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fast_rcnn.config.cfg.PIXEL_MEANS。
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 _vis_minibatch(im_blob, rois_blob, labels_blob, overlaps): """Visualize a mini-batch for debugging.""" import matplotlib.pyplot as plt for i in xrange(rois_blob.shape[0]): rois = rois_blob[i, :] im_ind = rois[0] roi = rois[1:] im = im_blob[im_ind, :, :, :].transpose((1, 2, 0)).copy() im += cfg.PIXEL_MEANS im = im[:, :, (2, 1, 0)] im = im.astype(np.uint8) cls = labels_blob[i] plt.imshow(im) print 'class: ', cls, ' overlap: ', overlaps[i] plt.gca().add_patch( plt.Rectangle((roi[0], roi[1]), roi[2] - roi[0], roi[3] - roi[1], fill=False, edgecolor='r', linewidth=3) ) plt.show()
def setup(self, bottom, top): # (1, 3, 1, 1) shaped arrays self.PIXEL_MEANS = \ np.array([[[[0.48462227599918]], [[0.45624044862054]], [[0.40588363755159]]]]) self.PIXEL_STDS = \ np.array([[[[0.22889466674951]], [[0.22446679341259]], [[0.22495548344775]]]]) # The default ("old") pixel means that were already subtracted channel_swap = (0, 3, 1, 2) self.OLD_PIXEL_MEANS = \ cfg.PIXEL_MEANS[np.newaxis, :, :, :].transpose(channel_swap) top[0].reshape(*(bottom[0].shape))
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(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 _vis_minibatch(im_blob, rois_blob, labels_blob, sublabels_blob): """Visualize a mini-batch for debugging.""" import matplotlib.pyplot as plt for i in xrange(rois_blob.shape[0]): rois = rois_blob[i, :] im_ind = rois[0] roi = rois[2:] im = im_blob[im_ind, :, :, :].transpose((1, 2, 0)).copy() im += cfg.PIXEL_MEANS im = im[:, :, (2, 1, 0)] im = im.astype(np.uint8) cls = labels_blob[i] subcls = sublabels_blob[i] plt.imshow(im) print 'class: ', cls, ' subclass: ', subcls plt.gca().add_patch( plt.Rectangle((roi[0], roi[1]), roi[2] - roi[0], roi[3] - roi[1], fill=False, edgecolor='r', linewidth=3) ) plt.show()
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(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
