def run(self, path, choice=[0, 0, 0], top=3):
preds = []
x = self.load_image(path)
if choice[0] == 1:
preds.extend(self.resnet.predict(x))
if choice[1] == 1:
preds.extend(self.inception.predict(x))
if choice[2] == 1:
preds.extend(self.inceptionresnet.predict(x))
scores = map_scores(preds, top)
return scores
def run(self,path,choice=[0,0,0],top=3):
"""
Inputs image and predicts using selected model
# Arguments
path: String path to image
choice: Array of binary values for selecting models to infer.
Each index of array used to represent a model.
Ex:
[1, 1, 1] toggles [ResNet152V2, InceptionV3, InceptionResNetV2]'
top: Integer indicating the top 'X' classes of prediction.
# Returns
A list of lists of model name and top class prediction tuples
Ex:
path = 'image_path'
choice = [1,0,1]
top = 3
[
[
'ResNet152V2',
('nv', 'melanocytic nevi', 0.9999974),
('bkl', 'benign keratosis-like lesions, solar lentigines / seborrheic keratoses and lichen-planus like keratoses', 2.5060908e-06),
('akiec', "Actinic keratoses and intraepithelial carcinoma / Bowen's disease", 1.4720392e-07)
],
[
'InceptionResNetV2',
('nv', 'melanocytic nevi', 1.0),
('mel', 'melanoma', 2.9319884e-13),
('bkl', 'benign keratosis-like lesions, solar lentigines / seborrheic keratoses and lichen-planus like keratoses', 8.238215e-15)
]
]
"""
preds = []
x = self.load_image(path)
if choice[0] == 1:
arr = ['ResNet152V2']
arr.extend(self.resnet.predict(x))
preds.append(arr)
if choice[1] == 1:
arr = ['InceptionV3']
arr.extend(self.inception.predict(x))
preds.append(arr)
if choice[2] == 1:
arr = ['InceptionResNetV2']
arr.extend(self.inceptionresnet.predict(x))
preds.append(arr)
scores = map_scores(preds,top)
return scores
import numpy as np
import tensorflow as tf
import time
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
tf.keras.backend.set_session(tf.Session(config=config))
start_time = time.time()
dim_size = 224
input_tensor = Input(shape=(dim_size, dim_size, 3))
model = InceptionResNetV2(input_tensor=input_tensor, weights=None, classes=7)
model.load_weights('weights_inceptionresnet224.h5', by_name=False)
img_path = str(
Path(__file__).resolve().parents[1]) + '/test_imgs/keratosis1.jpg'
img = image.load_img(img_path, target_size=(dim_size, dim_size))
x = image.img_to_array(img)
x = x / 255.0
x = np.expand_dims(x, axis=0)
#x = preprocess_input(x)
preds = model.predict(x)
# decode the results into a list of tuples (class, description, probability)
# (one such list for each sample in the batch)
print('Predicted:', map_scores(preds, 3)[0])
elapsed_time = time.time() - start_time
print('Runtime: ', elapsed_time)
def predict(x, model):
preds = model.predict(x)
return map_scores(preds, 3)[0]