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gtasapy_minor.py
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gtasapy_minor.py
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import numpy as np
import os
import six.moves.urllib as urllib
import sys
sys.path.insert(0, "E:\workspace_py\object_detection")
import tarfile
import tensorflow as tf
import zipfile
from collections import defaultdict
from io import StringIO
from matplotlib import pyplot as plt
from PIL import Image, ImageGrab
import cv2
# import pyHook, pythoncom
from directKeys import PressKey, ReleaseKey, W, A, S, D, J
# cap = cv2.VideoCapture(0)
# Object detection imports
# Here are the imports from the object detection module.
# utils is folder in object_detection/
from utils import label_map_util
from utils import visualization_utils as vis_util
# # Model preparation
# ## Variables
#
# Any model exported using the `export_inference_graph.py` tool can be loaded here simply by changing `PATH_TO_CKPT` to point to a new .pb file.
#
# By default we use an "SSD with Mobilenet" model here. See the [detection model zoo](https://github.com/tensorflow/models/blob/master/object_detection/g3doc/detection_model_zoo.md) for a list of other models that can be run out-of-the-box with varying speeds and accuracies.
# What model to download.
MODEL_NAME = 'ssd_mobilenet_v1_coco_11_06_2017' # model name.
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_CKPT = 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('object_detection/data', 'mscoco_label_map.pbtxt')
NUM_CLASSES = 90
# Download Model
download_model = False
if download_model:
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())
# Load a (frozen) Tensorflow model into memory.
detection_graph = tf.Graph()
with detection_graph.as_default():
od_graph_def = tf.GraphDef()
with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
serialized_graph = fid.read()
od_graph_def.ParseFromString(serialized_graph)
tf.import_graph_def(od_graph_def, name='')
# Loading label map
# Label maps map indices to category names, so that when our convolution network predicts `5`, we know that this corresponds to `airplane`.
# Here we use internal utility functions, but anything that returns a dictionary mapping integers to appropriate string labels would be fine
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)
print("categories: ", categories)
print("\n\ncategory_index:", category_index)
# Detection
real_height = 420.0 # experimental value
focal_length = 10.0 # experimental value
brain_ON = True
with detection_graph.as_default():
with tf.Session(graph=detection_graph) as sess:
while True:
# ret, image_np = cap.read()
game_screen = np.asarray(ImageGrab.grab(bbox=(1, 35, 640, 420)))
game_screen = cv2.cvtColor(game_screen, cv2.COLOR_BGR2RGB)
image_np = game_screen
# Expand dimensions since the model expects images to have shape: [1, None, None, 3]
image_np_expanded = np.expand_dims(image_np, axis=0)
image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
# Each box represents a part of the image where a particular object was detected.
boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
# Each score represent how level of confidence for each of the objects.
# Score is shown on the result image, together with the class label.
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')
# Actual detection.
(boxes, scores, classes, num_detections) = sess.run(
[boxes, scores, classes, num_detections],
feed_dict={image_tensor: image_np_expanded})
# Visualization of the results of a detection.
# print("num_detections", num_detections)
# print("boxes : ", boxes)
# print("\nbox : ", boxes[0][np.argmax(scores)]) # for some reasons boxes is 3d. # batch_size duh.
# print("\nscore : ", classes[np.argmax(scores)])
# print("classes : ", classes)
box_with_max_score = boxes[0][np.argmax(scores)] # [ymin, xmin, ymax, xmax]
apparent_height = (box_with_max_score[2] - box_with_max_score[0])*420.0 # multiplying to de-normalize.
print("\nApparent height = ", apparent_height)
print("\nApparent height (normalized) = ", apparent_height / 420.0)
distance = ((real_height*focal_length)/apparent_height) - focal_length
# velocity = (distance - )/5
print("\nDistance from object = ", distance)
# if brain_ON:
# if distance < 40.0:
# ReleaseKey(W)
# PressKey(S)
# else:
# PressKey(W)
# ReleaseKey(S)
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=4)
cv2.imshow('object detection', cv2.resize(image_np, (640,420)))
if cv2.waitKey(25) & 0xFF == ord('q'):
cv2.destroyAllWindows()
break
elif cv2.waitKey(25) & 0xFF == ord('b'):
brain_ON = not brain_ON
print("b pressed.")
# cap.close()