Added get_cropped_skin_im_shapes method, refactored landmarks_windowing

pyVHR/extraction/sig_processing.py

@@ -370,6 +370,7 @@ def extract_patches(self, videoFileName, region_type, sig_extraction_method):
         sig = []
         processed_frames_count = 0
         self.patch_landmarks = []
+        self.cropped_skin_im_shapes = [[], []]
         with mp_face_mesh.FaceMesh(
                 max_num_faces=1,
                 min_detection_confidence=0.5,
@@ -402,6 +403,10 @@ def extract_patches(self, videoFileName, region_type, sig_extraction_method):
 
                     ### skin extraction ###
                     cropped_skin_im, full_skin_im = skin_ex.extract_skin(image, ldmks)
+
+                    self.cropped_skin_im_shapes[0].append(cropped_skin_im.shape[0])
+                    self.cropped_skin_im_shapes[1].append(cropped_skin_im.shape[1])
+
                 else:
                     cropped_skin_im = np.zeros_like(image)
                     full_skin_im = np.zeros_like(image)
@@ -416,7 +421,7 @@ def extract_patches(self, videoFileName, region_type, sig_extraction_method):
                 sig.append(temp)
 
                 # save landmarks coordinates
-                self.patch_landmarks.append(magic_ldmks[:,0:2])
+                self.patch_landmarks.append(magic_ldmks[:,0:3])
 
                 # visualize patches and skin
                 if self.visualize_skin == True:
@@ -455,3 +460,12 @@ def get_landmarks(self):
             return np.array(self.patch_landmarks)
         else:
             return np.empty(0)
+
+    def get_cropped_skin_im_shapes(self):
+        """
+        Returns cropped skin shapes with shape [height, width] or empty array
+        """
+        if hasattr(self, "cropped_skin_im_shapes"):
+            return np.array(self.cropped_skin_im_shapes)
+        else:
+            return np.empty((0, 0))

pyVHR/extraction/utils.py

@@ -125,12 +125,13 @@ def sig_windowing(sig, wsize, stride, fps):
         block_signals.append(wind_signal)
     return block_signals, timesES
 
-def landmarks_windowing(landmarks, wsize, stride, fps):
+def movements_windowing(landmarks, area_x, wsize, stride, fps):
     """
     Calculation of overlapping windows of landmarks coordinates.
 
     Args:
         landmarks (float32 ndarray): ndarray with shape [num_frames, num_estimators, 2-coords].
+        area_x (float32 ndarray)   : ndarray with shape [num_frames]
         wsize (float)              : window size in seconds.
         stride (float)             : stride between overlapping windows in seconds.
         fps (float)                : frames per seconds.
@@ -142,12 +143,15 @@ def landmarks_windowing(landmarks, wsize, stride, fps):
     N = landmarks.shape[0]
     block_idx, timesLmks = sliding_straded_win_idx(N, wsize, stride, fps)
     lmks_xy = []
+    win_area_x = []
     for e in block_idx:
         st_frame = int(e[0])
         end_frame = int(e[-1])
         coords = np.copy(landmarks[st_frame: end_frame+1])
+        area_x_coords = np.copy(area_x[st_frame: end_frame+1])
         lmks_xy.append(coords)
-    return np.array(lmks_xy), timesLmks
+        win_area_x.append(area_x_coords)
+    return np.array(lmks_xy), np.array(win_area_x), timesLmks
 
 
 def raw_windowing(raw_signal, wsize, stride, fps):