add contrib/SpatialEmbeddings

contrib/SpatialEmbeddings/README.md

+# SpatialEmbeddings 
+
+## 模型概述
+本模型是基于proposal-free的实例分割模型，快速实时，同时准确率高，适用于自动驾驶等实时场景。
+
+本模型基于KITTI中MOTS数据集训练得到，是论文 Segment as Points for Efficient Online Multi-Object Tracking and Segmentation中的分割部分
+[论文地址](https://arxiv.org/pdf/2007.01550.pdf)
+
+## KITTI MOTS指标
+KITTI MOTS验证集AP:0.76, AP_50%:0.915
+
+## 代码使用说明
+
+### 1. 模型下载
+
+执行以下命令下载并解压SpatialEmbeddings预测模型：
+
+```
+python download_SpatialEmbeddings_kitti.py
+```
+
+或点击[链接](https://paddleseg.bj.bcebos.com/models/SpatialEmbeddings_kitti.tar)进行手动下载并解压。
+
+### 2. 数据下载
+
+前往KITTI官网下载MOTS比赛数据[链接](https://www.vision.rwth-aachen.de/page/mots)
+
+下载后解压到./data文件夹下, 并生成验证集图片路径的test.txt
+
+### 3. 快速预测
+
+使用GPU预测
+```
+python -u infer.py --use_gpu
+```
+
+使用CPU预测：
+```
+python -u infer.py
+```
+数据及模型路径等详细配置见config.py文件
+
+#### 4. 预测结果示例：
+  
+  原图：
+  
+  ![](imgs/kitti_0007_000518_ori.png)
+  
+  预测结果：
+  
+  ![](imgs/kitti_0007_000518_pred.png)
+  
+
+
+## 引用
+
+**论文** 
+
+*Instance Segmentation by Jointly Optimizing Spatial Embeddings and Clustering Bandwidth* 
+
+**代码**
+
+https://github.com/davyneven/SpatialEmbeddings

contrib/SpatialEmbeddings/config.py

+# -*- coding: utf-8 -*-
+from utils.util import AttrDict, merge_cfg_from_args, get_arguments
+import os
+
+args = get_arguments()
+cfg = AttrDict()
+
+# 待预测图像所在路径
+cfg.data_dir = "data"
+# 待预测图像名称列表
+cfg.data_list_file = os.path.join("data", "test.txt")
+# 模型加载路径
+cfg.model_path = 'SpatialEmbeddings_kitti'
+# 预测结果保存路径
+cfg.vis_dir = "result"
+
+# sigma值
+cfg.n_sigma = 2
+# 中心点阈值
+cfg.threshold = 0.94
+# 点集数阈值
+cfg.min_pixel = 160
+
+merge_cfg_from_args(args, cfg)

contrib/SpatialEmbeddings/data/test.txt

+kitti/0007/kitti_0007_000512.png
+kitti/0007/kitti_0007_000518.png

contrib/SpatialEmbeddings/download_SpatialEmbeddings_kitti.py

+# 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.
+
+import sys
+import os
+
+LOCAL_PATH = os.path.dirname(os.path.abspath(__file__))
+TEST_PATH = os.path.join(LOCAL_PATH, "..", "..", "test")
+sys.path.append(TEST_PATH)
+
+from test_utils import download_file_and_uncompress
+
+if __name__ == "__main__":
+    download_file_and_uncompress(
+        url='https://paddleseg.bj.bcebos.com/models/SpatialEmbeddings_kitti.tar',
+        savepath=LOCAL_PATH,
+        extrapath=LOCAL_PATH,
+        extraname='SpatialEmbeddings_kitti')
+
+    print("Pretrained Model download success!")

contrib/SpatialEmbeddings/infer.py

+# -*- coding: utf-8 -*-
+import os
+import numpy as np
+from utils.util import get_arguments
+from utils.palette import get_palette
+from utils.data_util import Cluster, pad_img
+from PIL import Image as PILImage
+import importlib
+import paddle.fluid as fluid
+
+args = get_arguments()
+config = importlib.import_module('config')
+cfg = getattr(config, 'cfg')
+
+cluster = Cluster()
+
+# 预测数据集类
+class TestDataSet():
+    def __init__(self):
+        self.data_dir = cfg.data_dir 
+        self.data_list_file = cfg.data_list_file
+        self.data_list = self.get_data_list()
+        self.data_num = len(self.data_list)
+    
+    def get_data_list(self):
+        # 获取预测图像路径列表
+        data_list = []
+        data_file_handler = open(self.data_list_file, 'r')
+        for line in data_file_handler:
+            img_name = line.strip()
+            name_prefix = img_name.split('.')[0]
+            if len(img_name.split('.')) == 1:
+                img_name = img_name + '.jpg'
+            img_path = os.path.join(self.data_dir, img_name)
+            data_list.append(img_path)
+        return data_list
+
+    def preprocess(self, img):
+        # 图像预处理
+        h, w = img.shape[:2]
+        h_new = (h//32 + 1 if h % 32 != 0 else h//32)*32
+        w_new = (w//32 + 1 if w % 32 != 0 else w//32)*32
+        img = np.pad(img, ((0, h_new - h), (0, w_new - w), (0, 0)), 'edge')
+        
+        img = img.astype(np.float32)/255.0
+        img = img.transpose((2, 0, 1))
+        img = np.expand_dims(img, axis=0)
+        return img
+
+    def get_data(self, index):
+        # 获取图像信息
+        img_path = self.data_list[index]
+        img = np.array(PILImage.open(img_path))
+        if img is None:
+            return img, img,img_path, None
+
+        img_name = img_path.split(os.sep)[-1]
+        name_prefix = img_name.replace('.'+img_name.split('.')[-1],'')
+        img_shape = img.shape[:2]
+        img_process = self.preprocess(img)
+
+        return img_process, name_prefix, img_shape
+
+
+def infer():
+    if not os.path.exists(cfg.vis_dir):
+        os.makedirs(cfg.vis_dir)
+
+    place = fluid.CUDAPlace(0) if cfg.use_gpu else fluid.CPUPlace()
+    exe = fluid.Executor(place)
+
+    # 加载预测模型
+    test_prog, feed_name, fetch_list = fluid.io.load_inference_model(
+        dirname=cfg.model_path, executor=exe, params_filename='__params__')
+
+    #加载预测数据集
+    test_dataset = TestDataSet()
+    data_num = test_dataset.data_num
+
+    for idx in range(data_num):
+        # 数据获取
+        image, im_name, im_shape = test_dataset.get_data(idx)
+        if image is None:
+            print(im_name, 'is None')
+            continue
+        # 预测
+        output = exe.run(program=test_prog, feed={feed_name[0]: image}, fetch_list=fetch_list)
+        instance_map, predictions = cluster.cluster(output[0][0], n_sigma=cfg.n_sigma, \
+                                    min_pixel=cfg.min_pixel, threshold=cfg.threshold)
+
+        # 预测结果保存
+        instance_map = pad_img(instance_map, image.shape[2:])
+        instance_map = instance_map[:im_shape[0], :im_shape[1]]
+        output_im = PILImage.fromarray(np.asarray(instance_map, dtype=np.uint8))
+        palette = get_palette(len(predictions) + 1)
+        output_im.putpalette(palette)
+        result_path = os.path.join(cfg.vis_dir, im_name+'.png')
+        output_im.save(result_path)
+
+        if (idx + 1) % 100 == 0:
+            print('%d  processd' % (idx + 1))
+            
+    print('%d  processd done' % (idx + 1))   
+    
+    return 0
+
+
+if __name__ == "__main__":
+    infer()

contrib/SpatialEmbeddings/run.sh

+export CUDA_VISIBLE_DEVICES=4
+python infer.py --use_gpu

contrib/SpatialEmbeddings/utils/data_util.py

+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+import os
+import numpy as np
+from PIL import Image as PILImage
+
+def sigmoid_np(x):
+    return 1/(1+np.exp(-x))
+
+class Cluster:
+
+    def __init__(self, ):
+        xm = np.repeat(np.linspace(0, 2, 2048)[np.newaxis, np.newaxis,:], 1024, axis=1)
+        ym = np.repeat(np.linspace(0, 1, 1024)[np.newaxis, :, np.newaxis], 2048, axis=2)
+        self.xym = np.vstack((xm, ym))
+
+    def cluster(self, prediction, n_sigma=1, min_pixel=160, threshold=0.5):
+        
+        height, width = prediction.shape[1:3]
+        xym_s = self.xym[:, 0:height, 0:width]
+        
+        spatial_emb = np.tanh(prediction[0:2]) + xym_s
+        sigma = prediction[2:2+n_sigma]
+        seed_map = sigmoid_np(prediction[2+n_sigma:2+n_sigma + 1])
+       
+        instance_map = np.zeros((height, width), np.float32)
+        instances = []
+        count = 1
+        mask = seed_map > 0.5
+
+        if mask.sum() > min_pixel:
+            spatial_emb_masked = spatial_emb[np.repeat(mask, \
+                                spatial_emb.shape[0], 0)].reshape(2, -1)
+            sigma_masked = sigma[np.repeat(mask, n_sigma, 0)].reshape(n_sigma, -1)
+            seed_map_masked = seed_map[mask].reshape(1, -1)
+
+            unclustered = np.ones(mask.sum(), np.float32)
+            instance_map_masked = np.zeros(mask.sum(), np.float32)
+
+            while(unclustered.sum() > min_pixel):
+
+                seed = (seed_map_masked * unclustered).argmax().item()
+                seed_score = (seed_map_masked * unclustered).max().item()
+                if seed_score < threshold:
+                    break
+                center = spatial_emb_masked[:, seed:seed+1]
+                unclustered[seed] = 0
+                s = np.exp(sigma_masked[:, seed:seed+1]*10)
+                dist = np.exp(-1*np.sum((spatial_emb_masked-center)**2 *s, 0))
+                proposal = (dist > 0.5).squeeze()
+
+                if proposal.sum() > min_pixel:
+                    if unclustered[proposal].sum()/proposal.sum()> 0.5:
+                        instance_map_masked[proposal.squeeze()] = count
+                        instance_mask = np.zeros((height, width), np.float32)
+                        instance_mask[mask.squeeze()] = proposal 
+                        instances.append(
+                            {'mask': (instance_mask.squeeze()*255).astype(np.uint8), \
+                            'score': seed_score})
+                        count += 1
+
+                unclustered[proposal] = 0
+
+            instance_map[mask.squeeze()] = instance_map_masked
+
+        return instance_map, instances
+
+def pad_img(img, dst_shape, mode='constant'):
+    img_h, img_w = img.shape[:2]
+    dst_h, dst_w = dst_shape
+    pad_shape = ((0, max(0, dst_h - img_h)), (0, max(0, dst_w - img_w)))
+    return np.pad(img, pad_shape, mode)
+
+def save_for_eval(predictions, infer_shape, im_shape, vis_dir, im_name):
+    txt_file = os.path.join(vis_dir, im_name + '.txt')
+    with open(txt_file, 'w') as f:
+        for id, pred in enumerate(predictions):
+            save_name = im_name + '_{:02d}.png'.format(id)
+            pred_mask = pad_img(pred['mask'], infer_shape)
+            pred_mask = pred_mask[:im_shape[0], :im_shape[1]]
+            im = PILImage.fromarray(pred_mask)
+            im.save(os.path.join(vis_dir, save_name))
+            cl = 26
+            score = pred['score']
+            f.writelines("{} {} {:.02f}\n".format(save_name, cl, score))
+

contrib/SpatialEmbeddings/utils/palette.py

+##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+## Created by: RainbowSecret
+## Microsoft Research
+## yuyua@microsoft.com
+## Copyright (c) 2018
+##
+## This source code is licensed under the MIT-style license found in the
+## LICENSE file in the root directory of this source tree
+##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+import numpy as np
+import cv2
+
+
+def get_palette(num_cls):
+    """ Returns the color map for visualizing the segmentation mask.
+    Args:
+        num_cls: Number of classes
+    Returns:
+        The color map
+    """
+    n = num_cls
+    palette = [0] * (n * 3)
+    for j in range(0, n):
+        lab = j
+        palette[j * 3 + 0] = 0
+        palette[j * 3 + 1] = 0
+        palette[j * 3 + 2] = 0
+        i = 0
+        while lab:
+            palette[j * 3 + 0] |= (((lab >> 0) & 1) << (7 - i))
+            palette[j * 3 + 1] |= (((lab >> 1) & 1) << (7 - i))
+            palette[j * 3 + 2] |= (((lab >> 2) & 1) << (7 - i))
+            i += 1
+            lab >>= 3
+    return palette

contrib/SpatialEmbeddings/utils/util.py

+from __future__ import division
+from __future__ import print_function
+from __future__ import unicode_literals
+import argparse
+import os
+
+def get_arguments():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--use_gpu",
+                        action="store_true",
+                        help="Use gpu or cpu to test.")
+    parser.add_argument('--example',
+                        type=str,
+                        help='RoadLine, HumanSeg or ACE2P')
+                        
+    return parser.parse_args()
+
+
+class AttrDict(dict):
+    def __init__(self, *args, **kwargs):
+        super(AttrDict, self).__init__(*args, **kwargs)
+
+    def __getattr__(self, name):
+        if name in self.__dict__:
+            return self.__dict__[name]
+        elif name in self:
+            return self[name]
+        else:
+            raise AttributeError(name)
+
+    def __setattr__(self, name, value):
+        if name in self.__dict__:
+            self.__dict__[name] = value
+        else:
+            self[name] = value
+
+def merge_cfg_from_args(args, cfg):
+    """Merge config keys, values in args into the global config."""
+    for k, v in vars(args).items():
+        d = cfg
+        try:
+            value = eval(v)
+        except:
+            value = v
+        if value is not None:
+            cfg[k] = value
+