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119 lines
6.9 KiB
119 lines
6.9 KiB
--- |
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comments: true |
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description: Learn how to crop and extract objects using Ultralytics YOLOv8 for focused analysis, reduced data volume, and enhanced precision. |
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keywords: Ultralytics, YOLOv8, object cropping, object detection, image processing, video analysis, AI, machine learning |
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--- |
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# Object Cropping using Ultralytics YOLOv8 |
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## What is Object Cropping? |
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Object cropping with [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics/) involves isolating and extracting specific detected objects from an image or video. The YOLOv8 model capabilities are utilized to accurately identify and delineate objects, enabling precise cropping for further analysis or manipulation. |
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<p align="center"> |
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<br> |
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<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/ydGdibB5Mds" |
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title="YouTube video player" frameborder="0" |
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allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" |
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allowfullscreen> |
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</iframe> |
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<br> |
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<strong>Watch:</strong> Object Cropping using Ultralytics YOLOv8 |
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</p> |
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## Advantages of Object Cropping? |
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- **Focused Analysis**: YOLOv8 facilitates targeted object cropping, allowing for in-depth examination or processing of individual items within a scene. |
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- **Reduced Data Volume**: By extracting only relevant objects, object cropping helps in minimizing data size, making it efficient for storage, transmission, or subsequent computational tasks. |
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- **Enhanced Precision**: YOLOv8's object detection accuracy ensures that the cropped objects maintain their spatial relationships, preserving the integrity of the visual information for detailed analysis. |
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## Visuals |
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| Airport Luggage | |
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| :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | |
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|  | |
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| Suitcases Cropping at airport conveyor belt using Ultralytics YOLOv8 | |
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!!! example "Object Cropping using YOLOv8 Example" |
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=== "Object Cropping" |
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```python |
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import os |
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import cv2 |
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from ultralytics import YOLO |
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from ultralytics.utils.plotting import Annotator, colors |
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model = YOLO("yolov8n.pt") |
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names = model.names |
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cap = cv2.VideoCapture("path/to/video/file.mp4") |
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assert cap.isOpened(), "Error reading video file" |
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w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS)) |
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crop_dir_name = "ultralytics_crop" |
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if not os.path.exists(crop_dir_name): |
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os.mkdir(crop_dir_name) |
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# Video writer |
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video_writer = cv2.VideoWriter("object_cropping_output.avi", cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h)) |
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idx = 0 |
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while cap.isOpened(): |
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success, im0 = cap.read() |
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if not success: |
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print("Video frame is empty or video processing has been successfully completed.") |
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break |
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results = model.predict(im0, show=False) |
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boxes = results[0].boxes.xyxy.cpu().tolist() |
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clss = results[0].boxes.cls.cpu().tolist() |
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annotator = Annotator(im0, line_width=2, example=names) |
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if boxes is not None: |
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for box, cls in zip(boxes, clss): |
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idx += 1 |
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annotator.box_label(box, color=colors(int(cls), True), label=names[int(cls)]) |
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crop_obj = im0[int(box[1]) : int(box[3]), int(box[0]) : int(box[2])] |
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cv2.imwrite(os.path.join(crop_dir_name, str(idx) + ".png"), crop_obj) |
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cv2.imshow("ultralytics", im0) |
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video_writer.write(im0) |
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if cv2.waitKey(1) & 0xFF == ord("q"): |
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break |
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cap.release() |
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video_writer.release() |
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cv2.destroyAllWindows() |
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``` |
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### Arguments `model.predict` |
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{% include "macros/predict-args.md" %} |
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## FAQ |
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### What is object cropping in Ultralytics YOLOv8 and how does it work? |
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Object cropping using [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics) involves isolating and extracting specific objects from an image or video based on YOLOv8's detection capabilities. This process allows for focused analysis, reduced data volume, and enhanced precision by leveraging YOLOv8 to identify objects with high accuracy and crop them accordingly. For an in-depth tutorial, refer to the [object cropping example](#object-cropping-using-ultralytics-yolov8). |
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### Why should I use Ultralytics YOLOv8 for object cropping over other solutions? |
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Ultralytics YOLOv8 stands out due to its precision, speed, and ease of use. It allows detailed and accurate object detection and cropping, essential for [focused analysis](#advantages-of-object-cropping) and applications needing high data integrity. Moreover, YOLOv8 integrates seamlessly with tools like OpenVINO and TensorRT for deployments requiring real-time capabilities and optimization on diverse hardware. Explore the benefits in the [guide on model export](../modes/export.md). |
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### How can I reduce the data volume of my dataset using object cropping? |
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By using Ultralytics YOLOv8 to crop only relevant objects from your images or videos, you can significantly reduce the data size, making it more efficient for storage and processing. This process involves training the model to detect specific objects and then using the results to crop and save these portions only. For more information on exploiting Ultralytics YOLOv8's capabilities, visit our [quickstart guide](../quickstart.md). |
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### Can I use Ultralytics YOLOv8 for real-time video analysis and object cropping? |
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Yes, Ultralytics YOLOv8 can process real-time video feeds to detect and crop objects dynamically. The model's high-speed inference capabilities make it ideal for real-time applications such as surveillance, sports analysis, and automated inspection systems. Check out the [tracking and prediction modes](../modes/predict.md) to understand how to implement real-time processing. |
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### What are the hardware requirements for efficiently running YOLOv8 for object cropping? |
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Ultralytics YOLOv8 is optimized for both CPU and GPU environments, but to achieve optimal performance, especially for real-time or high-volume inference, a dedicated GPU (e.g., NVIDIA Tesla, RTX series) is recommended. For deployment on lightweight devices, consider using CoreML for iOS or TFLite for Android. More details on supported devices and formats can be found in our [model deployment options](../guides/model-deployment-options.md).
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