[cherry-pick][MOT] remove cython_box and fix doc (#5531)

configs/mot/README.md

@@ -47,12 +47,11 @@ PP-Tracking 提供了简洁的GUI可视化界面，教程请参考[PP-Tracking
 ## 安装依赖
 一键安装MOT相关的依赖：
 ```
-pip install lap sklearn motmetrics openpyxl cython_bbox
+pip install lap sklearn motmetrics openpyxl
 或者
 pip install -r requirements.txt
 ```
 **注意：**
-- `cython_bbox`在windows上安装：`pip install -e git+https://github.com/samson-wang/cython_bbox.git#egg=cython-bbox`。可参考这个[教程](https://stackoverflow.com/questions/60349980/is-there-a-way-to-install-cython-bbox-for-windows)。
 - 预测需确保已安装[ffmpeg](https://ffmpeg.org/ffmpeg.html), Linux(Ubuntu)平台可以直接用以下命令安装：`apt-get update && apt-get install -y ffmpeg`。
 
 

configs/mot/README_en.md

@@ -49,12 +49,11 @@ PP-Tracking supports GUI predict and deployment. Please refer to this [doc](http
 ## Installation
 Install all the related dependencies for MOT:
 ```
-pip install lap sklearn motmetrics openpyxl cython_bbox
+pip install lap sklearn motmetrics openpyxl
 or
 pip install -r requirements.txt
 ```
 **Notes:**
-- Install `cython_bbox` for Windows: `pip install -e git+https://github.com/samson-wang/cython_bbox.git#egg=cython-bbox`. You can refer to this [tutorial](https://stackoverflow.com/questions/60349980/is-there-a-way-to-install-cython-bbox-for-windows).
 - Please make sure that [ffmpeg](https://ffmpeg.org/ffmpeg.html) is installed first, on Linux(Ubuntu) platform you can directly install it by the following command:`apt-get update && apt-get install -y ffmpeg`.
 
 

configs/mot/fairmot/README.md

@@ -86,7 +86,7 @@ PP-tracking provides an AI studio public project tutorial. Please refer to this
 ### Results on MOT-17 Half Set
 | backbone       | input shape | MOTA | IDF1 |  IDS  |    FP   |   FN   |    FPS    | download | config |
 | :--------------| :------- | :----: | :----: | :----: | :----: | :----: | :------: | :----: |:-----: |
-| DLA-34         | 1088x608 |  69.1  |  72.8  |  299  |  1957  | 14412 |    -     |[model](https://paddledet.bj.bcebos.com/models/mot/fairmot_dla34_30e_1088x608_bytetracker.pdparams) | [config](./fairmot_dla34_30e_1088x608.yml) |
+| DLA-34         | 1088x608 |  69.1  |  72.8  |  299  |  1957  | 14412 |    -     |[model](https://paddledet.bj.bcebos.com/models/mot/fairmot_dla34_30e_1088x608.pdparams) | [config](./fairmot_dla34_30e_1088x608.yml) |
 | DLA-34 + BYTETracker| 1088x608 |  70.3 |  73.2  |  234  |  2176  | 13598 |    -     |[model](https://paddledet.bj.bcebos.com/models/mot/fairmot_dla34_30e_1088x608_bytetracker.pdparams) | [config](./fairmot_dla34_30e_1088x608_bytetracker.yml) |
 
 **Notes:**

configs/mot/fairmot/README_cn.md

@@ -82,7 +82,7 @@ PP-Tracking 提供了AI Studio公开项目案例，教程请参考[PP-Tracking
 ### 在MOT-17 Half上结果
 |    骨干网络      |  输入尺寸 |  MOTA  |  IDF1  |   IDS  |   FP   |   FN   |    FPS   |  下载链接  | 配置文件 |
 | :--------------| :------- | :----: | :----: | :----: | :----: | :----: | :------: | :----: |:-----: |
-| DLA-34         | 1088x608 |  69.1  |  72.8  |  299  |  1957  | 14412 |    -     |[下载链接](https://paddledet.bj.bcebos.com/models/mot/fairmot_dla34_30e_1088x608_bytetracker.pdparams) | [配置文件](./fairmot_dla34_30e_1088x608.yml) |
+| DLA-34         | 1088x608 |  69.1  |  72.8  |  299  |  1957  | 14412 |    -     |[下载链接](https://paddledet.bj.bcebos.com/models/mot/fairmot_dla34_30e_1088x608.pdparams) | [配置文件](./fairmot_dla34_30e_1088x608.yml) |
 | DLA-34 + BYTETracker| 1088x608 |  70.3 |  73.2  |  234  |  2176  | 13598 |    -     |[下载链接](https://paddledet.bj.bcebos.com/models/mot/fairmot_dla34_30e_1088x608_bytetracker.pdparams) | [配置文件](./fairmot_dla34_30e_1088x608_bytetracker.yml) |
 
 

configs/mot/fairmot/fairmot_dla34_30e_1088x608_bytetracker.yml

@@ -14,8 +14,18 @@ TrainDataset:
     image_lists: ['mot17.half', 'caltech.all', 'cuhksysu.train', 'prw.train', 'citypersons.train', 'eth.train']
     data_fields: ['image', 'gt_bbox', 'gt_class', 'gt_ide']
 
+# for MOT evaluation
+# If you want to change the MOT evaluation dataset, please modify 'data_root'
+EvalMOTDataset:
+  !MOTImageFolder
+    dataset_dir: dataset/mot
+    data_root: MOT17/images/half
+    keep_ori_im: False # set True if save visualization images or video, or used in DeepSORT
+
 JDETracker:
   use_byte: True
   match_thres: 0.8
   conf_thres: 0.4
   low_conf_thres: 0.2
+  min_box_area: 200
+  vertical_ratio: 1.6 # for pedestrian

configs/mot/pedestrian/README_cn.md

@@ -18,7 +18,7 @@
 | :-------------| :-------- | :------- | :----: | :----: | :----: | :-----: |:------: |
 |  PathTrack    |   DLA-34  | 1088x608 |  44.9 |    59.3   |    -   |[下载链接](https://paddledet.bj.bcebos.com/models/mot/fairmot_dla34_30e_1088x608_pathtrack.pdparams) | [配置文件](./fairmot_dla34_30e_1088x608_pathtrack.yml) |
 |  VisDrone     |   DLA-34  | 1088x608 |  49.2 |   63.1 |    -   | [下载链接](https://paddledet.bj.bcebos.com/models/mot/fairmot_dla34_30e_1088x608_visdrone_pedestrian.pdparams) | [配置文件](./fairmot_dla34_30e_1088x608_visdrone_pedestrian.yml) |
-|  VisDrone     | HRNetv2-W18| 1088x608 |  40.5 |   54.7 |    -   | [下载链接](https://paddledet.bj.bcebos.com/models/mot/fairmot_hrnetv2_w18_dlafpn_30e_864x480_visdrone_pedestrian.pdparams) | [配置文件](./fairmot_hrnetv2_w18_dlafpn_30e_864x480_visdrone_pedestrian.yml) |
+|  VisDrone     | HRNetv2-W18| 1088x608 |  40.5 |   54.7 |    -   | [下载链接](https://paddledet.bj.bcebos.com/models/mot/fairmot_hrnetv2_w18_dlafpn_30e_1088x608_visdrone_pedestrian.pdparams) | [配置文件](./fairmot_hrnetv2_w18_dlafpn_30e_1088x608_visdrone_pedestrian.yml) |
 |  VisDrone     | HRNetv2-W18| 864x480 |  38.6 |   50.9 |    -   | [下载链接](https://paddledet.bj.bcebos.com/models/mot/fairmot_hrnetv2_w18_dlafpn_30e_864x480_visdrone_pedestrian.pdparams) | [配置文件](./fairmot_hrnetv2_w18_dlafpn_30e_864x480_visdrone_pedestrian.yml) |
 |  VisDrone     | HRNetv2-W18| 576x320 |  30.6 |   47.2 |    -   | [下载链接](https://paddledet.bj.bcebos.com/models/mot/fairmot_hrnetv2_w18_dlafpn_30e_576x320_visdrone_pedestrian.pdparams) | [配置文件](./fairmot_hrnetv2_w18_dlafpn_30e_576x320_visdrone_pedestrian.yml) |
 
@@ -124,8 +124,8 @@ month={Oct},}
 
 @ARTICLE{9573394,
   author={Zhu, Pengfei and Wen, Longyin and Du, Dawei and Bian, Xiao and Fan, Heng and Hu, Qinghua and Ling, Haibin},
-  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, 
-  title={Detection and Tracking Meet Drones Challenge}, 
+  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
+  title={Detection and Tracking Meet Drones Challenge},
   year={2021},
   volume={},
   number={},

deploy/pptracking/python/mot/matching/jde_matching.py

@@ -26,7 +26,7 @@ warnings.filterwarnings("ignore")
 __all__ = [
     'merge_matches',
     'linear_assignment',
-    'cython_bbox_ious',
+    'bbox_ious',
     'iou_distance',
     'embedding_distance',
     'fuse_motion',
@@ -68,22 +68,28 @@ def linear_assignment(cost_matrix, thresh):
     return matches, unmatched_a, unmatched_b
 
 
-def cython_bbox_ious(atlbrs, btlbrs):
-    ious = np.zeros((len(atlbrs), len(btlbrs)), dtype=np.float)
-    if ious.size == 0:
+def bbox_ious(atlbrs, btlbrs):
+    boxes = np.ascontiguousarray(atlbrs, dtype=np.float)
+    query_boxes = np.ascontiguousarray(btlbrs, dtype=np.float)
+    N = boxes.shape[0]
+    K = query_boxes.shape[0]
+    ious = np.zeros((N, K), dtype=boxes.dtype)
+    if N * K == 0:
         return ious
-    try:
-        import cython_bbox
-    except Exception as e:
-        print('cython_bbox not found, please install cython_bbox.'
-              'for example: `pip install cython_bbox`.')
-        exit()
-
-    ious = cython_bbox.bbox_overlaps(
-        np.ascontiguousarray(
-            atlbrs, dtype=np.float),
-        np.ascontiguousarray(
-            btlbrs, dtype=np.float))
+
+    for k in range(K):
+        box_area = ((query_boxes[k, 2] - query_boxes[k, 0] + 1) *
+                    (query_boxes[k, 3] - query_boxes[k, 1] + 1))
+        for n in range(N):
+            iw = (min(boxes[n, 2], query_boxes[k, 2]) - max(
+                boxes[n, 0], query_boxes[k, 0]) + 1)
+            if iw > 0:
+                ih = (min(boxes[n, 3], query_boxes[k, 3]) - max(
+                    boxes[n, 1], query_boxes[k, 1]) + 1)
+                if ih > 0:
+                    ua = float((boxes[n, 2] - boxes[n, 0] + 1) * (boxes[
+                        n, 3] - boxes[n, 1] + 1) + box_area - iw * ih)
+                    ious[n, k] = iw * ih / ua
     return ious
 
 
@@ -98,7 +104,7 @@ def iou_distance(atracks, btracks):
     else:
         atlbrs = [track.tlbr for track in atracks]
         btlbrs = [track.tlbr for track in btracks]
-    _ious = cython_bbox_ious(atlbrs, btlbrs)
+    _ious = bbox_ious(atlbrs, btlbrs)
     cost_matrix = 1 - _ious
 
     return cost_matrix

ppdet/metrics/mcmot_metrics.py

@@ -306,11 +306,12 @@ class MCMOTEvaluator(object):
 
     def load_annotations(self):
         assert self.data_type == 'mcmot'
-        self.gt_filename = os.path.join(self.data_root, '../',
-                                        'sequences',
+        self.gt_filename = os.path.join(self.data_root, '../', 'sequences',
                                         '{}.txt'.format(self.seq_name))
         if not os.path.exists(self.gt_filename):
-            logger.warning("gt_filename '{}' of MCMOTEvaluator is not exist, so the MOTA will be -inf.")
+            logger.warning(
+                "gt_filename '{}' of MCMOTEvaluator is not exist, so the MOTA will be -INF."
+            )
 
     def reset_accumulator(self):
         import motmetrics as mm

ppdet/metrics/mot_metrics.py

@@ -37,8 +37,11 @@ __all__ = ['MOTEvaluator', 'MOTMetric', 'JDEDetMetric', 'KITTIMOTMetric']
 def read_mot_results(filename, is_gt=False, is_ignore=False):
     valid_label = [1]
     ignore_labels = [2, 7, 8, 12]  # only in motchallenge datasets like 'MOT16'
-    logger.info("In MOT16/17 dataset the valid_label of ground truth is '{}', "
-        "in other dataset it should be '0' for single classs MOT.".format(valid_label[0]))
+    if is_gt:
+        logger.info(
+            "In MOT16/17 dataset the valid_label of ground truth is '{}', "
+            "in other dataset it should be '0' for single classs MOT.".format(
+                valid_label[0]))
     results_dict = dict()
     if os.path.isfile(filename):
         with open(filename, 'r') as f:
@@ -118,7 +121,9 @@ class MOTEvaluator(object):
         gt_filename = os.path.join(self.data_root, self.seq_name, 'gt',
                                    'gt.txt')
         if not os.path.exists(gt_filename):
-            logger.warning("gt_filename '{}' of MOTEvaluator is not exist, so the MOTA will be -inf.")
+            logger.warning(
+                "gt_filename '{}' of MOTEvaluator is not exist, so the MOTA will be -INF."
+            )
         self.gt_frame_dict = read_mot_results(gt_filename, is_gt=True)
         self.gt_ignore_frame_dict = read_mot_results(
             gt_filename, is_ignore=True)

ppdet/modeling/mot/matching/jde_matching.py

@@ -26,7 +26,7 @@ warnings.filterwarnings("ignore")
 __all__ = [
     'merge_matches',
     'linear_assignment',
-    'cython_bbox_ious',
+    'bbox_ious',
     'iou_distance',
     'embedding_distance',
     'fuse_motion',
@@ -68,22 +68,28 @@ def linear_assignment(cost_matrix, thresh):
     return matches, unmatched_a, unmatched_b
 
 
-def cython_bbox_ious(atlbrs, btlbrs):
-    ious = np.zeros((len(atlbrs), len(btlbrs)), dtype=np.float)
-    if ious.size == 0:
+def bbox_ious(atlbrs, btlbrs):
+    boxes = np.ascontiguousarray(atlbrs, dtype=np.float)
+    query_boxes = np.ascontiguousarray(btlbrs, dtype=np.float)
+    N = boxes.shape[0]
+    K = query_boxes.shape[0]
+    ious = np.zeros((N, K), dtype=boxes.dtype)
+    if N * K == 0:
         return ious
-    try:
-        import cython_bbox
-    except Exception as e:
-        print('cython_bbox not found, please install cython_bbox.'
-                     'for example: `pip install cython_bbox`.')
-        raise e
-
-    ious = cython_bbox.bbox_overlaps(
-        np.ascontiguousarray(
-            atlbrs, dtype=np.float),
-        np.ascontiguousarray(
-            btlbrs, dtype=np.float))
+
+    for k in range(K):
+        box_area = ((query_boxes[k, 2] - query_boxes[k, 0] + 1) *
+                    (query_boxes[k, 3] - query_boxes[k, 1] + 1))
+        for n in range(N):
+            iw = (min(boxes[n, 2], query_boxes[k, 2]) - max(
+                boxes[n, 0], query_boxes[k, 0]) + 1)
+            if iw > 0:
+                ih = (min(boxes[n, 3], query_boxes[k, 3]) - max(
+                    boxes[n, 1], query_boxes[k, 1]) + 1)
+                if ih > 0:
+                    ua = float((boxes[n, 2] - boxes[n, 0] + 1) * (boxes[
+                        n, 3] - boxes[n, 1] + 1) + box_area - iw * ih)
+                    ious[n, k] = iw * ih / ua
     return ious
 
 
@@ -98,7 +104,7 @@ def iou_distance(atracks, btracks):
     else:
         atlbrs = [track.tlbr for track in atracks]
         btlbrs = [track.tlbr for track in btracks]
-    _ious = cython_bbox_ious(atlbrs, btlbrs)
+    _ious = bbox_ious(atlbrs, btlbrs)
     cost_matrix = 1 - _ious
 
     return cost_matrix

requirements.txt

@@ -14,4 +14,3 @@ lap
 sklearn
 motmetrics
 openpyxl
-cython_bbox