def generate_x(self, cfg: SampleCfg):
img = scipy.misc.imread(dataset.image_crop_fn(cfg.fish_classification.video_id, cfg.fish_classification.frame))
crop = utils.get_image_crop(full_rgb=img, rect=cfg.rect,
scale_rect_x=cfg.scale_rect_x, scale_rect_y=cfg.scale_rect_y,
shift_x_ratio=cfg.shift_x_ratio, shift_y_ratio=cfg.shift_y_ratio,
angle=cfg.angle, out_size=INPUT_ROWS)
crop = crop.astype('float32')
if cfg.saturation != 0.5:
crop = img_augmentation.saturation(crop, variance=0.2, r=cfg.saturation)
if cfg.contrast != 0.5:
crop = img_augmentation.contrast(crop, variance=0.25, r=cfg.contrast)
if cfg.brightness != 0.5:
crop = img_augmentation.brightness(crop, variance=0.3, r=cfg.brightness)
if cfg.hflip:
crop = img_augmentation.horizontal_flip(crop)
if cfg.vflip:
crop = img_augmentation.vertical_flip(crop)
if cfg.blurred_by_downscaling != 1:
crop = img_augmentation.blurred_by_downscaling(crop, 1.0 / cfg.blurred_by_downscaling)
return crop * 255.0
def prepare_y(self, cfg: SampleCfg):
img = self.masks[cfg.img_idx].astype(np.float32) / 255.0
crop = utils.get_image_crop(full_rgb=img,
rect=Rect(0, 0, IMG_WITH, IMG_HEIGHT),
scale_rect_x=cfg.scale_rect_x,
scale_rect_y=cfg.scale_rect_y,
shift_x_ratio=cfg.shift_x_ratio,
shift_y_ratio=cfg.shift_y_ratio,
angle=cfg.angle,
out_size=IMG_WITH,
order=1)
crop = crop.astype('float32')
if cfg.hflip:
crop = img_augmentation.horizontal_flip(crop)
if cfg.vflip:
crop = img_augmentation.vertical_flip(crop)
return np.expand_dims(crop, axis=3)
def prepare_x(self, cfg: SampleCfg):
img = self.images[cfg.img_idx]
crop = utils.get_image_crop(full_rgb=img,
rect=Rect(0, 0, IMG_WITH, IMG_HEIGHT),
scale_rect_x=cfg.scale_rect_x,
scale_rect_y=cfg.scale_rect_y,
shift_x_ratio=cfg.shift_x_ratio,
shift_y_ratio=cfg.shift_y_ratio,
angle=cfg.angle,
out_size=IMG_WITH)
crop = crop.astype('float32')
if cfg.saturation != 0.5:
crop = img_augmentation.saturation(crop,
variance=0.2,
r=cfg.saturation)
if cfg.contrast != 0.5:
crop = img_augmentation.contrast(crop,
variance=0.25,
r=cfg.contrast)
if cfg.brightness != 0.5:
crop = img_augmentation.brightness(crop,
variance=0.3,
r=cfg.brightness)
if cfg.hflip:
crop = img_augmentation.horizontal_flip(crop)
if cfg.vflip:
crop = img_augmentation.vertical_flip(crop)
if cfg.blurred_by_downscaling != 1:
crop = img_augmentation.blurred_by_downscaling(
crop, 1.0 / cfg.blurred_by_downscaling)
return preprocess_input(crop * 255.0)
def gen_x(self, cfg: SampleCfg):
if cfg.scale == 1.0:
crop = self.gen_img_crop(img=cfg.img, row=cfg.row,
col=cfg.col).astype(np.float32)
else:
crop = utils.crop_zero_pad(cfg.img,
x=cfg.col,
y=cfg.row,
w=int(self.crop_size * cfg.scale),
h=int(self.crop_size * cfg.scale))
# print(crop.shape, crop.dtype)
crop = imresize(crop, [self.crop_size, self.crop_size],
interp='bicubic')
crop = crop / 255.0
if cfg.saturation != 0.5:
crop = img_augmentation.saturation(crop,
variance=0.25,
r=cfg.saturation)
if cfg.contrast != 0.5:
crop = img_augmentation.contrast(crop,
variance=0.5,
r=cfg.contrast)
if cfg.brightness != 0.5:
crop = img_augmentation.brightness(crop,
variance=0.7,
r=cfg.brightness)
if cfg.hflip:
crop = img_augmentation.horizontal_flip(crop)
if cfg.vflip:
crop = img_augmentation.vertical_flip(crop)
return crop * 255.0