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=(512, 512),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(FT)" example = [["https://huggingface.co/spaces/Sultannn/YOLOX-Demo/blob/main/images_1.jpeg ","YOLOXM"],["https://huggingface.co/spaces/Sultannn/YOLOX-Demo/blob/main/images_2.jpeg","YOLOXS"]] description = "Demo for YOLOX(Object Detection). Models are YOLOXNano - YOLOXX" article = "
Untuk penjelasan lihat di repo ku 😁
" # deploy iface = gr.Interface(main, inputs = input, outputs = output, title = title, article = article, description = description, examples = example, theme = "dark") iface.launch(debug = True)