Original text version of tutorial you can visit here.
This is third part of our CS:GO object detection tutorial. In this part we are going to merge jupyter API code from 1-st tutorial with code from 2-nd tutorial where we tested 3 different ways of grabbing screen.
To begin, we're going to modify the notebook first by converting it to a .py file. If you want to keep it in a notebook, that's fine too. To convert, you can go to file > download as > python file. Once that's done, we're going to comment out the lines we don't need.
Once you have your converted object detection file, go to your TensorFlow installation folder: research\object_detection\data and grab mscoco_label_map.pbtxt file, place it to you working directory.
Next you should download pretrained model from here, I am using faster_rcnn_inception_v2_coco, so I recommend you to use the same, at least at the beginning. Take frozen_inference_graph.pb file and transfer it to your local working repository.
So we begin by importing time, CV2, MSS libraries. If you don't have them, install before moving forward.
I personally imported line to disable CUDA devices, because I wanted to run this example on CPU, because running tensorflow-gpu takes more time to startup in backend.
os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
Then we don't need to import tarfile and import zipfile, because we are not working with these files, so we comment them out now.
Going further we comment #from matplotlib import pyplot as plt and #from PIL import Image lines, because we are doing things our way.
Next I am importing few lines from my second tutorial for grabing screen and measuring FPS:
# 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": 40, "left": 0, "width": 800, "height": 640}
Because we are not using notebook anymore we are not using and these lines:
#sys.path.append("..")
#if StrictVersion(tf.__version__) < StrictVersion('1.9.0'):
# raise ImportError('Please upgrade your TensorFlow installation to v1.9.* or later!')
We are not using matplotlib to display image, so we are commenting line used for that:
#get_ipython().run_line_magic('matplotlib', 'inline')
There are two lines of import before going to an actual code:
from utils import label_map_util
from utils import visualization_utils as vis_util
But if you will try to use them like this, you will get an error, so add object_detection. before utils, just like this:
from object_detection.utils import label_map_util
from object_detection.utils import visualization_utils as vis_util
Next is links to paths, if you would like to have everything in same folder, just like in my tutorial, comment all these lines:
# What model to download.
MODEL_NAME = 'ssd_mobilenet_v1_coco_2017_11_17'
MODEL_FILE = MODEL_NAME + '.tar.gz'
DOWNLOAD_BASE = 'http://download.tensorflow.org/models/object_detection/'
# Path to frozen detection graph. This is the actual model that is used for the object detection.
PATH_TO_FROZEN_GRAPH = MODEL_NAME + '/frozen_inference_graph.pb'
# List of the strings that is used to add correct label for each box.
PATH_TO_LABELS = os.path.join('data', 'mscoco_label_map.pbtxt')
And replace them with my used path lines (don't forget to add NUM_CLASSES = 99 line)
MODEL_NAME = 'inference_graph'
PATH_TO_FROZEN_GRAPH = 'frozen_inference_graph.pb'
PATH_TO_LABELS = 'mscoco_label_map.pbtxt'
NUM_CLASSES = 99
Next you can comment all [6] part, because we won't use it:
#opener = urllib.request.URLopener()
#opener.retrieve(DOWNLOAD_BASE + MODEL_FILE, MODEL_FILE)
#tar_file = tarfile.open(MODEL_FILE)
#for file in tar_file.getmembers():
# file_name = os.path.basename(file.name)
# if 'frozen_inference_graph.pb' in file_name:
# tar_file.extract(file, os.getcwd())
Next, add 3: label_map, categories and category_index lines before detection_graph code:
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)
and in part [8] comment or delete category_index line:
category_index = label_map_util.create_category_index_from_labelmap(PATH_TO_LABELS, use_display_name=True)
Comment all lines in part [7] where images were loaded to numpy array:
#def load_image_into_numpy_array(image):
# (im_width, im_height) = image.size
# return np.array(image.getdata()).reshape(
# (im_height, im_width, 3)).astype(np.uint8)
Next you can delete PATH_TO_TEST_IMAGES_DIR, TEST_IMAGE_PATHS and IMAGE_SIZE lines, but if you will leave them it wound effect our code:
PATH_TO_TEST_IMAGES_DIR = 'test_images'
TEST_IMAGE_PATHS = [ os.path.join(PATH_TO_TEST_IMAGES_DIR, 'image{}.jpg'.format(i)) for i in range(1, 3) ]
IMAGE_SIZE = (12, 8)
At the end of code, not to make any mistakes you can replace all [12] block code with this code:
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 ger real color we do this:
image_np = cv2.cvtColor(image_np, cv2.COLOR_BGR2RGB)
#image = Image.open(image_path)
# the array based representation of the image will be used later in order to prepare the
# result image with boxes and labels on it.
#image_np = load_image_into_numpy_array(image)
# 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.
output_dict = run_inference_for_single_image(image_np, detection_graph)
# Visualization of the results of a detection.
vis_util.visualize_boxes_and_labels_on_image_array(
image_np,
output_dict['detection_boxes'],
output_dict['detection_classes'],
output_dict['detection_scores'],
category_index,
instance_masks=output_dict.get('detection_masks'),
use_normalized_coordinates=True,
line_thickness=8)
#plt.figure(figsize=IMAGE_SIZE)
#plt.imshow(image_np)
cv2.imshow(title, cv2.cvtColor(image_np, cv2.COLOR_BGR2RGB))
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
So I tried to use this slow object detection method on image where you can see crowd of people walking across the street:
And here is the results of frames per second working with TensorFlow CPU version. In average, it is about 7 seconds to receive one frame per second. So if we would like to use it for real time purpose, this would be impossible to do something useful with it. So we need to make it work much faster, we are doing so in second part below.
Original text version of tutorial you can visit here.
In previous tutorial we ran actual pretrained object detection, but our code is messy and detection was working really slow. In this part we are cleaning the messy code and making some code modifications that our object detection would work in much faster way.
At first I went through all code and deleted all unecassary code, so instead of using object_detection_tutorial_grabscreen.py, better take object_detection_tutorial_grabscreen_pretty.py it will be much easier to understand how it works.
After cleaning the code, I started to make some changes to it. Mainly what I done is that I deleted def run_inference_for_single_image(image, graph): function and added needed lines to main while loop, and this changed object detection speed. Not taking into details I will upload code part I changed:
# # 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=3)
# 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
Same as in 3-rd part, we are testing how fast it is working. To compare results we got in 3-rd tutorial part we are taking the same picture, with the same object count in it. In bellow image, you can see significant difference comparing what we had before, it is in average 1 FPS. If you will run it on GPU you will get from 5 to 10 times boost.
