# Welcome to the object detection tutorial !

# # Imports
import multiprocessing
from multiprocessing import Pipe
import time
import cv2
import mss
import numpy as np
import os
import sys
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
import tensorflow as tf
from distutils.version import StrictVersion
from collections import defaultdict
from io import StringIO
import pyautogui
import time

# pyautogui settings
pyautogui.MINIMUM_DURATION = 0
pyautogui.MINIMUM_SLEEP = 0
pyautogui.PAUSE = 0

# title of our window
title = "FPS benchmark"
# set start time to current time
start_time = time.time()
# displays the frame rate every 2 second
display_time = 2
# Set primarry FPS to 0
fps = 0
# Load mss library as sct
sct = mss.mss()
# Set monitor size to capture to MSS
width = 800
height = 600

monitor = {"top": 80, "left": 0, "width": width, "height": height}

# ## Env setup
from object_detection.utils import ops as utils_ops
from object_detection.utils import label_map_util
from object_detection.utils import visualization_utils as vis_util

# # Model preparation
PATH_TO_FROZEN_GRAPH = 'CSGO_frozen_inference_graph.pb'
# List of the strings that is used to add correct label for each box.
PATH_TO_LABELS = 'CSGO_labelmap.pbtxt'
NUM_CLASSES = 4

# ## Load a (frozen) Tensorflow model into memory.
label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES, use_display_name=True)
category_index = label_map_util.create_category_index(categories)

detection_graph = tf.Graph()
with detection_graph.as_default():
  od_graph_def = tf.GraphDef()
  with tf.gfile.GFile(PATH_TO_FROZEN_GRAPH, 'rb') as fid:
    serialized_graph = fid.read()
    od_graph_def.ParseFromString(serialized_graph)
    tf.import_graph_def(od_graph_def, name='')

def Shoot(mid_x, mid_y):
  x = int(mid_x*width)
  #y = int(mid_y*height)
  y = int(mid_y*height+height/9)
  pyautogui.moveTo(x,y)
  pyautogui.click()

def grab_screen(p_input):
  while True:
    #Grab screen image
    img = np.array(sct.grab(monitor))
    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

    # Put image from pipe
    p_input.send(img)

def TensorflowDetection(p_output, p_input2):
  # Detection
  with detection_graph.as_default():
    with tf.Session(graph=detection_graph) as sess:
      while True:
        # Get image from pipe
        image_np = p_output.recv()
        # Expand dimensions since the model expects images to have shape: [1, None, None, 3]
        image_np_expanded = np.expand_dims(image_np, axis=0)
        # Actual detection.
        image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
        boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
        scores = detection_graph.get_tensor_by_name('detection_scores:0')
        classes = detection_graph.get_tensor_by_name('detection_classes:0')
        num_detections = detection_graph.get_tensor_by_name('num_detections:0')
        # Visualization of the results of a detection.
        (boxes, scores, classes, num_detections) = sess.run(
            [boxes, scores, classes, num_detections],
            feed_dict={image_tensor: image_np_expanded})
        vis_util.visualize_boxes_and_labels_on_image_array(
            image_np,
            np.squeeze(boxes),
            np.squeeze(classes).astype(np.int32),
            np.squeeze(scores),
            category_index,
            use_normalized_coordinates=True,
            line_thickness=2)

        # Send detection image to pipe2
        p_input2.send(image_np)

        array_ch = []
        array_c = []
        array_th = []
        array_t = []
        for i,b in enumerate(boxes[0]):
          if classes[0][i] == 2: # ch
            if scores[0][i] >= 0.5:
              mid_x = (boxes[0][i][1]+boxes[0][i][3])/2
              mid_y = (boxes[0][i][0]+boxes[0][i][2])/2
              array_ch.append([mid_x, mid_y])
              cv2.circle(image_np,(int(mid_x*width),int(mid_y*height)), 3, (0,0,255), -1)
          if classes[0][i] == 1: # c
            if scores[0][i] >= 0.5:
              mid_x = (boxes[0][i][1]+boxes[0][i][3])/2
              mid_y = boxes[0][i][0] + (boxes[0][i][2]-boxes[0][i][0])/6
              array_c.append([mid_x, mid_y])
              cv2.circle(image_np,(int(mid_x*width),int(mid_y*height)), 3, (50,150,255), -1)
          if classes[0][i] == 4: # th
            if scores[0][i] >= 0.5:
              mid_x = (boxes[0][i][1]+boxes[0][i][3])/2
              mid_y = (boxes[0][i][0]+boxes[0][i][2])/2
              array_th.append([mid_x, mid_y])
              cv2.circle(image_np,(int(mid_x*width),int(mid_y*height)), 3, (0,0,255), -1)
          if classes[0][i] == 3: # t
            if scores[0][i] >= 0.5:
              mid_x = (boxes[0][i][1]+boxes[0][i][3])/2
              mid_y = boxes[0][i][0] + (boxes[0][i][2]-boxes[0][i][0])/6
              array_t.append([mid_x, mid_y])
              cv2.circle(image_np,(int(mid_x*width),int(mid_y*height)), 3, (50,150,255), -1)

        team = "c" # shooting target
        if team == "c":
          if len(array_ch) > 0:
            Shoot(array_ch[0][0], array_ch[0][1])
          if len(array_ch) == 0 and len(array_c) > 0:
            Shoot(array_c[0][0], array_c[0][1])
        if team == "t":
          if len(array_th) > 0:
            Shoot(array_th[0][0], array_th[0][1])
          if len(array_th) == 0 and len(array_t) > 0:
            Shoot(array_t[0][0], array_t[0][1])


def Show_image(p_output2):
  global start_time, fps
  while True:
    image_np = p_output2.recv()
    # Show image with detection
    cv2.imshow(title, image_np)
    # Bellow we calculate our FPS
    fps+=1
    TIME = time.time() - start_time
    if (TIME) >= display_time :
      print("FPS: ", fps / (TIME))
      fps = 0
      start_time = time.time()
    # Press "q" to quit
    if cv2.waitKey(25) & 0xFF == ord("q"):
      cv2.destroyAllWindows()
      break

if __name__=="__main__":
    # Pipes
    p_output, p_input = Pipe()
    p_output2, p_input2 = Pipe()

    # creating new processes
    p1 = multiprocessing.Process(target=grab_screen, args=(p_input,))
    p2 = multiprocessing.Process(target=TensorflowDetection, args=(p_output,p_input2,))
    p3 = multiprocessing.Process(target=Show_image, args=(p_output2,))

    # starting our processes
    p1.start()
    p2.start()
    p3.start()