landmark_detection.py

### Function used for processing the data, fitted into a data generator.
def get_image_from_number(num, df):
    fname, label = df.iloc[num,:]
    fname = fname + ".jpg"
    f1 = fname[0]
    f2 = fname[1]
    f3 = fname[2]
    path = os.path.join(f1,f2,f3,fname)
    im = cv2.imread(os.path.join(base_path,path))
    return im, label

def image_reshape(im, target_size):
    return cv2.resize(im, target_size)
    
def get_batch(dataframe,start, batch_size):
    image_array = []
    label_array = []
    
    end_img = start+batch_size
    if end_img > len(dataframe):
        end_img = len(dataframe)

    for idx in range(start, end_img):
        n = idx
        im, label = get_image_from_number(n, dataframe)
        im = image_reshape(im, (224, 224)) / 255.0
        image_array.append(im)
        label_array.append(label)
        
    label_array = encode_label(label_array)
    return np.array(image_array), np.array(label_array)
batch_size = 16
epoch_shuffle = True
weight_classes = True
epochs = 15

# Split train data up into 80% and 20% validation
train, validate = np.split(df.sample(frac=1), [int(.8*len(df))])
print("Training on:", len(train), "samples")
print("Validation on:", len(validate), "samples")

    
for e in range(epochs):
    print("Epoch: ", str(e+1) + "/" + str(epochs))
    if epoch_shuffle:
        train = train.sample(frac = 1)
    for it in range(int(np.ceil(len(train)/batch_size))):

        X_train, y_train = get_batch(train, it*batch_size, batch_size)

        model.train_on_batch(X_train, y_train)
        

model.save("Model.h5")