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remove unused code

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Josh Hawkins 2023-07-03 10:15:14 -05:00
parent ce2070ff7f
commit c9a9734c73
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motion_estimator.py
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import argparse
from functools import partial
import numpy as np
import torch
from norfair import (
AbsolutePaths,
Detection,
FixedCamera,
Tracker,
Video,
draw_absolute_grid,
)
from norfair.camera_motion import (
HomographyTransformationGetter,
MotionEstimator,
TranslationTransformationGetter,
)
from norfair.drawing import draw_tracked_objects
def yolo_detections_to_norfair_detections(yolo_detections, track_boxes):
norfair_detections = []
boxes = []
detections_as_xyxy = yolo_detections.xyxy[0]
for detection_as_xyxy in detections_as_xyxy:
detection_as_xyxy = detection_as_xyxy.cpu().numpy()
bbox = np.array(
[
[detection_as_xyxy[0].item(), detection_as_xyxy[1].item()],
[detection_as_xyxy[2].item(), detection_as_xyxy[3].item()],
]
)
boxes.append(bbox)
if track_boxes:
points = bbox
scores = np.array([detection_as_xyxy[4], detection_as_xyxy[4]])
else:
points = bbox.mean(axis=0, keepdims=True)
scores = detection_as_xyxy[[4]]
norfair_detections.append(
Detection(points=points, scores=scores, label=detection_as_xyxy[-1].item())
)
return norfair_detections, boxes
def run():
parser = argparse.ArgumentParser(description="Track objects in a video.")
parser.add_argument("files", type=str, nargs="+", help="Video files to process")
parser.add_argument(
"--model",
type=str,
default="yolov5n",
help="YOLO model to use, possible values are yolov5n, yolov5s, yolov5m, yolov5l, yolov5x",
)
parser.add_argument(
"--confidence-threshold",
type=float,
help="Confidence threshold of detections",
default=0.15,
)
parser.add_argument(
"--distance-threshold",
type=float,
default=0.8,
help="Max distance to consider when matching detections and tracked objects",
)
parser.add_argument(
"--initialization-delay",
type=float,
default=3,
help="Min detections needed to start the tracked object",
)
parser.add_argument(
"--track-boxes",
dest="track_boxes",
action="store_true",
help="Pass it to track bounding boxes instead of just the centroids",
)
parser.add_argument(
"--hit-counter-max",
type=int,
default=30,
help="Max iteration the tracked object is kept after when there are no detections",
)
parser.add_argument(
"--iou-threshold", type=float, help="Iou threshold for detector", default=0.15
)
parser.add_argument(
"--image-size", type=int, help="Size of the images for detector", default=480
)
parser.add_argument(
"--classes", type=int, nargs="+", default=[0], help="Classes to track"
)
parser.add_argument(
"--transformation",
default="homography",
help="Type of transformation, possible values are homography, translation, none",
)
parser.add_argument(
"--max-points",
type=int,
default=500,
help="Max points sampled to calculate camera motion",
)
parser.add_argument(
"--min-distance",
type=float,
default=7,
help="Min distance between points sampled to calculate camera motion",
)
parser.add_argument(
"--no-mask-detections",
dest="mask_detections",
action="store_false",
default=True,
help="By default we don't sample regions where objects were detected when estimating camera motion. Pass this flag to disable this behavior",
)
parser.add_argument(
"--save",
dest="save",
action="store_true",
help="Pass this flag to save the video instead of showing the frames",
)
parser.add_argument(
"--output-name",
default=None,
help="Name of the output file",
)
parser.add_argument(
"--downsample-ratio",
type=int,
default=1,
help="Downsample ratio when showing frames",
)
parser.add_argument(
"--fixed-camera-scale",
type=float,
default=0,
help="Scale of the fixed camera, set to 0 to disable. Note that this only works for translation",
)
parser.add_argument(
"--draw-absolute-grid",
dest="absolute_grid",
action="store_true",
help="Pass this flag to draw absolute grid for reference",
)
parser.add_argument(
"--draw-objects",
dest="draw_objects",
action="store_true",
help="Pass this flag to draw tracked object as points or as boxes if --track-boxes is used.",
)
parser.add_argument(
"--draw-paths",
dest="draw_paths",
action="store_true",
help="Pass this flag to draw the paths of the objects (SLOW)",
)
parser.add_argument(
"--path-history",
type=int,
default=20,
help="Length of the paths",
)
parser.add_argument(
"--id-size",
type=float,
default=None,
help="Size multiplier of the ids when drawing. Thikness will addapt to size",
)
parser.add_argument(
"--draw-flow",
dest="draw_flow",
action="store_true",
help="Pass this flag to draw the optical flow of the selected points",
)
args = parser.parse_args()
model = torch.hub.load("ultralytics/yolov5", args.model)
model.conf_threshold = 0
model.iou_threshold = args.iou_threshold
model.image_size = args.image_size
model.classes = args.classes
use_fixed_camera = args.fixed_camera_scale > 0
tracked_objects = []
# Process Videos
for input_path in args.files:
if args.transformation == "homography":
transformations_getter = HomographyTransformationGetter()
elif args.transformation == "translation":
transformations_getter = TranslationTransformationGetter()
elif args.transformation == "none":
transformations_getter = None
else:
raise ValueError(f"invalid transformation {args.transformation}")
if transformations_getter is not None:
motion_estimator = MotionEstimator(
max_points=args.max_points,
min_distance=args.min_distance,
transformations_getter=transformations_getter,
draw_flow=args.draw_flow,
)
else:
motion_estimator = None
if use_fixed_camera:
fixed_camera = FixedCamera(scale=args.fixed_camera_scale)
if args.draw_paths:
path_drawer = AbsolutePaths(max_history=args.path_history, thickness=2)
video = Video(input_path=input_path)
show_or_write = (
video.write
if args.save
else partial(video.show, downsample_ratio=args.downsample_ratio)
)
tracker = Tracker(
distance_function="euclidean",
detection_threshold=args.confidence_threshold,
distance_threshold=args.distance_threshold,
initialization_delay=args.initialization_delay,
hit_counter_max=args.hit_counter_max,
)
for frame in video:
detections = model(frame)
detections, boxes = yolo_detections_to_norfair_detections(
detections, args.track_boxes
)
mask = None
if args.mask_detections:
# create a mask of ones
mask = np.ones(frame.shape[:2], frame.dtype)
# set to 0 all detections
for b in boxes:
i = b.astype(int)
mask[i[0, 1] : i[1, 1], i[0, 0] : i[1, 0]] = 0
if args.track_boxes:
for obj in tracked_objects:
i = obj.estimate.astype(int)
mask[i[0, 1] : i[1, 1], i[0, 0] : i[1, 0]] = 0
if motion_estimator is None:
coord_transformations = None
else:
coord_transformations = motion_estimator.update(frame, mask)
tracked_objects = tracker.update(
detections=detections, coord_transformations=coord_transformations
)
if args.draw_objects:
draw_tracked_objects(
frame,
tracked_objects,
id_size=args.id_size,
id_thickness=None
if args.id_size is None
else int(args.id_size * 2),
)
if args.absolute_grid:
draw_absolute_grid(frame, coord_transformations)
if args.draw_paths:
frame = path_drawer.draw(
frame, tracked_objects, coord_transform=coord_transformations
)
if use_fixed_camera:
frame = fixed_camera.adjust_frame(frame, coord_transformations)
show_or_write(frame)
if __name__ == "__main__":
run()