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import data |
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import cv2 |
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import torch |
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from PIL import Image, ImageDraw |
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from tqdm import tqdm |
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from models import imagebind_model |
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from models.imagebind_model import ModalityType |
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from segment_anything import build_sam, SamAutomaticMaskGenerator |
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from utils import ( |
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segment_image, |
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convert_box_xywh_to_xyxy, |
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get_indices_of_values_above_threshold, |
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) |
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device = "cuda" if torch.cuda.is_available() else "cpu" |
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""" |
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Step 1: Instantiate model |
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""" |
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mask_generator = SamAutomaticMaskGenerator( |
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build_sam(checkpoint=".checkpoints/sam_vit_h_4b8939.pth").to(device), |
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points_per_side=16, |
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) |
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bind_model = imagebind_model.imagebind_huge(pretrained=True) |
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bind_model.eval() |
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bind_model.to(device) |
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""" |
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Step 2: Generate auto masks with SAM |
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""" |
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image_path = ".assets/car_image.jpg" |
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image = cv2.imread(image_path) |
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image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) |
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masks = mask_generator.generate(image) |
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""" |
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Step 3: Get cropped images based on mask and box |
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""" |
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cropped_boxes = [] |
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image = Image.open(image_path) |
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for mask in tqdm(masks): |
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cropped_boxes.append(segment_image(image, mask["segmentation"]).crop(convert_box_xywh_to_xyxy(mask["bbox"]))) |
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""" |
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Step 4: Run ImageBind model to get similarity between cropped image and different modalities |
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""" |
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referring_image_path = ".assets/referring_car_image.jpg" |
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referring_image = Image.open(referring_image_path) |
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image_list = [] |
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image_list += cropped_boxes |
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image_list.append(referring_image) |
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def retriev_vision_and_vision(elements): |
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inputs = { |
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ModalityType.VISION: data.load_and_transform_vision_data_from_pil_image(elements, device), |
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} |
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with torch.no_grad(): |
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embeddings = bind_model(inputs) |
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cropped_box_embeddings = embeddings[ModalityType.VISION][:-1, :] |
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referring_image_embeddings = embeddings[ModalityType.VISION][-1, :] |
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vision_referring_result = torch.softmax(cropped_box_embeddings @ referring_image_embeddings.T, dim=0), |
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return vision_referring_result |
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vision_referring_result = retriev_vision_and_vision(image_list) |
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""" |
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Step 5: Merge the top similarity masks to get the final mask and save the merged mask |
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Image / Text mask |
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""" |
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threshold = 0.017 |
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index = get_indices_of_values_above_threshold(vision_referring_result[0], threshold) |
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segmentation_masks = [] |
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for seg_idx in index: |
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segmentation_mask_image = Image.fromarray(masks[seg_idx]["segmentation"].astype('uint8') * 255) |
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segmentation_masks.append(segmentation_mask_image) |
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original_image = Image.open(image_path) |
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overlay_image = Image.new('RGBA', image.size, (0, 0, 0, 255)) |
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overlay_color = (255, 255, 255, 0) |
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draw = ImageDraw.Draw(overlay_image) |
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for segmentation_mask_image in segmentation_masks: |
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draw.bitmap((0, 0), segmentation_mask_image, fill=overlay_color) |
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mask_image = overlay_image.convert("RGB") |
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mask_image.save("./image_referring_sam_merged_mask.jpg") |
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