def classify_random_image():
from PIL import Image
import random
import imageio
image_path = filename[random.randrange(0,len(filename),1)][0]
image = Image.open(image_path)
image.show()
my_image = imageio.imread(image_path)
my_image = np.expand_dims(my_image, axis = 0)
my_image = my_image/255.
my_image_prediction = round(shipModel.predict(my_image)[0,0])
print(class_dict[my_image_prediction])
def predict():
model = build_model()
model.load_weights('aug_model.h5')
image = load_img('fedex-logo.jpg', target_size=(224, 224))
image.show()
image = img_to_array(image)
# reshape data for the model
#image = image.reshape((1, image.shape[0], image.shape[1], image.shape[2]))
# prepare the image for the ResNet50 model
image = np.expand_dims(image, axis=0)
image = preprocess_input(image)
# predict the probability across all output classes
yhat = model.predict(image)
print(yhat)
max_val = np.argmax(yhat)
if yhat[0][max_val] * 100 >= 70:
print('%s (%.2f%%)' %
(parse_predictions[max_val], yhat[0][max_val] * 100))
else:
print("no logo detected")
''' Part e '''
path = 'dataset/single_prediction/diseased_or_healthy_2.jpg'
test_image = image.load_img(path, target_size=(128,128)) #Dimensions already specified
# Now since the data that we need to have should be a 3D, 3rd being the 3 for RGB
test_image = image.img_to_array(test_image)
# The data for the prediction needs to be 4D. 4th being the batch. Even though it is single but predict function
# expects the data to be in form of batches
test_image = np.expand_dims(test_image, axis=0)
result = classifier.predict(test_image)
# training_set.class_indices can be used to check the category encoding
animal = ""
if result==0:
animal = "Diseased"
else:
animal = "Healthy"
from PIL import Image, ImageDraw, ImageFont
image = Image.open(path)
font_type = ImageFont.truetype('arial.ttf',30)
draw = ImageDraw.Draw(image)
draw.text(xy=(0,0),text=animal,fill=(0,0,0),font=font_type)
image.show()
""" To improve accuracy, one more convulation layer can be added with either same or different parameters.
Add it after the max pooling and before the flattening.
Next layer won't be having input_shape parameter. Keras will automatically accept the pool of previous layer."""
model.summary()
# Start Fine-tuning
# Start Fine-tuning
model.fit(X_train, Y_train,batch_size=batch_size,epochs=nb_epoch,shuffle=True,verbose=1,validation_data=(X_valid, Y_valid))
# Make predictions
predictions_valid = model.predict(X_valid, batch_size=batch_size, verbose=1)
# Cross-entropy loss score
score = log_loss(Y_valid, predictions_valid)
'''
#https://machinelearningmastery.com/use-pre-trained-vgg-model-classify-objects-photographs/
'''
from keras.preprocessing.image import load_img
from keras.preprocessing.image import img_to_array
from keras.applications.vgg16 import preprocess_input
from keras.applications.vgg16 import decode_predictions
from keras.applications.vgg16 import VGG16
# load the model
model = VGG16()
# load an image from file
#image_path = "/home/terrence/Desktop/PHONE/Camera/"
#x = '20140218_170332'
#print(image_data(x))
image = load_img('/home/terrence/Desktop/PHONE/Camera/20140218_170332.jpg', target_size=(224, 224))
image.show()
# convert the image pixels to a numpy array
from keras.models import load_model
from keras.preprocessing.image import load_img, img_to_array
from numpy import array, expand_dims
from keras.preprocessing import image
#Loading Our Traied Model
model = load_model("face_actor_VGG_TL.h5")
# load an image from file
testing_image = "./data/httpabsolumentgratuitfreefrimagesbenaffleckjpg.jpg"
image = load_img(testing_image, target_size=(224, 224))
#Showing the image
image.show(testing_image)
# Convert to array and make 4D
image = array(image)
image = expand_dims(image, axis=0)
#Decoding Predictions
if model.predict(image)[0][0] > 0.9:
print("BEN_AFFLEK")
if model.predict(image)[0][1] > 0.9:
print("ELTON_JOHN")
if model.predict(image)[0][2] > 0.9:
print("JERRY_SEINFELD")
if model.predict(image)[0][3] > 0.9:
print("MADONNA")
if model.predict(image)[0][4] > 0.9:
def affiche_image(rawda):
data_set = ImageDataSet(filepath="data/01_raw/elephant.jpg")
image = data_set.load()
image.show()
def show(image):
image = image[0] * 255
image = image.astype(np.uint8)
image = Image.fromarray(image)
image.show()
