本文主要是介绍Object_Detection_API之Inference，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！
导入包
import numpy as np
import os
import six.moves.urllib as urllib
import sys
import tarfile
import tensorflow as tf
import zipfilefrom distutils.version import StrictVersion
from collections import defaultdict
from io import StringIO
from matplotlib import pyplot as plt
from PIL import Image# This is needed since the notebook is stored in the object_detection folder.
sys.path.append("../../")
from object_detection.utils import ops as utils_opsif StrictVersion(tf.__version__) < StrictVersion('1.9.0'):raise ImportError('Please upgrade your TensorFlow installation to v1.9.* or later!')%matplotlib inline
from utils import label_map_util
from utils import visualization_utils as vis_util
设置模型目录
MODEL_DIR = './model_from_200000_steps'
PATH_TO_FROZEN_GRAPH = MODEL_DIR + '/frozen_inference_graph.pb'
PATH_TO_LABELS = os.path.join('./dataset', 'traffic_light_label_map.pbtxt')
恢复计算图
detection_graph = tf.Graph()
with detection_graph.as_default():od_graph_def=tf.GraphDef()with tf.gfile.GFile(PATH_TO_FROZEN_GRAPH, 'rb') as fid:serialized_graph = fid.read()od_graph_def.ParseFromString(serialized_graph)tf.import_graph_def(od_graph_def, name='')ops = tf.get_default_graph().get_operations()all_tensor_names = {output.name for op in ops for output in op.outputs}
# print(all_tensor_names)print(len(all_tensor_names))
for name in all_tensor_names:
# if name.startswith('SecondStage'):
# print(name)if name.find('bottleneck')==-1:passelse:print(name)
读取labelmap
category_index=label_map_util.create_category_index_from_labelmap(PATH_TO_LABELS)
print(category_index)
def load_image_into_numpy_array(image):(im_width, im_height) = image.sizereturn np.array(image.getdata()).reshape((im_height, im_width, 3)).astype(np.uint8)
定义推理函数
def run_inference_for_single_image(image, graph):with graph.as_default():with tf.Session() as sess:# Get handles to input and output tensorsops = tf.get_default_graph().get_operations()all_tensor_names = {output.name for op in ops for output in op.outputs}tensor_dict = {}for key in ['num_detections', 'detection_boxes', 'detection_scores','detection_classes', 'detection_masks']:tensor_name = key + ':0'if tensor_name in all_tensor_names:tensor_dict[key] = tf.get_default_graph().get_tensor_by_name(tensor_name)if 'detection_masks' in tensor_dict:# The following processing is only for single imagedetection_boxes = tf.squeeze(tensor_dict['detection_boxes'], [0])detection_masks = tf.squeeze(tensor_dict['detection_masks'], [0])# Reframe is required to translate mask from box coordinates to image coordinates and fit the image size.real_num_detection = tf.cast(tensor_dict['num_detections'][0], tf.int32)detection_boxes = tf.slice(detection_boxes, [0, 0], [real_num_detection, -1])detection_masks = tf.slice(detection_masks, [0, 0, 0], [real_num_detection, -1, -1])detection_masks_reframed = utils_ops.reframe_box_masks_to_image_masks(detection_masks, detection_boxes, image.shape[0], image.shape[1])detection_masks_reframed = tf.cast(tf.greater(detection_masks_reframed, 0.5), tf.uint8)# Follow the convention by adding back the batch dimensiontensor_dict['detection_masks'] = tf.expand_dims(detection_masks_reframed, 0)image_tensor = tf.get_default_graph().get_tensor_by_name('image_tensor:0')# Run inferenceoutput_dict = sess.run(tensor_dict,feed_dict={image_tensor: np.expand_dims(image, 0)})# all outputs are float32 numpy arrays, so convert types as appropriateoutput_dict['num_detections'] = int(output_dict['num_detections'][0])output_dict['detection_classes'] = output_dict['detection_classes'][0].astype(np.uint8)output_dict['detection_boxes'] = output_dict['detection_boxes'][0]output_dict['detection_scores'] = output_dict['detection_scores'][0]if 'detection_masks' in output_dict:output_dict['detection_masks'] = output_dict['detection_masks'][0]return output_dict
进行推理
import random
import cv2
import glob
PATH_TO_TEST_IMAGES_DIR = './images_for_test'
TEST_IMAGE_PATHS = glob.glob(os.path.join(PATH_TO_TEST_IMAGES_DIR, "*.jpg"))
print(len(TEST_IMAGE_PATHS))
IMAGE_SIZE=(12,8)
推理
count=0
saved_dir_for_predicted_images="./images_predicted"
for count in range(10):chx = random.randint(0, len(TEST_IMAGE_PATHS)-1)image_path = TEST_IMAGE_PATHS[chx]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)# print(output_dict)# 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=4)im = Image.fromarray(image_np.astype('uint8'))saved_name = os.path.basename(image_path).split('.')[0]im.save(os.path.join(saved_dir_for_predicted_images, saved_name+'.jpg'))
# image_np = cv2.cvtColor(image_np, cv2.COLOR_RGB2BGR)
# cv2.imshow('image',image_np)
# cv2.waitKey(10)
# cv2.destroyAllWindows()
# if cv2.waitKey(1000)&0xff == 113:
# cv2.destroyAllWindows()
# plt.figure(figsize=IMAGE_SIZE)
# plt.imshow(image_np)
# plt.show()
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