我们从Python开源项目中，提取了以下50个代码示例，用于说明如何使用utils.blob.im_list_to_blob()。
def _get_image_blob(roidb, scale_inds): """Builds an input blob from the images in the roidb at the specified scales. """ num_images = len(roidb) processed_ims = [] im_scales = [] for i in range(num_images): im = cv2.imread(roidb[i]['image']) if roidb[i]['flipped']: im = im[:, ::-1, :] target_size = cfg.TRAIN.SCALES[scale_inds[i]] im, im_scale = prep_im_for_blob(im, cfg.PIXEL_MEANS, target_size, cfg.TRAIN.MAX_SIZE) im_scales.append(im_scale) processed_ims.append(im) # Create a blob to hold the input images blob = im_list_to_blob(processed_ims) return blob, im_scales
def _get_image_blob(roidb, scale_inds): """Builds an input blob from the images in the roidb at the specified scales. """ num_images = len(roidb) processed_ims = [] im_scales = [] im_shapes = np.zeros((0, 2), dtype=np.float32) for i in xrange(num_images): im = cv2.imread(roidb[i]['image']) if roidb[i]['flipped']: im = im[:, ::-1, :] target_size = cfg.TRAIN.SCALES[scale_inds[i]] im, im_scale, im_shape = prep_im_for_blob(im, cfg.PIXEL_MEANS, target_size) im_scales.append(im_scale) processed_ims.append(im) im_shapes = np.vstack((im_shapes, im_shape)) # Create a blob to hold the input images blob = im_list_to_blob(processed_ims) return blob, im_scales, im_shapes
def _get_image_blob(roidb, scale_inds): """Builds an input blob from the images in the roidb at the specified scales. """ num_images = len(roidb) processed_ims = [] im_scales = [] for i in xrange(num_images): im = cv2.imread(roidb[i]['image']) if roidb[i]['flipped']: im = im[:, ::-1, :] target_size = cfg.TRAIN.SCALES[scale_inds[i]] im, im_scale = prep_im_for_blob(im, cfg.PIXEL_MEANS, target_size, cfg.TRAIN.MAX_SIZE) im_scales.append(im_scale) processed_ims.append(im) # Create a blob to hold the input images blob = im_list_to_blob(processed_ims) return blob, im_scales
def _get_image_blob(roidb, scale_inds, data_i): """Builds an input blob from the images in the roidb at the specified scales. """ num_images = len(roidb) processed_ims = [] im_scales = [] for i in xrange(num_images): im = cv2.imread(roidb[i]['image'][data_i]) if roidb[i]['flipped']: im = im[:, ::-1, :] target_size = cfg.TRAIN.SCALES[scale_inds[i]] im, im_scale = prep_im_for_blob(im, cfg.PIXEL_MEANS, target_size, cfg.TRAIN.MAX_SIZE) im_scales.append(im_scale) processed_ims.append(im) # Create a blob to hold the input images blob = im_list_to_blob(processed_ims) return blob, im_scales
def _get_image_blob(self, sample): im_blob = [] labels_blob = [] for i in range(self.batch_size): ImgAsColr = 0 if cfg.CHANNEL_SIZE == 3: ImgAsColr = 1 im = cv2.imread(cfg.IMAGEPATH + sample[i]['picname'],ImgAsColr) if sample[i]['flipped']: im = im[:, ::-1, :] personname = sample[i]['picname'].split('/')[0] labels_blob.append(self._data._sample_label[personname]) im = prep_im_for_blob(im) im_blob.append(im) # Create a blob to hold the input images blob = im_list_to_blob(im_blob) return blob, labels_blob
def _get_image_blob(roidb, scale_inds): """Builds an input blob from the images in the roidb at the specified scales. """ num_images = len(roidb) processed_ims = [] im_scales = [] global_vars.image_files = [] for i in xrange(num_images): im = cv2.imread(roidb[i]['image']) global_vars.image_files.append(roidb[i]['image']) if roidb[i]['flipped']: im = im[:, ::-1, :] target_size = cfg.TRAIN.SCALES[scale_inds[i]] im, im_scale = prep_im_for_blob(im, cfg.PIXEL_MEANS, target_size, cfg.TRAIN.MAX_SIZE) im_scales.append(im_scale) processed_ims.append(im) # Create a blob to hold the input images blob = im_list_to_blob(processed_ims) return blob, im_scales
def _get_image_blob(ims, target_size): """Converts an image into a network input. Arguments: im (ndarray): a color image in BGR order Returns: blob (ndarray): a data blob holding an image pyramid im_infos(ndarray): a data blob holding input size pyramid """ processed_ims = [] for im in ims: im = im.astype(np.float32, copy = False) im = im - cfg.PIXEL_MEANS im_shape = im.shape[0:2] im = cv2.resize(im, None, None, fx = float(target_size) / im_shape[1], \ fy = float(target_size) / im_shape[0], interpolation = cv2.INTER_LINEAR) processed_ims.append(im) # Create a blob to hold the input images blob = im_list_to_blob(processed_ims) return blob
def _get_image_blob(roidb, scale_inds): """Builds an input blob from the images in the roidb at the specified scales. """ num_images = len(roidb) processed_ims = [] im_scales = [] for i in xrange(num_images): im = cv2.imread(roidb[i]['image']) if roidb[i]['flipped']: im = im[:, ::-1, :] im_orig = im.astype(np.float32, copy=True) im_orig -= cfg.PIXEL_MEANS im_scale = cfg.TRAIN.SCALES_BASE[scale_inds[i]] im = cv2.resize(im_orig, None, None, fx=im_scale, fy=im_scale, interpolation=cv2.INTER_LINEAR) im_scales.append(im_scale) processed_ims.append(im) # Create a blob to hold the input images blob = im_list_to_blob(processed_ims) return blob, im_scales
def _get_image_blob_multiscale(roidb): """Builds an input blob from the images in the roidb at multiscales. """ num_images = len(roidb) processed_ims = [] im_scales = [] scales = cfg.TRAIN.SCALES_BASE for i in xrange(num_images): im = cv2.imread(roidb[i]['image']) if roidb[i]['flipped']: im = im[:, ::-1, :] im_orig = im.astype(np.float32, copy=True) im_orig -= cfg.PIXEL_MEANS for im_scale in scales: im = cv2.resize(im_orig, None, None, fx=im_scale, fy=im_scale, interpolation=cv2.INTER_LINEAR) im_scales.append(im_scale) processed_ims.append(im) # Create a blob to hold the input images blob = im_list_to_blob(processed_ims) return blob, im_scales
def get_image_blob_noscale(self, im): im_orig = im.astype(np.float32, copy=True) im_orig -= self.PIXEL_MEANS im_shape = im_orig.shape im_size_min = np.min(im_shape[0:2]) im_size_max = np.max(im_shape[0:2]) processed_ims = [] im_scale_factors = [] target_size = self.SCALES[0] im_scale = float(target_size) / float(im_size_min) # Prevent the biggest axis from being more than MAX_SIZE if np.round(im_scale * im_size_max) > self.MAX_SIZE: im_scale = float(self.MAX_SIZE) / float(im_size_max) im = cv2.resize(im_orig, None, None, fx=im_scale, fy=im_scale, interpolation=cv2.INTER_LINEAR) im_scale_factors.append(im_scale) processed_ims.append(im) blob = im_list_to_blob(processed_ims) return blob, np.array(im_scale_factors)
def _get_image_blob(roidb, scale_inds): """Builds an input blob from the images in the roidb at the specified scales. """ num_images = len(roidb) processed_ims = [] im_scales = [] im_shapes = np.zeros((0, 2), dtype=np.float32) for i in xrange(num_images): im = cv2.imread(roidb[i]['image']) if roidb[i]['flipped']: im = im[:, ::-1, :] target_size = cfg.TRAIN.SCALES[scale_inds[i]] im, im_scale, im_shape = prep_im_for_blob(im, cfg.PIXEL_MEANS, target_size, cfg.TRAIN.MAX_SIZE) im_scales.append(im_scale) processed_ims.append(im) im_shapes = np.vstack((im_shapes, im_shape)) # Create a blob to hold the input images blob = im_list_to_blob(processed_ims) return blob, im_scales, im_shapes
def _get_image_blob(self, sample): im_blob = [] labels_blob = [] for i in range(self.batch_size): im = cv2.imread(cfg.IMAGEPATH + sample[i]['picname']) if sample[i]['flipped']: im = im[:, ::-1, :] personname = sample[i]['picname'].split('/')[0] labels_blob.append(self._data._sample_label[personname]) im = prep_im_for_blob(im) im_blob.append(im) # Create a blob to hold the input images blob = im_list_to_blob(im_blob) return blob, labels_blob
def _get_image_blob(imdb, roidb, scale_inds): """Builds an input blob from the images in the roidb at the specified scales. """ num_images = len(roidb) processed_ims = [] im_scales = [] for i in xrange(num_images): proto = imdb.get_proto_at(roidb[i]['image']) mem = BytesIO(proto.data) im = io.imread(mem) im = im[:,:,::-1] if roidb[i]['flipped']: im = im[:, ::-1, :] target_size = cfg.TRAIN.SCALES[scale_inds[i]] im, im_scale = prep_im_for_blob(im, cfg.PIXEL_MEANS, target_size, cfg.TRAIN.MAX_SIZE, cfg.TRAIN.SCALE_MULTIPLE_OF) im_scales.append(im_scale) processed_ims.append(im) # Create a blob to hold the input images blob = im_list_to_blob(processed_ims) return blob, im_scales
def _get_image_blob(roidb, scale_inds): """Builds an input blob from the images in the roidb at the specified scales. """ num_images = len(roidb) processed_ims = [] im_scales = [] for i in xrange(num_images): im = roidb[i]['image']() # use image getter if roidb[i]['flipped']: im = im[:, ::-1, :] target_size = cfg.TRAIN.SCALES[scale_inds[i]] im, im_scale = prep_im_for_blob(im, cfg.PIXEL_MEANS, target_size, cfg.TRAIN.MAX_SIZE) im_scales.append(im_scale) processed_ims.append(im) # Create a blob to hold the input images blob = im_list_to_blob(processed_ims) return blob, im_scales
