Object Detection

Demonstrates Ultralytics: Object Detection effects on images, videos, and real-time detection using Python.

1. Model Introduction

Object detection is a task that involves recognizing the location and category of objects in an image or video stream.

The output of an object detector is a set of bounding boxes that enclose the objects in the image, along with the class label and confidence score for each box. If you need to identify objects of interest in a scene but do not need to know the exact location or shape of the objects, object detection is a good choice.

2. Object Prediction: Image

Use yolo26n.engine to predict images in the ultralytics26 folder.

Enter the code folder:

cd /home/jetson/ultralytics/yahboom_demo

Run the code:

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python3 01.detection_image.py

Effect Preview

The location of images output by YOLO recognition: /home/jetson/ultralytics/output/

Example code:

​x
from ultralytics import YOLO
​
# Load a model
model = YOLO("/home/jetson/ultralytics/yolo26n.engine")
​
# Run batched inference on a list of images
results = model("/home/jetson/ultralytics/assets/bus.jpg")  # return a list of Results objects
​
# Process results list
for result in results:
    boxes = result.boxes  # Boxes object for bounding box outputs
    # masks = result.masks  # Masks object for segmentation masks outputs
    # keypoints = result.keypoints  # Keypoints object for pose outputs
    # probs = result.probs  # Probs object for classification outputs
    # obb = result.obb  # Oriented boxes object for OBB outputs
    result.show()  # display to screen
    result.save(filename="/home/jetson/ultralytics/output/bus_output.jpg")  # save to disk
​

3. Object Prediction: Video

Use yolo26n_ncnn_model to predict a video in the ultralytics26 folder (a video not included with ultralytics).

Enter the code folder:

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cd /home/jetson/ultralytics/yahboom_demo

Run the code:

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python3 01.detection_video.py

Effect Preview

The location of videos output by YOLO recognition: /home/jetson/ultralytics/output/

Example code:

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import cv2
from ultralytics import YOLO
​
# Load the YOLO model
model = YOLO("/home/jetson/ultralytics/yolo26n.engine")
​
# Open the video file
video_path = "/home/jetson/ultralytics/ultralytics/videos/people_animals.mp4"
cap = cv2.VideoCapture(video_path)
​
# Get the video frame size and frame rate
frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
fps = int(cap.get(cv2.CAP_PROP_FPS))
​
# Define the codec and create a VideoWriter object to output the processed video
output_path = "/home/jetson/output/01.people_animals_output.mp4"
fourcc = cv2.VideoWriter_fourcc(*'mp4v')  # You can use 'XVID' or 'mp4v' depending on your platform
out = cv2.VideoWriter(output_path, fourcc, fps, (frame_width, frame_height))
​
# Loop through the video frames
while cap.isOpened():
    # Read a frame from the video
    success, frame = cap.read()
​
    if success:
        # Run YOLO inference on the frame
        results = model(frame)
​
        # Visualize the results on the frame
        annotated_frame = results[0].plot()
​
        # Write the annotated frame to the output video file
        out.write(annotated_frame)
​
        # Display the annotated frame
        cv2.imshow("YOLO Inference", cv2.resize(annotated_frame, (640, 480)))
​
        # 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 and writer objects, and close the display window
cap.release()
out.release()
cv2.destroyAllWindows()

4. Object Prediction: Real-time Detection

4.1 USB Camera

Use yolo26n_ncnn_model to predict the USB camera feed.

Enter the code folder:

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cd /home/jetson/ultralytics/yahboom_demo

Run the code: Click on the preview window and press the q key to terminate the program!

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python3 01.detection_camera_usb.py

Effect Preview

The location of videos output by YOLO recognition: /home/jetson/ultralytics/output/

Example code:

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import cv2
from ultralytics import YOLO
​
# Load the YOLO model
model = YOLO("/home/jetson/ultralytics/yolo26n.engine")
​
# Open the cammera
cap = cv2.VideoCapture(0)
cap.set(6, cv2.VideoWriter.fourcc('M', 'J', 'P', 'G'))
cap.set(cv2.CAP_PROP_FRAME_WIDTH, 640)
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 480)
​
# Get the video frame size and frame rate
frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
fps = int(cap.get(cv2.CAP_PROP_FPS))
​
# Define the codec and create a VideoWriter object to output the processed video
output_path = "/home/jetson/ultralytics/output/01.detection_camera_usb.mp4"
fourcc = cv2.VideoWriter_fourcc(*'mp4v')  # You can use 'XVID' or 'mp4v' depending on your platform
out = cv2.VideoWriter(output_path, fourcc, fps, (frame_width, frame_height))
​
# Loop through the video frames
while cap.isOpened():
    # Read a frame from the video
    success, frame = cap.read()
​
    if success:
        # Run YOLO inference on the frame
        results = model(frame)
​
        # Visualize the results on the frame
        annotated_frame = results[0].plot()
​
        # Write the annotated frame to the output video file
        out.write(annotated_frame)
​
​
        # Display the annotated frame
        cv2.imshow("YOLO Inference", cv2.resize(annotated_frame, (640, 480)))
​
        # 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 and writer objects, and close the display window
cap.release()
out.release()
cv2.destroyAllWindows()
​

4.2 CSI Camera

For CSI camera-related features, please be sure to use VNC or watch directly on a physical display!! X11 forwarding services like MobaXTerm are not supported.

Use yolo26n_ncnn_model to predict the CSI camera feed.

Enter the code folder:

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cd /home/jetson/ultralytics/yahboom_demo

Run the code: Click on the preview window and press the q key to terminate the program!

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python3 01.detection_camera_csi.py

Effect Preview

The location of videos output by YOLO recognition: /home/jetson/ultralytics/output/

Example code:

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import cv2
from ultralytics import YOLO
from jetcam.csi_camera import CSICamera
​
# Load the YOLO model
model = YOLO("/home/jetson/ultralytics/yolo26n.engine")
​
# Open the camera (CSI Camera)
cap = CSICamera(capture_device=0, width=640, height=480)
​
# Get the video frame size and frame rate
frame_width = 640
frame_height = 480
fps = 30
​
# Define the codec and create a VideoWriter object to output the processed video
output_path = "/home/jetson/ultralytics/output/01.detection_camera_csi.mp4"
fourcc = cv2.VideoWriter_fourcc(*'mp4v')  # You can use 'XVID' or 'mp4v' depending on your platform
out = cv2.VideoWriter(output_path, fourcc, fps, (frame_width, frame_height))
​
# Loop through the video frames
while True:
    # Read a frame from the camera
    frame = cap.read()
​
    if frame is not None:
        # Run YOLO inference on the frame
        results = model(frame)
​
        # Visualize the results on the frame
        annotated_frame = results[0].plot()
​
        # Write the annotated frame to the output video file
        out.write(annotated_frame)
​
        # Display the annotated frame
        cv2.imshow("YOLO Inference", cv2.resize(annotated_frame, (640, 480)))
​
        # Break the loop if 'q' is pressed
        if cv2.waitKey(1) & 0xFF == ord("q"):
            break
    else:
        # Break the loop if no frame is received (camera error or end of stream)
        print("No frame received, breaking the loop.")
        break
​
# Release the video capture and writer objects, and close the display window
cap.release()
out.release()
cv2.destroyAllWindows()
​

Reference

Object Detection - Ultralytics YOLO Docs