-
Notifications
You must be signed in to change notification settings - Fork 0
/
predict_single_file_full_set.py
131 lines (106 loc) · 3.13 KB
/
predict_single_file_full_set.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
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
from keras.models import load_model
import cv2.cv2 as cv2
import numpy as np
import sys
import os
import sorting_robot
# arrays for saving
succes_rate = [0,0,0,0,0,0,0,0,0,0]
total_test = [0,0,0,0,0,0,0,0,0,0]
succes_percentage = [0,0,0,0,0,0,0,0,0,0]
bolts = sorting_robot.Bolts()
REV_CLASS_MAP, model = bolts.bolts_in_model(sub_ass=1)
#filepath = "Users\laure\Documents\SMR2\augmenting_image\1.jpg"
folderpath = os.getcwd()
main_folder = "dataset/image_data_blue_light_split"
filepath = os.path.join(folderpath, main_folder, "test")
files = os.listdir(filepath)
amount = len (files) - 1
folder = 0
# REV_CLASS_MAP = {
# 0: "m59557-10",
# 1: "m59557-16",
# 2: "m59557-20",
# 3: "nas1802-3-6",
# 4: "nas1802-3-7",
# 5: "nas1802-3-8",
# 6: "nas1802-3-9",
# 7: "nas1802-4-07",
# 8: "nas6305-10",
# 9: "v647p23b"
# }
def mapper(val):
return REV_CLASS_MAP[val]
while folder <= amount:
correct_amount = 0
boltname = files[folder]
picture_path = os.path.join(filepath, boltname)
picture_names = os.listdir(picture_path)
test_nr = 0
amount_test = len (picture_names) - 1
total_test[folder] = len (picture_names)
# print(boltname)
while test_nr <= amount_test:
picture_nr = picture_names[test_nr]
picpath = os.path.join(picture_path, picture_nr)
# prepare the image
img = cv2.imread(picpath)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = cv2.resize(img, (350, 350))
# predict the picture
pred = model.predict(np.array([img]))
# sets print of arrays to 2 decimal places
np.set_printoptions(formatter={'float': lambda x: "{0:0.3f}".format(x)})
pic_code = np.argmax(pred[0])
pic_name = mapper(pic_code)
print("Predicted: {}".format(pic_name))
# print(pic_code)
print(pred)
if str(pic_name) == str(boltname):
correct_amount += 1
succes_rate[folder] = correct_amount
# print(succes_rate)
# print("Correct prediction")
test_nr += 1
succes_percentage[folder] = (succes_rate[folder] / total_test[folder]) * 100
print(boltname, " ", succes_percentage[folder], "% succesfull identification")
folder += 1
# print(total_test)
# print(succes_rate)
print(succes_percentage)
#
# filepath = 'C:/Users/marce/PycharmProjects/SMR2/green_tes/green_tes_test/v647p23b/31.jpg'
#
#
# REV_CLASS_MAP = {
#
# 0: "m59557-16",
# 1: "m59557-20",
# 2: "nas1802-3-7",
# 3: "nas1802-3-9",
# 4: "nas6305-10",
# 5: "v647p23b"
# }
#
# def mapper(val):
# return REV_CLASS_MAP[val]
#
# model = load_model("C:/Users/marce/PycharmProjects/SMR2/green_tes_v1.h5")
#
# # prepare the image
# img = cv2.imread(filepath)
# img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# img = cv2.resize(img, (640, 640))
#
# # predict the picture
# pred = model.predict(np.array([img]))
#
# # sets print of arrays to 2 decimal places
# np.set_printoptions(formatter={'float': lambda x: "{0:0.3f}".format(x)})
#
# pic_code = np.argmax(pred[0])
# pic_name = mapper(pic_code)
#
# print("Predicted: {}".format(pic_name))
# print(pic_code)
# print(pred)