import gradio as gr
import torch
from PIL import Image
from ultralytics import YOLO
import matplotlib.pyplot as plt
import io
from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
model = YOLO('detect-best.pt')
def predict(img, conf, iou):
results = model.predict(img, conf=conf, iou=iou)
name = results[0].names
cls = results[0].boxes.cls
crazing = 0
inclusion = 0
patches = 0
pitted_surface = 0
rolled_inscale = 0
scratches = 0
for i in cls:
if i == 0:
crazing += 1
elif i == 1:
inclusion += 1
elif i == 2:
patches += 1
elif i == 3:
pitted_surface += 1
elif i == 4:
rolled_inscale += 1
elif i == 5:
scratches += 1
# 绘制柱状图
fig, ax = plt.subplots()
categories = ['1','2', '3' ,'4', '5' ,'6']
counts = [1,2,3,4,5,6]
ax.bar(categories, counts)
ax.set_title('Category-Count')
plt.ylim(0,5)
plt.xticks(rotation=45, ha="right")
ax.set_xlabel('Category')
ax.set_ylabel('Count')
# 将图表保存为字节流
buf = io.BytesIO()
canvas = FigureCanvas(fig)
canvas.print_png(buf)
plt.close(fig) # 关闭图形，释放资源
# 将字节流转换为PIL Image
image_png = Image.open(buf)
# 绘制并返回结果图片和类别计数图表
for i, r in enumerate(results):
# Plot results image
im_bgr = r.plot() # BGR-order numpy array
im_rgb = Image.fromarray(im_bgr[..., ::-1]) # RGB-order PIL image
# Show results to screen (in supported environments)
return im_rgb
base_conf, base_iou = 0.25, 0.45
title = "基于改进YOLOv8算法的工业瑕疵辅助检测系统"
des = "鼠标点击上传图片即可检测缺陷，可通过鼠标调整预测置信度，还可点击网页最下方示例图片进行预测"
interface = gr.Interface(
inputs=['image', gr.Slider(maximum=1, minimum=0, value=base_conf), gr.Slider(maximum=1, minimum=0, value=base_iou)],
outputs=["image"], fn=predict, title=title, description=des,
examples=[["example1.jpg", base_conf, base_iou],
["example2.jpg", base_conf, base_iou],
["example3.jpg", base_conf, base_iou]])
interface.launch()
