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--save-crop #8905
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👋 Hello @PriyamDalwadii, thank you for your interest in YOLOv5 🚀! Please visit our ⭐️ Tutorials to get started, where you can find quickstart guides for simple tasks like Custom Data Training all the way to advanced concepts like Hyperparameter Evolution. If this is a 🐛 Bug Report, please provide screenshots and minimum viable code to reproduce your issue, otherwise we can not help you. If this is a custom training ❓ Question, please provide as much information as possible, including dataset images, training logs, screenshots, and a public link to online W&B logging if available. For business inquiries or professional support requests please visit https://ultralytics.com or email support@ultralytics.com. RequirementsPython>=3.7.0 with all requirements.txt installed including PyTorch>=1.7. To get started: git clone https://github.com/ultralytics/yolov5 # clone
cd yolov5
pip install -r requirements.txt # install EnvironmentsYOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):
StatusIf this badge is green, all YOLOv5 GitHub Actions Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training (train.py), validation (val.py), inference (detect.py) and export (export.py) on macOS, Windows, and Ubuntu every 24 hours and on every commit. |
@PriyamDalwadii 👋 Hello! Thanks for asking about cropping results with YOLOv5 🚀. Cropping bounding box detections can be useful for training classification models on box contents for example. This feature was added in PR #2827. You can crop detections using either detect.py or YOLOv5 PyTorch Hub: detect.pyCrops will be saved under python detect.py --save-crop YOLOv5 PyTorch HubCrops will be saved under import torch
# Model
model = torch.hub.load('ultralytics/yolov5', 'yolov5s') # or yolov5m, yolov5l, yolov5x, custom
# Images
img = 'https://ultralytics.com/images/zidane.jpg' # or file, Path, PIL, OpenCV, numpy, list
# Inference
results = model(img)
# Results
crops = results.crop(save=True) # or .show(), .save(), .print(), .pandas(), etc. Good luck 🍀 and let us know if you have any other questions! |
@glenn-jocher Hey! I am using python detect.py --source 1 --save-crop. |
@PriyamDalwadii only one crop is produced per detection. If you have multiple crops you have multiple detections. |
@PriyamDalwadii this is probably just one image per frame as intended. |
@glenn-jocher Absolutely, I don't know if i quite got it, but I don't know why it keeps cropping same image for multiple time? I hope you get my point. Any fix to just crop one image per 1 detection |
@PriyamDalwadii I think you're not understanding. If your webcam is running at 30FPS you'll have 30 images processed in 1 second. If the object is in each image you'll have 30 crops in 1 second. Everything is working as intended. |
oh! Great Thanks! @glenn-jocher |
After collecting all images you can remove them by checking the similarity search method from images. |
@sourabmaity Thanks for the idea, do have any good resource that I can refer? |
you can check fastdup @PriyamDalwadii |
Thanks! This might help :D |
👋 Hello, this issue has been automatically marked as stale because it has not had recent activity. Please note it will be closed if no further activity occurs. Access additional YOLOv5 🚀 resources:
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Feel free to inform us of any other issues you discover or feature requests that come to mind in the future. Pull Requests (PRs) are also always welcomed! Thank you for your contributions to YOLOv5 🚀 and Vision AI ⭐! |
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Question
I am working on project on object detection and saving the crop image of detection. I am using --save-crop flag but it creates duplicates of same image. How to just save one crop image instead of multiple crop images?
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