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| import pandas as pd | |
| import numpy as np | |
| import torch | |
| import torch.nn as nn | |
| import torchvision.transforms as transforms | |
| import gradio as gr | |
| import matplotlib.pyplot as plt | |
| import matplotlib.image as mpimg | |
| device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') | |
| classes = { 0:'Speed limit (20km/h)', | |
| 1:'Speed limit (30km/h)', | |
| 2:'Speed limit (50km/h)', | |
| 3:'Speed limit (60km/h)', | |
| 4:'Speed limit (70km/h)', | |
| 5:'Speed limit (80km/h)', | |
| 6:'End of speed limit (80km/h)', | |
| 7:'Speed limit (100km/h)', | |
| 8:'Speed limit (120km/h)', | |
| 9:'No passing', | |
| 10:'No passing veh over 3.5 tons', | |
| 11:'Right-of-way at intersection', | |
| 12:'Priority road', | |
| 13:'Yield', | |
| 14:'Stop', | |
| 15:'No vehicles', | |
| 16:'Veh > 3.5 tons prohibited', | |
| 17:'No entry', | |
| 18:'General caution', | |
| 19:'Dangerous curve left', | |
| 20:'Dangerous curve right', | |
| 21:'Double curve', | |
| 22:'Bumpy road', | |
| 23:'Slippery road', | |
| 24:'Road narrows on the right', | |
| 25:'Road work', | |
| 26:'Traffic signals', | |
| 27:'Pedestrians', | |
| 28:'Children crossing', | |
| 29:'Bicycles crossing', | |
| 30:'Beware of ice/snow', | |
| 31:'Wild animals crossing', | |
| 32:'End speed + passing limits', | |
| 33:'Turn right ahead', | |
| 34:'Turn left ahead', | |
| 35:'Ahead only', | |
| 36:'Go straight or right', | |
| 37:'Go straight or left', | |
| 38:'Keep right', | |
| 39:'Keep left', | |
| 40:'Roundabout mandatory', | |
| 41:'End of no passing', | |
| 42:'End no passing veh > 3.5 tons' } | |
| def transform_images(img): | |
| transform = transforms.Compose( | |
| [transforms.ToTensor(), | |
| transforms.Resize((30, 30)), | |
| transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))] | |
| ) | |
| return transform(img) | |
| model = torch.jit.load('model_scripted.pt') | |
| model.eval() | |
| def classify_image(img): | |
| image = transform_images(img).to(device) | |
| outputs = model(image) | |
| _, predicted = torch.max(outputs.data, 1) | |
| return classes[int(predicted[0])] | |
| image = gr.inputs.Image(shape=(30,30)) | |
| label = gr.outputs.Label() | |
| examples = ['002_0003_j.png', '054_0024_j.png', '056_1_0001_1_j.png', '003_1_0009_1_j.png', '055_1_0005_1_j.png', '056_1_0013_1_j.png'] | |
| intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples) | |
| intf.launch(inline=False) |