Python tensorflow.crop_and_resize() Examples
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code examples of tensorflow.crop_and_resize().
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Example #1
Source File: anchor_projector.py From avod with MIT License | 6 votes |
def reorder_projected_boxes(box_corners): """Helper function to reorder image corners. This reorders the corners from [x1, y1, x2, y2] to [y1, x1, y2, x2] which is required by the tf.crop_and_resize op. Args: box_corners: tensor image corners in the format N x [x1, y1, x2, y2] Returns: box_corners_reordered: tensor image corners in the format N x [y1, x1, y2, x2] """ boxes_reordered = tf.stack([box_corners[:, 1], box_corners[:, 0], box_corners[:, 3], box_corners[:, 2]], axis=1) return boxes_reordered
Example #2
Source File: anchor_projector.py From TLNet with Apache License 2.0 | 6 votes |
def reorder_projected_boxes(box_corners): """Helper function to reorder image corners. This reorders the corners from [x1, y1, x2, y2] to [y1, x1, y2, x2] which is required by the tf.crop_and_resize op. Args: box_corners: tensor image corners in the format N x [x1, y1, x2, y2] Returns: box_corners_reordered: tensor image corners in the format N x [y1, x1, y2, x2] """ boxes_reordered = tf.stack([box_corners[:, 1], box_corners[:, 0], box_corners[:, 3], box_corners[:, 2]], axis=1) return boxes_reordered
Example #3
Source File: anchor_projector.py From avod-ssd with MIT License | 6 votes |
def reorder_projected_boxes(box_corners): """Helper function to reorder image corners. This reorders the corners from [x1, y1, x2, y2] to [y1, x1, y2, x2] which is required by the tf.crop_and_resize op. Args: box_corners: tensor image corners in the format N x [x1, y1, x2, y2] Returns: box_corners_reordered: tensor image corners in the format N x [y1, x1, y2, x2] """ boxes_reordered = tf.stack([box_corners[:, 1], box_corners[:, 0], box_corners[:, 3], box_corners[:, 2]], axis=1) return boxes_reordered
Example #4
Source File: roi_pool.py From Table-Detection-using-Deep-learning with BSD 3-Clause "New" or "Revised" License | 5 votes |
def _roi_crop(self, roi_proposals, conv_feature_map, im_shape): # Get normalized bounding boxes. bboxes = self._get_bboxes(roi_proposals, im_shape) # Generate fake batch ids bboxes_shape = tf.shape(bboxes) batch_ids = tf.zeros((bboxes_shape[0], ), dtype=tf.int32) # Apply crop and resize with extracting a crop double the desired size. crops = tf.image.crop_and_resize( conv_feature_map, bboxes, batch_ids, [self._pooled_width * 2, self._pooled_height * 2], name="crops" ) # Applies max pool with [2,2] kernel to reduce the crops to half the # size, and thus having the desired output. prediction_dict = { 'roi_pool': tf.nn.max_pool( crops, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding=self._pooled_padding ), } if self._debug: prediction_dict['bboxes'] = bboxes prediction_dict['crops'] = crops prediction_dict['batch_ids'] = batch_ids prediction_dict['conv_feature_map'] = conv_feature_map return prediction_dict
Example #5
Source File: roi_pool.py From Tabulo with BSD 3-Clause "New" or "Revised" License | 5 votes |
def _roi_crop(self, roi_proposals, conv_feature_map, im_shape): # Get normalized bounding boxes. bboxes = self._get_bboxes(roi_proposals, im_shape) # Generate fake batch ids bboxes_shape = tf.shape(bboxes) batch_ids = tf.zeros((bboxes_shape[0], ), dtype=tf.int32) # Apply crop and resize with extracting a crop double the desired size. crops = tf.image.crop_and_resize( conv_feature_map, bboxes, batch_ids, [self._pooled_width * 2, self._pooled_height * 2], name="crops" ) # Applies max pool with [2,2] kernel to reduce the crops to half the # size, and thus having the desired output. prediction_dict = { 'roi_pool': tf.nn.max_pool( crops, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding=self._pooled_padding ), } if self._debug: prediction_dict['bboxes'] = bboxes prediction_dict['crops'] = crops prediction_dict['batch_ids'] = batch_ids prediction_dict['conv_feature_map'] = conv_feature_map return prediction_dict
Example #6
Source File: roi_pool.py From luminoth with BSD 3-Clause "New" or "Revised" License | 5 votes |
def _roi_crop(self, roi_proposals, conv_feature_map, im_shape): # Get normalized bounding boxes. bboxes = self._get_bboxes(roi_proposals, im_shape) # Generate fake batch ids bboxes_shape = tf.shape(bboxes) batch_ids = tf.zeros((bboxes_shape[0], ), dtype=tf.int32) # Apply crop and resize with extracting a crop double the desired size. crops = tf.image.crop_and_resize( conv_feature_map, bboxes, batch_ids, [self._pooled_width * 2, self._pooled_height * 2], name="crops" ) # Applies max pool with [2,2] kernel to reduce the crops to half the # size, and thus having the desired output. prediction_dict = { 'roi_pool': tf.nn.max_pool( crops, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding=self._pooled_padding ), } if self._debug: prediction_dict['bboxes'] = bboxes prediction_dict['crops'] = crops prediction_dict['batch_ids'] = batch_ids prediction_dict['conv_feature_map'] = conv_feature_map return prediction_dict