@@ -41,8 +41,8 @@ def _data_aug_fn(im, ann):
4141 ann = ann .decode ()
4242 ann = tl .prepro .parse_darknet_ann_str_to_list (ann )
4343 clas , coords = tl .prepro .parse_darknet_ann_list_to_cls_box (ann )
44- ## random brightness, contrast and saturation
45- im = tl .prepro .brightness (im , gamma = 0.5 , gain = 1 , is_random = True )
44+ ## random brightness, contrast and saturation (tf.image API is faster)
45+ # im = tl.prepro.brightness(im, gamma=0.5, gain=1, is_random=True)
4646 # im = tl.prepro.illumination(im, gamma=(0.5, 1.5),
4747 # contrast=(0.5, 1.5), saturation=(0.5, 1.5), is_random=True) # TypeError: Cannot handle this data type
4848 ## random horizontal flip
@@ -65,7 +65,12 @@ def _map_fn(filename, annotation):
6565 image = tf .read_file (filename )
6666 image = tf .image .decode_jpeg (image , channels = 3 )
6767 image = tf .image .convert_image_dtype (image , dtype = tf .float32 )
68- ## data augmentation
68+ ## data augmentation for image only 0.02s
69+ image = tf .image .random_brightness (image , max_delta = 63 )
70+ image = tf .image .random_contrast (image , lower = 0.2 , upper = 1.8 )
71+ # subtract off the mean and divide by the variance of the pixels. (optional)
72+ # img = tf.image.per_image_standardization(img)
73+ ## data augmentation for image and bounding box
6974 image , annotation = tf .py_func (_data_aug_fn , [image , annotation ], [tf .float32 , tf .string ])
7075 return image , annotation
7176
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