Merge branch 'master' into feature/detect_format_function

src/js/pose_viewer/package.json

@@ -1,6 +1,6 @@
 {
   "name": "pose-viewer",
-  "version": "1.0.0",
+  "version": "1.0.1",
   "description": "Stencil Component Starter",
   "homepage": "https://github.com/sign-language-processing/pose",
   "main": "dist/index.cjs.js",

src/js/pose_viewer/src/components/pose-viewer/renderers/pose-renderer.tsx

@@ -59,7 +59,7 @@ export abstract class PoseRenderer {
 
     return lines
       .filter(Boolean) // Remove invalid lines
-      .sort((a, b) => a.z - b.z) // Sort lines by depth
+      .sort((a, b) => b.z - a.z) // Sort lines by depth
       .map(({from, to, color}) => this.renderLimb(from, to, color));
   }
 

src/python/pose_format/pose_visualizer.py

@@ -40,7 +40,8 @@ def __init__(self, pose: Pose, thickness=None):
         except ImportError:
             raise ImportError("Please install OpenCV with: pip install opencv-python")
 
-    def _draw_frame(self, frame: ma.MaskedArray, frame_confidence: np.ndarray, img,
+    def _draw_frame(self, frame: ma.MaskedArray,
+                    frame_confidence: np.ndarray, img,
                     transparency: bool = False) -> np.ndarray:
         """
         Draw frame of pose data of an image.
@@ -74,7 +75,6 @@ def _draw_frame(self, frame: ma.MaskedArray, frame_confidence: np.ndarray, img,
 
         for person, person_confidence in zip(frame, frame_confidence):
             c = person_confidence.tolist()
-            points = [p for p in person.tolist()]
             idx = 0
             for component in self.pose.header.components:
                 colors = [np.array(c[::-1]) for c in component.colors]
@@ -91,7 +91,7 @@ def _point_color(p_i: int):
                 # Collect Points
                 for i, point_name in enumerate(component.points):
                     if c[i + idx] > 0:
-                        center = points[i + idx]
+                        center = person[i + idx]
                         draw_operations.append({
                             'type': 'circle',
                             'center': center,
@@ -103,8 +103,8 @@ def _point_color(p_i: int):
                         })
 
                 if self.pose.header.is_bbox:
-                    point1 = points[0 + idx]
-                    point2 = points[1 + idx]
+                    point1 = person[0 + idx]
+                    point2 = person[1 + idx]
                     color = tuple(np.mean([_point_color(0), _point_color(1)], axis=0))
 
                     draw_operations.append({
@@ -119,8 +119,8 @@ def _point_color(p_i: int):
                     # Collect Limbs
                     for (p1, p2) in component.limbs:
                         if c[p1 + idx] > 0 and c[p2 + idx] > 0:
-                            point1 = points[p1 + idx]
-                            point2 = points[p2 + idx]
+                            point1 = person[p1 + idx]
+                            point2 = person[p2 + idx]
 
                             color = tuple(np.mean([_point_color(p1), _point_color(p2)], axis=0))
 
@@ -136,27 +136,30 @@ def _point_color(p_i: int):
 
                 idx += len(component.points)
 
-        draw_operations = sorted(draw_operations, key=lambda op: op['z'])
+        draw_operations = sorted(draw_operations, key=lambda op: op['z'], reverse=True)
+
+        def point_to_xy(point: ma.MaskedArray):
+            return tuple([round(p) for p in point[:2]])
 
         # Execute draw operations
         for op in draw_operations:
             if op['type'] == 'circle':
                 self.cv2.circle(img=img,
-                                center=tuple(op['center'][:2]),
+                                center=point_to_xy(op['center']),
                                 radius=op['radius'],
                                 color=op['color'],
                                 thickness=op['thickness'],
                                 lineType=op['lineType'])
             elif op['type'] == 'rectangle':
                 self.cv2.rectangle(img=img,
-                                   pt1=tuple(op['pt1'][:2]),
-                                   pt2=tuple(op['pt2'][:2]),
+                                   pt1=point_to_xy(op['pt1']),
+                                   pt2=point_to_xy(op['pt2']),
                                    color=op['color'],
                                    thickness=op['thickness'])
             elif op['type'] == 'line':
                 self.cv2.line(img,
-                              pt1=tuple(op['pt1'][:2]),
-                              pt2=tuple(op['pt2'][:2]),
+                              pt1=point_to_xy(op['pt1']),
+                              pt2=point_to_xy(op['pt2']),
                               color=op['color'],
                               thickness=op['thickness'],
                               lineType=op['lineType'])
@@ -184,12 +187,11 @@ def draw(self, background_color: Tuple[int, int, int] = (255, 255, 255), max_fra
         # ...
         if transparency:
             background_color += (0,)
-        int_frames = np.array(np.around(self.pose.body.data.data), dtype="int32")
         background = np.full(
             (self.pose.header.dimensions.height, self.pose.header.dimensions.width, len(background_color)),
             fill_value=background_color,
             dtype="uint8")
-        for frame, confidence in itertools.islice(zip(int_frames, self.pose.body.confidence), max_frames):
+        for frame, confidence in itertools.islice(zip(self.pose.body.data, self.pose.body.confidence), max_frames):
             yield self._draw_frame(frame, confidence, img=background.copy(), transparency=transparency)
 
     def draw_on_video(self, background_video, max_frames: int = None, blur=False):
@@ -471,9 +473,8 @@ def draw(self, background_color: int = 0, foreground_color: int = 255):
         np.ndarray
             frames with drawn pose
         """
-        int_frames = np.array(np.around(self.pose.body.data.data), dtype="int32")
         background = np.full((self.pose.header.dimensions.height, self.pose.header.dimensions.width),
                              fill_value=background_color,
                              dtype="uint8")
-        for frame in int_frames:
+        for frame in self.pose.body.data:
             yield self._draw_frame(frame, img=background.copy(), color=foreground_color)

src/python/pose_format/tensorflow/pose_representation_test.py

@@ -16,7 +16,7 @@
 
 dimensions = PoseHeaderDimensions(width=0, height=0, depth=0)
 components = [OpenPose_Hand_Component("hand_left_keypoints_2d")]
-header: PoseHeader = PoseHeader(version=0.1, dimensions=dimensions, components=components)
+header: PoseHeader = PoseHeader(version=0.2, dimensions=dimensions, components=components)
 
 representation = TensorflowPoseRepresentation(
     header=header,

src/python/pose_format/utils/generic.py

@@ -207,7 +207,7 @@ def fake_pose(num_frames: int, fps=25, dims=2, components=None):
     if components is None:
         components = copy.deepcopy(OpenPose_Components) # fixes W0102, dangerous default value
     dimensions = PoseHeaderDimensions(width=1, height=1, depth=1)
-    header = PoseHeader(version=0.1, dimensions=dimensions, components=components)
+    header = PoseHeader(version=0.2, dimensions=dimensions, components=components)
 
     total_points = header.total_points()
     data = np.random.randn(num_frames, 1, total_points, dims)

src/python/pose_format/utils/holistic.py

@@ -327,7 +327,7 @@ def load_holistic(frames: list,
     refine_face_landmarks = 'refine_face_landmarks' in additional_holistic_config and additional_holistic_config[
         'refine_face_landmarks']
     additional_face_points = 10 if refine_face_landmarks else 0
-    header: PoseHeader = PoseHeader(version=0.1,
+    header: PoseHeader = PoseHeader(version=0.2,
                                     dimensions=dimensions,
                                     components=holistic_components(pf, additional_face_points))
     body: NumPyPoseBody = process_holistic(frames, fps, width, height, kinect, progress, additional_face_points,
@@ -355,7 +355,7 @@ def formatted_holistic_pose(width: int, height: int, additional_face_points: int
         Formatted pose components
     """
     dimensions = PoseHeaderDimensions(width=width, height=height, depth=1000)
-    header = PoseHeader(version=0.1,
+    header = PoseHeader(version=0.2,
                         dimensions=dimensions,
                         components=holistic_components("XYZC", additional_face_points))
     body = NumPyPoseBody(
@@ -399,7 +399,7 @@ def load_mediapipe_directory(directory: str, fps: int, width: int, height: int,
         num_right_hand_points = first_frame["right_hand_landmarks"]["num_landmarks"]
         additional_face_points = 10 if (num_face_points == 478 or num_face_points == 128) else 0
     else:
-        return ValueError("No frames found in directory: {}".format(directory))
+        raise ValueError("No frames found in directory: {}".format(directory))
 
     def load_mediapipe_frame(frame):
         """

src/python/pose_format/utils/openpose.py

@@ -250,7 +250,7 @@ def load_openpose(frames: OpenPoseFrames,
     """
     dimensions = PoseHeaderDimensions(width=width, height=height, depth=depth)
 
-    header: PoseHeader = PoseHeader(version=0.1, dimensions=dimensions, components=OpenPose_Components)
+    header: PoseHeader = PoseHeader(version=0.2, dimensions=dimensions, components=OpenPose_Components)
 
     total_points = header.total_points()
 

src/python/pose_format/utils/openpose_135.py

@@ -128,7 +128,7 @@ def load_openpose_135_directory(*args, **kwargs) -> Pose:
 
 if __name__ == "__main__":
     dimensions = PoseHeaderDimensions(width=512, height=512, depth=0)
-    header = PoseHeader(version=0.1, dimensions=dimensions, components=OpenPose_Components)
+    header = PoseHeader(version=0.2, dimensions=dimensions, components=OpenPose_Components)
 
     with open(
             "/home/nlp/amit/sign-language/sign-language-datasets/sign_language_datasets/datasets/autsl/openpose_135.poseheader",

src/python/pyproject.toml

@@ -1,7 +1,7 @@
 [project]
 name = "pose_format"
 description = "Library for viewing, augmenting, and handling .pose files"
-version = "0.8.1"
+version = "0.8.2"
 keywords = ["Pose Files", "Pose Interpolation", "Pose Augmentation"]
 authors = [
     { name = "Amit Moryossef", email = "[email protected]" },