import json import math import cv2 import gradio as gr import matplotlib.pyplot as plt import numpy as np import onnxruntime as rt from huggingface_hub import hf_hub_download modele = hf_hub_download(repo_id="onnx/EfficientNet-Lite4", filename="efficientnet-lite4-11.onnx") # load the labels text file labels = json.load(open("onnx_guide/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(100.0 * out_height / scale) new_width = int(100.0 * 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 = [[hf_hub_download('nateraw/gradio-guides-files', 'catonnx.jpg', repo_type='dataset', force_filename='catonnx.jpg')]] interface = gr.Interface( inference, gr.inputs.Image(type="filepath"), "label", title=title, description=description, examples=examples ) if __name__ == '__main__': interface.launch(debug=True)