def _get_image_blob(roidb, scale_inds, args): """Builds an input blob from the images in the roidb at the specified scales. """ num_images = len(roidb) processed_ims = [] im_scales = [] crop_box = [] for i in range(num_images): im = cv2.imread(roidb[i]['image']) if roidb[i]['flipped']: im = im[:, ::-1, :] global_scale = args.scale_list[scale_inds[i]] im, im_scale, im_crop_box = prep_im_for_blob(im, cfg.PIXEL_MEANS, global_scale, args) crop_box.append(im_crop_box) 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, crop_box
def _get_image_blob(self, sample): im_blob = [] labels_blob = [] for i in range(len(sample)): if config.NUM_CHANNELS == 1: im = cv2.imread(sample[i], cv2.IMREAD_GRAYSCALE) else: im = cv2.imread(sample[i]) 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
def _get_image_blob(self,sample): im_blob = [] labels_blob = [] for i in range(self._batch_size): im = cv2.imread(config.IMAGEPATH+sample[i]) personname = sample[i].split('@')[0] #print str(i)+':'+personname+','+str(len(sample)) labels_blob.append(self.data_container._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(self, sample): im_blob = [] labels_blob = [] for i in range(self._batch_size): im = cv2.imread(config.IMAGEPATH + sample[i]) personname = sample[i].split('@')[0] #print str(i)+':'+personname+','+str(len(sample)) labels_blob.append(self.data_container._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(self,sample): im_blob = [] labels_blob = np.zeros((len(sample), 1), dtype=np.int32) for i in range(len(sample)): img_name = osp.join(cfg.IMAGEPATH, sample[i]) #print img_name im = cv2.imread(img_name) personname = sample[i].split('/')[0] #print str(i)+':'+personname+','+str(len(sample)) labels_blob[i, 0] = int(self.data_container._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(im): """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_scale_factors (list): list of image scales (relative to im) used in the image pyramid """ im_orig = im.astype(np.float32, copy=True) im_orig -= cfg.PIXEL_MEANS #[[[102.9801 115.9465 122.7717]]] 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 = [] for target_size in cfg.TEST.SCALES: # cfg.TEST.SCALES = [600] im_scale = float(target_size) / float( im_size_min) #600 / 输入图像比较近小的尺寸 # Prevent the biggest axis from being more than MAX_SIZE if np.round(im_scale * im_size_max) > cfg.TEST.MAX_SIZE: #1000 im_scale = float(cfg.TEST.MAX_SIZE) / float(im_size_max) im = cv2.resize(im_orig, None, None, fx=im_scale, fy=im_scale, interpolation=cv2.INTER_LINEAR) #fx fy是缩放的比例因子 im_scale_factors.append(im_scale) processed_ims.append(im) #以上就是 将 输入的图片 修改尺寸到 600 1000 限定在这个范围内 # processed_ims = [ im ]里面是修改内容图片尺寸在 600 1000 限定在这个范围内 blob = im_list_to_blob(processed_ims) # im_scale_factors.append(im_scale) #processed_ims.append(im) # for i in range(num_images): # im = ims[i] # blob[i, 0:im.shape[0], 0:im.shape[1], :] = im 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 = [] 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(im): """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_scale_factors (list): list of image scales (relative to im) used in the image pyramid """ im_orig = im.astype(np.float32, copy=True) im_orig -= cfg.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 = [] for target_size in cfg.TEST.SCALES: 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) > cfg.TEST.MAX_SIZE: im_scale = float(cfg.TEST.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) # Create a blob to hold the input images blob = im_list_to_blob(processed_ims) return blob, np.array(im_scale_factors)