def equalize_images(images_in, e_type):
images_out = imports.deepcopy(images_in)
for i in range(images_out.shape[0]):
arr_image = images_out[i]*255
image_to_eq = imports.array_to_img(arr_image)
if (e_type == 0):
images_out[i] = (imports.img_to_array(hsv_equalization(imports.np.asarray(image_to_eq))) / 255).astype('float32')
if (e_type == 1):
images_out[i] = (imports.img_to_array(rgb_equalization(imports.np.asarray(image_to_eq))) / 255).astype('float32')
if (e_type == 2):
images_out[i] = (imports.img_to_array(yuv_equalization(imports.np.asarray(image_to_eq))) / 255).astype('float32')
return images_out
def load_data_eq():
meta_data = imports.pd.read_csv('data/messidor/train/messidor_annotation.csv')
Y = meta_data['Retinopathy grade'].values
# Transform into binary classificaiton
if settings.nb_classes == 2:
Y[Y > 0] = 1
n_samples = Y.shape[0]
X = imports.np.empty((n_samples, settings.img_rows, settings.img_cols, 3))
for i in range(n_samples):
filename = './data/messidor/train/{}'.format(meta_data['Image name'][i])
img_cv = imports.cv2.resize(imports.cv2.imread(filename), (settings.img_rows, settings.img_cols))
x = imports.img_to_array(img_cv) / 255.0
X[i] = x.astype('float32')
input_shape_l = (settings.img_rows, settings.img_cols, 3)
return X, Y, input_shape_l