Ultralytics
zh/tasks/segment/ #8508
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Replies: 14 comments 30 replies
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Hello, may I ask how to count and print segmented objects of different categories? |
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@gaotaogaotao hello! To count and print segmented objects of different categories using a YOLOv8 segmentation model, you can iterate through the results after prediction and keep a tally for each category. Here's a quick example using Python: from ultralytics import YOLO
from collections import Counter
# Load your segmentation model
model = YOLO('yolov8n-seg.pt') # Assuming you're using the YOLOv8n-seg model
# Predict on an image
results = model('path/to/your/image.jpg')
# Count objects by category
object_counts = Counter([model.names[int(cls)] for cls in results[0].masks.cls])
# Print the counts
for category, count in object_counts.items():
print(f"{category}: {count}")This code loads the segmentation model, predicts on an image, and then uses a |
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Hello, may I ask how to count and print segmented objects of different categories? |
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Hello! To count and print segmented objects of different categories using a YOLOv8 segmentation model, you can iterate through the results after prediction and keep a tally of each category. Here's a quick example using Python: from ultralytics import YOLO
from collections import Counter
# Load a pretrained segmentation model
model = YOLO('yolov8n-seg.pt')
# Predict on an image
results = model('https://ultralytics.com/images/bus.jpg')
# Count objects by category
object_counts = Counter([model.names[int(cls)] for cls in results[0].masks.cls])
# Print counts
for category, count in object_counts.items():
print(f"{category}: {count}")This code loads a pretrained segmentation model, runs a prediction on an image, and then uses a |
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Hi, can you tell me how I can get the detected car plates in separate windows (as many as I have detected and output), my code outputs only one window. |
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@Boaruzhanchik hey there! 👋 To display each detected car plate in separate windows, you can loop through the detected boxes and create a window for each. Here's a quick example to get you started: for i, box in enumerate(boxes):
x1, y1, x2, y2 = map(int, box[:4])
plate = im0[y1:y2, x1:x2]
cv2.imshow(f'Plate {i}', plate)
cv2.waitKey(0)
cv2.destroyAllWindows()This code snippet extracts each detected plate based on its bounding box coordinates and displays it in a new window. Make sure to replace |
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Thanks a lot for the help, I did it this way, it seems to work too |
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@Boaruzhanchik hey there! 👋 It looks like you've found a way to handle different numbers of masks with your approach. Nicely done! 🎉 Just a quick tip: you might want to ensure |
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My segmentation dataset is VOC format, can I use the yolo v8-seg model to train it? My label file is in png format. |
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Yes, you can train the YOLOv8-seg model with your VOC format dataset. For the label files in PNG format, ensure they are properly annotated with instance segmentation masks. Here's a quick example to get you started with training: from ultralytics import YOLO
# Load the YOLOv8 segmentation model
model = YOLO('yolov8n-seg.pt') # Load a pretrained segmentation model
# Train the model on your dataset
results = model.train(data='your_dataset_config.yaml', epochs=100, imgsz=640)Make sure your dataset config file ( |
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My question is same as "how to count and print segmented objects of different categories". But if my source is a frame, it will report an error like ‘object_counts = Counter([model.names[int(cls)] for cls in results[0].masks.cls]) |
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What can i do? Hope for help. |
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My problem has been solved, and after reading the Results documentation, I see that cls is now in boxes instead of masks. But thank you for developing such a great yolov8 |
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@Bjyh22 hello! It looks like you're trying to count segmented objects using a detection model rather than a segmentation model. The error you encountered typically arises when attempting to access segmentation-specific attributes on results from a detection model. For instance segmentation, ensure you're working with a segmentation model ( Here's a quick fix if you're aiming for instance segmentation:
For instance segmentation with from ultralytics import YOLO
from collections import Counter
# Load the segmentation model
model = YOLO('yolov8n-seg.pt')
# Run prediction on your frame (assuming 'frame' is your image)
results = model(frame)
# Assuming segmentation model, we should check for presence of 'masks'
if results[0].masks:
object_counts = Counter([model.names[int(cls)] for cls in results[0].masks.cls])
# Printing the counted objects
print(object_counts)
else:
print("No segmented objects found or incorrect model type.")This code snippet will count and print the number of segmented objects per category if you're using an instance segmentation model and have segmented results. If Hope this helps! If you have more questions, feel free to ask. 😊 |
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hello, may i ask how to use this for semantic segmentation? |
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Hello! 😊 For semantic segmentation with YOLOv8, you'll want to use our segmentation models, such as
Make sure your dataset is in the correct format, detailed in our docs. For more customization options and commands, our documentation provides extensive guides. Enjoy experimenting with your segmentation tasks! 🚀 |
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Hello, I encountered the following error when I used pyside6 to put the segmentation result on the UI part of my code is below Could this be caused when the result is converted to an array with decimals in it or something else in the code? |
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Hey there! 👋 It seems the issue you're encountering is due to the shape of the A simple workaround is to modify the def convert2QImage(img):
if img.ndim == 2: # If img is grayscale (height, width)
img = np.stack((img,)*3, axis=-1) # Convert to RGB
height, width, channel = img.shape
bytesPerLine = 3 * width
return QImage(img.data, width, height, bytesPerLine, QImage.Format_RGB888)This checks if Regarding the segmentation results, since you're working with a segmentation model, the output would not directly be in a format suitable for Hope this helps! Let me know if you have any more questions. 😊 |
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Hello, can you give the frame diagrammodule of YOLOv8 segmentation model |
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Hey there! 😄 It seems like you're looking for the framework on how the YOLOv8 segmentation model is structured, right? The YOLOv8 segmentation models, including For instance, to train, validate or predict with the YOLOv8 segmentation models in Python you can use the following examples: 🔧 To Train: from ultralytics import YOLO
# Load and train a model
model = YOLO('yolov8n-seg.pt') # This loads the pretrained segmentation model
results = model.train(data='coco128-seg.yaml', epochs=100, imgsz=640)✅ To Validate: # Validate the trained model
metrics = model.val()👁 To Predict: # Predict on new images
results = model('path/to/your/image.jpg')And to export a trained segmentation model to say ONNX format, it’s as simple as: # Export the model
model.export(format='onnx')For a detailed dive, our documentation on instance segmentation is a great next stop: 实例分割 As of now, we don't provide a frame diagram of the segmentation model's architecture directly. However, the documentation and code snippets should give a clear understanding of how the model operates on tasks. If you're looking for something more detailed or specific, don't hesitate to let me know! 😊 |
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Is there any difference in the framework of the segmentation model compared to the detection model |
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@wkywwds hey there! 👋 The segmentation model, such as For instance, while a detection model might simply indicate a bounding box around a person, the segmentation model outlines the exact boundaries, even capturing subtle outlines around complex shapes. This makes segmentation models incredibly useful for tasks requiring a nuanced understanding of object shapes and their spatial layouts within images. Here's a quick peek into using a YOLOv8 segmentation model in Python: from ultralytics import YOLO
# Load the segmentation model
model = YOLO('yolov8n-seg.pt') # A pretrained segmentation model
# Predict and segment objects in an image
results = model('path/to/your/image.jpg')
# The results contain both the bounding boxes and the segmentation masksThis model is especially great for scenarios where precise object shapes, beyond just their locations, are crucial. 🎨 |
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Hello! I've been working with the segmentation model and encountered an issue I hope you can help with. |
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This is my CLI: And i also checked the attribute mask.xy of result. The output was the same. |
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Hi there! 👋 Thanks for reaching out and sharing the details of your CLI command. It seems you're facing an issue where the exported For segmentation tasks, the A quick thing you can check is to ensure the format and pathway for saving these detailed segmentation outputs are correctly specified and accessible. Moreover, verifying the For debugging, you might try explicitly specifying the path and format for saving segmentation outputs if not already. Here’s a code snippet that might help guide you on how to save these outputs: from ultralytics import YOLO
# Assume your model and source are correctly loaded
results = model.predict(source)
# For each result, save or process segmentation masks
for i, result in enumerate(results):
# Check and process result.masks here
# E.g., save mask details to a file in your preferred format
passPlease make sure your environment has the necessary permissions to write files to the specified locations, and review the output directory for any generated mask details. If the issue persists, please share more details about the output files and any errors/logs you might see. That would help in diagnosing the problem further! 🛠️ |
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I have already set |
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Hey there! It looks like One possible reason for the combining of parts could be how the model interprets the visual continuity of the railway, particularly when it's partially obscured. If the model's perception of object boundaries is primarily based on visual cues, the utility pole might not provide a strong enough signal to indicate a segmentation between parts A and C. A potential solution to improve segment differentiation in such scenarios could involve fine-tuning your model with more varied examples that specifically include cases of obscured or segmented objects of interest, like railways interrupted by other items. This way, your model can learn to recognize and segment objects more accurately even when partially blocked. It might also be helpful to adjust the model's confidence threshold ( Here's a small piece of code for fine-tuning with custom data, assuming you've already prepared a dataset with the mentioned characteristics: yolo segment train data=your_custom_dataset.yaml model=yolov8n-seg.pt epochs=50 imgsz=640Remember to replace Let me know if there's anything else I can do to assist! |
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Hello, may I ask how to output the area of each mask in the image, and count the number of masks corresponding to different categories？ |
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Hello! To output the area of each mask and count the number of masks for different categories, you can use the results object after running inference with your segmentation model. Here's a quick example on how you can achieve that with Python: from ultralytics import YOLO
# Load your segmentation model
model = YOLO('yolov8n-seg.pt') # Use the appropriate model for your task
# Predict on your image
results = model('path/to/your/image.jpg')
# Calculate areas and counts
areas = [mask.area.cpu().numpy() for mask in results.masks]
categories = [results.names[i] for i in results.masks.label.cpu().numpy()]
# Count masks per category
category_counts = {category: categories.count(category) for category in set(categories)}
print("Areas of masks:", areas)
print("Number of masks per category:", category_counts)This will give you the areas of each detected object's mask and the count of masks per category. Hope this helps! 😊 |
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Hello, I am doing an instance segmentation task on the COCO dataset, how to output its COCO evaluation metric when verifying. For example, map, map50, map75, map50-95, map(large, medium, small)... |
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Hello! 😊 For evaluating your instance segmentation tasks on the COCO dataset with metrics such as mAP, mAP50, mAP75, mAP50-95, and mAP for different object sizes, you can use the Here's a quick example of how you could validate a trained model on the COCO dataset: yolo segment val model=yolov8n-seg.pt data=coco.yamlThis command assumes you have a COCO dataset correctly set up and referenced by Let us know if you need further assistance! |
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Thanks for your reply. In fact, I do instance segmentation tasks on coco dataset. After I improve some modules of yolov8 for training, I verify the training model effect and want to obtain its coco type evaluation indexes, such as mAP, mAP75, mAP(large), mAP(medium), mAP(small). I generate JSON files to verify the training effect of the model, and then follow the pycocotools package in the Linux environment to try to obtain its coco type instance segmentation evaluation indexes. But the results are not ideal. Here is the code for the get_COCO_metric.py file.import argparse def parse_opt(): if name == 'main': ============================================================
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Hi there! Thanks for sharing your process and the output you're getting during COCO evaluation after making modifications to YOLOv8. Based on the output you've shared, it looks like the model is not performing as expected given the very low mAP values across the board. Given the significant difference in metrics between your custom
For better diagnostic insights:
Here’s a small adjustment tip: If you're not already using it, consider visualizing some predictions using the Ultralytics inference code to manually inspect if the segmentation masks and categories look reasonable. Hope this helps! If the issue persists, feel free to share more details about the modifications and perhaps some sample outputs for further assistance. 🤝 |
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How to use the segmentation function of YOLOv8 to segment a panoramic image and a target image simultaneously, and then use feature point matching to perform feature point matching on the segmented object in both images. Match feature points only between segmented objects in two images |
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@Daniel-lucifer to perform instance segmentation on both a panoramic image and a target image simultaneously using YOLOv8, you can follow these steps:
Here's a basic example in Python using Ultralytics YOLO: from ultralytics import YOLO
import cv2
# Load the segmentation model
model = YOLO('yolov8n-seg.pt')
# Load images
img1 = cv2.imread('panoramic.jpg')
img2 = cv2.imread('target.jpg')
# Perform segmentation
results1 = model(img1)
results2 = model(img2)
# Assuming you have a function to extract and match features
matches = match_features(results1, results2)
def match_features(results1, results2):
# Extract features and perform matching
# This is a placeholder for your feature matching logic
passFor feature point matching, you might consider using SIFT, SURF, or ORB algorithms provided by OpenCV, focusing only on the regions defined by the segmentation masks. This approach ensures that feature matching is constrained to the segmented objects, potentially improving the relevance and accuracy of the matches. |
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from ultralytics import YOLO 初始化模型model = YOLO('F:/ultralytics-main/yolov8n-seg.pt') 读取图像img1 = cv2.imread('C:/Users/801/Desktop/lab.jpg') 进行分割results1 = model(img1) def extract_features_and_keypoints(segment): def match_features(descriptors1, descriptors2): def process_and_match_segments(results1, results2): 处理分割结果并匹配特征keypoints1, keypoints2, matches, cropped1, cropped2 = process_and_match_segments(results1, results2) 可视化匹配结果img_matches = cv2.drawMatches(cropped1, keypoints1, cropped2, keypoints2, matches, None, |
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@Daniel-lucifer hello! It looks like you've set up your segmentation and feature matching pipeline quite well. Here are a few suggestions to ensure everything works as expected:
If you encounter any specific errors or unexpected results, feel free to share more details, and I'd be happy to help further! Good luck with your feature matching! 🚀 |
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The result after I ran it was that only two images were segmented, and there was no result of matching feature points. Can you help me check which part can be modified，thanks |
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@Daniel-lucifer hi there! It sounds like you might be facing an issue with the feature matching step after successfully segmenting your images. Here are a couple of things you might want to check:
If you're still facing issues, it might be helpful to visualize the keypoints on the segmented areas to ensure they are being detected correctly. If you can provide any specific errors or more details, I'd be happy to help further! 😊 |
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How to extract feature points on segmented objects |
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@Daniel-lucifer to extract feature points on segmented objects using a YOLOv8-seg model, you can follow these steps:
Here’s a quick example of how you might apply a simple feature detection on the masks using OpenCV: import cv2
import numpy as np
# Assuming 'masks' is a numpy array from the YOLO segmentation output
for i, mask in enumerate(masks):
mask = (mask * 255).astype(np.uint8) # Convert mask to 8-bit image
corners = cv2.goodFeaturesToTrack(mask, maxCorners=100, qualityLevel=0.01, minDistance=10)
# corners will contain the coordinates of the feature pointsThis will give you the feature points for each segmented object in your image. For more detailed processing or different feature extraction techniques, adjust the methods accordingly. |
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Hello, I would like to know how to add other evaluation metrics, such as Dice and accuracy, which are commonly used in semantic segmentation tasks. |
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Hello, Thank you for your question! Adding additional evaluation metrics such as Dice and accuracy to your YOLOv8 segmentation tasks is a great way to enhance the analysis of your model's performance. To integrate these metrics, you can modify the validation script to include the calculations for Dice coefficient and accuracy. Below is an example of how you can extend the existing validation code to include these metrics: Python Examplefrom ultralytics import YOLO
import numpy as np
# Load a model
model = YOLO("yolov8n-seg.pt") # load an official model
# or
# model = YOLO("path/to/best.pt") # load a custom model
# Define a function to calculate Dice coefficient
def dice_coefficient(y_true, y_pred, smooth=1):
intersection = np.sum(y_true * y_pred)
return (2. * intersection + smooth) / (np.sum(y_true) + np.sum(y_pred) + smooth)
# Define a function to calculate accuracy
def accuracy(y_true, y_pred):
return np.sum(y_true == y_pred) / y_true.size
# Validate the model
metrics = model.val()
# Calculate additional metrics
for result in metrics:
y_true = result.masks # Ground truth masks
y_pred = result.pred # Predicted masks
dice = dice_coefficient(y_true, y_pred)
acc = accuracy(y_true, y_pred)
print(f"Dice Coefficient: {dice}")
print(f"Accuracy: {acc}")CLI ExampleIf you prefer using the CLI, you can export the predictions and then calculate the metrics using a custom script: # Run validation and save predictions
yolo segment val model=yolov8n-seg.pt save_txt=True
# Custom script to calculate Dice and accuracy
python calculate_metrics.pyIn For more detailed information on instance segmentation and how to work with YOLOv8 models, you can refer to the Ultralytics documentation on segmentation. If you encounter any issues or need further assistance, please provide a minimum reproducible example of your code and ensure you are using the latest versions of Happy coding! 😊 |
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@HouNAiL hello, Thank you for your question! Adding additional evaluation metrics such as Dice and accuracy to your YOLOv8 segmentation tasks can provide more insights into your model's performance, especially for semantic segmentation tasks. To incorporate these metrics, you can modify the validation script to include custom metric calculations. Here's a basic example of how you can add Dice coefficient and accuracy to your evaluation: Python Examplefrom ultralytics import YOLO
import torch
def dice_coefficient(pred, target, smooth=1e-6):
intersection = (pred * target).sum()
union = pred.sum() + target.sum()
dice = (2. * intersection + smooth) / (union + smooth)
return dice
def accuracy(pred, target):
correct = (pred == target).sum().item()
total = target.numel()
return correct / total
# Load a model
model = YOLO("yolov8n-seg.pt") # load an official model
model = YOLO("path/to/best.pt") # load a custom model
# Validate the model
metrics = model.val() # no arguments needed, dataset and settings remembered
# Calculate additional metrics
for result in metrics:
pred_mask = result.masks # predicted masks
true_mask = result.labels # true masks
dice = dice_coefficient(pred_mask, true_mask)
acc = accuracy(pred_mask, true_mask)
print(f"Dice Coefficient: {dice:.4f}")
print(f"Accuracy: {acc:.4f}")CLI ExampleCurrently, the CLI does not directly support adding custom metrics. However, you can export your predictions and then calculate the metrics using a separate script. # Predict and save results
yolo segment predict model=yolov8n-seg.pt source='path/to/your/images' save=TrueAfter running the above command, you can write a script to load the saved predictions and calculate the Dice coefficient and accuracy. For more detailed information on segmentation tasks, you can refer to our Instance Segmentation Documentation. If you encounter any issues or need further assistance, please ensure you are using the latest versions of |
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zh/tasks/segment/
了解如何通过Ultralytics YOLO 使用实例分割模型。有关训练、验证、图像预测和模型导出的说明。
https://docs.ultralytics.com/zh/tasks/segment/
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