YOLOv8を使って簡単に画像認識する

python -m venv env

env\Scripts\Activate.ps1

pip install ultralytics

model = YOLO('ここを変更するだけ')  # load an official model

from ultralytics import YOLO

# Load a model
source = "https://ultralytics.com/images/bus.jpg"
model = YOLO('yolov8n.pt')  # load an official model

# Predict with the model
results = model.predict(source, save=True, imgsz=320, conf=0.5)

from ultralytics import YOLO

# Load a model
source = "https://ultralytics.com/images/bus.jpg"
model = YOLO('yolov8n-cls.pt')  # load an official model

# Predict with the model
results = model.predict(source, save=True, imgsz=320, conf=0.5)

from ultralytics import YOLO

# Load a model
source = "https://ultralytics.com/images/bus.jpg"
model = YOLO('yolov8n-seg.pt')  # load an official model

# Predict with the model
results = model.predict(source, save=True, imgsz=320, conf=0.5)

from ultralytics import YOLO

# Load a model
source = "https://ultralytics.com/images/bus.jpg"
model = YOLO('yolov8n.pt')  # load an official model

# Predict with the model
results = model.predict(source, save=True, imgsz=320, conf=0.5)

# results[0]からResultsオブジェクトを取り出す
result_object = results[0]

# バウンディングボックスの座標を取得
bounding_boxes = result_object.boxes.xyxy

# クラスIDを取得
class_ids = result_object.boxes.cls

# クラス名の辞書を取得
class_names_dict = result_object.names

# バウンディングボックスとクラス名を組み合わせて表示
for box, class_id in zip(bounding_boxes, class_ids):
    class_name = class_names_dict[int(class_id)]
    print(f"Box coordinates: {box}, Object: {class_name}")

import cv2
from ultralytics import YOLO

# Load the YOLOv8 model
model = YOLO('yolov8n.pt')

# Open the video file
cap = cv2.VideoCapture(0)

# Loop through the video frames
while cap.isOpened():
    # Read a frame from the video
    success, frame = cap.read()

    if success:
        # Run YOLOv8 inference on the frame
        results = model(frame)

        # Visualize the results on the frame
        annotated_frame = results[0].plot()

        # Display the annotated frame
        cv2.imshow("YOLOv8 Inference", annotated_frame)

        # Break the loop if 'q' is pressed
        if cv2.waitKey(1) & 0xFF == ord("q"):
            break
    else:
        # Break the loop if the end of the video is reached
        break

# Release the video capture object and close the display window
cap.release()
cv2.destroyAllWindows()

pip install labelImg

labelImg

p.drawRect(int(left_top.x()), int(left_top.y()), int(rect_width), int(rect_height))

p.drawLine(int(self.prev_point.x()), 0, int(self.prev_point.x()), int(self.pixmap.height()))

p.drawLine(0, int(self.prev_point.y()), int(self.pixmap.width()), int(self.prev_point.y()))

bar.setValue(int(bar.value() + bar.singleStep() * units))

from ultralytics import YOLO

# Load a model
model = YOLO('yolov8n.pt')  # load a pretrained model (recommended for training)

# Train the model
model.train(data='coco128.yaml', epochs=3, imgsz=640)

# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
# COCO128 dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics
# Example usage: python train.py --data coco128.yaml
# parent
# ├── yolov5
# └── datasets
#     └── coco128  ← downloads here (7 MB)


# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
path: ../datasets/coco128  # dataset root dir
train: images/train2017  # train images (relative to 'path') 128 images
val: images/train2017  # val images (relative to 'path') 128 images
test:  # test images (optional)

# Classes
names:
  0: person
  1: bicycle
  2: car
  3: motorcycle
  4: airplane
  5: bus
  6: train
  7: truck
  8: boat
  9: traffic light
  10: fire hydrant
  11: stop sign
  12: parking meter
  13: bench
  14: bird
  15: cat
  16: dog
  17: horse
  18: sheep
  19: cow
  20: elephant
  21: bear
  22: zebra
  23: giraffe
  24: backpack
  25: umbrella
  26: handbag
  27: tie
  28: suitcase
  29: frisbee
  30: skis
  31: snowboard
  32: sports ball
  33: kite
  34: baseball bat
  35: baseball glove
  36: skateboard
  37: surfboard
  38: tennis racket
  39: bottle
  40: wine glass
  41: cup
  42: fork
  43: knife
  44: spoon
  45: bowl
  46: banana
  47: apple
  48: sandwich
  49: orange
  50: broccoli
  51: carrot
  52: hot dog
  53: pizza
  54: donut
  55: cake
  56: chair
  57: couch
  58: potted plant
  59: bed
  60: dining table
  61: toilet
  62: tv
  63: laptop
  64: mouse
  65: remote
  66: keyboard
  67: cell phone
  68: microwave
  69: oven
  70: toaster
  71: sink
  72: refrigerator
  73: book
  74: clock
  75: vase
  76: scissors
  77: teddy bear
  78: hair drier
  79: toothbrush


# Download script/URL (optional)
download: https://ultralytics.com/assets/coco128.zip

from ultralytics import YOLO

source = "https://ultralytics.com/images/bus.jpg" # 自身が検出したいデータの位置
model = YOLO('best.pt') # 学習した重みデータ

model.predict(source, save=True, imgsz=640 conf=0.5)