Ánh xạ đối tượng bằng VisionEye với Ultralytics YOLO26#

VisionEye already draws the ray from vision_point to the bounding-box centroid (not the edge) via SolutionAnnotator.visioneye() as shown in the VisionEye guide and the SolutionAnnotator visioneye() reference; there isn’t a separate “viewing angle” parameter today since it’s a 2D overlay, so the intended control is moving vision_point, and if you need an actual distance value (vs just a line), please use the Distance Calculation solution and share your desired behavior here (plus a short clip/frame + vision_point) so the team can advise the cleanest extension.

@dixroby the requirements.txt file typically includes dependencies like torch, opencv-python, and other libraries necessary for running Ultralytics YOLO models. For a detailed list, please check the file directly in the repository.

@FarrelCulbert VisionEye Object Mapping is not limited to YOLOv8: it works with the tracking-capable Ultralytics models you pass via model=, including YOLO26 (recommended) and YOLO11. For more detailed information and examples, you can refer to the VisionEye documentation at https://docs.ultralytics.com/guides/vision-eye/.

Hi there!

Great question! If you want to test an image instead of a video using Ultralytics YOLO, it's quite straightforward. Below is a simple example to guide you through the process:

Testing on an Image

First, ensure you have the latest versions of torch and ultralytics installed. If not, please update them:

pip install --upgrade torch ultralytics

Next, you can use the following code to test an image:

import cv2
from ultralytics import YOLO
from ultralytics.utils.plotting import Annotator, colors

# Load the model
model = YOLO("yolo26n.pt")

# Load the image
image_path = "path/to/your/image.jpg"
im0 = cv2.imread(image_path)

# Perform prediction
results = model.predict(im0)
boxes = results[0].boxes.xyxy.cpu()
clss = results[0].boxes.cls.cpu().tolist()

# Annotate the image
annotator = Annotator(im0, line_width=2)
names = model.model.names

for box, cls in zip(boxes, clss):
    annotator.box_label(box, label=names[int(cls)], color=colors(int(cls)))

# Display the annotated image
cv2.imshow("YOLO Image Prediction", im0)
cv2.waitKey(0)
cv2.destroyAllWindows()

Explanation

1. Load the Model: We load the YOLO model.
2. Load the Image: Read the image using OpenCV.
3. Perform Prediction: Use the model to predict objects in the image.
4. Annotate the Image: Draw bounding boxes and labels on the image.
5. Display the Image: Show the annotated image using OpenCV.

This should help you get started with testing images. If you encounter any issues or have further questions, feel free to ask! 😊

For more detailed information, you can refer to the VisionEye Object Mapping Guide.

Happy coding! 🚀

how is this model trained? some details on dataset would be great help

Hi! The model is trained using the YOLOv8 framework. You can train it on custom datasets like KITTI by converting the dataset to the YOLO format. The dataset should have labels in the format <class_id> <x_center> <y_center> <width> <height>.

For more details on VisionEye Object Mapping, check out the Ultralytics VisionEye guide. If you have any specific questions, feel free to ask! 🚀

Here's a sample training command:

yolo task=detect mode=train model=yolo26s.pt data=your_dataset.yaml epochs=5 batch=16

Happy training! 😊

Hello! Great questions about the VisionEye Object Mapping using Ultralytics YOLOv8.

1. Distance Calculation: The phrase "It calculates this distance by analyzing the size and position of the object within the frame, using the camera's field of view and other calibration parameters if available" was inaccurate. The VisionEye distance example does not estimate camera-to-object distance from object size or field of view. It computes the pixel distance between the vision point and the bounding-box centroid and divides it by the pixel_per_meter value you set, so you calibrate that scale yourself.

2. Other Calibration Parameters: The example uses none of these. You would need data such as camera intrinsic parameters (focal length, optical centers), extrinsic parameters (orientation and position of the camera), object size references, or a depth sensor only if you build a true camera-to-object distance estimate yourself.

3. Model Transparency: YOLOv8 models are not black boxes in the sense that their architecture and training processes are open for examination and modification. They learn from labeled data (ground truth) where objects are manually annotated in images, teaching the model how to recognize similar objects in new images.

For further details or specific implementations, feel free to check out our guide on VisionEye Object Mapping at Ultralytics VisionEye Guide. If you have more questions or need clarification, don't hesitate to reach out! 😊

Hey there! 😊 It sounds like you're encountering some version compatibility issues. For Ultralytics YOLOv8, we generally recommend using Python 3.8 or newer as it strikes a good balance between being up-to-date and maintaining compatibility with most dependencies.

If you're using a Python version older than 3.7, you might run into issues, and it's a good idea to update to at least version 3.7 or newer if possible. Here's a quick way to check your current Python version from your terminal:

python --version
# or if you have Python 3 installed as 'python3'
python3 --version

And if you need to update Python, the official Python Downloads page is a great place to start.

If you're still facing issues after checking your Python version, could you please provide more details about the error messages you're seeing? This information can help us pinpoint the problem more accurately. Thanks!

it is telling distance 61m while object is almost 1 m far

@mirhasnainsiba hi there! 👋 It seems there might be a discrepancy with the distance calculation in your setup. Typically, the distance calculated by the model would depend on the scale set in your system (for example, pixel_per_meter in the example). If the object is being reported as 61m away while it's actually about 1m away, you might want to adjust this scale factor accordingly. Here's a brief example:

pixel_per_meter = 100  # Adjust this value based on your actual scale

This adjustment helps calibrate the distance calculation to more accurately reflect real-world distances. If you continue to experience issues, double-check the calibration process or consider revisiting the portion of your code responsible for calculating distances. If further assistance is needed, feel free to reach out! 🛠️

thanks

Hey there! 😊 Thanks for reaching out. If you're observing an object distance as 61m while it's almost 1m away, you might want to adjust the pixel_per_meter scaling factor in your VisionEye implementation. This adjustment is crucial for accurate distance calculation.

Here's a simplified approach:

pixel_per_meter = 100 # This is just an example value. 
# You'll need to calibrate this based on your specific setup and camera properties.

By fine-tuning this variable, you can achieve more accurate distance measurements. Calibration involves comparing known distances with the pixel distances detected by your model to find the most suitable pixel_per_meter value. 🛠️

Hope this helps! Let us know if you have any further questions or if there are more details you'd like to dive into.

Hey there! 🌟

The video files used in our examples are not provided in the repository. You can use any video file that you have access to by placing it in an accessible directory and then modifying the "path/to/video/file.mp4" placeholder in the code snippets to point to your video file's location.

For example:

cap = cv2.VideoCapture("your/video/path/here.mp4")

Just replace "your/video/path/here.mp4" with the actual path to your video. If you're looking for sample videos, online resources like Pexels Video or Pixabay Videos offer free footage that you might find useful for your projects.

If you have any more questions or need further assistance, don't hesitate to reach out. Happy coding! 🚀

Okay thankyou!

Hey! Thank you for checking out the VisionEye Object Mapping with Ultralytics YOLOv8. 🚀 If you're keen to dive into VisionEye Object Mapping for pinpoint precision or even integrate object tracking and distance calculation, the provided Python examples can guide you seamlessly through the process.

Just remember to replace "path/to/video/file.mp4" with the specific path to your video file, and you’re all set to explore the amazing capabilities of YOLOv8 with VisionEye!

For pinpoint object mapping:

model = YOLO("yolov8n.pt")  # Adjust model as needed
cap = cv2.VideoCapture("your/video/path.mp4")  # Your video path here

For any additional queries or discussions, feel free to drop a message in our GitHub Issue Section. Happy coding! 🎉

@AdadAlShabab hey there! 👋

No, the "Distance" in the VisionEye example is not a camera-to-object distance. It is the pixel distance between the vision point and the detected object's bounding-box centroid, divided by a pixel_per_meter scale that you set and calibrate yourself. Here's a simplified example of how you might calculate distance in a custom script:

import cv2
import math
from ultralytics import YOLO

model = YOLO("yolo26s.pt")
cap = cv2.VideoCapture("path/to/video.mp4")

while True:
    ret, im0 = cap.read()
    if not ret:
        break

    results = model.track(im0, persist=True)
    for box in results[0].boxes.xyxy.cpu():
        # Example: Calculate distance based on object size or position here
        # This is a placeholder for actual distance calculation logic
        distance = calculate_distance(box)
        print(f"Distance: {distance} meters")

In this example, calculate_distance would be a function you define based on your specific distance estimation method. Keep in mind, accurate distance measurement might require camera calibration and knowing the actual size of the object or using depth sensors in conjunction with the camera.

Hope this helps! Let us know if you have any more questions. 😊

Hi @pderrenger. Thanks a lot for your reply. I just wanted to know how can I divide the results by 10 ( ex: distance/10)? I'm dropping the code, please let me know in which line/lines I should make the changes? TIA.

Code:

if results[0].boxes.id is not None:
    track_ids = results[0].boxes.id.int().cpu().tolist()

    for box, track_id in zip(boxes, track_ids):
        annotator.box_label(box, label=str(track_id), color=bbox_clr)
        annotator.visioneye(box, center_point)

        x1, y1 = int((box[0] + box[2]) // 2), int((box[1] + box[3]) // 2)    # Bounding box centroid

        distance = (math.sqrt((x1 - center_point[0]) ** 2 + (y1 - center_point[1]) ** 2))/pixel_per_meter

        text_size, _ = cv2.getTextSize(f"Distance: {distance:.2f} m", cv2.FONT_HERSHEY_SIMPLEX,1.2, 3)
        cv2.rectangle(im0, (x1, y1 - text_size[1] - 10),(x1 + text_size[0] + 10, y1), txt_background, -1)
        cv2.putText(im0, f"Distance: {distance:.2f} m",(x1, y1 - 5), cv2.FONT_HERSHEY_SIMPLEX, 1.2,txt_color, 3)

Hi @AdadAlShabab! 👋

To divide the calculated distance by 10, you can adjust the line where distance is calculated. Here's how you can modify it:

distance = (math.sqrt((x1 - center_point[0]) ** 2 + (y1 - center_point[1]) ** 2)) / pixel_per_meter / 10

This change will effectively scale down the calculated distance by a factor of 10. Just add / 10 at the end of the existing distance calculation line in your code.

Hope this helps! Let me know if there's anything else you need. 😊

what does this mean "It calculates this distance by analyzing the size and position of the object within the frame, using the camera's field of view and other calibration parameters if available"? what do you mean by 'other calibration parameter if possible'? The model is not a blackbox. Is it? there must be some calibration, some ground truth from where model must have learned. please explain this.

why are we dividing "distance " by 'pixel_per_meter' ? what is the logic or explaination for this ?

Hey there! 👋

The division by pixel_per_meter is used to convert the measured pixel distance into real-world units like meters. This conversion factor helps translate the scale of the image to actual distances based on how many pixels represent one meter in the image. It's essential for applications where you need to know the physical separation between objects or from the camera to an object, not just the distance in pixels.

If you have more questions or need further clarification, feel free to ask! 😊

you mean to say, if there's 1 unit Euclidean distance between two pixels in an image, than it'd be 10m in real world scenarios? And this scale can be adjusted according to our will as per "pixel_per_meter = 10" this code. Isn't it?

if so, how is this calibration done? where's the ground truth?

Hey there! 👋

Yes, exactly! If the pixel_per_meter is set to 10, it means that 1 pixel in the image corresponds to 0.1 meters in the real world. You can adjust this scale to fit the specific context or calibration of your camera setup.

Calibration is typically done by measuring known distances in the real world and adjusting the pixel_per_meter value so that the distances in the image accurately reflect these measurements. Ground truth data for calibration might come from physical measurements or known dimensions within the scene.

If you have any more questions or need further clarification, feel free to ask! 😊

Thank you @pderrenger

You're welcome! 😊

The "VisionEye View with Distance" feature calculates the pixel distance between a reference point and the detected object's centroid, not a true camera-to-object distance. The pixel_per_meter value is used to convert pixel measurements to real-world distances. Calibration involves setting this value based on known distances in your scene.

To divide the distance by 10, modify the distance calculation line like this:

distance = (math.sqrt((x1 - center_point[0]) ** 2 + (y1 - center_point[1]) ** 2)) / pixel_per_meter / 10

For more details, check out the VisionEye guide.

I'm facing this issue, PLEASE HELP. Much Appreciated

@leongjy2005 visioneye is not a method of the base Annotator; it lives on SolutionAnnotator. Use from ultralytics.solutions.solutions import SolutionAnnotator and annotator = SolutionAnnotator(im0, line_width=2), or use the solutions.VisionEye solution from the VisionEye guide.

Problem Solved! Thanks!