코드에서 마스크 사용하기: 면적, 겹침, 윤곽, 분리 이미지

import cv2
import numpy as np

from ultralytics import YOLO

model = YOLO("yolo26n-seg.pt")
result = model("https://ultralytics.com/images/bus.jpg", retina_masks=True)[0]  # masks at original resolution

print("masks.data:", tuple(result.masks.data.shape), "orig_shape:", result.orig_shape)
for i, box in enumerate(result.boxes):
    name = result.names[int(box.cls)]
    mask = result.masks.data[i].cpu().numpy().astype(np.uint8)  # H x W, 1 = object
    polygon = result.masks.xy[i]  # P x 2 outline in original pixels
    x1, y1, x2, y2 = box.xyxy[0].int().tolist()
    mask_area = int(mask.sum())
    box_area = (x2 - x1) * (y2 - y1)
    print(f"{name:6s} conf={float(box.conf):.2f} mask={mask_area:7d}px box={box_area:7d}px fill={mask_area / box_area:.2f} polygon={len(polygon)} pts")

    # Cut the object out on a transparent background
    cutout = np.dstack([result.orig_img, mask * 255])[y1:y2, x1:x2]
    cv2.imwrite(f"{i}_{name}.png", cutout)

masks.data: (5, 1080, 810) orig_shape: (1080, 810)
person conf=0.88 mask=  49331px box=  97219px fill=0.51 polygon=575 pts
person conf=0.86 mask=  21316px box=  67760px fill=0.31 polygon=408 pts
bus    conf=0.86 mask= 255265px box= 409822px fill=0.62 polygon=1172 pts
person conf=0.81 mask=  33524px box=  55842px fill=0.60 polygon=350 pts
person conf=0.64 mask=  11109px box=  23828px fill=0.47 polygon=211 pts

import numpy as np

from ultralytics import YOLO

result = YOLO("yolo26n-seg.pt")("https://ultralytics.com/images/bus.jpg", retina_masks=True, verbose=False)[0]
names = [result.names[int(c)] for c in result.boxes.cls]
masks = result.masks.data.cpu().numpy().astype(bool)  # N x H x W at original resolution
boxes = np.zeros_like(masks)
for i, (x1, y1, x2, y2) in enumerate(result.boxes.xyxy.int().tolist()):
    boxes[i, y1:y2, x1:x2] = True  # the same objects as filled rectangles

bus = names.index("bus")
for i, name in enumerate(names):
    if name == "person":
        by_box = (boxes[i] & boxes[bus]).sum() / boxes[i].sum()
        by_mask = (masks[i] & masks[bus]).sum() / masks[i].sum()
        print(f"person {i}: {by_box:4.0%} of box overlaps the bus box, {by_mask:4.0%} of mask overlaps the bus mask")

person 0:  67% of box overlaps the bus box,   0% of mask overlaps the bus mask
person 1:  69% of box overlaps the bus box,   0% of mask overlaps the bus mask
person 3:  74% of box overlaps the bus box,   0% of mask overlaps the bus mask
person 4:  56% of box overlaps the bus box,   0% of mask overlaps the bus mask