[Cherry-Pick] fix error when all bbox are filtered (#5571)

* fix error when all bbox are filered

* remove filter in function get_person_from_rect

deploy/python/det_keypoint_unite_infer.py

@@ -36,10 +36,9 @@ KEYPOINT_SUPPORT_MODELS = {
 
 
 def predict_with_given_det(image, det_res, keypoint_detector,
-                           keypoint_batch_size, det_threshold,
-                           keypoint_threshold, run_benchmark):
+                           keypoint_batch_size, run_benchmark):
     rec_images, records, det_rects = keypoint_detector.get_person_from_rect(
-        image, det_res, det_threshold)
+        image, det_res)
     keypoint_vector = []
     score_vector = []
 
@@ -79,19 +78,20 @@ def topdown_unite_predict(detector,
             detector.gpu_util += gu
         else:
             results = detector.predict_image([image], visual=False)
+        results = detector.filter_box(results, FLAGS.det_threshold)
+        if results['boxes_num'] > 0:
+            keypoint_res = predict_with_given_det(
+                image, results, topdown_keypoint_detector, keypoint_batch_size,
+                FLAGS.run_benchmark)
 
-        if results['boxes_num'] == 0:
-            continue
-
-        keypoint_res = predict_with_given_det(
-            image, results, topdown_keypoint_detector, keypoint_batch_size,
-            FLAGS.det_threshold, FLAGS.keypoint_threshold, FLAGS.run_benchmark)
-
-        if save_res:
-            store_res.append([
-                i, keypoint_res['bbox'],
-                [keypoint_res['keypoint'][0], keypoint_res['keypoint'][1]]
-            ])
+            if save_res:
+                store_res.append([
+                    i, keypoint_res['bbox'],
+                    [keypoint_res['keypoint'][0], keypoint_res['keypoint'][1]]
+                ])
+        else:
+            results["keypoint"] = [[], []]
+            keypoint_res = results
         if FLAGS.run_benchmark:
             cm, gm, gu = get_current_memory_mb()
             topdown_keypoint_detector.cpu_mem += cm
@@ -138,7 +138,7 @@ def topdown_unite_predict_video(detector,
     if not os.path.exists(FLAGS.output_dir):
         os.makedirs(FLAGS.output_dir)
     out_path = os.path.join(FLAGS.output_dir, video_name)
-    fourcc = cv2.VideoWriter_fourcc(*'mp4v')
+    fourcc = cv2.VideoWriter_fourcc(* 'mp4v')
     writer = cv2.VideoWriter(out_path, fourcc, fps, (width, height))
     index = 0
     store_res = []
@@ -152,10 +152,14 @@ def topdown_unite_predict_video(detector,
         frame2 = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
 
         results = detector.predict_image([frame2], visual=False)
+        results = detector.filter_box(results, FLAGS.det_threshold)
+        if results['boxes_num'] == 0:
+            writer.write(frame)
+            continue
 
         keypoint_res = predict_with_given_det(
             frame2, results, topdown_keypoint_detector, keypoint_batch_size,
-            FLAGS.det_threshold, FLAGS.keypoint_threshold, FLAGS.run_benchmark)
+            FLAGS.run_benchmark)
 
         im = visualize_pose(
             frame,

deploy/python/keypoint_infer.py

@@ -95,12 +95,10 @@ class KeyPointDetector(Detector):
     def set_config(self, model_dir):
         return PredictConfig_KeyPoint(model_dir)
 
-    def get_person_from_rect(self, image, results, det_threshold=0.5):
+    def get_person_from_rect(self, image, results):
         # crop the person result from image
         self.det_times.preprocess_time_s.start()
-        det_results = results['boxes']
-        mask = det_results[:, 1] > det_threshold
-        valid_rects = det_results[mask]
+        valid_rects = results['boxes']
         rect_images = []
         new_rects = []
         org_rects = []