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May 14, 2018 15:31
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######## Image Object Detection Using Tensorflow-trained Classifier ######### | |
# | |
# Author: Evan Juras | |
# Date: 1/15/18 | |
# Description: | |
# This program uses a TensorFlow-trained classifier to perform object detection. | |
# It loads the classifier uses it to perform object detection on an image. | |
# It draws boxes and scores around the objects of interest in the image. | |
## Some of the code is copied from Google's example at | |
## https://github.com/tensorflow/models/blob/master/research/object_detection/object_detection_tutorial.ipynb | |
## and some is copied from Dat Tran's example at | |
## https://github.com/datitran/object_detector_app/blob/master/object_detection_app.py | |
## but I changed it to make it more understandable to me. | |
# Import packages | |
import os | |
import cv2 | |
import numpy as np | |
import tensorflow as tf | |
import sys | |
import requests | |
# This is needed since the notebook is stored in the object_detection folder. | |
sys.path.append("..") | |
# Import utilites | |
from utils import label_map_util | |
from utils import visualization_utils as vis_util | |
# Name of the directory containing the object detection module we're using | |
MODEL_NAME = 'inference_graph' | |
IMAGE_NAME = 'test0.jpg' | |
# Grab path to current working directory | |
CWD_PATH = os.getcwd() | |
# Path to frozen detection graph .pb file, which contains the model that is used | |
# for object detection. | |
PATH_TO_CKPT = os.path.join(CWD_PATH,MODEL_NAME,'frozen_inference_graph.pb') | |
# Path to label map file | |
PATH_TO_LABELS = os.path.join(CWD_PATH,'training','labelmap.pbtxt') | |
# Path to image | |
PATH_TO_IMAGE = os.path.join(CWD_PATH,IMAGE_NAME) | |
# Number of classes the object detector can identify | |
NUM_CLASSES = 6 | |
# Load the label map. | |
# Label maps map indices to category names, so that when our convolution | |
# network predicts `5`, we know that this corresponds to `king`. | |
# 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) | |
# Load the 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='') | |
sess = tf.Session(graph=detection_graph) | |
# Define input and output tensors (i.e. data) for the object detection classifier | |
# Input tensor is the image | |
image_tensor = detection_graph.get_tensor_by_name('image_tensor:0') | |
# Output tensors are the detection boxes, scores, and classes | |
# Each box represents a part of the image where a particular object was detected | |
detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0') | |
# Each score represents level of confidence for each of the objects. | |
# The score is shown on the result image, together with the class label. | |
detection_scores = detection_graph.get_tensor_by_name('detection_scores:0') | |
detection_classes = detection_graph.get_tensor_by_name('detection_classes:0') | |
# Number of objects detected | |
num_detections = detection_graph.get_tensor_by_name('num_detections:0') | |
# Load image using OpenCV and | |
# expand image dimensions to have shape: [1, None, None, 3] | |
# i.e. a single-column array, where each item in the column has the pixel RGB value | |
image = cv2.imread(PATH_TO_IMAGE) | |
image_expanded = np.expand_dims(image, axis=0) | |
# Perform the actual detection by running the model with the image as input | |
(boxes, scores, classes, num) = sess.run( | |
[detection_boxes, detection_scores, detection_classes, num_detections], | |
feed_dict={image_tensor: image_expanded}) | |
# Draw the results of the detection (aka 'visulaize the results') | |
vis_util.visualize_boxes_and_labels_on_image_array( | |
image, | |
np.squeeze(boxes), | |
np.squeeze(classes).astype(np.int32), | |
np.squeeze(scores), | |
category_index, | |
use_normalized_coordinates=True, | |
line_thickness=8, | |
min_score_thresh=0.80) | |
# All the results have been drawn on image. Now display the image. | |
cv2.imshow('Object detector', image) | |
# Press any key to close the image | |
cv2.waitKey(2000) | |
# Clean up | |
cv2.destroyAllWindows() | |
#QoS | |
while num_detections is not None: | |
requests.get('http://10.10.0.42:9090/api/qos/increase/eth',timeout=1) | |
break |
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