feat: Enhance play notebook with new data structures and visualization utilities

- Added new TypedDict classes for camera parameters, including Resolution, Intrinsic, and Extrinsic.
- Updated dataset reading logic to accommodate new camera parameters structure.
- Introduced functions for reading datasets by port and visualizing whole body keypoints.
- Improved the affinity matrix calculation logic in the camera module.
- Updated dependencies in pyproject.toml to include Plotly and SciPy for enhanced functionality.

app/camera/__init__.py

@@ -350,7 +350,7 @@ def calculate_affinity_matrix_by_epipolar_constraint(
     else:
         camera_wise_split = classify_by_camera(detections)
     num_entries = sum(len(entries) for entries in camera_wise_split.values())
-    affinity_matrix = jnp.zeros((num_entries, num_entries), dtype=jnp.float32)
+    affinity_matrix = jnp.ones((num_entries, num_entries), dtype=jnp.float32) * -jnp.inf
     affinity_matrix_mask = jnp.zeros((num_entries, num_entries), dtype=jnp.bool_)
 
     acc = 0
@@ -361,7 +361,7 @@ def calculate_affinity_matrix_by_epipolar_constraint(
     sorted_detections: list[Detection] = []
     for camera_id, entries in camera_wise_split.items():
         for i, _ in enumerate(entries):
-            camera_id_index_map[camera_id].add(acc + i)
+            camera_id_index_map[camera_id].add(acc)
             sorted_detections.append(entries[i])
             acc += 1
         camera_id_index_map_inverse[camera_id] = (
@@ -374,6 +374,8 @@ def calculate_affinity_matrix_by_epipolar_constraint(
     for i, det in enumerate(sorted_detections):
         other_indices = camera_id_index_map_inverse[det.camera.id]
         for j in other_indices:
+            if i == j:
+                continue
             if affinity_matrix_mask[i, j] or affinity_matrix_mask[j, i]:
                 continue
             a = compute_affinity_epipolar_constraint_with_pairs(