forked from mrbing47/Smart-Parking-System
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Slot Prediction.py
144 lines (109 loc) · 3.69 KB
/
Slot Prediction.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
132
133
134
135
136
137
138
139
140
141
142
143
# -*- coding: utf-8 -*-
"""Untitled0.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1EtxW8KSQZh76rQgJIzxUhveIRqWG4tA5
"""
from keras.models import Sequential
from keras.layers import *
from keras.utils import to_categorical
model = Sequential()
model.add(SimpleRNN(32, input_shape=(10,50)))
model.add(Dropout(0.4))
model.add(Dense(5))
model.add(Activation('softmax'))
model.summary()
model.compile(optimizer = 'adam', loss = 'categorical_crossentropy', metrics = ['acc'])
model.fit(xT_processed, YT, epochs = 40, validation_split = 0.1)
import keras
from keras.models import Sequential
from keras.layers import Dense, Activation, Dropout, Flatten, Conv2D, MaxPooling2D
from keras.layers.normalization import BatchNormalization
import numpy as np
np.random.seed(1000)
#Instantiate an empty model
model = Sequential()
# 1st Convolutional Layer
model.add(Conv2D(filters=16, input_shape=(224,224,3), kernel_size=(11,11), strides=(4,4), padding='valid'))
model.add(Activation('relu'))
# Max Pooling
model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='valid'))
# 2nd Convolutional Layer
model.add(Conv2D(filters=20, kernel_size=(11,11), strides=(1,1), padding='valid'))
model.add(Activation('relu'))
# Max Pooling
model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='valid'))
# 4th Convolutional Layer
model.add(Conv2D(filters=30, kernel_size=(3,3), strides=(1,1), padding='valid'))
model.add(Activation('relu'))
# Max Pooling
model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2), padding='valid'))
# Passing it to a Fully Connected layer
model.add(Flatten())
# 1st Fully Connected Layer
model.add(Dense(48, input_shape=(224*224*3,)))
model.add(Activation('relu'))
# Add Dropout to prevent overfitting
# model.add(Dropout(0.4))
# Output Layer
model.add(Dense(1))
model.add(Activation('softmax'))
model.summary()
# Compile the model
model.compile(loss=keras.losses.categorical_crossentropy, optimizer='adam', metrics=['accuracy'])
import cv2
train = []
for i in range(0,15000):
img = cv2.imread('PATCHES/'+data['path'][i])
img = cv2.resize(img, (224,224))
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
train.append((img, data['label'][i]))
from keras.utils import to_categorical
y_train = to_categorical(y_train)
print(y)
from keras.models import model_from_json
json_file = open('model.json', 'r')
loaded_model_json = json_file.read()
json_file.close()
loaded_model = model_from_json(loaded_model_json)
# load weights into new model
loaded_model.load_weights("model.h5")
print("Loaded model from disk")
loaded_model.summary()
camera_1 = {
'lot_1' : [(360, 160), (400, 199)],
'lot_2' : [(365, 289), (443, 366)],
'lot_3' : [(464,347), (573, 452)],
'lot_4' : [(111, 263), (165, 315)],
'lot_5' : [(71, 227), (118, 273)],
'lot_6' : [(41, 197), (79, 235)],
'lot_7' : [(12,173), (48, 203)],
'lot_8' : [(39, 98), (76, 134)],
}
import cv2
import numpy as np
import matplotlib.pyplot as plt
#image = cv2.imread('camera 1_1.jpg')
image = cv2.imread('2015-11-16_1710.jpg')
image = cv2.resize(image, (610, 457))
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
plt.imshow(image)
fig=plt.figure()
columns = 4
rows = 2
images = np.zeros((8, 48, 48, 3));
for no, v in enumerate(camera_1.values()):
x1 = v[0][0]
x2 = v[1][0]
y1 = v[0][1]
y2 = v[1][1]
img = np.zeros((y2 - y1, x2 - x1, 3), dtype = 'uint8')
for i in range(y1, y2):
for j in range(x1, x2):
img[i - y1][j - x1] = image[i][j]
img = cv2.resize(img, (48, 48)) / 255
images[no] = img
fig.add_subplot(rows, columns, no + 1)
plt.imshow(img)
plt.show()
loaded_model.predict(images)