@@ -874,6 +874,7 @@ def analyze_videos(
             auto_track=auto_track,
             identity_only=identity_only,
             overwrite=False,
+            cropping=cropping,
             **torch_kwargs,
         )
 
 
@@ -45,11 +45,14 @@ class VideoIterator(VideoReader):
     """A class to iterate over videos, with possible added context"""
 
     def __init__(
-        self, video_path: str | Path, context: list[dict[str, Any]] | None = None
+        self, video_path: str | Path, context: list[dict[str, Any]] | None = None, cropping: list[int] | None = None
     ) -> None:
         super().__init__(str(video_path))
         self._context = context
         self._index = 0
+        self._crop = cropping is not None
+        if self._crop:
+            self.set_bbox(*cropping)
 
     def get_context(self) -> list[dict[str, Any]] | None:
         if self._context is None:
@@ -68,7 +71,7 @@ def __iter__(self):
         return self
 
     def __next__(self) -> np.ndarray | tuple[str, dict[str, Any]]:
-        frame = self.read_frame()
+        frame = self.read_frame(crop=self._crop)
         if frame is None:
             self._index = 0
             self.reset()
@@ -94,9 +97,10 @@ def video_inference(
     detector_runner: InferenceRunner | None = None,
     with_identity: bool = False,
     return_video_metadata: bool = False,
+    cropping: list[int] | None = None,
 ) -> list[dict[str, np.ndarray]]:
     """Runs inference on a video"""
-    video = VideoIterator(str(video_path))
+    video = VideoIterator(str(video_path), cropping=cropping)
     n_frames = video.get_n_frames()
     vid_w, vid_h = video.dimensions
     print(f"Starting to analyze {video_path}")
@@ -167,6 +171,7 @@ def analyze_videos(
     auto_track: bool | None = True,
     identity_only: bool | None = False,
     overwrite: bool = False,
+    cropping: list[int] | None = None,
 ) -> str:
     """Makes prediction based on a trained network.
 
@@ -220,6 +225,11 @@ def analyze_videos(
         identity_only: sub-call for auto_track. If ``True`` and animal identity was
             learned by the model, assembly and tracking rely exclusively on identity
             prediction.
+        cropping: list or None, optional, default=None
+            List of cropping coordinates as [x1, x2, y1, y2].
+            Note that the same cropping parameters will then be used for all videos.
+            If different video crops are desired, run ``analyze_videos`` on individual
+            videos with the corresponding cropping coordinates.
 
     Returns:
         The scorer used to analyze the videos
@@ -252,6 +262,9 @@ def analyze_videos(
         cfg, model_cfg, snapshot_index, detector_snapshot_index,
     )
 
+    if cropping is None and cfg.get("cropping", False):
+        cropping = cfg["x1"], cfg["x2"], cfg["y1"], cfg["y2"]
+
     # Get general project parameters
     bodyparts = model_cfg["metadata"]["bodyparts"]
     unique_bodyparts = model_cfg["metadata"]["unique_bodyparts"]
@@ -328,6 +341,7 @@ def analyze_videos(
                 pose_runner=pose_runner,
                 task=pose_task,
                 detector_runner=detector_runner,
+                cropping=cropping,
             )
             runtime.append(time.time())
             metadata = _generate_metadata(
 
@@ -159,6 +159,24 @@ def get_bbox(self, relative=False):
             y2 = int(self._height * y2)
         return x1, x2, y1, y2
 
+    def set_bbox(self, x1, x2, y1, y2, relative=False):
+        if x2 <= x1 or y2 <= y1:
+            raise ValueError(
+                f"Coordinates look wrong... " f"Ensure {x1} < {x2} and {y1} < {y2}."
+            )
+        if not relative:
+            x1 /= self._width
+            x2 /= self._width
+            y1 /= self._height
+            y2 /= self._height
+        bbox = x1, x2, y1, y2
+        if any(coord > 1 for coord in bbox):
+            warnings.warn(
+                "Bounding box larger than the video... " "Clipping to video dimensions."
+            )
+            bbox = tuple(map(lambda x: min(x, 1), bbox))
+        self._bbox = bbox
+
     @property
     def fps(self):
         return self._fps
@@ -205,24 +223,6 @@ def __init__(self, video_path, codec="h264", dpi=100, fps=None):
         if fps:
             self.fps = fps
 
-    def set_bbox(self, x1, x2, y1, y2, relative=False):
-        if x2 <= x1 or y2 <= y1:
-            raise ValueError(
-                f"Coordinates look wrong... " f"Ensure {x1} < {x2} and {y1} < {y2}."
-            )
-        if not relative:
-            x1 /= self._width
-            x2 /= self._width
-            y1 /= self._height
-            y2 /= self._height
-        bbox = x1, x2, y1, y2
-        if any(coord > 1 for coord in bbox):
-            warnings.warn(
-                "Bounding box larger than the video... " "Clipping to video dimensions."
-            )
-            bbox = tuple(map(lambda x: min(x, 1), bbox))
-        self._bbox = bbox
-
     def shorten(
         self, start, end, suffix="short", dest_folder=None, validate_inputs=True
     ):
Original file line number	Diff line number	Diff line change
`@@ -874,6 +874,7 @@ def analyze_videos(`
`874`	`874`	`auto_track=auto_track,`
`875`	`875`	`identity_only=identity_only,`
`876`	`876`	`overwrite=False,`
	`877`	`+ cropping=cropping,`
`877`	`878`	`**torch_kwargs,`
`878`	`879`	`)`
`879`	`880`