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| # backend.py | |
| import numpy as np | |
| from PIL import Image, ImageDraw | |
| import torch | |
| from transformers import SamModel, SamProcessor | |
| from torchvision.transforms import v2 | |
| from samgeo.text_sam import LangSAM | |
| import os | |
| import logging | |
| preproc = v2.Compose([ | |
| v2.PILToTensor(), | |
| v2.ToDtype(torch.float32, scale=True), # to float32 in [0, 1] | |
| ]) | |
| # Load the necessary models. | |
| device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu') | |
| CHECKPOINT_FILE = os.getenv("SAM_FINETUNE_CHECKPOINT", "checkpoints/bbox_finetune.ckpt") | |
| processor = SamProcessor.from_pretrained("facebook/sam-vit-base") | |
| tuned_model = SamModel.from_pretrained("facebook/sam-vit-large").to(device) | |
| tuned_model.load_state_dict(torch.load(CHECKPOINT_FILE, | |
| map_location=device)) | |
| langsam_model = LangSAM("vit_l") | |
| def process_image(image: Image, bbox: list[int, int, int, int] = None) -> Image: | |
| logging.info("Logging image information.") | |
| if bbox is None: | |
| # No bbox information. Use default (filters out zeroes) | |
| logging.debug("Using default, null bounding box.") | |
| bbox = list(map(float, image.getbbox())) # List of floats. | |
| inputs = processor(preproc(image), input_boxes=[[bbox]], | |
| do_rescale=False, return_tensors="pt") | |
| inputs = {k: v.to(device) for k, v in inputs.items()} # Map objects to our device. | |
| mask = get_sidewalk_mask(tuned_model, inputs) | |
| # Get tree masks. | |
| # Union 'em?? | |
| return mask | |
| def get_sidewalk_mask(model, inputs) -> Image: | |
| logging.info("Calculating mask.") | |
| model.eval() | |
| with torch.no_grad(): | |
| outputs = model(**inputs, multimask_output=False) | |
| ## apply sigmoid | |
| mask_probabilities = torch.sigmoid(outputs.pred_masks.squeeze(1)) | |
| ## Convert to numpy for the rest of our stuff. | |
| mask_probabilities = mask_probabilities.cpu().numpy().squeeze() | |
| ## Filter out smaller probs. | |
| mask_probabilities[mask_probabilities < 0.5] = 0 | |
| ## Map probabilities to color intensity linearly. | |
| mask_probabilities *= 255 | |
| greyscale_img = Image.fromarray(mask_probabilities).convert('L') | |
| return greyscale_img | |
| def get_tree_masks(image: Image): | |
| langsam_model.predict(image, "tree", box_threshold=0.24, text_threshold=0.24) | |
| # masks, boxes, phrases, logits = tuned_model.predict(image_pil, bbox) | |
| # tree_data = langsam_model.predict(image_pil, text_prompt) | |
| # def draw_layer_on_image(model, im: Image, text_prompt: str='sidewalk') -> Image: | |