def prep_image(image, target):
if (image.mode != "RGB"):
image = image.convert("RGB")
image = image.resize(target)
image = img_to_array(image)
image = np.expand_dims(image, axis=0)
image = imagenet_utils.preprocess_input(image)
return image
def color_classification(single_path):
"""
This function does color classification for a certain path of a photo (of a clothes)
Input is a path on your computer
Output is a color
"""
image = Image.open(single_path)
image = image.convert('RGB')
return get_cloth_color(image)
def return_predictions(model, image):
if image.mode != "RGB":
image = image.convert("RGB")
adding_dimensions = np.expand_dims(image, axis=0)
prediction = model.predict(adding_dimensions)
if prediction > 0.5:
return 'uninfected'
else:
return 'parasitized'
return prediction
def preprocess_image(image, target_size):
'''function to preprocess input image'''
if image.mode != "RGB":
image = image.convert("RGB") # if image is not RGB then convert to RGB
image = image.resize(target_size) # resize image to defined target size
# image = image.img_to_array(image) # convert image to an array of numbers
image = asarray(image)
image = image.astype(np.float32) # convert from uint8 to float32
image = np.expand_dims(image, axis=0) # expand the dimension of the image
return image
def process_image(image):
#read image
image = Image.open(BytesIO(image))
if image.mode != "RGB":
image = image.convert("RGB")
# resize and convert to tensor
image = image.resize((96, 96))
image = img_to_array(image)
image = preprocess_input(image)
image = np.expand_dims(image, axis=0)
return image
def prepare_image(image, target):
# if the image mode is not RGB, convert it
if image.mode != "RGB":
image = image.convert("RGB")
# resize the input image and preprocess it
image = image.resize(target)
image = img_to_array(image)
image = np.expand_dims(image, axis=0)
image = imagenet_utils.preprocess_input(image)
# return the processed image
return image
def get(self, user_index):
credentials_json = open("credentials.json", "rb")
config = pickle.load(credentials_json)
credentials_json.close()
MODEL_PATH = 'models/covid_new_model1.model'
# Load your own trained model
model = load_model(MODEL_PATH)
model._make_predict_function()
firebase = pyrebase.initialize_app(config)
db = firebase.database()
nm = db.child("Appointments").child(user_index).get()
res = nm.val()
xray_frame = res['xraybase64']
image = base64_to_pil(xray_frame)
if image.mode != "RGB":
image = image.convert("RGB")
image = image.resize((224, 224))
image = img_to_array(image)
image /= 255
image = np.expand_dims(image, axis=0)
# Be careful how your trained model deals with the input
# otherwise, it won't make correct prediction!
preds = model.predict(image)
pred_proba = "{:.3f}".format(np.amax(preds)) # Max probability
pred_class = preds
print(pred_proba)
print(pred_class)
if pred_class[0][0] >= 0.5:
result = "Covid-19 tested positive. [Confidence ratio: " + str(
round(pred_class[0][0] * 100, 2)) + "%]"
# result += str(pred_class[0][0])
else:
result = "Covid-19 tested negative. [Confidence ratio: " + str(
round((1 - pred_class[0][0]) * 100, 2)) + "%]"
# result += str(pred_class[0][1])
print(result)
db.child("Appointments").child(user_index).update({"report": result})
return (1)
def upload_file():
if request.method == 'POST':
# check if the post request has the file part
if 'file' not in request.files:
flash('No file part')
return redirect(request.url)
file = request.files['file']
# if user does not select file, browser also
# submit an empty part without filename
if file.filename == '':
flash('No selected file')
return redirect(request.url)
if file and allowed_file(file.filename):
filename = secure_filename(file.filename)
file.save(os.path.join(app.config['UPLOAD_FOLDER'], filename))
filepath = os.path.join(app.config['UPLOAD_FOLDER'], filename)
# model = load_model('./animal_cnn_aug.h5')
model = VGG16(weights='imagenet')
image = Image.open(filepath)
image = image.convert('RGB')
image = image.resize((image_size, image_size))
data = np.asarray(image)
X = []
X.append(data)
X = np.array(X)
result = model.predict([X])[0]
predicted = result.argmax()
percentage = int(result[predicted] * 100)
return "ラベル： " + classes[predicted] + ", 確率："+ str(percentage) + " %"
# return redirect(url_for('uploaded_file',
# filename=filename))
return '''
def predict(features_array):
df = pd.DataFrame(columns=[
'dst_port', 'protocol', 'timestamp', 'flow_duration', 'tot_fwd_pkts',
'tot_bwd_pkts', 'totlen_fwd_pkts', 'totlen_bwd_pkts', 'flow_byts/s',
'flow_pkts/s', 'fwd_iat_tot', 'bwd_iat_tot', 'fwd_psh_flags',
'bwd_psh_flags', 'fwd_urg_flags', 'bwd_urg_flags', 'fwd_header_len',
'bwd_header_len', 'fwd_pkts/s', 'bwd_pkts/s', 'pkt_len_var',
'fin_flag_cnt', 'syn_flag_cnt', 'rst_flag_cnt', 'psh_flag_cnt',
'ack_flag_cnt', 'urg_flag_cnt', 'cwe_flag_count', 'ece_flag_cnt',
'down/up_ratio', 'pkt_size_avg', 'fwd_seg_size_avg',
'bwd_seg_size_avg', 'fwd_byts/b_avg', 'fwd_pkts/b_avg',
'fwd_blk_rate_avg', 'bwd_byts/b_avg', 'bwd_pkts/b_avg',
'bwd_blk_rate_avg', 'subflow_fwd_pkts', 'subflow_fwd_byts',
'subflow_bwd_pkts', 'subflow_bwd_byts', 'init_fwd_win_byts',
'init_bwd_win_byts', 'fwd_act_data_pkts'
])
df.loc[len(df), :] = features_array
x_1 = numeric_features(df)
image = convert_to_image(x_1)
image = image.convert("RGB")
img = np.array(image)
img = np.expand_dims(img, 0)
all_models = list()
all_models.append(model_res)
all_models.append(model_vgg)
yhats = [model.predict(img) for model in all_models]
# sum across ensemble members
summed = np.sum(yhats, axis=0)
# argmax across classes
result = np.argmax(summed, axis=1)
if (result == 0):
return False
else:
return True
