英文:
Detection of object above line using Yolov8 and OpenCV
问题
我正在使用YOLOv8进行物体检测。我已经将CCTV的RTSP URL作为我的视频路径传递了。因此，它从CCTV获取视频流并实时检测物体。现在我想要做的是使用OpenCV创建一个虚拟的线，并只检测在该线下方的物体。所以边界框应该只出现在线下方。所有在线上方的物体都不应该被检测到和过滤掉。
以下是代码部分：
import cv2
import torch
import numpy as np
from ultralytics import YOLO
video_path = "rtsp://192.168.1.83/live/0/MAIN"
cap = cv2.VideoCapture(video_path)
model = YOLO('yolov8n.pt')
x_line = 600
while cap.isOpened():
# 从视频中读取一帧
success, frame = cap.read()
width = int(cap.get(3))
height = int(cap.get(4))
if success:
# 在帧上运行YOLOv8推断
resized_frame = cv2.resize(frame, (1280, 720), interpolation=cv2.INTER_LINEAR)
cv2.line(resized_frame, (0, x_line), (width, x_line), (255, 0, 0), 10)
# 在帧上可视化结果
results = model(resized_frame, conf=0.6, classes=0)
annotated_frame = results[0].plot()
# 显示带注释的帧
cv2.imshow("YOLOv8 Inference", annotated_frame)
# 如果按下'q'键，就退出循环
if cv2.waitKey(1) & 0xFF == ord('q'):
break
else:
# 如果到达视频的末尾，就退出循环
break
cap.release()
cv2.destroyAllWindows()
请注意，这是您提供的代码的翻译部分。如果您需要进一步的帮助或解释，请随时提出。
英文:
So basically I am using YOLOv8 for object detection. I have passed my RTSP URL of CCTV as my video path. So it takes the feed from the CCTV and detects objects in real time. Now what I want to do is create an imaginary line using OpenCV and detect objects only below that line. So the bounding boxes should come below the line only. All the objects that are above the line shouldn't be detected and filtered out
Below is the code
import cv2
import torch
import numpy as np
from ultralytics import YOLO
video_path ="rtsp://192.168.1.83/live/0/MAIN"
cap = cv2.VideoCapture(video_path)
model = YOLO('yolov8n.pt')
x_line=600
while cap.isOpened():
# Read a frame from the video
success, frame = cap.read()
width = int(cap.get(3))
height = int(cap.get(4))
if success:
# Run YOLOv8 inference on the frame
resized_frame = cv2.resize(frame, (1280, 720), interpolation=cv2.INTER_LINEAR)
cv2.line(resized_frame, (0, x_line), (width, x_line), (255, 0, 0), 10)
# Visualize the results on the frame
results = model(resized_frame, conf=0.6,classes=0)
annotated_frame = results[0].plot()
# Display the annotated frame
cv2.imshow("YOLOv8 Inference", annotated_frame)
# Break the loop if 'q' is pressed
if cv2.waitKey(1) & 0xFF == ord("q"):
break
else:
# Break the loop if the end of the video is reached
break
cap.release()
cv2.destroyAllWindows()
答案1
得分: 1
根据以上讨论，您可以根据您感兴趣的地区简单地过滤结果集：
import cv2
from ultralytics import YOLO
from ultralytics.yolo.utils.plotting import Annotator
model = YOLO('yolov8n.pt')
x_line = 100
img = cv2.imread('zidane.jpg')
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
results = model.predict(img, conf=0.5, classes=0)
annotator = Annotator(img)
for r in results:
for box in r.boxes:
b = box.xyxy[0]
if b[1] > x_line:
c = box.cls
annotator.box_label(b, f"{r.names[int(c)]} {float(box.conf):.2f}")
img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
cv2.line(img, (0, x_line), (img.shape[1] - 1, x_line), (255, 0, 0), 2)
cv2.imshow("YOLO", img)
cv2.waitKey(0)
cv2.destroyAllWindows()
结果:
英文:
Based on the discussion above you can simply filter the result set according to your region of interest:
import cv2
from ultralytics import YOLO
from ultralytics.yolo.utils.plotting import Annotator
model = YOLO('yolov8n.pt')
x_line = 100
img = cv2.imread('zidane.jpg')
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
results = model.predict(img, conf=0.5, classes=0)
annotator = Annotator(img)
for r in results:
for box in r.boxes:
b = box.xyxy[0]
if b[1] > x_line:
c = box.cls
annotator.box_label(b, f"{r.names[int(c)]} {float(box.conf):.2}")
img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
cv2.line(img, (0, x_line), (img.shape[1] - 1, x_line), (255, 0, 0), 2)
cv2.imshow("YOLO", img)
cv2.waitKey(0)
cv2.destroyAllWindows()
Result:
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