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detector.py
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detector.py
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import numpy as np
import os
import six.moves.urllib as urllib
import sys
import tarfile
import tensorflow as tf
import zipfile
from collections import defaultdict
from io import StringIO
from PIL import Image
from utils import label_map_util
from utils import visualization_utils as vis_util
import draw
import cv2
import time
# Globals
class Detector:
def __init__(self):
self.NUM_CLASSES = 2
self.PATH_TO_LABELS = os.path.join('config', 'object_detection.pbtxt')
self.label_map = label_map_util.load_labelmap(self.PATH_TO_LABELS)
self.categories = label_map_util.convert_label_map_to_categories(self.label_map, max_num_classes=self.NUM_CLASSES, use_display_name=True)
self.category_index = label_map_util.create_category_index(self.categories)
# What model to download.
self.MODEL_NAME = 'trained_models/new_12_model'
# Path to frozen detection graph. This is the actual model that is used for the object detection.
self.PATH_TO_CKPT = self.MODEL_NAME + '/frozen_inference_graph.pb'
self.detection_graph = tf.Graph()
with self.detection_graph.as_default():
od_graph_def = tf.GraphDef()
with tf.gfile.GFile(self.PATH_TO_CKPT, 'rb') as fid:
serialized_graph = fid.read()
od_graph_def.ParseFromString(serialized_graph)
tf.import_graph_def(od_graph_def, name='')
self.detection_graph.as_default()
self.sess = tf.Session(graph=self.detection_graph)
def __load_graph__(self):
self.detection_graph.as_default()
od_graph_def = tf.GraphDef()
fid = tf.gfile.GFile(self.PATH_TO_CKPT, 'rb')
# 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='')
def predict(self, image, callback):
count = 0
# ret, image = cap.read()
# Expand dimensions since the model expects images to have shape: [1, None, None, 3]
image_np_expanded = np.expand_dims(image, axis=0)
image_tensor = self.detection_graph.get_tensor_by_name('image_tensor:0')
# Each box represents a part of the image where a particular object was detected.
boxes = self.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 = self.detection_graph.get_tensor_by_name('detection_scores:0')
classes = self.detection_graph.get_tensor_by_name('detection_classes:0')
num_detections = self.detection_graph.get_tensor_by_name('num_detections:0')
# Actual detection.
(boxes, scores, classes, num_detections) = self.sess.run(
[boxes, scores, classes, num_detections],
feed_dict={image_tensor: image_np_expanded})
# Visualization of the results of a detection.
draw.visualize_boxes_and_labels_on_image_array(
image,
np.squeeze(boxes),
np.squeeze(classes).astype(np.int32),
np.squeeze(scores),
self.category_index,
use_normalized_coordinates=True,
line_thickness=8)
callback(image)
# cv2.imshow('object detection', cv2.resize(image, (800,600)))
# cv2.imshow('object detection', image)
# cv2.waitKey()
# time.sleep(2)
def __del__(self):
# fid.close()
tf.reset_default_graph()
# self.sess.close()
# NUM_CLASSES = 2
# PATH_TO_LABELS = os.path.join('config', 'object_detection.pbtxt')
# 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)
# # What model to download.
# MODEL_NAME = 'trained_models/new_12_model'
# # 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'
# detection_graph = tf.Graph()
# sess = None
# def process(image, callback):
# print("Session ",sess)
# count = 0
# # ret, image = cap.read()
# # Expand dimensions since the model expects images to have shape: [1, None, None, 3]
# image_np_expanded = np.expand_dims(image, 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.
# draw.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)
# callback(image)
# # cv2.imshow('object detection', cv2.resize(image, (800,600)))
# # cv2.imshow('object detection', image)
# # cv2.waitKey()
# # time.sleep(2)
# def setup():
# load_graph()
# # detection_graph.as_default()
# sess = tf.Session(graph=detection_graph)
# def predict(path, callback):
# process(path, callback)
# def clean():
# tf.reset_default_graph()
# sess.close()