From e51fda35ebdee6619542d2bf6f90068acdb89409 Mon Sep 17 00:00:00 2001 From: afy <73753274+afyzone@users.noreply.github.com> Date: Sun, 10 Dec 2023 21:19:33 -0800 Subject: [PATCH] Create afy_raspberry_pi_pico_w_tensorrt.py (#138) * Create afy_raspberry_pi_pico_w_tensorrt.py * Update afy_raspberry_pi_pico_w_tensorrt.py --- .../afy_raspberry_pi_pico_w_tensorrt.py | 188 ++++++++++++++++++ 1 file changed, 188 insertions(+) create mode 100644 customScripts/afyScripts/afy_raspberry_pi_pico_w_tensorrt.py diff --git a/customScripts/afyScripts/afy_raspberry_pi_pico_w_tensorrt.py b/customScripts/afyScripts/afy_raspberry_pi_pico_w_tensorrt.py new file mode 100644 index 0000000..e126915 --- /dev/null +++ b/customScripts/afyScripts/afy_raspberry_pi_pico_w_tensorrt.py @@ -0,0 +1,188 @@ +from unittest import result +import torch +import numpy as np +import cv2 +import time +import win32api +import win32con +import pandas as pd +from utils.general import (cv2, non_max_suppression, xyxy2xywh) +from models.common import DetectMultiBackend +import cupy as cp +import socket + +ip = '' # raspberry board ip +port = 50123 # raspberry port +client = socket.socket(socket.AF_INET, socket.SOCK_STREAM) +print(f'Connecting to {ip}:{port}...') +try: + client.connect((ip, port)) +except TimeoutError as e: + print(f'ERROR: Could not connect. {e}') + client.close() + exit(1) + +def moveafy(x, y): + x = int(np.floor(x)) + y = int(np.floor(y)) + + if x != 0 or y != 0: + command = (f'M{x},{y}\r') + client.sendall(command.encode()) + get_response() + +def get_response(): + return f'Socket: {client.recv(4).decode()}' + +# Could be do with +# from config import * +# But we are writing it out for clarity for new devs +from config import aaMovementAmp, useMask, maskHeight, maskWidth, aaQuitKey, confidence, headshot_mode, cpsDisplay, visuals, centerOfScreen +import gameSelection + +def main(): + # External Function for running the game selection menu (gameSelection.py) + camera, cWidth, cHeight = gameSelection.gameSelection() + + # Used for forcing garbage collection + count = 0 + sTime = time.time() + + # Loading Yolo5 Small AI Model + model = DetectMultiBackend('afyfort.engine', device=torch.device('cuda'), dnn=False, data='', fp16=True) + stride, names, pt = model.stride, model.names, model.pt + + # 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 + with torch.no_grad(): + while win32api.GetAsyncKeyState(ord(aaQuitKey)) == 0: + + npImg = cp.array([camera.get_latest_frame()]) + if npImg.shape[3] == 4: + # If the image has an alpha channel, remove it + npImg = npImg[:, :, :, :3] + + if useMask: + npImg[:, -maskHeight:, :maskWidth, :] = 0 + + im = npImg / 255 + im = im.astype(cp.half) + + im = cp.moveaxis(im, 3, 1) + im = torch.from_numpy(cp.asnumpy(im)).to('cuda') + + # Detecting all the objects + results = model(im) + + pred = non_max_suppression( + results, confidence, confidence, 0, False, max_det=10) + + targets = [] + for i, det in enumerate(pred): + s = "" + gn = torch.tensor(im.shape)[[0, 0, 0, 0]] + if len(det): + for c in det[:, -1].unique(): + n = (det[:, -1] == c).sum() # detections per class + s += f"{n} {names[int(c)]}, " # add to string + + for *xyxy, conf, cls in reversed(det): + targets.append((xyxy2xywh(torch.tensor(xyxy).view( + 1, 4)) / gn).view(-1).tolist() + [float(conf)]) # normalized xywh + + targets = pd.DataFrame( + targets, columns=['current_mid_x', 'current_mid_y', 'width', "height", "confidence"]) + + center_screen = [cWidth, cHeight] + + # If there are people in the center bounding box + if len(targets) > 0: + if (centerOfScreen): + # Compute the distance from the center + targets["dist_from_center"] = np.sqrt((targets.current_mid_x - center_screen[0])**2 + (targets.current_mid_y - center_screen[1])**2) + + # Sort the data frame by distance from center + targets = targets.sort_values("dist_from_center") + + # 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[:, [0, 1]].values - targets.iloc[:, [4, 5]], 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 = targets.iloc[0].current_mid_x + yMid = targets.iloc[0].current_mid_y + + box_height = targets.iloc[0].height + if headshot_mode: + headshot_offset = box_height * 0.38 + else: + headshot_offset = box_height * 0.2 + + mouseMove = [xMid - cWidth, (yMid - headshot_offset) - cHeight] + + # Moving the mouse + if win32api.GetAsyncKeyState(0x02) < 0: + # win32api.mouse_event(win32con.MOUSEEVENTF_MOVE, int(mouseMove[0] * aaMovementAmp), int(mouseMove[1] * aaMovementAmp), 0, 0) + moveafy(int(mouseMove[0] * aaMovementAmp), int(mouseMove[1] * aaMovementAmp)) + last_mid_coord = [xMid, yMid] + + else: + last_mid_coord = None + + # See what the bot sees + if visuals: + npImg = cp.asnumpy(npImg[0]) + # Loops over every item identified and draws a bounding box + for i in range(0, len(targets)): + halfW = round(targets["width"][i] / 2) + halfH = round(targets["height"][i] / 2) + midX = targets['current_mid_x'][i] + midY = targets['current_mid_y'][i] + (startX, startY, endX, endY) = int( + midX + halfW), int(midY + halfH), int(midX - halfW), int(midY - halfH) + + idx = 0 + # draw the bounding box and label on the frame + label = "{}: {:.2f}%".format( + "Human", targets["confidence"][i] * 100) + cv2.rectangle(npImg, (startX, startY), (endX, endY), + COLORS[idx], 2) + y = startY - 15 if startY - 15 > 15 else startY + 15 + cv2.putText(npImg, label, (startX, y), + cv2.FONT_HERSHEY_SIMPLEX, 0.5, COLORS[idx], 2) + + # Forced garbage cleanup every second + count += 1 + if (time.time() - sTime) > 1: + if cpsDisplay: + print("CPS: {}".format(count)) + count = 0 + sTime = time.time() + + # Uncomment if you keep running into memory issues + # gc.collect(generation=0) + + # See visually what the Aimbot sees + if visuals: + cv2.imshow('Live Feed', npImg) + if (cv2.waitKey(1) & 0xFF) == ord('q'): + exit() + camera.stop() + + +if __name__ == "__main__": + try: + main() + except Exception as e: + import traceback + traceback.print_exception(e) + print(str(e)) + print("Ask @Wonder for help in our Discord in the #ai-aimbot channel ONLY: https://discord.gg/rootkitorg")