guides/object-cropping/

[giscus[bot]]

guides/object-cropping/

Learn how to isolate and extract specific objects from images and videos using YOLOv8 object cropping.

https://docs.ultralytics.com/guides/object-cropping/

[HsnKmlMt]

Hi! How can i specify classed to be cropped like for my case i just want people.

[HsnKmlMt]

Thank you, I really appreciate it!

[glenn-jocher]

Hello! We're really glad you found the materials helpful! If you ever need to specifically crop specific classes, such as 'people', you can do so by filtering the detections based on class IDs before cropping. Here's a quick example on how you might modify the example to only crop 'people':

from ultralytics import YOLO
import cv2
import os

model = YOLO("yolov8n.pt")  # Load your model
person_class_id = model.names.index('person')  # Assuming 'person' is the class name for people

cap = cv2.VideoCapture("path/to/video.mp4")
assert cap.isOpened(), "Error reading video file"

while cap.isOpened():
    ret, frame = cap.read()
    if not ret:
        break
    results = model.predict(frame)
    boxes = results.boxes.xyxy[results.boxes.cls == person_class_id].cpu().tolist()  # Filter detections to only 'person'

    for idx, box in enumerate(boxes):
        x1, y1, x2, y2 = map(int, box[:4])
        crop_img = frame[y1:y2, x1:x2]
        cv2.imwrite(f'cropped_person_{idx}.jpg', crop_img)

Feel free to replace 'person' with any class you're interested in, and make sure the class name matches what's defined in your model's class names. Let us know if you need further assistance! 🚀

[umairrkhn]

Hey Glenn,
I am getting this error using your implementation:

person_class_id = model.names.index('person') # Ensure 'person' is in the model's class names
^^^^^^^^^^^^^^^^^
AttributeError: 'dict' object has no attribute 'index'

[glenn-jocher]

Hey there!

It looks like you're trying to use the .index() method on a dictionary, which isn't supported. The model.names in YOLOv8 is a list, not a dictionary. Make sure that model.names is correctly referenced and is a list. Here’s how you can do it:

model = YOLO("yolov8n.pt")
person_class_id = model.names.index('person')  # This should work if model.names is a list

If model.names is somehow being altered to a dictionary in your code, you'll need to ensure it remains a list or adjust your approach accordingly. If the issue persists, could you share more of your code context? That might help diagnose the issue better. 😊

[glenn-jocher]

Hey there! It looks like you're trying to use the index() method on a dictionary, which isn't supported. The model.names is likely a dictionary, not a list. You can access the class index directly if you know the class name. Here’s how you can modify your code:

person_class_id = model.names['person']  # Directly access the 'person' class index

Make sure that 'person' is indeed the key for the class you're interested in. If model.names is structured differently, you might need to adjust the key accordingly. Let me know if this helps or if you need further assistance! 😊

[AdityaJaiswal99]

How can we change the frame height and width to a specific pixel and change FPS?

[pderrenger]

@AdityaJaiswal99 hi there!

To change the frame height, width, and FPS of a video using OpenCV, you can set these properties directly on the cv2.VideoCapture and cv2.VideoWriter objects. Below is an example of how you can achieve this:

import cv2

# Load the video
cap = cv2.VideoCapture("path/to/video/file.mp4")
assert cap.isOpened(), "Error reading video file"

# Set the desired frame width, height, and FPS
frame_width = 640  # Set your desired width
frame_height = 480  # Set your desired height
fps = 30  # Set your desired FPS

# Create a VideoWriter object to save the modified video
video_writer = cv2.VideoWriter("modified_video.avi", cv2.VideoWriter_fourcc(*"mp4v"), fps, (frame_width, frame_height))

while cap.isOpened():
    success, frame = cap.read()
    if not success:
        break

    # Resize the frame to the desired dimensions
    resized_frame = cv2.resize(frame, (frame_width, frame_height))

    # Write the resized frame to the output video
    video_writer.write(resized_frame)

    # Display the frame (optional)
    cv2.imshow("Resized Video", resized_frame)
    if cv2.waitKey(1) & 0xFF == ord("q"):
        break

# Release resources
cap.release()
video_writer.release()
cv2.destroyAllWindows()

This script reads a video, resizes each frame to the specified width and height, and writes the modified frames to a new video file with the desired FPS.

For more detailed information on object cropping using YOLOv8, you can refer to our Object Cropping Guide.

Feel free to reach out if you have any more questions! 😊

[AdityaJaiswal99]

Can we make the model, so it does not take the same pictures in the frame again?

[glenn-jocher]

@AdityaJaiswal99 thank you for your question! If you're looking to avoid capturing the same objects multiple times within a frame, you can leverage the Non-Maximum Suppression (NMS) feature of YOLOv8. NMS helps in eliminating redundant bounding boxes by keeping only the highest confidence detections and discarding others that overlap significantly.

Here's a brief example of how you can adjust the iou threshold to refine your detections and reduce duplicates:

from ultralytics import YOLO

# Load the model
model = YOLO("yolov8n.pt")

# Set the IoU threshold to a lower value to reduce overlapping detections
results = model.predict("path/to/your/image_or_video", iou=0.5)

# Process the results as needed
for result in results:
    boxes = result.boxes.xyxy.cpu().tolist()
    clss = result.boxes.cls.cpu().tolist()
    # Further processing...

In this example, setting the iou parameter to a lower value (e.g., 0.5) helps in reducing the number of overlapping boxes, thus minimizing the chances of capturing the same object multiple times.

For a more comprehensive guide on object cropping and additional tips, please visit our Object Cropping Guide.

If you encounter any issues or have further questions, feel free to provide a reproducible example, and ensure you are using the latest version of the packages. This will help us assist you more effectively. 😊

Happy coding!