Object detection on segment area? ( Two different model) about ultralytics

[glenn-jocher]

To accomplish the task of first segmenting an image and then detecting objects only within the segmented area using YOLOv8, you can follow these steps:

1. Load the Segmentation Model and Segment the Image:

   • Use your trained segmentation model to segment the image and obtain the segmented area.

2. Mask the Segmented Area:

   • Create a mask from the segmented area and apply this mask to the original image. This will zero out the regions of the image that are not of interest, effectively focusing the detection on the segmented area.

3. Run the Detection Model on the Masked Image:

   • Use your trained detection model to detect objects within the masked image.

Here's an outline of the code to achieve this:

import cv2
import numpy as np
from ultralytics import YOLO

# Load the segmentation and detection models
segmentation_model = YOLO("path/to/your/segmentation_model.pt")
detection_model = YOLO("path/to/your/detection_model.pt")

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

# Run segmentation model
segmentation_results = segmentation_model(image)

# Extract the segmentation mask
# Assuming the mask is in the format [batch, channels, height, width]
segmentation_mask = segmentation_results[0].masks

# Create a binary mask
binary_mask = (segmentation_mask > 0.5).astype(np.uint8) * 255

# Apply the mask to the original image
masked_image = cv2.bitwise_and(image, image, mask=binary_mask)

# Run detection model on the masked image
detection_results = detection_model(masked_image)

# Process and visualize the detection results
for result in detection_results:
    boxes = result.boxes
    for box in boxes:
        x1, y1, x2, y2 = map(int, box.xyxy[0])
        label = box.cls
        confidence = box.conf

        # Draw the bounding box
        cv2.rectangle(masked_image, (x1, y1), (x2, y2), (0, 255, 0), 2)
        cv2.putText(masked_image, f"{label}: {confidence:.2f}", (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)

# Display the result
cv2.imshow("Detection on Segmented Area", masked_image)
cv2.waitKey(0)
cv2.destroyAllWindows()

Key Points:

1. Loading Models: Load both the segmentation and detection models using the YOLO framework.
2. Segmentation: Run the segmentation model to get the segmented mask.
3. Mask Creation: Convert the segmented mask to a binary format.
4. Mask Application: Apply the binary mask to the original image to isolate the segmented area.
5. Detection: Run the detection model on the masked image.
6. Visualization: Draw the detection results on the image.

Make sure to adjust paths, thresholds, and other parameters according to your specific setup and requirements. This approach ensures that object detection is performed only within the areas of interest identified by the segmentation model.