use mlx which can annotate images as input to do object detection in matlab

To use mlx, which is a MATLAB function for annotating images, for object detection, here are the general steps:

1. Load the image(s) you want to annotate and the corresponding ground truth bounding boxes. You can use imread() and readtable() functions for this.

main.m
img = imread('image.jpg'); % replace with your image path
gt_bbox = readtable('gt_bbox.csv'); % replace with your ground truth bbox path
137 chars
3 lines

2. Use insertObjectAnnotation() function to annotate the image with ground truth bounding boxes.

main.m
annotated_img = insertObjectAnnotation(img, 'rectangle', gt_bbox{:,[2,3,4,5]}, gt_bbox{:,1},'LineWidth',3,'Color','green');
124 chars
2 lines

3. Create an imageDatastore() object to hold the annotated images and their ground truth data.

main.m
imds = imageDatastore('annotated_images_folder');
blds = boxLabelDatastore('annoated_images_folder', 'FileExtensions', '.csv');
imds = combine(imds, blds);
156 chars
4 lines

4. Define the object detection network architecture to use, such as yolov2 or fasterRCNN. Choose a pre-trained model, or train the network from scratch on your annotated data.

main.m
net = yolov2ObjectDetector(imds);
34 chars
2 lines

5. Use the trained model to detect objects in new input images.

main.m
[test_img, test_info] = imread('test_image.jpg');
[bboxes, scores, labels] = detect(net, test_img);
100 chars
3 lines

6. Visualize the detected objects in the input image.

main.m
detected_img = insertObjectAnnotation(test_img, 'rectangle', bboxes, cellstr(labels));
figure
imshow(detected_img)
115 chars
4 lines

Note: These steps are general, and you may need to tweak them based on your specific project requirements.