Grand Theft Auto 5 PC.60 FPS Ultra Max Settings 1080pMy PC Specs: Processor: Intel Core i7-4790kMemory: 16gb Ram. GPU: Asus Strix Geforce GTX 980Subscribe fo.
- Jul 03, 2013 So the 5-1 favorites will now win 31.88% of the time, instead of 16.67%, the 15-1 runners will win 11.95% of the time, and the underdogs at 30-1 odds will still win 6.17% of the time. When betting on this field, the expected return on your bet is 191.25%! This means that a max bet of 10,000 chips will result in an expected PROFIT of 9,125 chips.
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It's finally here.
Grand Theft Auto V took its time to migrate to PC, and from our preliminary overview and testing, it seems like the wait was worthwhile. GTA V's PC port exhibits unique PC features, like a VRAM consumption slider indicative of the maximum VRAM requirement of the current settings. The port also added first-person mode, complete with new 3D models and animations for the characters' arms, phone, guns, and what-have-you. As you'll find out in our benchmark results below, the game is also incredibly well-optimized across most graphics card configurations, something we can't say has been true for most games in recent history.
These things take time, and RockStar certainly took as much of that as it needed.
Using a suite of video cards spanning the Titan X, SLI GTX 980s, R9 290X and 270Xs, GTX 960s, 750 Ti cards, and more, we benchmarked GTA V in an intensive test. This GTA V PC benchmark compares FPS of various graphics cards at maximum settings in 1080p, 1440p, and 4K resolutions.
This article makes no intentions to comment on gameplay value.
Explorations of Using Python to play Grand Theft Auto 5
- GTA 5 is a great environment to practice in for a variety of reasons
- With GTA, we can use modes to control the time of day, weather,traffic,speeds, what happens when we crash, all kinds of things
- It is a just a completely customizable environment
- This method can be done on a variety of games
- This initial goal is to create a sort of self-driving car
- The method he will use to access the game should be do-able on almost any game
- Things like sun glare in GAT V will make computer vision much more challenging, but also more realistic
- We can teach an AI to play games by simply showing it how to play for a bit, using CNN on that information, and then letting the AI poke around
- Here are initial thoughts
- We can access frames from the scree
- We can mimic key-presses(sendkeys,pyautogui and probably many other options)
- This is enough for rudimentary tasks, but what about for deep learning?
- The only extra thing we might want is something that can also log various events from the game world
- Since most games are played almost completely visually, we can handle for that, and we can also track mouse position and key presses, allowing us to engage in deep learning
- Main concern is processing everything fast enough
- So this is quite a large project
- The initial goals are
- Access the game screen at a somewhat decent FPS Anything over 5 should be workable for us, unpleasant to watch, but workable,and we can always watch the actual live game, rather than the processing frames
- Send keyboard input to game screen.
- Try some form of joystick input if possible(especially considering throttle and turning)
- Simple self-driving car that stays in some lanes under simple conditions(High sun,clear day, no rain, no traffic…)
So step 1, how should we actually accesses our screen?
- refer to this implementation stackoverflow impl, it just appears to have a typo on the import, ImageGrab is part of PIL
- ImageGrab is only availeble for Windows or MacPython: Using Pyscreenshot image to get RGB values (Linux)
- This gives 12 ~ 13 FPS
The next thing we want to do is to run OpenCV on the captured screen data
We’ll convert the image to grayscale to simplify things and edge detection to eventually be used for finding the lines that will be our lanes
let’s add some grayscale and edges
pyautogui, Control the keyboard and mouse from a Python script
- But Some games want “Direct Input” instead of pyautogui sendkeys
- Window direct key input examples
We get a full list of direct x scan codes here: direct x scan codes
We’re interesting in W, A, S, and D for now:
W = 0x11
A = 0x1E
S = 0x1F
D = 0x20
Region of Interest for finding lanes
- We’re back on the task of trying to do some self-driving
- In order to do this, a common goal is to be able to detect lanes
Hough Lines
- HoughlinesP algorithm
- draw lines on the image
Gta 5 Max M1 Carbine
- use GausssinaBlur
Finding Lanes for self-driving car
- find the edges, selected a region of interest, and then finally have found lines
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