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| import onnx | |
| import numpy as np | |
| import onnxruntime as ort | |
| from PIL import Image | |
| import cv2 | |
| import os | |
| import gradio as gr | |
| os.system("wget https://s3.amazonaws.com/onnx-model-zoo/synset.txt") | |
| with open('synset.txt', 'r') as f: | |
| labels = [l.rstrip() for l in f] | |
| os.system("wget https://github.com/AK391/models/raw/main/vision/classification/densenet-121/model/densenet-9.onnx") | |
| os.system("wget https://s3.amazonaws.com/model-server/inputs/kitten.jpg") | |
| model_path = 'densenet-9.onnx' | |
| model = onnx.load(model_path) | |
| session = ort.InferenceSession(model.SerializeToString()) | |
| def get_image(path): | |
| with Image.open(path) as img: | |
| img = np.array(img.convert('RGB')) | |
| return img | |
| def preprocess(img): | |
| img = img / 255. | |
| img = cv2.resize(img, (256, 256)) | |
| h, w = img.shape[0], img.shape[1] | |
| y0 = (h - 224) // 2 | |
| x0 = (w - 224) // 2 | |
| img = img[y0 : y0+224, x0 : x0+224, :] | |
| img = (img - [0.485, 0.456, 0.406]) / [0.229, 0.224, 0.225] | |
| img = np.transpose(img, axes=[2, 0, 1]) | |
| img = img.astype(np.float32) | |
| img = np.expand_dims(img, axis=0) | |
| return img | |
| def predict(path): | |
| img = get_image(path) | |
| img = preprocess(img) | |
| ort_inputs = {session.get_inputs()[0].name: img} | |
| preds = session.run(None, ort_inputs)[0] | |
| preds = np.squeeze(preds) | |
| a = np.argsort(preds) | |
| results = {} | |
| for i in a[0:5]: | |
| results[labels[a[i]]] = float(preds[a[i]]) | |
| return results | |
| title="DenseNet-121" | |
| description="DenseNet-121 is a convolutional neural network for classification." | |
| examples=[['kitten.jpg']] | |
| gr.Interface(predict,gr.inputs.Image(type='filepath'),"label",title=title,description=description,examples=examples).launch(enable_queue=True,debug=True) | |