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Performing Image (The MIT Press)

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Our images have less of a blur effect than when using the averaging method in Figure 2; however, the blur itself is more natural due to the computation of the weighted mean, rather than allowing all pixels in the kernel neighborhood to have equal weight. You can select custom to freely crop the edges of your image or choose from any of the preset crop options. These will automatically crop your photo to one of the most common aspect ratios on social media. To apply NMS, we first extract the bounding boxes and associated prediction probabilities ( proba) via Lines 159 and 160. We then pass those results into my imultils implementation of NMS ( Line 161). For more details on non-maxima suppression, be sure to refer to my blog post. Image processing requires fixed sequences of operations that are performed at each pixel of an image. The image processor performs the first sequence of operations on the image, pixel by pixel. Once this is fully done, it will begin to perform the second operation, and so on. The output value of these operations can be computed at any pixel of the image. What Is Image Processing? For example, we can see that blurring is applied when building a simple document scanner on the PyImageSearch blog. We also apply smoothing to aid us in finding our marker when measuring the distance from an object to our camera. In both these examples the smaller details in the image are smoothed out and we are left with more of the structural aspects of the image.

Morphological processing is a set of processing operations for morphing images based on their shapes. Segmentation An average filter does exactly what you think it might do — takes an area of pixels surrounding a central pixel, averages all these pixels together, and replaces the central pixel with the average. where and are the respective distances to the horizontal and vertical center of the kernel and is the standard deviation of the Gaussian kernel. Isobel Harbison’s Performing Image is a timely contribution to the field of performance studies, as it appeals to the facets of the performing body and identity in our contemporary, image-centric mediascape. The book sets out a dual project: the first is to map a group of artists from the 1970s to 2010s whose work has plumbed the meaning of images in relation to the body and subjectivity; and the second is to meditate on how that work anticipates the imbrication of performance and image in the contemporary attention economy defined by social media. The fulcrum of these two projects is the technological, cultural, and economic phenomenon of prosumerism: a “new mode of image production” in which the producers are also consumers (6). Through a “speculative genealogy” (4) Harbison argues that certain artistic works — that is, works of “performing image”— both anticipate and illuminate the cycle of consumption- production-consumption that defines the prosumerist logic of contemporary cultural practice. This eponymous term, then, refers to images that negotiate, in a meta fashion, the meaning of images. It is through the study of such works that we might glean the subtle yet pervasive impulses to create, post, interface with, and — yes —performimages in and through new media. As such,Performing Imageaptly resonates with and contributes to discourses on mediatized performance, digital corporeality, and performance on new and social media — among others… […] the notion of performing image is a provocative concept that strikes a chord of resonance with myriad issues in contemporary digital culture. It may be mobilized, for example, toward inquiries into digital spatio-temporalities, notions of new media as archive, questions of contemporary performer agency, surveillance, or neoliberal infiltrations of the performing body. In this way, Performing Image will prove to be a valuable addition to the performance studies bookshelf.”

Again, there might be some slight differences in the final image because this image augmentation pipeline created with the Compose class doesn't apply all transformations to the image 100% of the time. If you want to try and get a different final result, just rerun the code that creates the custom dataset object and take a look at the resultant image: # Create a custom dataset To average blur an image, we use the cv2.blur function. This function requires two arguments: the image we want to blur and the size of the kernel. Our first script, blurring.py, will show you how to apply an average blur, Gaussian blur, and median blur to an image ( adrian.png) using OpenCV. The last parameter is our , the standard deviation of the Gaussian distribution. By setting this value to 0, we are instructing OpenCV to automatically compute based on our kernel size.

Many different styles of photos can convey emotion successfully, but none is as effective as lifestyle images, a.k.a photos of people. It’s just so much easier for viewers to identify emotions in their peers, as well as to see themselves in the scenes portrayed when other people are included. Now that my three functions are ready, I can combine them to create our CustomDataset class. # Create a custom dataset class Again, as we’ll see in the code below, when the size of our kernel increases so will the amount of blurring that is applied to our output image. However, the blurring will appear to be more “natural” and will preserve edges in our image better than simple average smoothing: # close all windows to cleanup the screen

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Ultimately, deciding on which techniques to use depends on the result of your testing. When possible, you should train your model using different combinations of the various techniques, and evaluate the result of each test. Using the Datature Platform for Image Augmentation and Training Your Model So with all the various image augmentation techniques, which one do we choose? There are no hard-and-fast rules on the best image augmentation techniques that you should adopt for your computer vision projects. It depends on the dataset you have, the type of images you are working with, and the context that you plan to use your model in. The second Python script, bilateral.py, will demonstrate how to use OpenCV to apply a bilateral blur to our input image. Average blurring ( cv2.blur ) When you post multiple images in a single tweet, just keep in mind that the image preview will crop differently depending on how many images you post. You can tweet up to four videos and images in a single post.

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