-
Notifications
You must be signed in to change notification settings - Fork 3
/
Copy pathinference_util.py
102 lines (87 loc) · 4.2 KB
/
inference_util.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
# coding: utf-8
# # Object Detection Demo
### Please refer to https://github.com/tensorflow/models/blob/master/research/object_detection/object_detection_tutorial.ipynb
### In the DEMO notebook, there is a function run inference for single image.
###
### Added a new function for running inference for multiple images
###
## v1
import datetime
import os
import sys
from collections import defaultdict
from io import StringIO
import numpy as np
import tensorflow as tf
from PIL import Image
def run_inference_for_images(images, graph):
with graph.as_default():
with tf.Session() as sess:
output_dict_array = []
for image in images:
# Get handles to input and output tensors
ops = 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:
detection_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 dimension
tensor_dict['detection_masks'] = tf.expand_dims(
detection_masks_reframed, 0)
image_tensor = tf.get_default_graph().get_tensor_by_name('image_tensor:0')
# Run inference
output_dict = sess.run(tensor_dict,
feed_dict={image_tensor: np.expand_dims(image, 0)})
# all outputs are float32 numpy arrays, so convert types as appropriate
output_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]
output_dict_array.append(output_dict)
return output_dict_array
###
### Refer to https://github.com/tensorflow/models/blob/master/research/object_detection/object_detection_tutorial.ipynb
###load your detection_graph
#detection_graph = tf.Graph()
#images = []
#### ...
####load your images
#### ...
# output_dict_array = run_inference_for_images(images,detection_graph)
#
# for idx in range(len(output_dict_array)):
# output_dict = output_dict_array[idx]
# image_np = images[idx]
# 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=6)
###
### show your images
### plt.imshow(image_np)
###