nettoyage gesture

sergeLabo · May 13, 2021 · a266777 · a266777
1 parent e2bb366
commit a266777
Show file tree

Hide file tree

Showing 3 changed files with 4 additions and 19 deletions.
diff --git a/blender-skeleton/scripts/utils.py b/blender-skeleton/scripts/utils.py
@@ -171,7 +171,7 @@ def get_points_blender(data):
             if val == [-1000000, -1000000, -1000000]:
                 points.append(None)
             else:
-                # Conversion
+                # Conversion cubemos vers blender
                 points.append([val[0]/1000, val[2]/1000, -val[1]/1000])
     else:
         points = None

diff --git a/get_skeleton/gestures.py b/get_skeleton/gestures.py
@@ -3,14 +3,7 @@
 from time import time, sleep
 from json import load
 import numpy as np
-from collections import deque
 
-try:
-    from scipy.signal import savgol_filter
-    SCIPY = True
-except:
-    print("Vous devez installer scipy !")
-    SCIPY = False
 
 GESTURES  = {   2: (7, 5),
                 3: (4, 2),
@@ -50,14 +43,6 @@ def __init__(self, client):
         self.points_old = None
         self.center = [0,0,0]
 
-        # Filtres: toutes les coordonnées sont dans une pile
-        self.piles = []
-        pile_size = 10
-        for i in range(18):
-            self.piles.append([])
-            for j in range(3):
-                self.piles[i].append(deque(maxlen=pile_size))
-
         # Lissage: 18 x 3 x LISSAGE
         self.liss = []
         for i in range(18):

diff --git a/get_skeleton/get_cubemos_skeleton.py b/get_skeleton/get_cubemos_skeleton.py
@@ -57,9 +57,9 @@ def render_ids_3d(self, color_image, skeletons_2d, depth_map,
                     low_bound_x = max(0, l)
                     m = int(joints_2D[joint_index].x + math.ceil(kernel/2))
                     upper_bound_x = min(cols - 1, m)
-                    n = int(joints_2D[joint_index].y - math.floor(kernel / 2))
+                    n = int(joints_2D[joint_index].y - math.floor(kernel/2))
                     low_bound_y = max(0, n)
-                    o = int(joints_2D[joint_index].y + math.ceil(kernel / 2))
+                    o = int(joints_2D[joint_index].y + math.ceil(kernel/2))
                     upper_bound_y = min(rows - 1, o)
                     for x in range(low_bound_x, upper_bound_x):
                         for y in range(low_bound_y, upper_bound_y):
@@ -123,7 +123,7 @@ def run(self):
                 continue
 
             # Convert images to numpy arrays
-            depth_image = np.asanyarray(depth.get_data())
+            # #depth_image = np.asanyarray(depth.get_data())
             color_image = np.asanyarray(color.get_data())
 
             # perform inference and update the tracking id