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May 13, 2020 03:23
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# vehicle_detector.py | |
# https://pythonprogramming.net/introduction-use-tensorflow-object-detection-api-tutorial/ | |
# # Object Detection Demo | |
# License: Apache License 2.0 (https://github.com/tensorflow/models/blob/master/LICENSE) | |
# source: https://github.com/tensorflow/models | |
# https://gpuopen.com/rocm-tensorflow-1-8-release/ | |
# py -m pip install Cython contextlib2 pillow lxml jupyter matplotlib | |
# http://www.mingw.org/wiki/Getting_Started | |
import os | |
import six.moves.urllib as urllib | |
import sys | |
import tarfile | |
# Installed | |
import cv2 | |
import tensorflow as tf | |
import numpy as np | |
# Project | |
from collections import defaultdict | |
from grabscreen import grab_screen | |
# This is needed since the notebook is stored in the object_detection folder. | |
sys.path.append("..") | |
# ## Object detection imports | |
# Here are the imports from the object detection module. | |
from utils import label_map_util | |
from utils import visualization_utils as vis_util | |
# # Model preparation | |
# What model to download. | |
MODEL_NAME = 'ssd_mobilenet_v1_coco_11_06_2017' | |
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('data', 'mscoco_label_map.pbtxt') | |
NUM_CLASSES = 90 | |
# ## 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) | |
# ## Helper code | |
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) | |
# Size, in inches, of the output images. | |
IMAGE_SIZE = (12, 8) | |
with detection_graph.as_default(): | |
with tf.Session(graph=detection_graph) as sess: | |
while True: | |
#screen = cv2.resize(grab_screen(region=(0,40,1280,745)), (WIDTH,HEIGHT)) | |
screen = cv2.resize(grab_screen(region=(0,40,1280,745)), (800,450)) | |
image_np = cv2.cvtColor(screen, 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) | |
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. | |
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=8) | |
cv2.imshow('window',image_np) | |
if cv2.waitKey(25) & 0xFF == ord('q'): | |
cv2.destroyAllWindows() | |
break |
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