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| import os | |
| os.system("pip install gradio==2.9b23") | |
| import numpy as np | |
| import math | |
| import matplotlib.pyplot as plt | |
| import onnxruntime as rt | |
| import cv2 | |
| import json | |
| import gradio as gr | |
| from huggingface_hub import hf_hub_download | |
| import onnxruntime as rt | |
| modele = hf_hub_download(repo_id="onnx/EfficientNet-Lite4", filename="efficientnet-lite4-11.onnx") | |
| # load the labels text file | |
| labels = json.load(open("labels_map.txt", "r")) | |
| # set image file dimensions to 224x224 by resizing and cropping image from center | |
| def pre_process_edgetpu(img, dims): | |
| output_height, output_width, _ = dims | |
| img = resize_with_aspectratio(img, output_height, output_width, inter_pol=cv2.INTER_LINEAR) | |
| img = center_crop(img, output_height, output_width) | |
| img = np.asarray(img, dtype='float32') | |
| # converts jpg pixel value from [0 - 255] to float array [-1.0 - 1.0] | |
| img -= [127.0, 127.0, 127.0] | |
| img /= [128.0, 128.0, 128.0] | |
| return img | |
| # resize the image with a proportional scale | |
| def resize_with_aspectratio(img, out_height, out_width, scale=87.5, inter_pol=cv2.INTER_LINEAR): | |
| height, width, _ = img.shape | |
| new_height = int(100. * out_height / scale) | |
| new_width = int(100. * out_width / scale) | |
| if height > width: | |
| w = new_width | |
| h = int(new_height * height / width) | |
| else: | |
| h = new_height | |
| w = int(new_width * width / height) | |
| img = cv2.resize(img, (w, h), interpolation=inter_pol) | |
| return img | |
| # crop the image around the center based on given height and width | |
| def center_crop(img, out_height, out_width): | |
| height, width, _ = img.shape | |
| left = int((width - out_width) / 2) | |
| right = int((width + out_width) / 2) | |
| top = int((height - out_height) / 2) | |
| bottom = int((height + out_height) / 2) | |
| img = img[top:bottom, left:right] | |
| return img | |
| sess = rt.InferenceSession(modele) | |
| def inference(img): | |
| img = cv2.imread(img) | |
| img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) | |
| img = pre_process_edgetpu(img, (224, 224, 3)) | |
| img_batch = np.expand_dims(img, axis=0) | |
| results = sess.run(["Softmax:0"], {"images:0": img_batch})[0] | |
| result = reversed(results[0].argsort()[-5:]) | |
| resultdic = {} | |
| for r in result: | |
| resultdic[labels[str(r)]] = float(results[0][r]) | |
| return resultdic | |
| title="EfficientNet-Lite4" | |
| description="EfficientNet-Lite 4 is the largest variant and most accurate of the set of EfficientNet-Lite model. It is an integer-only quantized model that produces the highest accuracy of all of the EfficientNet models. It achieves 80.4% ImageNet top-1 accuracy, while still running in real-time (e.g. 30ms/image) on a Pixel 4 CPU." | |
| examples=[['catonnx.jpg']] | |
| gr.Interface(inference,gr.inputs.Image(type="filepath"),"label",title=title,description=description,examples=examples).launch() |