| # Importing the requirements | |
| import numpy as np | |
| from PIL import Image | |
| import gradio as gr | |
| import torch | |
| from transformers import DPTImageProcessor, DPTForDepthEstimation | |
| # Load the model and feature extractor | |
| feature_extractor = DPTImageProcessor.from_pretrained("Intel/dpt-beit-large-512") | |
| model = DPTForDepthEstimation.from_pretrained("Intel/dpt-beit-large-512") | |
| # Function to process an image and return the formatted depth map as an image | |
| def process_image(image): | |
| """ | |
| Preprocesses an image, passes it through a model, and returns the formatted depth map as an image. | |
| Args: | |
| image (PIL.Image.Image): The input image. | |
| Returns: | |
| PIL.Image.Image: The formatted depth map as an image. | |
| """ | |
| # Check if the image is provided | |
| if not image: | |
| gr.Warning("No image provided") | |
| # Preprocess the image for the model | |
| encoding = feature_extractor(image, return_tensors="pt") | |
| # Forward pass through the model | |
| with torch.no_grad(): | |
| outputs = model(**encoding) | |
| predicted_depth = outputs.predicted_depth | |
| # Interpolate the predicted depth map to the original image 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") | |
| # Return the formatted depth map as an image | |
| return Image.fromarray(formatted) | |