From bc2247dd720c1a10b261d2876c060da3bcd00131 Mon Sep 17 00:00:00 2001 From: Qfc9 Date: Sat, 16 Apr 2022 13:29:40 -0400 Subject: [PATCH] Squashed commit of the following: commit 10957c41487023dc0b996a100b9ca11024ac7ea6 Merge: 3e54f7b edce98a Author: Elijah Harmon Date: Thu Apr 14 16:45:07 2022 -0400 Merge pull request #7 from wrp5031/feature/wade Faster analysis and softening of the aiming jitters commit edce98a36982f83461c4569f4033401ea9a2c546 Merge: 05c9b17 3e54f7b Author: Elijah Harmon Date: Thu Apr 14 16:44:21 2022 -0400 Merge branch 'main' into feature/wade commit 05c9b17ca50cbd1bdfe5a8d5a283d1b2c32ae4d3 Author: wade Date: Thu Apr 14 16:39:26 2022 -0400 Screen capture area is based around the center of the screen. Added headshot mode. If multiple people, there is logic to take the person that had a coordinate closest to the last recorded coordinate. commit 3e54f7ba975b4691fed2f9f61b163fed0f7d54df Author: Elijah Harmon Date: Sun Apr 10 00:02:39 2022 -0400 Create requirements.txt commit f1fa560e56ac6ed92e645b0b4c78dac08861459f Author: Elijah Harmon Date: Sat Apr 2 19:54:37 2022 -0400 Changed readme title commit a84ac9a238d47518bb45d64e27354f3fe65073ec Author: TazMatic <31835653+TazMatic@users.noreply.github.com> Date: Thu Mar 31 16:52:02 2022 -0400 Fix win32api and yaml package names commit 8e32d8bd309c9e6de926213499d766bdf3d10fc8 Author: Elijah Harmon Date: Tue Mar 15 16:54:36 2022 -0400 Update about pressing Q commit 3dc6835a9b33d7d27e9878b72550416b86fa2406 Author: Elijah Harmon Date: Tue Mar 15 16:48:20 2022 -0400 Update Readme commit ae24cc3f496e2a9c2810f2876c262c3bef46df09 Merge: 21d431d 42954e0 Author: Elijah Harmon Date: Tue Mar 15 16:42:44 2022 -0400 Merge pull request #3 from RootKit-Org/dev Now using YOLO --- README.md | 15 ++---------- main.py | 60 +++++++++++++++++++++++++++++++++++++----------- requirements.txt | 12 ++++++++++ 3 files changed, 60 insertions(+), 27 deletions(-) create mode 100644 requirements.txt diff --git a/README.md b/README.md index a4dc089..3328bc7 100644 --- a/README.md +++ b/README.md @@ -34,20 +34,9 @@ ANYTHING dealing with Machine Learning can be funky with your computer. So if yo 4. To install `PyTorch` go to this website, https://pytorch.org/get-started/locally/, and Select the stable build, your OS, Pip, Python and CUDA 11.3. Then select the text that is generated and run that command. -6. Copy and past the commands below into your terminal. This will install the Open Source packages needed to run the program. +6. Copy and past the command below into your terminal. This will install the Open Source packages needed to run the program. ``` -pip install PyAutoGUI -pip install PyDirectInput -pip install Pillow -pip install opencv-python -pip install mss -pip install numpy -pip install pandas -pip install win32api -pip install yaml -pip install tqdm -pip install matplotlib -pip install seaborn +pip install -r requirements.txt ``` ### Run diff --git a/main.py b/main.py index ce845fe..c489e8f 100644 --- a/main.py +++ b/main.py @@ -1,3 +1,4 @@ +from unittest import result import torch import pyautogui @@ -14,16 +15,20 @@ def main(): # Window title to go after and the height of the screenshots videoGameWindowTitle = "Counter" - screenShotHeight = 500 + # Portion of screen to be captured (This forms a square/rectangle around the center of screen) + screenShotHeight = 320 + screenShotWidth = 320 # How big the Autoaim box should be around the center of the screen - aaDetectionBox = 300 + aaDetectionBox = 320 # Autoaim speed - aaMovementAmp = 2 + aaMovementAmp = 1.1 - # 0 will point center mass, 40 will point around the head in CSGO - aaAimExtraVertical = 40 + # Person Class Confidence + confidence = 0.5 + + headshot_mode = True # Set to True if you want to get the visuals visuals = False @@ -41,7 +46,10 @@ def main(): videoGameWindow.activate() # Setting up the screen shots - sctArea = {"mon": 1, "top": videoGameWindow.top + round((videoGameWindow.height - screenShotHeight) / 2), "left": videoGameWindow.left, "width": videoGameWindow.width, "height": screenShotHeight} + sctArea = {"mon": 1, "top": videoGameWindow.top + (videoGameWindow.height - screenShotHeight) // 2, + "left": ((videoGameWindow.left + videoGameWindow.right) // 2) - (screenShotWidth // 2), + "width": screenShotWidth, + "height": screenShotHeight} #! Uncomment if you want to view the entire screen # sctArea = {"mon": 1, "top": 0, "left": 0, "width": 1920, "height": 1080} @@ -58,21 +66,24 @@ def main(): sTime = time.time() # Loading Yolo5 Small AI Model - model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True) - + model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True, force_reload=True) + model.classes = [0] + # Used for colors drawn on bounding boxes COLORS = np.random.uniform(0, 255, size=(1500, 3)) # Main loop Quit if Q is pressed + last_mid_coord = None + aimbot=False while win32api.GetAsyncKeyState(ord('Q')) == 0: # Getting screenshop, making into np.array and dropping alpha dimention. npImg = np.delete(np.array(sct.grab(sctArea)), 3, axis=2) # Detecting all the objects - results = model(npImg).pandas().xyxy[0] + results = model(npImg, size=320).pandas().xyxy[0] # Filtering out everything that isn't a person - filteredResults = results[results['class']==0] + filteredResults = results[(results['class']==0) & (results['confidence']>confidence)] # Returns an array of trues/falses depending if it is in the center Autoaim box or not cResults = ((filteredResults["xmin"] > cWidth - aaDetectionBox) & (filteredResults["xmax"] < cWidth + aaDetectionBox)) & \ @@ -83,15 +94,36 @@ def main(): # If there are people in the center bounding box if len(targets) > 0: - # All logic is just done on the random person that shows up first in the list + targets['current_mid_x'] = (targets['xmax'] + targets['xmin']) // 2 + targets['current_mid_y'] = (targets['ymax'] + targets['ymin']) // 2 + # Get the last persons mid coordinate if it exists + if last_mid_coord: + targets['last_mid_x'] = last_mid_coord[0] + targets['last_mid_y'] = last_mid_coord[1] + # Take distance between current person mid coordinate and last person mid coordinate + targets['dist'] = np.linalg.norm(targets.iloc[:, [7,8]].values - targets.iloc[:, [9,10]], axis=1) + targets.sort_values(by="dist", ascending=False) + + # Take the first person that shows up in the dataframe (Recall that we sort based on Euclidean distance) xMid = round((targets.iloc[0].xmax + targets.iloc[0].xmin) / 2) yMid = round((targets.iloc[0].ymax + targets.iloc[0].ymin) / 2) - mouseMove = [xMid - cWidth, yMid - (cHeight + aaAimExtraVertical)] + box_height = targets.iloc[0].ymax - targets.iloc[0].ymin + if headshot_mode: + headshot_offset = box_height * 0.38 + else: + headshot_offset = box_height * 0.2 + mouseMove = [xMid - cWidth, (yMid - headshot_offset) - cHeight] + cv2.circle(npImg, (int(mouseMove[0] + xMid), int(mouseMove[1] + yMid - headshot_offset)), 3, (0, 0, 255)) # Moving the mouse - win32api.mouse_event(win32con.MOUSEEVENTF_MOVE, round(mouseMove[0] * aaMovementAmp), round(mouseMove[1] * aaMovementAmp), 0, 0) - + if win32api.GetKeyState(0x14): + win32api.mouse_event(win32con.MOUSEEVENTF_MOVE, int(mouseMove[0] * aaMovementAmp), int(mouseMove[1] * aaMovementAmp), 0, 0) + last_mid_coord = [xMid, yMid] + + else: + last_mid_coord = None + # See what the bot sees if visuals: # Loops over every item identified and draws a bounding box diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..a813336 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,12 @@ +PyAutoGUI +PyDirectInput +Pillow +opencv-python +mss +numpy +pandas +pywin32 +pyyaml +tqdm +matplotlib +seaborn