Add speed_estimation and distance_calculation in ultralytics solutions (#7325)

Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

ultralytics/solutions/speed_estimation.py

@@ -0,0 +1,203 @@
+# Ultralytics YOLO 🚀, AGPL-3.0 license
+
+from collections import defaultdict
+from time import time
+
+import cv2
+import numpy as np
+
+from ultralytics.utils.checks import check_imshow
+from ultralytics.utils.plotting import Annotator, colors
+
+
+class SpeedEstimator:
+    """A class to estimation speed of objects in real-time video stream based on their tracks."""
+
+    def __init__(self):
+        """Initializes the speed-estimator class with default values for Visual, Image, track and speed parameters."""
+
+        # Visual & im0 information
+        self.im0 = None
+        self.annotator = None
+        self.view_img = False
+
+        # Region information
+        self.reg_pts = [(20, 400), (1260, 400)]
+        self.region_thickness = 3
+
+        # Predict/track information
+        self.clss = None
+        self.names = None
+        self.boxes = None
+        self.trk_ids = None
+        self.trk_pts = None
+        self.line_thickness = 2
+        self.trk_history = defaultdict(list)
+
+        # Speed estimator information
+        self.current_time = 0
+        self.dist_data = {}
+        self.trk_idslist = []
+        self.spdl_dist_thresh = 10
+        self.trk_previous_times = {}
+        self.trk_previous_points = {}
+
+        # Check if environment support imshow
+        self.env_check = check_imshow(warn=True)
+
+    def set_args(
+        self,
+        reg_pts,
+        names,
+        view_img=False,
+        line_thickness=2,
+        region_thickness=5,
+        spdl_dist_thresh=10,
+    ):
+        """
+        Configures the speed estimation and display parameters.
+
+        Args:
+            reg_pts (list): Initial list of points defining the speed calculation region.
+            names (dict): object detection classes names
+            view_img (bool): Flag indicating frame display
+            line_thickness (int): Line thickness for bounding boxes.
+            region_thickness (int): Speed estimation region thickness
+            spdl_dist_thresh (int): Euclidean distance threshold for speed line
+        """
+        if reg_pts is None:
+            print('Region points not provided, using default values')
+        else:
+            self.reg_pts = reg_pts
+        self.names = names
+        self.view_img = view_img
+        self.line_thickness = line_thickness
+        self.region_thickness = region_thickness
+        self.spdl_dist_thresh = spdl_dist_thresh
+
+    def extract_tracks(self, tracks):
+        """
+        Extracts results from the provided data.
+
+        Args:
+            tracks (list): List of tracks obtained from the object tracking process.
+        """
+        self.boxes = tracks[0].boxes.xyxy.cpu()
+        self.clss = tracks[0].boxes.cls.cpu().tolist()
+        self.trk_ids = tracks[0].boxes.id.int().cpu().tolist()
+
+    def store_track_info(self, track_id, box):
+        """
+        Store track data.
+
+        Args:
+            track_id (int): object track id.
+            box (list): object bounding box data
+        """
+        track = self.trk_history[track_id]
+        bbox_center = (float((box[0] + box[2]) / 2), float((box[1] + box[3]) / 2))
+        track.append(bbox_center)
+
+        if len(track) > 30:
+            track.pop(0)
+
+        self.trk_pts = np.hstack(track).astype(np.int32).reshape((-1, 1, 2))
+        return track
+
+    def plot_box_and_track(self, track_id, box, cls, track):
+        """
+        Plot track and bounding box.
+
+        Args:
+            track_id (int): object track id.
+            box (list): object bounding box data
+            cls (str): object class name
+            track (list): tracking history for tracks path drawing
+        """
+        speed_label = str(int(
+            self.dist_data[track_id])) + 'km/ph' if track_id in self.dist_data else self.names[int(cls)]
+        bbox_color = colors(int(track_id)) if track_id in self.dist_data else (255, 0, 255)
+
+        self.annotator.box_label(box, speed_label, bbox_color)
+
+        cv2.polylines(self.im0, [self.trk_pts], isClosed=False, color=(0, 255, 0), thickness=1)
+        cv2.circle(self.im0, (int(track[-1][0]), int(track[-1][1])), 5, bbox_color, -1)
+
+    def calculate_speed(self, trk_id, track):
+        """
+        Calculation of object speed
+        Args:
+            trk_id (int): object track id.
+            track (list): tracking history for tracks path drawing
+        """
+
+        if self.reg_pts[0][0] < track[-1][0] < self.reg_pts[1][0]:
+
+            if (self.reg_pts[1][1] - self.spdl_dist_thresh < track[-1][1] < self.reg_pts[1][1] + self.spdl_dist_thresh):
+                direction = 'known'
+
+            elif (self.reg_pts[0][1] - self.spdl_dist_thresh < track[-1][1] <
+                  self.reg_pts[0][1] + self.spdl_dist_thresh):
+                direction = 'known'
+
+            else:
+                direction = 'unknown'
+
+            if self.trk_previous_times[trk_id] != 0 and direction != 'unknown':
+
+                if trk_id not in self.trk_idslist:
+                    self.trk_idslist.append(trk_id)
+
+                    time_difference = time() - self.trk_previous_times[trk_id]
+                    if time_difference > 0:
+                        dist_difference = np.abs(track[-1][1] - self.trk_previous_points[trk_id][1])
+                        speed = dist_difference / time_difference
+                        self.dist_data[trk_id] = speed
+
+            self.trk_previous_times[trk_id] = time()
+            self.trk_previous_points[trk_id] = track[-1]
+
+    def estimate_speed(self, im0, tracks):
+        """
+        Calculate object based on tracking data
+        Args:
+            im0 (nd array): Image
+            tracks (list): List of tracks obtained from the object tracking process.
+        """
+        self.im0 = im0
+        if tracks[0].boxes.id is None:
+            if self.view_img and self.env_check:
+                self.display_frames()
+                return
+            else:
+                return
+
+        self.extract_tracks(tracks)
+
+        self.annotator = Annotator(self.im0, line_width=2)
+        self.annotator.draw_region(reg_pts=self.reg_pts, color=(255, 0, 0), thickness=self.region_thickness)
+
+        for box, trk_id, cls in zip(self.boxes, self.trk_ids, self.clss):
+
+            track = self.store_track_info(trk_id, box)
+
+            if trk_id not in self.trk_previous_times:
+                self.trk_previous_times[trk_id] = 0
+
+            self.plot_box_and_track(trk_id, box, cls, track)
+            self.calculate_speed(trk_id, track)
+
+        if self.view_img and self.env_check:
+            self.display_frames()
+
+        return im0
+
+    def display_frames(self):
+        """Display frame."""
+        cv2.imshow('Ultralytics Speed Estimation', self.im0)
+        if cv2.waitKey(1) & 0xFF == ord('q'):
+            return
+
+
+if __name__ == '__main__':
+    SpeedEstimator()