-
Notifications
You must be signed in to change notification settings - Fork 3
/
Copy pathserver.py
130 lines (112 loc) · 3.88 KB
/
server.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
# Covid Detection WebService
#
# $ curl -XPOST -F "[email protected]" http://127.0.0.1:5001
from flask import Flask, jsonify, request, redirect
from flask_ngrok import run_with_ngrok
import cv2
import PIL
import numpy as np
from sklearn.svm import NuSVC
import pickle
from scipy.io import loadmat
import time
# Create An SVM
FV = loadmat('features.mat')
X = FV['data']
Y = FV['labels']
# fix a bug where saving with scipy makes the matrix transposed.
Y = Y.transpose()
clf = NuSVC(nu=0.4, kernel='rbf', gamma=0.009876939713502824, shrinking=True, tol=0.00001,
max_iter=176, random_state=1, class_weight='balanced', probability=True)
clf.fit(X, Y)
# Save the SVM to be used on the endpoint.
with open('svm_model.pkl', 'wb') as f:
pickle.dump(clf, f)
# Perform all the steps at once and return a probabilty for covid.
def detect(imagePath, models):
s_time = time.time()
covid19 = False
image = imagePath
feature_extractor, clf = models
image = cv2.resize(image, (224, 224)) / 255.
features = feature_extractor(np.array([image]))
features = np.array(features).reshape(1024, 1)
pred = np.around(clf.predict(features.reshape(1, -1)))
if pred == 1:
covid19 = True
return {'covid19': covid19,
'response_time': time.time()-s_time}
# Utility func to load the models.
def load_models():
import tensorflow as tf
import pickle
inputs = tf.keras.Input(shape=(224, 224, 3))
model = tf.keras.applications.DenseNet121(include_top=False, weights='imagenet',
input_shape=(224,224,3))
model_outputs = model(inputs)
outputs = tf.keras.layers.GlobalAveragePooling2D(name='ga')(model_outputs)
feature_extractor = tf.keras.models.Model(inputs=inputs, outputs=outputs)
with open('svm_model.pkl', 'rb') as f:
clf = pickle.load(f)
return [feature_extractor, clf]
# Load Models.
models = load_models()
# You can change this to any folder on your system
ALLOWED_EXTENSIONS = {'png', 'jpg', 'jpeg', 'gif'}
app = Flask(__name__)
run_with_ngrok(app)
# Load RGB images.
def load_image_file(file, mode='RGB'):
"""
Loads an image file (.jpg, .png, etc) into a numpy array
:param file: image file name or file object to load
:param mode: format to convert the image to. Only 'RGB' (8-bit RGB, 3 channels) and 'L' (black and white) are supported.
:return: image contents as numpy array
"""
im = PIL.Image.open(file)
if mode:
im = im.convert(mode)
return np.array(im)
# Check for desired file extensions.
def allowed_file(filename):
return '.' in filename and \
filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS
# Prediction endpoint.
@app.route('/', methods=['GET', 'POST'])
def upload_image():
# Check if a valid image file was uploaded
if request.method == 'POST':
if 'file' not in request.files:
return redirect(request.url)
file = request.files['file']
if file.filename == '':
return redirect(request.url)
if file and allowed_file(file.filename):
# The image file seems valid! Detect faces and return the result.
return detect_covid(file)
# If no valid image file was uploaded, show the file upload form:
return '''
<!doctype html>
<title>Computerized Tomography COVID Detection.</title>
<head>
<style>
body {
background-color: gray;
}
</style>
</head>
<h1>Upload a CT image and see if its COVID positive!</h1>
<h2>The method gains an AUC of 95% So there is no gurantee of correct diagnosis.</h2>
<hr>
<form method="POST" enctype="multipart/form-data">
<input type="file" name="file">
<input type="submit" value="Upload">
</form>
'''
# Detection wrapper.
def detect_covid(file_stream):
image = load_image_file(file_stream)
result = detect(image, models)
return jsonify(result)
if __name__ == "__main__":
app.run()