Fix mot lazy import and warning (#6476)

* [Dy2St]Fix cascade_rcnn export model problem (#6468)

* [Dy2St]Fix cascade_rcnn export model problem

* [Dy2St]Fix cascade_rcnn export model problem

* fix import for mot

* fix import for mot deploy

* fix lazy import mot

configs/mot/README.md

@@ -81,7 +81,7 @@ PP-Human赋能社区智能精细化管理教程[链接](https://aistudio.baidu.c
 ```
 pip install -r requirements.txt
 # 或手动pip安装MOT相关的库
-pip install lap sklearn motmetrics openpyxl
+pip install lap motmetrics sklearn filterpy
 ```
 **注意：**
   - 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

@@ -49,7 +49,7 @@ 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
+pip install lap motmetrics sklearn filterpy
 or
 pip install -r requirements.txt
 ```

deploy/pipeline/pphuman/mtmct.py

@@ -18,8 +18,14 @@
 import cv2
 import gc
 import numpy as np
-from sklearn import preprocessing
-from sklearn.cluster import AgglomerativeClustering
+try:
+    from sklearn import preprocessing
+    from sklearn.cluster import AgglomerativeClustering
+except:
+    print(
+        'Warning: Unable to use MTMCT in PP-Human, please install sklearn, for example: `pip install sklearn`'
+    )
+    pass
 import pandas as pd
 from tqdm import tqdm
 from functools import reduce

deploy/pipeline/ppvehicle/vehicleplate_postprocess.py

@@ -17,7 +17,6 @@
 from paddle.nn import functional as F
 import re
 from shapely.geometry import Polygon
-import pyclipper
 import cv2
 import copy
 
@@ -111,6 +110,12 @@ def boxes_from_bitmap(self, pred, _bitmap, dest_width, dest_height):
         return np.array(boxes, dtype=np.int16), scores
 
     def unclip(self, box):
+        try:
+            import pyclipper
+        except Exception as e:
+            raise RuntimeError(
+                'Unable to use vehicleplate postprocess in PP-Vehicle, please install pyclipper, for example: `pip install pyclipper`, see https://github.com/fonttools/pyclipper'
+            )
         unclip_ratio = self.unclip_ratio
         poly = Polygon(box)
         distance = poly.area * unclip_ratio / poly.length

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

@@ -15,7 +15,14 @@
 This code is based on https://github.com/Zhongdao/Towards-Realtime-MOT/blob/master/tracker/matching.py
 """
 
-import lap
+try:
+    import lap
+except:
+    print(
+        'Warning: Unable to use JDE/FairMOT/ByteTrack, please install lap, for example: `pip install lap`, see https://github.com/gatagat/lap'
+    )
+    pass
+
 import scipy
 import numpy as np
 from scipy.spatial.distance import cdist
@@ -53,6 +60,12 @@ def merge_matches(m1, m2, shape):
 
 
 def linear_assignment(cost_matrix, thresh):
+    try:
+        import lap
+    except Exception as e:
+        raise RuntimeError(
+            'Unable to use JDE/FairMOT/ByteTrack, please install lap, for example: `pip install lap`, see https://github.com/gatagat/lap'
+        )
     if cost_matrix.size == 0:
         return np.empty(
             (0, 2), dtype=int), tuple(range(cost_matrix.shape[0])), tuple(

deploy/pptracking/python/mot/mtmct/camera_utils.py

@@ -19,7 +19,13 @@
 """
 
 import numpy as np
-from sklearn.cluster import AgglomerativeClustering
+try:
+    from sklearn.cluster import AgglomerativeClustering
+except:
+    print(
+        'Warning: Unable to use MTMCT in PP-Tracking, please install sklearn, for example: `pip install sklearn`'
+    )
+    pass
 from .utils import get_dire, get_match, get_cid_tid, combin_feature, combin_cluster
 from .utils import normalize, intracam_ignore, visual_rerank
 

deploy/pptracking/python/mot/mtmct/postprocess.py

@@ -20,7 +20,13 @@
 import cv2
 from tqdm import tqdm
 import numpy as np
-import motmetrics as mm
+try:
+    import motmetrics as mm
+except:
+    print(
+        'Warning: Unable to use motmetrics in MTMCT in PP-Tracking, please install motmetrics, for example: `pip install motmetrics`, see https://github.com/longcw/py-motmetrics'
+    )
+    pass
 from functools import reduce
 
 from .utils import parse_pt_gt, parse_pt, compare_dataframes_mtmc
@@ -201,6 +207,12 @@ def print_mtmct_result(gt_file, pred_file):
     summary.loc[:, 'idr'] *= 100
     summary.loc[:, 'idf1'] *= 100
     summary.loc[:, 'mota'] *= 100
+    try:
+        import motmetrics as mm
+    except Exception as e:
+        raise RuntimeError(
+            'Unable to use motmetrics in MTMCT in PP-Tracking, please install motmetrics, for example: `pip install motmetrics`, see https://github.com/longcw/py-motmetrics'
+        )
     print(
         mm.io.render_summary(
             summary,

deploy/pptracking/python/mot/mtmct/utils.py

@@ -20,9 +20,6 @@
 import cv2
 import gc
 import numpy as np
-from sklearn import preprocessing
-from sklearn.cluster import AgglomerativeClustering
-import motmetrics as mm
 import pandas as pd
 from tqdm import tqdm
 import warnings
@@ -195,10 +192,10 @@ def find_topk(a, k, axis=-1, largest=True, sorted=True):
 
     a = np.asanyarray(a)
     if largest:
-        index_array = np.argpartition(a, axis_size-k, axis=axis)
-        topk_indices = np.take(index_array, -np.arange(k)-1, axis=axis)
+        index_array = np.argpartition(a, axis_size - k, axis=axis)
+        topk_indices = np.take(index_array, -np.arange(k) - 1, axis=axis)
     else:
-        index_array = np.argpartition(a, k-1, axis=axis)
+        index_array = np.argpartition(a, k - 1, axis=axis)
         topk_indices = np.take(index_array, np.arange(k), axis=axis)
     topk_values = np.take_along_axis(a, topk_indices, axis=axis)
     if sorted:
@@ -228,7 +225,8 @@ def batch_numpy_topk(qf, gf, k1, N=6000):
         temp_qd = temp_qd / (np.max(temp_qd, axis=0)[0])
         temp_qd = temp_qd.T
         initial_rank.append(
-            find_topk(temp_qd, k=k1, axis=1, largest=False, sorted=True)[1])
+            find_topk(
+                temp_qd, k=k1, axis=1, largest=False, sorted=True)[1])
     del temp_qd
     del temp_gf
     del temp_qf
@@ -374,6 +372,12 @@ def visual_rerank(prb_feats,
 
 
 def normalize(nparray, axis=0):
+    try:
+        from sklearn import preprocessing
+    except Exception as e:
+        raise RuntimeError(
+            'Unable to use sklearn in MTMCT in PP-Tracking, please install sklearn, for example: `pip install sklearn`'
+        )
     nparray = preprocessing.normalize(nparray, norm='l2', axis=axis)
     return nparray
 
@@ -453,6 +457,12 @@ def parse_pt_gt(mot_feature):
 
 # eval result
 def compare_dataframes_mtmc(gts, ts):
+    try:
+        import motmetrics as mm
+    except Exception as e:
+        raise RuntimeError(
+            'Unable to use motmetrics in MTMCT in PP-Tracking, please install motmetrics, for example: `pip install motmetrics`, see https://github.com/longcw/py-motmetrics'
+        )
     """Compute ID-based evaluation metrics for MTMCT
     Return:
         df (pandas.DataFrame): Results of the evaluations in a df with only the 'idf1', 'idp', and 'idr' columns.
@@ -528,6 +538,12 @@ def get_labels(cid_tid_dict,
                use_ff=True,
                use_rerank=True,
                use_st_filter=False):
+    try:
+        from sklearn.cluster import AgglomerativeClustering
+    except Exception as e:
+        raise RuntimeError(
+            'Unable to use sklearn in MTMCT in PP-Tracking, please install sklearn, for example: `pip install sklearn`'
+        )
     # 1st cluster
     sim_matrix = get_sim_matrix(
         cid_tid_dict,

deploy/pptracking/python/mot/mtmct/zone.py

@@ -21,7 +21,13 @@
 import os
 import cv2
 import numpy as np
-from sklearn.cluster import AgglomerativeClustering
+try:
+    from sklearn.cluster import AgglomerativeClustering
+except:
+    print(
+        'Warning: Unable to use MTMCT in PP-Tracking, please install sklearn, for example: `pip install sklearn`'
+    )
+    pass
 
 BBOX_B = 10 / 15
 

deploy/pptracking/python/mot/tracker/ocsort_tracker.py

@@ -16,7 +16,13 @@
 """
 
 import numpy as np
-from filterpy.kalman import KalmanFilter
+try:
+    from filterpy.kalman import KalmanFilter
+except:
+    print(
+        'Warning: Unable to use OC-SORT, please install filterpy, for example: `pip install filterpy`, see https://github.com/rlabbe/filterpy'
+    )
+    pass
 
 from ..matching.ocsort_matching import associate, linear_assignment, iou_batch
 
@@ -84,6 +90,12 @@ class KalmanBoxTracker(object):
     count = 0
 
     def __init__(self, bbox, delta_t=3):
+        try:
+            from filterpy.kalman import KalmanFilter
+        except Exception as e:
+            raise RuntimeError(
+                'Unable to use OC-SORT, please install filterpy, for example: `pip install filterpy`, see https://github.com/rlabbe/filterpy'
+            )
         self.kf = KalmanFilter(dim_x=7, dim_z=4)
         self.kf.F = np.array([[1, 0, 0, 0, 1, 0, 0], [0, 1, 0, 0, 0, 1, 0],
                               [0, 0, 1, 0, 0, 0, 1], [0, 0, 0, 1, 0, 0, 0],

ppdet/metrics/mcmot_metrics.py

@@ -21,16 +21,21 @@
 import sys
 import math
 from collections import defaultdict
-from motmetrics.math_util import quiet_divide
 
 import numpy as np
 import pandas as pd
 
 from .metrics import Metric
-import motmetrics as mm
-import openpyxl
-metrics = mm.metrics.motchallenge_metrics
-mh = mm.metrics.create()
+try:
+    import motmetrics as mm
+    from motmetrics.math_util import quiet_divide
+    metrics = mm.metrics.motchallenge_metrics
+    mh = mm.metrics.create()
+except:
+    print(
+        'Warning: Unable to use MCMOT metric, please install motmetrics, for example: `pip install motmetrics`, see https://github.com/longcw/py-motmetrics'
+    )
+    pass
 from ppdet.utils.logger import setup_logger
 logger = setup_logger(__name__)
 
@@ -300,6 +305,13 @@ def __init__(self, data_root, seq_name, data_type, num_classes):
         self.num_classes = num_classes
 
         self.load_annotations()
+        try:
+            import motmetrics as mm
+            mm.lap.default_solver = 'lap'
+        except Exception as e:
+            raise RuntimeError(
+                'Unable to use MCMOT metric, please install motmetrics, for example: `pip install motmetrics`, see https://github.com/longcw/py-motmetrics'
+            )
         self.reset_accumulator()
 
         self.class_accs = []
@@ -314,13 +326,9 @@ def load_annotations(self):
             )
 
     def reset_accumulator(self):
-        import motmetrics as mm
-        mm.lap.default_solver = 'lap'
         self.acc = mm.MOTAccumulator(auto_id=True)
 
     def eval_frame_dict(self, trk_objs, gt_objs, rtn_events=False, union=False):
-        import motmetrics as mm
-        mm.lap.default_solver = 'lap'
         if union:
             trk_tlwhs, trk_ids, trk_cls = unzip_objs_cls(trk_objs)[:3]
             gt_tlwhs, gt_ids, gt_cls = unzip_objs_cls(gt_objs)[:3]
@@ -394,9 +402,6 @@ def get_summary(accs,
                     names,
                     metrics=('mota', 'num_switches', 'idp', 'idr', 'idf1',
                              'precision', 'recall')):
-        import motmetrics as mm
-        mm.lap.default_solver = 'lap'
-
         names = copy.deepcopy(names)
         if metrics is None:
             metrics = mm.metrics.motchallenge_metrics

ppdet/metrics/mot_metrics.py

@@ -28,6 +28,15 @@
 from .metrics import Metric
 from .munkres import Munkres
 
+try:
+    import motmetrics as mm
+    mm.lap.default_solver = 'lap'
+except:
+    print(
+        'Warning: Unable to use MOT metric, please install motmetrics, for example: `pip install motmetrics`, see https://github.com/longcw/py-motmetrics'
+    )
+    pass
+
 from ppdet.utils.logger import setup_logger
 logger = setup_logger(__name__)
 
@@ -114,6 +123,13 @@ def __init__(self, data_root, seq_name, data_type):
         self.data_type = data_type
 
         self.load_annotations()
+        try:
+            import motmetrics as mm
+            mm.lap.default_solver = 'lap'
+        except Exception as e:
+            raise RuntimeError(
+                'Unable to use MOT metric, please install motmetrics, for example: `pip install motmetrics`, see https://github.com/longcw/py-motmetrics'
+            )
         self.reset_accumulator()
 
     def load_annotations(self):
@@ -129,13 +145,9 @@ def load_annotations(self):
             gt_filename, is_ignore=True)
 
     def reset_accumulator(self):
-        import motmetrics as mm
-        mm.lap.default_solver = 'lap'
         self.acc = mm.MOTAccumulator(auto_id=True)
 
     def eval_frame(self, frame_id, trk_tlwhs, trk_ids, rtn_events=False):
-        import motmetrics as mm
-        mm.lap.default_solver = 'lap'
         # results
         trk_tlwhs = np.copy(trk_tlwhs)
         trk_ids = np.copy(trk_ids)
@@ -193,8 +205,6 @@ def get_summary(accs,
                     names,
                     metrics=('mota', 'num_switches', 'idp', 'idr', 'idf1',
                              'precision', 'recall')):
-        import motmetrics as mm
-        mm.lap.default_solver = 'lap'
         names = copy.deepcopy(names)
         if metrics is None:
             metrics = mm.metrics.motchallenge_metrics
@@ -231,8 +241,6 @@ def update(self, data_root, seq, data_type, result_root, result_filename):
         self.result_root = result_root
 
     def accumulate(self):
-        import motmetrics as mm
-        import openpyxl
         metrics = mm.metrics.motchallenge_metrics
         mh = mm.metrics.create()
         summary = self.MOTEvaluator.get_summary(self.accs, self.seqs, metrics)

ppdet/modeling/mot/matching/jde_matching.py

@@ -15,7 +15,14 @@
 This code is based on https://github.com/Zhongdao/Towards-Realtime-MOT/blob/master/tracker/matching.py
 """
 
-import lap
+try:
+    import lap
+except:
+    print(
+        'Warning: Unable to use JDE/FairMOT/ByteTrack, please install lap, for example: `pip install lap`, see https://github.com/gatagat/lap'
+    )
+    pass
+
 import scipy
 import numpy as np
 from scipy.spatial.distance import cdist
@@ -53,6 +60,12 @@ def merge_matches(m1, m2, shape):
 
 
 def linear_assignment(cost_matrix, thresh):
+    try:
+        import lap
+    except Exception as e:
+        raise RuntimeError(
+            'Unable to use JDE/FairMOT/ByteTrack, please install lap, for example: `pip install lap`, see https://github.com/gatagat/lap'
+        )
     if cost_matrix.size == 0:
         return np.empty(
             (0, 2), dtype=int), tuple(range(cost_matrix.shape[0])), tuple(