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| import gradio as gr | |
| import torch | |
| # from sahi.prediction import ObjectPrediction | |
| # from sahi.utils.cv import visualize_object_predictions, read_image | |
| import os | |
| import requests | |
| import json | |
| from PIL import Image | |
| from huggingface_hub import hf_hub_download | |
| from ultralyticsplus import YOLO, render_result | |
| # from ultralyticsplus import render_result | |
| # import requests | |
| # import cv2 | |
| image_path = [['test_images/2a998cfb0901db5f8210.jpg','linhcuem/cham_diem_yolov8', 640, 0.25, 0.45],['test_images/2ce19ce0191acb44920b.jpg','linhcuem/cham_diem_yolov8', 640, 0.25, 0.45], | |
| ['test_images/2daab6ea3310e14eb801.jpg','linhcuem/cham_diem_yolov8', 640, 0.25, 0.45], ['test_images/4a137deefb14294a7005 (1).jpg','linhcuem/cham_diem_yolov8', 640, 0.25, 0.45], | |
| ['test_images/7e77c596436c9132c87d.jpg','linhcuem/cham_diem_yolov8', 640, 0.25, 0.45], ['test_images/170f914014bac6e49fab.jpg','linhcuem/cham_diem_yolov8', 640, 0.25, 0.45], | |
| ['test_images/3355ec3269c8bb96e2d9.jpg','linhcuem/cham_diem_yolov8', 640, 0.25, 0.45], ['test_images/546306a88052520c0b43.jpg','linhcuem/cham_diem_yolov8', 640, 0.25, 0.45], | |
| ['test_images/33148464019ed3c08a8f.jpg','linhcuem/cham_diem_yolov8', 640, 0.25, 0.45], ['test_images/a17a992a1cd0ce8e97c1.jpg','linhcuem/cham_diem_yolov8', 640, 0.25, 0.45], | |
| ['test_images/b5db5e42d8b80ae653a9 (1).jpg','linhcuem/cham_diem_yolov8', 640, 0.25, 0.45],['test_images/b8ee1f5299a84bf612b9.jpg','linhcuem/cham_diem_yolov8', 640, 0.25, 0.45], | |
| ['test_images/b272fec7783daa63f32c.jpg','linhcuem/cham_diem_yolov8', 640, 0.25, 0.45],['test_images/bb202b3eaec47c9a25d5.jpg','linhcuem/cham_diem_yolov8', 640, 0.25, 0.45], | |
| ['test_images/bf1e22b0a44a76142f5b.jpg','linhcuem/cham_diem_yolov8', 640, 0.25, 0.45], ['test_images/ea5473c5f53f27617e2e.jpg','linhcuem/cham_diem_yolov8', 640, 0.25, 0.45], | |
| ['test_images/ee106392e56837366e79.jpg','linhcuem/cham_diem_yolov8', 640, 0.25, 0.45], ['test_images/f88d2214a4ee76b02fff.jpg','linhcuem/cham_diem_yolov8', 640, 0.25, 0.45]] | |
| # Load YOLO model | |
| model = YOLO('linhcuem/cham_diem_yolov8') | |
| # model = YOLO('linhcuem/cham_diem_yolov8_ver20') | |
| ################################################### | |
| def yolov8_img_inference( | |
| image: gr.inputs.Image = None, | |
| model_path: gr.inputs.Dropdown = None, | |
| image_size: gr.inputs.Slider = 640, | |
| conf_threshold: gr.inputs.Slider = 0.25, | |
| iou_threshold: gr.inputs.Slider = 0.45, | |
| ): | |
| model = YOLO(model_path) | |
| # model.conf = conf_threshold | |
| # model.iou = iou_threshold | |
| model.overrides['conf'] = conf_threshold | |
| model.overrides['iou'] = iou_threshold | |
| model.overrides['agnostic_nms'] = False | |
| model.overrides['max_det'] = 1000 | |
| # image = read_image | |
| results = model.predict(image) | |
| render = render_result(model=model, image=image, result=results[0]) | |
| # get the model names list | |
| names = model.names | |
| # get the 'obj' class id | |
| # obj_id = list(names)[list(names.values()).index('lo_ytv')] | |
| # ('hop_dln','hop_jn','hop_vtg','hop_ytv','lo_kids', 'lo_ytv','loc_dln','loc_jn','loc_kids','loc_ytv')] | |
| # obj_id = list(names)[list(names.values()).index([0])] | |
| # count 'car' objects in the results | |
| # count_result = results[0].boxes.cls[0].item() | |
| # count_result = results[0].boxes.cls.tolist() | |
| clist= results[0].boxes.cls.tolist() | |
| cls = set() | |
| for cno in clist: | |
| cls.add(model.names[int(cno)]) | |
| # for r in results: | |
| # for c in r.boxes.cls: | |
| # count = names[int(c)] | |
| # count_result = results.pandas().xyxy[0].value_counts('name') | |
| return render, count | |
| # results = model.predict(image, imgsz=image_size, return_outputs=True) | |
| # results = model.predict(image) | |
| # object_prediction_list = [] | |
| # for _, image_results in enumerate(results): | |
| # if len(image_results)!=0: | |
| # image_predictions_in_xyxy_format = image_results['det'] | |
| # for pred in image_predictions_in_xyxy_format: | |
| # x1, y1, x2, y2 = ( | |
| # int(pred[0]), | |
| # int(pred[1]), | |
| # int(pred[2]), | |
| # int(pred[3]), | |
| # ) | |
| # bbox = [x1, y1, x2, y2] | |
| # score = pred[4] | |
| # category_name = model.model.names[int(pred[5])] | |
| # category_id = pred[5] | |
| # object_prediction = ObjectPrediction( | |
| # bbox=bbox, | |
| # category_id=int(category_id), | |
| # score=score, | |
| # category_name=category_name, | |
| # ) | |
| # object_prediction_list.append(object_prediction) | |
| # image = read_image(image) | |
| # output_image = visualize_object_predictions(image=image, object_prediction_list=object_prediction_list) | |
| # return output_image['image'] | |
| # render = render_result(model=model, image=image, result=results[0]) | |
| inputs_image = [ | |
| # gr.inputs.Image(type="filepath", label="Input Image"), | |
| gr.inputs.Image(type="pil"), | |
| gr.inputs.Dropdown(["linhcuem/linhcuem/cham_diem_yolov8"], | |
| default="linhcuem/cham_diem_yolov8", label="Model"), | |
| gr.inputs.Slider(minimum=320, maximum=1280, default=640, step=32, label="Image Size"), | |
| gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.25, step=0.05, label="Confidence Threshold"), | |
| gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.45, step=0.05, label="IOU Threshold"), | |
| ] | |
| # outputs_image =gr.outputs.Image(type="filepath", label="Output Image") | |
| # count_obj = gr.Textbox(show_label=False) | |
| title = "Tất cả do anh Đạt" | |
| interface_image = gr.Interface( | |
| fn=yolov8_img_inference, | |
| inputs=inputs_image, | |
| outputs=[gr.Image(type="pil"),gr.Textbox(show_label=False)], | |
| title=title, | |
| examples=image_path, | |
| cache_examples=True, | |
| theme='huggingface' | |
| ) | |
| # gr.TabbedInterface( | |
| # [interface_image], | |
| # tab_names=['Image inference'] | |
| # ).queue().launch() | |
| interface_image.launch(debug=True, enable_queue=True) |