优化计算过程

Author: yhu-ctrl

angle.py

@@ -61,16 +61,16 @@ def cal(coords, confidence, keypoint_thresh=0.2):
     # 角度对比
     def compare(self, angles):
         stdAngle = self.stdAngles[self.pos]     # 每次读取一行标准角度
-        scores = []
+        scores = np.empty(len(angles))
         visibles = ~np.isnan(stdAngle)          # 样本中没有缺失值的
         stdAngle = stdAngle[visibles]           # 过滤掉样本中的缺失值
-        for angle in angles:
+        for i, angle in enumerate(angles):
             angle_v = angle[visibles]           # 过滤样本中也有缺失值的点
             if np.isnan(angle_v).any():         # 还有缺失值
-                scores.append('NaN')
+                scores[i] = np.nan
             else:
-                score = 1 - np.tanh(mean_squared_error(angle_v, stdAngle))
-                scores.append('{:.4f}'.format(score))
+                score = 100 * (1 - np.tanh(mean_squared_error(angle_v, stdAngle)))
+                scores[i] = score
         self.pos += 1
 
         return scores

run.py

@@ -41,16 +41,15 @@
     pose_input, upscale_bbox = detector_to_simple_pose(img, class_IDs, scores, bounding_boxs, ctx=ctx)
 
     # 姿态识别
+    results = None
     if len(upscale_bbox) > 0:
         predicted_heatmap = estimator(pose_input)
         pred_coords, confidence = heatmap_to_coord(predicted_heatmap, upscale_bbox)
         img = cv_plot_keypoints(img, pred_coords, confidence, class_IDs, bounding_boxs, scores)
 
         # 动作对比
         angles = AngeleCal.cal(pred_coords, confidence)
-        results = angeleCal.compare(angles)
-    else:
-        results = ['NaN']
+        results = angeleCal.compare(angles).astype('U5')
 
     # 缩放示例视频并合并显示
     height = int(img.shape[0])