| |
| """Tooth Decay Detection.ipynb |
| |
| Automatically generated by Colab. |
| |
| Original file is located at |
| https://colab.research.google.com/drive/1tMmGs2M07jYAPI0z-FtK3edk1gxIQ6K- |
| """ |
|
|
| !pip install -q git+https://github.com/THU-MIG/yolov10.git |
|
|
| !wget -P -q https://github.com/jameslahm/yolov10/releases/download/v1.0/yolov10n.pt |
|
|
| !pip install -q roboflow |
|
|
| from roboflow import Roboflow |
| rf = Roboflow(api_key= "pZEBEeL3bfGXXSQHCJHH") |
| project = rf.workspace("meproject-4mubm/").project("mydata-wrg2p-t8nuc") |
| version = project.version(1) |
| dataset = version.download("yolov8") |
|
|
| !yolo task=detect mode=train epochs =35 batch = 32 plots= True\ |
| model = '/content/-q/yolov10n.pt'\ |
| data = '/content/mydata-1/data.yaml' |
|
|
| from ultralytics import YOLOv10 |
| model_path = "/content/runs/detect/train/weights/best.pt" |
| model = YOLOv10(model_path) |
| result = model(source = "/content/mydata-1/valid/images", conf = 0.25, save =True) |
|
|
| import glob |
| import matplotlib.pyplot as plt |
| import matplotlib.image as mpimg |
| images = glob.glob("/content/runs/detect/predict/*.jpg") |
|
|
| images_to_display = images[:10] |
| fig, axes =plt.subplots(2,5, figsize =(20,10)) |
|
|
| for i , ax in enumerate(axes.flat): |
| if i < len(images_to_display): |
| img = mpimg.imread(images_to_display[i]) |
| ax.imshow(img) |
| ax.axis('off') |
| else: |
| ax.axis('off') |
| plt.tight_layout() |
| plt.show() |
|
|
| result =model.predict(source = '/content/mydata-1/valid/images/1014_jpg.rf.e559597d59faaf5c86aa3c9c177620f6.jpg',imgsz = 640, conf = 0.25) |
| annotated_img =result[0].plot() |
| annotated_img[:, :, ::-1] |
|
|
| !pip install gradio |
|
|
| import gradio as gr |
| import cv2 |
| import numpy as np |
|
|
| def predict(image): |
| result =model.predict(source =image,imgsz = 640, conf = 0.25) |
| annotated_image =result[0].plot() |
| annotated_image[:, :, ::-1] |
| return annotated_image |
|
|
| app = gr.Interface( |
| fn = predict, |
| inputs = gr.Image(type = "numpy", label ="Upload an Image"), |
| outputs = gr.Image(type = "numpy", label ="Detect a Tooth Cavity"), |
| title = "Tooth Cavity Detection Using YOLO V10 made by Pulastya 😎", |
| description = "Upload an Image ant eh YOLO V10 model will detect and Annotate the Tooth Decay" |
| ) |
| app.launch() |
