Welcome to DOFBOT-Ultra repository
3. Object Detection
Object Detection
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Object Detection1. Model Introduction2. Object Prediction: ImagePreview3. Object Prediction: VideoPreview4. Object Prediction: Real-time Detection4.1. USB CameraPreview4.2. CSI CameraPreviewReferences
Demonstrating Ultralytics Object Detection effects on images, videos, and real-time detection using Python.
1. Model Introduction
Object detection is a task that involves identifying the location and class of objects in an image or video stream.
The output of an object detector is a set of bounding boxes surrounding objects in the image, along with class labels and confidence scores for each box. If you need to identify objects of interest in a scene but do not need to know their exact location or precise shape, object detection is a good choice.
2. Object Prediction: Image
Use yolo26n.pt to predict images that come with the ultralytics project.
Navigate to the code folder:
cd /home/pi/ultralytics/ultralytics/yahboom_demo_26
Run the code:
xxxxxxxxxxpython3 01.detection_image.py
Preview
yolo recognition output image location: /home/pi/ultralytics/ultralytics/output/
Sample code:
xfrom ultralytics import YOLO
# Load a modelmodel = YOLO("/home/pi/ultralytics/ultralytics/yolo26n.pt")
# Run batched inference on a list of imagesresults = model("/home/pi/ultralytics/ultralytics/assets/bus.jpg") # return a list of Results objects
# Process results listfor 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/pi/ultralytics/ultralytics/output/bus_output.jpg") # save to disk3. Object Prediction: Video
Use yolo26n.pt to predict videos in the ultralytics project (non-ultralytics built-in videos).
Navigate to the code folder:
xxxxxxxxxxcd /home/pi/ultralytics/ultralytics/yahboom_demo_26
Run the code:
xxxxxxxxxxpython3 01.detection_video.py
Preview
yolo recognition output video location: /home/pi/ultralytics/ultralytics/output/
Sample code:
xxxxxxxxxximport cv2from ultralytics import YOLO
# Load the YOLO modelmodel = YOLO("/home/pi/ultralytics/ultralytics/yolo26n.pt")
# Open the video filevideo_path = "/home/pi/ultralytics/ultralytics/videos/people_animals.mp4"cap = cv2.VideoCapture(video_path)
# Get the video frame size and frame rateframe_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 videooutput_path = "/home/pi/ultralytics/ultralytics/output/01.people_animals_output.mp4"fourcc = cv2.VideoWriter_fourcc(*'mp4v') # You can use 'XVID' or 'mp4v' depending on your platformout = cv2.VideoWriter(output_path, fourcc, fps, (frame_width, frame_height))
# Loop through the video frameswhile 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", 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 and writer objects, and close the display windowcap.release()out.release()cv2.destroyAllWindows()4. Object Prediction: Real-time Detection
4.1. USB Camera
Use yolo26n.pt to predict USB camera feed.
Navigate to the code folder:
xxxxxxxxxxcd /home/pi/ultralytics/ultralytics/yahboom_demo_26
Run the code: Click on the preview window, press the q key to terminate the program!
xxxxxxxxxxpython3 01.detection_camera_usb.py
Preview
yolo recognition output video location: /home/pi/ultralytics/ultralytics/output/
Sample code: