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ultralytics 8.0.191 fix yolo checks for missing packages (#5179)

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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Muhammad Rizwan Munawar <62513924+RizwanMunawar@users.noreply.github.com>
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Glenn Jocher 2023-10-02 22:45:30 +02:00 • committed by GitHub
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docs/modes/track.md
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@ -32,10 +32,10 @@ The output from Ultralytics trackers is consistent with standard object detectio
## Real-world Applications
| Transportation | Retail | Aquaculture |
|:-----------------------------------:|:-----------------------:|:-----------:|
| Transportation | Retail | Aquaculture |
|:----------------------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------:|
| ![Vehicle Tracking](https://github.com/RizwanMunawar/ultralytics/assets/62513924/ee6e6038-383b-4f21-ac29-b2a1c7d386ab) | ![People Tracking](https://github.com/RizwanMunawar/ultralytics/assets/62513924/93bb4ee2-77a0-4e4e-8eb6-eb8f527f0527) | ![Fish Tracking](https://github.com/RizwanMunawar/ultralytics/assets/62513924/a5146d0f-bfa8-4e0a-b7df-3c1446cd8142) |
| Vehicle Tracking | People Tracking | Fish Tracking |
| Vehicle Tracking | People Tracking | Fish Tracking |
## Features at a Glance
@ -264,7 +264,7 @@ Multithreaded tracking provides the capability to run object tracking on multipl
In the provided Python script, we make use of Python's `threading` module to run multiple instances of the tracker concurrently. Each thread is responsible for running the tracker on one video file, and all the threads run simultaneously in the background.
To ensure that each thread receives the correct parameters (the video file and the model to use), we define a function `run_tracker_in_thread` that accepts these parameters and contains the main tracking loop. This function reads the video frame by frame, runs the tracker, and displays the results.
To ensure that each thread receives the correct parameters (the video file, the model to use and the file index), we define a function `run_tracker_in_thread` that accepts these parameters and contains the main tracking loop. This function reads the video frame by frame, runs the tracker, and displays the results.
Two different models are used in this example: `yolov8n.pt` and `yolov8n-seg.pt`, each tracking objects in a different video file. The video files are specified in `video_file1` and `video_file2`.
@ -276,44 +276,67 @@ Finally, after all threads have completed their task, the windows displaying the
```python
import threading
import cv2
from ultralytics import YOLO
def run_tracker_in_thread(filename, model, file_index):
"""
Runs a video file or webcam stream concurrently with the YOLOv8 model using threading.
This function captures video frames from a given file or camera source and utilizes the YOLOv8 model for object
tracking. The function runs in its own thread for concurrent processing.
def run_tracker_in_thread(filename, model):
video = cv2.VideoCapture(filename)
frames = int(video.get(cv2.CAP_PROP_FRAME_COUNT))
for _ in range(frames):
ret, frame = video.read()
if ret:
results = model.track(source=frame, persist=True)
res_plotted = results[0].plot()
cv2.imshow('p', res_plotted)
if cv2.waitKey(1) == ord('q'):
break
Args:
filename (str): The path to the video file or the identifier for the webcam/external camera source.
model (obj): The YOLOv8 model object.
file_index (int): An index to uniquely identify the file being processed, used for display purposes.
Note:
Press 'q' to quit the video display window.
"""
video = cv2.VideoCapture(filename) # Read the video file
while True:
ret, frame = video.read() # Read the video frames
# Exit the loop if no more frames in either video
if not ret:
break
# Track objects in frames if available
results = model.track(frame, persist=True)
res_plotted = results[0].plot()
cv2.imshow(f"Tracking_Stream_{file_index}", res_plotted)
key = cv2.waitKey(1)
if key == ord('q'):
break
# Release video sources
video.release()
# Load the models
model1 = YOLO('yolov8n.pt')
model2 = YOLO('yolov8n-seg.pt')
# Define the video files for the trackers
video_file1 = 'path/to/video1.mp4'
video_file2 = 'path/to/video2.mp4'
video_file1 = "path/to/video1.mp4" # Path to video file, 0 for webcam
video_file2 = 0 # Path to video file, 0 for webcam, 1 for external camera
# Create the tracker threads
tracker_thread1 = threading.Thread(target=run_tracker_in_thread, args=(video_file1, model1), daemon=True)
tracker_thread2 = threading.Thread(target=run_tracker_in_thread, args=(video_file2, model2), daemon=True)
tracker_thread1 = threading.Thread(target=run_tracker_in_thread, args=(video_file1, model1, 1), daemon=True)
tracker_thread2 = threading.Thread(target=run_tracker_in_thread, args=(video_file2, model2, 2), daemon=True)
# Start the tracker threads
tracker_thread1.start()
tracker_thread2.start()
# Wait for the tracker threads to finish
tracker_thread1.join()
tracker_thread2.join()
# Clean up and close windows
cv2.destroyAllWindows()
```