[MOT] refine mtmct deploy (#4633)

* fix mtmct deploy

* add enable_static

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

@@ -341,6 +341,7 @@ def visual_rerank(prb_feats,
         prb_feats, gal_feats = run_fac(prb_feats, gal_feats, prb_labels,
                                        gal_labels, 0.08, 20, 0.5, 1, 1)
     if use_rerank:
+        paddle.enable_static()
         print('current use rerank finetuned parameters....')
         # Step2: k-reciprocal. finetuned parameters: [k1,k2,lambda_value]
         sims = ReRank2(

deploy/pptracking/python/mot_sde_infer.py

@@ -32,7 +32,7 @@ from benchmark_utils import PaddleInferBenchmark
 from visualize import plot_tracking
 
 from mot.tracker import DeepSORTTracker
-from mot.utils import MOTTimer, write_mot_results, flow_statistic
+from mot.utils import MOTTimer, write_mot_results, flow_statistic, scale_coords, clip_box, preprocess_reid
 
 from mot.mtmct.utils import parse_bias
 from mot.mtmct.postprocess import trajectory_fusion, sub_cluster, gen_res, print_mtmct_result
@@ -59,50 +59,6 @@ def bench_log(detector, img_list, model_info, batch_size=1, name=None):
     log(name)
 
 
-def scale_coords(coords, input_shape, im_shape, scale_factor):
-    im_shape = im_shape[0]
-    ratio = scale_factor[0][0]
-    pad_w = (input_shape[1] - int(im_shape[1])) / 2
-    pad_h = (input_shape[0] - int(im_shape[0])) / 2
-    coords[:, 0::2] -= pad_w
-    coords[:, 1::2] -= pad_h
-    coords[:, 0:4] /= ratio
-    coords[:, :4] = np.clip(coords[:, :4], a_min=0, a_max=coords[:, :4].max())
-    return coords.round()
-
-
-def clip_box(xyxy, input_shape, im_shape, scale_factor):
-    im_shape = im_shape[0]
-    ratio = scale_factor[0][0]
-    img0_shape = [int(im_shape[0] / ratio), int(im_shape[1] / ratio)]
-    xyxy[:, 0::2] = np.clip(xyxy[:, 0::2], a_min=0, a_max=img0_shape[1])
-    xyxy[:, 1::2] = np.clip(xyxy[:, 1::2], a_min=0, a_max=img0_shape[0])
-    w = xyxy[:, 2:3] - xyxy[:, 0:1]
-    h = xyxy[:, 3:4] - xyxy[:, 1:2]
-    mask = np.logical_and(h > 0, w > 0)
-    keep_idx = np.nonzero(mask)
-    return xyxy[keep_idx[0]], keep_idx
-
-
-def preprocess_reid(imgs,
-                    w=64,
-                    h=192,
-                    mean=[0.485, 0.456, 0.406],
-                    std=[0.229, 0.224, 0.225]):
-    im_batch = []
-    for img in imgs:
-        img = cv2.resize(img, (w, h))
-        img = img[:, :, ::-1].astype('float32').transpose((2, 0, 1)) / 255
-        img_mean = np.array(mean).reshape((3, 1, 1))
-        img_std = np.array(std).reshape((3, 1, 1))
-        img -= img_mean
-        img /= img_std
-        img = np.expand_dims(img, axis=0)
-        im_batch.append(img)
-    im_batch = np.concatenate(im_batch, 0)
-    return im_batch
-
-
 class SDE_Detector(Detector):
     """
     Args:
@@ -146,8 +102,7 @@ class SDE_Detector(Detector):
         assert batch_size == 1, "The JDE Detector only supports batch size=1 now"
         self.pred_config = pred_config
 
-    def postprocess(self, boxes, input_shape, im_shape, scale_factor, threshold,
-                    scaled):
+    def postprocess(self, boxes, ori_image_shape, threshold, scaled):
         over_thres_idx = np.nonzero(boxes[:, 1:2] >= threshold)[0]
         if len(over_thres_idx) == 0:
             pred_dets = np.zeros((1, 6), dtype=np.float32)
@@ -165,8 +120,8 @@ class SDE_Detector(Detector):
         else:
             pred_bboxes = boxes[:, 2:]
 
-        pred_xyxys, keep_idx = clip_box(pred_bboxes, input_shape, im_shape,
-                                        scale_factor)
+        pred_xyxys, keep_idx = clip_box(pred_bboxes, ori_image_shape)
+
         if len(keep_idx[0]) == 0:
             pred_dets = np.zeros((1, 6), dtype=np.float32)
             pred_xyxys = np.zeros((1, 4), dtype=np.float32)
@@ -183,10 +138,12 @@ class SDE_Detector(Detector):
 
         return pred_dets, pred_xyxys
 
-    def predict(self, image, scaled, threshold=0.5, warmup=0, repeats=1):
+    def predict(self, image_path, ori_image_shape, scaled, threshold=0.5, warmup=0, repeats=1):
         '''
         Args:
-            image (np.ndarray): image numpy data
+            image_path (list[str]): path of images, only support one image path
+                (batch_size=1) in tracking model
+            ori_image_shape (list[int]: original image shape
             threshold (float): threshold of predicted box' score
             scaled (bool): whether the coords after detector outputs are scaled,
                 default False in jde yolov3, set True in general detector.
@@ -194,7 +151,7 @@ class SDE_Detector(Detector):
             pred_dets (np.ndarray, [N, 6])
         '''
         self.det_times.preprocess_time_s.start()
-        inputs = self.preprocess(image)
+        inputs = self.preprocess(image_path)
         self.det_times.preprocess_time_s.end()
 
         input_names = self.predictor.get_input_names()
@@ -221,12 +178,8 @@ class SDE_Detector(Detector):
             pred_dets = np.zeros((1, 6), dtype=np.float32)
             pred_xyxys = np.zeros((1, 4), dtype=np.float32)
         else:
-            input_shape = inputs['image'].shape[2:]
-            im_shape = inputs['im_shape']
-            scale_factor = inputs['scale_factor']
-
             pred_dets, pred_xyxys = self.postprocess(
-                boxes, input_shape, im_shape, scale_factor, threshold, scaled)
+                boxes, ori_image_shape, threshold, scaled)
 
         self.det_times.postprocess_time_s.end()
         self.det_times.img_num += 1
@@ -727,7 +680,9 @@ def predict_mtmct_seq(detector, reid_model, seq_name, output_dir):
         if frame_id % 40 == 0:
             print('Processing frame {} of seq {}.'.format(frame_id, seq_name))
         frame = cv2.imread(os.path.join(fpath, img_file))
-        pred_dets, pred_xyxys = detector.predict([frame], FLAGS.scaled,
+        ori_image_shape = list(frame.shape[:2])
+        frame_path = os.path.join(fpath, img_file)
+        pred_dets, pred_xyxys = detector.predict([frame_path], ori_image_shape, FLAGS.scaled,
                                                  FLAGS.threshold)
 
         if len(pred_dets) == 1 and np.sum(pred_dets) == 0:
@@ -855,8 +810,6 @@ def predict_mtmct(detector, reid_model, mtmct_dir, mtmct_cfg):
             use_roi=use_roi,
             roi_dir=roi_dir)
 
-    pred_mtmct_file = os.path.join(output_dir, 'mtmct_result.txt')
-
     if FLAGS.save_images:
         carame_results, cid_tid_fid_res = get_mtmct_matching_results(
             pred_mtmct_file)
@@ -872,6 +825,11 @@ def predict_mtmct(detector, reid_model, mtmct_dir, mtmct_cfg):
             save_dir=save_dir,
             save_videos=FLAGS.save_images)
 
+    # evalution metrics
+    data_root_gt = os.path.join(mtmct_dir, '..', 'gt', 'gt.txt')
+    if os.path.exists(data_root_gt):
+        print_mtmct_result(data_root_gt, pred_mtmct_file)
+
 
 def main():
     pred_config = PredictConfig(FLAGS.model_dir)