企业官网建设流程全解析

参考

grounding-dino-tiny · 模型库

不支持中文，试过了

Downloading Model from https://www.modelscope.cn to directory: C:\Users\njsgcs\.cache\modelscope\hub\models\IDEA-Research\grounding-dino-tiny Downloading Model from https://www.modelscope.cn to directory: C:\Users\njsgcs\.cache\modelscope\hub\models\IDEA-Research\grounding-dino-tiny 检测结果: Result 0: Boxes shape: torch.Size([3, 4]) e:\code\my_python_server\micromambavenv\lib\site-packages\transformers\models\grounding_dino\processing_grounding_dino.py:93: FutureWarning: The key `labels` is will return integer ids in `GroundingDinoProcessor.post_process_grounded_object_detection` output since v4.51.0. Use `text_labels` instead to retrieve string object names. warnings.warn(self.message, FutureWarning) Labels: ['a cat', 'a cat', 'a remote control'] Scores: tensor([0.4785, 0.4381, 0.4759], device='cuda:0') Text Labels: ['a cat', 'a cat', 'a remote control'] 结果已保存到 result.jpg

import requests import torch from PIL import Image, ImageDraw, ImageFont import numpy as np from modelscope import AutoProcessor, AutoModelForZeroShotObjectDetection def visualize_results(image, results, text_labels): """ 可视化检测结果 """ draw = ImageDraw.Draw(image) # 从结果中获取检测框、标签和分数 boxes = results[0]['boxes'] labels = results[0]['text_labels'] if 'text_labels' in results[0] else results[0]['labels'] scores = results[0]['scores'] for i in range(len(boxes)): box = boxes[i].cpu().numpy() score = scores[i].item() label = labels[i] # 现在是字符串，不需要 .item() # 只绘制置信度高的框 if score > 0.4: x0, y0, x1, y1 = box color = tuple(np.random.randint(0, 255, size=3).tolist()) draw.rectangle([x0, y0, x1, y1], outline=color, width=3) # 使用标签文本 text_to_draw = f"{label} {score:.2f}" # 绘制标签 font = ImageFont.load_default() if hasattr(font, "getbbox"): bbox = draw.textbbox((x0, y0), text_to_draw, font) else: w, h = draw.textsize(text_to_draw, font) bbox = (x0, y0, x0 + w, y0 + h) draw.rectangle(bbox, fill=color) draw.text((x0, y0), text_to_draw, fill="white", font=font) return image model_id = "IDEA-Research/grounding-dino-tiny" device = "cuda" if torch.cuda.is_available() else "cpu" processor = AutoProcessor.from_pretrained(model_id) model = AutoModelForZeroShotObjectDetection.from_pretrained(model_id).to(device) image_url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(image_url, stream=True).raw) # Check for cats and remote controls # VERY important: text queries need to be lowercased + end with a dot text = "a cat. a remote control." inputs = processor(images=image, text=text, return_tensors="pt").to(device) with torch.no_grad(): outputs = model(**inputs) # 使用正确的参数名 results = processor.post_process_grounded_object_detection( outputs, input_ids=inputs.input_ids, threshold=0.4, # 使用 threshold 而不是 box_threshold text_threshold=0.3, target_sizes=[image.size[::-1]] ) # 打印结果 print("检测结果:") for i, result in enumerate(results): print(f"Result {i}:") print(f" Boxes shape: {result['boxes'].shape}") print(f" Labels: {result['labels']}") print(f" Scores: {result['scores']}") print(f" Text Labels: {result.get('text_labels', 'N/A')}") # 可视化结果 result_image = visualize_results(image.copy(), results, text) result_image.save("result.jpg") print("结果已保存到 result.jpg")

想让它识别点赞和收藏按钮识别不出来，效果很拉