# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import absolute_import from __future__ import division from __future__ import print_function import os, sys # add python path of PadleDetection to sys.path parent_path = os.path.abspath(os.path.join(__file__, *(['..'] * 2))) if parent_path not in sys.path: sys.path.append(parent_path) # ignore numba warning import warnings warnings.filterwarnings('ignore') import glob import numpy as np from PIL import Image import paddle from paddle.distributed import ParallelEnv from ppdet.core.workspace import load_config, merge_config, create from ppdet.utils.check import check_gpu, check_version, check_config from ppdet.utils.visualizer import visualize_results from ppdet.utils.cli import ArgsParser from ppdet.utils.checkpoint import load_weight from ppdet.utils.eval_utils import get_infer_results import logging FORMAT = '%(asctime)s-%(levelname)s: %(message)s' logging.basicConfig(level=logging.INFO, format=FORMAT) logger = logging.getLogger(__name__) def parse_args(): parser = ArgsParser() parser.add_argument( "--infer_dir", type=str, default=None, help="Directory for images to perform inference on.") parser.add_argument( "--infer_img", type=str, default=None, help="Image path, has higher priority over --infer_dir") parser.add_argument( "--output_dir", type=str, default="output", help="Directory for storing the output visualization files.") parser.add_argument( "--draw_threshold", type=float, default=0.5, help="Threshold to reserve the result for visualization.") parser.add_argument( "--use_vdl", type=bool, default=False, help="whether to record the data to VisualDL.") parser.add_argument( '--vdl_log_dir', type=str, default="vdl_log_dir/image", help='VisualDL logging directory for image.') args = parser.parse_args() return args def get_save_image_name(output_dir, image_path): """ Get save image name from source image path. """ if not os.path.exists(output_dir): os.makedirs(output_dir) image_name = os.path.split(image_path)[-1] name, ext = os.path.splitext(image_name) return os.path.join(output_dir, "{}".format(name)) + ext def get_test_images(infer_dir, infer_img): """ Get image path list in TEST mode """ assert infer_img is not None or infer_dir is not None, \ "--infer_img or --infer_dir should be set" assert infer_img is None or os.path.isfile(infer_img), \ "{} is not a file".format(infer_img) assert infer_dir is None or os.path.isdir(infer_dir), \ "{} is not a directory".format(infer_dir) # infer_img has a higher priority if infer_img and os.path.isfile(infer_img): return [infer_img] images = set() infer_dir = os.path.abspath(infer_dir) assert os.path.isdir(infer_dir), \ "infer_dir {} is not a directory".format(infer_dir) exts = ['jpg', 'jpeg', 'png', 'bmp'] exts += [ext.upper() for ext in exts] for ext in exts: images.update(glob.glob('{}/*.{}'.format(infer_dir, ext))) images = list(images) assert len(images) > 0, "no image found in {}".format(infer_dir) logger.info("Found {} inference images in total.".format(len(images))) return images def run(FLAGS, cfg, place): # Model main_arch = cfg.architecture model = create(cfg.architecture) # data dataset = cfg.TestDataset test_images = get_test_images(FLAGS.infer_dir, FLAGS.infer_img) dataset.set_images(test_images) test_loader, _ = create('TestReader')(dataset, cfg['worker_num'], place) # TODO: support other metrics imid2path = dataset.get_imid2path() from ppdet.utils.coco_eval import get_category_info anno_file = dataset.get_anno() with_background = cfg.with_background use_default_label = dataset.use_default_label clsid2catid, catid2name = get_category_info(anno_file, with_background, use_default_label) # Init Model load_weight(model, cfg.weights) # Run Infer for iter_id, data in enumerate(test_loader): # forward fields = cfg.TestReader['inputs_def']['fields'] model.eval() outs = model( data=data, input_def=cfg.TestReader['inputs_def']['fields'], mode='infer') for key, value in outs.items(): outs[key] = value.numpy() im_shape = data[fields.index('im_shape')].numpy() scale_factor = data[fields.index('scale_factor')].numpy() im_ids = data[fields.index('im_id')].numpy() im_info = [im_shape, scale_factor, im_ids] if 'mask' in outs and 'bbox' in outs: mask_resolution = model.mask_post_process.mask_resolution from ppdet.py_op.post_process import mask_post_process outs['mask'] = mask_post_process(outs, im_shape, scale_factor, mask_resolution) eval_type = [] if 'bbox' in outs: eval_type.append('bbox') if 'mask' in outs: eval_type.append('mask') batch_res = get_infer_results([outs], eval_type, clsid2catid, [im_info]) logger.info('Infer iter {}'.format(iter_id)) bbox_res = None mask_res = None bbox_num = outs['bbox_num'] start = 0 for i, im_id in enumerate(im_ids): image_path = imid2path[int(im_id)] image = Image.open(image_path).convert('RGB') end = start + bbox_num[i] # use VisualDL to log original image if FLAGS.use_vdl: original_image_np = np.array(image) vdl_writer.add_image( "original/frame_{}".format(vdl_image_frame), original_image_np, vdl_image_step) if 'bbox' in batch_res: bbox_res = batch_res['bbox'][start:end] if 'mask' in batch_res: mask_res = batch_res['mask'][start:end] image = visualize_results(image, bbox_res, mask_res, int(im_id), catid2name, FLAGS.draw_threshold) # use VisualDL to log image with bbox if FLAGS.use_vdl: infer_image_np = np.array(image) vdl_writer.add_image("bbox/frame_{}".format(vdl_image_frame), infer_image_np, vdl_image_step) vdl_image_step += 1 if vdl_image_step % 10 == 0: vdl_image_step = 0 vdl_image_frame += 1 # save image with detection save_name = get_save_image_name(FLAGS.output_dir, image_path) logger.info("Detection bbox results save in {}".format(save_name)) image.save(save_name, quality=95) start = end def main(): FLAGS = parse_args() cfg = load_config(FLAGS.config) merge_config(FLAGS.opt) check_config(cfg) check_gpu(cfg.use_gpu) check_version() place = 'gpu:{}'.format(ParallelEnv().dev_id) if cfg.use_gpu else 'cpu' place = paddle.set_device(place) run(FLAGS, cfg, place) if __name__ == '__main__': main()