add core code

Author: yangxue0827

README.md

@@ -114,6 +114,12 @@ tensorboard --logdir=.
 If this is useful for your research, please consider cite.
 
 ```
+@article{yang2020on,
+    title={On the Arbitrary-Oriented Object Detection: Classification based Approaches Revisited},
+    author={Yang, Xue and Yan, Junchi and He, Tao},
+    year={2020}
+}
+
 @article{yang2020arbitrary,
     title={Arbitrary-Oriented Object Detection with Circular Smooth Label},
     author={Yang, Xue and Yan, Junchi},
@@ -129,21 +135,6 @@ If this is useful for your research, please consider cite.
     year={2019}
 }
 
-@article{yang2020scrdet++,
-    title={SCRDet++: Detecting Small, Cluttered and Rotated Objects via Instance-Level Feature Denoising and Rotation Loss Smoothing},
-    author={Yang, Xue and Yan, Junchi and Yang, Xiaokang and Tang, Jin and Liao, Wenglong and He, Tao},
-    journal={arXiv preprint arXiv:2004.13316},
-    year={2020}
-}
-
-@inproceedings{yang2019scrdet,
-    title={SCRDet: Towards more robust detection for small, cluttered and rotated objects},
-    author={Yang, Xue and Yang, Jirui and Yan, Junchi and Zhang, Yue and Zhang, Tengfei and Guo, Zhi and Sun, Xian and Fu, Kun},
-    booktitle={Proceedings of the IEEE International Conference on Computer Vision (ICCV)},
-    pages={8232--8241},
-    year={2019}
-}
-
 @inproceedings{xia2018dota,
     title={DOTA: A large-scale dataset for object detection in aerial images},
     author={Xia, Gui-Song and Bai, Xiang and Ding, Jian and Zhu, Zhen and Belongie, Serge and Luo, Jiebo and Datcu, Mihai and Pelillo, Marcello and Zhang, Liangpei},

data/__init__.py

@@ -0,0 +1,230 @@
+import os
+from xml.dom.minidom import Document
+import numpy as np
+import copy
+import cv2
+import sys
+sys.path.append('../../..')
+
+from help_utils.tools import mkdir
+from libs.box_utils.coordinate_convert import backward_convert
+
+
+def save_to_xml(save_path, im_height, im_width, objects_axis, label_name):
+    im_depth = 0
+    object_num = len(objects_axis)
+    doc = Document()
+
+    annotation = doc.createElement('annotation')
+    doc.appendChild(annotation)
+
+    folder = doc.createElement('folder')
+    folder_name = doc.createTextNode('VOC2007')
+    folder.appendChild(folder_name)
+    annotation.appendChild(folder)
+
+    filename = doc.createElement('filename')
+    filename_name = doc.createTextNode('000024.jpg')
+    filename.appendChild(filename_name)
+    annotation.appendChild(filename)
+
+    source = doc.createElement('source')
+    annotation.appendChild(source)
+
+    database = doc.createElement('database')
+    database.appendChild(doc.createTextNode('The VOC2007 Database'))
+    source.appendChild(database)
+
+    annotation_s = doc.createElement('annotation')
+    annotation_s.appendChild(doc.createTextNode('PASCAL VOC2007'))
+    source.appendChild(annotation_s)
+
+    image = doc.createElement('image')
+    image.appendChild(doc.createTextNode('flickr'))
+    source.appendChild(image)
+
+    flickrid = doc.createElement('flickrid')
+    flickrid.appendChild(doc.createTextNode('322409915'))
+    source.appendChild(flickrid)
+
+    owner = doc.createElement('owner')
+    annotation.appendChild(owner)
+
+    flickrid_o = doc.createElement('flickrid')
+    flickrid_o.appendChild(doc.createTextNode('knautia'))
+    owner.appendChild(flickrid_o)
+
+    name_o = doc.createElement('name')
+    name_o.appendChild(doc.createTextNode('yang'))
+    owner.appendChild(name_o)
+
+    size = doc.createElement('size')
+    annotation.appendChild(size)
+    width = doc.createElement('width')
+    width.appendChild(doc.createTextNode(str(im_width)))
+    height = doc.createElement('height')
+    height.appendChild(doc.createTextNode(str(im_height)))
+    depth = doc.createElement('depth')
+    depth.appendChild(doc.createTextNode(str(im_depth)))
+    size.appendChild(width)
+    size.appendChild(height)
+    size.appendChild(depth)
+    segmented = doc.createElement('segmented')
+    segmented.appendChild(doc.createTextNode('0'))
+    annotation.appendChild(segmented)
+    for i in range(object_num):
+        objects = doc.createElement('object')
+        annotation.appendChild(objects)
+        object_name = doc.createElement('name')
+        object_name.appendChild(doc.createTextNode(label_name[int(objects_axis[i][-1])]))
+        objects.appendChild(object_name)
+        pose = doc.createElement('pose')
+        pose.appendChild(doc.createTextNode('Unspecified'))
+        objects.appendChild(pose)
+        truncated = doc.createElement('truncated')
+        truncated.appendChild(doc.createTextNode('1'))
+        objects.appendChild(truncated)
+        difficult = doc.createElement('difficult')
+        difficult.appendChild(doc.createTextNode('0'))
+        objects.appendChild(difficult)
+        bndbox = doc.createElement('bndbox')
+        objects.appendChild(bndbox)
+
+        x0 = doc.createElement('x0')
+        x0.appendChild(doc.createTextNode(str((objects_axis[i][0]))))
+        bndbox.appendChild(x0)
+        y0 = doc.createElement('y0')
+        y0.appendChild(doc.createTextNode(str((objects_axis[i][1]))))
+        bndbox.appendChild(y0)
+
+        x1 = doc.createElement('x1')
+        x1.appendChild(doc.createTextNode(str((objects_axis[i][2]))))
+        bndbox.appendChild(x1)
+        y1 = doc.createElement('y1')
+        y1.appendChild(doc.createTextNode(str((objects_axis[i][3]))))
+        bndbox.appendChild(y1)
+
+        x2 = doc.createElement('x2')
+        x2.appendChild(doc.createTextNode(str((objects_axis[i][4]))))
+        bndbox.appendChild(x2)
+        y2 = doc.createElement('y2')
+        y2.appendChild(doc.createTextNode(str((objects_axis[i][5]))))
+        bndbox.appendChild(y2)
+
+        x3 = doc.createElement('x3')
+        x3.appendChild(doc.createTextNode(str((objects_axis[i][6]))))
+        bndbox.appendChild(x3)
+        y3 = doc.createElement('y3')
+        y3.appendChild(doc.createTextNode(str((objects_axis[i][7]))))
+        bndbox.appendChild(y3)
+
+    f = open(save_path, 'w')
+    f.write(doc.toprettyxml(indent=''))
+    f.close()
+
+
+class_list = ['plane', 'baseball-diamond', 'bridge', 'ground-track-field',
+              'small-vehicle', 'large-vehicle', 'ship',
+              'tennis-court', 'basketball-court',
+              'storage-tank', 'soccer-ball-field',
+              'roundabout', 'harbor',
+              'swimming-pool', 'helicopter', 'container-crane']
+
+
+def format_label(txt_list):
+    format_data = []
+    for i in txt_list:
+        if len(i.split(' ')) < 9:
+            continue
+        format_data.append(
+            [float(xy) for xy in i.split(' ')[:8]] + [class_list.index(i.split(' ')[8])]
+        )
+
+        if i.split(' ')[8] not in class_list:
+            print('warning found a new label :', i.split(' ')[8])
+            exit()
+    return np.array(format_data)
+
+
+def clip_image(file_idx, image, boxes_all, width, height, stride_w, stride_h):
+    min_pixel = 5
+    print(file_idx)
+    boxes_all_5 = backward_convert(boxes_all[:, :8], False)
+    print(boxes_all[np.logical_or(boxes_all_5[:, 2] <= min_pixel, boxes_all_5[:, 3] <= min_pixel), :])
+    boxes_all = boxes_all[np.logical_and(boxes_all_5[:, 2] > min_pixel, boxes_all_5[:, 3] > min_pixel), :]
+
+    if boxes_all.shape[0] > 0:
+        shape = image.shape
+        for start_h in range(0, shape[0], stride_h):
+            for start_w in range(0, shape[1], stride_w):
+                boxes = copy.deepcopy(boxes_all)
+                box = np.zeros_like(boxes_all)
+                start_h_new = start_h
+                start_w_new = start_w
+                if start_h + height > shape[0]:
+                    start_h_new = shape[0] - height
+                if start_w + width > shape[1]:
+                    start_w_new = shape[1] - width
+                top_left_row = max(start_h_new, 0)
+                top_left_col = max(start_w_new, 0)
+                bottom_right_row = min(start_h + height, shape[0])
+                bottom_right_col = min(start_w + width, shape[1])
+
+                subImage = image[top_left_row:bottom_right_row, top_left_col: bottom_right_col]
+
+                box[:, 0] = boxes[:, 0] - top_left_col
+                box[:, 2] = boxes[:, 2] - top_left_col
+                box[:, 4] = boxes[:, 4] - top_left_col
+                box[:, 6] = boxes[:, 6] - top_left_col
+
+                box[:, 1] = boxes[:, 1] - top_left_row
+                box[:, 3] = boxes[:, 3] - top_left_row
+                box[:, 5] = boxes[:, 5] - top_left_row
+                box[:, 7] = boxes[:, 7] - top_left_row
+                box[:, 8] = boxes[:, 8]
+                center_y = 0.25 * (box[:, 1] + box[:, 3] + box[:, 5] + box[:, 7])
+                center_x = 0.25 * (box[:, 0] + box[:, 2] + box[:, 4] + box[:, 6])
+
+                cond1 = np.intersect1d(np.where(center_y[:] >= 0)[0], np.where(center_x[:] >= 0)[0])
+                cond2 = np.intersect1d(np.where(center_y[:] <= (bottom_right_row - top_left_row))[0],
+                                       np.where(center_x[:] <= (bottom_right_col - top_left_col))[0])
+                idx = np.intersect1d(cond1, cond2)
+                if len(idx) > 0 and (subImage.shape[0] > 5 and subImage.shape[1] > 5):
+                    mkdir(os.path.join(save_dir, 'images'))
+                    img = os.path.join(save_dir, 'images',
+                                       "%s_%04d_%04d.png" % (file_idx, top_left_row, top_left_col))
+                    cv2.imwrite(img, subImage)
+
+                    mkdir(os.path.join(save_dir, 'labeltxt'))
+                    xml = os.path.join(save_dir, 'labeltxt',
+                                       "%s_%04d_%04d.xml" % (file_idx, top_left_row, top_left_col))
+                    save_to_xml(xml, subImage.shape[0], subImage.shape[1], box[idx, :], class_list)
+
+
+print('class_list', len(class_list))
+raw_data = '/data/yangxue/dataset/DOTA/val/'
+raw_images_dir = os.path.join(raw_data, 'images', 'images')
+raw_label_dir = os.path.join(raw_data, 'labelTxt', 'labelTxt')
+
+save_dir = '/data/yangxue/dataset/DOTA/DOTA1.0/trainval/'
+
+images = [i for i in os.listdir(raw_images_dir) if 'png' in i]
+labels = [i for i in os.listdir(raw_label_dir) if 'txt' in i]
+
+print('find image', len(images))
+print('find label', len(labels))
+
+min_length = 1e10
+max_length = 1
+
+img_h, img_w, stride_h, stride_w = 600, 600, 450, 450
+
+for idx, img in enumerate(images):
+    print(idx, 'read image', img)
+    img_data = cv2.imread(os.path.join(raw_images_dir, img))
+
+    txt_data = open(os.path.join(raw_label_dir, img.replace('png', 'txt')), 'r').readlines()
+    box = format_label(txt_data)
+
+    if box.shape[0] > 0:
+        clip_image(img.strip('.png'), img_data, box, img_w, img_h, stride_w, stride_h)