commit 1df2271308133846f9803e670d1c94ca32452112
Author: monoKim <mingoooose@gmail.com>
Date:   Mon Nov 2 19:59:37 2020 +0100

    first commit

diff --git a/aimbot.py b/aimbot.py
new file mode 100644
index 0000000..a3d4af8
--- /dev/null
+++ b/aimbot.py
@@ -0,0 +1,73 @@
+import tensorflow as tf
+import tensorflow_hub as hub
+import numpy as np
+import pyautogui
+import win32api, win32con, win32gui
+import cv2
+import math
+import time
+
+detector = hub.load("https://tfhub.dev/tensorflow/centernet/resnet50v1_fpn_512x512/1")
+size_scale = 3
+
+while True:
+    # Get rect of Window
+    hwnd = win32gui.FindWindow(None, 'Counter-Strike: Global Offensive')
+    rect = win32gui.GetWindowRect(hwnd)
+    region = rect[0], rect[1], rect[2] - rect[0], rect[3] - rect[1]
+
+    # Get image of screen
+    image = np.array(pyautogui.screenshot(region=region))
+    image = cv2.resize(image, (image.shape[1] // size_scale, image.shape[0] // size_scale))
+    image = np.expand_dims(image, 0)
+    img_w, img_h = image.shape[2], image.shape[1]
+
+    # Detection
+    result = detector(image)
+    result = {key:value.numpy() for key,value in result.items()}
+    boxes = result['detection_boxes'][0]
+    scores = result['detection_scores'][0]
+    classes = result['detection_classes'][0]
+
+    # Check every detected object
+    detected_boxes = []
+    for i, box in enumerate(boxes):
+        # Choose only person(class:1)
+        if classes[i] == 1 and scores[i] >= 0.5:
+            ymin, xmin, ymax, xmax = tuple(box)
+            if ymin > 0.5 and ymax > 0.8:
+                continue
+            left, right, top, bottom = int(xmin * img_w), int(xmax * img_w), int(ymin * img_h), int(ymax * img_h)
+            detected_boxes.append((left, right, top, bottom))
+
+    print("Detected:", len(detected_boxes))
+
+    # Check Closest
+    if len(detected_boxes) >= 1:
+        min = 99999
+        at = 0
+        centers = []
+        for i, box in enumerate(detected_boxes):
+            x1, x2, y1, y2 = box
+            c_x = ((x2 - x1) / 2) + x1
+            c_y = ((y2 - y1) / 2) + y1
+            centers.append((c_x, c_y))
+            dist = math.sqrt(math.pow(img_w/2 - c_x, 2) + math.pow(img_h/2 - c_y, 2))
+            if dist < min:
+                min = dist
+                at = i
+
+        x = centers[at][0] - img_w/2
+        y = centers[at][1] - img_h/2 - (detected_boxes[at][3] - detected_boxes[at][2]) * 0.45
+
+        # Move mouse and shoot
+        scale = 1.7 * size_scale
+        x = int(x * scale)
+        y = int(y * scale)
+        win32api.mouse_event(win32con.MOUSEEVENTF_MOVE, x, y, 0, 0)
+        time.sleep(0.05)
+        win32api.mouse_event(win32con.MOUSEEVENTF_LEFTDOWN, x, y, 0, 0)
+        time.sleep(0.1)
+        win32api.mouse_event(win32con.MOUSEEVENTF_LEFTUP, x, y, 0, 0)
+
+    time.sleep(0.1)