Project-Rainfall/imageClassification.py at master · Ayu-99/Project-Rainfall · GitHub

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 matplotlib.pyplot as plt
import os
import cv2
import random
import pickle
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Activation,Flatten, Conv2D, MaxPooling2D


DATADIR="C://Users/Ayushi Sharma/Pictures/potholes/pothole_image_data"
CATEGORIES=["with", "without"] # 0-with 1-without


training_data=[]
IMG_SIZE = 250


def create_training_data():
    for category in CATEGORIES:
        path = os.path.join(DATADIR, category)  # path to with or without
        class_num=CATEGORIES.index(category)
        for img in os.listdir(path):
            try:
                img_array = cv2.imread(os.path.join(path, img), cv2.IMREAD_GRAYSCALE)
                new_array = cv2.resize(img_array, (IMG_SIZE, IMG_SIZE))
                training_data.append([new_array, class_num])

            except Exception as e:
                pass

create_training_data()
random.shuffle(training_data)

# print(len(training_data))

# for sample in training_data[:10]:
#     print(sample[1])

# import numpy as np
X=[] #feature set
y=[] #labels

for features, labels in training_data:
    X.append(features)
    y.append(labels)

X=np.array(X).reshape(-1, IMG_SIZE, IMG_SIZE, 1)

pickle_out=open("X.pickle", "wb")
pickle.dump(X, pickle_out)
pickle_out.close()

pickle_out=open("y.pickle", "wb")
pickle.dump(y, pickle_out)
pickle_out.close()

pickle_in=open("X.pickle", "rb")
X=pickle.load(pickle_in)
# print(X[1])

X = pickle.load(open("X.pickle", "rb"))
y = np.array(pickle.load(open("y.pickle", "rb")))


X = X/255.0
model = keras.Sequential()

model.add(Conv2D(64, (3,3), input_shape=X.shape[1:]))
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size=(2,2)))

model.add(Conv2D(64, (3,3)))
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size=(2,2)))

model.add(Flatten())
model.add(Dense(64))

model.add(Dense(1))
model.add(Activation('sigmoid'))

model.compile(loss="binary_crossentropy",
              optimizer="adam",
              metrics=['accuracy'])

model.fit(X,y, batch_size=32, validation_split=0.1)


model.save('64X3-CNN.model1')