YOLOv8 imagesegmentation

 Ultralytics YOLOv8은Ultralytics 에서 개발한 YOLO(You Only Look Once) 객체 감지 및 이미지 분할 모델의 최신 버전입니다. YOLOv8은 이전 YOLO 버전의 성공을 바탕으로 새로운 기능과 개선 사항을 도입하여 성능과 유연성을 더욱 향상시키는 최첨단 SOTA(최신 기술) 모델입니다.

 

https://docs.ultralytics.com/

YOLOv8 Docs

 

설치방법

      

      

      

    
    

      <python />
    
pip install ultralytics

 

Train

 

      

      

      

    
    

      <python />
    
from ultralytics import YOLO

model = YOLO("yolov8n.pt") # pass any model type
model.train(epochs=5)

Validation

      

      

      

    
    

      <python />
    
  from ultralytics import YOLO

  model = YOLO("model.pt")
  # It'll use the data yaml file in model.pt if you don't set data.
  model.val()
  # or you can set the data you want to val
  model.val(data="coco128.yaml")

Predict

      

      

      

    
    

      <python />
    
from ultralytics import YOLO
from PIL import Image
import cv2

model = YOLO("model.pt")
# accepts all formats - image/dir/Path/URL/video/PIL/ndarray. 0 for webcam
results = model.predict(source="0")
results = model.predict(source="folder", show=True) # Display preds. Accepts all YOLO predict arguments

# from PIL
im1 = Image.open("bus.jpg")
results = model.predict(source=im1, save=True)  # save plotted images

# from ndarray
im2 = cv2.imread("bus.jpg")
results = model.predict(source=im2, save=True, save_txt=True)  # save predictions as labels

# from list of PIL/ndarray
results = model.predict(source=[im1, im2])

 

 

간단한 테스트

      

      

      

    
    

      <python />
    
from ultralytics import YOLO
import numpy as np

class YOLOSegmentation:
    def __init__(self, model_path):
        self.model = YOLO(model_path)

    def detect(self, img):
        # Get img shape
        height, width, channels = img.shape

        results = self.model.predict(source=img.copy(), save=False, save_txt=False)
        result = results[0]
        segmentation_contours_idx = []
        for seg in result.masks.segments:
            # contours
            seg[:, 0] *= width
            seg[:, 1] *= height
            segment = np.array(seg, dtype=np.int32)
            segmentation_contours_idx.append(segment)

        bboxes = np.array(result.boxes.xyxy.cpu(), dtype="int")
        # Get class ids
        class_ids = np.array(result.boxes.cls.cpu(), dtype="int")
        # Get scores
        scores = np.array(result.boxes.conf.cpu(), dtype="float").round(2)
        return bboxes, class_ids, segmentation_contours_idx, scores

 

 

      

      

      

    
    

      <python />
    
#pip install ultralytics

import os
os.chdir("E:\GoogleDrive\pycv\YOLOv8_segmentation")
os.getcwd()
import cv2
from YOLOv8_segmentation.yolov8_segmentation.yoloseg import YOLOSegmentation

img2 = cv2.imread("img.png")
img2 = cv2.resize(img2, None, fx=0.7, fy=0.7)

# Segmentation detector
ys = YOLOSegmentation("yolov8m-seg.pt")

bboxes, classes, segmentations, scores = ys.detect(img2)
for bbox, class_id, seg, score in zip(bboxes, classes, segmentations, scores):
    # print("bbox:", bbox, "class id:", class_id, "seg:", seg, "score:", score)
    (x, y, x2, y2) = bbox
    # if class_id == 32:
    cv2.rectangle(img2, (x, y), (x2, y2), (255, 0, 0), 2)

    cv2.polylines(img2, [seg], True, (0, 0, 255), 4)

    cv2.putText(img2, str(class_id), (x, y - 10), cv2.FONT_HERSHEY_PLAIN, 2, (0, 0, 255), 2)

cv2.imshow("image", img2)
cv2.waitKey(0)