Spaces:
Runtime error
Runtime error
from keras.models import load_model | |
import numpy as np | |
from PIL import Image | |
import cv2 | |
import os | |
import gradio as gr | |
weight_path = r'basic.h5' #权重文件 | |
model = load_model(weight_path) | |
labels=['天气:小雨, 降雨强度(mm/min):0.8, 能见度(m):500, 道路摩擦系数:0.64, 限速(km/h):80', | |
'天气:中雨, 降雨强度(mm/min):1.2, 能见度(m):250, 道路摩擦系数:0.58, 限速(km/h):50', | |
'天气:大雨, 降雨强度(mm/min):1.6, 能见度(m):50-150, 道路摩擦系数:0.45, 限速(km/h):30', | |
'天气:薄雾, 降雨强度(mm/min):0, 能见度(m):500:, 道路摩擦系数:0.8, 限速(km/h):110', | |
'天气:大雾, 降雨强度(mm/min):0, 能见度(m):200, 道路摩擦系数:0.8, 限速(km/h):70', | |
'天气:浓雾, 降雨强度(mm/min):0:, 能见度(m):50-100, 道路摩擦系数:0.8, 限速(km/h):40', | |
'天气:晴, 降雨强度(mm/min):0, 能见度(m):>1000:, 道路摩擦系数:0.8, 限速(km/h):120'] | |
def classify_image(inp): | |
inp = inp.resize((256,256), Image.ANTIALIAS) #缩放到事先指定的大小 | |
inp = np.expand_dims(inp, axis=0) | |
prediction = model.predict(inp) | |
confidences = {labels[i]: float(prediction[0][i]) for i in range(7)} | |
return confidences | |
gr.Interface( | |
fn=classify_image, | |
inputs=gr.Image(type="pil",shape=(256,256)), | |
outputs=gr.Label(num_top_classes=1), | |
examples=["sunny.jpg"], | |
interpretation="default",cache_examples=True,title="恶劣天气图像识别与预警" | |
).launch(enable_queue=True) |