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| from transformers import DPTImageProcessor, DPTForDepthEstimation | |
| from segment_anything import SamAutomaticMaskGenerator, sam_model_registry | |
| import gradio as gr | |
| import supervision as sv | |
| import torch | |
| import numpy as np | |
| from PIL import Image | |
| import requests | |
| class DepthPredictor: | |
| def __init__(self): | |
| self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| self.feature_extractor = DPTImageProcessor.from_pretrained("Intel/dpt-large") | |
| self.model = DPTForDepthEstimation.from_pretrained("Intel/dpt-large") | |
| self.model.eval() | |
| def predict(self, image): | |
| # prepare image for the model | |
| encoding = self.feature_extractor(image, return_tensors="pt") | |
| # forward pass | |
| with torch.no_grad(): | |
| outputs = self.model(**encoding) | |
| predicted_depth = outputs.predicted_depth | |
| # interpolate to original size | |
| prediction = torch.nn.functional.interpolate( | |
| predicted_depth.unsqueeze(1), | |
| size=image.size[::-1], | |
| mode="bicubic", | |
| align_corners=False, | |
| ).squeeze() | |
| output = prediction.cpu().numpy() | |
| formatted = (output * 255 / np.max(output)).astype('uint8') | |
| #img = Image.fromarray(formatted) | |
| return formatted | |
| class SegmentPredictor: | |
| def __init__(self): | |
| MODEL_TYPE = "vit_b" | |
| checkpoint = "sam_vit_b_01ec64.pth" | |
| sam = sam_model_registry[MODEL_TYPE](checkpoint=checkpoint) | |
| self.mask_generator = SamAutomaticMaskGenerator(sam) | |
| def predict(self, image): | |
| image = np.array(image) | |
| sam_result = self.mask_generator.generate(image) | |
| mask_annotator = sv.MaskAnnotator() | |
| detections = sv.Detections.from_sam(sam_result=sam_result) | |
| annotated_image = mask_annotator.annotate(scene=image.copy(), detections=detections) | |
| return annotated_image |