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| import os | |
| os.system("pip install tensorflow") | |
| os.system("pip install modelscope") | |
| os.system("pip install thop") | |
| os.system("pip install easydict ") | |
| import gradio as gr | |
| import PIL.Image as Image | |
| import torch | |
| from modelscope.pipelines import pipeline | |
| from modelscope.utils.constant import Tasks | |
| import cv2 | |
| import numpy as np | |
| import random | |
| import warnings | |
| warnings.filterwarnings("ignore") | |
| def object_detection(img_pil, confidence_threshold, device): | |
| # 加载模型 | |
| p = pipeline(task='image-object-detection', model='damo/cv_tinynas_object-detection_damoyolo', device=device) | |
| # 传入图片进行推理 | |
| result = p(img_pil) | |
| # 读取图片 | |
| img_cv = cv2.cvtColor(np.asarray(img_pil), cv2.COLOR_RGB2BGR) | |
| # 获取bbox和类别 | |
| scores = result['scores'] | |
| boxes = result['boxes'] | |
| labels = result['labels'] | |
| # 遍历每个bbox | |
| for i in range(len(scores)): | |
| # 只绘制置信度大于设定阈值的bbox | |
| if scores[i] > confidence_threshold: | |
| # 随机生成颜色 | |
| class_color = (random.randint(0, 255), random.randint(0, 255), random.randint(0, 255)) | |
| # 获取bbox坐标 | |
| x1, y1, x2, y2 = boxes[i] | |
| x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2) | |
| # 绘制bbox | |
| cv2.rectangle(img_cv, (x1, y1), (x2, y2), class_color, thickness=2) | |
| # 绘制类别标签 | |
| label = f"{labels[i]}: {scores[i]:.2f}" | |
| cv2.putText(img_cv, label, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, class_color, thickness=2) | |
| img_pil = Image.fromarray(cv2.cvtColor(img_cv, cv2.COLOR_BGR2RGB)) | |
| return img_pil | |
| def download_test_image(): | |
| # Images | |
| torch.hub.download_url_to_file( | |
| 'https://user-images.githubusercontent.com/59380685/266264420-21575a83-4057-41cf-8a4a-b3ea6f332d79.jpg', | |
| 'bus.jpg') | |
| torch.hub.download_url_to_file( | |
| 'https://user-images.githubusercontent.com/59380685/266264536-82afdf58-6b9a-4568-b9df-551ee72cb6d9.jpg', | |
| 'dogs.jpg') | |
| torch.hub.download_url_to_file( | |
| 'https://user-images.githubusercontent.com/59380685/266264600-9d0c26ca-8ba6-45f2-b53b-4dc98460c43e.jpg', | |
| 'zidane.jpg') | |
| if __name__ == '__main__': | |
| download_test_image() | |
| # 定义输入和输出 | |
| input_image = gr.inputs.Image(type='pil') | |
| input_slide = gr.inputs.Slider(minimum=0, maximum=1, step=0.05, default=0.5, label="Confidence Threshold") | |
| input_device = gr.inputs.Radio(["cpu", "cuda", "gpu"], default="cpu") | |
| output_image = gr.outputs.Image(type='pil') | |
| examples = [['bus.jpg', 0.45, "cpu"], | |
| ['dogs.jpg', 0.45, "cpu"], | |
| ['zidane.jpg', 0.45, "cpu"]] | |
| title = "DAMO-YOLO web demo" | |
| description = "<div align='center'><img src='https://raw.githubusercontent.com/tinyvision/DAMO-YOLO/master/assets/logo.png' width='800''/><div>" \ | |
| "<p style='text-align: center'><a href='https://github.com/tinyvision/DAMO-YOLO'>DAMO-YOLO</a> DAMO-YOLO DAMO-YOLO DAMO-YOLO：一种快速准确的目标检测方法，采用了一些新技术，包括 NAS 主干、高效的 RepGFPN、ZeroHead、AlignedOTA 和蒸馏增强。" \ | |
| "DAMO-YOLO: a fast and accurate object detection method with some new techs, including NAS backbones, efficient RepGFPN, ZeroHead, AlignedOTA, and distillation enhancement..</p>" | |
| article = "<p style='text-align: center'><a href='https://github.com/tinyvision/DAMO-YOLO'>DAMO-YOLO</a></p>" \ | |
| "<p style='text-align: center'><a href='https://github.com/isLinXu'>gradio build by gatilin</a></a></p>" | |
| # 创建 Gradio 接口并运行 | |
| gr.Interface( | |
| fn=object_detection, | |
| inputs=[ | |
| input_image, input_slide, input_device | |
| ], | |
| outputs=output_image, | |
| title=title, | |
| examples=examples, | |
| description=description, article=article | |
| ).launch() | |