct to compare it wit Sobel operator. More over hed causes headache when you try to apply it on larger image, where there are e.
I would be really interested in how hed can be applied in such case. Great Post! Seems to be a little CPU heavy. Do you have any suggestions on a good GPU implementation? Thank you so much for keeping us updated on new and better CV technologies. A lot of your work has been helpful for me to get into computer vision. Also, can you recommend forums and blogs to follow like yours that would help me know more about the latest trends in computer vision?
The PyImageSearch Gurus course has dedicated forums. I spend more time in there than in the blog post comments. The model that you have given was run by me on a Macbook Air and Pro. It turned out to be very heavy on processing.
Is it expected to behave like this? How can we train the model to make it lighter on processing? My use-case is an edge detector which can crop an identity card from an image. What approach would you recommend for this? Yes, it is expected to be very computationally heavy.
Make sure you are reading the entire blog post as I discuss the benefits and tradeoffs of HED vs. Adrian, I have a failure on cv2. What version of OpenCV are you using? Well, I used your blog to find that out. It now prints the version just before the offending line. OpenCV version 3. Upgrade to OpenCV 3. That should resolve the error v3. Thanks Adrian, you are brilliant. What is the exact string for that as neither conda nor pip can find OpenCV4?
Otherwise, you should be all set. I suggest you execute the code via the command line instead of an IDE. If you are using an IDE you likely need to set the command line arguments.
Hi Adrian, thanks for this great post. I am curious to know how this can be used to implement better object detection not recognition algorithms, or can it help to have better background subtraction,�.? Do you have any efficient conventional object detection ignoring recognition method using this? Unfortunately without knowing more information I cannot provide any suggestions. Thank you Adrian�interesting as always.
Can we make this detector detects only vertical or horizontal edges like what Sobel does? Thank Adrian for this blogp post! In the image I can see booth coast and the water. I need to get the coastline, can I use this method to get a better segmentation? The coastline will be used to perform some autonomous navigation of a boat.
Hey Leonardo � I would suggest giving it a try and seeing for yourself. What do your results look like? MasyaAllah,,,your article always great. May God Always Bless You. Most likely the system does not have enough memory to process the large image and hold the deep learning model. Check your memory usage during execution and see if it spikes. Hello Adrian, Thanks for perfect work share. Do u have any information about it?
Thank you for your tutorials! After walking through one I always learn something useful that I can apply in a number of projects. I do have one question on this post. What is the CropLayer Class doing?
Hi Adrian, very appreciate your post. As mentioned above, HED method can help detect edge in an image. I am wondering whether it is possible to use HED for blur dectection. Have you tried following my blur detection post here?
You could try using the HED as a Build Your Own Boat Wrap Zip replacement for the Laplacian of the image. That might require some tuning though. Nice work here. But can you tell me how I could use this when I want to detect an object?
I am making a project wherein I have to detect a specific leaf out of many different kinds of leaves in the background. Please help. Thank you. That sounds more like an object detection or instance segmentation problem rather than an edge detection one. If I want to blur out a face in a photo with two faces in close proximity facing each other, could I used a combo of edge detection and skin color detection to designate the area to blur out?
The results will be more aesthetically pleasing. The other option is to fit a minimum enclosing circle to the detected face bounding box and then only blur the circular region.
The best part is, these CG visual effects shot can then be incorporated into the main edit, as the editor and compositing tools work together in a single package. With a streamlined design, creators can freely resort to features such as masking, animation, 3D camera tracking, and keying to breathe new life into the project.
Read our full review of HitFilm Express to learn more. Fusion by Blackmagic is one of the best free VFX software for motion graphics and compositing. The node-based workflow allows you to connect processing types and transmit attributes. There are powerful features such as the planar tracker and delta keyer introduced in the latest release. The planar tracker works in a way to follow an area of a shot, creating a flat plane � as opposed to the classic point tracker � and thus allowing for a more solid tracking to stabilize the shot.
Delta keyer takes color difference into account, and works nice even if your green screen is unevenly lit. With advanced algorithm, the delta keyer can discriminate colors that represent the foreground object from an uneven colored screen. For Independent filmmakers, hobbyists or editors looking to expand their VFX knowledge, Fusion is a nice choice.
As an already powerful and comprehensive visual effects suite, together with DaVinci Resolve , the artists will have a powerful combo for the post work, at much lower price than the After Effects plus Premiere workflow. Developed by Germany-based company Maxon, Cinema 4D has been around for 30 years. This high-end tool excels in 3D modeling, visual effects, animation, painting and rendering, making it a popular program among 3D animators and motion graphics professionals.
Its popularity and degree of professionalism can be seen from a dazzling array of films and TV episodes it has been used for, such as Spiderman , The Lion King , Avengers: Endgame , Beowulf and many more. Cinema 4D offers users a seamless experience thanks to the Build Your Own Boat Wrap Video integration with a wide range of design tools, including After Effects, Photoshop, Illustrator, and other CAD applications.
There are various modeling modes available: parametric, polygon, volume modeling, sculpting, procedural modeling, with advanced texturing and lighting. Autodesk Maya is powerful video effects software that helps artists to create 3D animation, rendering, modeling and simulation.
It is probably the largest software ever coded on this planet, and all those powerful features bring us the epic scenes in Blockbusters such as The Lord of the Rings: The Two Towers and Star Wars. For VFX creators, there are many handy tools in Maya that helps to render your imagination into reality. You can resort to auto-rigging, hair tools, and hypershade tool for more technical control, and create models and assets with organic shapes and texture.
As for performance, the biggest improvement in the lately release is the animation cache playback. Users now can enjoy a smoother experience, thanks to the dynamic support with a new layered evaluation system and GPU enabled smoothing. Models and shaders update in almost real-time in the viewport. It delivers smother interactions with facial rigs and muscle systems by calculating how the geometry manipulates the surface model.
Nuke is a versatile visual effects and compositing tool produced by the Foundry. Unlike layer-based compositing software, Nuke adopts a node-based system that can make compositing quite easier. Yet it would be challenging if you are from After Effects. The node-based workflow allows you to obtain a complete overview of the whole project, with a clear display of the relationship between the different nodes.
Nuke finds its popularity for in-house use, and you can see big names such as Walt Disney Animation studios, Blizzard Entertainment, and DreamWorks among its users. This VFX software allows you to build up 3D stuff and combine it with any 2d footage. You can create fake lights to render real-world effects, and mix the 3D compositing to the live footage. With advanced analyzer, you can key out live elements from the shot, and generate layers for compositing.
Nuke embraces customization, allowing maximum flexibility for its users. There are bunch of official plug-ins for keying and blurring effects, and you can create your own tool sets and plug-ins. Houdini is most renowned for its powerful dynamic simulation tools, which delivers particle effects that simulate life-like elements, such as liquid effects. It also has powerful volumetric systems in-built for the creation of fire, clouds, smoke simulations and other video special effects.
Houdini uses the node-based system that allows you to return to a previous version or review the iterations easily, thus making the complex 3D modeling tasks more efficient, especially when dealing with a client's feedback. You can experiment with numerous nodes to build or remove certain parameters. For plug-ins and extensions, it supports 3-rd party rendering engines, and scripting via Python API, allowing for the automation of certain tasks to enhance efficiency.
Applying special effects and VFX in a video isn't exclusive to professionals. You can use green screen software to create interesting content. If you have no idea of where to get started, you can learn from movies by going through scenes, and analyze all the elements needed to achieve those visual effects. Here is a detailed guide on how to break down a VFX scene.
Cecilia Hwung is the marketing manager of Digiarty Software and the editor-in-chief of VideoProc team. She pursues common progress with her team and expects to share more creative content and useful information to readers. She has strong interest in copywriting and rich experience in video editing tips. Here are some visual effects fundamentals: Keying uses green screen or blue screen and remove it with VFX software to create desired effects. Tracking replicates the camera movement from the shot and apply it to the elements that are added in post production.
It analyzes the movement, offering you several tracking points to select as a reference, so as to parent the elements to the movement. In this way, the elements blend in the shot, as if it was there on set. Rotoscoping , or roto, allows you to trace an object frame by frame, and create a matte to key it out.
In this way, you can put something in between the cut out part and the background. It is useful when the green screen isn't working, or you don't have a pure colored background at all. Compositing stacks all the elements into the shot and makes them fit to the scene.
For instance, you key out a "ghost" and put it over a TV, as if the ghost is cropping out from the TV.
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