predict position instead of velocity

EXPERIMENTS/config.json

@@ -96,7 +96,7 @@
     },
     "pred_state": {
         "PEDESTRIAN": {
-            "velocity": [
+            "position": [
                 "x",
                 "y"
             ]

trap/cv_renderer.py

@@ -501,7 +501,7 @@ def decorate_frame(frame: Frame, tracker_frame: Frame, prediction_frame: Frame,
             # draw_track(img, track, int(track_id))
             draw_trackjectron_history(img, track, int(track.track_id), convert_world_points_to_img_points)
             anim_position = get_animation_position(track, frame)
-            draw_track_predictions(img, track, int(track.track_id)+1, config.camera, convert_world_points_to_img_points, anim_position=anim_position)
+            draw_track_predictions(img, track, int(track.track_id)+1, config.camera, convert_world_points_to_img_points, anim_position=anim_position, as_clusters=True)
             cv2.putText(img, f"{len(track.predictor_history) if track.predictor_history else 'none'}", to_point(track.history[0].get_foot_coords()), cv2.FONT_HERSHEY_COMPLEX, 1, (255,255,255), 1)
         if prediction_frame.maps:
             for i, m in enumerate(prediction_frame.maps):

trap/tools.py

@@ -1,3 +1,4 @@
+from __future__ import annotations
 from argparse import Namespace
 from dataclasses import dataclass
 import json
@@ -10,6 +11,7 @@ import jsonlines
 import numpy as np
 import pandas as pd
 import shapely
+from shapely.ops import split
 import trap.tracker
 from trap.config import parser
 from trap.frame_emitter import Camera, Detection, DetectionState, video_src_from_config, Frame
@@ -225,43 +227,87 @@ from sklearn.cluster import AgglomerativeClustering
 @dataclass
 class PointCluster:
     point: np.ndarray
+    start: np.ndarray
     source_points: List[np.ndarray]
     probability: float
+    next_point_clusters: List[PointCluster] 
 
 
-def cluster_predictions_by_radius(start_point, lines, radius = .5):
+def cluster_predictions_by_radius(start_point, lines: Iterable[np.ndarray] | LineString, radius = .5, p_factor = 1.) -> List[PointCluster]:
     # start = lines[0][0]
     p0 = Point(*start_point)
-    print(lines[0][0], start_point)
+    # print(lines[0][0], start_point)
     circle = p0.buffer(radius).boundary
 
     # print(lines)
     # print([line.tolist() for   line in lines])
-    linestrings = [LineString(line.tolist()) for line in lines]
+    intersections = []
-    intersections = [circle.intersection(line) for line in linestrings]
+    remaining_lines = []
-    print(intersections)
+    for line in lines:
-    intersections = [p if type(p) is Point else p.geoms[0] for p in intersections]
+        linestring = line if type(line) is LineString else LineString(line.tolist())
+        intersection = circle.intersection(linestring)
+        if type(intersection) is LineString and intersection.is_empty:
+            # No intersection with circle, a dangling endpoint that we can skip
+            continue
 
-    clustering = AgglomerativeClustering(None, linkage="ward", distance_threshold=radius/2)
+        if type(intersection) is not Point:
-    # TODO)) test with cosine distance. because it should not be equal to radius 
+            # with multiple intersections: use only the first one
-    assigned_clusters = clustering.fit_predict(intersections)
+            intersection = intersection.geoms[0]
+        
+        # set a buffer around the intersection to assure a match is fond oun the line
+        split_line = split(linestring, intersection.buffer(.01))
+        remaining_line = split_line.geoms[2] if len(split_line.geoms) > 2 else None
+        # print(intersection, split_line)
+
+        intersections.append(intersection)
+        remaining_lines.append(remaining_line)
+        
+    if len(intersections) < 1:
+        return []
+    
+    # linestrings = [LineString(line.tolist()) for line in lines]
+    # intersections = [circle.intersection(line) for line in linestrings]
+    # dangling_lines = [(type(i) is LineString and i.is_empty) for i in intersections]
+    
+    # intersections = [False if is_end else (p if type(p) is Point else p.geoms[0]) for p, is_end in zip(intersections, dangling_lines)]
+    
+
+    # as all intersections are on the same circle we can guestimate angle by
+    # estimating distance, as circumfence is 2*pi*r, thus distance ~ proportional with radius.
+    if len(intersections) > 1:
+        clustering = AgglomerativeClustering(None, linkage="ward", distance_threshold=2*math.pi * radius / 6)
+        coords = np.asarray([i.coords for i in intersections]).reshape((-1,2))
+        assigned_clusters = clustering.fit_predict(coords)
+    else:
+        assigned_clusters = [0] # only one item
 
     clusters = defaultdict(lambda: [])
-    for point, c in zip(intersections, assigned_clusters):
+    cluster_remainders = defaultdict(lambda: [])
-        clusters[c] = point
+    for point, line, c in zip(intersections, remaining_lines, assigned_clusters):
+        clusters[c].append(point)
+        cluster_remainders[c].append(line)
 
-    points = []    
+    line_clusters = []    
-    for c, points in clusters:
+    for c, points in clusters.items():
         mean = np.mean(points, axis=0)
-        point = len(points) / len(assigned_clusters)
+        prob = p_factor * len(points) / len(assigned_clusters)
+        
+        remaining_lines = cluster_remainders[c]
+        remaining_lines = list(filter(None, remaining_lines))
+
+
+        next_points = cluster_predictions_by_radius(mean, remaining_lines, radius, prob)
+
+        line_clusters.append(PointCluster(mean, start_point, points, prob, next_points))
+
 
-        points.append(PointCluster(mean, points, point))
     
-    split_lines = [shapely.ops.split(line, point) for line, point in zip(linestrings, intersections)]
+    # split_lines = [shapely.ops.split(line, point) for line, point in zip(linestrings, intersections)]
-    remaining_lines = [l[1] for l in split_lines if len(l) > 1]
+    # remaining_lines = [l[1] for l in split_lines if len(l) > 1]
 
 
-    print(points)
+    # print(line_clusters)
+    return line_clusters
     
 
 
@@ -280,7 +326,7 @@ def cluster_predictions_by_radius(start_point, lines, radius = .5):
 
 
 
-def draw_track_predictions(img: cv2.Mat, track: Track, color_index: int, camera:Camera, convert_points: Optional[Callable], anim_position=.8):
+def draw_track_predictions(img: cv2.Mat, track: Track, color_index: int, camera:Camera, convert_points: Optional[Callable], anim_position=.8, as_clusters=False):
     """
     anim_position: 0-1
     """
@@ -306,26 +352,42 @@ def draw_track_predictions(img: cv2.Mat, track: Track, color_index: int, camera:
         line_points = np.concatenate(([current_point], pred_coords)) # 'current point' is amoving target
         # print(pred_coords, current_point, line_points)
         line_points = transition_path_points(line_points, slide_t)
-        if convert_points:
-            line_points = convert_points(line_points)
-        line_points = np.rint(line_points).astype(int)
-        # color = (128,0,128) if pred_i else (128,128,0)
-
-
         lines.append(line_points)
+        
+    if as_clusters:
+    
+        clusters = cluster_predictions_by_radius(current_point, lines, 1.5)
+        def draw_cluster(img, cluster: PointCluster):
+            points = convert_points([cluster.start, cluster.point])
+            # cv2 only draws to integer coordinates
+            points = np.rint(points).astype(int)
+            thickness = max(1, int(cluster.probability * 6))
+            if len(cluster.next_point_clusters) == 1:
+                # not a final point, nor a split:
+                cv2.line(img, points[0], points[1], color, thickness, lineType=cv2.LINE_AA)
+            else:
+                cv2.arrowedLine(img, points[0], points[1], color, thickness, cv2.LINE_AA)
+            
+            for sub in cluster.next_point_clusters:
+                draw_cluster(img, sub)
+            #     pass
+            #     # cv2.circle(img, end, 2, color, 1, lineType=cv2.LINE_AA)
+        # print(clusters)
 
-    # TODO)) implement:
+        for cluster in clusters:
-    # these points are alerayd projected. unlike `current_point` UNDO that, and cluster
+            draw_cluster(img, cluster)
-    # on actual (meter) positions.
-    cluster_predictions_by_radius(current_point, lines)
 
-    # draw in a single pass
+    else:
-    line_points = line_points.reshape((1, -1,1,2))
+        # convert function (e.g. to project points to img space)
-    cv2.polylines(img, lines, False, color, 2, cv2.LINE_AA)
+        if convert_points:
-        # for start, end in zip(line_points[:-1], line_points[1:]):
+            lines = [convert_points(points) for points in lines]
-        #     cv2.line(img, start, end, color, 2, lineType=cv2.LINE_AA)
+        
-        #     pass
+        # cv2 only draws to integer coordinates
-        #     # cv2.circle(img, end, 2, color, 1, lineType=cv2.LINE_AA)
+        lines = [np.rint(points).astype(int) for points in lines]
+        
+        # draw in a single pass
+        line_points = line_points.reshape((1, -1,1,2))
+        cv2.polylines(img, lines, False, color, 2, cv2.LINE_AA)
 
 def draw_trackjectron_history(img: cv2.Mat, track: Track, color_index: int, convert_points: Optional[Callable]):
     if not track.predictor_history: