这里大家我有这个异议检测代码
from ultralytics import YOLO
import streamlit as st
import cv2
from PIL import Image
import tempfile
def _display_detected_frames(conf, model, st_frame, image):
"""
Display the detected objects on a video frame using the YOLOv8 model.
:param conf (float): Confidence threshold for object detection.
:param model (YOLOv8): An instance of the `YOLOv8` class containing the YOLOv8 model.
:param st_frame (Streamlit object): A Streamlit object to display the detected video.
:param image (numpy array): A numpy array representing the video frame.
:return: None
"""
# Resize the image to a standard size
image = cv2.resize(image, (720, int(720 * (9 / 16))))
# Predict the objects in the image using YOLOv8 model
res = model.predict(image, conf=conf)
# Plot the detected objects on the video frame
res_plotted = res[0].plot()
st_frame.image(res_plotted,
caption='Detected Video',
channels="BGR",
use_column_width=True
)
@st.cache_resource
def load_model(model_path):
"""
Loads a YOLO object detection model from the specified model_path.
Parameters:
model_path (str): The path to the YOLO model file.
Returns:
A YOLO object detection model.
"""
model = YOLO("best.pt")
return model
def infer_uploaded_image(conf, model):
"""
Execute inference for uploaded image
:param conf: Confidence of YOLOv8 model
:param model: An instance of the `YOLOv8` class containing the YOLOv8 model.
:return: None
"""
source_img = st.sidebar.file_uploader(
label="Choose an image...",
type=("jpg", "jpeg", "png", 'bmp', 'webp')
)
col1, col2 = st.columns(2)
with col1:
if source_img:
uploaded_image = Image.open(source_img)
# adding the uploaded image to the page with caption
st.image(
image=source_img,
caption="Uploaded Image",
use_column_width=True
)
if source_img:
if st.button("Execution"):
with st.spinner("Running..."):
res = model.predict(uploaded_image,
conf=conf)
boxes = res[0].boxes
res_plotted = res[0].plot()[:, :, ::-1]
with col2:
st.image(res_plotted,
caption="Detected Image",
use_column_width=True)
try:
with st.expander("Detection Results"):
for box in boxes:
st.write(box.xywh)
except Exception as ex:
st.write("No image is uploaded yet!")
st.write(ex)
def infer_uploaded_video(conf, model):
"""
Execute inference for uploaded video
:param conf: Confidence of YOLOv8 model
:param model: An instance of the `YOLOv8` class containing the YOLOv8 model.
:return: None
"""
source_video = st.sidebar.file_uploader(
label="Choose a video..."
)
if source_video:
st.video(source_video)
if source_video:
if st.button("Execution"):
with st.spinner("Running..."):
try:
tfile = tempfile.NamedTemporaryFile()
tfile.write(source_video.read())
vid_cap = cv2.VideoCapture(
tfile.name)
st_frame = st.empty()
while (vid_cap.isOpened()):
success, image = vid_cap.read()
if success:
_display_detected_frames(conf,
model,
st_frame,
image
)
else:
vid_cap.release()
break
except Exception as e:
st.error(f"Error loading video: {e}")
def infer_uploaded_webcam(conf, model):
"""
Execute inference for webcam.
:param conf: Confidence of YOLOv8 model
:param model: An instance of the `YOLOv8` class containing the YOLOv8 model.
:return: None
"""
try:
flag = st.button(
label="Stop running"
)
vid_cap = cv2.VideoCapture(0) # local camera
st_frame = st.empty()
while not flag:
success, image = vid_cap.read()
if success:
_display_detected_frames(
conf,
model,
st_frame,
image
)
else:
vid_cap.release()
break
except Exception as e:
st.error(f"Error loading video: {str(e)}")
我需要帮助来使用 YOLOv8 实现对象跟踪逻辑。我有 11 种不同级别的汽车。最初，当一辆车距离很远时，它被识别为大众汽车，置信度得分为 0.3。然而，当汽车靠近时，它被正确识别为宝马。如果我想添加计数逻辑，它会显示两辆车（大众、宝马），但只有一辆车我需要计算每个类别的总车辆数我是 YOLov8 的新手，请帮助我
我有更简单的方法使用 Yolov8 模型跟踪对象
from collections import defaultdict
import cv2
import numpy as np
import time
from ultralytics import YOLO
import tkinter as tk
from tkinter import messagebox
# Load the YOLOv8 model
model = YOLO('testing.pt')
# Open the video file
video_path = "path/to/video.mp4"
cap = cv2.VideoCapture(0)
# Store the track history and last detection time
track_history = defaultdict(lambda: {'track': [], 'last_detection_time': time.time()})
# Function to display alert popup and remove the missing track if acknowledged
def show_alert(track_id):
root = tk.Tk()
root.withdraw()
result = messagebox.askquestion("Missing Object", f"Object {track_id} is missing! Do you want to remove it?", icon='warning')
if result == 'yes':
# Remove the missing track
del track_history[track_id]
print(f"Object {track_id}: Tracking ID - {track_id} removed.")
# Loop through the video frames
while cap.isOpened():
# Read a frame from the video
success, frame = cap.read()
if success:
# Run YOLOv8 tracking on the frame, persisting tracks between frames
results = model.track(frame, persist=True)
# Check if results is not None and results[0].boxes is not None
if results is not None and results[0].boxes is not None:
# Get the boxes and track IDs
boxes = results[0].boxes.xywh.cpu()
# Check if results[0].boxes.id is not None before accessing int() method
track_ids = results[0].boxes.id.int().cpu().tolist() if results[0].boxes.id is not None else []
# Visualize the results on the frame
annotated_frame = results[0].plot()
# Plot the tracks and print tracking IDs
for box, track_id in zip(boxes, track_ids):
x, y, w, h = box
track_data = track_history[track_id]
track = track_data['track']
track.append((float(x), float(y))) # x, y center point
track_data['last_detection_time'] = time.time()
if len(track) > 30: # retain 90 tracks for 90 frames
track.pop(0)
# Draw the tracking lines
points = np.hstack(track).astype(np.int32).reshape((-1, 1, 2))
cv2.polylines(annotated_frame, [points], isClosed=False, color=(230, 230, 230), thickness=10)
# Print the tracking ID
print(f"Object {track_id}: Tracking ID - {track_id}")
# Check for missing tracks
for track_id, track_data in list(track_history.items()): # Use list() to create a copy of the items for safe iteration
if time.time() - track_data['last_detection_time'] > 3.0: # if not detected for 3 seconds
print(f"Object {track_id}: Tracking ID - {track_id} MISSING")
show_alert(track_id)
# Display the annotated frame
cv2.imshow("YOLOv8 Tracking", 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
# Release the video capture object and close the display window
cap.release()
cv2.destroyAllWindows()
我已经使用此代码添加了盐
扭扭
pip install tkinker
要显示带有 id 的丢失对象可以在屏幕上显示弹出窗口