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DetectionTeamUCAS · Apr 25, 2019 · 5d5ecce · 5d5ecce
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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+env/
+build/
+develop-eggs/
+dist/
+downloads/
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+.eggs/
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+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# PyInstaller
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+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
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+
+# Unit test / coverage reports
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+.tox/
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+*.cover
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+
+# Translations
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+# Django stuff:
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+# Flask stuff:
+instance/
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+# PyBuilder
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+# Jupyter Notebook
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+
+# pyenv
+.python-version
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+# celery beat schedule file
+celerybeat-schedule
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+# SageMath parsed files
+*.sage.py
+
+# dotenv
+.env
+
+# virtualenv
+.venv
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+# Spyder project settings
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+# Rope project settings
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+*/data/tfrecord/*
diff --git a/LICENSE b/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2018 DetectionTeamUCAS
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/README.md b/README.md
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+# Cascade R-CNN: Delving into High Quality Object Detection 
+
+## Abstract     
+This repo is based on [FPN](https://github.com/DetectionTeamUCAS/FPN_Tensorflow), and completed by [YangXue](https://github.com/yangxue0827).     
+
+## Train on COCO train2017 and test on COCO val2017 (coco minival).   
+|Model|Backbone|Train Schedule|GPU|Image/GPU|FP16|Box AP(Mask AP)|test stage|
+|-----|--------|--------------|---|---------|----|---------------|---|
+|Faster (paper)|R50v1-FPN|1X|8X TITAN XP|1|no|38.3|3|
+|Faster (ours)|R50v1-FPN|1X|8X 2080 Ti|1|no|38.2|3|
+|Faster (Face++)|R50v1-FPN|1X|8X 2080 Ti|2|no|39.1|3|
+
+![2](comparison.png)
+
+## My Development Environment
+1、python3.5 (anaconda recommend)             
+2、cuda9.0 **(If you want to use cuda8, please set CUDA9 = False in the cfgs.py file.)**                    
+3、[opencv(cv2)](https://pypi.org/project/opencv-python/)    
+4、[tfplot](https://github.com/wookayin/tensorflow-plot)             
+5、tensorflow == 1.12                   
+
+## Download Model
+### Pretrain weights
+1、Please download [resnet50_v1](http://download.tensorflow.org/models/resnet_v1_50_2016_08_28.tar.gz), [resnet101_v1](http://download.tensorflow.org/models/resnet_v1_101_2016_08_28.tar.gz) pre-trained models on Imagenet, put it to data/pretrained_weights.       
+2、Or you can choose to use a better backbone, refer to [gluon2TF](https://github.com/yangJirui/gluon2TF). [Pretrain Model Link](https://pan.baidu.com/s/1HF3G5XSxXm7W4pk10RuOlw), password: q4jg.
+
+### Trained weights
+**Select a configuration file in the folder ($PATH_ROOT/libs/configs/) and copy its contents into cfgs.py, then download the corresponding [weights](https://github.com/DetectionTeamUCAS/Models/tree/master/Cascade_FPN_Tensorflow).**      
+
+## Compile
+```  
+cd $PATH_ROOT/libs/box_utils/cython_utils
+python setup.py build_ext --inplace
+```
+
+## Train
+
+1、If you want to train your own data, please note:  
+```     
+(1) Modify parameters (such as CLASS_NUM, DATASET_NAME, VERSION, etc.) in $PATH_ROOT/libs/configs/cfgs.py
+(2) Add category information in $PATH_ROOT/libs/label_name_dict/lable_dict.py     
+(3) Add data_name to $PATH_ROOT/data/io/read_tfrecord_multi_gpu.py 
+```     
+
+2、make tfrecord
+```  
+cd $PATH_ROOT/data/io/  
+python convert_data_to_tfrecord_coco.py --VOC_dir='/PATH/TO/JSON/FILE/' 
+                                        --save_name='train' 
+                                        --dataset='coco'
+```     
+
+3、multi-gpu train
+```  
+cd $PATH_ROOT/tools
+python multi_gpu_train.py
+```
+
+## Eval
+```  
+cd $PATH_ROOT/tools
+python eval_coco.py --eval_data='/PATH/TO/IMAGES/'  
+                    --eval_gt='/PATH/TO/TEST/ANNOTATION/'
+                    --GPU='0'
+```
+
+## Tensorboard
+```  
+cd $PATH_ROOT/output/summary
+tensorboard --logdir=.
+``` 
+![3](images.png)
+
+![4](scalars.png)
+
+## Reference
+1、https://github.com/endernewton/tf-faster-rcnn    
+2、https://github.com/zengarden/light_head_rcnn     
+3、https://github.com/tensorflow/models/tree/master/research/object_detection        
+4、https://github.com/CharlesShang/FastMaskRCNN       
+5、https://github.com/matterport/Mask_RCNN      
+6、https://github.com/msracver/Deformable-ConvNets         
+7、https://github.com/tensorpack/tensorpack       
diff --git a/comparison.png b/comparison.png
diff --git a/data/__init__.py b/data/__init__.py
diff --git a/data/io/BDD100K/BDD2VOC.py b/data/io/BDD100K/BDD2VOC.py
@@ -0,0 +1,131 @@
+import json
+import os
+import cv2
+from xml.dom.minidom import Document
+import xml.dom.minidom
+
+label_map = {'bus': 1, 'traffic light': 2, 'traffic sign': 3, 'person': 4, 'bike': 5,
+             'truck': 6, 'motor': 7, 'car': 8, 'train': 9, 'rider': 10}
+FLAG = ['train', 'val']
+
+
+def write_xml(save_path, name, box_list, label_list, w, h, d):
+
+    # dict_box[filename]=json_dict[filename]
+    doc = xml.dom.minidom.Document()
+    root = doc.createElement('annotation')
+    doc.appendChild(root)
+
+    foldername = doc.createElement("folder")
+    foldername.appendChild(doc.createTextNode("JPEGImages"))
+    root.appendChild(foldername)
+
+    nodeFilename = doc.createElement('filename')
+    nodeFilename.appendChild(doc.createTextNode(name))
+    root.appendChild(nodeFilename)
+
+    pathname = doc.createElement("path")
+    pathname.appendChild(doc.createTextNode("xxxx"))
+    root.appendChild(pathname)
+
+    sourcename=doc.createElement("source")
+
+    databasename = doc.createElement("database")
+    databasename.appendChild(doc.createTextNode("Unknown"))
+    sourcename.appendChild(databasename)
+
+    annotationname = doc.createElement("annotation")
+    annotationname.appendChild(doc.createTextNode("xxx"))
+    sourcename.appendChild(annotationname)
+
+    imagename = doc.createElement("image")
+    imagename.appendChild(doc.createTextNode("xxx"))
+    sourcename.appendChild(imagename)
+
+    flickridname = doc.createElement("flickrid")
+    flickridname.appendChild(doc.createTextNode("0"))
+    sourcename.appendChild(flickridname)
+
+    root.appendChild(sourcename)
+
+    nodesize = doc.createElement('size')
+    nodewidth = doc.createElement('width')
+    nodewidth.appendChild(doc.createTextNode(str(w)))
+    nodesize.appendChild(nodewidth)
+    nodeheight = doc.createElement('height')
+    nodeheight.appendChild(doc.createTextNode(str(h)))
+    nodesize.appendChild(nodeheight)
+    nodedepth = doc.createElement('depth')
+    nodedepth.appendChild(doc.createTextNode(str(d)))
+    nodesize.appendChild(nodedepth)
+    root.appendChild(nodesize)
+
+    segname = doc.createElement("segmented")
+    segname.appendChild(doc.createTextNode("0"))
+    root.appendChild(segname)
+
+    for (box, label) in zip(box_list, label_list):
+
+        nodeobject = doc.createElement('object')
+        nodename = doc.createElement('name')
+        nodename.appendChild(doc.createTextNode(str(label)))
+        nodeobject.appendChild(nodename)
+        nodebndbox = doc.createElement('bndbox')
+        nodex1 = doc.createElement('x1')
+        nodex1.appendChild(doc.createTextNode(str(box[0])))
+        nodebndbox.appendChild(nodex1)
+        nodey1 = doc.createElement('y1')
+        nodey1.appendChild(doc.createTextNode(str(box[1])))
+        nodebndbox.appendChild(nodey1)
+        nodex2 = doc.createElement('x2')
+        nodex2.appendChild(doc.createTextNode(str(box[2])))
+        nodebndbox.appendChild(nodex2)
+        nodey2 = doc.createElement('y2')
+        nodey2.appendChild(doc.createTextNode(str(box[3])))
+        nodebndbox.appendChild(nodey2)
+
+        nodeobject.appendChild(nodebndbox)
+        root.appendChild(nodeobject)
+    fp = open(save_path, 'w')
+    doc.writexml(fp, indent='\n')
+    fp.close()
+
+
+for flag in FLAG:
+    BDD_path = '/unsullied/sharefs/_research_detection/GeneralDetection/BDD100K/bdd100k/'
+    BDD_labels_dir = os.path.join(BDD_path, 'labels/bdd100k_labels_images_{}.json'.format(flag))
+    BDD_labels = json.load(open(BDD_labels_dir, 'r'))
+    BDD_images_dir = os.path.join(BDD_path, 'images/100k/{}'.format(flag))
+
+    for cnt, bdd in enumerate(BDD_labels):
+        img_name = bdd['name']
+        img_path = os.path.join(BDD_images_dir, img_name)
+        # img = cv2.imread(img_path)
+        # h, w, d = img.shape
+        h, w, d = 720, 1280, 3
+        bdd_boxes = bdd['labels']
+        box_list, label_list = [], []
+        for bb in bdd_boxes:
+            if bb['category'] not in label_map.keys():
+                continue
+            box = bb['box2d']
+            box_list.append([round(box['x1']), round(box['y1']),
+                             round(box['x2']), round(box['y2'])])
+            label_list.append(bb['category'])
+
+        if len(box_list) != 0:
+            save_path = os.path.join('/unsullied/sharefs/yangxue/isilon/yangxue/data/BDD100K/BDD100K_VOC/bdd100k_{}/Annotations'.format(flag),
+                                     img_name.replace('.jpg', '.xml'))
+            write_xml(save_path, img_name, box_list, label_list, w, h, d)
+        if cnt % 100 == 0:
+            print('{} process: {}/{}'.format(flag, cnt+1, len(BDD_labels)))
+print('Finish!')
+
+
+
+
+
+
+
+
+