import numpy as np
from termcolor import colored
import timeit
import thread
import imutils
import time
import mss
import cv2
import os
import signal
import sys
import pynput
import ctypes
import pyautogui
from lib.grab import grab_screen


import win32gui
import win32api
import win32con
import mouse
import keyboard


sct = mss.mss()
Wd, Hd = sct.monitors[1]["width"], sct.monitors[1]["height"]
SendInput = ctypes.windll.user32.SendInput
PUL = ctypes.POINTER(ctypes.c_ulong)

class KeyBdInput(ctypes.Structure):
    fields = [("wVk", ctypes.c_ushort),
                ("wScan", ctypes.c_ushort),
                ("dwFlags", ctypes.c_ulong),
                ("time", ctypes.c_ulong),
                ("dwExtraInfo", PUL)]
class HardwareInput(ctypes.Structure):
    fields = [("uMsg", ctypes.c_ulong),
                ("wParamL", ctypes.c_short),
                ("wParamH", ctypes.c_ushort)]
class MouseInput(ctypes.Structure):
    fields = [("dx", ctypes.c_long),
                ("dy", ctypes.c_long),
                ("mouseData", ctypes.c_ulong),
                ("dwFlags", ctypes.c_ulong),
                ("time", ctypes.c_ulong),
                ("dwExtraInfo", PUL)]
class Input_I(ctypes.Union):
    fields = [("ki", KeyBdInput),
                ("mi", MouseInput),
                ("hi", HardwareInput)]
class Input(ctypes.Structure):
    fields = [("type", ctypes.c_ulong),
                ("ii", Input_I)]

def set_pos(x, y):
    if mouse.is_pressed(button='left') or keyboard.is_pressed('alt'):
        x = int(x - (1360 / 2))
        y = int(y - (768 / 2))
        
        extra = ctypes.c_ulong(0)
        ii = pynput._util.win32.INPUT_union()
        ii.mi = pynput._util.win32.MOUSEINPUT(x, y, 0, (0x0001 | 0x8000), 0, ctypes.cast(ctypes.pointer(extra), ctypes.c_void_p))
        command=pynput._util.win32.INPUT(ctypes.c_ulong(0), ii)
        SendInput(1, ctypes.pointer(command), ctypes.sizeof(command))

if __name__ == "__main__":
    print("Do not run this file directly.")

def start(ENABLE_AIMBOT):

    # Config
    YOLO_DIRECTORY = "models"
    CONFIDENCE = 0.20
    THRESHOLD = 0.01
    #THRESHOLD = 0.22 Estava usando esete antes de 0.01

    #
    #   Size (in pixels) of the screen capture box to feed the neural net.
    #   This box is in the center of your screen. Lower value makes the network faster.
    #
    #   Example: "ACTIVATION_RANGE = 400" means a 400x400 pixel box.
    #
    ACTIVATION_RANGE = 200

    # load the COCO class labels our YOLO model was trained on
    labelsPath = os.path.sep.join([YOLO_DIRECTORY, "obj.names"])
    LABELS = open(labelsPath).read().strip().split("\n")

    # initialize a list of colors to represent each possible class label
    np.random.seed(42)
    COLORS = np.random.randint(0, 255, size=(len(LABELS), 3),
                               dtype="uint8")

    # derive the paths to the YOLO weights and model configuration
    weightsPath = os.path.sep.join([YOLO_DIRECTORY, "PESOCNN/yolov4-tiny-obj_final.weights"])
    configPath = os.path.sep.join([YOLO_DIRECTORY, "PESOCNN/yolov4-tiny-obj.cfg"])

    # Wait for buffering
    time.sleep(0.4)

    # load our YOLO object detector trained on COCO dataset (80 classes)
    # and determine only the output layer names that we need from YOLO
    print("[INFO] loading neural-network from disk…")
    net = cv2.dnn.readNetFromDarknet(configPath, weightsPath)

    # Check if GPU is available and use it
    if cv2.cuda.getCudaEnabledDeviceCount() > 0:
        print(colored("[OKAY] CUDA GPU Detected!", "green"))
        net.setPreferableBackend(cv2.dnn.DNN_BACKEND_CUDA)
        net.setPreferableTarget(cv2.dnn.DNN_TARGET_CUDA)
    elif cv2.ocl.haveOpenCL():
        print(colored("[OKAY] OpenCL GPU Detected!", "green"))
        net.setPreferableBackend(cv2.dnn.DNN_BACKEND_DEFAULT)
        net.setPreferableTarget(cv2.dnn.DNN_TARGET_OPENCL)
    else:
        print(colored("[WARNING] No GPU detected, using CPU.", "yellow"))
        net.setPreferableBackend(cv2.dnn.DNN_BACKEND_DEFAULT)
        net.setPreferableTarget(cv2.dnn.DNN_TARGET_CPU)

    ln = net.getLayerNames()
    ln = [ln[i - 1] for i in net.getUnconnectedOutLayers()]

    # Define screen capture area
    print("[INFO] loading screencapture device…")
    W, H = None, None
    origbox = (int(Wd/2 - ACTIVATION_RANGE/2),
               int(Hd/2 - ACTIVATION_RANGE/2),
               int(Wd/2 + ACTIVATION_RANGE/2),
               int(Hd/2 + ACTIVATION_RANGE/2))

    # Log whether aimbot is enabled
    if not ENABLE_AIMBOT:
        print("[INFO] aimbot disabled, using visualizer only…")
    else:
        print(colored("[OKAY] Aimbot enabled!", "green"))

    # Handle Ctrl+C in terminal, release pointers
    def signal_handler(sig, frame):
        # release the file pointers
        print("\n[INFO] cleaning up…")
        sct.exit()
        cv2.destroyAllWindows()
        sys.exit(0)
    signal.signal(signal.SIGINT, signal_handler)

    print()

    # loop over frames from the video file stream
    while True:
        start_time = timeit.default_timer()
        frame = np.array(grab_screen(region=origbox))
        frame = cv2.cvtColor(frame, cv2.COLOR_RGBA2BGR)

        # if the frame dimensions are empty, grab them
        if W is None or H is None:
            (H, W) = frame.shape[: 2]

        frame = cv2.UMat(frame)

        # construct a blob from the input frame and then perform a forward
        # pass of the YOLO object detector, giving us our bounding boxes
        # and associated probabilities
        blob = cv2.dnn.blobFromImage(frame, 1/260, (150, 150), 
                                     swapRB=True, crop=True)
        net.setInput(blob)
        layerOutputs = net.forward(ln)

        # initialize our lists of detected bounding boxes, confidences,
        # and class IDs, respectively
        boxes = []
        confidences = []
        classIDs = []

        # loop over each of the layer outputs
        for output in layerOutputs:
            # loop over each of the detections
            for detection in output:
                # extract the class ID and confidence (i.e., probability)
                # of the current object detection
                scores = detection[5:]
                classID = 0  # person = 0
                confidence = scores[classID]

                # filter out weak predictions by ensuring the detected
                # probability is greater than the minimum probability
                if confidence > CONFIDENCE:
                    box = detection[0: 4] * np.array([W, H, W, H])
                    (centerX, centerY, width, height) = box.astype("int")

                    # use the center (x, y)-coordinates to derive the top
                    # and and left corner of the bounding box
                    x = int(centerX - (width / 2))
                    y = int(centerY - (height / 2))

                    # update our list of bounding box coordinates,
                    # confidences, and class IDs
                    boxes.append([x, y, int(width), int(height)])
                    confidences.append(float(confidence))
                    classIDs.append(classID)

        # apply non-maxima suppression to suppress weak, overlapping
        # bounding boxes
        idxs = cv2.dnn.NMSBoxes(boxes, confidences, CONFIDENCE, THRESHOLD)

        # ensure at least one detection exists
        if len(idxs) > 0:
            # Find best player match
            bestMatch = confidences[np.argmax(confidences)]

            # loop over the indexes we are keeping
            for i in idxs.flatten():
                # extract the bounding box coordinates
                (x, y) = (boxes[i][0], boxes[i][1])
                (w, h) = (boxes[i][2], boxes[i][3])

                # draw target dot on the frame
                cv2.circle(frame, (int(x + w / 2), int(y + h / 3)), 2, (0, 0, 255), -1)

                # draw a bounding box rectangle and label on the frame
                cv2.rectangle(frame, (x, y),
                                (x + w, y + h), (0, 255, 0), 1)

                text = "enemy {}%".format(int(confidences[i] * 100))
                cv2.putText(frame, text, (x, y - 5),
                            cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)

                if ENABLE_AIMBOT and bestMatch == confidences[i]:
                    mouseX = origbox[0] + (x + w/1.55)
                    mouseY = origbox[1] + (y + h/2.50)
                    set_pos(mouseX, mouseY)

        cv2.imshow("Neural Net Vision (Pine)", frame)
        elapsed = timeit.default_timer() - start_time
        sys.stdout.write(
            "\r{1} FPS with {0} MS interpolation delay \t".format(int(elapsed*1000), int(1/elapsed)))
        sys.stdout.flush()
        if cv2.waitKey(1) & 0xFF == ord('0'):
            break

    # Clean up on exit
    signal_handler(0, 0)


preciso que seja adapitado para qualquer resolução de monitor! nao somente para 
 = int(x - (1360 / 2))
        y = int(y - (768 / 2))