YOLOV8_SiamFC

First Step

• Prepare the video

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Second Step

Run demo.py to generate the image (video -> image). It will take a long time, so be prepared.

• Comment below code (siamfc/ops.py) before running demo.py.

seq_dir = os.path.expanduser(videodata)
img_files = sorted(glob.glob(seq_dir + '/img/*.jpg'))
tracker = TrackerSiamFC(net_path=modelpth)
tracker.track(img_files, rect, visualize=True)

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Third Step

python demo.py --model pretrained --data video

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[After preparing all of the data]

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How to Run ?

python demo_image.py --model pretrained --data video\img

• Checklist
  • You must check whether the pretrained model is in the pretrained folder, and also check the evaluation images in the folder.

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main part

def track(self, img_files, box, visualize=False):
    frame_num = len(img_files)
    boxes = np.zeros((frame_num, 4))
    boxes[0] = box
    times = np.zeros(frame_num)

    for f, img_file in enumerate(img_files):
        img = ops.read_image(img_file)

        begin = time.time()
        if f == 0:
            self.init(img, box)
        else:
            boxes[f, :] = self.update(img)
        times[f] = time.time() - begin

        if visualize:
            ops.show_image(img, boxes[f, :])
            
    return boxes, times

• Add this code to the top of the ops.py file.

from ultralytics import YOLO
from ultralytics.utils.plotting import Annotator, colors

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# choose your yolov8 model
# v8n, v8s, v8m, v8l, v8x
model = YOLO("yolov8n-seg.pt")
model.to('cuda')
names = model.model.names

# select the object you want to segment/detect
objects_of_interest = ['bird']

ops.show_image(img, boxes[f, :]) Track this code, then add the following code to line number 93 of ops.py.

if visualize:
    # Select the roi size.
    roi = img[pt1[1]-30:pt2[1]+30, pt1[0]-30:pt2[0]+30]
    
    # Before predicting, you have to change the data type to a NumPy array.
    roi = np.array(roi)

    # There are many hyperparameters in there. Please refer to the official website.
    # https://docs.ultralytics.com/zh/usage/cfg/#predict
    results = model.predict(roi, device='0', conf=0.25, max_det=1)
    if results[0].masks is not None:
        clss = results[0].boxes.cls.cpu().tolist()
        masks = results[0].masks.xy

        annotator = Annotator(roi, line_width=2)

        for idx, (mask, cls) in enumerate(zip(masks, clss)):
            det_label = names[int(cls)]
            if det_label in objects_of_interest:
                annotator.seg_bbox(mask=mask,
                                det_label=det_label)

    img[pt1[1]-30:pt2[1]+30, pt1[0]-30:pt2[0]+30] = roi
    winname = 'window_{}'.format(fig_n)
    cv2.imshow(winname, img)
    cv2.waitKey(delay)

Result

siamfc_yolov8.mp4