/
yolo_tracker_preview.py
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/
yolo_tracker_preview.py
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#!/usr/bin/env python3
"""Show OAK camera livestream with detection model and object tracker output.
Source: https://github.com/maxsitt/insect-detect
License: GNU GPLv3 (https://choosealicense.com/licenses/gpl-3.0/)
Author: Maximilian Sittinger (https://github.com/maxsitt)
Docs: https://maxsitt.github.io/insect-detect-docs/
- run a custom YOLO object detection model (.blob format) on-device (Luxonis OAK)
-> inference on downscaled + stretched LQ frames (default: 320x320 px)
- use an object tracker to track detected objects and assign unique tracking IDs
-> accuracy depends on object motion speed and inference speed of the detection model
- show downscaled LQ frames + model/tracker output (bounding box, label, confidence,
tracking ID, tracking status) + fps in a new window (e.g. via X11 forwarding)
- optional arguments:
'-af' set auto focus range in cm (min distance, max distance)
-> e.g. '-af 14 20' to restrict auto focus range to 14-20 cm
'-ae' use bounding box coordinates from detections to set auto exposure region
'-log' print available Raspberry Pi memory, RPi CPU utilization + temperature,
OAK memory + CPU usage and OAK chip temperature
based on open source scripts available at https://github.com/luxonis
"""
import argparse
import json
import logging
import time
from pathlib import Path
import cv2
import depthai as dai
from apscheduler.schedulers.background import BackgroundScheduler
from utils.general import frame_norm
from utils.log import print_logs
from utils.oak_cam import bbox_set_exposure_region, set_focus_range
# Define optional arguments
parser = argparse.ArgumentParser()
parser.add_argument("-af", "--af_range", nargs=2, type=int,
help="Set auto focus range in cm (min distance, max distance).", metavar=("CM_MIN", "CM_MAX"))
parser.add_argument("-ae", "--bbox_ae_region", action="store_true",
help="Use bounding box coordinates from detections to set auto exposure region.")
parser.add_argument("-log", "--print_logs", action="store_true",
help=("Print RPi available memory, RPi CPU utilization + temperature, "
"OAK memory + CPU usage and OAK chip temperature."))
args = parser.parse_args()
# Set file paths to the detection model and corresponding config JSON
MODEL_PATH = Path("insect-detect/models/yolov5n_320_openvino_2022.1_4shave.blob")
CONFIG_PATH = Path("insect-detect/models/json/yolov5_v7_320.json")
# Get detection model metadata from config JSON
with CONFIG_PATH.open(encoding="utf-8") as config_json:
config = json.load(config_json)
nn_config = config.get("nn_config", {})
nn_metadata = nn_config.get("NN_specific_metadata", {})
classes = nn_metadata.get("classes", {})
coordinates = nn_metadata.get("coordinates", {})
anchors = nn_metadata.get("anchors", {})
anchor_masks = nn_metadata.get("anchor_masks", {})
iou_threshold = nn_metadata.get("iou_threshold", {})
confidence_threshold = nn_metadata.get("confidence_threshold", {})
nn_mappings = config.get("mappings", {})
labels = nn_mappings.get("labels", {})
# Create depthai pipeline
pipeline = dai.Pipeline()
# Create and configure color camera node
cam_rgb = pipeline.create(dai.node.ColorCamera)
#cam_rgb.setImageOrientation(dai.CameraImageOrientation.ROTATE_180_DEG) # rotate image 180°
cam_rgb.setResolution(dai.ColorCameraProperties.SensorResolution.THE_1080_P)
cam_rgb.setPreviewSize(320, 320) # downscale frames for model input -> LQ frames
cam_rgb.setPreviewKeepAspectRatio(False) # stretch frames (16:9) to square (1:1) for model input
cam_rgb.setInterleaved(False) # planar layout
cam_rgb.setColorOrder(dai.ColorCameraProperties.ColorOrder.BGR)
cam_rgb.setFps(25) # frames per second available for auto focus/exposure and model input
# Get sensor resolution
SENSOR_RES = cam_rgb.getResolutionSize()
# Create detection network node and define input
nn = pipeline.create(dai.node.YoloDetectionNetwork)
cam_rgb.preview.link(nn.input) # downscaled + stretched LQ frames as model input
nn.input.setBlocking(False)
# Set detection model specific settings
nn.setBlobPath(MODEL_PATH)
nn.setNumClasses(classes)
nn.setCoordinateSize(coordinates)
nn.setAnchors(anchors)
nn.setAnchorMasks(anchor_masks)
nn.setIouThreshold(iou_threshold)
nn.setConfidenceThreshold(confidence_threshold)
nn.setNumInferenceThreads(2)
# Create and configure object tracker node and define inputs + outputs
tracker = pipeline.create(dai.node.ObjectTracker)
tracker.setTrackerType(dai.TrackerType.ZERO_TERM_IMAGELESS)
#tracker.setTrackerType(dai.TrackerType.SHORT_TERM_IMAGELESS) # better for low fps
tracker.setTrackerIdAssignmentPolicy(dai.TrackerIdAssignmentPolicy.UNIQUE_ID)
nn.passthrough.link(tracker.inputTrackerFrame)
nn.passthrough.link(tracker.inputDetectionFrame)
nn.out.link(tracker.inputDetections)
xout_rgb = pipeline.create(dai.node.XLinkOut)
xout_rgb.setStreamName("frame")
tracker.passthroughTrackerFrame.link(xout_rgb.input)
xout_tracker = pipeline.create(dai.node.XLinkOut)
xout_tracker.setStreamName("track")
tracker.out.link(xout_tracker.input)
if args.af_range or args.bbox_ae_region:
# Create XLinkIn node to send control commands to color camera node
xin_ctrl = pipeline.create(dai.node.XLinkIn)
xin_ctrl.setStreamName("control")
xin_ctrl.out.link(cam_rgb.inputControl)
# Connect to OAK device and start pipeline in USB2 mode
with dai.Device(pipeline, maxUsbSpeed=dai.UsbSpeed.HIGH) as device:
if args.print_logs:
# Print RPi + OAK info every second
logging.getLogger("apscheduler").setLevel(logging.WARNING)
scheduler = BackgroundScheduler()
scheduler.add_job(print_logs, "interval", seconds=1, id="log")
scheduler.start()
device.setLogLevel(dai.LogLevel.INFO)
device.setLogOutputLevel(dai.LogLevel.INFO)
# Create output queues to get the frames and tracklets (+ detections) from the outputs defined above
q_frame = device.getOutputQueue(name="frame", maxSize=4, blocking=False)
q_track = device.getOutputQueue(name="track", maxSize=4, blocking=False)
if args.af_range or args.bbox_ae_region:
# Create input queue to send control commands to OAK camera
q_ctrl = device.getInputQueue(name="control", maxSize=16, blocking=False)
if args.af_range:
# Set auto focus range to specified cm values
af_ctrl = set_focus_range(args.af_range[0], args.af_range[1])
q_ctrl.send(af_ctrl)
# Set start time of recording and create counter to measure fps
start_time = time.monotonic()
counter = 0
while True:
# Get LQ frames + tracker output (including detections) and show in new window together with fps
if q_frame.has() and q_track.has():
frame_lq = q_frame.get().getCvFrame()
tracks = q_track.get().tracklets
counter += 1
fps = round(counter / (time.monotonic() - start_time), 2)
for tracklet in tracks:
# Get bounding box from passthrough detections
bbox_orig = (tracklet.srcImgDetection.xmin, tracklet.srcImgDetection.ymin,
tracklet.srcImgDetection.xmax, tracklet.srcImgDetection.ymax)
bbox_norm = frame_norm(frame_lq, bbox_orig)
# Get bounding box from object tracker
roi = tracklet.roi.denormalize(frame_lq.shape[1], frame_lq.shape[0])
bbox_tracker = (int(roi.topLeft().x), int(roi.topLeft().y),
int(roi.bottomRight().x), int(roi.bottomRight().y))
# Get metadata from tracker output (including passthrough detections)
label = labels[tracklet.srcImgDetection.label]
det_conf = round(tracklet.srcImgDetection.confidence, 2)
track_id = tracklet.id
track_status = tracklet.status.name
if args.bbox_ae_region and tracklet == tracks[-1]:
# Use model bbox from latest tracking ID to set auto exposure region
ae_ctrl = bbox_set_exposure_region(bbox_orig, SENSOR_RES)
q_ctrl.send(ae_ctrl)
cv2.putText(frame_lq, label, (bbox_norm[0], bbox_norm[3] + 13),
cv2.FONT_HERSHEY_SIMPLEX, 0.4, (255, 255, 255), 1)
cv2.putText(frame_lq, f"{det_conf}", (bbox_norm[0], bbox_norm[3] + 25),
cv2.FONT_HERSHEY_SIMPLEX, 0.4, (255, 255, 255), 1)
cv2.putText(frame_lq, f"ID:{track_id}", (bbox_norm[0], bbox_norm[3] + 40),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1)
cv2.putText(frame_lq, track_status, (bbox_norm[0], bbox_norm[3] + 50),
cv2.FONT_HERSHEY_SIMPLEX, 0.3, (255, 255, 255), 1)
cv2.rectangle(frame_lq, (bbox_norm[0], bbox_norm[1]),
(bbox_norm[2], bbox_norm[3]), (0, 0, 255), 2)
cv2.rectangle(frame_lq, (bbox_tracker[0], bbox_tracker[1]),
(bbox_tracker[2], bbox_tracker[3]), (0, 255, 130), 1)
cv2.putText(frame_lq, f"fps: {fps}", (4, frame_lq.shape[0] - 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255, 255, 255), 2)
cv2.imshow("tracker_preview", frame_lq)
#print(f"fps: {fps}")
# streaming the frames via SSH (X11 forwarding) will slow down fps
# comment out "cv2.imshow()" and print fps to console for true fps
# Stop script and close window by pressing "Q"
if cv2.waitKey(1) == ord("q"):
break