更换文档检测模型

paddle_detection/tools/box_distribution.py

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+# Copyright (c) 2022 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.
+
+import matplotlib.pyplot as plt
+import json
+import numpy as np
+import argparse
+from pycocotools.coco import COCO
+from tqdm import tqdm
+
+
+def median(data):
+    data.sort()
+    mid = len(data) // 2
+    median = (data[mid] + data[~mid]) / 2
+    return median
+
+
+def draw_distribution(width, height, out_path):
+    w_bins = int((max(width) - min(width)) // 10)
+    h_bins = int((max(height) - min(height)) // 10)
+    plt.figure()
+    plt.subplot(221)
+    plt.hist(width, bins=w_bins, color='green')
+    plt.xlabel('Width rate *1000')
+    plt.ylabel('number')
+    plt.title('Distribution of Width')
+    plt.subplot(222)
+    plt.hist(height, bins=h_bins, color='blue')
+    plt.xlabel('Height rate *1000')
+    plt.title('Distribution of Height')
+    plt.savefig(out_path)
+    print(f'Distribution saved as {out_path}')
+    plt.show()
+
+
+def get_ratio_infos(jsonfile, out_img, eval_size, small_stride):
+    coco = COCO(annotation_file=jsonfile)
+    allannjson = json.load(open(jsonfile, 'r'))
+    be_im_id = allannjson['annotations'][0]['image_id'] 
+    be_im_w = []
+    be_im_h = []
+    ratio_w = []
+    ratio_h = []
+    im_wid,im_hei=[],[]
+    for ann in tqdm(allannjson['annotations']):
+        if ann['iscrowd']:
+            continue
+        x0, y0, w, h = ann['bbox'][:]
+        if be_im_id == ann['image_id']:
+            be_im_w.append(w)
+            be_im_h.append(h)
+        else:
+            im_w = coco.imgs[be_im_id]['width']
+            im_h = coco.imgs[be_im_id]['height']
+            im_wid.append(im_w)
+            im_hei.append(im_h)
+            im_m_w = np.mean(be_im_w)
+            im_m_h = np.mean(be_im_h)
+            dis_w = im_m_w / im_w
+            dis_h = im_m_h / im_h
+            ratio_w.append(dis_w)
+            ratio_h.append(dis_h)
+            be_im_id = ann['image_id']
+            be_im_w = [w]
+            be_im_h = [h]
+        
+
+    im_w = coco.imgs[be_im_id]['width']
+    im_h = coco.imgs[be_im_id]['height']
+    im_wid.append(im_w)
+    im_hei.append(im_h)
+    all_im_m_w = np.mean(im_wid)
+    all_im_m_h = np.mean(im_hei)
+
+
+    im_m_w = np.mean(be_im_w)
+    im_m_h = np.mean(be_im_h)
+    dis_w = im_m_w / im_w
+    dis_h = im_m_h / im_h
+    ratio_w.append(dis_w)
+    ratio_h.append(dis_h)
+    mid_w = median(ratio_w)
+    mid_h = median(ratio_h)
+
+    reg_ratio = []
+    ratio_all = ratio_h + ratio_w
+    for r in ratio_all:
+        if r < 0.2:
+            reg_ratio.append(r)
+        elif r < 0.4:
+            reg_ratio.append(r/2)
+        else:
+            reg_ratio.append(r/4)
+    reg_ratio = sorted(reg_ratio)
+    max_ratio = reg_ratio[int(0.95*len(reg_ratio))]
+    reg_max = round(max_ratio*eval_size/small_stride)
+    
+    ratio_w = [i * 1000 for i in ratio_w]
+    ratio_h = [i * 1000 for i in ratio_h]
+    print(f'Suggested reg_range[1] is {reg_max+1}' )
+    print(f'Mean of all img_w is {all_im_m_w}')
+    print(f'Mean of all img_h is {all_im_m_h}') 
+    print(f'Median of ratio_w is {mid_w}')
+    print(f'Median of ratio_h is {mid_h}')
+    print('all_img with box: ', len(ratio_h))
+    print('all_ann: ', len(allannjson['annotations']))
+    draw_distribution(ratio_w, ratio_h, out_img)
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        '--json_path', type=str, default=None, help="Dataset json path.")
+    parser.add_argument(
+        '--eval_size', type=int, default=640, help="eval size.")
+    parser.add_argument(
+        '--small_stride', type=int, default=8, help="smallest stride.")
+    parser.add_argument(
+        '--out_img',
+        type=str,
+        default='box_distribution.jpg',
+        help="Name of distibution img.")
+    args = parser.parse_args()
+
+    get_ratio_infos(args.json_path, args.out_img, args.eval_size, args.small_stride)
+
+
+if __name__ == "__main__":
+    main()