def rgb_processing(self, rgb_img, center, scale, rot, flip, pn, is_train): rgb_img = crop(rgb_img.copy(), center, scale, [constants.IMG_RES, constants.IMG_RES], rot=rot) if is_train: if flip: rgb_img = flip_img(rgb_img) rgb_img = np.transpose(rgb_img.astype('float32'),(2,0,1))/255.0 return rgb_img
def rgb_processing(self, rgb_img, center, scale, rot, flip, pn, img_size): """Process rgb image and do augmentation.""" if self.is_rotate: rgb_img = crop_v2(rgb_img, center, scale, [constants.IMG_RES, constants.IMG_RES], rot=rot) # flip the image if flip: rgb_img = flip_img(rgb_img) # in the rgb image we add pixel noise in a channel-wise manner rgb_img[:, :, 0] = np.minimum(255.0, np.maximum(0.0, rgb_img[:, :, 0] * pn[0])) rgb_img[:, :, 1] = np.minimum(255.0, np.maximum(0.0, rgb_img[:, :, 1] * pn[1])) rgb_img[:, :, 2] = np.minimum(255.0, np.maximum(0.0, rgb_img[:, :, 2] * pn[2])) if img_size == 224: rgb_img_up = rgb_img.copy() # add color jitter if self.is_train: rgb_img_up = color_jitter(rgb_img_up, brightness=0.4, contrast=0.4, saturation=0.4, prob=0.5) rgb_img_up = rgb_img_up.clip(0, 255) else: shape = rgb_img.shape rgb_img_lr = scipy.misc.imresize(rgb_img, (img_size, img_size), interp='bicubic') rgb_img_lr = rgb_img_lr.clip(0, 255) rgb_img_up = scipy.misc.imresize(rgb_img_lr, (shape[0], shape[1]), interp='bicubic') # naive upsampling # add color jitter if self.is_train: rgb_img_up = color_jitter(rgb_img_up, brightness=0.4, contrast=0.4, saturation=0.4, prob=0.5) rgb_img_up = rgb_img_up.clip(0, 255) rgb_img_up = np.transpose(rgb_img_up.astype('float32'), (2, 0, 1)) / 255.0 return rgb_img_up
def rgb_processing(self, rgb_img, center, scale, rot, flip, pn): """Process rgb image and do augmentation.""" rgb_img = crop(rgb_img, center, scale, [constants.IMG_RES, constants.IMG_RES], rot=rot) # flip the image if flip: rgb_img = flip_img(rgb_img) # in the rgb image we add pixel noise in a channel-wise manner rgb_img[:,:,0] = np.minimum(255.0, np.maximum(0.0, rgb_img[:,:,0]*pn[0])) rgb_img[:,:,1] = np.minimum(255.0, np.maximum(0.0, rgb_img[:,:,1]*pn[1])) rgb_img[:,:,2] = np.minimum(255.0, np.maximum(0.0, rgb_img[:,:,2]*pn[2])) # (3,224,224),float,[0,1] rgb_img = np.transpose(rgb_img.astype('float32'),(2,0,1))/255.0 return rgb_img
def rgb_processing(self, rgb_img, center, scale, rot, flip, pn): """Process rgb image and do augmentation.""" # crop and rotate the image if self.use_augmentation_rot: rgb_img = crop(rgb_img, center, scale, [self.options.img_res, self.options.img_res], rot=rot) else: rgb_img = crop(rgb_img, center, scale, [self.options.img_res, self.options.img_res], rot=0) # flip the image if flip: rgb_img = flip_img(rgb_img) # in the rgb image we add pixel noise in a channel-wise manner if self.use_augmentation_rgb: rgb_img[:,:,0] = np.minimum(255.0, np.maximum(0.0, rgb_img[:,:,0]*pn[0])) rgb_img[:,:,1] = np.minimum(255.0, np.maximum(0.0, rgb_img[:,:,1]*pn[1])) rgb_img[:,:,2] = np.minimum(255.0, np.maximum(0.0, rgb_img[:,:,2]*pn[2])) # (3,224,224),float,[0,1] rgb_img = np.transpose(rgb_img.astype('float32'),(2,0,1))/255.0 return rgb_img