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''' |
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https://huggingface.co/spaces/merve/OWLSAM |
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text,letter,watermark |
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vim run_text_mask.py |
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from gradio_client import Client, handle_file |
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from datasets import load_dataset, Image as HfImage |
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from PIL import ImageOps, Image |
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import numpy as np |
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import os |
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from tqdm import tqdm |
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# 初始化客户端 |
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client = Client("http://localhost:7860") |
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# 加载数据集 |
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dataset_name = "svjack/InfiniteYou_PosterCraft_Wang_Leehom_Poster_FP8_WAV" |
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dataset = load_dataset(dataset_name) |
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# 创建保存 mask 的文件夹 |
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os.makedirs("mask_images", exist_ok=True) |
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#### 832, 1216 |
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#### (864, 1152) |
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def combine_non_white_regions(annotations): |
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canvas = None |
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for i, annotation in enumerate(annotations): |
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img = Image.open(annotation["image"]).convert("RGBA") |
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img_array = np.array(img) |
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if canvas is None: |
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height, width = img_array.shape[:2] |
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canvas = np.zeros((height, width, 4), dtype=np.uint8) |
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rgb = img_array[..., :3] |
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non_white_mask = np.any(rgb < 240, axis=-1, keepdims=True) |
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alpha_layer = np.where(non_white_mask, img_array[..., 3:], 0) |
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processed_img = np.concatenate([rgb, alpha_layer], axis=-1) |
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canvas = np.where(processed_img[..., 3:] > 0, processed_img, canvas) |
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if canvas is None: |
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height = 1152 |
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width = 864 |
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result_array = np.zeros((height, width, 4), dtype=np.uint8) |
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result_array[..., :3] = 255 |
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result_array[..., 3] = 255 |
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return Image.fromarray(result_array.astype(np.uint8)) |
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result_array = np.zeros((height, width, 4), dtype=np.uint8) |
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result_array[..., :3] = 255 |
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result_array[..., 3] = 255 |
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result_array = np.where(canvas[..., 3:] > 0, canvas, result_array) |
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non_white_mask = np.any(result_array[..., :3] < 255, axis=-1) |
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result_array[non_white_mask] = [0, 0, 0, 255] |
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return Image.fromarray(result_array.astype(np.uint8)) |
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def generate_mask(image, idx): |
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try: |
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# 保存原始图片为临时文件 |
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temp_input_path = f"mask_images/temp_{idx:04d}.jpg" |
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image.save(temp_input_path) |
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# 调用 Gradio API |
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result = client.predict( |
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image=handle_file(temp_input_path), |
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texts="text,letter,watermark", |
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threshold=0.05, |
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sam_threshold=0.88, |
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api_name="/predict" |
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) |
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# 生成 mask 图像 |
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mask_image = combine_non_white_regions(result["annotations"]) |
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mask_image = ImageOps.invert(mask_image.convert("RGB")) |
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# 保存 mask 图像 |
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output_mask_path = f"mask_images/mask_{idx:04d}.jpg" |
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mask_image.save(output_mask_path) |
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return {"mask_image": output_mask_path} |
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except Exception as e: |
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print(f"生成 mask 时出错 (index={idx}): {e}") |
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return {"mask_image": None} |
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# 使用 map 处理整个数据集 |
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updated_dataset = dataset["train"].map( |
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lambda example, idx: generate_mask(example["Wang_Leehom_poster_image"], idx), |
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with_indices=True, |
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num_proc=1, |
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batched=False |
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) |
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# 转换列类型为 Image |
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updated_dataset = updated_dataset.cast_column("mask_image", HfImage()) |
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# 保存更新后的数据集 |
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output_path = "Wang_Leehom_PosterCraft_with_Mask" |
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updated_dataset.save_to_disk(output_path) |
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print(f"✅ 已生成包含 mask 的数据集并保存至: {output_path}") |
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''' |
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from transformers import pipeline, SamModel, SamProcessor |
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import torch |
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import numpy as np |
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import gradio as gr |
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import spaces |
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checkpoint = "google/owlv2-base-patch16-ensemble" |
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detector = pipeline(model=checkpoint, task="zero-shot-object-detection", device="cuda") |
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sam_model = SamModel.from_pretrained("facebook/sam-vit-base").to("cuda") |
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sam_processor = SamProcessor.from_pretrained("facebook/sam-vit-base") |
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@spaces.GPU |
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def query(image, texts, threshold, sam_threshold): |
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texts = texts.split(",") |
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predictions = detector( |
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image, |
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candidate_labels=texts, |
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threshold=threshold |
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) |
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result_labels = [] |
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for pred in predictions: |
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box = pred["box"] |
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score = pred["score"] |
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label = pred["label"] |
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box = [round(pred["box"]["xmin"], 2), round(pred["box"]["ymin"], 2), |
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round(pred["box"]["xmax"], 2), round(pred["box"]["ymax"], 2)] |
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inputs = sam_processor( |
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image, |
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input_boxes=[[box]], |
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return_tensors="pt" |
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).to("cuda") |
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with torch.no_grad(): |
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outputs = sam_model(**inputs) |
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mask = sam_processor.image_processor.post_process_masks( |
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outputs.pred_masks.cpu(), |
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inputs["original_sizes"].cpu(), |
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inputs["reshaped_input_sizes"].cpu() |
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) |
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iou_scores = outputs["iou_scores"] |
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masks, iou_scores, boxes = sam_processor.image_processor.filter_masks( |
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mask[0], |
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iou_scores[0].cpu(), |
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inputs["original_sizes"][0].cpu(), |
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box, |
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pred_iou_thresh=sam_threshold, |
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) |
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result_labels.append((mask[0][0][0].numpy(), label)) |
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return image, result_labels |
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description = "This Space combines OWLv2, the state-of-the-art zero-shot object detection model with SAM, the state-of-the-art mask generation model. SAM normally doesn't accept text input. Combining SAM with OWLv2 makes SAM text promptable. Try the example or input an image and comma separated candidate labels to segment." |
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demo = gr.Interface( |
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query, |
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inputs=[gr.Image(type="pil", label="Image Input"), gr.Textbox(label = "Candidate Labels"), gr.Slider(0, 1, value=0.05, label="Confidence Threshold for OWL"), gr.Slider(0, 1, value=0.88, label="IoU threshold for SAM")], |
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outputs="annotatedimage", |
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title="OWL 🤝 SAM", |
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description=description, |
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examples=[ |
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["./cats.png", "cat", 0.1, 0.88], |
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], |
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cache_examples=True |
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) |
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demo.launch(debug=True, share = True) |