Release Fortnite Neural Network Python Hack +Tutorial How to install?

Discussion in 'Fortnite Cheats, Hacks and Mods' started by tel1014, Jul 8, 2018.

  1. tel1014

    tel1014 New Member

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    Warning: It does not always detect every player.

    How to setup the hack?
    So to set this up you first need to install python 2.7.14 or above, and a editor of your choice. I am using the Atom editor.
    Please login or register to view links or downloads!
    Please login or register to view links or downloads!
    install the .exe file and once it is done, run it and setup python. Next find where you put your python folder and type env into the search bar at the bottom left. do edit enviornment variable for your account and go to path in the top half. select it and press edit. Make two new paths and put these two in each, or whatever your pythons path is.
    C:\Python27\Scripts
    C:\Python27\

    the first one is to be able to use pip anywhere and the second is to use python anywhere through commandline. next install whatever text editor you are using and just leave it for now. Next go to your command prompt and put in the following commands 1 at a time.
    pip install pywin32
    pip install keyboard
    pip install pygame
    pip install pyscreenshot
    pip install imutils
    pip install numpy
    pip install argparse
    pip install opencv-python
    pip install pyautogui

    these are to install the dependancys that the python script needs to run.
    now that this is done open up your text editor and make a new .py file. You can call it whatever you want, and it should look like this: hack.py. if it looks like hack.py.txt dont worry, just rename it and you will be fine. once you have the document made copy paste the code in and edit it however you want. This is how im using it:
    Code:

    Code:
    # USAGE
    # python real_time_object_detection.py --prototxt MobileNetSSD_deploy.prototxt.txt --model MobileNetSSD_deploy.caffemodel
    # import the necessary packages
    import win32api
    import keyboard as keyboard
    import pygame as pygame
    import pythoncom
    import win32con
    from PIL import ImageGrab
    from imutils.video import VideoStream
    from imutils.video import FPS
    import numpy as np
    import argparse
    import imutils
    import time
    import cv2
    import pyautogui
    # construct the argument parse and parse the arguments
    from keyboard._mouse_event import RIGHT
    ap = argparse.ArgumentParser()
    ap.add_argument("-p", "--prototxt", required=False,
                    help="path to Caffe 'deploy' prototxt file")
    ap.add_argument("-m", "--model", required=False,
                    help="path to Caffe pre-trained model")
    ap.add_argument("-c", "--confidence", type=float, default=0.6,
                    help="minimum probability to filter weak detections")
    args = vars(ap.parse_args())
    prott1 = r'C:\Users\ianyy\Documents\MobileNetSSD_deploy.prototxt.txt'
    prott2 = r'C:\Users\ianyy\Documents\MobileNetSSD_deploy.caffemodel'
    # initialize the list of class labels MobileNet SSD was trained to
    # detect, then generate a set of bounding box colors for each class
    CLASSES = ["background", "aeroplane", "bicycle", "bird", "boat",
               "bottle", "bus", "car", "cat", "chair", "cow", "diningtable",
               "dog", "horse", "motorbike", "person", "pottedplant", "sheep",
               "sofa", "train", "tvmonitor"]
    COLORS = np.random.uniform(0, 255, size=(len(CLASSES), 3))
    # load our serialized model from disk
    print("[INFO] loading model...")
    net = cv2.dnn.readNetFromCaffe(prott1, prott2)
    # initialize the video stream, allow the cammera sensor to warmup,
    # and initialize the FPS counter
    print("[INFO] starting video stream...")
    #vs = VideoStream(src=0).start()
    #time.sleep(2.0)
    #fps = FPS().start()
    # loop over the frames from the video stream
    HSX = 100;
    LSX = 1000;
    HSY = 100;
    LSY = 1000;
    HEX = 100;
    LEX = 1000;
    HEY = 100;
    LEY = 1000;
    while True:
        # grab the frame from the threaded video stream and resize it
        # to have a maximum width of 400 pixels
        frame = np.array(ImageGrab.grab(bbox=(0, 40, 1820, 1240)))
        # frame = imutils.resize(frame, width=400)
        # grab the frame dimensions and convert it to a blob
        (h, w) = frame.shape[:2]
        blob = cv2.dnn.blobFromImage(cv2.resize(frame, (300, 300)),
                                     0.007843, (300, 300), 127.5)
        # pass the blob through the network and obtain the detections and
        # predictions
        net.setInput(blob)
        detections = net.forward()
        # loop over the detections
        for i in np.arange(0, detections.shape[2]):
            # extract the confidence (i.e., probability) associated with
            # the prediction
            confidence = detections[0, 0, i, 2]
            # filter out weak detections by ensuring the `confidence` is
            # greater than the minimum confidence
            if confidence > args["confidence"]:
                # extract the index of the class label from the
                # `detections`, then compute the (x, y)-coordinates of
                # the bounding box for the object
                idx = int(detections[0, 0, i, 1])
                box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
                (startX, startY, endX, endY) = box.astype("int")
                # draw the prediction on the frame
                label = "{}: {:.2f}%".format(CLASSES[idx],
                                             confidence * 100)
                cv2.rectangle(frame, (startX, startY), (endX, endY),
                              COLORS[idx], 2)
                y = startY - 15 if startY - 15 > 15 else startY + 15
                cv2.putText(frame, label, (startX, y),
                            cv2.FONT_HERSHEY_SIMPLEX, 0.5, COLORS[idx], 2)
               if 'person' in label:
                    pygame.init()
                    pygame.event.get()
                    if pygame.mouse.get_pressed():
                        print 'pressing'
                        #tried to detect my character's offset and add the best way to exclude it, failed most tests.
                        if startX > 369 & startX < 1402 & startY > -1 & startY < 725 & endX > 339 & endX < 1805 & endY > 806 & endY < 1017:
                            print 'found myself'
                        else:
                            #print 'found somebody else'
                            nosum = int(round(startX * 1)) + int(round(startX * 0.06))
                            nosum2 = int(round(y * 1)) + int(round(y * 0.7))
                            halfX = (endX - startX) / 2
                            halfY = (endY - startY) / 2
                            finalX = startX + halfX
                            finalY = startY + halfY
                        #    pyautogui.moveTo(finalX, finalY)
                            #win32api.SetCursorPos((finalX, finalY))
                        #    win32api.mouse_event(win32con.MOUSEEVENTF_LEFTDOWN, finalX, finalY, 0, 0)
                        #    win32api.mouse_event(win32con.MOUSEEVENTF_LEFTUP, finalX, finalY, 0, 0)
                            #print 'Pressed L'
                        if 'HSX' not in locals():
                            HSX = startX
                        if 'LSX' not in locals():
                            LSX = startX
                        if 'HSY' not in locals():
                            HSY = startY
                        if 'LSY' not in locals():
                            LSY = startY
                        if 'HEX' not in locals():
                            HEX = endX
                        if 'LEX' not in locals():
                            LEX = endX
                        if 'HEY' not in locals():
                            HEY = endY
                        if 'LEY' not in locals():
                            LEY = endY
                        if startX > HSX:
                            HSX = startX
                        if startX < LSX:
                            LSX = startX
                        if startY > HSY:
                            HSY = startY
                        if startY < LSY:
                            LSY = startY
                        if endX > HEX:
                            HEX = endX
                        if endX < LEX:
                            LEX = endX
                        if endY > HEY:
                            HEY = endY
                        if endY < LEY:
                            LEY = endY
                        print 'HStartX: ' + str(HSX)
                        print 'LStartX: ' + str(LSX)
                        print 'HStartY: ' + str(HSY)
                        print 'LStartY: ' + str(LSY)
                        print 'HendX: ' + str(HEX)
                        print 'LendX: ' + str(LEX)
                        print 'HendY: ' + str(HEY)
                        print 'LendY: ' + str(LEY)
                    #print args["confidence"]
    #             click(10,10)
        # show the output frame
        cv2.imshow("Frame", frame)
        key = cv2.waitKey(1) & 0xFF
        # if the `k` key was pressed, break from the loop
        if key == ord("k"):
            break
        # update the FPS counter
    # stop the timer and display FPS information
    # do a bit of cleanup
    cv2.destroyAllWindows()
    
    Once you have gotten your code in make sure to save it and remember what directory it is in. now go to the search bar and open command prompt and it should look something like this : C:\Users\[YOUR USERNAME]> now use cd to navigate to the directory it is in. if it is in C:\Python27 then just do
    cd C:\Python27 and it will put you in the directory and then do
    python hack.py to run the script.

    If you have any questions put them here as a comment.



    Edit: also one more thing, the code i pasted in here simply just comments out the part that moves your mouse to shoot
     
    Last edited by a moderator: Aug 8, 2018
    CabCon likes this.
  2. Marc Swinther

    Marc Swinther Moderator Staff Member

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    Thanks for sharing, needs testing.
    Would you be able to provide some photos or a video. Also explaining what this is for, might be a good idea.
     
    CabCon likes this.
  3. Levieichelberg2000

    Levieichelberg2000 Known Member

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    Please post a video or Image tut, I couldn't follow your text tut.
     
  4. thegaotu

    thegaotu New Member

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    frame = np.array(ImageGrab.grab(bbox=(0, 40, 1820, 1240)))
    is where i get a "indentationError: expected an indented block?
    How would i fix that?
     
  5. CabCon

    CabCon Head Administrator Staff Member Head Staff Team

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    Like @Marc Swinther already mentioned. Can we get any preview of it?
     
  6. CabCon

    CabCon Head Administrator Staff Member Head Staff Team

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    I also found this tutorial, it might help you:
     
  7. osmand

    osmand New Member

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    C:\Python27>hacking.py
    File "C:\Python27\hacking.py", line 90
    if 'person' in label:
    ^
    IndentationError: unindent does not match any outer indentation level

    C:\Python27>
     
  8. xxprodigyxx

    xxprodigyxx New Member

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    when i did it it just saif syntax error so
     
  9. cobrasteel

    cobrasteel New Member

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    Same error here
    if 'person' in label:
    ^
    IndentationError: unindent does not match any outer indentation level
     
  10. Fossil

    Fossil New Member

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    Same here... Please go fix
     

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