computer-vision

问题 I am trying to implement a paper on Semantic Segmentation and I am confused about how to Upsample the prediction map produced by my segmentation network to match the input image size. For example, I am using a variant of Resnet101 as the segmentation network (as used by the paper). With this network structure, an input of size 321x321 (again used in the paper) produces a final prediction map of size 41x41xC (C is the number of classes). Because I have to make pixel-level predictions, I need

问题 I have an input image of a paragraph of text in single line spacing. I'm trying to implement something like the line spacing option to increase/decrease space between text lines in Microsoft Word. The current image is in single space, how can I convert the text into double space? Or say .5 space? Essentially I'm trying to dynamically restructure the spacing between text lines, preferably with an adjustable parameter. Something like this: Input image Desired result My current attempt looks

问题 I have an input image of a paragraph of text in single line spacing. I'm trying to implement something like the line spacing option to increase/decrease space between text lines in Microsoft Word. The current image is in single space, how can I convert the text into double space? Or say .5 space? Essentially I'm trying to dynamically restructure the spacing between text lines, preferably with an adjustable parameter. Something like this: Input image Desired result My current attempt looks

问题 I want to load more than 10000 images in my 8gb ram in the form of numpy arrays.So far I have tried cv2.imread,keras.preprocessing.image.load_image,pil,imageio,scipy.I want to do it the fastest way possible but I can't figure out which on is it. 回答1: One of the Fastest way is to get your multiprocessors do your job in Parallel it asks for parallelisation of your desired job, it brings multiple processors to work on your tasks at the same time when concurrent running isn't an issue. This

问题 I'm converting RGB triplets to LAB in this way. from colormath.color_objects import sRGBColor, LabColor from colormath.color_conversions import convert_color for p in range(0,h): for q in range(0,w): rgb_color = sRGBColor(img_arr[p][q][0],img_arr[p][q][1],img_arr[p][q][2]) lab_color = convert_color(rgb_color, LabColor) But this method is slow. Is there a way I can convert img_arr from RGB to LAB without loops? I want to use colormath only. 来源： https://stackoverflow.com/questions/37608210/how

问题 Suppose I would like to build a tool that would tell me on a scale from 0 to 1 the similarity between images. I could go ahead and download a pre-trained mage models such as vgg16, remove its last layers (classifier), convert the images into feature vectors using the pre-trained model and finally compute the similarity between the vectors using something like the cosine similarity. The method is better explained here: https://mc.ai/find-more-like-this-product-using-transfer-learning/ ... but

问题 Suppose I would like to build a tool that would tell me on a scale from 0 to 1 the similarity between images. I could go ahead and download a pre-trained mage models such as vgg16, remove its last layers (classifier), convert the images into feature vectors using the pre-trained model and finally compute the similarity between the vectors using something like the cosine similarity. The method is better explained here: https://mc.ai/find-more-like-this-product-using-transfer-learning/ ... but

问题 Use-case Generate a synthetic 3D scene using random points Generate two synthetic cameras Get the 2 camera 2D projections Derive the Fundamental & Essential matrices Derive rotation & translation using the Essential matrix Triangulate the 2 2D projections to result the initial 3D scene Implementation Random 3D points ( x, y, z ) are generated Camera intrinsic matrix is statically defined Rotation matrix is statically defined ( 25 deg rotation on the Z-axis ) Identity Translation Matrix ( no

问题 I have seen people using IOU as the metric for detection tasks and Dice Coeff for segmentation tasks. The two metrics looks very much similar in terms of equation except that dice gives twice the weightage to the intersection part. If I am correct, then Dice: (2 x (A*B) / (A + B)) IOU : (A * B) / (A + B) Is there any particular reason for preferring dice for segmentation and IOU for detection? 回答1: This is not exactly right. The Dice coefficient (also known as the Sørensen–Dice coefficient

问题 I have seen people using IOU as the metric for detection tasks and Dice Coeff for segmentation tasks. The two metrics looks very much similar in terms of equation except that dice gives twice the weightage to the intersection part. If I am correct, then Dice: (2 x (A*B) / (A + B)) IOU : (A * B) / (A + B) Is there any particular reason for preferring dice for segmentation and IOU for detection? 回答1: This is not exactly right. The Dice coefficient (also known as the Sørensen–Dice coefficient