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| import numpy as np | |
| from keras_cv_attention_models.yolox import * # import all yolox model | |
| from keras_cv_attention_models.coco import data | |
| import matplotlib.pyplot as plt | |
| import gradio as gr | |
| # semua yolox model | |
| choices = ["YOLOXNano", "YOLOXTiny", "YOLOXS", "YOLOXM", "YOLOXL", "YOLOXX"] | |
| def main(input_img, models): | |
| # | |
| fig, ax = plt.subplots() # pakai ini,jika tidak akan muncul error | |
| # YOLOXNano models | |
| if models == "YOLOXNano": | |
| model = YOLOXNano(pretrained="coco") | |
| # YOLOXTiny models | |
| elif models == "YOLOXTiny": | |
| model = YOLOXTiny(pretrained="coco") | |
| # YOLOXS models | |
| elif models == "YOLOXS": | |
| model = YOLOXS(pretrained="coco") | |
| # YOLOXM models | |
| elif models == "YOLOXM": | |
| model = YOLOXM(pretrained="coco") | |
| # YOLOXL models | |
| elif models == "YOLOXL": | |
| model = YOLOXL(pretrained="coco") | |
| # YOLOXX models | |
| elif models == "YOLOXX": | |
| model = YOLOXX(pretrained="coco") | |
| # pass | |
| else: | |
| pass | |
| # image pre processing yolox | |
| preds = model(model.preprocess_input(input_img)) | |
| bboxs, lables, confidences = model.decode_predictions(preds)[0] | |
| data.show_image_with_bboxes(input_img, bboxs, lables, confidences, num_classes=100,label_font_size=17, ax=ax) | |
| return fig | |
| # define params | |
| input = [gr.inputs.Image(shape=(2000, 1500),label = "Input Image"), | |
| gr.inputs.Dropdown(choices= choices, type="value", default='YOLOXS', label="Model")] | |
| output = gr.outputs.Image(type="plot", label="Output Image") | |
| title = "YoLoX Demo" | |
| description = "Demo for YOLOX(Object Detection). Models are YOLOXNano - YOLOXX" | |
| # deploy | |
| iface = gr.Interface(main, | |
| inputs = input, | |
| outputs = output, | |
| title = title, | |
| article = article, | |
| description = description, | |
| theme = "dark") | |
| iface.launch(debug = True) |