-
Notifications
You must be signed in to change notification settings - Fork 14
/
deploy.py
96 lines (73 loc) · 2.87 KB
/
deploy.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
import numpy as np
import cv2
import time
from func import tf_init
from keras.models import load_model
from keras.preprocessing.image import load_img
from keras.preprocessing.image import img_to_array
from keras.applications import resnet50, inception_resnet_v2
from keras.applications.resnet50 import ResNet50
tf_init()
model = load_model('./model/resnet50.h5')
model_type = 'resnet50'
csize = 224
def classify(img_path='', img_arr=None, thresh=0.5):
if img_arr is None:
img_arr = img_to_array(
load_img(img_path, target_size=(csize, csize))
)
else:
img_arr = cv2.resize(img_arr, (csize, csize), interpolation=cv2.INTER_NEAREST)
img_arr = np.expand_dims(img_arr, axis=0)
if model_type == 'resnet50':
img_arr = resnet50.preprocess_input(img_arr)
else:
img_arr = inception_resnet_v2.preprocess_input(img_arr)
match = model.predict(img_arr)
return (1 if match[0][0] > thresh else 0, match[0][0])
def classify_from_file(file_path, thresh=0.5, batch_size=32):
imgs = open(file_path, 'r').readlines()
labels = np.array([])
matches = np.array([])
batch_imgs = np.empty((0, csize, csize, 3))
id = 0
for img in imgs:
img = img.strip().split(' ')
img_path = img[0]
label = img[1]
labels = np.concatenate((labels, [label]), axis=0)
img_arr = img_to_array(
load_img(img_path, target_size=(csize, csize))
)
img_arr = np.expand_dims(img_arr, axis=0)
if model_type == 'resnet50':
img_arr = resnet50.preprocess_input(img_arr)
else:
img_arr = inception_resnet_v2.preprocess_input(img_arr)
batch_imgs = np.concatenate((batch_imgs, img_arr), axis=0)
if batch_imgs.shape[0] == batch_size:
start_time = time.time()
batch_matches = model.predict_on_batch(batch_imgs)
print('Predict took', time.time() - start_time)
for match in batch_matches:
type = '1' if match[0] > thresh else '0'
print(type, end=' ')
matches = np.concatenate((matches, [type]), axis=0)
print('')
batch_imgs = np.empty((0, csize, csize, 3))
id += 1
print('Batch', id, '.....', 'acc=', np.sum(
matches == labels) / matches.shape[0])
if batch_imgs.shape[0] != 0:
batch_matches = model.predict_on_batch(batch_imgs)
for match in batch_matches:
type = '0' if match[0] > thresh else '1'
print(type, end=' ')
matches = np.concatenate((matches, [type]), axis=0)
print('')
id += 1
print('Batch', id, '.....', 'acc=', np.sum(
matches == labels) / matches.shape[0])
print('Accuracy:', np.sum(matches == labels) / len(imgs))
if __name__ == "__main__":
classify_from_file('./data/test.txt')