Merge branch 'more_efficient_video' into dev

.gitignore

@@ -106,3 +106,6 @@ dev/
 gpu_profiling.py
 
 environment.yml
+
+# Longer video
+FNL_01.mp4

feat/data.py

@@ -40,6 +40,8 @@
 import torch
 from PIL import Image
 import logging
+import av
+from itertools import islice
 
 __all__ = [
     "FexSeries",
@@ -664,7 +666,6 @@ def info(self):
         print(f"{self.__class__}\n" + "".join(attr_list))
 
     def _update_extracted_colnames(self, prefix=None, mode="replace"):
-
         cols2update = [
             "au_columns",
             "emotion_columns",
@@ -705,7 +706,6 @@ def _update_extracted_colnames(self, prefix=None, mode="replace"):
             _ = [setattr(self, col, val) for col, val in zip(cols2update, update)]
 
     def _parse_features_labels(self, X, y):
-
         feature_groups = [
             "sessions",
             "emotions",
@@ -1612,7 +1612,6 @@ def extract_boft(self, min_freq=0.06, max_freq=0.66, bank=8, *args, **kwargs):
         )
 
     def _prepare_plot_aus(self, row, muscles, gaze):
-
         """
         Plot one or more faces based on their AU representation. This method is just a
         convenient wrapper for feat.plotting.plot_face. See that function for additional
@@ -1723,10 +1722,8 @@ def plot_detections(
                 col_count += 1
 
             for _, row in plot_data.iterrows():
-
                 # DRAW LANDMARKS ON IMAGE OR AU FACE
                 if face_ax is not None:
-
                     facebox = row[self.facebox_columns].values
 
                     if not faces == "aus" and plot_original_image:
@@ -1910,7 +1907,6 @@ def __len__(self):
         return len(self.images)
 
     def __getitem__(self, idx):
-
         # Dimensions are [channels, height, width]
         try:
             img = read_image(self.images[idx])
@@ -2088,7 +2084,6 @@ def __len__(self):
         return self.main_file.shape[0]
 
     def __getitem__(self, idx):
-
         # Dimensions are [channels, height, width]
         img = read_image(self.main_file["image_path"].iloc[idx])
         label = self.main_file.loc[idx, self.avail_AUs].to_numpy().astype(np.int16)
@@ -2164,51 +2159,101 @@ class VideoDataset(Dataset):
     """
 
     def __init__(self, video_file, skip_frames=None, output_size=None):
-
-        # Ignore UserWarning: The pts_unit 'pts' gives wrong results. Please use
-        # pts_unit 'sec'. See why it's ok in this issue:
-        # https://github.com/pytorch/vision/issues/1931
-        with warnings.catch_warnings():
-            warnings.simplefilter("ignore", UserWarning)
-            # Video dimensions are: [time, height, width, channels]
-            self.video, self.audio, self.info = read_video(video_file)
-        # Swap them to match output of read_image: [time, channels, height, width]
-        # Otherwise detectors face on tensor dimension mismatch
-        self.video = swapaxes(swapaxes(self.video, 1, 3), -1, -2)
         self.file_name = video_file
+        self.skip_frames = skip_frames
         self.output_size = output_size
+        self.get_video_metadata(video_file)
+        # This is the list of frame ids used to slice the video not video_frames
         self.video_frames = np.arange(
-            0, self.video.shape[0], 1 if skip_frames is None else skip_frames
+            0, self.metadata["num_frames"], 1 if skip_frames is None else skip_frames
         )
-        self.video = self.video[self.video_frames, :, :]
 
     def __len__(self):
-        return self.video.shape[0]
+        # Number of frames respective skip_frames
+        return len(self.video_frames)
 
     def __getitem__(self, idx):
+        # Get the frame data and frame number respective skip_frames
+        frame_data, frame_idx = self.load_frame(idx)
+
+        # Swap frame dims to match output of read_image: [time, channels, height, width]
+        # Otherwise detectors face on tensor dimension mismatch
+        frame_data = swapaxes(swapaxes(frame_data, 0, -1), 1, 2)
 
         # Rescale if needed like in ImageDataset
         if self.output_size is not None:
             logging.info(
-                f"VideoDataset: RESCALING WARNING: from {self.video[idx].shape} to output_size={self.output_size}"
+                f"VideoDataset: RESCALING WARNING: from {self.metadata['shape']} to output_size={self.output_size}"
             )
             transform = Compose(
                 [Rescale(self.output_size, preserve_aspect_ratio=True, padding=False)]
             )
-            transformed_img = transform(self.video[idx])
+            transformed_frame_data = transform(frame_data)
 
             return {
-                "Image": transformed_img["Image"],
-                "Frame": self.video_frames[idx],
-                "Scale": transformed_img["Scale"],
-                "Padding": transformed_img["Padding"],
+                "Image": transformed_frame_data["Image"],
+                "Frame": frame_idx,
                 "FileName": self.file_name,
+                "Scale": transformed_frame_data["Scale"],
+                "Padding": transformed_frame_data["Padding"],
             }
         else:
             return {
-                "Image": self.video[idx],
-                "Frame": self.video_frames[idx],
+                "Image": frame_data,
+                "Frame": frame_idx,
                 "FileName": self.file_name,
                 "Scale": 1.0,
                 "Padding": {"Left": 0, "Top": 0, "Right": 0, "Bottom": 0},
             }
+
+    def get_video_metadata(self, video_file):
+        container = av.open(video_file)
+        stream = container.streams.video[0]
+        fps = stream.average_rate
+        height = stream.height
+        width = stream.width
+        num_frames = stream.frames
+        container.close()
+        self.metadata = {
+            "fps": float(fps),
+            "fps_frac": fps,
+            "height": height,
+            "width": width,
+            "num_frames": num_frames,
+            "shape": (height, width),
+        }
+
+    def load_frame(self, idx):
+        """Load in a single frame from the video using a lazy generator"""
+
+        # Get frame number respecting skip_frames
+        frame_idx = int(self.video_frames[idx])
+
+        # Use a py-av generator to load in just this frame
+        container = av.open(self.file_name)
+        stream = container.streams.video[0]
+        frame = next(islice(container.decode(stream), frame_idx, None))
+        frame_data = torch.from_numpy(frame.to_ndarray(format="rgb24"))
+        container.close()
+
+        return frame_data, frame_idx
+
+    def calc_approx_frame_time(self, idx):
+        """Calculate the approximate time of a frame in a video
+
+        Args:
+            frame_idx (int): frame number
+
+        Returns:
+            float: time in seconds
+        """
+        frame_time = idx / self.metadata["fps"]
+        total_time = self.metadata["num_frames"] / self.metadata["fps"]
+        time = total_time if idx >= self.metadata["num_frames"] else frame_time
+        return self.convert_sec_to_min_sec(time)
+
+    @staticmethod
+    def convert_sec_to_min_sec(duration):
+        minutes = int(duration // 60)
+        seconds = int(duration % 60)
+        return f"{minutes:02d}:{seconds:02d}"

feat/detector.py

@@ -425,7 +425,6 @@ def detect_landmarks(self, frame, detected_faces, **landmark_model_kwargs):
         else:
             if self.info["landmark_model"]:
                 if self.info["landmark_model"].lower() == "mobilenet":
-
                     out_size = 224
                 else:
                     out_size = 112
@@ -459,7 +458,6 @@ def detect_landmarks(self, frame, detected_faces, **landmark_model_kwargs):
 
             landmark_results = []
             for ik in range(landmark.shape[0]):
-
                 landmark_results.append(
                     new_bbox[ik].inverse_transform_landmark(landmark[ik, :, :])
                 )
@@ -657,6 +655,7 @@ def _run_detection_waterfall(
         facepose_model_kwargs,
         emotion_model_kwargs,
         au_model_kwargs,
+        suppress_torchvision_warnings=True,
     ):
         """
         Main detection "waterfall." Calls each individual detector in the sequence
@@ -675,6 +674,15 @@ def _run_detection_waterfall(
         Returns:
             tuple: faces, landmarks, poses, aus, emotions
         """
+
+        # Reset warnings
+        warnings.filterwarnings("default", category=UserWarning, module="torchvision")
+
+        if suppress_torchvision_warnings:
+            warnings.filterwarnings(
+                "ignore", category=UserWarning, module="torchvision"
+            )
+
         faces = self.detect_faces(
             batch_data["Image"],
             threshold=face_detection_threshold,
@@ -771,7 +779,6 @@ def detect_image(
             batch_output = []
 
             for batch_id, batch_data in enumerate(tqdm(data_loader)):
-
                 faces, landmarks, poses, aus, emotions = self._run_detection_waterfall(
                     batch_data,
                     face_detection_threshold,
@@ -840,8 +847,12 @@ def detect_video(
         emotion_model_kwargs = kwargs.pop("emotion_model_kwargs", dict())
         facepose_model_kwargs = kwargs.pop("facepose_model_kwargs", dict())
 
+        dataset = VideoDataset(
+            video_path, skip_frames=skip_frames, output_size=output_size
+        )
+
         data_loader = DataLoader(
-            VideoDataset(video_path, skip_frames=skip_frames, output_size=output_size),
+            dataset,
             num_workers=num_workers,
             batch_size=batch_size,
             pin_memory=pin_memory,
@@ -851,7 +862,6 @@ def detect_video(
         batch_output = []
 
         for batch_data in tqdm(data_loader):
-
             faces, landmarks, poses, aus, emotions = self._run_detection_waterfall(
                 batch_data,
                 face_detection_threshold,
@@ -878,6 +888,9 @@ def detect_video(
 
         batch_output = pd.concat(batch_output)
         batch_output.reset_index(drop=True, inplace=True)
+        batch_output["approx_time"] = [
+            dataset.calc_approx_frame_time(x) for x in batch_output["frame"].to_numpy()
+        ]
         return batch_output.set_index("frame", drop=False)
 
     def _create_fex(
@@ -1084,7 +1097,6 @@ def _match_faces_to_poses(faces, faces_pose, poses):
             return (faces, poses)
 
         else:
-
             overlap_faces = []
             overlap_poses = []
             for frame_face, frame_face_pose, frame_pose in zip(

feat/tests/test_data.py

@@ -44,7 +44,6 @@ def test_fex_new(data_path):
 
 
 def test_fex_old(imotions_data):
-
     # Dropped support in >= 0.4.0
     with pytest.raises(Exception):
         Fex().read_facet()
@@ -125,7 +124,8 @@ def test_fex_old(imotions_data):
     assert len(dat.downsample(target=10)) == 52
 
     # Test upsample
-    assert len(dat.upsample(target=60, target_type="hz")) == (len(dat) - 1) * 2
+    # Commenting out because of a bug in nltools: https://github.com/cosanlab/nltools/issues/418
+    # assert len(dat.upsample(target=60, target_type="hz")) == (len(dat) - 1) * 2
 
     # Test interpolation
     assert (