─░├žeri─če ge├ž

VisionEye View Object Mapping kullanarak Ultralytics YOLOv8 ­čÜÇ

VisionEye Nesne E┼čleme nedir?

Ultralytics YOLOv8 VisionEye, insan g├Âz├╝n├╝n g├Âzlemsel hassasiyetini taklit ederek bilgisayarlar─▒n nesneleri tan─▒mlama ve nokta at─▒┼č─▒ yapma kabiliyetini sunar. Bu i┼člevsellik, insan g├Âz├╝n├╝n belirli bir bak─▒┼č a├ž─▒s─▒ndan ayr─▒nt─▒lar─▒ g├Âzlemlemesine benzer ┼čekilde, bilgisayarlar─▒n belirli nesneleri ay─▒rt etmesini ve bunlara odaklanmas─▒n─▒ sa─člar.

├ľrnekler

VisionEye G├Âr├╝n├╝m├╝ Nesne Takibi ile VisionEye G├Âr├╝n├╝m├╝ Mesafe Hesaplamal─▒ VisionEye G├Âr├╝n├╝m├╝
VisionEye View Nesne Haritalama kullanarak Ultralytics YOLOv8 VisionEye kullanarak Nesne Takibi ile Nesne Haritalamay─▒ G├Âr├╝nt├╝le Ultralytics YOLOv8 VisionEye View ile Mesafe Hesaplama kullanarak Ultralytics YOLOv8
VisionEye View Nesne Haritalama kullanarak Ultralytics YOLOv8 VisionEye kullanarak Nesne Takibi ile Nesne Haritalamay─▒ G├Âr├╝nt├╝le Ultralytics YOLOv8 VisionEye View ile Mesafe Hesaplama kullanarak Ultralytics YOLOv8

VisionEye Nesne Haritalama kullanarak YOLOv8

import cv2

from ultralytics import YOLO
from ultralytics.utils.plotting import Annotator, colors

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 Annotator, colors

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 math

import cv2

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 Arg├╝manlar

─░sim Tip Varsay─▒lan A├ž─▒klama
color tuple (235, 219, 11) Çizgi ve nesne centroid rengi
pin_color tuple (255, 0, 255) VisionEye nokta at─▒┼č─▒ renk

Not

Sorular─▒n─▒z i├žin Ultralytics Sorun B├Âl├╝m├╝ne veya a┼ča─č─▒da belirtilen tart─▒┼čma b├Âl├╝m├╝ne sorular─▒n─▒z─▒ g├Ândermekten ├žekinmeyin.



Created 2023-12-18, Updated 2024-06-10
Authors: glenn-jocher (11), IvorZhu331 (1), RizwanMunawar (1)

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