Bounding boxes example

examples/load_and_explore_bboxes.py

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+"""Load and explore bboxes tracks
+===============================
+
+Load and explore an example dataset of bounding boxes tracks.
+"""
+
+# %%
+# Imports
+# -------
+# For interactive plots: install ipympl with `pip install ipympl` and uncomment
+# the following line in your notebook
+# %matplotlib widget
+from cycler import cycler
+from matplotlib import pyplot as plt
+
+from movement import sample_data
+from movement.io import load_bboxes
+
+# %%
+# Select sample data file
+# --------------------
+# For the sake of this example, we will use the path to one of
+# the sample datasets provided with ``movement``.
+
+file_path = sample_data.fetch_dataset_paths(
+    "VIA_multiple-crabs_5-frames_labels.csv"
+)["bboxes"]
+print(file_path)
+
+# %%
+# Read file as a `movement` dataset
+# ----------------------------------
+ds = load_bboxes.from_via_tracks_file(file_path)
+
+# print some information about the dataset
+print(ds)
+print("-----")
+print(f"Number of individuals: {ds.sizes['individuals']}")
+print(f"Number of frames: {ds.sizes['time']}")
+
+
+# %%
+# The dataset contains bounding boxes for 86 individuals, tracked for
+# 5 frames, in the xy plane.
+#
+# We can also see from the printout of the dataset that it contains
+# three data arrays: ``position``, ``shape`` and ``confidence``.
+#
+# We will use these three arrays in the following sections to produce
+# informative plots of the tracked trajectories
+# %%
+# Plot trajectories and color by individual
+# -----------------------------------------
+
+fig, ax = plt.subplots(1, 1)  # , figsize=(15, 15))
+
+# add color cycler to axes
+plt.rcParams["axes.prop_cycle"] = cycler(color=plt.get_cmap("tab10").colors)
+# get the list of colors in the cycle
+color_cycle = plt.rcParams["axes.prop_cycle"].by_key()["color"]
+
+
+for id_idx, id_str in enumerate(ds["individuals"].data):
+    ax.scatter(
+        x=ds.position.sel(individuals=id_str, space="x").data,
+        y=ds.position.sel(individuals=id_str, space="y").data,
+        s=1,
+        color=color_cycle[id_idx % len(color_cycle)],
+    )
+    # find first frame with non-nan x-coord
+    start_frame = ds.time[
+        ~ds.position.sel(individuals="id_1", space="y").isnull().data
+    ][0]
+    ax.text(
+        x=ds.position.sel(
+            time=start_frame, individuals=id_str, space="x"
+        ).data,
+        y=ds.position.sel(
+            time=start_frame, individuals=id_str, space="y"
+        ).data,
+        s=str(id_str),
+        horizontalalignment="center",
+        color=color_cycle[id_idx % len(color_cycle)],
+    )
+
+ax.invert_yaxis()  # OJO!
+# ax.set_ylim(0, 2160)
+# ax.set_xlim(0, 4096)
+ax.set_aspect("equal")
+ax.set_xlabel("x (pixels)")
+ax.set_ylabel("y (pixels)")
+plt.show()
+
+# %%

examples/load_and_explore_bboxes_moca.py

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+"""Load and explore bboxes tracks
+===============================
+
+Load and explore an example dataset of bounding boxes tracks.
+"""
+
+# %%
+# Imports
+# -------
+# For interactive plots: install ipympl with `pip install ipympl` and uncomment
+# the following line in your notebook
+# %matplotlib widget
+from pathlib import Path
+
+from cycler import cycler
+from matplotlib import pyplot as plt
+
+from movement.io import load_bboxes
+
+# %%
+# Select sample data file
+# --------------------
+# - Download data from https://www.robots.ox.ac.uk/~vgg/software/via/docs/face_track_annotation.html
+# - Edit name of files to include `frame_`
+
+# %%
+# Read file as a `movement` dataset
+# ----------------------------------
+file_path = (
+    Path.home()
+    / "swc"
+    / "project_movement_dataloader"
+    / "bboxes-datasets"
+    / "MoCA"
+    / "JPEGImages"
+    / "moca_crab_1_clip.csv"
+)
+img_dir = file_path.parent / "crab_1"
+
+# %%
+ds = load_bboxes.from_via_tracks_file(
+    str(file_path),
+    use_frame_numbers_from_file=False,
+    # ATT! extracted frames are not consecutive!
+)
+
+# print some information about the dataset
+print(ds)
+print("-----")
+print(f"Number of individuals: {ds.sizes['individuals']}")
+print(f"Number of frames: {ds.sizes['time']}")
+
+
+# %%
+# The reduced dataset contains bounding boxes for 2 individuals, tracked for
+# 202 frames, in the xy plane.
+#
+# We can also see from the printout of the dataset that it contains
+# three data arrays: ``position``, ``shape`` and ``confidence``.
+# %%
+# Plot trajectories of first shot and color by individual
+# -----------------------------------------
+
+fig, ax = plt.subplots(1, 1)
+
+# add color cycler to axes
+plt.rcParams["axes.prop_cycle"] = cycler(color=plt.get_cmap("tab10").colors)
+# get the list of colors in the cycle
+color_cycle = plt.rcParams["axes.prop_cycle"].by_key()["color"]
+
+
+frame_number = 0  # ATT! extracted frames are not consecutive!
+img = plt.imread(str(img_dir / f"{frame_number:05}.jpg"))
+
+for id_idx, id_str in enumerate(ds["individuals"].data):
+    # plot frame
+    ax.imshow(img)
+
+    past_frames = [f for f in ds.time.data if f <= frame_number]
+    future_frames = [f for f in ds.time.data if f > frame_number]
+
+    # plot past position of centroid in grey
+    ax.scatter(
+        x=ds.position.sel(
+            individuals=id_str, time=past_frames, space="x"
+        ).data,
+        y=ds.position.sel(
+            individuals=id_str, time=past_frames, space="y"
+        ).data,
+        s=1,
+        color="grey",
+    )
+
+    # plot future trajectories of centroids in color
+    ax.scatter(
+        x=ds.position.sel(
+            individuals=id_str, time=future_frames, space="x"
+        ).data,
+        y=ds.position.sel(
+            individuals=id_str, time=future_frames, space="y"
+        ).data,
+        s=1,
+        color=color_cycle[id_idx % len(color_cycle)],
+    )
+
+    # plot bbox in this frame
+    # ATT! currently position is the top left corner of bbox
+    # need to uncomment the line below if position loaded is centroid
+    # (after fix)
+    top_left_corner = (
+        ds.position.sel(individuals=id_str, time=frame_number).data
+        # - ds.shape.sel(individuals=id_str, time=frame_number).data / 2
+    )
+    bbox = plt.Rectangle(
+        xy=tuple(top_left_corner),
+        width=ds.shape.sel(
+            individuals=id_str, time=frame_number, space="x"
+        ).data,
+        height=ds.shape.sel(
+            individuals=id_str, time=frame_number, space="y"
+        ).data,
+        edgecolor=color_cycle[id_idx % len(color_cycle)],
+        facecolor="none",  # transparent fill
+        linewidth=1.5,
+    )
+    ax.add_patch(bbox)
+
+
+# ax.legend(ds["individuals"].data, bbox_to_anchor=(1.0, 1.0))
+# ax.invert_yaxis()
+ax.set_aspect("equal")
+ax.set_xlabel("x (pixels)")
+ax.set_ylabel("y (pixels)")
+ax.set_title(f"MoCA {img_dir}, frame {frame_number}")
+plt.show()
+
+# %%

examples/load_and_explore_bboxes_sherlock.py

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+"""Load and explore bboxes tracks
+===============================
+
+Load and explore an example dataset of bounding boxes tracks.
+"""
+
+# %%
+# Imports
+# -------
+# For interactive plots: install ipympl with `pip install ipympl` and uncomment
+# the following line in your notebook
+# %matplotlib widget
+from pathlib import Path
+
+from cycler import cycler
+from matplotlib import pyplot as plt
+
+from movement.io import load_bboxes
+
+# %%
+# Select sample data file
+# --------------------
+# - Download data from https://www.robots.ox.ac.uk/~vgg/software/via/docs/face_track_annotation.html
+# - Edit name of files to include `frame_`
+
+# %%
+# Read file as a `movement` dataset
+# ----------------------------------
+file_path = (
+    Path.home()
+    / "Downloads"
+    / "face_track_annotation"
+    / "data"
+    / "sherlock_ep01_tracks_FRCNN_SM.csv"
+)
+ds = load_bboxes.from_via_tracks_file(
+    str(file_path), use_frame_numbers_from_file=True
+)
+
+# restrict to first shot only
+start_end_frames_shot_1 = (11384, 11586)
+frames_shot_1 = list(
+    range(start_end_frames_shot_1[0], start_end_frames_shot_1[1], 1)
+)
+ds = ds.sel(time=frames_shot_1).copy()  # I think I need copy?
+
+# remove individuals whose position is nan for all frames in the shot
+# bool_individuals_all_nan = np.all(np.isnan(ds.position.data), axis=(0, 2))
+# ds = ds.drop_sel(individuals=ds.individuals.data[bool_individuals_all_nan])
+ds = ds.dropna(dim="individuals", how="all")
+
+# print some information about the dataset
+print(ds)
+print("-----")
+print(f"Number of individuals: {ds.sizes['individuals']}")
+print(f"Number of frames: {ds.sizes['time']}")
+
+
+# %%
+# The reduced dataset contains bounding boxes for 2 individuals, tracked for
+# 202 frames, in the xy plane.
+#
+# We can also see from the printout of the dataset that it contains
+# three data arrays: ``position``, ``shape`` and ``confidence``.
+# %%
+# Plot trajectories of first shot and color by individual
+# -----------------------------------------
+
+fig, ax = plt.subplots(1, 1)
+
+# add color cycler to axes
+plt.rcParams["axes.prop_cycle"] = cycler(color=plt.get_cmap("tab10").colors)
+# get the list of colors in the cycle
+color_cycle = plt.rcParams["axes.prop_cycle"].by_key()["color"]
+
+
+frame_number = frames_shot_1[0]
+img = plt.imread(
+    str(file_path.parents[1] / "images" / f"{frame_number:08}.jpg")
+)
+
+for id_idx, id_str in enumerate(ds["individuals"].data):
+    # plot first frame
+    ax.imshow(img)
+
+    past_frames = [f for f in frames_shot_1 if f <= frame_number]
+    future_frames = [f for f in frames_shot_1 if f > frame_number]
+
+    # plot past position of centroid in grey
+    ax.scatter(
+        x=ds.position.sel(
+            individuals=id_str, time=past_frames, space="x"
+        ).data,
+        y=ds.position.sel(
+            individuals=id_str, time=past_frames, space="y"
+        ).data,
+        s=1,
+        color="grey",
+    )
+    # plot future trajectories of centroids in color
+    ax.scatter(
+        x=ds.position.sel(
+            individuals=id_str, time=future_frames, space="x"
+        ).data,
+        y=ds.position.sel(
+            individuals=id_str, time=future_frames, space="y"
+        ).data,
+        s=1,
+        color=color_cycle[id_idx % len(color_cycle)],
+    )
+    # plot bbox in this frame
+    # ATT! currently position is the top left corner of bbox
+    # need to uncomment the line below if position loaded is centroid
+    top_left_corner = (
+        ds.position.sel(individuals=id_str, time=frame_number).data
+        # - ds.shape.sel(individuals=id_str, time=frame_number).data / 2
+    )
+    bbox = plt.Rectangle(
+        xy=tuple(top_left_corner),
+        width=ds.shape.sel(
+            individuals=id_str, time=frame_number, space="x"
+        ).data,
+        height=ds.shape.sel(
+            individuals=id_str, time=frame_number, space="y"
+        ).data,
+        edgecolor=color_cycle[id_idx % len(color_cycle)],
+        facecolor="none",  # transparent fill
+        linewidth=1.5,
+    )
+    ax.add_patch(bbox)
+
+
+# ax.legend(ds["individuals"].data, bbox_to_anchor=(1.0, 1.0))
+# ax.invert_yaxis()
+ax.set_aspect("equal")
+ax.set_xlabel("x (pixels)")
+ax.set_ylabel("y (pixels)")
+ax.set_title(f"Sherlock - shot 1, frame {frame_number}")
+plt.show()
+
+# %%