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Fortnite Neural Network Python Hack +Tutorial How to install?
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<blockquote data-quote="tel1014" data-source="post: 36817" data-attributes="member: 217129"><p><em>Warning: It does not always detect every player.</em></p><p></p><p><strong>How to setup the hack?</strong></p><p>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.</p><p><a href="https://atom.io/" target="_blank">Atom</a></p><p><a href="https://www.python.org/downloads/release/python-2714/" target="_blank">Python Release Python 2.7.14 | Python.org</a></p><p>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.</p><p>C:\Python27\Scripts</p><p>C:\Python27\</p><p></p><p>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.</p><p>pip install pywin32</p><p>pip install keyboard</p><p>pip install pygame</p><p>pip install pyscreenshot</p><p>pip install imutils</p><p>pip install numpy</p><p>pip install argparse</p><p>pip install opencv-python</p><p>pip install pyautogui</p><p></p><p>these are to install the dependancys that the python script needs to run.</p><p>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:</p><p>Code:</p><p></p><p>[CODE]</p><p># USAGE</p><p># python real_time_object_detection.py --prototxt MobileNetSSD_deploy.prototxt.txt --model MobileNetSSD_deploy.caffemodel</p><p># import the necessary packages</p><p>import win32api</p><p>import keyboard as keyboard</p><p>import pygame as pygame</p><p>import pythoncom</p><p>import win32con</p><p>from PIL import ImageGrab</p><p>from imutils.video import VideoStream</p><p>from imutils.video import FPS</p><p>import numpy as np</p><p>import argparse</p><p>import imutils</p><p>import time</p><p>import cv2</p><p>import pyautogui</p><p># construct the argument parse and parse the arguments</p><p>from keyboard._mouse_event import RIGHT</p><p>ap = argparse.ArgumentParser()</p><p>ap.add_argument("-p", "--prototxt", required=False,</p><p> help="path to Caffe 'deploy' prototxt file")</p><p>ap.add_argument("-m", "--model", required=False,</p><p> help="path to Caffe pre-trained model")</p><p>ap.add_argument("-c", "--confidence", type=float, default=0.6,</p><p> help="minimum probability to filter weak detections")</p><p>args = vars(ap.parse_args())</p><p>prott1 = r'C:\Users\ianyy\Documents\MobileNetSSD_deploy.prototxt.txt'</p><p>prott2 = r'C:\Users\ianyy\Documents\MobileNetSSD_deploy.caffemodel'</p><p># initialize the list of class labels MobileNet SSD was trained to</p><p># detect, then generate a set of bounding box colors for each class</p><p>CLASSES = ["background", "aeroplane", "bicycle", "bird", "boat",</p><p> "bottle", "bus", "car", "cat", "chair", "cow", "diningtable",</p><p> "dog", "horse", "motorbike", "person", "pottedplant", "sheep",</p><p> "sofa", "train", "tvmonitor"]</p><p>COLORS = np.random.uniform(0, 255, size=(len(CLASSES), 3))</p><p># load our serialized model from disk</p><p>print("[INFO] loading model...")</p><p>net = cv2.dnn.readNetFromCaffe(prott1, prott2)</p><p># initialize the video stream, allow the cammera sensor to warmup,</p><p># and initialize the FPS counter</p><p>print("[INFO] starting video stream...")</p><p>#vs = VideoStream(src=0).start()</p><p>#time.sleep(2.0)</p><p>#fps = FPS().start()</p><p># loop over the frames from the video stream</p><p>HSX = 100;</p><p>LSX = 1000;</p><p>HSY = 100;</p><p>LSY = 1000;</p><p>HEX = 100;</p><p>LEX = 1000;</p><p>HEY = 100;</p><p>LEY = 1000;</p><p>while True:</p><p> # grab the frame from the threaded video stream and resize it</p><p> # to have a maximum width of 400 pixels</p><p> frame = np.array(ImageGrab.grab(bbox=(0, 40, 1820, 1240)))</p><p> # frame = imutils.resize(frame, width=400)</p><p> # grab the frame dimensions and convert it to a blob</p><p> (h, w) = frame.shape[:2]</p><p> blob = cv2.dnn.blobFromImage(cv2.resize(frame, (300, 300)),</p><p> 0.007843, (300, 300), 127.5)</p><p> # pass the blob through the network and obtain the detections and</p><p> # predictions</p><p> net.setInput(blob)</p><p> detections = net.forward()</p><p> # loop over the detections</p><p> for i in np.arange(0, detections.shape[2]):</p><p> # extract the confidence (i.e., probability) associated with</p><p> # the prediction</p><p> confidence = detections[0, 0, i, 2]</p><p> # filter out weak detections by ensuring the `confidence` is</p><p> # greater than the minimum confidence</p><p> if confidence > args["confidence"]:</p><p> # extract the index of the class label from the</p><p> # `detections`, then compute the (x, y)-coordinates of</p><p> # the bounding box for the object</p><p> idx = int(detections[0, 0, i, 1])</p><p> box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])</p><p> (startX, startY, endX, endY) = box.astype("int")</p><p> # draw the prediction on the frame</p><p> label = "{}: {:.2f}%".format(CLASSES[idx],</p><p> confidence * 100)</p><p> cv2.rectangle(frame, (startX, startY), (endX, endY),</p><p> COLORS[idx], 2)</p><p> y = startY - 15 if startY - 15 > 15 else startY + 15</p><p> cv2.putText(frame, label, (startX, y),</p><p> cv2.FONT_HERSHEY_SIMPLEX, 0.5, COLORS[idx], 2)</p><p> if 'person' in label:</p><p> pygame.init()</p><p> pygame.event.get()</p><p> if pygame.mouse.get_pressed():</p><p> print 'pressing'</p><p> #tried to detect my character's offset and add the best way to exclude it, failed most tests.</p><p> if startX > 369 & startX < 1402 & startY > -1 & startY < 725 & endX > 339 & endX < 1805 & endY > 806 & endY < 1017:</p><p> print 'found myself'</p><p> else:</p><p> #print 'found somebody else'</p><p> nosum = int(round(startX * 1)) + int(round(startX * 0.06))</p><p> nosum2 = int(round(y * 1)) + int(round(y * 0.7))</p><p> halfX = (endX - startX) / 2</p><p> halfY = (endY - startY) / 2</p><p> finalX = startX + halfX</p><p> finalY = startY + halfY</p><p> # pyautogui.moveTo(finalX, finalY)</p><p> #win32api.SetCursorPos((finalX, finalY))</p><p> # win32api.mouse_event(win32con.MOUSEEVENTF_LEFTDOWN, finalX, finalY, 0, 0)</p><p> # win32api.mouse_event(win32con.MOUSEEVENTF_LEFTUP, finalX, finalY, 0, 0)</p><p> #print 'Pressed L'</p><p> if 'HSX' not in locals():</p><p> HSX = startX</p><p> if 'LSX' not in locals():</p><p> LSX = startX</p><p> if 'HSY' not in locals():</p><p> HSY = startY</p><p> if 'LSY' not in locals():</p><p> LSY = startY</p><p> if 'HEX' not in locals():</p><p> HEX = endX</p><p> if 'LEX' not in locals():</p><p> LEX = endX</p><p> if 'HEY' not in locals():</p><p> HEY = endY</p><p> if 'LEY' not in locals():</p><p> LEY = endY</p><p> if startX > HSX:</p><p> HSX = startX</p><p> if startX < LSX:</p><p> LSX = startX</p><p> if startY > HSY:</p><p> HSY = startY</p><p> if startY < LSY:</p><p> LSY = startY</p><p> if endX > HEX:</p><p> HEX = endX</p><p> if endX < LEX:</p><p> LEX = endX</p><p> if endY > HEY:</p><p> HEY = endY</p><p> if endY < LEY:</p><p> LEY = endY</p><p> print 'HStartX: ' + str(HSX)</p><p> print 'LStartX: ' + str(LSX)</p><p> print 'HStartY: ' + str(HSY)</p><p> print 'LStartY: ' + str(LSY)</p><p> print 'HendX: ' + str(HEX)</p><p> print 'LendX: ' + str(LEX)</p><p> print 'HendY: ' + str(HEY)</p><p> print 'LendY: ' + str(LEY)</p><p> #print args["confidence"]</p><p># click(10,10)</p><p> # show the output frame</p><p> cv2.imshow("Frame", frame)</p><p> key = cv2.waitKey(1) & 0xFF</p><p> # if the `k` key was pressed, break from the loop</p><p> if key == ord("k"):</p><p> break</p><p> # update the FPS counter</p><p># stop the timer and display FPS information</p><p># do a bit of cleanup</p><p>cv2.destroyAllWindows()</p><p>[/CODE]</p><p>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</p><p>cd C:\Python27 and it will put you in the directory and then do</p><p>python hack.py to run the script.</p><p></p><p>If you have <strong>any questions put them here</strong> as a comment.</p><p></p><p></p><p></p><p>Edit: also one more thing, the code i pasted in here simply just comments out the part that moves your mouse to shoot</p></blockquote><p></p>
[QUOTE="tel1014, post: 36817, member: 217129"] [I]Warning: It does not always detect every player.[/I] [B]How to setup the hack?[/B] 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. [URL='https://atom.io/']Atom[/URL] [URL='https://www.python.org/downloads/release/python-2714/']Python Release Python 2.7.14 | Python.org[/URL] 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() [/CODE] 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 [B]any questions put them here[/B] 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 [/QUOTE]
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