4/10/2023 0 Comments Virtual ruler![]() imgBlur = cv2.GaussianBlur(imgGrey, (5, 5), 1) In the first line of the function, we greyscale the image, since it makes computation in real time faster by getting rid of the extra RGB channels. The other arguments will be explained in a bit. We pass in several arguments into the function, the first of which is the image we would like to process. def getContours(img, cThr=, showCanny=False, minArea=1000, filter=0, draw=False): imgGrey = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) Let’s build out a function called getCountours to get a better understanding. With these edges, we can define where the paper is as well as where the item being measured is. This can be done using a specific convolutional kernel that detects edges on an image. Now that we have some standardization, we can use computer vision packages like CV2 to help determine where each element is on the page. In this image, for example, the letter paper will serve as a ‘calibration’, and any objects placed onto its surface will be detected and measured. Middle stages might be able to pick out body components such as eyes and the snout, while final stages will be able to identify the dog as a whole. In our dog example, early layers maybe be able to pick up the rounded edges of the eyes and the tongue, as well as the lines along its ears and back. Early filters pick up basic patterns including lines and curves, while later filters learn what entire objects look like. ![]() In essence, CNNs function by repeatedly passing filters over the pixel values of an image. The most popular method in computer vision models has been the Convolutional Neural Network (CNN). Luckily, machine learning developers have discovered various methods of interpreting this data. From this perspective, it’s now impossibly difficult to decipher what the image contains without prior knowledge of what is passed in. In this case, each individual pixel in the image is converted to components of red, green, and blue, and passed on to the computer. ![]() A computer can only interpret images as arrays of values. ![]()
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