is there one that works in co-op or not? if it doesn't exist, i can not play this game. Some guidance on this subject would surely be appreciated. I really need a solution for this, i hope enough time has elapsed by now for someone to have finally come up with a solution that really works but i have yet to come across it. i believe it is mainly required to keep it from fisheyeing at 90 degrees. also the widescreenfixer program can only provide very minor relief from this without being able to actually change the fov. anyway it really makes me want to play with my friends because single player sucks even with the proper fov.
after seeing it again with the proper fov, it looks like a new game, beautiful, it's also about 10X easier to play when you can actually see the zombies that get in your face instead of having them be right under your nose attacking you when you can't see them. I had to quit playing this game years ago because after moving to a large 1080p monitor it got to the point where the fov gives me terrible headaches. The cheat enabler for world at war that comes from gamecopyworld only works for the FOV in single player, it does enable cheats in co-op but the FOV is not included in that. The first 90% of the dataset is used for training (171000 patches), while the last 10% is used for validation (19000 patches).Yes, i know it's an old thread but the problem is still unresolved. different patches may contain same part of the original images, no further data augmentation is performed. A set of 190000 patches is obtained by randomly extracting 9500 patches in each of the 20 DRIVE training images. Also the patches partially or completely outside the Field Of View (FOV) are selected, in this way the neural network learns how to discriminate the FOV border from blood vessels. Each patch, of dimension 48x48, is obtained by randomly selecting its center inside the full image. The training of the neural network is performed on sub-images (patches) of the pre-processed full images. Also on the STARE datasets, this method reports one of the best performances. The performance of this neural network is tested on the DRIVE database, and it achieves the best score in terms of area under the ROC curve in comparison to the other methods published so far. The neural network structure is derived from the U-Net architecture, described in this paper. This is a binary classification task: the neural network predicts if each pixel in the fundus image is either a vessel or not. This repository contains the implementation of a convolutional neural network used to segment blood vessels in retina fundus images. Retina-unet - Retina blood vessel segmentation with a convolutional neural network
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