def rotate(): return tf_image.random_rotate90(image, bboxes, xs, ys)
def rotate(): return tf_image.random_rotate90(image, bboxes, xs, ys)
def preprocess_for_train(image, labels, bboxes, xs, ys, out_shape, data_format='NHWC', scope='ssd_preprocessing_train'): """Preprocesses the given image for training. Note that the actual resizing scale is sampled from [`resize_size_min`, `resize_size_max`]. Args: image: A `Tensor` representing an image of arbitrary size. output_height: The height of the image after preprocessing. output_width: The width of the image after preprocessing. resize_side_min: The lower bound for the smallest side of the image for aspect-preserving resizing. resize_side_max: The upper bound for the smallest side of the image for aspect-preserving resizing. Returns: A preprocessed image. """ fast_mode = False with tf.name_scope(scope, 'ssd_preprocessing_train', [image, labels, bboxes]): if image.get_shape().ndims != 3: raise ValueError('Input must be of size [height, width, C>0]') # Randomly flip the image horizontally. if FLIP: image, bboxes, xs, ys = tf_image.random_flip_left_right_bbox( image, bboxes, xs, ys) if ROTATE: # random rotate the image [-10, 10] image, bboxes, xs, ys = tf_rotate_image(image, xs, ys) # samples = tf.multinomial(tf.log([[0.25, 0.25, 0.25, 0.25]]), 1) # note log-prob # scale=elems[tf.cast(samples[0][0], tf.int32)] # if SCALE: # image,bboxes,xs,ys=tf_scale_image(image,bboxes,xs,ys,640) image_shape = tf.cast(tf.shape(image), dtype=tf.float32) image_h, image_w = image_shape[0], image_shape[1] if USE_NM_CROP: mask = tf.greater_equal(labels, 1) valid_bboxes = tf.boolean_mask(bboxes, mask) # FIXME bboxes may is empty # NOTE tf_func must return value must be numpy, or you will madding!!!!!! crop_bbox = tf.py_func(generate_sample, [image_shape, valid_bboxes], tf.float32) else: scales = tf.random_shuffle([0.5, 1.]) scales = tf.Print(scales, [crop]) target_h = tf.cast(640 / scales[0], dtype=tf.float32) target_w = tf.cast(640 / scales[0], dtype=tf.float32) bbox_begin_h_max = tf.maximum(image_h - target_h, 0) bbox_begin_w_max = tf.maximum(image_w - target_w, 0) bbox_begin_h = tf.random_uniform([], minval=0, maxval=bbox_begin_h_max, dtype=tf.float32) bbox_begin_w = tf.random_uniform([], minval=0, maxval=bbox_begin_w_max, dtype=tf.float32) crop_bbox = [bbox_begin_h/image_h, bbox_begin_w/image_w, \ (bbox_begin_h+target_h)/image_h, (bbox_begin_w+target_w)/image_w] image = tf.image.crop_and_resize(tf.expand_dims(image, 0), [crop_bbox], [0], (640, 640), extrapolation_value=128) image = tf.squeeze(image, 0) bboxes, xs, ys = tfe.bboxes_resize(crop_bbox, bboxes, xs, ys) labels, bboxes, xs, ys = tfe.bboxes_filter_overlap( labels, bboxes, xs, ys, threshold=BBOX_CROP_OVERLAP, assign_value=LABEL_IGNORE) if ROTATE_90: rnd = tf.random_uniform((), minval=0, maxval=1) image, bboxes, xs, ys = tf.cond( tf.less(rnd, 0.2), lambda: tf_image.random_rotate90(image, bboxes, xs, ys), lambda: (image, bboxes, xs, ys)) # tf_summary_image(tf.to_float(image), bboxes, 'crop_image') # what is the enpand's meanoing? # expand image if MAX_EXPAND_SCALE > 1: rnd2 = tf.random_uniform((), minval=0, maxval=1) def expand(): scale = tf.random_uniform([], minval=1.0, maxval=MAX_EXPAND_SCALE, dtype=tf.float32) image_shape = tf.cast(tf.shape(image), dtype=tf.float32) image_h, image_w = image_shape[0], image_shape[1] target_h = tf.cast(image_h * scale, dtype=tf.int32) target_w = tf.cast(image_w * scale, dtype=tf.int32) tf.logging.info('expanded') return tf_image.resize_image_bboxes_with_crop_or_pad( image, bboxes, xs, ys, target_h, target_w) def no_expand(): return image, bboxes, xs, ys image, bboxes, xs, ys = tf.cond(tf.less(rnd2, config.expand_prob), expand, no_expand) # Convert to float scaled [0, 1]. # if image.dtype != tf.float32: # image = tf.image.convert_image_dtype(image, dtype=tf.float32) # tf_summary_image(image, bboxes, 'image_with_bboxes') # Distort image and bounding boxes. dst_image = image # use tf.image.sample_distorted_bounding_box() random crop train patch, but can't control the scale if False: dst_image, labels, bboxes, xs, ys, distort_bbox = \ distorted_bounding_box_crop(image, labels, bboxes, xs, ys, min_object_covered=MIN_OBJECT_COVERED, aspect_ratio_range=CROP_ASPECT_RATIO_RANGE, area_range=AREA_RANGE) # Resize image to output size. dst_image = tf_image.resize_image( dst_image, out_shape, method=tf.image.ResizeMethod.BILINEAR, align_corners=False) # Filter bboxes using the length of shorter sides if USING_SHORTER_SIDE_FILTERING: xs = xs * out_shape[1] ys = ys * out_shape[0] labels, bboxes, xs, ys = tfe.bboxes_filter_by_shorter_side( labels, bboxes, xs, ys, min_height=MIN_SHORTER_SIDE, max_height=MAX_SHORTER_SIDE, assign_value=LABEL_IGNORE) xs = xs / out_shape[1] ys = ys / out_shape[0] # Randomly distort the colors. There are 4 ways to do it. dst_image = apply_with_random_selector( dst_image / 255.0, lambda x, ordering: distort_color(x, ordering, fast_mode), num_cases=4) dst_image = dst_image * 255. # tf_summary_image(dst_image, bboxes, 'image_color_distorted') # FIXME: change the input value # NOTE: resnet v1 use VGG data process, so we use the same way image = tf_image_whitened(image, [_R_MEAN, _G_MEAN, _B_MEAN]) # Image data format. if data_format == 'NCHW': image = tf.transpose(image, perm=(2, 0, 1)) return image, labels, bboxes, xs, ys