# coding: utf8 # copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve. # # 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 # GPU memory garbage collection optimization flags os.environ['FLAGS_eager_delete_tensor_gb'] = "0.0" import sys import argparse import pprint import cv2 import numpy as np import paddle.fluid as fluid from PIL import Image as PILImage from utils.config import cfg from reader import SegDataset from models.model_builder import build_model from models.model_builder import ModelPhase from tools.gray2pseudo_color import get_color_map_list def parse_args(): parser = argparse.ArgumentParser(description='PaddeSeg visualization tools') parser.add_argument( '--cfg', dest='cfg_file', help='Config file for training (and optionally testing)', default=None, type=str) parser.add_argument( '--use_gpu', dest='use_gpu', help='Use gpu or cpu', action='store_true') parser.add_argument( '--vis_dir', dest='vis_dir', help='visual save dir', type=str, default='visual') parser.add_argument( '--local_test', dest='local_test', help='if in local test mode, only visualize 5 images for testing', action='store_true') parser.add_argument( 'opts', help='See config.py for all options', default=None, nargs=argparse.REMAINDER) if len(sys.argv) == 1: parser.print_help() sys.exit(1) return parser.parse_args() def makedirs(directory): if not os.path.exists(directory): os.makedirs(directory) def to_png_fn(fn): """ Append png as filename postfix """ directory, filename = os.path.split(fn) basename, ext = os.path.splitext(filename) return basename + ".png" def visualize(cfg, vis_file_list=None, use_gpu=False, vis_dir="visual", ckpt_dir=None, log_writer=None, local_test=False, **kwargs): if vis_file_list is None: vis_file_list = cfg.DATASET.TEST_FILE_LIST dataset = SegDataset( file_list=vis_file_list, mode=ModelPhase.VISUAL, data_dir=cfg.DATASET.DATA_DIR) startup_prog = fluid.Program() test_prog = fluid.Program() pred, logit = build_model(test_prog, startup_prog, phase=ModelPhase.VISUAL) # Clone forward graph test_prog = test_prog.clone(for_test=True) # Generator full colormap for maximum 256 classes color_map = get_color_map_list(256) # Get device environment place = fluid.CUDAPlace(0) if use_gpu else fluid.CPUPlace() exe = fluid.Executor(place) exe.run(startup_prog) ckpt_dir = cfg.TEST.TEST_MODEL if not ckpt_dir else ckpt_dir fluid.io.load_params(exe, ckpt_dir, main_program=test_prog) save_dir = vis_dir makedirs(save_dir) fetch_list = [pred.name] test_reader = dataset.batch(dataset.generator, batch_size=1, is_test=True) img_cnt = 0 for imgs, grts, img_names, valid_shapes, org_shapes in test_reader: pred_shape = (imgs.shape[2], imgs.shape[3]) pred, = exe.run( program=test_prog, feed={'image': imgs}, fetch_list=fetch_list, return_numpy=True) num_imgs = pred.shape[0] # TODO: use multi-thread to write images for i in range(num_imgs): # Add more comments res_map = np.squeeze(pred[i, :, :, :]).astype(np.uint8) img_name = img_names[i] res_shape = (res_map.shape[0], res_map.shape[1]) if res_shape[0] != pred_shape[0] or res_shape[1] != pred_shape[1]: res_map = cv2.resize( res_map, pred_shape, interpolation=cv2.INTER_NEAREST) valid_shape = (valid_shapes[i, 0], valid_shapes[i, 1]) res_map = res_map[0:valid_shape[0], 0:valid_shape[1]] org_shape = (org_shapes[i, 0], org_shapes[i, 1]) res_map = cv2.resize( res_map, (org_shape[1], org_shape[0]), interpolation=cv2.INTER_NEAREST) png_fn = to_png_fn(img_name) # colorful segment result visualization vis_fn = os.path.join(save_dir, png_fn) dirname = os.path.dirname(vis_fn) makedirs(dirname) pred_mask = PILImage.fromarray(res_map.astype(np.uint8), mode='P') pred_mask.putpalette(color_map) pred_mask.save(vis_fn) img_cnt += 1 print("#{} visualize image path: {}".format(img_cnt, vis_fn)) # Use Tensorboard to visualize image if log_writer is not None: # Calulate epoch from ckpt_dir folder name epoch = int(os.path.split(ckpt_dir)[-1]) print("Tensorboard visualization epoch", epoch) pred_mask_np = np.array(pred_mask.convert("RGB")) log_writer.add_image( "Predict/{}".format(img_name), pred_mask_np, epoch, dataformats='HWC') # Original image # BGR->RGB img = cv2.imread( os.path.join(cfg.DATASET.DATA_DIR, img_name))[..., ::-1] log_writer.add_image( "Images/{}".format(img_name), img, epoch, dataformats='HWC') # add ground truth (label) images grt = grts[i] if grt is not None: grt = grt[0:valid_shape[0], 0:valid_shape[1]] grt_pil = PILImage.fromarray(grt.astype(np.uint8), mode='P') grt_pil.putpalette(color_map) grt_pil = grt_pil.resize((org_shape[1], org_shape[0])) grt = np.array(grt_pil.convert("RGB")) log_writer.add_image( "Label/{}".format(img_name), grt, epoch, dataformats='HWC') # If in local_test mode, only visualize 5 images just for testing # procedure if local_test and img_cnt >= 5: break if __name__ == '__main__': args = parse_args() if args.cfg_file is not None: cfg.update_from_file(args.cfg_file) if args.opts: cfg.update_from_list(args.opts) cfg.check_and_infer() print(pprint.pformat(cfg)) visualize(cfg, **args.__dict__)