Squashed commit of the following:

commit 10957c41487023dc0b996a100b9ca11024ac7ea6
Merge: 3e54f7b edce98a
Author: Elijah Harmon <elijahharmon@gmail.com>
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 <elijahharmon@gmail.com>
Date:   Thu Apr 14 16:44:21 2022 -0400

    Merge branch 'main' into feature/wade

commit 05c9b17ca50cbd1bdfe5a8d5a283d1b2c32ae4d3
Author: wade <wpines@clarityinnovates.com>
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 <elijahharmon@gmail.com>
Date:   Sun Apr 10 00:02:39 2022 -0400

    Create requirements.txt

commit f1fa560e56ac6ed92e645b0b4c78dac08861459f
Author: Elijah Harmon <elijahharmon@gmail.com>
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 <elijahharmon@gmail.com>
Date:   Tue Mar 15 16:54:36 2022 -0400

    Update about pressing Q

commit 3dc6835a9b33d7d27e9878b72550416b86fa2406
Author: Elijah Harmon <elijahharmon@gmail.com>
Date:   Tue Mar 15 16:48:20 2022 -0400

    Update Readme

commit ae24cc3f496e2a9c2810f2876c262c3bef46df09
Merge: 21d431d 42954e0
Author: Elijah Harmon <elijahharmon@gmail.com>
Date:   Tue Mar 15 16:42:44 2022 -0400

    Merge pull request #3 from RootKit-Org/dev

    Now using YOLO

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 -r requirements.txt
-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
 ```
 
 ### Run

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
+    # Person Class Confidence
-    aaAimExtraVertical = 40
+    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)) & \
@@ -83,14 +94,35 @@ 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:

requirements.txt

@@ -0,0 +1,12 @@
+PyAutoGUI
+PyDirectInput
+Pillow
+opencv-python
+mss
+numpy
+pandas
+pywin32
+pyyaml
+tqdm
+matplotlib
+seaborn