Discussion Neural Network Python hack need assistance

[tel1014]

1. So this was on another site and I was trying to set this up. The OP said that the aimbot kept locking onto his character and he couldnt fix it. I decided to just remove the aimbot all together and only use the glow and i was in the middle of installing everything when i realized that 1. import cv2 is a library which is exclusive to python 2.7 and 2. the code is in python 2.7 format(you can tell by the fact that there is no parenthesis around the variables). Now i would just go and install tensorflow, however tensorflow is no longer supported for python 2.7 and is only python 3.5 and 3.6. If anyone can either find a version of tensorflow for python 2.7, or cv2 for python 3.5(in which case i will revise the code to be updated to python 3) then this will be a undetectable cheat as all it does is record the frame screens and find people

also this is not 100% accurate so any improvements to the code will be extremely helpful.
Happy hacking!

1. # USAGE
2. # python real_time_object_detection.py --prototxt MobileNetSSD_deploy.prototxt.txt --model MobileNetSSD_deploy.caffemodel
3. # import the necessary packages
4. import win32api
5. import keyboard as keyboard
6. import pygame as pygame
7. import pythoncom
8. import win32con
9. from PIL import ImageGrab
10. from imutils.video import VideoStream
11. from imutils.video import FPS
12. import numpy as np
13. import argparse
14. import imutils
15. import time
16. import cv2
17. import pyautogui
18. # construct the argument parse and parse the arguments
19. from keyboard._mouse_event import RIGHT
20. ap = argparse.ArgumentParser()
21. ap.add_argument("-p", "--prototxt", required=False,
22. help="path to Caffe 'deploy' prototxt file")
23. ap.add_argument("-m", "--model", required=False,
24. help="path to Caffe pre-trained model")
25. ap.add_argument("-c", "--confidence", type=float, default=0.6,
26. help="minimum probability to filter weak detections")
27. args = vars(ap.parse_args())
28. prott1 = 'C:\Users\Saehi\PycharmProjects/testing\MobileNetSSD_deploy.prototxt.txt'
29. prott2 = 'C:\Users\Saehi\PycharmProjects/testing\MobileNetSSD_deploy.caffemodel'
30. # initialize the list of class labels MobileNet SSD was trained to
31. # detect, then generate a set of bounding box colors for each class
32. CLASSES = ["background", "aeroplane", "bicycle", "bird", "boat",
33. "bottle", "bus", "car", "cat", "chair", "cow", "diningtable",
34. "dog", "horse", "motorbike", "person", "pottedplant", "sheep",
35. "sofa", "train", "tvmonitor"]
36. COLORS = np.random.uniform(0, 255, size=(len(CLASSES), 3))
37. # load our serialized model from disk
38. print("[INFO] loading model...")
39. net = cv2.dnn.readNetFromCaffe(prott1, prott2)
40. # initialize the video stream, allow the cammera sensor to warmup,
41. # and initialize the FPS counter
42. print("[INFO] starting video stream...")
43. #vs = VideoStream(src=0).start()
44. #time.sleep(2.0)
45. #fps = FPS().start()
46. # loop over the frames from the video stream
47. HSX = 100;
48. LSX = 1000;
49. HSY = 100;
50. LSY = 1000;
51. HEX = 100;
52. LEX = 1000;
53. HEY = 100;
54. LEY = 1000;
55. while True:
56. # grab the frame from the threaded video stream and resize it
57. # to have a maximum width of 400 pixels
58. frame = np.array(ImageGrab.grab(bbox=(0, 40, 1820, 1240)))
59. # frame = imutils.resize(frame, width=400)
60. # grab the frame dimensions and convert it to a blob
61. (h, w) = frame.shape[:2]
62. blob = cv2.dnn.blobFromImage(cv2.resize(frame, (300, 300)),
63. 0.007843, (300, 300), 127.5)
64. # pass the blob through the network and obtain the detections and
65. # predictions
66. net.setInput(blob)
67. detections = net.forward()
68. # loop over the detections
69. for i in np.arange(0, detections.shape[2]):
70. # extract the confidence (i.e., probability) associated with
71. # the prediction
72. confidence = detections[0, 0, i, 2]
73. # filter out weak detections by ensuring the `confidence` is
74. # greater than the minimum confidence
75. if confidence > args["confidence"]:
76. # extract the index of the class label from the
77. # `detections`, then compute the (x, y)-coordinates of
78. # the bounding box for the object
79. idx = int(detections[0, 0, i, 1])
80. box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
81. (startX, startY, endX, endY) = box.astype("int")
82. # draw the prediction on the frame
83. label = "{}: {:.2f}%".format(CLASSES[idx],
84. confidence * 100)
85. cv2.rectangle(frame, (startX, startY), (endX, endY),
86. COLORS[idx], 2)
87. y = startY - 15 if startY - 15 > 15 else startY + 15
88. cv2.putText(frame, label, (startX, y),
89. cv2.FONT_HERSHEY_SIMPLEX, 0.5, COLORS[idx], 2)
90. if 'person' in label:
91. pygame.init()
92. pygame.event.get()
93. if pygame.mouse.get_pressed():
94. print 'pressing'
95. #tried to detect my character's offset and add the best way to exclude it, failed most tests.
96. if startX > 369 & startX < 1402 & startY > -1 & startY < 725 & endX > 339 & endX < 1805 & endY > 806 & endY < 1017:
97. print 'found myself'
98. else:
99. #print 'found somebody else'
100. nosum = int(round(startX * 1)) + int(round(startX * 0.06))
101. nosum2 = int(round(y * 1)) + int(round(y * 0.7))
102. halfX = (endX - startX) / 2
103. halfY = (endY - startY) / 2
104. finalX = startX + halfX
105. finalY = startY + halfY
106. pyautogui.moveTo(finalX, finalY)
107. #win32api.SetCursorPos((finalX, finalY))
108. win32api.mouse_event(win32con.MOUSEEVENTF_LEFTDOWN, finalX, finalY, 0, 0)
109. win32api.mouse_event(win32con.MOUSEEVENTF_LEFTUP, finalX, finalY, 0, 0)
110. #print 'Pressed L'
111. if 'HSX' not in locals():
112. HSX = startX
113. if 'LSX' not in locals():
114. LSX = startX
115. if 'HSY' not in locals():
116. HSY = startY
117. if 'LSY' not in locals():
118. LSY = startY
119. if 'HEX' not in locals():
120. HEX = endX
121. if 'LEX' not in locals():
122. LEX = endX
123. if 'HEY' not in locals():
124. HEY = endY
125. if 'LEY' not in locals():
126. LEY = endY
127. if startX > HSX:
128. HSX = startX
129. if startX < LSX:
130. LSX = startX
131. if startY > HSY:
132. HSY = startY
133. if startY < LSY:
134. LSY = startY
135. if endX > HEX:
136. HEX = endX
137. if endX < LEX:
138. LEX = endX
139. if endY > HEY:
140. HEY = endY
141. if endY < LEY:
142. LEY = endY
143. print 'HStartX: ' + str(HSX)
144. print 'LStartX: ' + str(LSX)
145. print 'HStartY: ' + str(HSY)
146. print 'LStartY: ' + str(LSY)
147. print 'HendX: ' + str(HEX)
148. print 'LendX: ' + str(LEX)
149. print 'HendY: ' + str(HEY)
150. print 'LendY: ' + str(LEY)
151. #print args["confidence"]
152. # click(10,10)
153. # show the output frame
154. cv2.imshow("Frame", frame)
155. key = cv2.waitKey(1) & 0xFF
156. # if the `q` key was pressed, break from the loop
157. if key == ord("q"):
158. break
159. # update the FPS counter
160. # stop the timer and display FPS information
161. # do a bit of cleanup
162. cv2.destroyAllWindows()

 

[tel1014]

Also one more thing, you would set this up by installing python with tensorflow (most likely using pip) then install these libraries

1. import win32api
2. import keyboard as keyboard
3. import pygame as pygame
4. import pythoncom
5. import win32con
6. from PIL import ImageGrab
7. from imutils.video import VideoStream
8. from imutils.video import FPS
9. import numpy as np
10. import argparse
11. import imutils
12. import time(not this one its default library)
13. import cv2
14. import pyautogui
15. and in lines 33 and 34 add r before the string as it needs to be raw input.

 

[CabCon]

Could you please add a short preview of the hack? In kind of a video.

 

[candy]

Could you please add a short preview of the hack? In kind of a video.

This is just a spam / joke thread.

 

[tel1014]

Could you please add a short preview of the hack? In kind of a video.

Sorry i dont have a video because i havent gotten it working yet. Thats why i need help with just setting it up as the libraries arent supported on python2.7 anymore (specifically tensorflow)