CSGO_object_detection.py

# Welcome to the object detection tutorial !

# # Imports
import time
import cv2
import mss
import numpy as np
import os
import sys
#os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
import tensorflow as tf
from distutils.version import StrictVersion
from collections import defaultdict
from io import StringIO


# 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
monitor = {"top": 80, "left": 0, "width": 800, "height": 640}



# ## 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='')


# # Detection
with detection_graph.as_default():
  with tf.Session(graph=detection_graph) as sess:
    while True:
      # Get raw pixels from the screen, save it to a Numpy array
      image_np = np.array(sct.grab(monitor))
      # To get real color we do this:
      image_np = cv2.cvtColor(image_np, cv2.COLOR_BGR2RGB)
      # 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)
      # Show image with detection
      cv2.imshow(title, cv2.cvtColor(image_np, cv2.COLOR_BGR2RGB))
      # 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