我们从Python开源项目中，提取了以下44个代码示例，用于说明如何使用utils.blob.prep_im_for_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(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
def _get_image_blob(self, roidb, scale_inds): """Builds an input blob from the images in the roidb at the specified scales. """ num_images = 1 # len(roidb) processed_ims = [] im_scales = [] for i in xrange(num_images): im = cv2.imread(roidb['image']) if roidb['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, roidb, scale_inds, im_names): """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(im_names[i]) # here [0][0] is due to the nature of scipy.io.savemat # since it will change True/False to [[1]] or [[0]] with shape (1,1) # so we judge whether flip image in this un-normal way if roidb[i]['Flip'][0][0]: 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): imname1 = roidb[i]['image'][data_i] imname2= imname1 + '_norm.png' im1= cv2.imread(imname1) im2= cv2.imread(imname2) if roidb[i]['flipped']: im1 = im1[:, ::-1, :] im2 = im2[:, ::-1, :] im2[:,:,2] = 255 - im2[:,:,2] im = np.zeros((im1.shape[0], im1.shape[1], 6)) im = im.astype('uint8') im1 = im1[:, :, ::-1] im2 = im2[:, :, ::-1] im[:,:,0:3] = im1 im[:,:,3:6] = im2 target_size = cfg.TRAIN.SCALES[scale_inds[i]] im, im_scale = prep_im_for_blob(im, 127.5, 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 _image_preprocess(img): target_scale = npr.choice(cfg.TRAIN.SCALES) im, im_scale = prep_im_for_blob(img, cfg.PIXEL_MEANS, target_scale, cfg.TRAIN.MAX_SIZE) return im, im_scale
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): if DEBUG: print 'Loading:', roidb[i]['image'] if cfg.TRAIN.FOURCHANNELS: im = cv2.imread(roidb[i]['image'], cv2.IMREAD_UNCHANGED) else: 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, four_channels=cfg.TRAIN.FOURCHANNELS) return blob, im_scales
