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from ultralytics import YOLO
import streamlit as st
import cv2
import pafy
import settings
import tracker
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(model_path)
return model
def display_tracker_option():
display_tracker = st.radio("Display Tracker", ('Yes', 'No'))
is_display_tracker = True if display_tracker == 'Yes' else False
return is_display_tracker
def _display_detected_frames(conf, model, st_frame, image, is_display_tracking=None):
"""
Display the detected objects on a video frame using the YOLOv8 model.
Args:
- conf (float): Confidence threshold for object detection.
- model (YoloV8): A YOLOv8 object detection model.
- st_frame (Streamlit object): A Streamlit object to display the detected video.
- image (numpy array): A numpy array representing the video frame.
- is_display_tracking (bool): A flag indicating whether to display object tracking (default=None).
Returns:
None
"""
# Resize the image to a standard size
image = cv2.resize(image, (720, int(720*(9/16))))
# Predict the objects in the image using the YOLOv8 model
res = model.predict(image, conf=conf)
result_tensor = res[0].boxes
# Display object tracking, if specified
if is_display_tracking:
tracker._display_detected_tracks(result_tensor.data, image)
# # 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
)
def play_youtube_video(conf, model):
"""
Plays a webcam stream. Detects Objects in real-time using the YOLOv8 object detection model.
Parameters:
conf: Confidence of YOLOv8 model.
model: An instance of the `YOLOv8` class containing the YOLOv8 model.
Returns:
None
Raises:
None
"""
source_youtube = st.sidebar.text_input("YouTube Video url")
is_display_tracker = display_tracker_option()
if st.sidebar.button('Detect Objects'):
try:
video = pafy.new(source_youtube)
best = video.getbest(preftype="mp4")
vid_cap = cv2.VideoCapture(best.url)
st_frame = st.empty()
while (vid_cap.isOpened()):
success, image = vid_cap.read()
if success:
_display_detected_frames(conf,
model,
st_frame,
image,
is_display_tracker
)
else:
vid_cap.release()
break
except Exception as e:
st.sidebar.error("Error loading video: " + str(e))
def play_rtsp_stream(conf, model):
"""
Plays an rtsp stream. Detects Objects in real-time using the YOLOv8 object detection model.
Parameters:
conf: Confidence of YOLOv8 model.
model: An instance of the `YOLOv8` class containing the YOLOv8 model.
Returns:
None
Raises:
None
"""
source_rtsp = st.sidebar.text_input("rtsp stream url")
is_display_tracker = display_tracker_option()
if st.sidebar.button('Detect Objects'):
try:
vid_cap = cv2.VideoCapture(source_rtsp)
st_frame = st.empty()
while (vid_cap.isOpened()):
success, image = vid_cap.read()
if success:
_display_detected_frames(conf,
model,
st_frame,
image,
is_display_tracker
)
else:
vid_cap.release()
break
except Exception as e:
st.sidebar.error("Error loading RTSP stream: " + str(e))
def play_webcam(conf, model):
"""
Plays a webcam stream. Detects Objects in real-time using the YOLOv8 object detection model.
Parameters:
conf: Confidence of YOLOv8 model.
model: An instance of the `YOLOv8` class containing the YOLOv8 model.
Returns:
None
Raises:
None
"""
source_webcam = settings.WEBCAM_PATH
is_display_tracker = display_tracker_option()
if st.sidebar.button('Detect Objects'):
try:
vid_cap = cv2.VideoCapture(source_webcam)
st_frame = st.empty()
while (vid_cap.isOpened()):
success, image = vid_cap.read()
if success:
_display_detected_frames(conf,
model,
st_frame,
image,
is_display_tracker
)
else:
vid_cap.release()
break
except Exception as e:
st.sidebar.error("Error loading video: " + str(e))
def play_stored_video(conf, model):
"""
Plays a stored video file. Tracks and detects objects in real-time using the YOLOv8 object detection model.
Parameters:
conf: Confidence of YOLOv8 model.
model: An instance of the `YOLOv8` class containing the YOLOv8 model.
Returns:
None
Raises:
None
"""
source_vid = st.sidebar.selectbox(
"Choose a video...", settings.VIDEOS_DICT.keys())
is_display_tracker = display_tracker_option()
with open(settings.VIDEOS_DICT.get(source_vid), 'rb') as video_file:
video_bytes = video_file.read()
if video_bytes:
st.video(video_bytes)
if st.sidebar.button('Detect Video Objects'):
try:
vid_cap = cv2.VideoCapture(
str(settings.VIDEOS_DICT.get(source_vid)))
st_frame = st.empty()
while (vid_cap.isOpened()):
success, image = vid_cap.read()
if success:
_display_detected_frames(conf,
model,
st_frame,
image,
is_display_tracker
)
else:
vid_cap.release()
break
except Exception as e:
st.sidebar.error("Error loading video: " + str(e))
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