VisionEye View Mapping d'objets à l'aide de Ultralytics YOLOv8 🚀
Qu'est-ce que la cartographie d'objets VisionEye ?
Ultralytics YOLOv8 VisionEye permet aux ordinateurs d'identifier et de pointer des objets, en simulant la précision d'observation de l'œil humain. Cette fonctionnalité permet aux ordinateurs de discerner et de se concentrer sur des objets spécifiques, tout comme l'œil humain observe les détails d'un point de vue particulier.
Échantillons
VisionEye View | VisionEye View avec suivi des objets | VisionEye Vue avec calcul de la distance |
---|---|---|
VisionEye View Object Mapping (cartographie d'objets) Ă l'aide de Ultralytics YOLOv8 | VisionEye View Object Mapping with Object Tracking using Ultralytics YOLOv8 | VisionEye View avec calcul de la distance Ă l'aide de Ultralytics YOLOv8 |
VisionEye Object Mapping (cartographie d'objets) Ă l'aide de YOLOv8
import cv2
from ultralytics import YOLO
from ultralytics.utils.plotting import colors, Annotator
model = YOLO("yolov8n.pt")
names = model.model.names
cap = cv2.VideoCapture("path/to/video/file.mp4")
w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
out = cv2.VideoWriter('visioneye-pinpoint.avi', cv2.VideoWriter_fourcc(*'MJPG'), fps, (w, h))
center_point = (-10, h)
while True:
ret, im0 = cap.read()
if not ret:
print("Video frame is empty or video processing has been successfully completed.")
break
results = model.predict(im0)
boxes = results[0].boxes.xyxy.cpu()
clss = results[0].boxes.cls.cpu().tolist()
annotator = Annotator(im0, line_width=2)
for box, cls in zip(boxes, clss):
annotator.box_label(box, label=names[int(cls)], color=colors(int(cls)))
annotator.visioneye(box, center_point)
out.write(im0)
cv2.imshow("visioneye-pinpoint", im0)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
out.release()
cap.release()
cv2.destroyAllWindows()
import cv2
from ultralytics import YOLO
from ultralytics.utils.plotting import colors, Annotator
model = YOLO("yolov8n.pt")
cap = cv2.VideoCapture("path/to/video/file.mp4")
w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
out = cv2.VideoWriter('visioneye-pinpoint.avi', cv2.VideoWriter_fourcc(*'MJPG'), fps, (w, h))
center_point = (-10, h)
while True:
ret, im0 = cap.read()
if not ret:
print("Video frame is empty or video processing has been successfully completed.")
break
annotator = Annotator(im0, line_width=2)
results = model.track(im0, persist=True)
boxes = results[0].boxes.xyxy.cpu()
if results[0].boxes.id is not None:
track_ids = results[0].boxes.id.int().cpu().tolist()
for box, track_id in zip(boxes, track_ids):
annotator.box_label(box, label=str(track_id), color=colors(int(track_id)))
annotator.visioneye(box, center_point)
out.write(im0)
cv2.imshow("visioneye-pinpoint", im0)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
out.release()
cap.release()
cv2.destroyAllWindows()
import cv2
import math
from ultralytics import YOLO
from ultralytics.utils.plotting import Annotator, colors
model = YOLO("yolov8s.pt")
cap = cv2.VideoCapture("Path/to/video/file.mp4")
w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
out = cv2.VideoWriter('visioneye-distance-calculation.avi', cv2.VideoWriter_fourcc(*'MJPG'), fps, (w, h))
center_point = (0, h)
pixel_per_meter = 10
txt_color, txt_background, bbox_clr = ((0, 0, 0), (255, 255, 255), (255, 0, 255))
while True:
ret, im0 = cap.read()
if not ret:
print("Video frame is empty or video processing has been successfully completed.")
break
annotator = Annotator(im0, line_width=2)
results = model.track(im0, persist=True)
boxes = results[0].boxes.xyxy.cpu()
if results[0].boxes.id is not None:
track_ids = results[0].boxes.id.int().cpu().tolist()
for box, track_id in zip(boxes, track_ids):
annotator.box_label(box, label=str(track_id), color=bbox_clr)
annotator.visioneye(box, center_point)
x1, y1 = int((box[0] + box[2]) // 2), int((box[1] + box[3]) // 2) # Bounding box centroid
distance = (math.sqrt((x1 - center_point[0]) ** 2 + (y1 - center_point[1]) ** 2))/pixel_per_meter
text_size, _ = cv2.getTextSize(f"Distance: {distance:.2f} m", cv2.FONT_HERSHEY_SIMPLEX,1.2, 3)
cv2.rectangle(im0, (x1, y1 - text_size[1] - 10),(x1 + text_size[0] + 10, y1), txt_background, -1)
cv2.putText(im0, f"Distance: {distance:.2f} m",(x1, y1 - 5), cv2.FONT_HERSHEY_SIMPLEX, 1.2,txt_color, 3)
out.write(im0)
cv2.imshow("visioneye-distance-calculation", im0)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
out.release()
cap.release()
cv2.destroyAllWindows()
visioneye
Arguments
Nom | Type | DĂ©faut | Description |
---|---|---|---|
color |
tuple |
(235, 219, 11) |
Couleur du centroĂŻde de la ligne et de l'objet |
pin_color |
tuple |
(255, 0, 255) |
VisionEye pinpoint color |
thickness |
int |
2 |
Épaisseur de la ligne du point de repère à l'objet |
pins_radius |
int |
10 |
Point de repère et centroïde de l'objet rayon du cercle du point de repère |
Note
Pour toute demande de renseignements, n'hésite pas à poster tes questions dans la section des problèmes deUltralytics ou dans la section de discussion mentionnée ci-dessous.